r/EFft
United States
Environmental Protection
Agency
Child-Specific
Exposure Factors
Handbook
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EPA-600-P-00-002B
September 2002
Interim Report
CHILD-SPECIFIC EXPOSURE FACTORS HANDBOOK
National Center for Environmental Assessment-Washington Office
Office of Research and Development
U.S. Environmental Protection Agency
Washington, D.C. 20460
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DISCLAIMER
This interim document has been reviewed in accordance with U.S. Environmental
Protection Agency policy and approved for publication. Mention of trade names or commercial
products does not constitute endorsement or recommendation for use.
ABSTRACT
Children are often more heavily exposed to environmental toxicants than adults. They
consume more food and water and have higher inhalation rates per pound of body weight than
adults. Young children play close to the ground and come into contact with contaminated soil
outdoors and with contaminated dust on surfaces and carpets indoors. As another example,
exposure to chemicals in breast milk affects infants and young children.
Although NCEA has published the Exposure Factors Handbook in 1997 (EPA/600/P-
95/002Fa-c), that include exposure factors and related data on both adults and children, the EPA
Program Offices identified the need to consolidate all children exposure data into one document.
The goal of the Child-Specific Exposure Factors Handbook is to fulfill this need. The document
provides a summary of the available and up-to-date statistical data on various factors assessing
children exposures. These factors include drinking water consumption, soil ingestion, inhalation
rates, dermal factors including skin area and soil adherence factors, consumption of fruits and
vegetables, fish, meats, dairy products, homegrown foods, breast milk, activity patterns, body
weight, consumer products and life expectancy.
Preferred Citation:
U.S. Environmental Protection Agency (EPA). (2002) Child-specific exposure factors handbook.
National Center for Environmental Assessment, Washington, DC; EPA/600/P-00/002B.
Available from: National Information Service, Springfield, VA; PB2003-101678 and
.
11
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CONTENTS—CHILD-SPECIFIC EXPOSURE FACTORS HANDBOOK
LIST OF TABLES ix
LIST OF FIGURES xxiv
FOREWARD xxv
PREFACE xxvi
AUTHORS, CONTRIBUTORS, AND REVIEWERS xxvii
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. PURPOSE 1-3
1.3. INTENDED AUDIENCE 1-4
1.4. SELECTION OF STUDIES FOR THE HANDBOOK 1-4
1.4.1. General Considerations 1-4
1.5. APPROACH USED TO DEVELOP RECOMMENDATIONS FOR
EXPOSURE FACTORS 1-6
1.6. CHARACTERIZING VARIABILITY 1-8
1.7. USING THE HANDBOOK IN AN EXPOSURE ASSESSMENT 1-10
1.8. GENERAL EQUATION FOR CALCULATING DOSE 1-12
1.9. ADJUSTMENT OF DOSE FROM ADULTS
TO CHILDREN 1-15
1.10. AGE GROUPING 1-17
1.11. CUMULATIVE RISK 1-19
1.12. FUTURE OR ONGOING WORK 1-20
1.13. RESEARCH NEEDS 1-22
1.14. ORGANIZATION 1-23
APPENDIX A FOR CHAPTER 1: VARIABILITY AND UNCERTAINTY 1-31
REFERENCES FOR CHAPTER 1 1-43
2. BREAST MILK INTAKE 2-1
2.1. INTRODUCTION 2-1
2.2. STUDIES ON BREAST MILK INTAKE 2-2
2.2.1. Pao et al., 1980 2-2
2.2.2. Dewey and Lonnerdal, 1983 2-3
2.2.3. Butte et al., 1984 2-3
2.2.4. Neville et al., 1988 2-4
2.2.5. Dewey et al., 1991a, b 2-4
2.3. STUDIES ON LIPID CONTENT AND FAT INTAKE FROM
BREAST MILK 2-5
2.3.1. Butte et al., 1984 2-5
2.3.2. Maxwell and Burmaster, 1993 2-6
in
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CONTENTS (continued)
2.4. OTHER FACTORS 2-6
2.4.1. Population of Nursing Infants 2-6
2.4.2. Intake Rates Based on Nutritional Status 2-8
2.5. RECOMMENDATIONS 2-8
2.5.1. Breast Milk Intake 2-9
2.5.2. Lipid Content and Lipid Intake 2-9
REFERENCES FOR CHAPTER 2 2-20
FOOD INTAKE 3-1
3.1. INTRODUCTION 3-1
3.2. INTAKE RATE DISTRIBUTIONS FOR VARIOUS FOOD TYPES 3-3
3.2.1. USDA, 1999 3-3
3.2.2. U.S. EPA, 2000 3-5
3.3. FISH INTAKE RATES 3-7
3.3.1. General Population Studies 3-7
3.3.1.1. U.S. EPA, 1996 3-7
3.3.1.2. Tsang and Klepeis, 1996 3-9
3.3.2. Freshwater Recreational Study 3-10
3.3.3. Native American Subsistence Studies 3-12
3.3.3.1. Columbia River Inter-Tribal Fish Commission
(CRITFC), 1994 3-12
3.3.3.2. Toy et al., 1996 3-13
3.3.3.3. The Suquamish Tribe, 2000 3-14
3.4. FAT INTAKE 3-15
3.5. TOTAL DIETARY INTAKE AND CONTRIBUTIONS TO DIETARY
INTAKE 3-16
3.5.1. U.S. EPA, 2000 3-16
3.6. INTAKE OF HOME-PRODUCED FOODS 3-17
3.7. SERVING SIZE STUDY BASED ON THE USDANFCS 3-22
3.8. CONVERSION BETWEEN "AS CONSUMED" AND DRY WEIGHT
INTAKE RATES 3-23
3.9. FAT CONTENT OF MEAT AND DAIRY PRODUCTS 3-23
3.10. RECOMMENDATIONS 3-24
APPENDIX A FOR CHAPTER 3: CALCULATIONS USED IN THE
1994-1996 CSFII ANALYSIS TO CORRECT FOR MIXTURES 3-99
APPENDIX B FOR CHAPTER 3 3-101
APPENDIX C FOR CHAPTER 3: SAMPLE CALCULATION OF MEAN
DAILY FAT INTAKE BASED ON CDC (1994) DATA 3-112
IV
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CONTENTS (continued)
APPENDIX D FOR CHAPTERS 3-113
APPENDIX E FOR CHAPTER 3: STATISTICAL NOTES 3-127
REFERENCES FOR CHAPTER 3 3-129
4. DRINKING WATER INTAKE 4-1
4.1. INTRODUCTION 4-1
4.2. DRINKING WATER INTAKE STUDIES 4-2
4.2.1. U.S. EPA, 2000a 4-2
4.2.2. U.S. EPA, 2000b 4-4
4.3. RECOMMENDATIONS 4-5
REFERENCES FOR CHAPTER 4 4-18
5. SOIL INGESTION AND PICA 5-1
5.1. INTRODUCTION 5-1
5.2. SOIL INTAKE STUDIES 5-1
5.2.1. Binder et al., 1986 5-1
5.2.2. Clausing et al., 1987 5-3
5.2.3. Calabrese et al., 1989 5-4
5.2.4. Davis et al., 1990 5-5
5.2.5. Van Wijnen et al., 1990 5-7
5.2.6. Stanek and Calabrese, 1995a 5-8
5.2.7. Stanek and Calabrese, 1995b 5-10
5.2.8. Thompson and Burmaster, 1991 5-11
5.2.9. Sedman and Mahmood, 1994 5-12
5.2.10. Calabrese and Stanek, 1995 5-13
5.2.11. Calabrese et al., 1997 5-15
5.3. SOIL PICA 5-16
5.3.1. Prevalence 5-16
5.3.2. Pica Among Children 5-18
5.3.2.1. Calabrese et al., 1991 5-18
5.3.2.2. Calabrese and Stanek, 1992 5-18
5.3.2.3. Calabrese and Stanek, 1993 5-19
5.4. RECOMMENDATIONS 5-20
REFERENCES FOR CHAPTER 5 5-39
6. OTHER NONDIETARY INGESTION FACTORS 6-1
6.1. INTRODUCTION 6-1
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CONTENTS (continued)
6.2. STUDIES RELATED TO NONDIETARYINGESTION 6-2
6.2.1. Davis, 1995 6-2
6.2.2. Groot et al., 1998 6-5
6.2.3. Reed te al., 1999 6-6
6.2.4. Zartarian et al., 1997 6-7
6.2.5. Stanek et al., 1998 6-8
6.3. RECOMMENDATIONS 6-10
REFERENCES FOR CHAPTER 6 6-18
7. INHALATION ROUTE 7-1
7.1. INTRODUCTION 7-1
7.2. INHALATION RATE STUDIES 7-1
7.2.1. Linn et al., 1992 7-1
7.2.2. Spier et al., 1992 7-2
7.2.3. Adams, 1993 7-3
7.2.4. Layton, 1993 7-4
7.2.4.1. First Approach 7-5
7.2.4.2. Second Approach 7-6
7.2.5. Rusconi et al., 1994 7-6
7.3. RECOMMENDATIONS 7-8
APPENDIX A FOR CHAPTER: VENTILATION RATES 7-22
REFERENCES FOR CHAPTER 7 7-24
8. DERMAL ROUTE 8-1
8.1. INTRODUCTION 8-1
8.2. SURFACE AREA 8-1
8.2.1. Background 8-1
8.2.2. Measurement Techniques 8-2
8.2.3. Body Surface Area Studies 8-3
8.2.3.1. Costeff, 1966 8-3
8.2.3.2. U.S. EPA, 1985 8-3
8.2.3.3. Phillips et al., 1993 8-4
8.2.3.4. Wong et al., 2000 8-5
8.2.4. Application of Body Surface Area Data 8-6
8.3. SOIL ADHERENCE TO SKIN 8-7
8.3.1. Background 8-7
VI
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CONTENTS (continued)
8.3.2. Soil Adherence to Skin Studies 8-7
8.3.2.1. Kissel et al., 1996a 8-7
8.3.2.2. Kissel et al., 1996b 8-7
8.3.2.3. Holmes et al., 1999 8-8
8.3.2.4. Kissel et al., 1998 8-9
8.4. RECOMMENDATIONS 8-10
8.4.1. Body Surface Area 8-10
8.4.2. Soil Adherence to Skin 8-11
APPENDIX A FOR CHAPTER 8: FORMULAE FOR TOTAL BODY
SURFACE AREA 8-27
REFERENCES FOR CHAPTER 8 8-33
9. ACTIVITY FACTORS 9-1
9.1. INTRODUCTION 9-1
9.2. ACTIVITY PATTERNS 9-1
9.2.1. Timmer et al., 1985 9-1
9.2.2. Robinson and Thomas, 1991 9-2
9.2.3. Wiley et al., 1991 9-3
9.2.4. U.S. EPA, 1992 9-4
9.2.5. Tsang and Klepeis, 1996 9-5
9.2.6. Funk et al., 1998 9-8
9.2.7. Hubal et al., 2000 9-9
9.2.8. Wong et al., 2000 9-9
9.3. RECOMMENDATIONS 9-11
9.3.1. Recommendations for Activity Patterns 9-11
9.3.1.1. Time Spent Indoors Versus Outdoors 9-11
9.3.1.2. Showering 9-12
9.3.1.3. Swimming 9-12
9.3.1.4. Residential Time Spent Indoors and Outdoors 9-12
9.3.1.5. Playing on Sand or Gravel and on Grass 9-13
9.3.2. Summary of Recommended Activity Factors 9-13
REFERENCES FOR CHAPTER 9 9-61
10. CONSUMER PRODUCTS 10-1
10.1. BACKGROUND 10-1
10.2. CONSUMER PRODUCTS USE STUDIES 10-1
10.2.1. Tsang and Klepeis, 1996 10-1
10.3. RECOMMENDATIONS 10-2
REFERENCES FOR CHAPTER 10 10-8
vn
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CONTENTS (continued)
11. BODY WEIGHT STUDIES 11-1
11.1. INTRODUCTION 11-1
11.2. BODY WEIGHT STUDIES 11-1
11.2.1. Hamill et al., 1979 11-1
11.2.2. National Center for Health Statistics, 1987 11-1
11.2.3. Burmaster and Crouch, 1997 11-2
11.2.4. U.S. EPA, 2000 11-3
11.3. RECOMMENDATIONS 11-3
REFERENCES FOR CHAPTER 11 11-16
12. LIFETIME 12-1
12.1. INTRODUCTION 12-1
12.2. RECOMMENDATIONS 12-1
REFERENCES FOR CHAPTER 12 12-3
Vlll
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LIST OF TABLES
1-1. Considerations used to rate confidence in recommended values 1-25
1-2. Summary of exposure factor recommendations and confidence ratings 1-26
1-3. Characterization of variability in exposure factors 1-29
1-4. Proposed set of childhood age groups for agency exposure assessments 1-30
2-1. Daily intakes of breast milk 2-10
2-2. Breast milk intake for infants aged 1 to 6 months 2-10
2-3. Breast milk intake among exclusively breast-fed infants during the first 4
months of life 2-11
2-4. Breast milk intake during a 24-hour period 2-12
2-5. Breast milk intake estimated by the Darling Study 2-13
2-6. Lipid content of human milk and estimated lipid intake among exclusively
breast-fed infants 2-13
2-7. Percentage of mothers breast-feeding newborn infants in the hospital and
infants at 5 or 6 months of age in the United States in 1989, by ethnic
background and selected demographic variables 2-14
2-8. Confidence in breast milk intake recommendations 2-16
2-9. Breast milk intake rates derived from key studies 2-18
2-10. Summary of recommended breast milk and lipid intake rates 2-19
3-1. Grain products: mean quantities consumed per individual, by sex and age,
1 day, 1994-1996/1998 3-26
3-2. Grain products: percentages of individuals consuming, by sex and age,
1 day, 1994-1996/1998 3-27
3-3. Vegetables: mean quantities consumed per individual, by sex and age,
1 day, 1994-1996/1998 3-28
IX
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LIST OF TABLES (continued)
3-4. Vegetables: mean quantities consumed per individual, by sex and age,
1 day, 1994-1996/1998 3-29
3-5. Fruits: mean quantities consumed per individual, by sex and age, 1 day,
1994-1996/1998 3-30
3-6. Fruits: percentages of individuals consuming, by sex and age, 1 day,
1994-1996/1998 3-31
3-7. Milk and milk products: mean quantities consumed per individual, by sex and age,
1 day, 1994-1996/1998 3-32
3-8. Milk and milk products: percentages of individuals consuming, by sex and age,
1 day, 1994-1996/1998 3-33
3-9. Meat, poultry, and fish: mean quantities consumed per individual, by sex and age,
1 day, 1994-1996/1998 3-34
3-10. Meat, poultry, and fish: percentages of individuals consuming, by sex and age,
1 day, 1994-1996/1998 3-35
3-11. Eggs, legumes, nuts and seeds, fats and oils, sugars and sweets: mean quantities
consumed per individual, by sex and age, 1 day, 1994-1996/1998 3-36
3-12. Eggs, legumes, nuts and seeds, fats and oils, sugars and sweets: percentage of
individuals consuming, by sex and age, 1 day, 1994-1996/1998 3-37
3-13. Beverages: mean quantities consumed per individual, by sex and age, 1 day,
1994-1996/1998 3-38
3-14. Beverages: percentages of individuals consuming, by sex and age, 1 day,
1994-1996/1998 3-39
3-15. Weighted and unweighted number of observations, 1994-1996 CSFII analysis .... 3-40
3-16. Per capita intake of the major food groups (g/kg-day as consumed) 3-41
3-17. Per capita intake of individual foods (g/kg-day as consumed) 3-42
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LIST OF TABLES (continued)
3-18. Per capita intake of USD A categories of vegetables and fruits
(g/kg-day as consumed) 3-44
3-19. Per capita intake of exposed/protected fruit and vegetable categories
(g/kg-day as consumed) 3-45
3-20. Per capita distribution offish (finfish and shellfish) intake by age and gender,
as consumed 3-46
3-21. Consumers only distribution offish (finfish and shellfish) intake by age and gender,
as consumed 3-47
3-22. Per capita distribution offish (finfish and shellfish) intake by age and gender,
uncooked fish weight 3-48
3-23. Per capita distribution offish (finfish and shellfish) intake by age and gender,
uncooked fish weight 3-49
3-24. Number of respondents reporting consumption of seafood, by number of servings
and source 3-50
3-25. Mean fish intake among individuals who eat fish and reside in households with
recreational fish consumption 3-50
3-26. Fish consumption rates throughout year of 194 children ages 5 years and under .... 3-51
3-27. Mean, 50th, and 90th percentiles of consumption rates for children ages birth
to 5 years (g/kg-day) 3-52
3-28. Children's consumption rate (g/kg-day): individual finfish and shellfish and
fish groups 3-53
3-29. Children's consumption rate (g/kg-day) for consumers only: individual finfish and
shellfish and fish groups 3-54
3-30. Fat intake among children based on data from the Bogalusa Heart Study,
1973-1982 (g/day) 3-55
XI
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LIST OF TABLES (continued)
3-31. Fat intake among children based on data from the Bogalusa Heart Study,
1973-1982 (g/kg-day) 3-56
3-32. Mean total daily dietary fat intake grouped by age and gender 3-57
3-33. Per capita total dietary intake 3-58
3-34. Per capita intake of major food groups (g/day, as consumed) 3-59
3-35. Per capita intake of major food groups (g/kg/day, as consumed) 3-61
3-36. Per capita intake of total foods and major food groups, and percent of total food
intake for individuals with low-end, mid-range, and high-end total food intake .... 3-63
3-37. Per capita intake of total foods and major food groups, and percent of total food
intake for individuals with low-end, mid-range, and high-end total meat intake .... 3-65
3-38. Per capita intake of total foods and major food groups, and percent of
total food intake for individuals with low-end, mid-range, and high-end
total meat and dairy intake 3-67
3-39. Per capita intake of total foods and major food groups, and percent of
total food intake for individuals with low-end, mid-range, and high-end
total fish intake 3-69
3-40. Per capita intake of total foods and major food groups, and percent of
total food intake for individuals with low-end, mid-range, and high-end
total fruit and vegetable intake 3-71
3-41. Per capita intake of total foods and major food groups, and percent of
total food intake for individuals with low-end, mid-range, and high-end
total dairy intake 3-73
3-42. Weighted (w) and unweighted number (uw) of observations (individuals) for
NFCS data used in analysis of food intake 3-75
3-43. Consumer-only intake of homegrown foods (g/kg-day), all regions combined 3-76
3-44. Percent weight losses from food preparation 3-77
xn
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LIST OF TABLES (continued)
3-45. Quantity (as consumed) of food groups consumed per eating occasion and
the percentage of individuals using these foods over a 3-day period in
a 1997-78 survey, by age group 3-78
3-46. Mean moisture content of selected food groups expressed as percentages
of edible portions 3-80
3-47. Percent moisture content for selected fish species 3-84
3-48. Percentage lipid content (expressed as percentages of 100 grams of edible
portions) of selected meat, dairy, and fish products 3-87
3-49. Fat content of meat products 3-92
3-50. Summary of recommended values (g/kg-day) for per capita intake of
foods, as consumed 3-93
3-51. Confidence intake recommendations for various foods, including fish
(general population) 3-95
3-52. Confidence intake recommendations for fish consumption, recreational
freshwater angler population 3-96
3-53. Summary of fish intake rates among Native American children
(consumers only) 3-97
3-54. Confidence intake recommendations for fish consumption, Native
American subsistence population 3-98
4-1. Estimated direct and indirect community total water ingestion by source for
U.S. population 4-6
4-2. Estimate of total direct and indirect water ingestion, all sources by broad age
category for U.S. children 4-7
4-3. Estimate of direct and indirect community water ingestion by fine age
category for U.S. children 4-8
4-4. Estimate of direct and indirect community water ingestion by broad age
category for U.S. children 4-9
Xlll
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LIST OF TABLES (continued)
4-5. Estimate of direct and indirect bottled water ingestion by fine age category
for U.S. children 4-10
4-6. Estimate of direct and indirect bottled water ingestion by broad age category
for U.S. children 4-11
4-7. Estimate of direct and indirect other water ingestion by fine age category
for U.S. children 4-12
4-8. Estimate of direct and indirect other water ingestion by broad age category
for U.S. children 4-13
4-9. Chi-square GOF statistics for 12 models, tapwater data, based on maximum
likelihood method of parameter estimation 4-14
4-10. P-values for chi-square GOF tests of 12 models, tapwater data 4-14
4-11. Results of statistical modeling of tapwater data (intake rates in dL/kg-day)
using 5-parameter generalized F and 2-parameter gamma, lognormal and
Weibull models 4-15
4-12. Summary of recommended community drinking water intake rates 4-16
4-13. Confidence in tapwater intake recommendations 4-17
5-1. Estimated daily soil ingestion based on aluminum, silicon, and titanium
concentrations 5-23
5-2. Calculated soil ingestion by nursery school children 5-24
5-3. Calculated soil ingestion by hospitalized, bedridden children 5-25
5-4. Mean and standard deviation percentage recovery of eight tracer elements 5-25
5-5. Soil and dust ingestion estimates for children ages 1-4 years 5-26
5-6. Average daily soil ingestion values based on aluminum, silicon, and titanium
as tracer elements 5-26
xiv
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LIST OF TABLES (continued)
5-7. Geometric mean (GM) and standard deviation (GSD) LTM values for children
at daycare centers and campgrounds 5-27
5-8. Estimated geometric mean (GM) LTM values of children attending daycare centers
according to weather category, age, and sampling period (mg/day) 5-28
5-9. Per child distribution of average (mean) daily soil ingestion estimates by trace
for 64 children 5-29
5-10. Estimated distribution of individual mean daily soil ingestion based on data for
64 subjects projected over 365 days 5-30
5-11. Summary statistics and parameters for distributions of estimated soil ingestion by
tracer element 5-31
5-12. Positive/negative error (bias) in soil ingestion estimates in the Calabrese et al. (1989)
mass-balance study—effect on mean soil ingestion estimate (mg/day) 5-32
5-13. Soil ingestion estimates for the median of best four trace elements based on
food/soil ratios for 64 Anaconda children using aluminum, silicon, titanium,
yttrium, and zirconium 5-33
5-14. Dust ingestion estimates for the median of best four trace elements based on
food/dust ratios for 64 Anaconda children using using aluminum, silicon, titanium,
yttrium, and zirconium 5-33
5-15. Daily soil ingestion estimation in a soil-pica child by tracer and by week 5-34
5-16. Ratios of soil, residual fecal, and dust samples in the soil pica child 5-35
5-17. Daily variation of soil ingestion by children displaying soil pica in Wong (1988) . . . 5-36
5-18. Summary of estimates of soil ingestion by children 5-37
5-19. Summary of recommended values for soil ingestion 5-37
5-20. Confidence in soil intake recommendation 5-38
6-1. Extrapolated total mouthing times minutes per day 6-11
xv
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LIST OF TABLES (continued)
6-2. Frequency of contacts 6-11
6-3. Prevalence of non-food ingestion/mouthing behaviors by age 6-12
6-4. Average outdoor object mouthing scores for children by age, frequency of sand/dirt
play, and general mouthing quartiles 6-15
6-5. Summary of studies on mouthing behavior 6-15
6-6. Summary of mouthing frequency data 6-16
6-7. Summary of recommended values for mouthing behavior 6-16
6-8. Confidence in mouthing behavior recommendations 6-17
7-1. Calibration and field protocols for self-monitoring of activities, grouped by
subj ect panels 7-9
7-2. Subject panel inhalation rates by mean ventilation rate, upper percentiles, and
self-estimated breathing rates 7-9
7-3. Distribution of predicted inhalation rates by location and activity levels for
elementary and high school students who participated in the survey 7-10
7-4. Average hours spent per day in a given location and activity level for elementary
and high school students 7-10
7-5. Distribution patterns of daily inhalation rates for elementary and high school
students grouped by activity level 7-11
7-6. Summary of average inhalation rates by age group and activity levels for
laboratory protocols 7-12
7-7. Summary of average inhalation rates by age group and activity levels in field
protocols 7-12
7-8. Comparisons of estimated basal metabolic rates (BMR) with average food-energy
intakes for individuals sampled in the 1977-1978 NFCS 7-13
7-9. Daily inhalation rates calculated from food-energy intakes 7-14
xvi
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LIST OF TABLES (continued)
7-10. Daily inhalation rates obtained from the ratios of total energy expenditure to basal
metabolic rate (BMR) 7-15
7-11. Inhalation rates for short-term exposures 7-16
7-12. Mean, median, and SD of respiratory rate according to waking or sleeping in
618 infants and children grouped in classes of age (breaths/minute) 7-17
7-13. Confidence in inhalation rate recommendations 7-18
7-14. Summary of recommended values for inhalation 7-19
7-15. Summary of arithmetic mean (m3/hr) of children's inhalation rates by activity
level for short-term exposure studies 7-20
8-1. Total body surface area of male children in m2 8-13
8-2. Total body surface area of female children in m2 8-14
8-3. Percentage of total body surface area by body part for children 8-15
8-4. Descriptive statistics for surface area/body weight (SA/BW) ratios (m2/kg) 8-16
8-5. Clothing choices and assumed body surface areas exposed 8-17
8-6. Estimated skin surface exposed during warm weather outdoor play for
children under age 5 (based on SCS-I data) 8-17
8-7. Summary of field studies 8-18
8-8. Geometric mean (Geometric Standard Deviations) of soil adherence by
activity and body region 8-19
8-9. Summary of groups assayed in round 2 of field measurements 8-20
8-10. Attire for individuals within children's groups studied 8-20
8-11. Geometric means (geometric standard deviations) of round 2 post-activity
loadings 8-21
xvn
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LIST OF TABLES (continued)
8-12. Summary of controlled greenhouse trials, children playing 8-21
8-13. Preactivity loadings recovered from greenhouse trial children volunteers 8-22
8-14. Summary of recommended values for skin surface area 8-22
8-15. Confidence in body surface area measurement recommendations 8-23
8-16. Confidence in soil adherence to skin recommendations 8-24
9-1. Mean time spent performing major activities grouped by age, sex and type of day . . 9-14
9-2. Mean time spent in major activities grouped by type of day for five different age
groups 9-15
9-3. Mean time spent indoors and outdoors grouped by age and time of the week 9-16
9-4. Mean time spent at three locations for both CARB and national studies (ages
12 years and older) 9-16
9-5. Mean time spent in various microenvironments grouped by total population
and gender (12 years and over) in the national and CARB data 9-17
9-6. Mean time spent in various microenvironments by type of day for the CARB
and national surveys (sample population ages 12 years and older) 9-18
9-7. Mean time spent in various microenvironments by age groups for the national
and CARB surveys 9-19
9-8. Mean time children ages 12 years and under spent in 10 major activity categories
for all respondents 9-20
9-9. Mean time children spent in 10 major activity categories grouped by age
and gender 9-21
9-10. Mean time children ages 12 years and under spent in 10 major activity categories
grouped by seasons and regions 9-22
9-11. Mean time children ages 12 years and under spent in six major location categories
for all respondents 9-23
xvin
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LIST OF TABLES (continued)
9-12. Mean time children spent in six location categories grouped by age and gender .... 9-23
9-13. Mean time children spent in six location categories grouped by season and region . . 9-24
9-14. Mean time children spent in proximity to three potential exposures grouped by all
respondents, age, and gender 9-25
9-15. Mean time spent indoors and outdoors grouped by age 9-25
9-16. Range of recommended defaults for dermal exposure factors 9-26
9-17. Number of times taking a shower by number of respondents 9-26
9-18. Time spent taking a shower and spent in the shower room after taking a shower
by the number of respondents 9-27
9-19. Time spent taking a shower and spent in the shower room immediately after
showering 9-27
9-20. Total time spent altogether in the shower or bathtub and in the bathroom
immediately after by number of respondents 9-28
9-21. Total number of minutes spent altogether in the shower or bathtub and spent in the
bathroom immediately following a shower or bath 9-28
9-22. Number of times hands were washed at specified daily frequencies by the number
of respondents 9-29
9-23. Number of minutes spent working or being near excessive dust in the air
(mins/day) 9-29
9-24. Number of times per day a motor vehicle was started in a garage or carport and
started with the garage door closed by number of respondents 9-30
9-25. Number of minutes spent playing on sand, gravel, dirt, or grass by number
of respondents 9-31
9-26. Number of minutes spent playing in sand, gravel, dirt or grass by percentiles 9-31
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LIST OF TABLES (continued)
9-27. Number of minutes per day spent playing on grass in a day by the number
of respondents 9-32
9-28. Number of minutes spent playing on grass by percentile 9-32
9-29. Number of times swimming in a month in freshwater swimming pool by the
number of respondents 9-33
9-30. Number of minutes spent swimming in a month in freshwater swimming pool
percentile 9-34
9-31. Range of the average amount of time actually spent in the water by swimmers
by the number of respondents 9-34
9-32. Statistics for twenty-four hour cumulative number of minutes spent playing
indoors and outdoors by percentiles 9-35
9-33. Statistics for twenty-four hour cumulative number of minutes spent
sleeping/napping by percentiles 9-35
9-34. Statistics for twenty-four hour cumulative number of minutes spent attending
full-time school by percentiles 9-36
9-3 5. Statistics for twenty-four hour cumulative number of minutes spent in active
sports and for time spent in sports/exercise by percentiles 9-36
9-36. Statistics for twenty-four hour cumulative number of minutes spent in outdoor
recreation and spent walking by percentiles 9-37
9-37. Statistics for twenty-four hour cumulative number of minutes spent in bathing
by percentiles 9-37
9-38. Statistics for twenty-four hour cumulative number of minutes eating or drinking
by percentiles 9-38
9-39. Statistics for twenty-four hour cumulative number of minutes spent indoors at
school and indoors at a restaurant by percentiles 9-38
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LIST OF TABLES (continued)
9-40. Statistics for twenty-four hour cumulative number of minutes spent outdoors on
school grounds/playground, at a park/golf course, and at a pool/river/lake
by percentiles 9-39
9-41. Statistics for twenty-four hour cumulative number of minutes spent at home in
the kitchen bathroom, bedroom, and in a residence (all rooms) by percentiles 9-40
9-42. Statistics for twenty-four hour cumulative number of minutes spent traveling
inside a vehicle by percentiles 9-41
9-43. Statistics for twenty-four hour cumulative number of minutes spent outdoors
(outside the residence) and outdoors other than near a residence or vehicle,
such as parks, golf courses, or farms by percentiles 9-41
9-44. Statistics for twenty-four hour cumulative number of minutes spent in malls,
grocery stores, or other stores by percentiles 9-42
9-45. Statistics for twenty-four hour cumulative number of minutes spent with smokers
present by percentiles 9-43
9-46. Gender and age groups 9-43
9-47. Assignment of at-home activities to ventilation levels for children 9-44
9-48. Aggregate time spent (mins/day) at home in activity groups by adolescents
and children 9-45
9-49. Comparison of mean time (mins/day) spent at home by gender (adolescents) 9-45
9-50. Comparison of mean time (mins/day) spent at home by gender and age for
children 9-46
9-51. Number of person-days/individuals for children in CHAD database 9-47
9-52. Number of hours per day children spent in various microenvironments, by age:
average ± SD (percent of children reporting > 0 hours in microenvironment) 9-48
9-53. Average number of hours per day children spent doing various macroactivities
while indoors at home, by age (percent of children reporting > 0 hours of
microenvironment/macroactivity) 9-49
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LIST OF TABLES (continued)
9-54. Respondents with children and those reporting outdoor play activities in both warm
and cold weather 9-49
9-55. Play frequency and duration for all child players (from SCS-II data) by percentiles . 9-50
9-56. Hand washing and bathing frequency for all child players (from SCS-II data)
by percentiles 9-50
9-57. NHAPS and SCS-II play duration comparison 9-50
9-58. Comparison of NHAPS and SCS-II hand-washing frequencies 9-51
9-59. Confidence in activity patterns recommendations 9-52
9-60. Summary of activity pattern studies 9-57
9-61. Summary of mean time spent indoors and outdoors from several studies 9-58
9-62. Summary of recommended values for activity factors 9-59
10-1. Consumer products commonly found in some U.S. households 10-3
10-2. Number of minutes per day spent in activities working with or near household
cleaning agents such as scouring powders or ammonia 10-6
10-3. Number of minutes per day spent using any microwave oven 10-6
10-4. Number of respondents using a humidifier at home 10-6
10-5. Number of respondents reporting that pesticides were applied by a professional at
their home by number of times applied over a 6-month period 10-7
10-6. Number of respondents reporting that pesticides were applied by the consumer at
home by number of times applied over a 6-month period 10-7
11-1. Smoothed percentiles of weight by sex and age: statistics from NCHS and data
from Pels Research Institute 11-4
11-2. Weight in kilograms for males 6 months to 19 years of age by sex and age,
U.S. population 1976-1980 11-5
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LIST OF TABLES (continued)
11-3. Weight in kilograms for females 6 months to 19 years of age by sex and age,
U.S. population 1976-1980 11-6
11-4. Statistics for probability plot regression analyses female's body weights 6 months
to 20 years of age 11-7
11-5. Statistics for probability plot regression analyses male's body weights 6 months
to 20 years of age 11-8
11-6. Body weight estimates (in kilograms) by age and gender, U.S. population
1988-1994 11-9
11-7. Body weight estimates by age, U.S. population 1988-1994 11-10
11-8. Summary of recommended values for body weight 11-10
11-9. Confidence in body weight recommendations 11-11
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LIST OF FIGURES
1-1. Schematic of dose and exposure: oral route 1-13
7-1. 5th, 10th, 25th, 50th, 75th, 90th, and 95th smoothed centiles by age in awake
subjects 7-21
7-2. 5th, 10th, 25th, 50th, 75th, 90th, and 95th smoothed centiles by age in asleep
subjects 7-21
8-1. Schematic of dose and exposure: dermal route 8-25
8-2. Skin coverage as determined by fluorescence versus body part for adults
transplanting plants and for children playing in wet soils 8-26
8-3. Gravimetric loading versus body part for adult transplanting plants in wet soil
and for children playing in wet and dry soils 8-26
11-1. Weight by age percentiles for girls ages birth to 36 months 11-12
11-2. Weight by age percentiles for boys ages birth to 36 months 11-13
11-3. Mean body weights estimates, U.S. population, 1988-1994 11-14
11-4. Median body weights estimates, U.S. population, 1988-1994 11-15
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FOREWORD
The National Center for Environmental Assessment (NCEA) of EPA's Office of
Research and Development (ORD) has five main functions: (1) providing risk assessment
research, methods, and guidelines; (2) performing health and ecological assessments; (3)
developing, maintaining, and transferring risk assessment information and training; (4) helping
ORD set research priorities; and (5) developing and maintaining resource support systems for
NCEA. The activities under each of these functions are supported by and respond to the needs
of the various program offices. In relation to the first function, NCEA sponsors projects aimed
at developing or refining techniques used in exposure assessments.
Exposure Factors Handbook was first published in 1989 to provide statistical data on the
various factors used in assessing exposure for the general population; it was revised and
published again in 1997. Child-Specific Exposure Factors Handbook is being prepared to focus
on various factors used in assessing exposure, specifically for children ages 0-19 years. The
recommended values are based solely on our interpretation of the available data. In many
situations, the use of different values may be appropriate in consideration of policy, precedent, or
other factors. This handbook contains numerous tables where data are presented using two and
sometimes three significant figures. The use of significant figures implies that these values are
known with some degree of certainty. However, in many cases, the data do not allow for this
degree of precision and the user should understand this limitation and apply rounding rules as
necessary to obtain a more appropriate value.
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PREFACE
The National Center for Environmental Assessment (NCEA) of EPA's Office of
Research and Development prepared this handbook to address factors commonly used in
exposure assessments for children. Children are often more heavily exposed than adults to
environmental toxicants. They consume more food and water and have higher inhalation rates
per pound of body weight than do adults. Young children play close to the ground and come into
contact with contaminated soil outdoors and with contaminated dust on surfaces and carpets
indoors. Furthermore, exposure to chemicals in breast milk affects infants and young children.
NCEA published the latest version of Exposure Factors Handbook in 1997. It includes
exposure factors and related data on children as well as adults. However, the EPA program
offices have identified the need to prepare a document specifically for children's exposure
factors. The goal of Child-Specific Exposure Factors Handbook is to fulfill this need.
This handbook will be continuously updated as new data become available. For example,
the Agency is currently developing guidance on the use of a standard set of age groups that are
needed to assess exposures in children. This guidance is expected to be completed by Fall 2002.
The handbook will be revised to ensure consistency with new Agency guidance. In an effort to
keep the handbook up-to-date, NCEA will incorporate new data as they become available in the
published literature. Please submit comments, recommendations, suggested revisions, and
corrections to moya.jacqueline@epa.gov.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The National Center for Environmental Assessment (NCEA) of EPA's Office of
Research and Development (ORD) was responsible for the publication of this handbook. The
handbook was prepared by the Exposure Assessment Division of Versar, Inc., in Springfield,
VA, under EPA Contract No. 68-W-99-041. Jacqueline Moya served as Work Assignment
Manager, providing overall direction and technical assistance and serving as contributing author.
AUTHORS WORD PROCESSING
Versar. Inc. Versar. Inc.
Linda Phillips Susan Perry
Patricia Wood Valerie Schwartz
Marit Espevik
Todd Ferryman
Clarkson Meredith
Diane Sinkowski
U.S. EPA
Jacqueline Moya
The following individuals at EPA reviewed an earlier draft of this handbook and
provided valuable comments:
Amina Wilkins, National Center for Environmental Assessment
Denis R. Borum, Office of Water, Health, and Ecological Criteria Division
Lynn Flowers, Region III
Youngmoo Kim, Region VI
Tom McCurdy, National Exposure Research Laboratory
Nicole Tulve, National Exposure Research Laboratory
Valerie Zartarian, National Exposure Research Laboratory
In addition, ORD's National Exposure Research Laboratory made an important contribution to
this handbook by conducting additional analyses of mouthing behavior data from the Davis
(1995) study. Data analyses were conducted by Nicole Tulve.
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This document was reviewed by an external panel of experts. The panel was composed
of the following individuals:
Dr. Robert Blaisdell California Environmental Protection Agency
1515 Clay St., 16th Floor
Oakland, CA 94612
Dr. Annette Guiseppi-Elie DuPont Spruance Plant
5401 Jefferson Davis Highway
P.O. Box 126
Richmond, VA 23234
Dr. Barbara Peterson Novigen Sciences, Inc.
1730 Rhode Island Ave., NW
Suite 1100
Washington, DC 20036
Dr. P. Barry Ryan Rollins School of Public Health
Department of Environmental and Occupational Health
1518 Clifton Rd.
Atlanta, GA 30322
Comments were also provided by:
The American Chemistry Council
1300 Wilson Boulevard
Arlington, VA
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1. INTRODUCTION
1.1. BACKGROUND
Because of differences in physiology and behaviors, exposures among children are
expected to be different than exposures among adults. Children may be more exposed to
environmental toxicants because they consume more food and water per unit of body, they have
higher inhalation rates per unit of body weight, and they have higher surface area to volume than
adults.
Recent studies have shown that young children can be exposed to pesticides during
normal oral exploration of their environment and by touching floors, surfaces, and objects such
as toys (Eskenazi et al., 1999; Gurunathan et al., 1998; Lewis et al., 1999; Nishioka et al., 1999).
Dust and tracked-in soil accumulates most effectively in carpets, where young children spend a
significant amount of time (Lewis et al., 1999). Children living in agricultural areas may
experience higher exposures to pesticides than do other children. Pesticides may be tracked into
their homes by family members. In addition, children living in agricultural areas may also play
in nearby fields or be exposed via consumption of contaminated breast milk from their
farmworker mother (Eskenazi et al., 1999).
In terms of risk, children may also be more vulnerable to environmental pollutants
because of differences in absorption, excretion, and metabolism (U.S. EPA, 1997a). The cellular
immaturity of children and the ongoing growth processes account for elevated risk (AAP, 1997).
Toxic chemicals in the environment can cause neurodevelopmental disabilities. The developing
brain can be particularly sensitive to environmental contaminants. For example, elevated blood
lead levels and prenatal exposures to even relatively low levels of lead result in reductions of
intellectual function and behavior disorders. Exposure to high levels of methylmercury can
result in developmental disabilities (Myers and Davidson, 2000). Other authors have described
the importance of exposure timing (i.e., preconceptional, prenatal, and postnatal) and how it
affects the outcomes observed (Selevan et al., 2000).
On April 21, 1997, President Clinton signed Executive Order 13045: Protection of
Children From Environmental Health Risks and Safety Risks. The order requires all federal
agencies to address health and safety risks to children, to coordinate research priorities on
children's health, and to ensure that their standards take into account special risks to children. To
implement the order, the U.S. Environmental Protection Agency (the EPA Agency) established
the Office of Children's Health Protection (OCHP), and offices within EPA increased their
efforts to provide a safe and healthy environment for children by ensuring that all regulations,
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standards, policies, and risk assessments take into account risks to children. Recent legislation
such as the Food Quality Protection Act and the Safe Drinking Water Act amendments has made
children's health issues more explicit, and research on children's health issues is continually
expanding. As a result of the emphasis on children's risk, the EPA Office of Research and
Development's (ORD's) National Center for Environmental Assessment (NCEA) issued a
Children's Risk Policy, which emphasizes the need to evaluate exposures and risks among this
population (U.S. EPA, 1997a; 1999a). ORD also developed a Strategy for Research on Risks to
Children (Children's Research Strategy). The goal of the Children's Research Strategy is to
improve risk assessments for children. This Child-Specific Exposure Factors Handbook is
intended to support EPA/ORD/NCEA's efforts to improve exposure and risk assessments for
children.
In 1997, EPA/ORD/NCEA published Exposure Factors Handbook (U.S. EPA, 1997b).
The handbook includes exposure factors and related data on both adults and children. OCHP
recently issued its child-related risk assessment policy and methodology guidance document
survey (U.S. EPA, 1999b), which highlighted Exposure Factors Handbook as a source of
information on exposure factors for children. EPA's Children's Environmental Health Yearbook
(U.S. EPA, 1998) also lists Exposure Factors Handbook as a source of exposure information for
children. However, the EPA Program Offices identified the need to consolidate all children
exposure data into one document. The goal of this Child-Specific Exposure Factors Handbook is
to fulfill this need. This handbook provides nonchemical-specific data on exposure factors that
can be used to assess doses from dietary and nondietary ingestion exposure, dermal exposure,
and inhalation exposure among children. Data are provided in the following areas:
• breast milk ingestion;
• food ingestion, including homegrown foods and other dietary-related areas;
• drinking water ingestion;
• soil ingestion;
• rates of hand-to-mouth and object-to-mouth activity;
• dermal exposure factors such as surface areas and soil adherence;
• inhalation rates;
• duration and frequency in different locations and various microenvironments;
• duration and frequency of consumer product use;
• body weight data; and
• duration of lifetime.
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This handbook is a compilation of available data from a variety of sources. Most of these
data have been presented in detail in EPA's Exposure Factors Handbook (U.S. EPA, 1997b), but
data that have been published subsequent to the release of Exposure Factors Handbook are also
included here. With very few exceptions, the data are the result of analyses by the individual
study authors. Because the studies included in this handbook vary in terms of their objectives,
design, scope, presentation of results, etc., the level of detail, statistics, and terminology may
vary from study to study and from factor to factor. For example, some authors used geometric
means to present their results, while others used arithmetic means or distributions. Authors have
sometimes used different age ranges to describe data for children. Within the constraint of
presenting the original material as accurately as possible, EPA has made an effort to present
discussions and results in a consistent manner. The strengths and limitations of each study are
discussed to provide the reader with a better understanding of the uncertainties associated with
the values derived from the study.
Because of their large number, the tables in this handbook are presented at the end of the
appropriate chapter, before the appendices, if any, and the references for the chapter.
1.2. PURPOSE
The dual purpose of Child-Specific Exposure Factors Handbook is to: (1) summarize
key data on human behaviors and characteristics that affect children's exposure to environmental
contaminants, and (2) recommend values to use for these factors. These recommendations are
not legally binding on any EPA program and should be interpreted as suggestions that Program
Offices or individual exposure assessors can consider and modify as needed. Most of the factors
are best quantified on a site- or situation-specific basis. The data presented in this handbook
have been compiled from various sources, which include EP A's Exposure Factor Handbook
(U.S. EPA, 1997b), government reports, and information presented in the scientific literature.
The handbook has strived to include discussions of issues that assessors should consider in
assessing exposure among children, and it may be used in conjunction with the EPA document
entitled, Sociodemographic Data Used for Identifying Potentially Highly Exposed
Subpopulations of Children, which is currently being drafted and provides population data for
children.
This handbook is intended to be a continuously evolving document. Updates will be
posted in the NCEA home page as new data become available.
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1.3. INTENDED AUDIENCE
Child-Specific Exposure Factors Handbook may be used by exposure assessors inside as
well as outside the Agency who need to obtain data on standard factors needed to calculate
childhood exposure to toxic chemicals.
1.4. SELECTION OF STUDIES FOR THE HANDBOOK
The information in this handbook has been summarized from studies documented in the
scientific literature and from other available sources. Studies were chosen that were seen as
useful and appropriate for estimating exposure factors. The handbook contains summaries of
selected studies published through June 2000.
1.4.1. General Considerations
Many scientific studies were reviewed for possible inclusion in this handbook.
Generally, studies identified in Exposure Factors Handbook (U.S. EPA, 1997b) as key studies
are included, as are new studies that became available after its publication. Key studies from
Exposure Factors Handbook were generally defined as the most useful for deriving exposure
factors. The recommended values for most exposure factors are based on the results of these
studies. As in Exposure Factors Handbook, the key studies were selected on the basis of
following considerations:
• Level of peer review. Studies were selected predominantly from the peer-
reviewed literature and final government reports. Internal or interim reports were
therefore avoided.
• Accessibility: Studies were preferred that the user could access in their entirety if
needed.
• Reproducibility: Studies were sought that contain sufficient information so that
methods can be reproduced or at least so the details of the author's work can be
accessed and evaluated.
• Focus on exposure factor of interest: Studies were chosen that directly address
the exposure factor of interest or address related factors that have significance for
the factor under consideration. As an example of the latter case, a selected study
contains useful ancillary information concerning fat content in fish, although it
does not directly address fish consumption.
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Pertinence of data to the U.S.: Studies were selected that address the U.S.
population. Data from populations outside the U.S. were sometimes included if
U.S. data were limited for a specific exposure factor. Studies similar in
methodology are also used to support or enhance the U.S. data.
Use of primary data: Studies were deemed preferable if they are based on
primary data, but studies based on secondary sources are also included when they
offer an original analysis. For example, the handbook cites studies of food
consumption that are based on original data collected by the U.S. Department of
Agriculture (USDA) National Food Consumption Survey.
Currency of information: Studies were chosen only if they are sufficiently recent
to represent current exposure conditions. This is an important consideration for
those factors that change with time. In some instances, recent data were very
limited. Therefore, the data provided in these instances are the only available
data. Limitations as to the age of the data are noted.
Adequacy of data collection period: Because most users of the handbook are
primarily addressing chronic exposures, studies were sought that used the most
appropriate techniques for collecting data to characterize long-term behavior.
Validity of approach: Studies that used experimental procedures or approaches
that more likely or closely capture the desired measurement were selected. In
general, direct exposure data collection techniques, such as direct observation,
personal monitoring devices, or other known methods were preferred where
available. If studies that used direct measurement were not available, studies
were selected that relied on validated indirect measurement methods, such as
surrogate measures (e.g., heart rate for inhalation rate) and questionnaires.
If questionnaires or surveys were used, proper design and procedures include an
adequate sample size for the population under consideration, a response rate large
enough to avoid biases, and avoidance of bias in the design of the instrument and
interpretation of the results.
Representativeness of the population: Studies seeking to characterize the national
population, a particular region, or a subpopulation were selected if they were
appropriately representative of that population. Studies with limitations in areas
where little data exist were included and are noted as such.
Variability in the population: Studies were sought that characterize any
variability within populations.
Minimal (or defined) bias in study design: Studies were sought that were
designed with minimal bias, or if biases were suspected to be present, the
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direction of the bias (i.e., an overestimate or underestimate of the parameter) is
either stated or apparent from the study design.
• Minimal (or defined) uncertainty in the data: Studies were sought that have
minimal uncertainty in the data, which was judged by evaluating all the
considerations listed above. At least, studies were preferred that identified
uncertainties, such as those due to inherent variability in environmental and
exposure-related parameters or possible measurement error. Studies that
document quality assurance/quality control measures were preferable.
1.5. APPROACH USED TO DEVELOP RECOMMENDATIONS FOR EXPOSURE
FACTORS
As discussed above, EPA first reviewed all the literature pertaining to a factor and
determined key studies. These key studies were used to derive recommendations for the values
of each factor. The recommended values were derived solely from EPA's interpretation of the
available data. Different values may be appropriate for the user in consideration of policy,
precedent, strategy, or other factors such as site-specific information. EPA's procedure for
developing recommendations was as follows:
1. Key studies were evaluated in terms of both quality and relevance to specific populations
(general U.S. population, age groups, gender, etc.). The criteria for assessing the quality
of the studies are described in section 1.4.
2. If only one study was classified as key for a particular factor, the mean value from that
study was selected as the recommended central value for that population. If multiple key
studies with reasonably equal quality, relevance, and study design information were
available, a weighted mean (if appropriate, considering sample size and other statistical
factors) of the studies was chosen as the recommended mean value. If the key studies
were judged to be unequal in quality, relevance, or study design, the range of means is
presented and the user of this handbook must employ judgment in selecting the most
appropriate value for the population of interest. In cases where the national population
was of interest, the midpoint of the range was usually judged to be the most appropriate
value.
3. The variability of the factor across the population was discussed. If adequate data were
available, the variability was described as either a series of percentiles or a distribution.
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4. Uncertainties were discussed in terms of data limitations, the range of circumstances over
which the estimates were (or were not) applicable, possible biases in the values
themselves, a statement about parameter uncertainties (measurement error, sampling
error), and model or scenario uncertainties if models or scenarios were used to derive the
recommended value.
5. Finally, EPA assigned a confidence rating of low, medium, or high to each recommended
value. This rating is not intended to represent an uncertainty analysis, rather it represents
EPA's judgment on the quality of the underlying data used to derive the
recommendation. This judgment was made using the guidelines shown in Table 1-1.
Table 1-1 is an adaptation of the General Considerations discussed in section 1.4.
Clearly this is a continuum from low to high, and judgment was used to determine these
ratings. Recommendations given in this handbook are accompanied by a discussion of the
rationale for their rating.
Table 1-2 summarizes EPA's recommendations and confidence ratings for the various exposure
factors that apply to children.
It is important to note that the study elements listed in Table 1-1 do not have the same
weight when arriving at the overall confidence rating for the various exposure factors. The
relative weight of each of these elements depends on the exposure factor of interest. Also, the
relative weights given to the elements for the various factors were subjective and based on the
professional judgement of the authors of this handbook. In general, most studies would rank
high with regard to "level of peer review," "accessibility," "focus on exposure factor of interest,"
and "data pertinent to the U.S."
These elements are important considerations for inclusion of a study in this handbook.
However, a high score on these elements does not necessarily translate into a high overall score.
Other elements in Table 1-1 were also examined to determine the overall score. For example,
the adequacy of the data collection period may be more important when determining usual intake
of foods in a population; on the other hand, it is not as important for factors where long-term
variability may be small, such as tapwater intake. In the case of tapwater intake, the currency of
the data was a critical element in determining the final rating. In addition, some exposure factors
are more easily measured than others. For example, soil ingestion by children is estimated by
measuring the levels in the feces of certain elements found in soil. Body weight, however, can
be measured directly, and it is, therefore, a more reliable measurement. The fact that soil
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ingestion is more difficult to measure than body weight is reflected in the confidence rating
given to both of these factors. In general, the better the methodology used to measure the
exposure factor, the higher the confidence in the value.
1.6. CHARACTERIZING VARIABILITY
This handbook attempts to characterize the variability of each of the factors. Variability
is characterized in one or more of three ways: (1) as a table with various percentiles or ranges of
values, (2) as an analytical distribution with specified parameters, and/or (3) as a qualitative
discussion. Analyses to fit standard or parametric distributions (e.g., normal, lognormal) to the
exposure data have not been performed by the authors of this handbook, but they have been
reproduced as they were found in the literature. Recommendations on the use of these
distributions are made where appropriate, based on the adequacy of the supporting data. The list
of exposure factors and the way in which variability has been characterized throughout this
handbook (i.e., average, median, upper percentiles, multiple percentiles, fitted distribution) are
presented in Table 1-3. The term "upper percentile" is used throughout this handbook, and it is
intended to represent values in the upper tail (i.e., between the 90th and the 99.9th percentile) of
the distribution of values for a particular exposure factor. A detailed presentation on variability
and uncertainty for exposure factors and algorithms used in estimating exposure is presented in
Appendix A of this chapter.
In the recommendations, an attempt was made to present percentile values that are
consistent with the exposure estimators defined in Guidelines for Exposure Assessment (U.S.
EPA, 1992a) (i.e., mean, 50th, 90th, 95th, 98th, and 99.9th percentile). However, this was not
always possible, because either the data available were limited for some factors or the authors of
the study did not provide such information. It is important to note, however, that these
percentiles were discussed in the guidelines within the context of risk descriptors and not
individual exposure factors. For example, the guidelines state that the assessor may derive a
high-end estimate of exposure by using maximum or near maximum values for one or more
sensitive exposure factors, leaving others at their mean value.
The use of Monte Carlo or other probabilistic analysis requires a selection of
distributions or histograms for the input parameters. Although this handbook is not intended to
provide complete guidance on the use of Monte Carlo and other probabilistic analyses, the
following should be considered when using such techniques:
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• The exposure assessor should only consider using probabilistic analysis when
there are credible distribution data (or ranges) for the factor under consideration.
Even if these distributions are known, it may not be necessary to apply this
technique. For example, if only average exposure values are needed, these can
often be computed accurately by using average values for each of the input
parameters. Probabilistic analysis is also not necessary when conducting
assessments for screening purposes, i.e., to determine whether unimportant
pathways can be eliminated. In this case, bounding estimates can be calculated
using maximum or near maximum values for each of the input parameters.
• It is important to note that the selection of distributions can be highly site specific
and will always involve some degree of judgment. The assessor needs to evaluate
the site-specific data, when available, to assess their quality and applicability.
Distributions derived from national data may not represent local conditions. An
assessor should use distributions or frequency histograms derived from local
surveys to assess risks locally if they are determined to be of adequate quality and
representative of the site conditions. The assessor may decide to use
distributional data drawn from the national or other surrogate population. In this
case, it is important that the assessor address the extent to which local conditions
may differ from the surrogate data.
In addition to a qualitative statement of uncertainty, the representativeness assumption
should be appropriately addressed as part of a sensitivity analysis.
• Distribution functions to be used in Monte Carlo analysis may be derived by fitting
an appropriate function to empirical data. In doing this, it should be recognized that
in the lower and upper tails of the distribution the data are scarce, so that several
functions with radically different shapes in the extreme tails may be consistent with
the data. To avoid introducing errors into the analysis by the arbitrary choice of an
inappropriate function, several techniques can be used. One technique is to avoid the
problem by using the empirical data itself rather than an analytic function. Another is
to do separate analyses with several functions that have adequate fit but form upper
and lower bounds to the empirical data. A third way is to use truncated analytical
distributions. Judgment must be used in choosing the appropriate goodness-of-fit
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test. Information on the theoretical basis for fitting distributions can be found in a
standard statistics text (e.g., Gilbert, 1987, among others). Off-the-shelf computer
software such as Best-Fit by Palisade Corporation can be used to statistically
determine the distributions that fit the data.
• If only a range of values is known for an exposure factor, the assessor has several
options:
Keep that variable constant at its central value.
Assume several values within the range of values for the exposure factor.
- Calculate a point estimate(s) instead of using probabilistic analysis.
- Assume a distribution. (The rationale for the selection of a distribution should be
discussed at length.) There are, however, cases where assuming a distribution is
not recommended. These include:
— data are missing or are very limited for a key parameter;
— data were collected over a short time period and may not represent long-term
trends (the respondent usual behavior), for example, food consumption
surveys, activity pattern data;
— data are not representative of the population of interest because sample size
was small or the population studied was selected from a local area and was
therefore not representative of the area of interest, for example, soil ingestion
by children; and
— ranges for a key variable are uncertain due to experimental error or other
limitations in the study design or methodology, for example, soil ingestion by
children.
1.7. USING THE HANDBOOK IN AN EXPOSURE ASSESSMENT
Some of the steps for performing an exposure assessment are determining the pathways
of exposure; identifying the environmental media that transports the contaminant; determining
the contaminant concentration; determining the exposure time, frequency, and duration; and
identifying the exposed population. Many of the issues related to characterizing exposure from
selected exposure pathways have been addressed in a number of existing EPA guidance
documents. These include, but are not limited to the following:
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• Guidelines for Exposure Assessment (U.S. EPA, 1992a)
• Dermal Exposure Assessment: Principles and Applications (U.S. EPA, 1992b)
• Methodology for Assessing Health Risks Associated with Indirect Exposure to
Combustor Emissions (U.S. EPA, 1990)
• Risk Assessment Guidance for Superfund (U. S. EPA, 1989)
• Estimating Exposures to Dioxin-Like Compounds (U.S. EPA, 1994a)
• Superfund Exposure Assessment Manual (U.S. EPA, 1988a)
• Selection Criteria for Mathematical Models Used in Exposure Assessments:
Groundwater Models (U.S. EPA, 1988b)
• Selection Criteria for Mathematical Models Used in Exposure Assessments: Surface
Water Models (U.S. EPA, 1987)
• Standard Scenarios for Estimating Exposure to Chemical Substances During Use of
Consumer Products (U.S. EPA, 1986a)
• Pesticide assessment guidelines, subdivision K (U.S. EPA, 1984) and U (U.S. EPA,
1986b)
• Methods for Assessing Exposure to Chemical Substances, volumes 1-13 (U.S. EPA,
1983-1989)
• Guiding Principles for Monte Carlo Analysis (U.S. EPA, 1997c)
• "Policy for Use of Probabilistic Analysis in Risk Assessment at the U.S."
Environmental Protection Agency, May 15, 1997, available at
http://www.epa.gov/ncea/mcpolicy.htm
• "Guiding Principles for Monte Carlo Assessments" (EPA/600/R-97/001), available at
http://www.epa.gov/ncea/monteabs.htm
• Options for Developing Parametric Probability Distributions for Exposure Factors
(U.S. EPA, 2000a)
• Identifying Potentially Highly Exposed Children's Populations (U.S. EPA, 2001)
• Proposed Cancer Guidelines for Carcinogen Risk Assessment (U.S. EPA, 1999c)
These documents may serve as valuable information resources to assist in the assessment of
exposure. The reader is encouraged to refer to them for more detailed discussion.
Most of the data presented in this handbook are derived from studies that target (1) the
general population (e.g., USDA food consumption surveys), and (2) a sample population from a
specific area or group (e.g., the Calabrese et al. (1989) soil ingestion study which uses children
from the Amherst, MA, area). It is necessary for risk or exposure assessors who are
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characterizing a diverse population to identify and enumerate certain groups within the general
population who are at risk for greater contaminant exposures or who exhibit a heightened
sensitivity to particular chemicals. For further guidance on addressing susceptible populations,
the reader is referred to Socio-demographic Data Used for Identifying Potentially Highly
ExposedSubpopulations (U.S. EPA, 2001).
EPA is also developing guidance on the use of exposure factors data in exposure
assessments. For further information on the status of this guidance, consult the NCEA's home
page, www.epa.gov/ncea.
1.8. GENERAL EQUATION FOR CALCULATING DOSE
The definition of exposure, as used in Guidelines for Exposure Assessment (U.S. EPA,
1992a), is "condition of a chemical contacting the outer boundary of a human." This means
contact with the visible exterior of a person, such as the skin, and openings such as the mouth,
nostrils, and lesions. The process of a chemical entering the body can be described in two steps:
contact (exposure) followed by entry (crossing the boundary). The magnitude of exposure (the
dose) is the amount of agent available at human exchange boundaries (skin, lungs, gut) where
absorption takes place during some specified time. An example of exposure and dose for the
oral route, as presented in the guidelines, is shown in Figure 1-1. Starting with a general integral
equation for exposure, several dose equations can be derived depending upon boundary
assumptions. One of the more useful of these derived equations is the average daily dose
(ADD). The ADD, which is used for many noncancer effects, averages exposures or doses over
the period of time over which exposure occurred. The ADD can be calculated by averaging the
potential dose (Dpot) over body weight and an averaging time.
Total Potential Dose
ADDnot = (1-1)
p Body Weight x Averaging Time
For cancer effects, where the biological response is usually described in terms of lifetime
probabilities even though exposure does not occur over the entire lifetime, doses are often
presented as lifetime ADDs (LADDs). The LADD takes the form of eq. 1-1, with lifetime
replacing averaging time. The LADD is a very common term used in carcinogen risk assessment
where linear nonthreshold models are employed.
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Biologically
Effective
Dose
Exposure
Chemical
Effect
Mouth
G.I. Tract
Intake
Uptake
Figure 1-1. Schematic of dose and exposure: oral route
Source: U.S. EPA, 1992a
The total exposure can be expressed as follows:
Total Potential Dose = C x IR x ED (1 -2)
Where:
C = Contaminant Concentration
IR = Intake Rate
ED = Exposure Duration
Contaminant concentration is the concentration of the contaminant in the medium (air,
food, soil, etc.) contacting the body; it has units of mass/volume or mass/mass.
The intake rate refers to the rates of inhalation, ingestion, and dermal contact, depending
on the route of exposure. For ingestion, the intake rate is simply the amount of food containing
the contaminant of interest that an individual ingests during some specific time period (units of
mass/time). Much of this handbook is devoted to rates of ingestion for some broad classes of
food. For inhalation, the intake rate is the rate at which contaminated air is inhaled. Factors that
affect dermal exposure are the amount of material that comes into contact with the skin and the
rate at which the contaminant is absorbed.
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The exposure duration is the length of time of contaminant contact. The time a person
lives in an area, frequency of bathing, time spent indoors versus outdoors, etc., all affect the
exposure duration. Chapter 9, Activity Factors, gives some examples of population
behavior/activity patterns that may be useful for estimating exposure durations.
When the parameter values IR and ED remain constant over time, they are substituted
directly into the exposure equation. When they change with time, a summation approach is
needed to calculate exposure. In either case, exposure duration is the length of time exposure
occurs at the concentration and at the intake rate specified by the other parameters in the
equation.
Dose can be expressed as a total amount (with units of mass, e.g., mg) or as a dose rate in
terms of mass/time (e.g., mg/day) or as a rate normalized to body mass (e.g., with units of mg of
chemical per kg of body weight per day [mg/kg-day]). The LADD is usually expressed in terms
of mg/kg-day or other mass/mass-time units.
In most cases (inhalation and ingestion exposure), the dose-response parameters for
carcinogen risks have been adjusted for the difference in absorption across body barriers
between humans and the experimental animals used to derive such parameters. Therefore, the
exposure assessment in these cases is based on the potential dose, with no explicit correction for
the fraction absorbed. However, the exposure assessor needs to make such an adjustment when
calculating dermal exposure and in other specific cases where current information indicates that
the human absorption factor used in the derivation of the dose-response factor is inappropriate.
The lifetime value used in the LADD version of eq. 1-1 is the period of time over which
the dose is averaged. For carcinogens, the derivation of the dose-response parameters usually
assumes no explicit number of years as the duration of a lifetime, and the nominal value of 70
years has been considered a reasonable approximation. For exposure estimates that are to be
used for assessments other than carcinogenic risk, various averaging periods have been used.
For acute exposures, the administered doses are usually averaged over a day or a single event.
For nonchronic, noncancer effects, the time period used is the actual period of exposure. The
objective in selecting the exposure averaging time is to express the exposure in such a way that it
can be combined with the dose-response relationship to calculate risk.
The body weight to be used in the exposure eq. 1-1 depends on the units of the exposure
data presented in this handbook. For food ingestion, the body weights of the surveyed
populations were known in the USDA surveys, and they were explicitly factored into the food
intake data in order to calculate the intake as g/day/kgr body weight. In this case, the body
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weight has already been included in the "intake rate" term in eq. 1-2, and the exposure assessor
does not need to explicitly include body weight.
The units of intake in this handbook for the ingestion offish and breast milk and the
inhalation of air are not normalized to body weight. In this case, the exposure assessor needs to
use (in eq. 1-1) the average weight of the exposed population during the time when the exposure
actually occurs. If the body weight of the individuals in the population whose risk is being
evaluated is nonstandard in some way (e.g., children may be smaller than the national
population) and if reasonable values are not available in the literature, then a model of intake as a
function of body weight must be used. One such model is discussed in Appendix 1A of
Exposure Factors Handbook (U.S. EPA, 1997b). Some of the parameters (primarily
concentrations) used in estimating exposure are exclusively site specific, and therefore default
recommendations could not be used. It should be noted that body weight is correlated with food
consumption rates and inhalation rates.
The link between the intake rate value and the exposure duration value is a common
source of confusion in defining exposure scenarios. It is important to define the duration
estimate so that it is consistent with the intake rate:
• The intake rate can be based on an individual event (e.g., serving size per event). The
duration should be based on the number of events or, in this case, meals.
• The intake rate also can be based on a long-term average, such as 10 g/day. In this
case, the duration should be based on the total time interval over which the exposure
occurs.
The objective is to define the terms so that, when multiplied, they give the appropriate
estimate of mass of contaminant contacted. This can be accomplished by basing the intake rate
on either a long-term average (chronic exposure) or an event (acute exposure) basis, as long as
the duration value is selected appropriately.
1.9. ADJUSTMENT OF DOSE FROM ADULTS TO CHILDREN
Section 1.8 discusses the general equation to calculate dose. To assess risk, dose
estimates are combined with slope factors for chemicals that have a carcinogenic effect and with
reference doses (RfDs) for those that have noncancer effects. For assessing risk via the
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inhalation route, the exposure concentration is compared with unit risk estimates for cancer
effects and with reference concentrations (RfCs) for noncancer effects.
Slope factors and unit risk estimates for lifetime exposure incorporate exposure factors
that are based on adults (specifically, body weight, breathing rate, and drinking water ingestion
rate). When slope factors and unit risks are used to assess risks from less-than-lifetime
exposures that occur during childhood, adjustments to toxicity values to account for differences
between adults and children may be appropriate (U.S. EPA, 1999c). The reader is referred to the
EPA's proposed cancer guidelines (U.S. EPA, 1999c) for further guidance on how to make these
adjustments for slope factors and unit risks.
As it is the case with slope factors, RfCs are developed using default exposure values
based on adults. The RfC methodology, which is described in Methods for Derivation of
Inhalation Reference Concentrations and Applications of Inhalation Dosimetry (U.S. EPA,
1994b), allows the user to incorporate population-specific assumptions into the models. The
reader is referred to EPA guidance (U.S. EPA, 1994b) on how to make these adjustments. It is
important to note, however, that the EPA's Risk Assessment Forum is currently considering this
existing guidance on animal-to-human cross-species extrapolation procedures described in U.S.
EPA (1994b) and is recommending the development of procedures for inhalation adjustments to
incorporate the most current scientific thought and data to address, as needed, issues of
variability due to life stage and other intrinsic factors.
There are no specific exposure factor assumptions in the derivation of RfDs. The
assessment of the potential for adverse health effects in infants and children is part of the overall
hazard and dose-response assessment for a chemical. Available data pertinent to children's
health risks are evaluated along with data on adults and the no-observed-adverse-effect-level
(NOAEL) or benchmark dose (BMD) for the most sensitive critical effect(s), based on
consideration of all health effects. By doing this, protection of the health of children will be
considered along with that of other sensitive populations. In some cases, it is appropriate to
evaluate the potential hazard to children separately from the assessment for the general
population or other population subgroups. However, the Food Quality Protection Act (FQPA) of
1996 states that for threshold effects,
an additional tenfold margin of safety for the chemical residue and other sources
of exposure shall be applied for infants and children to take into account potential
pre- and post-natal toxicity and completeness of data with respect to exposure and
toxicity to infants and children. Notwithstanding such requirement for an
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additional margin of safety, the Administrator may use a different margin of
safety for the pesticide chemical residue only if, on the basis of reliable data, such
margin of safety will be safe for infants and children.
In addition, FQPA lists several factors that must be considered when assessing risks to
children, such as available information concerning the special susceptibility of children to
pesticide chemical residues, neurological differences between children and adults, and effects of
in utero exposure. In response to FQPA requirements, EPA's Office of Pesticide Programs is
developing guidance on the use of this tenfold safety factor.
1.10. AGE GROUPING
Currently, there is no consistent EPA-wide approach for grouping children by age when
assessing exposure to environmental contaminants. Existing approaches vary from case to case
and among Program Offices within EPA. Often, the age groups depend on the availability of
data and are based on professional judgment. This handbook presents data from a variety of
studies in a manner that preserves, to the extent possible, the format followed by the study
authors. Therefore, the childhood age groupings presented in the handbook may not be
consistent across all studies. These age groupings typically reflect the expert judgment of the
study authors and will vary with respect to how well they reflect behavioral and physiological
changes during the childhood lifestage.
The development of standardized age groupings (or bins) was the subject of discussion in
a recent workshop sponsored by EPA's Risk Assessment Forum. The workshop was titled "The
Technical Workshop on Issues Associated with Considering Developmental Changes in
Behavior and Anatomy When Assessing Exposure to Children." The purpose of this workshop
was to gain insight and input into factors that need to be considered when developing
standardized age groups and to identify future research necessary to accomplish these goals.
During the workshop, participants were divided into two groups. One of the groups focused its
discussions on defining and characterizing the important facets of behavioral development
during childhood; the other group focused its discussions on defining and characterizing aspects
of physiological development. EPA is using the input obtained during the workshop to develop
guidance on the age groupings for children. This guidance is expected to be finalized by Fall
2002.
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During the workshop, participants concluded that, ideally, exposure assessment would
predict chemical exposures as a continuous function over a lifetime. However, given the paucity
of existing data, this is not likely to be accomplished in the near future, and assessors will need
to classify individuals into age groups in order to simplify the exposure model. The reader is
referred to the workshop report in the Risk Assessment Forum home page
(http://www.epa.gov/ncea/raf/rafpub.html) for more detailed information about the workshop
results. Summarized below are some of the key findings and recommendations made by the
workshop participants. These recommendations are currently under consideration by EPA as the
guidance development process continues.
• Panelists agreed that child development is a series of discrete events, but these events
occur along a continuum.
• Age grouping/bins are a useful tool for approximating the underlying exposure/dose
distributions. Ideally, sufficient data should be generated to develop a continuous
multivariate model that best reflects actual exposure/dose.
• Adequacy of existing exposure data is highly variable.
• A considerable amount of additional information already exists, but it is dispersed in
the literature. It was recommended that EPA consult with experts in developmental
biology, physiology, pharmacology, and toxicology and conduct an in-depth review
of the literature.
• Long-term research should include the development of integrated data sets that
combine information about the exposure factors with biomarkers of exposure and
effects.
• The definition of age groups/bins for childhood exposure assessment is inextricably
linked to toxicokinetic and toxicodynamic issues.
• The two break-out groups (i.e., behavioral and physiological) offered the following
preliminary ideas for age groupings:
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Age grouping based on behavioral characteristics
0 to < 3 months
3 to < 6 months
6 to < 12 months
1 to < 2 years
2 to < 6 years
6 to < 11 years
11 to < 16 years
16 to < 21 years
Age grouping based on physiological characteristics
0 to < 1 month
1 to < 3 months
3 to < 6 months
6 to < 12 months
1 to < 3 years
3 to < 8 (female) or 9 (male) years
8/9 to < 16 (female) or 18 (male) years
One can observe that there was fairly good agreement between the two groups with
regard to the age groupings that are important for infants and toddlers. However, there was some
disagreement with regard to the older children. Appropriate age groupings depend on not only
behavioral and physiological characteristics, but also on the specific scenario being studied and
the chemical of concern.
On the basis of the recommendations made by the workshop participants, the Risk
Assessment Forum workgroup on childhood exposure has proposed a recommended set of age
groups to be considered in risk assessments for children (Tablel-4). The age groups and
guidance on how to use them are currently under development and will be subjected to peer
review. It is important to note that EPA will be directing efforts to revise this handbook to
conform with the age grouping guidance as soon as guidance is finalized. A revised handbook is
expected in Fall 2003.
1.11. CUMULATIVE RISK
EPA recognizes that children may be exposed to mixtures of chemicals both indoors and
outdoors. Exposure may also occur through more than one pathway. New directions in risk
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assessments in EPA put more emphasis on total exposures via multiple pathways (U.S. EPA,
2000). However, methods and approaches to assess cumulative risks are not well developed.
EPA has initiated the development of guidelines for assessing cumulative risks. An external
peer review draft framework is expected in Spring 2002.
1.12. FUTURE OR ONGOING WORK
This section provides a summary of several ongoing activities in and outside of the
Agency to address various aspects of children's exposures. Results from these activities, which
are listed below, will be included in future updates to this handbook.
• EPA is developing guidance on the use of exposure data. For future information on
the status of this guidance, consult NCEA's homepage (www.epa.gov/ncea).
• EPA is summarizing the information in Sociodemographic Data Used for Identifying
Potentially Highly Exposed Children's Populations. An external review draft of this
document was completed in April 2001.
• EPA is currently analyzing arsenic data in urine to backcalculate soil intake rates in a
community living near a smelter. Results of this study are expected to be available in
the Fall 2002.
• EPA is currently developing guidance on the age groupings that are more appropriate
for characterizing children exposures. Guidance is expected to be finalized in
September 2002.
• EPA, the National Institute of Environmental Health Sciences, and the Centers for
Disease Control and Prevention (CDC) have established eight Centers of Excellence
in Children's Environmental Health and Disease Prevention Research. These Centers
are conducting basic and applied research in combination with community-based
prevention efforts. Their aim is to better understand the causes of environmentally
induced diseases among children and to eventually decrease their prevalence. More
information about these centers is available at
http://es.epa.gov/ncerqa/centers/cecehdpr.html.
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The Consumer Product Safety Commission (CPSC) is directing a study to collect
frequency and duration of mouthing behavior from 200 children of ages ranging from
3 months through 36 months. A report is expected to be issued in May 2002. This
report will be posted in the CPSC web page at www.cpsc.gov/library/library.htm.
• The President's Task Force on Environmental Health Risks and Safety Risks to
Children is planning to conduct a national longitudinal cohort study to identify and
quantify the effects of environmental factors on the health and development of
children. This study is called the National Children's Study, and more information
can be found at www.hichd.nih.gov/about/despr/cohort/.
• EPA's National Exposure Research Lab is directing research to study the transfer
efficiency of pesticides to foods. This research emphasizes accurate measurement of
pesticides transferred to foods from various surfaces, quantifying activities of
children through videotape analysis, and analyzing field study data from three home
environments to evaluate the dietary intake model. For more information, the reader
is referred to http://www.epa.gov/nerl/research/2000/pdf_conversions/G3-2.pdf.
• The U.S. Department of Housing and Urban Development, in collaboration with
EPA's National Exposure Research Laboratory and CPSC, conducted the first
national survey of licensed child care centers to evaluate the levels of lead, pesticides,
and allergens within and around these centers. Data will be available in 2003.
The Duval County Health Department (Jacksonville, FL), in collaboration with CDC
and EPA's National Exposure Research Laboratory, conducted a survey to evaluate
organophosphate and pyrethroid pesticide metabolite levels in urine from young
children, pesticide use practices, and a total aggregate exposure assessment in the
greater Jacksonville area. Data will be available in 2003.
• EPA's National Exposure Research Laboratory, in collaboration with the
Environmental and Occupational Health Sciences Institute at Rutgers University,
conducted a research study to reduce the uncertainties associated with dermal
exposures following a residential pesticide application.
1.13. RESEARCH NEEDS
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The data for several exposure factors for children are limited. The following list is a
compilation of areas for future research related to childhood exposure factors:
• More recent information is needed on breast milk consumption and the incidence and
duration of breastfeeding.
• Information on children's food handling practices that might exacerbate exposure is
needed to better characterize exposures among children.
• Further research on fish consumption rates among children, particularly recreational
and subsistence populations, is needed.
• Further research is needed on consumption of ethnic foods by children.
• Research is needed to better estimate soil intake rates, particularly, how to extrapolate
short-term data to chronic exposures. Research is also needed to refine the methods
for calculating soil intake rates (inconsistencies among tracers and input/output
misalignment errors indicate a fundamental problem with the methods). In particular,
additional information on soil ingestion among children that provides better estimates
of upper percentile rates is needed. Research is also needed to provide a better
understanding of the relative contribution of soil versus dust ingestion.
• Further research is needed on nondietary ingestion exposure factors, such as the
microenvironments in which children spend time and the types of materials that they
contact. More information is needed on the rate at which they contact contaminated
surfaces, the fraction of the contaminants that are transferred to skin and object
surfaces, and the amount of the object/skin entering the mouth.
• Additional data on dermal exposure factors, such as the microenvironments in which
children spend time and the types of materials that they contact, as well as
information on the rate at which they contact contaminated surfaces and the fraction
of the contaminants that are transferred to skin and object surfaces are needed.
• Further research is needed to obtain better soil adherence rates for additional
activities involving children.
• Further data are needed on the frequency and duration of use and kinds of consumer
products used by children.
• Additional information on derivation of new surface area based on newer body
weight data is needed.
• New surface areas based on newer body weight data need to be derived.
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• Additional data on inhalation rates that are specific to children's activities and overall
24-hour breathing rates are needed.
• Research is needed to derive a methodology to extrapolate from short-term data to
long-term or chronic exposures.
• Further research is needed to estimate food consumption rates by children based on
the CSFII supplemental survey on children.
1.14. ORGANIZATION
The handbook is organized as follows:
Chapter 1 provides the overall introduction to the handbook.
Chapter 2 provides factors for estimating exposure through ingestion of breast milk.
Chapter 3 provides factors for estimating human exposure through the ingestion of foods,
including fish.
Chapter 4 provides factors for estimating exposure through the ingestion of drinking
water.
Chapter 5 provides factors for estimating exposure as a result of ingestion of soil.
Chapter 6 presents factors for estimating exposure to environmental contaminants from
other nondietary ingestion, such as hand-to-mouth and object-to-mouth activity.
Chapter 7 provides factors for estimating exposure as a result of inhalation of vapors and
particulates.
Chapter 8 provides factors for estimating dermal exposure to environmental contaminants
that come in contact with the skin.
Chapter 9 presents data on activity factors (activity patterns, population mobility, and
occupational mobility).
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Chapter 10 presents data on consumer product use.
Chapter 11 presents data on body weight.
Chapter 12 presents data on life expectancy.
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Table 1-1. Considerations used to rate confidence in recommended values
Considerations
High Confidence
Low Confidence
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Study sizes2
Representativeness of the
population
Variability in the population
Lack of bias in study design
(a high rating is desirable)
Response rates:
In-person interviews
Telephone interviews
Mail surveys
Measurement error
The studies received a high level of peer
review (e.g., they appeared in peer review
journals).
The studies are widely available to the
public.
The results can be reproduced or the
methodology can be followed and
evaluated.
The studies focus on the exposure factor of
interest.
The studies focused on the U.S. population.
The studies analyzed primary data.
The data were published after 1990.
The study design captures the measurement
of interest (e.g., usual consumption patterns
of a population).
The studies used the best methodology
available to capture the measurement of
interest.
The sample size is greater than 100
samples.
The study population is the same as the
population of interest.
The studies characterize variability in the
population studied.
Potential bias in the studies are stated or can
be determined from the study design.
The response rate is greater than 80 percent.
The response rate is greater than 80 percent.
The response rate is greater than 70 percent.
The study design minimizes measurement
errors.
The studies received limited peer review.
The studies are difficult to obtain (e.g., draft
reports, unpublished data).
The results cannot be reproduced, the
methodology is hard to follow, and the
author(s) cannot be located.
The purpose of the studies was to
characterize a related factor.
The studies focus on populations outside the
U.S.
The studies are based on secondary sources.
The data were published before 1990.
The study design does not very accurately
capture the measurement of interest.
There are serious limitations with the
approach used.
The sample size is less than 20 samples.
The study population is very different from
the population of interest. b
The characterization of variability is
limited.
The study design introduces biases in the
results.
The response rate is less than 40 percent.
The response rate is less than 40 percent.
The response rate is less than 40 percent.
Uncertainties with the data exist due to
measurement error.
Other Elements
Number of studies
Agreement between
researchers
The number of studies is greater than 3.
The results of studies from different
researchers are in agreement.
The number of studies is 1 .
The results of studies from different
researchers are in disagreement.
a The sample size depends on how the target population is defined. As the size of a sample relative to the
total size of the target population increases, estimates are made with greater statistical assurance that the
sample results reflect actual characteristics of the target population.
b Differences include age, sex, race, income, or other demographic parameters.
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Table 1-2. Summary of exposure factor recommendations and confidence
ratings
Exposure Factor
Breast milk intake rate
(1-6 months)
Drinking water intake rate
Total fruit intake rate
Total vegetable intake rate
Total meat intake rate
Total dairy intake rate
Total grain intake
Fat Intake
Fish intake rate
Home-produced food intake
Soil ingestion rate
Recommendation"
742 mL/day (average)
1033 mL/day (upper percentile)
See Table 4-15 L/day (average)
See Table 4-15 L/day (90th percentile)
See Table 3-2 (per capita average)
See Table 3-2 (per capita 95th percentile)
See Table 3-2 (per capita average)
See Table 3-2 (per capita 95th percentile)
See Table 3-2 (per capita average)
See Table 3-2 (per capita 95th percentile)
See Table 3-2 (per capita average)
See Table 3-2 (per capita 95th percentile)
See Table 3-2 (per capita average)
See Table 3-2 (per capita 95th percentile)
See Table 3-17
General Population
See Table 3-6 (total fish)
See Table 3-6 (marine)
See Table 3-6 (freshwater/estuarine)
Recreational fish intake
1-5 years, 370 mg/kg/day (average)
6-10 years, 280 mg/kg/day (average)
Native American Subsistence Population
< 5 years, 1 1 g/day (average)
See Table 3-28
Children
100 mg/day (average)
400 mg/day (upper percentile)
Pica child
10 g/day
Confidence Rating
Medium
Medium
High
High
High
Low
High
Low
High
Low
High
Low
High
Low
—
High (average)
Low (upper percentile)
Low
Low
Low
Low (for means and short-
term distributions)
Low (for long-term
distributions)
Medium
Low
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Table 1-2. Summary of exposure factor recommendations and confidence
ratings (continued)
Exposure Factor
Mouthing behavior
Inhalation rate
Surface area
Soil adherence
Life expectancy
Body weights for children
Body weights for infants (birth
to 6 months)
Showering/bathing
Swimming
Recommendation"
3-60 months
46 min/day (average)
24-72 months
Hand-to-mouth, 9 contacts/hour (average)
Hand-to-mouth, 20 contact/hour (90th
percentile)
Object-to-mouth, 16.3 contact/hour (average)
10-72 months
Total mouthing, 49 contacts/hour (average)
Children (< 1 year)
4.5 mVday (average)
Children (1-12 years)
8.7 mVday ( average)
Water contact (bathing and swimming)
Use total body surface area for children in
Tables 8-1 through 8-2;
Soil contact (outdoor activities)
Use body part area based on Table 8-3
Use values presented in Table 8-13 depending
on activity and body part (central estimates
only)
70 years
Use values presented in Tables 11-2 and 11-3
(percentiles); Tables 11-6 and 11-7 (mean)
Use values presented in Table 11-1
(percentiles); Tables 11-6 and 11-7 (mean)
Showering time
10 min/day (average)
1 shower event/day
45 min (95th percentile)
Frequency
1 event/month
Duration
60 min/event (median)
Confidence Rating
Low
Low
Low
Low
Low
Medium
Medium
Medium
Medium
Medium
High
High
High
Medium
Medium
Medium
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Table 1-2. Summary of exposure factor recommendations and confidence
ratings (continued)
Exposure Factor
Recommendation"
Confidence Rating
Time indoors
At Residence:
Ages 1-17 years
18 hrs/day (average)
24 hrs/day (95th percentile)
Ages 1-4, 5-11, and 12-17 years
(use values in Table 9-41 for distribution
data)
Total Time Indoors
Ages 3-17 years
19 hrs/day (total average)
Ages 3-5, 6-8, 9-11, 12-14, and 15-17 years
(use Table 9-3 for mean values spent
indoors weekend and weekday)
Medium
Medium
Medium
Medium
Time outdoors
At Residence
Ages 1-17 years
3 hrs/day (average value)
8 hrs/day (95th percentile)
Ages 1-4, 5-11, and 12-17 years
(use values in Table 9-43 for distribution
data)
Total Time Outdoors
Ages 3-17 years
2 hrs/day (total average)
Medium
Medium
Medium
' For drinking water and food intake, the reader is referred to the specific table within Chapters 3 and 4 to
select the appropriate value, based on the age grouping and percentile of choice.
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Table 1-3. Characterization of variability in exposure factors
Exposure Factors
Breast milk intake rate
Total intake rate for major
food groups
Individual food intake rate
Drinking water intake rate
Fish intake rate for general
population, recreational
marine, recreational
freshwater, and Native
American
Serving size for foods
Home-produced food
intake rates
Soil intake rate
Inhalation rate
Surface area
Soil adherence
Body weight
Time indoors
Time outdoors
Showering time
Occupational tenure
Population mobility
Average
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
/
Median
/
/
/
/
/
/
Upper
percentile
/
/
Qualitative
discussion for
long-term
/
/
/
/
/
Qualitative
discussion for
long-term
/
/
/
/
/
Multiple
Percentiles
/
/
/
/
/
/
/
/
/
Fitted
Distributions
/
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Table 1-4. Proposed set of childhood age groups for agency
exposure assessments
Age groups < 1 year
Birth to < 1 month
1 to < 3 months
3 to < 6 months
6 to < 12 months
Age groups ^ 1 year
1 to < 2 years
2 to < 3 years
3 to < 6 years
6 to < 11 years
11 to< 16 years
16 to < 18 years
18to<21 years3
1 To be considered on a case-by-case basis.
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Appendix A for Chapter 1: Variability and Uncertainty
The sections that follow discuss exposure factors and algorithms for estimating exposure.
Exposure factor values can be used to obtain a range of exposure estimates such as average,
high-end and bounding estimates. It is instructive here to review the general equation for
potential average daily dose (ADDpot):
AT™ Contaminant Concentration x Intake Rate x Exposure Duration
ADD t = *- HA-1^
pot Body Weight x Averaging Time l '
With the exception of the contaminant concentration, all parameters in the above
equation are considered exposure factors and, thus, are treated in fair detail in other chapters of
this handbook. Each of the exposure factors involves humans, either in terms of their
characteristics (e.g., body weight) or behaviors (e.g., amount of time spent in a specific location,
which affects exposure duration). Although the topics of variability and uncertainty apply
equally to contaminant concentrations and the rest of the exposure factors in eq. 1 A-l, the focus
of this appendix is on variability and uncertainty as they relate to exposure factors.
Consequently, examples provided in this appendix relate primarily to exposure factors, although
contaminant concentrations may be used when they better illustrate the point under discussion.
This appendix also is intended to acquaint the exposure assessor with some of the
fundamental concepts and precepts related to variability and uncertainty, together with methods
and considerations for evaluating and presenting the uncertainty associated with exposure
estimates. Subsequent sections in this appendix are devoted to the following topics:
• Distinction between variability and uncertainty;
• Types of variability;
• Methods of confronting variability;
• Types of uncertainty and reducing uncertainty;
• Analysis of variability and uncertainty; and
• Presenting results of variability/uncertainty analysis.
Fairly extensive treatises on the topic of uncertainty have been provided by, for example,
Morgan and Henri on (1990), the National Research Council (NRC) (NRC, 1994) and, to a lesser
extent, the EPA (U.S. EPA, 1992a, 1995). The topic commonly has been treated as it relates to
the overall process of conducting risk assessments. Because exposure assessment is a
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component of the risk-assessment process, the general concepts apply equally to the exposure-
assessment component.
1A.1. VARIABILITY VERSUS UNCERTAINTY
Although some authors have treated variability as a specific type or component of
uncertainty, the EPA (U.S. EPA, 1995) has advised the risk assessor (and, by analogy, the
exposure assessor) to distinguish between variability and uncertainty. Uncertainty represents a
lack of knowledge about factors affecting exposure or risk, whereas variability arises from true
heterogeneity across people, places, or time. In other words, uncertainty can lead to inaccurate
or biased estimates, whereas variability can affect the precision of the estimates and the degree to
which they can be generalized. Most of the data presented in this handbook concern variability.
Variability and uncertainty can complement or confound one another. An instructive
analogy has been drawn by the NRC (1994), based on the objective of estimating the distance
between the earth and the moon. Prior to fairly recent technology developments, it was difficult
to make accurate measurements of this distance, resulting in measurement uncertainty. Because
the moon's orbit is elliptical, the distance is a variable quantity. If only a few measurements
were to be taken without knowledge of the elliptical pattern, then either of the following
incorrect conclusions might be reached:
That the measurements were faulty, thereby ascribing to uncertainty what was
actually caused by variability; or
That the moon's orbit was random, thereby not allowing uncertainty to shed light on
seemingly unexplainable differences that are, in fact, variable and predictable.
A more fundamental error in the above situation would be to incorrectly estimate the true
distance, by assuming that a few observations were sufficient. This latter pitfall—treating a
highly variable quantity as if it were invariant or only uncertain—is probably the most relevant
to the exposure or risk assessor.
Now consider a situation that relates to exposure, such as estimating the average daily
dose by one exposure route: ingestion of contaminated drinking water. Suppose that it is
possible to measure an individual's daily water consumption (and concentration of the
contaminant) exactly, thereby eliminating uncertainty in the measured daily dose. The daily
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dose still has an inherent day-to-day variability, however, due to changes in the individual's
daily water intake or the contaminant concentration in water.
It is impractical to measure the individual's dose every day. For this reason, the exposure
assessor may estimate the ADD on the basis of a finite number of measurements in an attempt to
"average out" the day-to-day variability. The individual has a true (but unknown) ADD, which
has now been estimated on the basis of a sample of measurements. Because the individual's true
average is unknown, it is uncertain how close the estimate is to the true value. Thus, the
variability across daily doses has been translated into uncertainty in the ADD. Although the
individual's true ADD has no variability, the estimate of the ADD has some uncertainty.
The above discussion pertains to the ADD for one person. Now consider a distribution of
ADDs across individuals in a defined population (e.g., the general U.S. population). In this case,
variability refers to the range and distribution of ADDs across individuals in the population. By
comparison, uncertainty refers to the exposure assessor's state of knowledge about that
distribution or about parameters describing the distribution (e.g., mean, standard deviation,
general shape, various percentiles).
As noted by the NRC (1994), the realms of variability and uncertainty have
fundamentally different ramifications for science and judgment. For example, uncertainty may
force decisionmakers to judge how probable it is that exposures have been overestimated or
underestimated for every member of the exposed population, whereas variability forces them to
cope with the certainty that different individuals are subject to exposures both above and below
any of the exposure levels chosen as a reference point.
1A.2. TYPES OF VARIABILITY
Variability in exposure is related to an individual's location, activity, and behavior or
preferences at a particular point in time, as well as pollutant emission rates and
physical/chemical processes that affect concentrations in various media (e.g., air, soil, food, and
water). The variations in pollutant-specific emissions or processes and in individual locations,
activities, or behaviors are not necessarily independent of one another. For example, both
personal activities and pollutant concentrations at a specific location might vary in response to
weather conditions or between weekdays and weekends.
At a more fundamental level, three types of variability can be distinguished:
• Variability across locations (spatial variability);
• Variability over time (temporal variability); and
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• Variability among individuals (inter-individual variability).
Spatial variability can occur at both the regional (macroscale) and the local (microscale)
level. For example, fish intake rates can vary, depending on the region of the country. Higher
consumption may occur among populations located near large bodies of water, such as the Great
Lakes or coastal areas. As another example, outdoor pollutant levels can be affected at the
regional level by industrial activities and at the local level by activities of individuals. In
general, higher exposures tend to be associated with closer proximity to the pollutant source,
whether it be an industrial plant or a source related to a personal activity, such as showering or
gardening. In the context of exposure to airborne pollutants, the concept of a
"microenvironment" has been introduced (Duan, 1982) to denote a specific locality (e.g., a
residential lot or a room in a specific building) where the airborne concentration can be treated
as homogeneous (i.e., invariant) at a particular point in time.
Temporal variability refers to variations over time, whether long- or short-term.
Seasonal fluctuations in weather, pesticide applications, use of woodburning appliances, and
fraction of time spent outdoors are examples of longer-term variability. Examples of shorter-
term variability are differences in industrial or personal activities on weekdays versus weekends
or at different times of the day.
Inter-individual variability can be either of two types: (1) human characteristics such as
age or body weight, and (2) human behaviors such as location and activity patterns. Each of
these variabilities, in turn, may be related to several underlying phenomena that vary. For
example, the natural variability in human weight is due to a combination of genetic, nutritional,
and other lifestyle or environmental factors. Variability arising from independent factors that
combine multiplicatively generally will lead to an approximately lognormal distribution across
the population or across spatial/temporal dimensions.
1A.3. CONFRONTING VARIABILITY
According to NRC (1994), variability can be confronted in four basic ways (Table 1 A-l)
when dealing with science-policy questions surrounding issues such as exposure or risk
assessment. The first way is to ignore the variability and hope for the best. This strategy tends
to work best when the variability is relatively small. For example, the assumption that all adults
weigh 70 kg is likely to be correct within ±25% for most adults.
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The second strategy involves disaggregating the variability in some explicit way in
order to better understand it or reduce it. Mathematical models are appropriate in some cases, as
in fitting a sine wave to the annual outdoor concentration cycle for a particular pollutant and
location. In other cases, particularly those involving human characteristics or behaviors, it is
easier to disaggregate the data by considering all the relevant subgroups or subpopulations. For
example, distributions of body weight could be developed separately for adults, adolescents, and
children and even for males and females within each of these subgroups. Temporal and spatial
analogies for this concept involve measurements on appropriate time scales and choosing
appropriate subregions or microenvironments.
The third strategy is to use the average value of a quantity that varies. Although this
strategy might appear as tantamount to ignoring variability, it needs to be based on a decision
that the average value can be estimated reliably in light of the variability (e.g., when the
variability is known to be relatively small, as in the case of adult body weight).
The fourth strategy involves using the maximum or minimum value for an exposure
factor. In this case, the variability is characterized by the range between the extreme values and
a measure of central tendency. This is perhaps the most common method of dealing with
variability in exposure or risk assessment—to focus on one time period (e.g., the period of peak
exposure), one spatial region (e.g., in close proximity to the pollutant source of concern), or one
subpopulation (e.g., exercising asthmatics). As noted by EPA (U.S. EPA, 1992a), when an
exposure assessor develops estimates of high-end individual exposure and dose, care must be
taken not to set all factors to values that maximize exposure or dose; such an approach will
almost always lead to an overestimate.
1A.4. CONCERN ABOUT UNCERTAINTY
Why should the exposure assessor be concerned with uncertainty? As noted by EPA
(U.S. EPA, 1992a), exposure assessment can involve a broad array of information sources and
analysis techniques. Even in situations where actual exposure-related measurements exist,
assumptions or inferences will still be required, because data are not likely to be available for all
aspects of the exposure assessment. Moreover, the data that are available may be of
questionable or unknown quality. Thus, exposure assessors have a responsibility to present not
just numbers but also a clear and explicit explanation of the implications and limitations of their
analyses.
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Table 1A-1. Four strategies for confronting variability
Strategy
Ignore variability
Disaggregate the
variability
Use the average
value
Use a maximum or
minimum value
Example
Assume that all adults weigh 70 kg
Develop distributions of body
weight for age/gender groups
Use average body weight for adults
Use a lower-end value from the
weight distribution
Comment
Works best when variability is small
Variability will be smaller in each group
Can the average be estimated reliably given what is
known about the variability?
Conservative approach — can lead to unrealistically
high exposure estimate if taken for all factors
Morgan and Henrion (1990) provide an argument by analogy. When scientists report
quantities that they have measured, they are expected to routinely report an estimate of the
probable error associated with such measurements. Because uncertainties inherent in policy
analysis (of which exposure assessment is a part) tend to be even greater than those in the natural
sciences, exposure assessors also should be expected to report or comment on the uncertainties
associated with their estimates.
Additional reasons for addressing uncertainty in exposure or risk assessments (U.S. EPA,
1992a; Morgan and Henrion, 1990) include the following:
• Uncertain information from different sources of different quality often must be
combined for the assessment,
• Decisions need to be made about whether or how to expend resources to acquire
additional information,
• Biases may result in so-called "best estimates" that in actuality are not very accurate,
and
• Important factors and potential sources of disagreement in a problem can be
identified.
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Addressing uncertainty will increase the likelihood that results of an assessment or
analysis will be used in an appropriate manner. Problems rarely are solved to everyone's
satisfaction, and decisions rarely are reached on the basis of a single piece of evidence. Results
of prior analyses can shed light on current assessments, particularly if they are couched in the
context of prevailing uncertainty at the time of analysis. Exposure assessment tends to be an
iterative process, beginning with a screening-level assessment that may identify the need for
more in-depth assessment. One of the primary goals of the more detailed assessment is to reduce
uncertainty in estimated exposures. This objective can be achieved more efficiently if guided by
presentation and discussion of factors thought to be primarily responsible for uncertainty in prior
estimates.
1A.5. TYPES OF UNCERTAINTY AND REDUCING UNCERTAINTY
The problem of uncertainty in exposure or risk assessment is relatively large and can
quickly become too complex for facile treatment unless it is divided into smaller and more
manageable topics. One method of division (Bogen, 1990) involves classifying sources of
uncertainty according to the step in the risk assessment process (hazard identification, dose-
response assessment, exposure assessment, or risk characterization) at which they can occur. A
more abstract and generalized approach preferred by some scientists is to partition all
uncertainties among the three categories of bias, randomness, and true variability. These ideas
are discussed later in some examples.
The EPA (U.S. EPA, 1992a) has classified uncertainty in exposure assessment into three
broad categories:
1. Uncertainty regarding missing or incomplete information needed to fully define
exposure and dose (scenario uncertainty).
2. Uncertainty regarding some parameter (parameter uncertainty).
3. Uncertainty regarding gaps in scientific theory required to make predictions on the
basis of causal inferences (model uncertainty).
Identification of the sources of uncertainty in an exposure assessment is the first step in
determining how to reduce that uncertainty. The types of uncertainty listed above can be further
defined by examining their principal causes. Sources and examples for each type of uncertainty
are summarized in Table 1A-2.
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Because uncertainty in exposure assessments is fundamentally tied to a lack of
knowledge concerning important exposure factors, strategies for reducing uncertainty necessarily
involve reduction or elimination of knowledge gaps. Example strategies to reduce uncertainty
include (1) collection of new data using a larger sample size, an unbiased sample design, a more
direct measurement method, or a more appropriate target population, and (2) use of more
sophisticated modeling and analysis tools.
1A.6. ANALYZING VARIABILITY AND UNCERTAINTY
Exposure assessments often are developed in a phased approach. The initial phase
usually screens out the exposure scenarios or pathways that are not expected to pose much risk to
eliminate them from more detailed, resource-intensive review. Screening-level assessments
typically examine exposures that would fall on or beyond the high end of the expected exposure
distribution. Because screening-level analyses usually are included in the final exposure
assessment, the final document may contain scenarios that differ quite markedly in
sophistication, data quality, and amenability to quantitative expressions of variability or
uncertainty.
According to EPA (U.S. 1992a), uncertainty characterization and uncertainty assessment
are two ways of describing uncertainty at different degrees of sophistication. Uncertainty
characterization usually involves a qualitative discussion of the thought processes used to select
or reject specific data, estimates, scenarios, etc. Uncertainty assessment is a more quantitative
process that may range from simpler measures (e.g., ranges) and simpler analytical techniques
(e.g., sensitivity analysis) to more complex measures and techniques. Its goal is to provide
decisionmakers with information concerning the quality of an assessment, including the potential
variability in the estimated exposures, major data gaps, and the effect that these data gaps have
on the exposure estimates developed.
A distinction between variability and uncertainty was made in section 1A.1. Although
the quantitative process mentioned above applies more directly to variability and the qualitative
approach more to uncertainty, there is some degree of overlap. In general, either method
provides the assessor or decisionmaker with insights to better evaluate the assessment in the
context of available data and assumptions. The following paragraphs describe some of the more
common procedures for analyzing variability and uncertainty in exposure assessments.
Principles that pertain to presenting the results of variability/uncertainty analysis are discussed in
the next section.
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Table 1A-2. Three types of uncertainty and associated sources and examples
Type of uncertainty
Scenario uncertainty
Parameter
uncertainty
Model uncertainty
Sources
Descriptive errors
Aggregation errors
Judgment errors
Incomplete analysis
Measurement errors
Sampling errors
Variability
Surrogate data
Relationship errors
Modeling errors
Examples
Incorrect or insufficient information
Spatial or temporal approximations
Selection of an incorrect model
Overlooking an important pathway
Imprecise or biased measurements
Small or unrepresentative samples
In time, space, or activities
Structurally related chemicals
Incorrect inference on the basis for
correlations
Excluding relevant variables
Several approaches can be used to characterize uncertainty in parameter values. When
uncertainty is high, the assessor may use order-of-magnitude bounding estimates of parameter
ranges (e.g., from 0.1 to 10L for daily water intake). Another method describes the range for
each parameter, including the lower and upper bounds as well as a "best estimate" (e.g., 1.4L per
day) determined by available data or professional judgement.
When sensitivity analysis indicates that a parameter profoundly influences exposure
estimates, the assessor should develop a probabilistic description of its range. If there are
enough data to support their use, standard statistical methods are preferred. If the data are
inadequate, expert judgment can be used to generate a subjective probabilistic representation.
Such judgments should be developed in a consistent, well-documented manner. Morgan and
Henrion (1990) and Rish (1988) describe techniques to solicit expert judgment.
Most approaches to quantitative analysis examine how variability and uncertainty in
values of specific parameters translate into the overall uncertainty of the assessment. Details
may be found in reviews such as Cox and Baybutt (1981), Whitmore (1985), Inman and Helton
(1988), Seller (1987), and Rish and Marnicio (1988). These approaches can generally be
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described (in order of increasing complexity and data needs) as (1) sensitivity analysis, (2)
analytical uncertainty propagation, (3) probabilistic uncertainty analysis, or (4) classical
statistical methods (U.S. EPA, 1992a). The four approaches are summarized in Table 1A-3.
1A.7. PRESENTING RESULTS OF VARIABILITY AND UNCERTAINTY ANALYSIS
Comprehensive qualitative analysis and rigorous quantitative analysis are of little value
for use in the decision-making process if their results are not clearly presented. In this appendix,
variability (the receipt of different levels of exposure by different individuals) has been
distinguished from uncertainty (the lack of knowledge about the correct value for a specific
exposure measure or estimate). Most of the data that are presented in this handbook deal with
variability directly, through inclusion of statistics that pertain to the distributions for various
exposure factors.
Not all approaches historically used to construct measures or estimates of exposure have
attempted to distinguish between variability and uncertainty. The assessor is advised to use a
variety of exposure descriptors—and where possible, the full population distribution—when
presenting the results. This information will provide risk managers with a better understanding
of how exposures are distributed over the population and how variability in population activities
influences this distribution.
Although incomplete analysis is essentially unquantifiable as a source of uncertainty, it
should not be ignored. At a minimum, the assessor should describe the rationale for excluding
particular exposure scenarios; characterize the uncertainty in these decisions as high, medium, or
low; and state whether they were based on data, analogy, or professional judgment. Where
uncertainty is high, a sensitivity analysis can be used to credible upper limits on exposure by
way of a series of "what if questions.
Although assessors have always used descriptors to communicate the kind of scenario
being addressed, the Guidelines for Exposure Assessment (U.S. EPA, 1992a) establish clear
quantitative definitions for these risk descriptors. These definitions were established to ensure
that consistent terminology is used throughout the Agency. The risk descriptors defined in the
guidelines include descriptors of individual risk and population risk. Individual risk descriptors
are intended to address questions dealing with risks borne by individuals within a population,
including not only measures of central tendency (e.g., average or median), but also those risks at
the high end of the distribution. Population risk descriptors refer to an assessment of the extent
of harm to the population being addressed. It can be either an estimate of the number of cases of
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a particular effect that might occur in a population (or population segment) or a description of
what fraction of the population receives exposures, doses, or risks greater than a specified value.
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Table 1A-3. Approaches to quantitative analysis of uncertainty
Approach
Description
Example
Sensitivity
analysis
Changing one input variable at a
time while leaving others constant
to examine effect on output
Fix each input at lower (then
upper) bound while holding others
at nominal values (e.g., medians)
Analytical
uncertainty
propagation
Examining how uncertainty in
individual parameters affects the
overall uncertainty of the exposure
assessment
Analytically or numerically obtain
a partial derivative of the exposure
equation with respect to each input
parameter
Probabilistic
uncertainty
analysis
Varying each of the input
variables over various values of
their respective probability
distributions
Assign probability density function
to each parameter; randomly
sample values from each
distribution and insert them in the
exposure equation (Monte Carlo)
Classical
statistical methods
Estimating the population
exposure distribution directly,
based on measured values from a
representative sample
Compute confidence interval
estimates for various percentiles of
the exposure distribution
The data presented in the Exposure Factors Handbook (U.S. EPA, 1997b) is one of the tools
available to exposure assessors to construct the various risk descriptors.
However, it is not sufficient to merely present the results using different exposure
descriptors. Risk managers should also be presented with an analysis of the uncertainties
surrounding these descriptors. Uncertainty may be presented using simple or very sophisticated
techniques, depending on the requirements of the assessment and the amount of data available.
It is beyond the scope of this handbook to discuss the mechanics of uncertainty analysis in detail.
At a minimum, the assessor should address uncertainty qualitatively by answering questions
such as:
• What is the basis or rationale for selecting these assumptions/parameters—data,
modeling, scientific judgment, Agency policy, "what if considerations, etc.?
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• What is the range or variability of the key parameters? How were the parameter
values selected for use in the assessment? Were average, median, or upper-percentile
values chosen? If other choices had been made, how would the results have differed?
• What is the assessor's confidence (including qualitative confidence aspects) in the
key parameters and the overall assessment? What are the quality and the extent of
the data base(s) supporting the selection of the chosen values?
Any exposure estimate developed by an assessor will have associated assumptions about
the setting, the chemical, and population characteristics and how contact with the chemical
occurs through various exposure routes and pathways. The exposure assessor will need to
examine many sources of information that bear either directly or indirectly on these components
of the exposure assessment. In addition, the assessor will be required to make many decisions
regarding the use of existing information in constructing scenarios and setting up the exposure
equations. In presenting the scenario results, the assessor should strive for a balanced and
impartial treatment of the evidence bearing on the conclusions, with the key assumptions
highlighted. For these key assumptions, one should cite data sources and explain any
adjustments of the data.
The exposure assessor also should qualitatively describe the rationale for selecting of any
conceptual or mathematical models that may have been used. This discussion should address
their verification and validation status, how well they represent the situation being assessed (e.g.,
average vs. high-end estimates), and any plausible alternatives in terms of their acceptance by
the scientific community.
Table 1A-2 summarizes the three types of uncertainty, associated sources, and examples.
Table 1A-3 summarizes four approaches to analyzing uncertainty quantitatively. These are
described further in the Exposure Guidelines (U.S. EPA, 1992a).
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Cox, DC; Baybutt, PC. (1981) Methods for uncertainty analysis: a comparative survey. Risk Anal 1(4):251-258.
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Eskenazi, B; Bradman, A; Castriona, R. (1999) Exposure of children to organophosphate pesticides and their
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Gilbert, RO. (1987) Statistical methods for environmental pollution monitoring. New York: Van Nostrand Reinhold.
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Inman, RL; Helton, JC. (1988) An investigation of uncertainty and sensitivity analysis techniques for computer
models. Risk Anal 8(1):71-91.
Lewis, RG; Fortune, C; Willis, RD; et al. (1999) Distribution of pesticides and polycyclic aromatic hydrocarbons in
house dust as a function of particle size. Environ Health Perspect 107(9):721-7-26.
Morgan, MG; Henrion, M. (1990) Uncertainty: a guide to dealing with uncertainty in quantitative risk and policy
analysis. New York: Cambridge University Press.
Myers, GJ; Davidson, PW. (2000) Does methylmercury have a role in causing developmental disabilities in
children? Environ Health Perspect 108(3):413-419.
Nishioka, MG; Burkholder, HM; Brinkman, MC; et al. (1999) Distribution of 2,4-dihlorophenoxyacetic acid in floor
dust throughout homes following homeowner and commercial lawn application: quantitative effects of children, pets,
and shoes. Environ Sci Technol 33:1359-1365.
NRC (National Research Council). (1994) Science and judgment in risk assessment. Washington, DC: National
Academy Press.
NRDC (National Resources Defense Council). (1997) Our children at risk: the 5 worst environmental threats to their
health. Washington, DC: National Academy Press.
Rish, WR. (1988) Approach to uncertainty in risk analysis. Oak Ridge National Laboratory, Oak Ridge, TN.
ORNL/TM-10746.
Rish, WR; Marnicio, RJ. (1988) Review of studies related to uncertainty in risk analysis. Oak Ridge National
Laboratory, Oak Ridge, TN. ORNL/TM-10776.
Seller, FA. (1987) Error propagation for large errors. Risk Anal 7(4):509-518.
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Selevan, SG; Kimmel, CA; Mendola, P. (2000) Identifying critical windows of exposure for children's health
monograph based on papers developed from the Workshop: Identifying Critical Windows of Exposure for Children's
Health held September 14-15, 1999 in Richmond, VA. Environ Health Perspect 108(Suppl 3):451-455.
U.S. EPA (Environmental Protection Agency). (1983-1989) Methods for assessing exposure to chemical substances.
Volumes 1-13. Office of Toxic Substances, Exposure Evaluation Division, Washington, DC.
U.S. EPA. (1984) Pesticide assessment guidelines subdivision K, exposure: reentry protection. Office of Pesticide
Programs, Washington, DC. EPA/540/9-48/001. Available from NTIS, Springfield, VA; PB-85-120962.
U.S. EPA. (1986a) Standard scenarios for estimating exposure to chemical substances during use of consumer
products. Volumes I and II. Office of Toxic Substance, Exposure Evaluation Division, Washington, DC.
U.S. EPA. (1986b) Pesticide assessment guidelines subdivision U, applicator exposure monitoring. Office of
Pesticide Programs, Washington, DC. EPA/540/9-87/127. Available from NTIS, Springfield, VA; PB-85-133286.
U.S. EPA. (1987) Selection criteria for mathematical models used in exposure assessments: surface water models.
Exposure Assessment Group, Office of Health and Environmental Assessment, Washington, DC.
WPA/600/8-87/042. Available from NTIS, Springfield, VA; PB-88-139928/AS.
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Washington, DC. EPA/540/1-88/001. Available from NTIS, Springfield, VA; PB-89-135859.
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2. BREAST MILK INTAKE
2.1. INTRODUCTION
Breast milk is a potential source of exposure to toxic substances for nursing infants.
Lipid-soluble chemical compounds accumulate in body fat and may be transferred to breast-fed
infants in the lipid portion of breast milk. Because nursing infants obtain most (if not all) of
their dietary intake from breast milk, they are especially vulnerable to exposures to these
compounds. Estimating the magnitude of the potential dose to infants from breast milk requires
information on the milk intake rate (quantity of breast milk consumed per day) and the duration
(months) over which breast-feeding occurs. Information on the fat content of breast milk is also
needed for estimating dose from breast milk residue concentrations that have been indexed to
lipid content.
Several studies have generated data on breast milk intake. Typically, breast milk intake
has been measured over a 24-hour period by weighing the infant before and after each feeding
without changing its clothing (test weighing). The sum of the difference between the measured
weights over the 24-hour period is assumed to be equivalent to the amount of breast milk
consumed daily. Intakes measured using this procedure are often corrected for evaporative water
losses (insensible water losses) between infant weighings (NAS, 1991). Neville et al. (1988)
evaluated the validity of the test weighing approach among bottle-fed infants by comparing the
weights of milk taken from bottles with the differences between the infants' weights before and
after feeding. When test weighing data were corrected for insensible water loss, they were not
significantly different from bottle weight data. Conversions between weight and volume of
breast milk consumed are made using the density of human milk (approximately 1.03 g/mL)
(NAS, 1991). Recently, techniques for measuring breast milk intake using stable isotopes have
been developed. However, few data based on this new technique have been published (NAS,
1991).
Studies among nursing mothers in industrialized countries have shown that intakes
among infants average approximately 750 to 800 g/day (728 to 777 mL/day) during the first 4 to
5 months of life, with a range of 450 to 1200 g/day (437 to 1165 mL/day) (NAS, 1991). Similar
intakes have also been reported for developing countries (NAS, 1991). Infant birth weight has
been shown to influence the rate of intake (NAS, 1991). Infants who are larger at birth and/or
nurse more frequently have been shown to have higher intake rates.
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Key studies on breast milk intake are summarized in the following sections.
Recommended intake rates are based on the results of these key studies, as described in
Exposure Factors Handbook (U.S. EPA, 1997). Relevant data on lipid content and fat intake,
breast-feeding duration, and the estimated percentage of the U.S. population that breast-feeds are
also presented.
2.2. STUDIES ON BREAST MILK INTAKE
2.2.1. Pao et al., 1980
Pao et al. (1980) conducted a study of 22 healthy breast-fed infants to estimate breast
milk intake rates. Infants were categorized as completely breast-fed or partially breast-fed.
Breast-feeding mothers were recruited through LaLeche League groups. Except for one black
infant, all the infants were from white middle-class families in southwestern Ohio. The goal of
the study was to enroll infants as close to 1 month of age as possible and to obtain records near
1, 3, 6, and 9 months of age. However, not all mother/infant pairs participated at each time
interval.
Data were collected for the 22 infants using the test weighing method. Records were
collected for three consecutive 24-hour periods at each test interval. The weight of breast milk
was converted to volume by assuming a density of 1.03 g/mL. Daily intake rates were calculated
for each infant, based on the mean of the three 24-hour periods. Reported mean daily breast milk
intake rates for the infants surveyed at each time interval are presented in Table 2-1. For
completely breast-fed infants, the mean intake rates were 600 mL/day at 1 month of age and
833 mL/day at 3 months of age. Partially breast-fed infants had mean intake rates of
485 mL/day, 467 mL/day, 395 mL/day, and 554 mL/day at 1, 3, 6, and 9 months of age,
respectively. The investigations also noted that intake rates for boys in both groups were slightly
higher than those for girls.
The advantage of this study is that data for both exclusively and partially breast-fed
infants were collected for multiple time periods. Also, data for individual infants were collected
over 3 consecutive days, which would account for some individual variability. However, the
number of infants in the study was relatively small. In addition, this study did not account for
insensible water loss, which may underestimate the amount of breast milk ingested.
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2.2.2. Dewey and Lonnerdal, 1983
Dewey and Lonnerdal (1983) monitored the dietary intake of 20 breast-fed infants
between the ages of 1 and 6 months. Most of the infants in the study were exclusively breast-fed
(five were given some formula, and several were given small amounts of solid foods after
3 months of age). According to the investigations, the mothers were all well educated and
recruited through Lamaze childbirth classes in the Davis area of California. Breast milk intake
volume was estimated on the basis of two 24-hour test weighings per month. Breast milk intake
rates for the various age groups are presented in Table 2-2. Breast milk intake averaged 673,
782, and 896 mL/day at 1, 3, and 6 months of age, respectively.
The advantage of this study is that it evaluated breast-fed infants for a period of 6
months, two 24-hour observations per infant per month. Corrections for insensible water loss
apparently were not made. Also, the number of infants in the study was relatively small.
2.2.3. Butte et al., 1984
Breast milk intake was studied in exclusively breast-fed infants during the first 4 months
of life (Butte et al., 1984). Breast-feeding mothers were recruited through the Baylor Milk Bank
Program in Texas. Forty-five mother/infant pairs participated in the study. However, data for
some time periods (i.e., 1, 2, 3, or 4 months) were missing for some mothers as a result of illness
or other factors. The mothers were from the middle to upper socioeconomic stratum and had a
mean age of 28.0 ±3.1 years. A total of 41 mothers were white, 2 were Hispanic, 1 was Asian,
and 1 was West Indian. Infant growth progressed satisfactorily over the course of the study.
The amount of milk ingested over a 24-hour period was determined using the test
weighing procedure. Test weighing occurred over a 24-hour period for most participants, but
intake among several infants was studied over longer periods (48 to 96 hours) to assess
individual variation in intake. Mean breast milk intake ranged from 723 g/day (702 mL/day) at 3
months to 751 g/day (729 mL/day) at 1 month, with an overall mean of 733 g/day (712 mL/day)
for the entire study period (Table 2-3). Intakes were also calculated on the basis of body weight
(Table 2-3). Based on the results of test weighings conducted over 48 to 96 hours, the mean
variation in individual daily intake was estimated to be 7.9 ± 3.6%.
The advantage of this study is that data for a larger number of exclusively breast-fed
infants were collected than were collected by Pao et al. (1980). However, data were collected
over a shorter time period (i.e., 4 months compared to 6 months) and day-to-day variability was
not characterized for all infants.
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2.2.4. Neville et al., 1988
Neville et al. (1988) studied breast milk intake among 13 infants during their first year of
life. The mothers were all multiparous, nonsmoking, Caucasian women of middle to upper
socioeconomic status living in Denver, CO. All the women in the study practiced exclusive
breast-feeding for at least 5 months. Solid foods were introduced at a mean age of 7 months.
Daily milk intake was estimated by the test weighing method, with corrections for insensible
weight loss. Data were collected daily from birth to 14 days, weekly from weeks 3 through 8,
and monthly until the study period ended at 1 year after inception. The estimated breast milk
intakes for this study are listed in Table 2-4. Mean breast milk intakes were 770 g/day
(748 mL/day), 734 g/day (713 mL/day), 766 g/day (744 mL/day), and 403 g/day (391 mL/day) at
1, 3, 6, and 12 months of age, respectively.
Compared with the previously described studies, data were collected in this study on
numerous days over a relatively long time period (12 months), and they were corrected for
insensible weight loss. However, the intake rates presented in Table 2-4 are estimated, based on
intake during only a 24-hour period. Consequently, these intake rates are based on short-term
data that do not account for day-to-day variability among individual infants. Also, a smaller
number of subjects was included than in the previous studies.
2.2.5. Dewey et al., 1991a, b
The Davis Area Research on Lactation, Infant Nutrition, and Growth (DARLING) study
was conducted in 1986 to evaluate growth patterns, nutrient intake, morbidity, and activity levels
in infants who were breast-fed for at least the first 12 months of life (Dewey et al., 1991a, b).
Seventy-three infants aged 3 months were included in the study. At subsequent time intervals,
the number of infants was somewhat lower as a result of attrition. All infants in the study were
healthy and of normal gestational age and weight at birth, and they did not consume solid foods
until after the first 4 months of age. The mothers were from the Davis area of California and
were highly educated and of "relatively high socioeconomic status."
Breast milk intake was estimated by weighing the infants before and after each feeding
and correcting for insensible water loss. Test weighings were conducted over a 4-day period
every 3 months. The results of the study indicate that breast milk intake declined over the first
12 months of life. This decline was associated with the intake of solid food. Mean breast milk
intake was estimated to be 812 g/day (788 mL/day) at 3 months and 448 g/day (435 mL/day) at
12 months (Table 2-5). Based on the estimated intakes at 3 months of age, variability between
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individuals (coefficient of variation ([CV] = 16.3%) was higher than individual day-to-day
variability ([CV] = 5.4%) for the infants in the study (Dewey et al., 1991a).
The advantages of this study are that data were collected over a relatively long time
period (4 days) at each test interval, which would account for some day-to-day infant variability,
and corrections for insensible water loss were made.
2.3. STUDIES ON LIPID CONTENT AND FAT INTAKE FROM BREAST MILK
Human milk contains over 200 constituents including lipids, various proteins,
carbohydrates, vitamins, minerals, and trace elements as well as enzymes and hormones. The
lipid content of breast milk varies according to the length of time that an infant nurses, and it
increases from the beginning to the end of a single nursing session (NAS, 1991). The lipid
portion accounts for approximately 4% of human breast milk (39 ± 4.0 g/L) (NAS, 1991). This
value is supported by various studies that evaluated lipid content from human breast milk.
Several studies also estimated the quantity of lipid consumed by breast-feeding infants. These
values are appropriate for performing exposure assessments for nursing infants when the
contaminant(s) have residue concentrations that are indexed to the fat portion of human breast
milk.
2.3.1. Butte et al., 1984
Butte et al., (1984) analyzed the lipid content of breast milk samples taken from women
who participated in a study of breast milk intake among exclusively breast-fed infants. The
study was conducted with over 40 women during a 4-month period. The mean lipid content of
breast milk at various infants' ages is presented in Table 2-6. The overall lipid content for the
4-month study period was 34.3 ± 6.9 mg/g (3.4%). The investigators also calculated lipid
intakes from 24-hour breast milk intakes and the lipid content of the human milk samples. Lipid
intake was estimated to range from 23.6 g/day (3.8 g/kg-day) to 28.0 g/day (5.9 g/kg-day).
The number of women included in this study was small, and these women were selected
primarily from middle to upper socioeconomic classes. Thus, data on breast milk lipid content
from this study may not be entirely representative of breast milk lipid content among the U.S.
population. Also, these estimates are based on short-term data, and day-to-day variability was
not characterized.
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2.3.2. Maxwell and Burmaster, 1993
Maxwell and Burmaster (1993) used a hypothetical population of 5000 infants between
birth and 1 year of age to simulate a distribution of daily lipid intake from breast milk. The
hypothetical population represented both bottle-fed and breast-fed infants aged 1 to 365 days. A
distribution of daily lipid intake was developed, based on data in Dewey et al. (1991b) on breast
milk intake for infants at 3, 6, 9, and 12 months and breast milk lipid content and survey data in
Ryan et al. (1991) on the percentage of breast-fed infants under the age of 12 months (i.e.,
approximately 22%). A model was used to simulate intake among 1113 of the 5000 infants that
were expected to be breast-fed. The results of the model indicated that lipid intake among
nursing infants under 12 months of age can be characterized by a normal distribution with a
mean of 26.8 g/day and a standard deviation of 7.4 g/day (minimum intake was 1 g/day,
maximum intake was 51.5 g/day). The model assumes that nursing infants are completely
breast-fed and does not account for infants who are breast-fed longer than 1 year. Based on data
collected by Dewey et al. (1991b), the investigators estimated the lipid content of breast milk to
be 36.7 g/L at 3 months (35.6 mg/g or 3.6%) and 40.2 g/L (39.0 mg/g or 3.9%) at 12 months.
The advantage of this study is that it provides a "snapshot" of daily lipid intake from
breast milk for breast-fed infants. However, these results are based on a simulation model, and
there are uncertainties associated with the assumptions made. The estimated mean lipid intake
rate represents the average daily intake for nursing infants under 12 months of age. These data
are useful for performing exposure assessments when the age of the infant cannot be specified
(i.e., 3 months or 6 months). Also, because intake rates are indexed to the lipid portion of the
breast milk, they may be used in conjunction with residue concentrations indexed to fat content.
However, the study did not generate "new" data. A reanalysis of previously reported data on
breast milk intake and breast milk lipid intake were provided.
2.4. OTHER FACTORS
Other factors associated with breast milk intake include the frequency of breast-feeding
sessions per day, the duration of breast-feeding per event, the duration of breast-feeding during
childhood, and the magnitude and nature of the population that breast-feeds.
2.4.1. Population of Nursing Infants
According to the National Academy of Sciences (NAS), the percentage of breast-feeding
women has changed dramatically over the years (NAS, 1991). The Ross Products Division of
Abbott Laboratories conducted a large national mail survey in 1995 to determine patterns of
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breast feeding during the first 6 months of life. The Ross Laboratory Mothers's Survey was first
developed in 1955 and has since been expanded to include many more infants. Before 1991, the
survey was conducted on a quarterly basis, and approximately 40,000 to 50,000 questionnaires
were mailed each quarter (Ryan, 1997). Beginning 1991, the survey was conducted monthly;
35,000 were mailed each month. Over time, the response rate has been consistently in the range
of 50 ± 5%. In 1989 and 1995, 196,000 and 720,000 questionnaires were mailed, respectively.
Ryan (1997) reported rates of breast-feeding through 1995 and compared them with rates in
1989.
The survey demonstrates recent increases in both the initiation of breast-feeding and
continued breast-feeding at 6 months of age. Table 2-7 presents the proportion of breast-feeding
in hospitals and at 6 months of age by selected demographic characteristics. In 1995, the
incidence of breast-feeding at birth and at 6 months for all infants was approximately 60% and
22%, respectively. The largest increases in the initiation of breast-feeding between 1989 and
1995 occurred among women who (1) were Black, were < 25 years of age, were in the < $10,000
income level, had no more than grade school education, and were living in the South Atlantic
region of the U.S.; (2) had infants of low birth weight; (3) were employed full time outside the
home at the time they received the survey; and (4) participated in the Women, Infants, and
Children (WIC) program. In 1995, as in 1989, the initiation of breast-feeding was highest
among women who were > 35 years of age, in the > $25,000 income group, and college
educated; women who did not participate in the WIC program; and women who were living in
the Mountain and Pacific regions of the U.S.
Data on the actual length of time that infants continue to breast-feed beyond 5 or 6
months are limited (NAS, 1991). However, Maxwell and Burmaster (1993) estimated that
approximately 22% of infants under 1 year of age are breast-fed. This estimate is based on a
reanalysis of the survey data in Ryan et al. (1991) collected by Ross Laboratories (Maxwell and
Burmaster, 1993).
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2.4.2. Intake Rates Based on Nutritional Status
Information on differences in the quality and quantity of breast milk consumed on the
basis of ethnic or socioeconomic characteristics of the population is limited. Lonnerdal et al.
(1976) studied breast milk volume and composition (nitrogen, lactose, proteins) among
underprivileged and privileged Ethiopian mothers. No significant differences were observed
between the data for these two groups. Similar data were observed for well-nourished Swedish
mothers. Lonnerdal et al. (1976) stated that these results indicate that breast milk quality and
quantity are not affected by maternal malnutrition. However, Brown et al. (1986a, b) noted that
the lactational capacity and energy concentration of marginally nourished women in Bangladesh
were "modestly less than in better-nourished mothers." Breast milk intake rates for infants of
marginally-nourished women in this study were 690 ± 122 g/day at 3 months, 722 ±105 g/day at
6 months, and 719 ±119 g/day at 9 months of age (Brown et al., 1986a). Brown et al. (1986a)
observed that breast milk from women with larger measurements of arm circumference and
triceps skinfold thickness had higher concentrations of fat and energy than mothers with less
body fat. Positive correlations between maternal weight and milk fat concentrations were also
observed. These results suggest that milk composition may be affected by maternal nutritional
status.
2.5. RECOMMENDATIONS
The studies described in this section were used in selecting recommended values for
breast milk intake, fat content and fat intake, and other related factors. Although different survey
designs, testing periods, and populations were used in the studies to estimate intake, the mean
and standard deviation estimates reported in these studies are relatively consistent. There are,
however, limitations with the data. Data are not available for infants under 1 month of age. This
subpopulation may be of particular concern, because a larger number of newborns are totally
breast-fed. In addition, with the exception of Butte (1984), data were not presented on a body
weight basis. This is particularly important because intake rates may be higher on a body-weight
basis for younger infants. Also, the data used to derive the recommendations are more than 10
years old, and the sample sizes of the studies were small. Other subpopulations of concern, such
as mothers highly committed to breast-feeding—sometimes for periods longer than 1 year—may
not be captured by the studies presented in this chapter. Further research is needed to identify
these subgroups and to get better estimates of breast milk intake rates. Table 2-8 presents the
confidence rating for breast milk intake recommendations.
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2.5.1. Breast Milk Intake
The breast milk intake rates for nursing infants that have been reported in the studies
described in this section are summarized in Table 2-9. Based on the combined results of these
studies, 742 mL/day is recommended to represent an average breast milk intake rate, and 1033
mL/day represents an upper-percentile intake rate (based on the middle range of the mean plus 2
standard deviations) for infants between the ages of 1 and 6 months of age. The average value is
the mean of the average intakes at 1, 3, and 6 months from the key studies listed in Table 2-9. It
is consistent with the average intake rate of 718 to 777 mL/day estimated by NAS (1991) for
infants during the first 4 to 5 months of life. Intake among older infants is somewhat lower,
averaging 427 mL/day for 12-month-olds (Neville et al. 1988; Dewey et al. 1991a, b). When a
time-weighted average is calculated for the 12-month period, average breast milk intake is
approximately 688 mL/day, and upper-percentile intake is approximately 980 mL/day. Table
2-10 summarizes these recommended intake rates.
2.5.2. Lipid Content and Lipid Intake
Recommended lipid intake rates are based on data from Butte et al. (1984) and Maxwell
and Burmaster (1993). Butte et al. (1984) estimated that average lipid intake ranges from
23.6 ± 7.2 g/day (22.9 ± 7.0 mL/day) to 28.0 ± 8.5 g/day (27.2 ± 8.3 mL/day) in infants between
1 and 4 months of age. These intake rates are consistent with those reported by Maxwell and
Burmaster (1993) of 26.8 ± 7.4 g/day (26.0 ± 7.2 mL/day) for infants under 1 year of age.
Therefore, the recommended breast milk lipid intake rate for infants under 1 year of age is 26.0
mL/day, and the upper-percentile value is 40.4 mL/day (based on the mean plus 2 standard
deviations). The recommended value for breast milk fat content is 4.0% based on data from
NAS (1991), Butte et al. (1984), and Maxwell and Burmaster (1993).
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Table 2-1. Daily intakes of breast milk
Age
Completely breast-fed
1 month
3 months
6 months
Partially breast-fed
1 month
3 months
6 months
9 months
Number of infants
11
2
1
4
11
6
3
Mean ± SD
(mL/day)a
600 ± 159
833
682
485 ± 79
467 ± 100
395 ± 175
<554
Intake Range
(mL/day)
426-989
645-1,000
616-786
398-655
242-698
147-684
451-732
1 Data expressed as mean ± standard deviation.
Source: Pao et al., 1980
Table 2-2. Breast milk intake for infants aged 1 to 6 months
Age
(months)
1
2
3
4
5
6
Number of Infants
16
19
16
13
11
11
Intake
Mean ± SD
(mL/day)
673 ± 192
756 ± 170
782 ± 172
810 ± 142
805 ± 117
896 ± 122
Range
(mL/day)
341-1003
449-1055
492-1053
593-1045
554-1045
675-1096
Source: Dewey and Lonnerdal, 1983
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Table 2-3. Breast milk intake among exclusively breast-fed infants during the
first 4 months of life
Age
(months)
1
2
3
4
Number of
Infants
37
40
37
41
Intake (g/day)
Mean ± SD
751.0 ± 130.0
725.0 ± 131.0
723.0 ± 114.0
740.0 ± 128.0
Intake (g/kg-day)
Mean ± SD
159.0 ±24.0
129.0 ± 19.0
117.0 ±20.0
lll.Oi 17.0
Body Weight3
(kg)
4.7
5.6
6.2
6.7
' Calculated by dividing breast milk intake (g/day) by breast milk intake (g/kg-day).
Source: Butteetal., 1984
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Table 2-4. Breast milk intake during a 24-hour period
Age
(days)
1
2
O
4
5
6
7
8
9
10
11
14
21
28
35
42
49
56
90
120
150
180
210
240
270
300
330
360
Number of Infants
7
10
11
11
12
10
8
9
10
10
8
10
10
13
12
12
10
13
12
13
13
13
12
10
12
11
9
9
Intake (g/day)
Mean ± SD
44 ±71
182 ±86
371 ± 153
451 ± 176
498 ± 129
508 ± 167
573 ± 167
581 ± 159
580 ±76
589 ± 132
615 ± 168
653 ± 154
651 ±84
770 ± 179
668 ± 117
711± 111
709 ± 115
694 ± 98
734 ± 114
711 ± 100
838 ± 134
766 ± 121
721 ± 154
622 ±210
618 ±220
551 ±234
554 ±240
403 ± 250
Range
-3 1-149 a
44-355
209-688
164-694
323-736
315-861
406-842
410-923
470-720
366-866
398-934
416-922
554-786
495-1144
465-930
554-896
559-922
556-859
613-942
570-847
688-1173
508-936
486-963
288-1002
223-871
129-894
120-860
65-770
' Negative value due to insensible water loss correction.
Source: Neville etal., 1988
2-12
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Table 2-5. Breast milk intake estimated by the Darling Study
Age
(months)
3
9
9
12
Number of Infants
73
60
50
42
Intake
Mean ± SD
812± 133
769 ± 171
646 ±217
448 ±251
Source: Dewey etal., 1991b
Table 2-6. Lipid content of human milk and estimated lipid intake among
exclusively breast-fed infants
Age
(months)
1
2
3
4
Number of
Observations
37
40
37
41
Lipid Content
(mg/g)
Mean ± SD
36.2 ±7.5
34.4 ±6.8
32.2 ±7.8
34.8 ± 10.8
Lipid
Content % a
3.6
3.4
3.2
3.5
Lipid
Intake
(g/day)
Mean ± SD
28.0 ±8. 5
25.2 ±7.1
23.6 ±7.2
25.6 ±8.6
Lipid
Intake
(g/kg-day)
Mean ± SD
5.9 ± 1.7
4.4 ± 1.2
3.8 ± 1.2
3.8 ± 1.3
1 Percents calculated from lipid content reported in mg/g.
Source: Butteetal., 1984
2-13
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Table 2-7. Percentage of mothers breast-feeding newborn infants in the
hospital and infants at 5 or 6 months of age in the United States in 1989% by
ethnic background and selected demographic variablesb
Characteristic
All Infants
White
Black
Hispanic
Maternal age (years)
<20
20-24
25-29
30-34
35+
Total family income
< $10,000
$10,000 -$14,999
$15,000 -$24,999
> $25,000
Maternal education
Grade school
High school
College
Maternal employment
Employed full time
Employed part time
Not employed
Birth weight
Low (<2,500 g)
Normal
Parity
Primiparous
Multiparous
WIC participation
Participant
Nonparticipant
Percentage of mothers breast-feeding
In hospital
1989
52.2
58.5
23.0
48.4
30.2
4.2
58.8
65.5
66.5
31.8
47.1
54.7
66.3
31.7
42.5
70.7
50.8
59.4
51.0
36.2
53.5
52.6
51.7
34.2
62.9
1995
59.7
64.3
37.0
61.0
42.8
52.6
63.1
68.1
70.0
41.8
51.7
58.8
70.7
43.8
49.7
74.4
60.7
63.5
58.0
47.7
60.5
61.6
57.8
46.6
71.0
Change
14.4
9.9
60.9
26.0
41.7
16.4
7.3
4.0
5.3
31.4
9.8
7.5
6.6
38.2
16.9
5.2
19.5
6.9
13.7
31.8
13.1
17.1
11.8
36.3
12.9
At 6 months
1989
18.1
21.0
6.4
13.9
5.6
11.5
21.1
29.3
34.0
8.2
13.9
18.9
25.5
11.5
12.4
28.8
8.9
21.1
21.6
9.8
18.8
15.1
21.1
8.4
23.8
1995
21.6
24.1
11.2
19.6
9.1
14.6
22.9
29.0
33.8
11.4
15.4
19.8
28.5
17.1
15.0
31.2
14.3
23.4
25.0
12.6
22.3
19.5
23.6
12.7
29.2
Change
19.3
14.8
75.0
41.0
62.5
27.0
8.5
(0.6)b
39.0
10.8
4.8
11.8
48.7
21.0
8.3
60.7
10.9
15.7
28.6
18.6
29.1
11.8
51.2
22.7
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Table 2-7. Percentage of mothers breast-feeding newborn infants in the
hospital and infants at 5 or 6 months of age in the United States in 1989% by
ethnic background and selected demographic variablesb (continued)
Characteristic
U.S. census region
New England
Middle Atlantic
East North Central
West North Central
South Atlantic
East South Central
West South Central
Mountain
Pacific
Percentage of mothers breast-feeding
In hospital
1989
52.2
47.4
47.6
55.9
43.8
37.9
46.0
70.2
70.3
1995
61.2
53.8
54.6
61.9
54.8
44.1
54.4
75.1
75.1
Change
17.2
13.5
14.7
10.7
25.1
16.4
18.3
7.0
6.8
At 6 months
1989
18.6
16.8
16.7
18.4
13.7
11.5
13.6
28.3
26.6
1995
22.2
19.6
18.9
21.4
18.6
13.0
17.0
30.3
30.9
Change
19.4
16.7
13.2
16.3
35.8
13.0
25.0
7.1
16.2
a The percent change was calculated using the following formula: % breast fed in 1984 - % breast fed in
1989 / % breast fed in 1984.
b Figures in parentheses indicate a decrease in the rate of breast-feeding from 1989 to 1995.
WIC = Women, Infants, and Children supplemental food program.
Source: Ryan, 1997
2-15
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Table 2-8. Confidence in breast milk intake recommendations
Considerations
Rationale
Rating
Study Elements
Level of peer
review
Accessibility
Reproducibility
Focus on factor of
interest
Data pertinent to
U.S.
Primary data
Currency
Adequacy of data
collection period
Validity of
approach
Study size
Representativeness
of the population
Characterization of
variability
All key studies are from peer-reviewed literature.
Papers are widely available from peer review journals.
Methodology used was clearly presented.
The focus of the studies was on estimating breast milk
intake.
Subpopulations of the U.S. were the focus of all the key
studies.
All the studies were based on primary data.
Studies were conducted between 1980-1997.
Infants were not studied long enough to fully characterize
day-to-day variability. With the exception of Neville et
al. (1988), the measurements were made for frequency
(e.g., once a month) and the data may not represent the
potential first year of lactation (both for less than 1 month
of age and for longitudinal measurements of more than 6
months).
Methodology uses changes in body weight as a surrogate
for total ingestion. This is the best methodology there is
to estimate breast milk ingestion. Mothers were
instructed in the use of infant scales to minimize
measurement errors. Three out of the five studies
corrected data for insensible water loss.
The sample sizes used in the key studies were fairly small
(range 13-73).
Population are representative of the general mother-infant
pair population.
Not very well characterized. Infants under 1 month not
captured, mothers committed to breast-feeding over 1
year not captured.
High
High
High
High
High
High
Medium-
High
Medium
Low
Low
High
Low
2-16
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Table 2-8. Confidence in breast milk intake recommendations (continued)
Considerations
Lack of bias in
study design (high
rating is desirable)
Measurement error
Rationale
Bias in the studies was not characterized. Two out of five
studies corrected for insensible water loss.
All mothers were well-educated and trained in the use of
the scale, which helped minimize measurement error.
Not correcting for insensible water loss may
underestimate intake. Mothers selected for the studies
were volunteers; therefore, response rate does not apply.
Population studied may introduce some bias in the results
(see above).
Rating
Low
Medium
Other Elements
Number of studies
Agreement between
researchers
Overall Rating
There are five key studies.
There is good agreement among researchers.
Studies were well-designed. Results were consistent.
Sample size was fairly low. Variability cannot be
characterized due to limitations in data collection period.
Medium
High
Medium
2-17
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Table 2-9. Breast milk intake rates derived from key studies
Age
(months)
1
3
6
9
12
Mean intake
(mL/day)
600
729
747
673
weighted avg = 702
833
702
712
782
788
weighted avg = 759
682
744
896
747
weighted avg = 765
600
627
avg = 622
391
435
weighted avg = 427
Number of
Children
11
37
13
16
2
37
12
16
73
1
13
11
60
12
50
9
42
12-month time weighted average
688
Upper Percentile
(mL/day)a
918
981
1095
1057
1007b
—
923
934
1126
1046
1025b
—
978
1140
1079
1059b
1027
1049
1038
877
923
900
Range 900-1059
(middle of the
range 980)
Reference
Paoetal., 1980
Butteetal., 1984
Neville et al., 1988
Dewey and Lonnerdal, 1983
Paoetal., 1980
Butteetal., 1984
Neville et al., 1988
Dewey and Lonnerdal, 1983
Dewey etal., 199 Ib
Paoetal., 1980
Neville et al., 1988
Dewey and Lonnerdal, 1983
Dewey etal., 199 Ib
Neville et al., 1988
Dewey etal., 199 Ib
Neville et al., 1988
Dewey etal., 1991a, b
a Upper percentile is reported (mean plus 2 standard deviations), except as noted.
b Middle of the range.
Table 2-10. Summary of recommended breast milk and lipid intake rates
2-18
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Age
Breast Milk
1-6 months
1 2-month-average
Lipidsa
< 1 Year
Mean intake
(mL/day)
742
688
26
Upper percentile
(mL/day)
1033.0
980.0
40.4
1 The recommended value for the lipid content of breastmilk is 4%.
2-19
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REFERENCES FOR CHAPTER 2
Brown, KH; Akhtar, NA; Robertson, AD; et al. (1986a) Lactational capacity of marginally nourished mothers:
relationships between maternal nutritional status and quantity and proximate composition of milk. Pediatrics
78:909-919.
Brown, KH; Robertson, AD; Akhtar, NA. (1986b) Lactational capacity of marginally nourished mothers: infants'
milk nutrient consumption and patterns of growth. Pediatrics 78:920-927.
Butte, NF; Garza, C; Smith, EO; et al. (1984) Human milk intake and growth in exclusively breast-fed infants.
JPediatr 104:187-195.
Dewey, KG; Lonnerdal, B. (1983) Milk and nutrient intake of breast-fed infants from 1 to 6 months: relation to
growth and fatness. J Pediatr Gastroenterol Nutr 2:497-506.
Dewey, KG; Heinig, J; Nommsen LA; et al. (1991a) Maternal versus infant factors related to breast milk intake and
residual volume: the DARLING study. Pediatrics 87:829-837.
Dewey, KG; Heinig, J; Nommsen, L; et al. (1991b) Adequacy of energy intake among breast-fed infants in the
DARLING study: relationships to growth, velocity, morbidity, and activity levels. J Pediatr 119:538-547.
Lonnerdal, B; Forsum, E; Gebre-Medhim, M; et al. (1976) Breast milk composition in Ethiopian and Swedish
mothers: lactose, nitrogen, and protein contents. Am JClin Nutr 29:1134-1141.
Maxwell, NI; Burmaster, DE. (1993) A simulation model to estimate a distribution of lipid intake from breast milk
during the first year of life. J Expo Anal Environ Epidemiol 3:383^06.
NAS (National Academy of Sciences). (1991) Nutrition during lactation. Washington, DC: National Academy
Press.
Neville, MC; Keller, R; Seacat, J; et al. (1988) Studies in human lactation: milk volumes in lactating women during
the onset of lactation and full lactation. Am J Clin Nutr 48:1375-1386.
Pao, EM; Hines, JM; Roche, AF. (1980) Milk intakes and feeding patterns of breast-fed infants. Journal Am Diet
Assoc 77:540-545.
Ryan, AS. (1997) The resurgence of breast-feeding in the United States. Pediatrics 99(4):E12.
Ryan, AS; Rush, D; Krieger, FW; et al. (1991) Recent declines in breast feeding in the United States, 1984-1989.
Pediatrics 88:719-727.
U.S. EPA (Environment Protection Agency). (1997) Exposure factors handbook. National Center for Environmental
Assessment, Washington, DC. EPA/600/P-95/002Fa,b,c.
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3. FOOD INTAKE
3.1. INTRODUCTION
Ingestion of contaminated foods is a potential pathway of exposure to toxic chemicals
among children. Fruits, vegetables, and grains may become contaminated with toxic chemicals
by several different pathways. Ambient pollutants from the air may be deposited on or absorbed
by the plants or dissolved in rainfall or irrigation waters that contact the plants. Pollutants may
also be absorbed through plant roots from contaminated soil and ground water. The addition of
pesticides, soil additives, and fertilizers may also result in food contamination. Meat, poultry,
and dairy products can become contaminated if animals are exposed to contaminated media (i.e.,
soil, water, or feed crops). Contaminated finfish and shellfish are also potential sources of
human exposure to toxic chemicals. Pollutants are carried in the surface waters but also may be
stored and accumulated in the sediments as a result of complex physical and chemical processes.
Consequently, finfish and shellfish are exposed to these pollutants and may become sources of
contaminated food. Intake rates for home-produced food products are needed to assess exposure
to local contaminants present in homegrown or home-caught foods.
Children's exposure from food ingestion may differ from that of adults because of
differences in the type and amounts of food eaten. Also, for many foods, the intake per unit
body weight is greater for children than for adults. The most common foods eaten by children
include nonfat milk solids, apple juice, fresh apples, orange juice, fresh pears, milk fat and
solids, fresh peaches, carrots, lean beef, milk sugar (lactose), fresh bananas, milled rice,
succulent garden peas, succulent garden beans, oats, soybean oil, coconut oil, and wheat flour
(Goldman, 1995).
The primary sources of recent information on consumption rates of foods among children
are USDA's Nationwide Food Consumption Survey (NFCS) and the USD A Continuing Survey
of Food Intakes by Individuals (CSFII). Data from the 1989-1991 and 1994-1996 CSFIIs have
been used in various studies to generate children's per capita intake rates for both individual
foods and the major food groups. Earlier studies have used the NFCS from 1977-1978 or
1987-1988. Because data from the 1989-1991 and 1994-1996 CSFIIs are available, data from
the older surveys are not reported here, except in the case of data on homegrown foods, which
are based on the 1987-1988 NFCS, and serving size information, which is based on the
1977-1978 NFCS. Older USDA data analyses can be found Exposure Factors Handbook (U.S.
EPA 1997).
5-1
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It should be noted that a variety of terms may be used to define intake (e.g., consumer-
only intake, per capita intake, as-consumed intake, dry-weight intake. These terms are defined
below to assist the reader in interpreting and using the intake rates that are appropriate for the
exposure scenario being assessed.
Consumer-only intake is defined as the quantity of foods consumed only by children who
ate these food items during the survey period. Per capita intake rates are generated by averaging
consumer-only intakes over the entire population of children (i.e., both users and nonusers). In
general, per capita intake rates are appropriate for use in exposure assessment for which average
dose estimates for children are of interest, because they represent children who ate the foods
during the survey period, as well as children who may eat the food items at some time but did
not consume them during the survey period. Intake rates for the major food categories include
all forms of that food type. For example, total fruit intake refers to the sum of all fruits
consumed in a day, including canned, dried, frozen, and fresh fruits. Likewise, total vegetable
intake refers to the sum of all vegetables consumed in a day, including canned, dried, frozen, and
fresh vegetables.
Intake rates may be presented on an "as consumed" (e.g., cooked) basis or on the basis of
an uncooked weight. As-consumed intake rates (g/day) are based on the weight of the food in
the form in which it is consumed and should be used in assessments where the basis for the
contaminant concentrations in foods is whole weight. When data are based on as-consumed
form, corrections to account for changes in portion sizes from cooking losses are generally not
required. When dry-weight contaminant concentrations in foods are available, dry-weight intake
rates must be used. Dry-weight intake rates are based on the weight of the food consumed after
the moisture content has been removed.
The food ingestion rate values provided in this handbook are generally expressed as "as
consumed" since this is the fashion in which data are reported by survey respondents. This is
important because concentration data to be used in the dose equation are generally measured in
uncooked food samples. In most situations, the only practical choice is to use the as-consumed
ingestion rate and the uncooked concentration. However, it should be recognized that cooking
generally results in some reductions in weight (e.g., loss of moisture) and that if the mass of the
contaminant in the food remains constant, then the concentration of the contaminant in the
cooked food item will increase. Therefore, if the as-consumed ingestion rate and the uncooked
concentration are used in the dose equation, the dose may be underestimated. On the other hand,
cooking may cause a reduction in mass of contaminant and other ingredients, such that the
overall concentration of contaminant does not change significantly. In this case, combining
O O
3-2
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cooked ingestion rates and uncooked concentration will provide an appropriate estimate of dose.
Ideally, food concentration data should be adjusted to account for changes after cooking; then
the as-consumed intake rates are appropriate. In the absence of data, it is reasonable to assume
that no change in contaminant concentration occurs after cooking. Except for general population
fish consumption and home-produced foods, uncooked intake rate data were not available for
presentation in this handbook. Data on the general population fish consumption are presented in
this handbook on both an as-consumed and an uncooked basis (Chapter 3). It is important for
the assessor to be aware of these issues and to choose intake rate data that best match the
concentration data that are being used.
Estimating source-specific exposures to toxic chemicals in fruits and vegetables may also
require information on the amount of fruits and vegetables that are exposed to or protected from
contamination as a result of cultivation practices or the physical nature of the food product itself
(e.g., those having protective coverings that are removed before eating would be considered
protected) or the amount grown beneath the soil (e.g., most root crops such as carrots). The
percentages of foods grown above and below ground will be useful when the concentrations of
contaminants in foods are estimated from concentrations in soil, water, and air. For example,
vegetables grown below ground may be more likely to be contaminated by soil pollutants, but
leafy above-ground vegetables may be more likely to be contaminated by deposition of air
pollutants on plant surfaces.
The purpose of this section is to provide (1) intake data for individual foods, the major
food groups, and total foods among children, including homegrown foods; (2) guidance for
converting between as-consumed and dry-weight intake rates; and (3) intake data for exposed
and protected fruits and vegetables and those grown below ground. Recommendations are based
on average and upper-percentile intake among the general population of the U.S.
3.2. INTAKE RATE DISTRIBUTIONS FOR VARIOUS FOOD TYPES
3.2.1. USDA, 1999
The Supplemental Children's Survey to the 1994-1996 Continuing Survey of Food
Intakes by Individuals (CSFII1998) was conducted in response to the Food Quality Protection
Act of 1996, which required the USDA to provide data from a larger sample of children for use
by the EPA in estimating exposure to pesticide residues in the diets of children. The 1998
survey adds intake data from 5559 children from birth through 9 years of age to the intake data
collected from 4253 children of the same age who participated in the CSFII 1994-1996. The
-------
1994-1996 survey included the collection of data from persons of all ages. Both surveys are
nationally representative samples of persons living in U.S. households.
The CSFII 1998 was designed to be combined with the CSFII 1994-1996; thus, the
approaches to sample selection, data collection, data file preparation, and weighting were
consistent. The design, methodology, and operation of the CSFII 1994-1996 are detailed in a
separate report (Tippett and Cypel, 1997). The CSFII 1998 was conducted between December
1997 and December 1998 by USDA's Agricultural Research Service.
The results presented in Tables 3-1 through 3-14 include national probability estimates
based on all 4 years of the CSFII (1994-1996 and 1998) for children ages 9 years and under and
on CSFII 1994-1996 only for individuals ages 10 years and over. The results are weighted to
adjust for differential rates of sample selection and nonresponse, to calibrate the sample to match
population characteristics that are correlated with eating behavior, and to equalize intakes over
the four quarters of the year and the 7 days of the week. Users should note that some weights
calculated for the purpose of combining data from 1994-1996 with those from 1998 yield
estimates for individuals 12 through 19 years of age that may be slightly different from estimates
issued earlier from the CSFII 1994-1996.
The sample sizes on which estimates are based are provided in the tables; readers using
data for young children should note that 503 breast-fed children were excluded from the
estimates. Pasters (individuals reporting no food or beverage consumed for the day) were
included in the calculations. In general, the sample sizes for each sex-age group provide a
sufficient level of precision to ensure statistical reliability of the estimates. For CSFII 1998, the
overall day l(the first day surveyed) response rate was 85.6% and the overall 2-day response rate
was 81.7%. The CSFII 1994-1996 day 1 response rate was 80%, and the 2-day response rate
was 76.1%.
Tables that present data on mean intakes or mean percentages are based on respondents'
day 1 intakes so that readers can track trends over time from surveys with different numbers of
days of dietary information. Tables that present percentages of individuals meeting
recommendations are based on respondents' 2-day average intakes. The data for food intakes
from this analysis are presented in Tables 3-1 through 3-14. Data are presented for mean
quantities in grams of food products/groups consumed per individual for 1 day and the percent
consuming. The foods presented include grain products; vegetables; fruits; milk and milk
products; meat, poultry, and fish; and beverages. Data are also provided for eggs, legumes, nuts
and seeds, fats and oils, and sugars and sweets.
3-4
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3.2.2. U.S. EPA, 2000
EPA's National Center for Environmental Assessment (NCEA) analyzed 3 years of data
from USDA's CSFII to generate distributions of intake rates for various food items/groups.
USDA conducts CSFII annually to "assess food consumption behavior and nutritional content of
diets for policy implications relating to food production and marketing, food safety, food
assistance, and nutrition education" (USDA, 1995). The survey uses a statistical sampling
technique designed to ensure that all seasons, geographic regions of the U.S., and demographic
and socioeconomic groups are represented. Using a stratified sampling technique, individuals of
all ages living in selected households in the 50 states and Washington, DC, were surveyed.
Individuals provided data for 2 nonconsecutive days, based on 24-hour recall. The 2-day
response rate for the 1994-1996 CSFII was approximately 76%. Data from the 1994, 1995, and
1996 CFSII were combined into a single data set to increase the number of observations
available for analysis. Approximately 15,000 individuals provided intake data over the 3 survey
years (USDA, 1998).
The food groups selected for this analysis include the major food groups: total fruits, total
vegetables, total grains, total meats, and total dairy. Individual foods include fruit and vegetable
items such as apples, bananas, peaches, pears, strawberries, and other berries; individual
vegetables such as asparagus, beets, broccoli, cabbage, carrots, corn, cucumbers, lettuce, lima
beans, okra, onions, peas, peppers, pumpkin, snap beans, tomatoes, and white potatoes; fruits and
vegetables, categorized as exposed, protected and roots; and various USDA categories (i.e.,
citrus and other fruits and dark green, deep yellow, and other vegetables). Individual meats
include beef, eggs, game, pork, and poultry; individual grain items include breads, breadfast
foods, cereals, pasta, rice, snacks, and sweets. Intake rates of total vegetables, tomatoes, and
white potatoes, total meats, fish, beef, pork, poultry, dairy, eggs, and total grains were adjusted
to account for the amount of these food items eaten as meat and grain mixtures as described in
Appendix 3 A. Food items/groups were identified in the CSFII database according to USDA-
defmed food codes. Appendix 3B presents the codes and definitions used to determine the
various food groups used in the analysis. Intake rates for these food items/groups represent
intake of all forms of the product (i.e., home produced and commercially produced).
Individual identifiers in the database were used throughout the analysis to categorize
populations according to demographics. These identifiers included identification number, age,
body weight, weighting factor, and number of days that data were reported. Distributions of
intake were determined for children who provided data for 2 days of the survey. Individuals who
3-5
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did not provide information on body weight or for whom identifying information was
unavailable were excluded from the analysis. Two-day average intake rates were calculated for
all individuals in the database for each of the food items/groups. These average daily intake
rates were divided by each individual's reported body weight to generate intake rates in units of
grams per kilogram of body weight per day (g/kg-day). The data were also weighted according
to the 2-day weights provided in the 1994-1996 CSFII. USDA sample weights are calculated to
account for inherent biases in the sample selection process and to adjust the sample population to
reflect the national population.
Summary statistics for individual intake rates were generated on a per capita basis. That
is, both users and nonusers of the food item were included in the analysis. Mean consumer-only
intake rates may be calculated by dividing the mean per capita intake rate by the percent of the
population consuming the food item of interest. Intake data from the CSFII are based on "as
eaten" (i.e., cooked or prepared) forms of the food items/groups. Thus, corrections to account
for changes in portion sizes from cooking losses are not generally required. Summary statistics
included are number of weighted and unweighted observations, percentage of the population
using the food item/group being analyzed, mean intake rate, standard error, and percentiles of the
intake rate distribution (i.e., 0, 1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 99th, and 100th percentile or
maximum observed in the survey). Data were provided for the total population using the food
item being evaluated and for several age groups of children, including < 1, 1-2, 3-5, 6-11, and
12-19 years. The total numbers of individuals in the data set, by age group are presented in
Table 3-15. The food analysis was accomplished using the SAS statistical programming system
(SAS, 1990).
The results of this analysis are presented in Table 3-16 for total fruits, total vegetables,
total grains, total meats, total fish, and total dairy products. Table 3-17 provides data for
individual foods, and Table 3-18 for the various USDA categories. The data for
exposed/protected and root food items are presented in Table 3-19. Because the results are
presented in units of g/kg-day, use of these data in calculating potential dose does not require the
body-weight factor to be included in the denominator of the average daily dose (ADD) equation.
It should be noted that converting these intake rates into units of g/day by multiplying by a single
average body weight is inappropriate, because individual intake rates were indexed to the
reported body weights of the survey respondents. However, if there is a need to compare the
intake data presented here to intake data in units of g/day, a body weight for the age group of
interest, as presented in Chapter 10 of this handbook, may be used.
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The distribution of average daily intake rates generated using short-term data (e.g., 2-
day) does not necessarily reflect the long-term distribution of average daily intake rates. The
distributions generated from short-term and long-term data will differ to the extent that each
individual's intake varies from day to day; the distributions will be similar to the extent that
individual's intakes are constant from day to day.
Day-to-day variation in intake among individuals will be great for foods that are highly
seasonal and for foods that are eaten year-round but that are not typically eaten every day. For
these foods, the intake distribution generated from short-term data will not be a good reflection
of the long-term distribution. On the other hand, for broad categories of foods (e.g., vegetables)
that are eaten on a daily basis throughout the year with minimal seasonality, the short-term
distribution may be a reasonable approximation of the true long-term distribution, although it
will show somewhat more variability. Distributions are shown only for the major food groups
and broad categories of foods. For individual foods, only the mean standard deviation and
percent consuming are provided. Because of the increased variability of the short-term
distribution, the short-term upper percentiles shown here will overestimate somewhat the
corresponding percentiles of the long-term distribution.
The advantages of using the 1949-1996 CSFII data set are that the data are expected to
be generally representative of the U.S. population and that it includes data on a wide variety of
food types. The data set is the most recent of a series of publicly available USDA data sets, and
should reflect recent eating patterns in the U.S. The data set includes 3 years of intake data
combined and is based on a 2-day survey period. Short-term dietary data may not accurately
reflect long-term eating patterns. This is particularly true for the tails (extremes) of the
distribution of food intake. In addition, the adjustment for including mixtures adds uncertainty
to the intake rate distributions. The calculation for including mixtures assumes that intake of any
mixture includes all of the foods identified in Table 3A-1 in Appendix A in the proportions
specified in that table. This may under- or overestimate intake of certain foods among some
individuals.
3.3. FISH INTAKE RATES
3.3.1. General Population Studies
3.3.1.1. U.S. EPA, 1996
EPA's Office of Water used the 1989, 1990, and 1991 CSFII data to generate fish intake
estimates. Participants in the CSFII provided 3 consecutive days of dietary data. For the first
day's data, participants supplied dietary recall information to an in-home interviewer. Second-
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and third-day dietary intakes were recorded by participants. Data collection for the CSFII started
in April of the given year and was completed in March of the following year.
The CSFII contains 469 fish-related food codes; survey respondents reported
consumption across 284 of these codes. Respondents estimated the weight of each food that they
consumed. The fish component (by weight) of these foods was calculated using data from the
recipe file for release 7 of the USD A's Nutrient Data Base for Individual Food Intake Surveys.
The amount offish consumed by each individual was then calculated by summing, over all fish-
containing foods, the product of the weight of food consumed and the fish component (i.e., the
percentage fish by weight) of the food.
The recipe file also contains cooking loss factors associated with each food. These were
used to convert, for each fish-containing food, the as-eaten fish weight consumed into an
uncooked equivalent weight offish. Analyses offish intake were performed on both an as-eaten
and uncooked basis.
Each (fish-related) food code was assigned by EPA a habitat type of either freshwater/
estuarine or marine. Food codes were also designated as finfish or shellfish. Average daily
individual consumption (g/day) for a given fish type-by-habitat category (e.g., marine finfish)
was calculated by summing the amount offish consumed by the individual across the 3 reporting
days for all fish-related food codes in the given fish-by-habitat category and then dividing by 3.
Individual consumption per day consuming fish (g/day) was calculated similarly except that total
fish consumption was divided by the specific number of survey days the individual reported
consuming fish; this was calculated for fish consumers only (i.e., those consuming fish on at
least one of the three survey days). The reported bodyweight of the individual was used to
convert consumption in g/day to consumption in g/kg-day.
A total of 11,912 respondents in the combined data set who had three-day dietary intake
data. Survey weights were assigned to this data set to make it representative of the U.S.
population with respect to various demographic characteristics related to food intake.
U.S. EPA (1996) reported means, medians, upper percentiles, and 90% interval estimates
for the 90th, 95th, and 99th percentiles. The 90% interval estimates are nonparametric estimates
from bootstrap techniques. The bootstrap estimates result from the percentile method, which
estimates the lower and upper bounds for the interval estimate by the lOOoc percentile and 100(1-
oc) percentile estimates from the nonparametric distribution of the given point estimate. Analyses
offish intake were performed on an as-eaten as well as on an uncooked equivalent basis and on a
g/day and g/kg-day basis.
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Table 3-20 presents data for daily average per capita fish consumption by age and gender
in g/day and in mg/kg/day, as consumed. Table 3-21 provides consumer-only data in units of
g/day and mg/kg/day, as consumed. Tables 3-22 and 3-23 provide similar data on an uncooked
basis. These data are presented by selected age groupings (14 years and under and 15-44 years)
and gender.
The advantages of this study are its large size, its relative currency, and its
representativeness. In addition, through use of the USDA recipe files, the analysis identified all
fish-related food codes and estimated the percent fish content of each of these codes. By
contrast, some analyses of the USDA NFCSs that reported per capita fish intake rates ( e.g., Pao
et al., 1982; USDA, 1992) excluded certain fish-containing foods (e.g., fish mixtures, frozen
plate meals) in their calculations.
The Office of Water is currently in the process of analyzing data from the 1994, 1995,
and 1996 CSFIIs. Total fish intake was estimated from the 1994-1996 CSFII by NCEA (see
section 3.2). The data will be included in this handbook when they are available.
3.3.1.2. Tsang and Klepeis, 1996
EPA's National Human Activity Pattern Survey (NHAPS) collected information for the
general population on the duration and frequency of time spent in selected activities and time
spent in selected microenvironments via 24-hour diaries. More than 9000 individuals from the
48 contiguous states participated in NHAPS. Approximately 4700 participants also provided
information on seafood consumption. More than 900 of these participants were children
between the ages of 1 and 17 years. The survey was conducted between October 1992 and
September 1994. Data were collected on (1) the number of people who ate seafood in the last
month, (2) the number of servings of seafood consumed, and (3) whether the seafood consumed
was caught or purchased (Tsang and Klepeis, 1996). The participant responses were weighted
according to selected demographics such as age, gender, and race to ensure that results were
representative of the U.S. population. Of the 900 children who participated in the survey,
approximately 43% reported eating seafood (including shellfish, eels, or squid) in the last month.
The number of servings per month were categorized in ranges of 1-2, 3-5, 6-10, 11-19, and 20+
servings per month (Table 3-24). The highest number of respondents for all ages of children had
one to two servings per month. Most of the respondents purchased the seafood they ate (Table
3-24).
Intake data were not provided in the survey. However, intake offish can be estimated
using the information from this study on the number of servings offish eaten and serving size
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data for each age group from other studies (e.g., Pao et al., 1982) (see section 3.7). Using this
mean value for serving size and the assumption that the average child eats one to two servings
per month (Table 3-24), the age-specific amount of seafood eaten per month can be estimated.
The advantages of the NHAPS is that the data were collected for a large number of
individuals and are representative of the U.S. general population. However, evaluation of
seafood intake was not the primary purpose of the study, and the data do not reflect the actual
amount of seafood that was eaten. However, using the assumption described above, the
estimated seafood intake from this study is comparable to that observed in the EPA CSFII
analysis. It should be noted that an all-inclusive description for seafood was not presented in
Tsang and Klepeis (1996). It is not known whether processed or canned seafood and seafood
mixtures are included in the seafood category.
3.3.2. Freshwater Recreational Study
The Michigan Sport Anglers Fish Consumption Survey gathered data from a stratified
random sample of Michigan residents who had fishing licenses. The sample was divided into 18
cohorts, with one cohort receiving a mail questionnaire each week between January and May
1989. The survey included both a short-term recall component that recorded respondents' fish
intake over a 7-day period and a usual frequency component. For the short-term component,
respondents were asked to identify all household members and list all fish meals consumed by
each household member during the past 7 days. The source of the fish for each meal was
requested (i.e., self-caught, gift, market, or restaurant). Respondents were asked to categorize
serving size by comparison with pictures of 8 oz. fish portions; serving sizes could be designated
as "about the same size," "less," or "more." Data on fish species, locations of self-caught fish,
and methods of preparation and cooking were also obtained.
The usual frequency component of the survey asked about the frequency offish meals
during each of the four seasons and requested respondents to give the overall percentage of
household fish meals that came from recreational sources. A sample of 2600 individuals was
selected from state records to receive survey questionnaires. A total of 2334 survey
questionnaires were deliverable, and 1104 were completed and returned, giving a response rate
of 47.3%.
In an analysis of the survey data by West et. al. (1989), the authors did not attempt to
generate the distribution of recreationally caught fish intake in the survey population. EPA
obtained the raw data of this survey for the purpose of generating fish intake distributions and
other specialized analyses.
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As described elsewhere in this handbook, percentiles of the distribution of average daily
intake reflective of long-term consumption patterns cannot in general be estimated using
short-term (e.g., one-week) data. Such data can be used to estimate mean average daily intake
rates (reflective of short- or long-term consumption); in addition, short-term data can serve to
validate estimates of usual intake based on longer recall.
EPA first analyzed the short-term data with the intent of estimating mean fish intake
rates. In order to compare these results with those based on usual intake, only respondents who
had information on both short-term and usual intake were included in this analysis. For the
analysis of the short-term data, EPA modified the serving size weights used by West et al.
(1989), which were 5, 8 and 10 oz., respectively, for portions that were less than, about the same
as, and more than the 8 oz. picture. EPA examined the percentiles of the distribution offish
meal sizes reported in Pao et al. (1982) derived from the 1977-1978 USDANFCS and observed
that a lognormal distribution provided a good visual fit to the percentile data. Using this
lognormal distribution, the mean values for serving sizes greater than 8 oz. and for serving sizes
at least 10% greater than 8 oz. were determined. In both cases, a serving size of 12 oz. was
consistent with the Pao et al. (1982) distribution. The weights used in the EPA analysis, then,
were 5, 8, and 12 oz. respectively, for fish meals described as less than, about the same as, and
more than the 8 oz. picture. It should be noted that the mean serving size from Pao et al. (1982)
was about 5 oz., well below the value of 8 oz. most commonly reported by respondents in the
West et al. (1989) survey.
Table 3-25 displays the mean number of total and recreational fish meals for each
household member between age 1 and 20 years, based on the 7-day recall data. Also shown are
mean fish intake rates derived by applying the weights described above to each fish meal. Intake
was calculated on both a g/day and g/kg-day basis. This analysis was restricted to individuals
who ate fish and who resided in households reporting some recreational fish consumption during
the previous year. About 75% of survey respondents (i.e., licensed anglers) and about 84% of
respondents who fished in the prior year reported some household recreational fish consumption.
The advantages of this data set and analysis are that the survey was relatively large and
contained both short-term and usual intake data. The response rate of this survey, 47%, was
relatively low. This study was conducted in the winter and spring months, a period that does not
include the summer months, when peak fishing activity can be anticipated, leading to the
possibility that intake results based on the 7-day recall data may understate individuals' usual
(annual average) fish consumption.
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3.3.3. Native American Subsistence Studies
3.3.3.1. Columbia River Inter-Tribal Fish Commission (CRITFC), 1994
CRITFC conducted a fish consumption survey among four Columbia River Basin Native
American tribes during the fall and winter of 1991-1992. The target population included all
adult tribal members who lived on or near the Yakama, Warm Springs, Umatilla, or Nez Perce
reservations. The survey was based on a stratified random sampling design where respondents
were selected from patient registration files at the Indian Health Service. Interviews were
performed in person at a central location on the member's reservation. Information for 204
children 5 years old and less was provided by the participating adult respondent. The overall
response rate was 69%.
Information requested included annual and seasonal numbers offish meals, average
serving size per fish meal, species and part(s) offish consumed, and preparation methods, based
on 24-hour dietary recall. Foam sponge food models approximating 4, 8, and 12 oz. fish fillets
were provided to help respondents estimate average fish meal size. Fish intake rates were
calculated by multiplying the annual frequency offish meals by the average serving size per fish
meal.
The study was designed to give essentially equal sample sizes for each tribe. However,
because the population sizes of the tribes were highly unequal, it was necessary to weight the
data (in proportion to tribal population size) so that the survey results were representative of the
overall population of the four tribes. Such weights were applied to the analysis of adults;
however, because the sample size for children was considered small, only an unweighted
analysis was performed for this population.
A total of 49% of respondents of the total survey population reported that they caught
fish from the Columbia River basin and its tributaries for personal use or for tribal ceremonies
and distributions to other tribe members, and 88% reported that they obtained fish from either
self-harvesting, family or friends, at tribal ceremonies, or from tribal distributions. Of all fish
consumed, 41% came from self-harvesting or family harvesting, 11% from the harvest of friends,
35% from tribal ceremonies or distribution, 9% from stores and 4% from other sources.
The analysis of seasonal intake showed that May and June tended to be high-
consumption months and December and January low-consumption months. Table 3-26 shows
the fish intake distribution for children under 5 years of age. The mean intake rate was 19.6
g/day, and the 95th percentile was approximately 70 g/day, which includes consumers and
nonconsumers.
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The authors noted that some nonresponse bias may have occurred in the survey because
respondents were more likely to live near the reservation and were more likely to be female than
were nonrespondents. In addition, they hypothesized that nonfish consumers may have been
more likely than fish consumers to be nonrespondents, because nonconsumers may have thought
their contribution to the survey would be meaningless; if this was the case, this study would
overestimate the mean intake rate. It was also noted that the timing of the survey, which was
conducted during low fish consumption months, may have led to underestimation of actual fish
consumption; the authors conjectured that an individual may report higher annual consumption if
interviewed during a relatively high consumption month and lower annual consumption if
interviewed during a relatively low consumption month. Finally, with respect to children's
intake, it was observed that some of the respondents provided the same information for their
children as for themselves, so the reliability of some of its data is questioned.
Although the authors have noted its limitations, this study does present information on
fish consumption patterns and habits for a Native American subpopulation. It should be noted
that the number of surveys that address subsistence subpopulations is very limited.
3.3.3.2. Toy et al, 1996
Toy et al. (1996) conducted a study to determine fish and shellfish consumption rates of
the Tulalip and Squaxin Island tribes living in the Puget Sound region. These two tribes were
selected nonrandomly to represent the expected range of fishing and fish consumption activities
of tribes in the Puget Sound region.
A survey was conducted to describe fish consumption for Puget Sound tribal members
over the age of 18 and their dependents ages 5 and under in terms of their consumption rate of
anadromous, pelagic and bottom fish and shellfish in g/kg-day. Data were also collected on fish
parts consumed, preparation methods, patterns of acquisition for all fish and shellfish
consumption, and children's consumption rates. Interviews were conducted between February
25 and May 15, 1994. A total of 190 tribal members ages 18 years old and older and 69 children
5 years and younger were included in the survey on consumption of 52 fish species. The
response rate was 77% for the Squaxin Island Tribe and 76% for the Tulalip Tribes.
The mean and median consumption rate for children 5 years and younger was 0.53 and
0.17 g/kg-day, respectively, which was significantly lower than that of adults, even when the
consumption rate was adjusted for body weight (Table 3-27). Squaxin island children tended to
consume more fish (mean 0.825 g/kg-day vs. 0.239 g/kg-day).
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The advantage of this study is that the data can be used to improve the manner in which
exposure assessments are conducted for high-consumer populations and to identify cultural
characteristics that place tribal members at disproportionate risk to chemical contamination. The
survey of Tulalip and Squaxin Island tribes showed considerable higher consumption rates for
both adults and children than the 0.09 g/kg-day reported for the general population by SRI
international (Toy et al., 1996). The median total fish consumption rate for women of both tribes
was four to five times higher than the rate (6.5 g/day) recommended as a national default value
by EPA. For males of both tribes, the median consumption rate was 8 to 10 times higher than
the recommended national default value.
One limitation associated with this study is that although data from the Tulalip and
Squaxin Island tribes may be representative of consumption rates of these specific tribes, fish
consumption rates, habits, and patterns can vary among tribes and other subpopulations. The
authors noted that the total fish consumption rates were similar for both tribes; however,
consumption pattern by fish species and other factors differed. In some instances, these
differences were statistically significant. Another limitation noted by the authors is that the
distribution presented in this study is skewed toward higher rates, and it might be more
appropriate to use the 90th or 95th percentiles rather than means or medians for analysis of risk
(Toy et al., 1996). There might also be a possible bias due to the time the survey was conducted;
many species in the survey are seasonal. For example, because of the timing of the survey,
respondents may have overestimated the annual consumption of shellfish (Toy et al., 1996).
3.3.3.3. The Suquamish Tribe, 2000
The Suquamish Tribal Council conducted a study of the Suquamish tribal members living on and
near the Port Madison Indian Reservation in the Puget Sound region. The study was funded by
the Agency for Toxic Substances and Disease Registry through a grant to the Washington State
Department of Health. The purpose of the study was to determine seafood consumption rates,
patterns, and habits of the members of the Suquamish Tribe. The second objective was to
identify cultural practices and attributes that affect consumption rates, patterns, and habits of
members of the Suquamish Tribe.
A systematic random sample of adults ages 16 and older was selected from a sorted tribal
enrollment roster. The study had a participation rate of 64.8%, which was calculated on the
basis of 92 respondents out of a total of 142 potentially eligible adults on the list of those
selected into the sample. Consumption data for children under 6 years of age were gathered
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through adult respondents who had children under 6 years of age living in the household at the
time of the survey since birth or for at least 1 year.
A survey questionnaire was administered by personal interview. The survey included
four parts: (1) a 24-hour dietary recall; (2) identification, portions, frequency of consumption,
preparation, harvest location offish; (3) shellfish consumption, preparation, harvest location; and
(4) changes in consumption over time and cultural, physical, and socioeconomic information.
A display booklet was developed to assist respondents in providing consumption data and
identifying harvest locations of seafood consumed. Physical models of finfish and shellfish were
constructed to assist respondents in determining typical food portions. Finfish and shellfish were
grouped into categories on the basis of similarities in life history as well as practices of tribal
members who fish for subsistence, ceremonial, and commercial purposes.
Interviewers collected data from 92 adults and for 31 children under 6 years of age.
Table 3-28 provides the consumption rate for children in terms of g/kg-day. Table 3-29 provides
consumption rates for consumers only. Because all the children involved in the study consumed
some form offish, the consumption distribution of all fish is the same in both tables. The mean,
median, and 95th percentile consumption of all fish were 1.5, 0.72, and 7.3 g/kg-day,
respectively.
An important attribute of this survey is that it provides consumption rates by individual
type offish and shellfish. It is important to note that the report indicates that increased levels of
development as well as pollutants from residential, industrial, and commercial uses have resulted
in degraded habitats and harvesting restrictions. There were 11 Superfund sites within the
immediate area of the Port Madison Indian Reservation at the time the fish consumption survey
was conducted. Despite degraded water quality and habitat, tribal members continue to rely on
fish and shellfish as a significant part of their diet.
3.4. FAT INTAKE
Cresanta et al. (1988), Nicklas et al. (1993), and Frank et al. (1986) analyzed dietary fat
intake data as part of the Bogalusa heart study. The Bogalusa study, an epidemiologic
investigation of cardiovascular risk-factor variables and environmental determinants, has
collected dietary data on subjects residing in Bogalusa, LA, since 1973. Among other things, the
study collected fat intake data for children, adolescents, and young adults. Researchers have
examined various cohorts of subjects, including (1) six cohorts of 10-year olds, (2) two cohorts
of 13-year olds, (3) one cohort of subjects from 6 months to 4 years of age, and (4) one cohort of
subjects from 10 to 17 years of age (Nicklas, 1995). In order to collect the data, interviewers
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used the 24-hour dietary recall method. According to Nicklas (1995), "the diets of children in
the Bogalusa study are similar to those reported in national studies of children." Thus, these data
are useful in evaluating the variability of fat intake among the general population for the
purposes of evaluating variability in exposure for dioxin-like compounds among this group.
Data for 6-month-old to 17-year-old individuals collected from 1973 to 1982 are presented in
Tables 3-30 and 3-31 (Frank et al., 1986). Data are presented for total fats, animal fats,
vegetable fats, and fish fats in units of g/day (Table 3-30) and g/kg-day (Table 3-31).
Total fat intake and intake of individual fat products were also estimated by NCEA using
data from the 1994/1996 CSFII. It should be noted that the fat intake rates presented here
include all forms of fats (i.e., added fats such as butter and vegetable oil as well as fats consumed
in meats and fish).
The Centers for Disease Control and Prevention (CDC, 1994) used data from NHANES
III to calculate daily total food energy intake (TFEI), total dietary fat intake, and saturated fat
intake for the U.S. population during 1988 to 1991. The sample population comprised 20,277
individuals ages 2 months and older, of which 14,001 respondents (73% response rate) provided
dietary information based on a 24-hour recall. TFEI was defined as "all nutrients (i.e., protein,
fat, carbohydrate, and alcohol) derived from consumption of foods and beverages (excluding
plain drinking water) measured in kilocalories (kcal)." Total dietary fat intake was defined as
"all fat (i.e., saturated and unsaturated) derived from consumption of foods and beverages
measured in grams" (CDC, 1994).
The authors estimated and provided data on the mean daily TFEI and the mean
percentages of TFEI from total dietary fat grouped by age and gender. The overall mean daily
TFEI was 2095 kcal for the total population, and 34% (or 82 g) of the TFEI was from total
dietary fat. Based on this information, the mean daily fat intake was calculated for the various
age groups and genders (see Appendix 3D for detailed calculation). Table 3-32 presents the
grams of fat per day obtained from the daily consumption of foods and beverages, grouped by
age and gender for the U.S. population, based on this calculation.
3.5. TOTAL DIETARY INTAKE AND CONTRIBUTIONS TO DIETARY INTAKE
3.5.1. U.S. EPA, 2000
Using data from the 1994-1996 CSFII, EPA evaluated total dietary intake (U.S. EPA,
2000). Total dietary intake was defined as intake of the sum of all foods in the following major
food groups using the same foods codes as those described in Appendix 3B and the same method
for allocation of mixtures as described in Appendix 3B: dairy, eggs, meats, fish, fats, grains,
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vegetables, and fruits. Beverages, sugar, candy, sweets, and nuts and nut products were not
included. Distributions of total dietary intake were generated, as described previously, for
various age groups. Means, standard errors, and percentiles of total dietary intake were
estimated in units of g/kg-day as well as g/day.
To evaluate variability in the contributions of the major food groups to total dietary
intake, individuals were ranked from lowest to highest, based on total dietary intake. Three
subsets of individuals were defined, as follows: a group at the low end of the distribution of total
intake (i.e., below the 10th percentile of total intake), a central group (i.e., the 45th to 55th
percentile of total intake), and a group at the high end of the distribution of total intake (i.e.,
above the 90th percentile of total intake). Mean total dietary intake (in g/day and g/kg-day),
mean intake of each of the major food groups (in g/day and g/kg-day), and the percent of total
dietary intake that each of these food groups represents was calculated for each of the three
populations (i.e., individuals with low-end, central, and high-end total dietary intake). A similar
analysis was conducted to estimate the contribution of the major food groups to total dietary
intake for individuals at the low end, central, and high end of the distribution of total meat
intake, total dairy intake, total meat and dairy intake, total fish intake, and fruit and vegetable
intake. For example, to evaluate the variability in the diets of individuals at the low end, central
range, and high-end of the distribution of total meat intake, surveyed individuals were ranked
according to their reported total meat intake. Three subsets of individuals were formed as
described above. Mean total dietary intake, intake of the major food groups, and the percent of
total dietary intake represented by each of the major food groups were tabulated. This analysis
was conducted for the following age groups of the population: < 1 year, 1-2 years, 3-5 years,
6-11 years, and 12-19 years. The data were tabulated in units of g/kg-day and g/day.
Distributions of total dietary intake are presented in Table 3-33. Tables 3-34, and 3-35
compare total dietary intake to intake of the various major food groups for the various age
groups in units of g/day and g/kg/day, respectively. Tables 3-36 through 3-41 present the
contributions of the major food groups to total dietary intake for individuals (in the various age
groups) at the low end, central range, and high end of the distribution of total dietary intake, total
meat intake, total meat and dairy intake, total fish intake, total fruit and vegetable intake, and
total dairy intake in units of g/day and g/kg-day, respectively.
3.6. INTAKE OF HOME-PRODUCED FOODS
In EPA's analysis of the 1987-1988 NFCS to estimate homegrown intake rates (U.S.
EPA, 1997). NFCS data were used to generate intake rates for home produced foods. USDA
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conducts the NFCS every 10 years to analyze the food consumption behavior and dietary status
of Americans (USD A, 1992). The most recent NFCS was conducted in 1987-88 (USDA, 1987-
88). The survey used a statistical sampling technique designed to ensure that all seasons,
geographic regions of the 48 conterminous states in the U.S., and socioeconomic and
demographic groups were represented (USD A, 1994). There were two components of the
NFCS. The household component collected information over a 7-day period on the
socioeconomic and demographic characteristics of households and the types, amount, value, and
sources of foods consumed by the household (USDA, 1994). The individual intake component
collected information on food intakes of individuals within each household over a 3-day period
(USDA, 1993). The sample size for the 1987-88 survey was approximately 4300 households
(more than 10,000 individuals). This is a decrease over the previous survey conducted in
1977-1978 which sampled approximately 15,000 households (more than 36,000 individuals)
(USD A, 1994). The sample size was lower in the 1987-1988 survey as a result of budgetary
constraints and low response rate (i.e., 38% for the household survey and 31% for the individual
survey) (USDA, 1993). However, NFCS data from 1987-1988 were used to generate
homegrown intake rates because they were the most recent data available and were believed to
be more reflective of current eating patterns among the U.S. population.
The USDA data were adjusted by applying the sample weights calculated by USDA to
the data set prior to analysis. The USDA sample weights were designed to "adjust for survey
non-response and other vagaries of the sample selection process" (USDA, 1987-88). Also, the
USDA weights are calculated "so that the weighted sample total equals the known population
total, in thousands, for several characteristics thought to be correlated with eating behavior"
(USDA, 1987-88).
For the purposes of this study, home-produced foods were defined as homegrown fruits
and vegetables, meat and dairy products derived from consumer-raised livestock or game meat,
and home-caught fish. The food items/groups selected for analysis included major food groups,
such as total fruits, total vegetables, total meats, total dairy, total fish and shellfish. Individual
food items for which fewer than 30 households reported eating the home-produced form of the
item; fruits and vegetables categorized as exposed, protected, and roots; and various USDA fruit
and vegetable subcategories (e.g., dark green vegetables, citrus fruits) were also evaluated for the
general population (U.S. EPA, 1997). However, age-specific data for children are not presented
here because of the small number of observations for children eating individual homegrown
foods in the data set. Food items/groups were identified in the NFCS database according to
3-18
-------
NFCS-defined food codes. Appendix 3D presents the codes and definitions used to determine
the various food groups.
Although the individual intake component of the NFCS gives the best measure of the
amount of each food group eaten by each individual in the household, it could not be used
directly to measure consumption of home-produced food because the individual component does
not identify the source of the food item (i.e., as home produced or not). Therefore, an analytical
method that incorporated data from both the household and the individual survey components
was developed to estimate individual home-produced food intake. The USDA household data
were used to determine (1) the amount of each home-produced food item used during a week by
household members, and (2) the number of meals eaten in the household by each household
member during a week. Note that the by household survey reports the total amount of each food
item used in the household (whether by guests or household members); the amount used by
household members was derived by multiplying the total amount used in the household by the
proportion of all meals served in the household (during the survey week) that were consumed by
household members.
The individual survey data were used to generate average sex- and age-specific serving
sizes for each food item. The age categories used in the analysis were 1-2, 3-5, 6-11, and
12-19 years (intake rates were not calculated for children younger than 1; the rationale for this is
discussed below). These serving sizes were used during subsequent analyses to generate
homegrown food intake rates for individual household members. Assuming that the proportion
of the household quantity of each homegrown food item/group was a function of the number of
meals and the mean sex- and age-specific serving size for each family member, individual
intakes of home produced food were calculated for all members of the survey population using
SAS programming in which the following general equation was used:
= W
f
mtqt
(3-1)
3-19
-------
where:
w; = homegrown amount of food item/group attributed to member "i" during the week
(g/wk);
wf = total quantity of homegrown food item/group used by the family members (g/wk);
m; = number of meals of household food consumed by member "i" during the week
(meals/wk); and
q; = serving size for an individual within the age and sex category of the member
(g/meal).
Daily intake of a homegrown food item/group was determined by dividing the weekly value (w;)
by 7. Intake rates were indexed to the self-reported body weight of the survey respondent and
reported in units of g/kg-day. Intake rates were not calculated for children under 1 year of age
because their diet differs markedly from that of other household members, and thus the
assumption that all household members share all foods would be invalid for this age group.
For the major food groups (fruits, vegetables, meats, dairy, and fish) consumed by at least
30 households, distributions of home-produced intake among consumers were generated by age
group. Consumers were defined as members of survey households who reported consumption of
the food item/group of interest during the 1-week survey period. Finally, the percentages of total
intake of the food items/groups consumed within survey households that could be attributed to
home production were tabulated. The percentage of intake that was homegrown was calculated
as the ratio of total intake of the homegrown food item/group by the survey population to the
total intake of all forms of the food by the survey population. As discussed previously,
percentiles of average daily intake derived from short time intervals (e.g., 7 days) will not, in
general, be reflective of long-term patterns.
The intake data presented here for consumers of home-produced foods and the total
number of individuals surveyed may be used to calculate the mean and the percentiles of the
distribution of home-produced food consumption in the overall population (consumers and non-
consumers) as follows:
Assuming that IRp is the homegrown intake rate of food item/group at the pth percentile,
Nc is the weighted number of individuals consuming the homegrown food item, and NT is the
weighted total number of individuals surveyed, then NT-NC is the weighted number of
individuals who reported zero consumption of the food item. In addition, there are (p/100 x Nc)
individuals below the pth percentile. Therefore, the percentile that corresponds to a particular
intake rate (IRp) for the overall distribution of homegrown food consumption (including
consumers and nonconsumers) can be obtained by:
3-20
-------
pth
P
100
r overall 1UU A
xNc + (>
NT
JT-NC)
(3-2)
Table 3-42 displays the weighted numbers (NT) as well as the unweighted total survey
sample sizes for each subcategory and overall. It should be noted that the total unweighted
number of observations in Table 3-42 (9852) is somewhat lower than the number of observations
reported by USDA because this study used only observations for family members for whom age
and body weight were specified.
Table 3-43 present homegrown intake rates for fruits, vegetables, meats, and fish. As
mentioned above, the intake rates derived in this section are based on the amount of household
food consumption. As measured by the NFCS, the amount of food "consumed" by the
household is a measure of consumption in an economic sense, that is, a measure of the weight of
food brought into the household that has been consumed (used up) in some manner. In addition
to food being consumed by persons, food may be used up by spoilage, by being discarded
(e.g., inedible parts), through cooking processes, etc.
USDA (1975) estimated preparation losses for various foods. For meats, a net cooking
loss, which includes dripping and volatile losses, and a net post-cooking loss, which involves
losses from cutting, bones, excess fat, scraps, and juices, were derived for a variety of cuts and
cooking methods. For each meat type (e.g., beef) EPA has averaged these losses across all cuts
and cooking methods to obtain a mean net cooking loss and a mean net post-cooking loss. Mean
values for all meats and fish are provided in Table 3-44. For individual fruits and vegetables,
USDA (1975) also gave cooking and post-cooking losses. These data, averaged across all types
of fruits and vegetables to give mean net cooking and post cooking losses, are also provided in
Table 3-44.
The following formula can be used to convert the homegrown intake rates tabulated here
to rates reflecting actual consumption:
(3-3)
3-21
-------
where:
IA = the adjusted intake rate, I is the tabulated intake rate;
Lj = the cooking or preparation loss; and
L2 = the post-cooking loss.
For fruits, corrections based on post-cooking losses only apply to fruits that are eaten in cooked
forms. For raw forms of the fruits, paring or preparation loss data should be used to correct for
losses from removal of skin, peel, core, caps, pits, stems, and defects or from draining of liquids
from canned or frozen forms.
In calculating ingestion exposure, assessors should use consistent forms in combining
intake rates with contaminant concentrations, as previously discussed.
3.7. SERVING SIZE STUDY BASED ON THE USDA NFCS
Using data gathered in the 1977-1978 USDA NFCS, Pao et al. (1982) calculated
distributions for the quantities of individual fruit and vegetables consumed per eating occasion
(i.e., serving sizes) by members of the U.S. population over a 3-day period. The data were
collected during NFCS home interviews of 37,874 respondents, who were asked to recall food
intake for the day preceding the interview and record food intake the day of the interview and the
day after the interview.
Serving size data are presented on an "as consumed" (g/eating occasion) basis in Table 3-
45 for various age groups of the population. Only the mean and standard deviation serving size
data and percent of the population consuming the food during the 3-day survey period are
presented in this handbook. Percentiles of serving sizes of the foods consumed by these age
groups of the U.S. population can be found in Pao et al. (1982).
The advantages of using these data are that they were derived from the USDA NFCS and
are representative of the U.S. population. This data set provides serving sizes for a number of
commonly eaten foods, but the list of foods is limited and does not account for fruits and
vegetables included in complex food dishes. Also, these data represent the quantity of foods
consumed per eating occasion. Although these estimates are based on USDA NFCS 1977-1978
data, serving size data have been collected but not published for the more recent USDA surveys.
These estimates may be useful for assessing acute exposures to contaminants in specific foods or
other assessments where the amount consumed per eating occasion is necessary. However, it
should be noted that serving sizes may have changed since the data were collected in 1977-1978.
3-22
-------
Serving sizes can also be calculated directly from the USDA CSFII data sets that are
available on CD-ROM from NTIS. Default serving sizes which were assumed by USDA when
the respondents did not know how much they ate, are also on the CD-ROM.
3.8. CONVERSION BETWEEN "AS CONSUMED" AND DRY WEIGHT INTAKE
RATES
As noted previously, intake rates may be reported in terms of units as consumed or units
of dry weight. It is essential that exposure assessors be aware of this difference so that they may
ensure consistency between the units used for intake rates and those used for concentration data
(i.e., if the unit of food consumption is grams dry weight per day, then the unit for the amount of
pollutant in the food should be grams dry weight).
If necessary, "as consumed" intake rates may be converted to dry weight intake rates
using the moisture content percentages presented in Table 3-46 and Table 3-47 and the following
equation:
IRdw= IRac*[(100-W)/100] (3.4)
"Dry weight" intake rates may be converted to "as consumed" rates as follows:
IRac= IRdw/[(100-W)/100] (3-5)
where:
IRdw = dry weight intake rate;
IRac = as consumed intake rate; and
W = percent water content.
3.9. FAT CONTENT OF MEAT AND DAIRY PRODUCTS
In some cases, the residue levels of contaminants in meat and dairy products are reported
as the concentration of contaminant per gram of fat. This may be particularly true for lipophilic
compounds. When using these residue levels, the assessor should ensure consistency in the
exposure assessment calculations by using consumption rates that are based on the amount of fat
consumed for the meat or dairy product of interest. Alternately, residue levels for the "as
consumed" portions of these products may be estimated by multiplying the levels based on fat by
the fraction of fat per product as follows:
3-23
-------
residue level residue level g-fat
= x —2 (3-6)
g-product g-fat g-product
The resulting residue levels may then be used in conjunction with "as consumed" consumption
rates. The percentages of lipid fat in meat and dairy products have been reported in various
publications. USD A's Agricultural Handbook Number 8 (USD A, 1979-1986) provides
composition data for agricultural products. It includes a listing of the total saturated,
monounsaturated, and polyunsaturated fats for various meat and dairy items. Table 3-48
presents the total fat content for selected meat and dairy products taken from Handbook
Number 8. The total percent fat content is based on the sum of saturated, monounsaturated, and
polyunsaturated fats.
The National Livestock and Meat Board (NLMB) (1993) used data from Agricultural
Handbook Number 8 to estimate total fat content in grams, based on a 3-oz. (85.05 g) cooked
serving size, and the corresponding percent fat content values for several categories of meats
(Table 3-49). The authors also reported that 0.17 g of fat are consumed per gram of meat (i.e.,
beef, pork, lamb, veal, game, processed meats, and variety meats) (17%) and 0.08 grams of fat
are consumed per gram of poultry (8%).
3.10. RECOMMENDATIONS
The 1994-1996 CSFII data described in this section were used in selecting recommended
intake rates for most food groups for children in the general population. For fish intake among
these children, the 1989-1991 CSFII analyses were used to recommend intake rates. For
children, the data for recreational fish intake, the data for children are limited. The studies that
address these populations should be used in exposure assessments where these populations are of
interest (see Table 3-11). Table 3-50 presents a summary of the recommended values for food
intake and Table 3-51 presents the confidence ratings for the food intake (including fish)
recommendations for general population children. Table 3-52 presents the confidence ratings for
fish intake recommendations for the freshwater recreational population.
Fish consumption data for Native American children are limited. Three Native American
fish consumption studies were identified (CRITFC, 1994; Toy et al., 1996; The Suquamish
Tribe, 2000). The means of these studies ranged from 11 to 25 g/day. The consumers-only
weighted mean based on those three studies is 21 g/day for children younger than 6 years of age.
CRITFC (1994) and Toy et al. (1996) did not present the distributions for consumersonly. EPA
3-24
-------
calculated the consumers-only distributions on the basis of the total number of the population
surveyed and the reported percentage of nonconsumers. Toy et al. (1996), however, presented
only the mean, 50th, 75th, and 90th percentile values of intake rates for the population of
consumers and nonconsumers. When those percentiles are converted to consumers only, they
result in the 32nd, 66th, and 86th percentiles, respectively. Therefore, the 95th percentile cannot be
estimated without the raw data. Based on CRITFC (1994) and The Suquamish Tribe (2000), the
weighted 90th and 95th percentiles for children younger than 6 years of age are 60 g/day and 78
g/day, respectively. Table 3-53 presents the summary of intake rates for Native American
children and Table 3-54 provides the confidence ratings.
Per capita intake rates for specific food items, on a g/kg-day basis, may be obtained from
Table 3-3. Percentiles of the per capita intake rate distributions for the major food groups in the
general population are presented in Table 3-2. It is important to note that these distributions are
based on data collected over a 2-day period and may not necessarily reflect the long-term
distribution of average daily intake rates. However, for these broad categories of food, because
they are eaten on a daily basis throughout the year with minimal seasonality, the short-term
distribution may be a reasonable approximation of the long-term distribution, although it will
display somewhat increased variability. This implies that the upper percentiles shown here will
tend to overestimate the corresponding percentiles of the true long-term distribution.
3-25
-------
Table 3-1. Grain products: mean quantities consumed per individual, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
size
Mean quantity consumed (g) per individual
Total
grams
Yeast
breads
and
rolls
Cereals and pasta
Total
Ready-to-
eat cereals
Rice
Pasta
Quick
breads,
pancakes,
French
toast
Cakes,
cookies,
pastries,
pies
Crackers,
popcorn,
pretzels,
corn chips
Mixtures,
mainly
grain
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
56
192
219
206
242
264
284
264
219
2
16
26
21
30
36
41
36
27
29
57
62
59
64
67
76
69
61
1
11
16
13
19
22
24
22
16
2
9
15
12
13
15
17
15
13
lc
9
12
11
12
11
11
11
10
1
9
12
11
16
17
15
16
12
3
16
22
19
23
30
33
29
22
1
7
9
8
11
13
13
12
9
20
87
87
87
98
102
107
102
87
Males
6-9
6-11
12-19
787
1,031
737
310
318
406
45
46
54
77
80
82
28
31
29
18
16
27
15
18
17
23
23
26
39
40
49
16
15
19
109
115
175
Females
6-9
6-11
12-19
704
969
732
284
280
306
43
43
40
61
62
67
21
20
17
12
14
19
15
15
22
18
19
15
42
42
37
13
14
15
107
101
132
All individuals
<9
< 19
9,309
11,287
250
298
34
40
64
69
20
22
14
17
12
15
16
18
30
36
12
14
96
120
OJ
to
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for
age 10 years and over.
b Excludes breast-fed children.
; Appendix E, Statistical Notes.
Source: USDA, 1998
-------
Table 3-2. Grain products: percentages of individuals consuming, by sex and age, 1 day, 1994-1996/1998 a'b
Gender
Age
(Years)
Sample
size
Percentage of individuals consuming
Total
grams
Yeast
breads
and
rolls
Cereals and pasta
Total
Ready-to-
eat cereals
Rice
Pasta
Quick
breads,
pancakes,
French
toast
Cakes,
cookies,
pastries,
pies
Crackers,
popcorn,
pretzels,
corn chips
Mixtures,
mainly
grain
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
90.6
98.2C
99.0C
98.7
99.4C
99.5C
99.9C
99.6C
95.8
10.9
48.4
58.7
53.7
64.1
67.0
69.2
66.8
55.5
62.8
70.6
71.1
70.9
69.7
69.1
70.4
69.7
69.3
9.1
45.3
51.9
48.7
53.3
54.8
54.9
54.3
46.9
3.4
11.3
14.4
12.9
11.1
11.4
11.4
11.3
10.9
2.1
9.4
9.4
9.4
8.6
7.1
6.8
7.5
7.5
4.4
23.0
27.5
25.3
28.8
28.6
25.2
27.5
24.0
16.5
47.0
46.6
46.8
46.1
52.3
52.4
50.3
45.0
10.3
39.0
37.9
38.4
38.5
39.4
32.1
36.7
34.1
20
87
87
87
98
102
107
102
87
Males
6-9
6-11
12-19
787
1,031
737
98.9C
99.0C
98.2C
69.8
69.1
62.7
62.6
64.0
44.6
50.8
52.4
33.2
10.5
9.7
10.0
7.4
8.1
5.9
28.1
27.1
24.4
52.5
52.3
41.3
36.0
33.8
27.2
109
115
175
Females
6-9
6-11
12-19
704
969
732
99.7C
99.3C
97.6C
71.5
71.0
60.9
61.2
59.3
45.9
47.6
45.6
30.3
9.0
9.4
8.6
7.9
7.1
9.3
26.3
27.1
19.8
57.1
55.0
40.6
38.3
37.1
30.9
107
101
132
All individuals
<9
< 19
9,309
11,287
97.2
97.6
61.6
62.4
66.4
57.6
47.9
41.7
10.5
9.9
7.6
7.6
25.3
24.2
48.9
46.1
35.3
32.5
96
120
OJ
to
a Estimates based on combined data from 1994-1996 and 1998 for individuals 9 years of age and under and on 1994-1996 data alone for age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
Source: USDA, 1998
-------
Table 3-3. Vegetables: mean quantities consumed per individual, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
size
Mean quantity consumed per individual (g)
Total
grams
White potatoes
Total
Fried
Dark-green
vegetables
Deep-yellow
vegetables
Tomatoes
Lettuce,
lettuce-
based
salads
Green
beans
Corn,
green
peas, lima
beans
Other
vegetables
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
57
79
87
83
91
97
103
97
88
9
26
32
29
34
37
44
38
31
1
11
17
14
17
19
22
20
16
2
5
4
5
5
6
4
5
4
19
9
5
7
5
5
6
5
7
lc
7
11
9
13
11
12
12
10
c, d
1
2
1
2
3
3
3
2
6
8
7
7
5
5
6
5
6
5
9
10
9
11
12
12
11
10
16
16
17
17
16
18
17
17
17
Males
6-9
6-11
12-19
787
1,031
737
110
115
176
47
50
85
26
27
44
4
5
6
5
5
6
15
16
28
5
5
12
5
5
3C
11
11
10
16
18
25
Females
6-9
6-11
12-19
704
969
732
110
116
145
42
46
61
22
25
31
5
5
9
4
4
4
14
15
18
6
7
12
5
5
4
13
12
8
21
22
28
All individuals
<9
< 19
9,309
11,287
97
125
37
53
19
27
4
6
6
6
12
17
3
7
6
5
11
10
18
22
to
oo
a Estimates based on combined data from 1994-1996 and 1998 for individuals 9 years of age and under and on 1994-1996 data alone for age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USDA, 1998
-------
Table 3-4. Vegetables: mean quantities consumed per individual, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
size
Percentage of individuals consuming
Total
grams
White potatoes
Total
Fried
Dark-green
vegetables
Deep-yellow
vegetables
Tomatoes
Lettuce,
lettuce-
based
salads
Green
beans
Corn,
green
peas,
lima
beans
Other
vegetables
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
47.2
73.3
78.4
75.9
80.5
80.7
83.0
81.4
75.4
12.3
40.4
46.7
43.6
46.7
47.3
50.7
48.2
42.3
4.3
25.2
34.5
29.9
34.7
34.8
38.3
35.9
30.1
2.3
6.4
7.6
7.0
7.0
7.2
4.6
6.3
6.1
20.5
13.3
10.5
11.8
10.7
12.0
13.3
12.0
13.0
1.8
18.0
30.8
24.6
34.1
33.0
36.5
34.5
27.2
0.2C
3.9
7.5
5.7
8.3
10.0
13.4
10.6
7.6
7.8
13.7
11.5
12.6
10.1
9.0
10.4
9.9
10.5
8.5
17.6
15.0
16.2
14.6
16.4
16.1
15.7
15.0
14.8
19.4
22.3
20.9
24.7
26.5
28.8
26.7
23.3
Males
6-9
6-11
12-19
787
1,031
737
78.8
79.3
78.2
47.9
48.7
49.5
38.0
38.4
38.6
6.3
6.1
3.6
12.5
12.4
8.0
38.2
38.7
43.0
13.1
13.9
23.8
7.8
6.7
3.5
15.0
13.8
7.4
29.7
30.8
33.2
Females
6-9
6-11
12-19
704
969
732
80.5
81.7
79.5
48.2
50.8
46.4
36.3
38.9
34.6
5.9
5.4
7.0
11.9
11.4
10.6
33.8
33.5
35.3
15.8
17.1
25.1
8.4
7.8
4.4
15.9
15.1
7.4
26.6
29.2
34.5
All individuals
<9
< 19
9,309
11,287
77.1
78.3
44.6
46.8
32.9
35.3
6.1
5.6
12.7
11.2
30.7
34.6
10.3
16.6
9.6
7.0
15.2
11.9
25.2
29.4
to
VO
a Estimates based on combined data from 1994-1996 and 1998 for individuals 9 years of age and under and on 1994-1996 data alone for age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
Source: USDA, 1998
-------
Table 3-5. Fruits: mean quantities consumed per individual, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
size
Mean quantity consumed (g)
Total
grams
Citrus fruits
Total
Juices
Dried Fruits
Other fruits, mixtures, and juices
Total
Apples
Bananas
Melons
and
berries
Other fruits
and mixtures
mainly fruits
Noncitrus
juices and
nectars
Males and Females
< 1
1
2
1-2
3
4
5
3-5
< 5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
131
267
276
271
256
243
218
239
237
4
47
65
56
61
62
55
59
52
4
42
56
49
51
52
44
49
44
c, d
2
2
2
1
1
c, d
1
1
126
216
207
212
191
177
160
176
182
14
22
27
24
27
31
31
30
26
10
23
20
22
18
17
14
16
17
lc
8
10
9
13
14
13
13
10
39
29
20
24
24
22
24
23
26
61
134
130
132
110
92
78
93
103
Males
6-9
6-11
12-19
787
1,031
737
194
183
174
58
67
102
51
60
94
c, d
c, d
1°
133
113
70
32
28
13
11
11
8
21
16
llc
20
19
10
50
40
29
Females
6-9
6-11
12-19
704
969
732
180
169
157
63
64
72
54
54
67
1°
c, d
c, d
113
103
83
23
21
13
10
8
5
10
8
15
25
23
14
46
42
35
All individuals
<9
<19
9,309
11,287
217
191
55
70
47
62
1
1
159
118
27
21
15
11
12
12
24
19
81
56
OJ
o
a Estimates based on combined data from 1994-1996 and 1998 for individuals 9 years of age and under and on 1994-1996 data alone for age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USDA, 1998
-------
Table 3-6. Fruits: percentages of individuals consuming, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
size
Percentages of individuals consuming
Total
grams
Citrus fruits
Total
Juices
Dried Fruits
Other fruits, mixtures, and juices
Total
Apples
Bananas
Melons
and
berries
Other fruits
and mixtures
mainly fruits
Noncitrus
juices and
nectars
Males and Females
< 1
1
2
1-2
3
4
5
3-5
< 5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
59.7
81.0
76.6
78.8
74.5
72.6
67.6
71.6
72.6
3.6
23.6
30.6
27.2
27.9
28.0
26.9
27.6
24.6
2.7
19.0
23.4
21.3
21.4
21.8
19.5
20.9
18.8
0.4C
5.9
5.3
5.6
4.1
3.0
1.3C
2.8
3.5
59.0
73.0
64.7
68.8
64.2
62.1
56.9
61.0
63.5
15.7
23.4
24.0
23.7
22.4
23.7
21.9
22.7
22.2
13.3
25.1
20.2
22.6
17.5
15.7
12.6
15.3
17.6
1.8
6.9
8.5
7.7
7.8
7.6
7.4
7.6
6.9
29.9
26.5
19.4
22.9
20.1
20.0
19.0
19.7
22.0
33.0
43.2
37.0
40.0
33.3
30.8
24.5
29.5
33.5
Males
6-9
6-11
12-19
787
1,031
737
59.0
56.5
44.5
24.8
25.2
24.7
20.5
21.6
21.7
0.8C
l.lc
1.0C
49.1
44.2
27.1
20.3
18.2
8.2
8.7
8.0
6.0
7.3
6.6
4.1
16.8
15.4
7.1
15.5
12.7
8.2
Females
6-9
6-11
12-19
704
969
732
180
169
157
63
64
72
54
54
67
1.5C
l.lc
l.lc
50.4
47.2
30.2
17.3
16.2
8.2
8.8
7.3
4.4
7.4
7.4
6.0
20.4
19.0
11.3
17.3
14.9
9.7
All individuals
<9
<19
9,309
11,287
217
191
55
70
47
62
2.5
1.8
58.0
44.4
20.9
15.2
14.0
9.7
7.1
6.2
20.6
15.5
26.7
17.9
a Estimates based on combined data from 1994-1996 and 1998 for individuals 9 years of age and under and on 1994-1996 data alone for age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
Source: USDA, 1998
-------
Table 3-7. Milk and milk products: mean quantities consumed per individual, by sex and age, 1 day,
1994-1996/19983 b
Gender
Age
(Years)
Sample
Size
Mean quantity consumed per individual (g)
Total
grams
Milk, milk drinks, yogurt
Total
Fluid milk
Total
Whole
Low fat
Skim
Yogurt
Milk desserts
Cheese
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
762
546
405
474
419
407
417
414
477
757
526
377
450
384
369
376
376
447
61
475
344
408
347
328
330
335
327
49
347
181
262
166
147
137
150
177
11
115
141
128
150
149
159
153
127
c, d
5C
17
11
26
27
25
26
18
4
14
10
12
10
10
9
10
10
3
11
16
14
22
23
25
23
18
1
9
11
10
12
14
14
13
11
Males
6-9
6-11
12-19
787
1,031
737
450
450
409
405
402
358
343
335
303
127
121
99
176
172
158
29
33
40
6
6
3C
31
35
29
13
12
19
Females
6-9
6-11
12-19
704
969
732
380
382
269
337
336
220
288
283
190
105
108
66
146
136
92
26
29
30
4
4
4C
29
30
29
13
14
14
All individuals
<9
< 19
9,309
11,287
55
70
47
62
1
1
159
118
27
21
15
11
8
6
23
27
12
14
OJ
to
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for
age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USD A, 1998
-------
Table 3-8. Milk and milk products: percentages of individuals consuming, by sex and age, 1 day,
1994-1996/19983 b
Gender
Age
(Years)
Sample
Size
Percentages of individuals consuming
Total
grams
Milk, milk drinks, yogurt
Total
Fluid milk
Total
Whole
Low fat
Skim
Yogurt
Milk desserts
Cheese
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
85.4
95.3
91.6
93.4
94.3
93.2
93.1
93.5
92.5
84.6
92.7
87.3
90.0
88.3
87.8
86.4
87.5
88.0
11.1
87.7
84.3
86.0
84.6
85.0
81.2
83.6
75.7
8.3
61.7
44.8
53.0
42.5
41.3
38.1
40.6
41.0
11
115
141
128
150
149
159
153
127
2.4
26.5
36.3
31.5
39.5
40.4
41.7
40.6
32.9
0.2C
1.5C
5.2
3.4
6.8
7.7
6.5
7.0
4.9
4.5
13.9
17.5
15.8
21.4
21.7
21.4
21.5
17.5
6.0
29.7
32.6
31.2
37.0
36.9
34.9
36.3
30.9
Males
6-9
6-11
12-19
787
1,031
737
93.2
92.3
81.3
85.5
84.6
65.8
80.7
79.0
59.6
32.4
30.8
22.6
176
172
158
44.3
43.1
30.7
8.6
9.5
7.0
24.0
25.0
13.6
34.6
32.3
37.1
Females
6-9
6-11
12-19
704
969
732
90.2
90.2
75.4
82.5
81.5
54.0
77.5
76.0
49.7
31.5
33.2
17.5
146
136
92
40.8
37.8
23.9
8.1
8.4
9.5
24.1
22.4
17.1
30.9
31.9
36.1
All individuals
<9
< 19
9,309
11,287
92.2
86.7
86.4
75.6
77.1
68.1
37.4
30.1
27
21
36.8
33.1
6.3
7.5
20.1
18.6
31.7
33.5
OJ
oo
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for
age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
Source: USD A, 1998
-------
Table 3-9. Meat, poultry, and fish: mean quantities consumed per individual, by sex and age, 1 day,
1994-1996/19983 b
Gender
Age
(Years)
Sample
size
Mean quantity consumed per individual (g)
Total
grams
Beef
Pork
Lamb, veal,
game
Organ meats
Frank-
furters,
sausages,
luncheon
meats
Poultry
Total
Chicken
Fish and
shellfish
Mixtures
mainly meat
poultry, fish
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
24
80
94
87
101
115
121
112
93
r
5
7
6
8
10
14
11
8
c, d
2
6
4
6
6
6
6
5
c, d
c, d
c, d
c, d
c, d
c, d
c, d
d
d
c, d
c, d
c, d
c, d
c, d
c, d
c, d
c, d
c, d
2
13
18
15
19
22
22
21
17
3
12
17
15
19
20
22
21
16
2
12
16
14
18
19
19
19
15
c, d
3
4
3
4
5
5
5
4
16
43
41
42
43
49
51
47
42
Males
6-9
6-11
12-19
787
1,031
737
151
154
250
18
19
30
51
60
94
c, d
c, d
r
c, d
c, d
0
24
24
28
23
22
31
21
20
26
7
6
8
71
72
134
Females
6-9
6-11
12-19
704
969
732
121
130
158
17
18
21
54
54
67
c, d
c, d
c, d
c, d
c, d
c, d
18
19
15
19
20
21
16
17
19
5
5
6
55
60
85
All individuals
<9
<19
9,309
11,287
110
152
12
18
47
62
d
c, d
d
c, d
19
20
18
22
17
19
5
6
50
76
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for age 10
years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USD A, 1998
-------
Table 3-10. Meat, poultry, and fish: percentages of individuals consuming, by sex and age, 1 day,
1994-1996/19983 b
Gender
Age
(Years)
Sample
size
Percentages of individuals consuming
Total
grams
Beef
Pork
Lamb, veal,
game
Organ meats
Frank-
furters,
sausages,
luncheon
meats
Poultry
Total
Chicken
Fish and
shellfish
Mixtures
mainly meat
poultry, fish
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
26.0
77.4
85.2
81.4
86.2
86.2
87.1
86.5
77.5
2.1
11.9
16.2
14.1
13.8
16.1
18.2
16.0
13.7
i.r
7.3
14.9
11.2
13.3
13.8
13.2
13.4
11.2
0.2C
0.8C
0.8C
0.8C
0.5C
0.5C
0.6C
0.5
0.6
0.2C
0.2C
0.2C
0.2C
c, d
0.2C
0.2C
0.2C
0.2C
6.1
26.3
33.2
29.9
36.4
37.0
35.1
36.1
30.4
6.3
24.0
27.6
25.8
28.3
27.4
27.7
27.8
24.5
5.0
23.1
25.6
24.4
26.0
25.1
24.8
25.3
22.6
1.0C
5.4
6.1
5.8
6.4
6.4
6.2
6.3
5.5
13.7
32.2
31.4
31.8
29.2
30.5
30.8
30.2
28.8
Males
6-9
6-11
12-19
787
1,031
737
87.4
87.8
86.8
20.1
22.0
24.2
11.9
12.2
15.8
0.4C
0.4C
0.6C
O.lc
0.2C
0.0
37.4
36.2
31.8
24.8
22.9
20.6
22.3
20.5
17.6
5.1
5.4
5.0
36.2
35.7
38.3
Females
6-9
6-11
12-19
704
969
732
84.6
86.5
80.1
19.4
20.2
22.0
9.2
10.0
11.2
0.4C
0.4C
O.lc
0.2C
O.lc
O.lc
33.5
33.1
24.6
23.1
22.9
21.6
20.2
19.8
18.9
6.4
6.1
5.8
32.4
32.8
34.0
All individuals
<9
<19
9,309
11.287
80.9
82.8
16.1
19.6
10.9
12.1
0.5
0.4
0.2C
O.lc
32.4
30.9
24.3
22.7
22.0
20.1
5.6
5.5
31.0
33.3
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for age
10 years and over.
b Excludes breast-fed children.
; Appendix E, Statistical Notes.
Source: USD A, 1998
-------
Table 3-11. Eggs, legumes, nuts and seeds, fats and oils, sugars and sweets: mean quantities consumed
per individual, by sex and age, 1 day, 1994-1996/19983 b
Gender
Age
(Years)
Sample
size
Mean quantity consumed per individual (g)
Eggs
Legumes
Nuts and
seeds
Fats and oils
Total
Table fats
Salad
dressings
Sugars and sweets
Total
Sugars
Candy
Males and Females
<1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
3
13
18
16
13
13
13
13
13
151
26
12
19
13
15
12
13
32
c, d
2
4
3
5
5
6
5
4
d
2
3
3
4
5
5
5
4
d
1
2
2
2
2
2
2
2
c, d
1
1
1
2
2
3
2
2
2
13
22
18
31
33
33
32
23
d
d
d
d
1
1
1
1
1
c, d
3
5
4
7
8
9
8
6
Males
6-9
6-11
12-19
787
1,031
737
11
12
22
11
13
17
5
5
5
8
7
12
3
3
3
4
4
9
46
42
35
1
1
2
13
12
13
Females
6-9
6-11
12-19
704
969
732
10
11
13
14
12
14
5
5
3
7
7
10
3
3
2
3
4
7
41
41
31
1
1
2
11
12
12
All individuals
<9
< 19
9,309
11.287
12
14
24
20
4
4
5
8
2
2
3
5
32
33
1
1
8
10
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for
age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USD A, 1998
-------
Table 3-12. Eggs, legumes, nuts and seeds, fats and oils, sugars and sweets: percentages of individuals
consuming, by sex and age, 1 day, 1994-1996/19983 b
Gender
Age
(Years)
Sample
size
Percentages of individuals consuming
Eggs
Legumes
Nuts and
seeds
Fats and oils
Total
Table fats
Salad
dressings
Sugars and sweets
Total
Sugars
Candy
Males and Females
<1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
6.7
22.8
27.3
25.1
19.8
16.9
16.4
17.7
18.9
18.7
12.7
10.9
11.8
11.1
12.5
11.2
11.6
12.5
i.r
12.4
16.8
14.7
20.5
20.4
21.1
20.7
16.3
6.0
31.5
41.1
36.4
42.1
44.3
44.7
43.7
36.6
5.3
25.6
30.9
28.3
30.2
30.3
29.0
29.8
26.4
0.7C
7.5
14.0
10.8
15.6
18.1
20.1
17.9
13.4
6.9
39.3
50.2
44.9
57.5
58.4
57.3
57.7
47.2
1.9
7.9
8.2
8.1
10.4
11.3
11.7
11.1
9.0
0.5C
12.1
21.0
16.7
24.1
24.6
25.7
24.8
19.1
Males
6-9
6-11
12-19
787
1,031
737
15.1
15.6
17.0
9.3
9.8
10.9
17.0
15.7
8.7
48.1
46.9
43.1
30.8
29.0
20.8
24.0
24.6
27.7
61.3
59.6
46.7
11.9
12.2
13.3
31.2
29.3
21.0
Females
6-9
6-11
12-19
704
969
732
13.4
13.4
15.0
12.7
11.0
10.7
18.7
17.2
7.8
52.3
49.3
45.6
33.3
31.0
23.9
23.0
23.4
28.6
61.0
60.3
46.3
12.2
12.9
11.9
28.5
28.9
23.9
All individuals
<9
<19
9,309
11,287
17.1
16.4
11.9
11.2
16.9
13.2
42.0
43.2
28.6
25.9
17.5
22.4
52.8
50.8
10.2
11.5
23.4
23.5
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for age 10
years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
Source: USD A, 1998
-------
Table 3-13. Beverages: mean quantities consumed per individuals, by sex and age, 1 day, 1994-1996/1998"''
Gender
Age
(Years)
Sample
Size
Mean quantity consumed per individual (g)
Total
grams
Alcoholic
Total
Wine
Beer
and
ale
Nonalcoholic
Total
Coffee
Tea
Fruit drinks and ades
Total
Regular
Low
calorie
Carbonated soft drinks
Total
Regular
Low
calorie
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
19
120
196
159
240
268
299
269
201
0
0
0
0
c, d
c, d
0
c, d
c, d
0
0
0
0
0
c, d
0
c, d
c, d
0
0
0
0
c, d
0
0
c, d
c, d
19
120
196
159
240
268
299
269
201
0
c, d
c, d
c, d
r
c, d
1
1
1
2C
15
21
18
18
20
28
22
18
15
79
113
96
137
141
149
143
111
7
69
100
85
126
130
140
132
101
3C
7
llc
9
8
8
6C
8
8
lc
25
62
44
84
106
121
104
71
lc
24
56
40
77
95
112
95
65
c, d
1°
5
3
7
11
7
8
6
Males
6-9
6-11
12-19
787
1,031
737
385
413
995
c, d
c, d
44C
0
0
r
0
0
40C
385
413
951
2C
2C
21
39
39
114
163
155
205
145
137
158
17
17
44
181
217
609
159
194
584
21
23
25
Females
6-9
6-11
12-19
704
969
732
322
370
645
c, d
c, d
8C
0
0
r
0
0
6C
322
370
637
r
2C
14
32
34
93
135
134
134
126
125
113
7
8C
20
154
200
395
143
181
349
11
19
43
All individuals
<9
<19
9,309
11,287
263
502
c, d
10
c, d
c, d
c, d
9c
263
492
1
8
25
57
127
144
115
124
9
19
110
282
99
260
10
21
OJ
oo
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for
age 10 years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USD A, 1998
-------
Table 3-14. Beverages: percentages of individuals consuming, by sex and age, 1 day, 1994-1996/1998a'b
Gender
Age
(Years)
Sample
Size
Percentages of individuals consuming
Total
grams
Alcoholic
Total
Wine
Beer
and
ale
Nonalcoholic
Total
Coffee
Tea
Fruit drinks and ades
Total
Regular
Low
calorie
Carbonated soft drinks
Total
Regular
Low
calorie
Males and Females
< 1
1
2
1-2
3
4
5
3-5
<5
1,126
1,016
1,102
2,118
1,831
1,859
884
4,574
7,818
8.4
40.8
57.1
49.1
61.6
67.8
70.9
66.8
53.7
0.0
0.0
0.0
0.0
O.lc
c, d
0.0
c, d
c, d
0.0
0.0
0.0
0.0
0.0
c, d
0.0
c, d
c, d
0.0
0.0
0.0
0.0
o.r
0.0
0.0
c, d
c, d
8.4
40.8
57.1
49.1
61.6
67.8
70.9
66.8
53.7
0.0
o.r
0.3C
0.2C
0.7C
0.6C
0.8C
0.7
0.5
1.4C
5.9
7.4
6.6
6.5
7.4
9.1
7.7
6.6
6.5
27.7
34.0
30.9
38.9
41.2
38.8
39.6
32.6
3.8
24.6
31.2
28.0
36.6
38.4
37.3
37.4
30.1
1.2C
2.7
3.0
2.8
2.5
2.6
2.2
2.4
2.4
1.2"
14.2
27.5
21.0
31.7
36.9
39.0
35.9
26.6
i.r
13.6
24.7
19.3
29.1
32.8
36.1
32.7
24.3
0.2C
0.8C
3.0
1.9
2.9
4.5
2.9
3.4
2.5
Males
6-9
6-11
12-19
787
1,031
737
73.2
74.2
87.4
0.3C
0.2C
2.9
0.0
0.0
0.3C
0.0
0.0
2.3C
73.2
74.2
86.9
0.9C
1.2"
6.1
8.8
8.9
16.2
41.6
39.0
28.4
38.1
35.4
23.7
5.3
4.8
5.6
43.1
47.1
69.2
38.8
43.2
66.2
5.4
5.5
5.2
Females
6-9
6-11
12-19
704
969
732
69.4
72.8
87.0
0.2C
o.r
1.8C
0.0
0.0
0.4C
0.0
0.0
0.9C
69.4
72.8
86.7
1.7C
0.8C
3.7
10.4
10.7
19.2
37.9
36.2
27.2
35.6
33.9
23.9
1.9C
2.1
4.0
39.1
44.8
62.2
36.4
40.9
56.1
3.7
5.8
8.5
All individuals
<9
<19
9,309
11,287
60.7
72.8
O.lc
1.0
c, d
O.lc
c, d
0.6
60.7
72.7
0.6
2.4
7.8
11.9
35.5
32.3
32.8
29.1
2.9
3.7
32.4
47.8
29.6
44.1
3.3
5.2
OJ
VO
a Estimates based on combined data from 1994-1996 and 1998 for
individuals 9 years of age and under and on 1994-1996 data alone for age 10
years and over.
b Excludes breast-fed children.
0 Appendix E, Statistical Notes.
d Value less than 0.5, but greater than 0.
Source: USD A, 1998
-------
Table 3-15. Weighted and unweighted number of observations, 1994-1996
CSFII analysis
Population
Group
Total
Weighted
Number of
Observations
261,897,260
Unweighted
Number of
Observations
15,303
Age group (years)
<01
01-02
03-05
06-11
12-19
20-39
40-69
70+
3,772,296
8,270,523
12,376,836
23,408,882
29,657,098
81,672,622
81,480,145
21,258,858
359
1,356
1,435
1,432
1,398
2,992
4,921
1,410
Season
Fall
Spring
Summer
Winter
65,474,320
65,474,321
65,474,320
65,474,299
3,653
4,015
4,143
3,492
Urbanization
Central City
Nonmetropolitan
Suburban
83,904,160
55,263,514
122,729,586
4,600
3,778
6,925
Race
Asian
Black
Native American
Other/NA
White
7,764,799
33,466,094
1,669,637
14,321,336
204,675,394
387
1,963
115
972
11,866
Region
Midwest
Northeast
South
West
61,512,403
51,416,379
91,294,341
57,674,137
3,658
2,737
5,474
3,434
3-40
-------
Table 3-16. Per capita intake of the major food groups (g/kg-day as consumed)"
Percentile
Population Percent Mean SE
Group Consuming 1st
5th
10th
25th
50th
75th
90th
95th
99th
100th
Fruits
Age (years)
<01
1-2
3-5
6-11
12-19
56.8
85.5
79.0
71.2
60.7
13.183 1.106 0
19.308 0.521 0
11.022 0.341 0
5.393 0.200 0
2.771 0.133 0
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
6.351
2.273
0.000
0.000
7.559
15.524
8.102
3.351
1.371
22.669
27.447
16.342
7.874
4.116
35.694
41.622
26.441
13.628
7.978
41.181
53.898
32.675
17.952
10.970
63.725
77.260
52.991
28.447
16.639
110.230
125.321
105.159
44.574
32.229
Vegetables
Age (years)
<01
1-2
3-5
6-11
12-19
50.1
95.4
92.7
93.2
97.9
6.902 0.721 0
9.528 0.213 0
7.295 0.159 0
5.337 0.118 0
4.034 0.085 0
0.000
0.471
0.000
0.000
0.633
0.000
1.929
1.348
1.120
1.121
0.000
4.534
3.411
2.480
2.140
2.337
8.013
6.231
4.334
3.404
12.227
12.580
9.690
7.103
5.145
17.857
18.716
13.933
10.438
7.399
24.179
23.278
18.274
13.539
9.346
36.281
33.457
28.992
21.210
14.684
102.646
83.285
45.543
52.266
42.431
Grains
Age (years)
<01
1-2
3-5
6-11
12-19
64.9
95.6
93.1
93.4
98.2
4.124 0.416 0
11.211 0.202 0
10.291 0.197 0
7.200 0.122 0
4.401 0.080 0
0.000
1.686
0.000
0.000
1.130
0.000
3.594
3.674
2.452
1.543
0.000
6.434
6.292
4.285
2.452
1.575
9.807
9.177
6.656
3.788
5.438
14.267
13.132
9.413
5.541
12.972
21.042
17.766
12.924
7.899
20.235
24.706
21.073
15.548
9.702
26.610
34.672
33.638
19.893
14.079
40.133
47.987
120.900
36.296
34.571
Meats
Age (years)
<01
1-2
3-5
6-11
12-19
32.3
94.0
92.2
92.4
97.3
1.132 0.198 0
4.422 0.094 0
4.144 0.080 0
2.919 0.060 0
2.158 0.046 0
0.000
0.000
0.000
0.000
0.266
0.000
0.759
0.768
0.523
0.527
0.000
1.909
2.125
1.418
1.106
0.000
3.845
3.814
2.520
1.947
1.383
6.195
5.624
3.996
2.835
3.870
8.869
7.847
5.555
3.930
5.853
10.159
9.436
6.802
4.865
10.585
14.662
13.103
10.232
7.459
12.369
24.436
20.743
17.597
26.747
Fish
Age (years)
<01
1-2
3-5
6-11
12-19
20.9
58.2
56.4
57.5
62.9
0.108 0.047 0
0.368 0.037 0
0.316 0.030 0
0.259 0.025 0
0.204 0.017 0
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.080
0.069
0.058
0.055
0.000
0.286
0.245
0.178
0.172
0.325
0.783
0.661
0.479
0.417
0.527
1.791
1.736
1.346
1.100
1.562
4.687
4.567
4.234
2.499
4.685
14.415
9.553
6.686
5.354
Dairy Products
Age (years)
<01
1-2
3-5
6-11
12-19
83.6
95.7
92.9
93.3
96.9
111.360 4.855 0
37.478 0.779 0
20.914 0.402 0
13.918 0.276 0
6.110 0.160 0
0.000
0.412
0.000
0.000
0.168
2.522
6.677
3.473
2.167
0.413
63.886
17.754
10.177
6.438
1.832
102.208
31.756
18.727
12.348
4.467
158.574
51.441
29.155
19.249
8.803
197.773
73.894
41.241
27.337
13.488
235.341
90.146
48.750
33.463
17.789
318.289
132.802
66.157
43.426
27.837
576.345
182.812
89.717
80.780
38.005
1 Food codes used and category definitions are provided in Appendix 3.
Source: Based on U.S. EPA, 2000
3-41
-------
Table 3-17. Per capita intake of individual foods (g/kg-day as consumed)"
Population
Group
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Percent
Consuming
41.2
55.1
47.7
34.1
20.0
0.6
3.8
5.7
6.7
5.8
0.3
1.6
0.8
1.0
0.5
14.8
8.5
5.0
5.2
1.7
Mean
Apples
7.030
8.020
4.103
1.437
0.582
Cabbage
0.023
0.071
0.099
0.074
0.039
Lima Beans
0.000
0.037
0.010
0.018
0.007
Pears
1.354
0.393
0.178
0.114
0.023
SE
0.977
0.448
0.273
0.135
0.090
0.209
0.070
0.060
0.040
0.020
0.000
0.070
0.040
0.060
0.060
0.490
0.159
0.114
0.070
0.040
Percent
Consuming
0.0
0.7
0.7
0.8
0.3
12.3
14.5
15.1
17.8
13.1
0.0
1.0
0.3
0.8
0.7
9.2
12.3
9.1
7.8
5.6
Strawberries
Age (years)
<01
1-2
3-5
0.6
4.4
4.4
0.007
0.116
0.096
0.090
0.090
0.080
28.7
88.8
87.7
Mean
Asparagus
0.000
0.014
0.009
0.014
0.003
Carrots
0.678
0.343
0.182
0.153
0.057
Okra
0.000
0.010
0.006
0.008
0.003
Peas
0.603
0.257
0.163
0.111
0.060
Tomatoes
0.518
2.139
1.741
SE
0.000
0.080
0.040
0.070
0.020
0.348
0.177
0.040
0.030
0.020
0.000
0.040
0.080
0.030
0.020
0.313
0.070
0.050
0.050
0.040
Percent
Consuming
21.4
35.0
20.8
14.2
9.4
2.2
18.5
19.2
21.0
12.8
0.3
4.1
4.7
6.7
12.9
0.3
1.5
3.1
4.7
7.4
Mean
Bananas
1.153
1.688
0.713
0.353
0.119
Corn
0.164
0.462
0.426
0.316
0.144
Onions
0.010
0.019
0.022
0.026
0.044
Peppers
0.000
0.010
0.018
0.018
0.018
SE
0.34
0.14
0.10
0.10
0.00
0.36
0.10
0.10
0.00
0.00
Percent
Consuming
0.6
0.4
0.6
0.3
0.2
0.3
6.9
11.2
14.7
15.2
Mean
Beets
0.032
0.000
0.012
0.000
0.000
Cucumbers
0.000
0.089
0.130
0.123
0.094
SE
0.25
0
0.1
0
0
0
0.1
0.1
0
0
Other Berries
0.14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.3
1.5
1.7
1.8
1.4
7.5
1.0
0.3
0.1
0.1
White Potatoes
0.119
0.080
0.060
27.6
77.4
77.6
0.537
2.245
2.027
0.15
0.10
0.10
15.0
76.9
85.6
0.010
0.073
0.034
0.029
0.016
Pumpkins
0.433
0.054
0.000
0.000
0.000
Breads
0.256
1.950
2.289
0.1
0.23
0.1
0.1
0
0.38
0.17
0
0
0
0.11
0.1
0.1
Percent
Consuming Mean SE
Broccoli
1.1 0.017 0.110
8.6 0.242 0.100
7.8 0.137 0.060
6.8 0.108 0.060
5.8 0.064 0.040
Lettuce
0.0 0.000 0.000
11.0 0.109 0.040
18.9 0.166 0.030
24.7 0.184 0.030
35.6 0.177 0.020
Peaches
12.8 0.856 0.393
9.7 0.447 0.145
7.2 0.248 0.117
5.6 0.125 0.080
4.0 0.064 0.050
Snap Beans
11.7 0.624 0.267
19.4 0.49 0.090
15.3 0.239 0.050
12.2 0.16 0.060
7.9 0.063 0.020
Breakfast Foods (Grains)
1.7 0.048 0.162
19.5 0.429 0.070
21.5 0.391 0.060
OJ
to
-------
Table 3-17. Per capita intake of individual foods (g/kg-day as consumed)" (continued)
Population
Group
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Age (years)
<01
1-2
3-5
6-11
12-19
Percent
Consuming Mean
4.5
3.8
52.9
6.5
0.3
0.1
0.0
13.9
57.5
54.5
51.0
45.6
29.0
86.7
84.5
85.0
90.2
0.6
9.1
14.8
16.4
21.5
Strawberries
0.064
0.032
Cereals (Baby)
1.595
0.162
0.004
0.000
0.000
Snacks (Grains)
0.135
0.738
0.701
0.461
0.287
Pork
0.092
0.400
0.375
0.265
0.209
Mayonnaise
0.001
0.024
0.036
0.028
0.032
Percent
SE Consuming Mean
0.050 89.4
0.030 94.8
0.265 5.6
0.100 16.6
0.060 14.7
0.000 8.7
0.000 5.9
0.060 10.6
0.040 53.9
0.040 62.1
0.030 63.4
0.020 54.6
0.030 30.4
0.030 89.7
0.020 88.1
0.020 87.8
0.010 93.3
0.000 0.0
0.010 0.4
0.000 0.8
0.000 0.7
0.000 1.3
Tomatoes
1.217
1.010
Cereals (Cooked)
0.931
1.618
1.260
0.471
0.164
Sweets (Grains)
0.158
1.155
1.342
1.151
0.621
Poultry
0.350
1.408
1.307
0.829
0.619
Sauce
0.000
0.004
0.003
0.003
0.005
Percent
SE Consuming Mean
0.040 79.0
0.030 84.3
White Potatoes
1.510
1.243
Cereals (Ready-to-Es
0.819 8.6
0.286 65.0
0.283 68.5
0.171 63.1
0.090 44.6
0.100 29.0
0.070 88.9
0.060 86.1
0.060 87.7
0.030 92.9
0.100 1.1
0.050 12.9
0.050 13.7
0.030 14.9
0.020 11.6
0.000 0.6
0.030 0.4
0.020 0.7
0.010 0.4
0.010 0.5
0.059
0.965
1.100
0.794
0.360
Beef
0.508
1.389
1.311
1.073
0.917
Butter
0.002
0.034
0.040
0.030
0.015
Vegetable Oil
0.005
0.001
0.002
0.001
0.000
Percent
SE Consuming
0.060 87.0
0.050 86.4
it)
0.050 2.5
0.040 16.2
0.040 12.5
0.030 12.3
0.020 12.1
0.111 29.0
0.050 88.8
0.040 84.5
0.040 85.3
0.030 91.0
0.000 2.2
0.010 30.1
0.010 31.6
0.000 31.4
0.000 24.0
0.060
0.010
0.000
0.000
0.000
Mean
Breads
1.698
1.068
Pasta
0.066
0.795
0.552
0.488
0.264
Eggs
0.405
1.174
0.650
0.400
0.286
Margarine
0.004
0.073
0.085
0.062
0.034
SE
0.040
0.030
0.149
0.152
0.128
0.115
0.090
0.142
0.060
0.040
0.030
0.020
0.010
0.000
0.000
0.000
0.000
Percent
Consuming
Breakfa
21.9
12.7
3.9
19.1
16.3
16.1
17.2
0.0
0.5
0.6
1.0
0.8
0.8
11.7
18.3
23.1
24.2
Mean SE
st Foods (Grains)
0.370 0.050
0.130 0.030
Rice
0.167 0.283
0.905 0.166
0.795 0.179
0.492 0.100
0.462 0.105
Game
0.000 0.000
0.010 0.070
0.010 0.050
0.013 0.050
0.010 0.030
Dressing
0.000 0.020
0.062 0.020
0.084 0.020
0.094 0.010
0.080 0.010
OJ
oo
' Food codes used and category definitions are provided in Appendix 3.
Source: Based on U.S. EPA, 2000
-------
Table 3-18. Per capita intake of USD A categories of vegetables and fruits
(g/kg-day as consumed)"
Percentile
Population Percent
Group Consuming
Mean
SE
1st
5th
10th 25th
50th
75th
90th
95th
99th
100th
Dark Green Vegetables
Age (years)
<01 1.7
1-2 12.5
3-5 10.9
6-11 9.9
12-19 9.4
0.045
0.328
0.197
0.154
0.124
0.219
0.098
0.063
0.054
0.041
0
0
0
0
0
0
0
0
0
0
0 0.000
0 0.000
0 0.000
0 0.000
0 0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.845
0.224
0.162
0.150
0.000
2.315
1.488
1.042
0.935
0.678
6.513
4.127
3.655
2.792
9.770
20.944
12.724
6.761
4.333
Deep Yellow Vegetables
Age (years)
< 01 4.5
1-2 15.2
3-5 16.9
6-11 19.3
12-19 14.3
0.162
0.276
0.243
0.180
0.071
0.217
0.065
0.051
0.035
0.021
0
0
0
0
0
0
0
0
0
0
0 0.000
0 0.000
0 0.000
0 0.000
0 0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.728
0.716
0.658
0.152
0.372
2.131
1.729
1.180
0.506
5.708
4.235
4.299
2.450
1.387
7.862
11.719
8.268
10.844
4.850
Citrus Fruits
Age (years)
< 01 4.5
1-2 37.7
3-5 38.9
6-11 35.0
12-19 36.1
0.213
4.018
2.946
1.900
1.409
0.392
0.341
0.22
0.163
0.121
0
0
0
0
0
0
0
0
0
0
0 0.000
0 0.000
0 0.000
0 0.000
0 0.000
0.000
0.000
0.000
0.000
0.000
0.000
5.741
4.704
2.745
1.92
0.000
12.866
9.308
6.329
4.652
0.000
18.714
13.032
9.465
7.160
8.578
37.074
21.209
16.736
12.871
30.252
113.369
66.539
27.935
17.935
Other Fruits
Age (years)
<01 55.4
1-2 79.6
3-5 71.4
6-11 62.0
12-19 43.1
12.929
15.266
8.071
3.493
1.362
1.110
0.496
0.311
0.163
0.104
0
0
0
0
0
0
0
0
0
0
0 0.000
0 2.817
0 0.000
0 0.000
0 0.000
7.266
10.692
4.92
1.901
0.000
22.669
23.001
11.758
5.102
1.833
35.384
35.155
20.53
9.341
4.153
41.181
48.171
27.381
12.808
6.261
63.419
70.309
44.078
22.222
12.706
110.230
105.506
84.574
38.467
32.229
Other Vegetables
Age (years)
< 01 10.9
1-2 62.4
3-5 64.5
6-11 66.3
12-19 68.8
0.466
2.161
1.726
1.328
0.804
0.293
0.125
0.091
0.067
0.042
0
0
0
0
0
0
0
0
0
0
0 0.000
0 0.000
0 0.000
0 0.000
0 0.000
0.000
0.75
0.706
0.62
0.33
0.000
2.961
2.239
1.836
1.127
0.565
6.35
4.693
3.639
2.086
2.853
8.871
7.206
4.858
2.961
11.069
16.072
13.350
9.762
6.270
14.759
53.611
21.713
28.579
12.563
a Food codes used and category definitions are provided in Appendix 3.
Source: U.S. EPA, 2000
3-44
-------
Table 3-19. Per capita intake of exposed/protected fruit and vegetable
categories (g/kg-day as consumed)"
Population Percent
Group Consuming
Percentile
Mean
SE
1st
5th
10th
25th 50th
75th
90th
95th
99th
100th
Exposed Fruits
Age (years)
<01 49.9
1-2 68.6
3-5 60.7
6-1 1 49.3
12-19 31.9
10.017
10.902
5.637
2.197
0.872
0.995
0.469
0.277
0.136
0.087
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 4.449
0 5.695
0 2.717
0 0.000
0 0.000
16.534
15.681
8.096
3.075
1.070
30.093
29.372
15.837
6.338
2.857
38.775
38.988
22.178
8.777
4.85
58.459
65.811
34.985
17.549
8.787
69.610
101.307
77.078
32.203
14.911
Protected Fruits
Age (years)
<01 27.0
1-2 62.1
3-5 54.5
6-1 1 49.0
12-19 46.4
1.719
6.449
4.356
2.702
1.809
0.392
0.309
0.223
0.165
0.124
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0 0.000
0 3.590
0 2.062
0 0.165
0 0.000
1.957
9.186
6.721
3.817
2.612
6.013
17.836
12.142
8.074
5.417
8.344
24.183
17.155
11.436
8.402
16.608
39.032
27.900
19.811
15.427
30.252
113.369
66.539
31.710
27.022
Exposed Vegetables
Age (years)
<01 18.1
1-2 63.4
3-5 68.2
6-1 1 70.6
12-19 76.4
1.189
1.996
1.630
1.235
0.966
0.371
0.114
0.083
0.058
0.041
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.000 0.000
0.000 0.591
0.000 0.674
0.000 0.601
0.055 0.530
0.000
2.678
2.241
1.580
1.338
4.991
5.753
4.442
3.417
2.530
7.353
8.551
6.378
4.836
3.610
14.654
14.869
12.787
8.102
5.767
19.040
45.031
25.072
19.600
13.022
Protected Vegetables
Age (years)
<01 18.9
1-2 41.4
3-5 38.8
6-11 38.7
12-19 31.2
1.281
1.469
1.079
0.778
0.462
0.371
0.125
0.09
0.065
0.055
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000 0.000
0.000
1.863
1.402
1.042
0.437
5.420
4.422
3.520
2.583
1.517
7.785
7.042
5.417
3.894
2.348
11.899
14.162
10.300
7.496
5.766
23.097
27.812
17.992
26.510
21.550
Root Vegetables
Age (years)
<01 30.4
1-2 68.2
3-5 71.1
6-11 73.7
12-19 76.2
1.812
2.572
2.191
1.620
1.263
0.355
0.134
0.091
0.063
0.053
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.000 0.000
0.000 1.447
0.000 1.355
0.000 1.034
0.094 0.823
2.307
3.562
3.215
2.315
1.747
6.944
6.774
5.512
4.171
3.015
9.582
8.331
7.125
5.325
3.992
15.585
16.777
14.063
9.492
7.661
32.922
83.285
32.045
20.591
22.474
a Food codes used and category definitions are provided in Appendix 3.
Source: Based on U.S. EPA, 2000
3-45
-------
Table 3-20. Per capita distribution of fish (finfish and shellfish) intake
by age and gender, as consumed
Percentile
Age (years)
Sample
Size
Mean
(g/day)
90th
(g/day)
95th
(g/day)
99th
(g/day)
Mean
(mg/kg-day)
Percentile
90th
(mg/kg-day)
95th
(mg/kg-day)
99th
(mg/kg-day)
Freshwater and Estuarine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
1.58
4.28
2.17
6.14
1.88
5.17
1.44
10.90
0.99
18.19
1.31
13.88
12.51
28.80
14.94
48.61
13.90
36.21
36.09
70.87
48.72
96.32
40.77
86.14
67.12
66.22
73.93
75.35
70.59
70.58
57.30
174.96
28.10
230.13
53.24
197.11
460.16
451.04
723.93
577.84
556.34
502.26
1356.54
1188.16
1290.10
1132.23
1347.67
1167.57
Marine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
6.60
9.97
7.25
13.33
6.93
11.58
24.84
36.83
24.85
52.73
24.88
44.24
37.32
55.53
49.89
71.49
42.07
62.18
87.05
105.32
92.64
116.51
91.64
110.07
256.90
159.79
230.25
165.92
243.31
162.72
936.94
573.49
846.57
626.85
873.87
602.58
1545.15
873.73
1504.37
933.05
1522.52
893.82
3060.22
1700.21
2885.08
1472.98
3059.93
1576.09
All Fish
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
8.19
14.25
9.42
19.46
8.82
16.74
32.28
47.13
34.85
68.60
32.88
57.88
43.09
71.58
52.85
93.65
50.95
84.59
95.19
120.84
98.36
149.07
98.33
138.21
324.02
226.01
304.17
241.27
313.90
233.30
1091.52
755.51
1172.17
867.70
1128.26
828.12
1690.99
1126.02
1575.43
1208.43
1679.91
1155.30
3982.60
2195.86
3393.84
1760.48
3419.49
2003.46
Source: U.S. EPA, 1996
3-46
-------
Table 3-21. Consumers only distribution offish (finfish and shellfish) intake
by age and gender, as consumed
Percentile
Age (years)
Sample
Size
Mean
(g/day)
90th
(g/day)
95th
(g/day)
99th
(g/day)
Mean
(mg/kg-day)
Percentile
90th
(mg/kg-day)
95th
(mg/kg-day)
99th
(mg/kg-day)
Freshwater and Estuarine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
138
445
157
356
295
801
38.44
61.40
52.44
81.56
45.73
71.44
91.30
148.83
112.05
224.01
108.36
180.67
128.97
185.44
154.44
275.00
136.24
230.95
182.66
363.56
230.74
371.00
214.62
371.52
1639.20
961.58
1798.24
1004.96
1721.99
983.19
3915.56
2578.81
3759.29
2744.61
3760.67
2616.63
6271.09
3403.75
3952.99
3348.86
4208.18
3360.85
10113.24
6167.24
7907.38
4569.62
9789.49
5089.78
Marine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
315
774
348
565
663
1339
69.04
76.53
78.44
104.57
73.62
89.93
114.23
149.78
160.97
191.29
153.20
171.88
162.37
178.74
190.68
227.56
176.90
209.17
336.59
271.06
336.98
316.69
337.24
308.06
2591.57
1227.41
2471.15
1302.62
2532.95
1263.35
5074.80
2469.67
4852.33
2390.20
5068.69
2464.80
6504.67
3007.98
5860.72
2882.91
6376.47
2961.92
9970.44
4800.68
8495.57
3887.23
8749.02
4251.47
All Fish
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
378
952
429
702
807
1654
69.54
88.80
79.72
124.78
74.80
106.06
126.22
170.01
161.62
230.77
153.70
203.33
165.27
212.56
190.00
296.66
178.08
271.66
338.04
361.04
308.59
397.70
337.46
372.77
2683.51
1414.54
2568.93
1545.93
2624.35
1477.57
5299.68
2726.46
4714.97
2854.49
5020.14
2798.37
7160.73
3740.83
5818.08
3773.51
6904.83
3747.88
12473.65
6703.25
9350.89
5254.04
10384.82
5386.43
Source: U.S. EPA, 1996
3-47
-------
Table 3-22. Per capita distribution of fish (finfish and shellfish) intake by
age and gender, uncooked fish weight
Percentile
Age (years)
Sample
Size
Mean
(g/day)
90th
(g/day)
95th
(g/day)
99th
(g/day)
Mean
(mg/kg-day)
Percentile
90th
(mg/kg-day)
95th
(mg/kg-day)
99th
(mg/kg-day
Freshwater and Estuarine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
1.99
5.50
2.69
7.87
2.35
6.64
1.81
13.62
1.07
22.10
1.72
18.30
15.88
36.68
18.47
63.26
17.46
47.31
46.82
94.93
57.07
126.61
50.14
109.66
84.78
85.15
91.62
96.91
88.26
90.77
70.75
202.83
38.98
281.17
66.00
250.26
599.06
584.79
868.97
740.91
717.37
631.31
1713.06
1411.42
1642.60
1589.97
1688.55
1529.94
Marine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
8.61
12.84
9.40
17.11
9.02
14.88
31.23
46.66
31.32
66.06
31.52
55.99
49.75
72.16
65.37
93.32
56.35
80.70
104.26
133.69
118.42
155.16
117.75
138.23
333.99
206.03
296.99
212.88
315.12
209.30
1132.99
762.54
1089.46
800.79
1123.28
780.16
1959.91
1137.58
1907.65
1191.75
1909.37
1174.69
3776.60
2174.21
3723.81
1890.42
3820.21
2019.59
All Fish
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
1431
2891
1546
2151
2977
5042
10.60
18.35
12.09
24.98
11.36
21.51
41.10
62.21
45.59
87.15
43.00
75.15
56.16
93.13
68.18
122.29
65.34
109.57
130.78
155.75
127.20
197.15
130.41
175.73
418.76
291.18
388.61
309.78
403.38
300.06
1389.10
993.92
1476.31
1096.57
1442.72
1040.98
2341.90
1436.00
2038.58
1566.39
2191.90
1514.82
4985.96
2726.50
4294.12
2275.15
4425.27
2481.23
Source: U.S. EPA, 1996
3-48
-------
Table 3-23. Per capita distribution of fish (finfish and shellfish) intake by
age and gender, uncooked fish weight
s
Age (years)
Freshwater and
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
Size
Estuarine
138
445
157
356
295
801
Percentile
Mean
(g/day)
48.30
78.56
64.91
104.86
56.95
91.66
90th
(g/day)
117.27
191.95
141.35
269.96
134.89
237.27
95th
(g/day)
161.44
242.76
193.79
343.66
166.32
322.06
99th
(g/day)
230.63
472.21
287.28
494.38
262.87
494.64
Mean
(mg/kg-day)
2070.41
1229.97
2229.31
1294.27
2153.11
1261.99
Percentile
90th
(mg/kg-day)
4450.54
3045.41
4638.34
3318.89
4634.82
3276.06
95th
(mg/kg-day)
6915.31
4191.25
5071.41
4275.83
5756.93
4246.63
99th
(mg/kg-day
13269.61
7711.43
9622.15
5974.96
12388.27
6625.15
Marine
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
315
774
348
565
663
1339
89.92
98.53
101.50
133.86
95.56
115.41
169.23
194.59
205.49
244.46
189.32
223.99
198.62
231.22
242.28
297.67
231.72
263.76
432.51
317.42
408.68
393.14
442.87
383.16
3359.10
1582.77
3180.45
1666.42
3272.13
1622.75
6058.97
3129.41
6434.20
3102.24
6278.74
3120.60
8573.62
3854.14
8089.26
3651.10
8424.77
3682.17
13050.09
5961.80
10764.01
4998.14
11838.54
5517.95
All Fish
Females
14 or under
15-44
Males
14 or under
15-44
Both Sexes
14 or under
15-44
378
952
429
702
807
1654
89.73
114.04
102.01
160.06
96.07
136.12
163.47
220.63
205.25
305.61
195.35
262.15
204.14
277.69
244.46
379.38
232.85
343.86
476.56
461.54
386.47
495.51
466.09
488.90
3448.73
1818.32
3273.63
1983.16
3358.33
1897.40
7100.43
3506.20
5734.46
3720.05
6333.46
3674.88
9012.18
4661.96
7570.83
4769.44
8611.73
4709.78
15381.13
8789.33
11891.85
6121.56
12406.35
7276.18
Source: U.S. EPA, 1996
3-49
-------
Table 3-24. Number of respondents reporting consumption of seafood, by
number of servings and source
Age
(years)
1^
5-11
12-17
N
102
166
137
Number of servings in a month
1-2
55
72
68
3-5
29
57
54
6-10
12
21
9
11-19
2
6
2
20+
NA
4
1
Don't
know
4
6
3
Source
Mostly
purchased
94
153
129
Mostly
caught
8
9
6
Don't
know
NA
4
2
Source: Tsang and Klepeis, 1996
Table 3-25. Mean fish intake among individuals who eat fish and reside
in households with recreational fish consumption
Age
(years)
1-5
6-10
11-20
All fish
(meals/week)
0.463
0.490
0.407
Recreational
fish
(meals/week)
0.223
0.278
0.229
N
121
151
349
Total
fish
(g/day)
11.4
13.6
12.3
Recreational
fish
(g/day)
5.63
7.94
7.27
Total fish
(g/kg-day)
0.737
0.481
0.219
Recreational
fish
(g/kg-day)
0.369
0.276
0.123
Source: U.S. EPA analysis using data from West et al., 1989
3-50
-------
Table 3-26. Fish consumption rates throughout year of 194 children ages
5 years and under
Number of grams/day
0.0
0.4
0.8
1.6
2.4
3.2
4.1
4.9
6.5
8.1
9.7
12.2
13.0
16.2
19.4
20.3
24.3
32.4
48.6
64.8
72.9
81.0
97.2
162.0
Unweighted Cumulative Percent = 19.6 grams/day
21.1
21.6
22.2
24.7
25.3
28.4
32.0
33.5
35.6
47.4
48.5
51.0
51.5
72.7
73.2
74.2
76.3
87.1
91.2
94.3
96.4
97.4
98.5
100.0
Unweighted SE = 1.94
Source: CRITFC, 1994
3-51
-------
Table 3-27. Mean, 50th, and 90th percentiles of consumption rates for
children ages birth to 5 years (g/kg-day)
Population
Mean (SE)
95% CI
Percentile
50th
90th
Tulalip Tribes (N = 21)
Shellfish
Total finfish
Total, all fish
0.125(0.056)
0.114(0.030)
0.239 (0.077)
(0.014, 0.236)
(0.056,0.173)
(0.088, 0.390)
0.000
0.060
0.078
0.597
0.290
0.738
Squaxin Island Tribe (N = 48)
Shellfish
Total finfish
Total, all fish
0.228 (0.053)
0.250 (0.063)
0.825(0.143)
(0.126,0.374)
(0.126,0.374)
(0.546, 1.105)
0.045
0.061
0.508
0.574
0.826
2.056
Both tribes combined (weighted)
Shellfish
Total finfish
Total, all fish
0.177(0.039)
0.182(0.035)
0.532(0.081)
(0.101,0.253)
(0.104,0.251)
(0.373, 0.691)
0.012
0.064
0.173
0.574
0.615
1.357
Source: Toy etal., 1996
3-52
-------
Table 3-28. Children's consumption rate (g/kg-day): individual finfish and shellfish and fish groups a''
All children (Including nonconsumers)
Species/Group
Manila/Littleneck clams
Horse clams
Butter clams
Geoduck
Cockles
Oysters
Mussels
Moon snails
Shrimp
Dungeness crab
Red rock crab
Scallops
Squid
Sea urchin
Sea cucumber
Group A
Group B
Group C
Group D
Group F°
All Finfish
All Shellfish
All Seafood
Mean
0.095
0.022
0.021
0.112
0.117
0.019
0.001
0.000
0.093
0.300
0.007
0.011
0.002
0.000
0.000
0.271
0.004
0.131
0.030
0.240
0.677
0.801
1.477
SE
0.051
0.013
0.014
0.041
0.079
0.012
0.001
0.038
0.126
0.003
0.006
0.002
0.117
0.002
0.040
0.011
0.075
0.168
0.274
0.346
95%
LCL
0.000
0.000
0.000
0.033
0.000
0.000
0.000
0.019
0.053
0.001
0.000
0.000
0.043
0.000
0.052
0.008
0.094
0.346
0.265
0.799
95%
UCL
0.195
0.048
0.048
0.191
0.271
0.043
0.002
0.168
0.547
0.014
0.022
0.005
0.499
0.008
0.210
0.053
0.387
1.007
1.337
2.155
Consumers Only
Percentile
5th
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.026
0.000
0.042
75th
0.063
0.006
0.000
0.116
0.054
0.056
0.000
0.000
0.059
0.166
0.000
0.005
0.000
0.000
0.000
0.216
0.000
0.205
0.037
0.254
0.740
0.799
1.983
90th
0.181
0.048
0.041
0.252
0.240
0.058
0.000
0.000
0.394
1.251
0.046
0.031
0.000
0.000
0.000
0.532
0.015
0.339
0.081
0.684
2.110
2.319
3.374
95th
0.763
0.269
0.247
0.841
1.217
0.205
0.011
0.000
0.712
2.689
0.064
0.089
0.000
0.000
0.000
2.064
0.038
0.838
0.191
1.571
3.549
4.994
7.272
Median
0.031
0.000
0.000
0.027
0.000
0.000
0.000
0.000
0.004
0.047
0.000
0.000
0.000
0.000
0.000
0.663
0.000
0.036
0.010
0.092
0.306
0.287
0.724
Max.
1.597
0.348
0.422
1.075
2.433
0.362
0.026
0.000
0.982
2.833
0.082
0.174
0.411
0.000
0.000
3.559
0.069
1.014
0.342
1.901
4.101
7.948
9.063
N
23
12
6
22
10
10
1
0
17
21
5
8
2
0
0
28
5
25
17
24
31
28
31
%
74
39
19
71
32
32
3
0
55
68
16
26
6
0
0
90
16
81
55
77
100
90
100
GM
0.050
0.015
0.041
0.054
0.123
0.020
0.026
0.050
0.116
0.040
0.026
0.032
0.100
0.014
0.069
0.033
0.140
0.312
0.314
0.729
MSE
1.278
1.587
1.844
1.480
1.545
1.606
1.000
1.527
1.442
1.308
1.410
1.265
1.312
1.618
1.309
1.262
1.315
1.273
1.360
1.268
OJ
oo
a The minimum consumption for all species and groups was zero, except for "all finfish" and "all seafood." The minimum rate for "all finfish" was 0.023,
and for "all seafood" was 0.035. GM = Geometric Mean MSE = Multiplicative Standard Error.
b N = 31, LCL = Lower Confidence Limit, UCL = Upper Confidence Limit.
0 Group F includes tuna, other finfish, and all others not included in Groups A, B, C, and D.
Source: The Suquamish Tribe, 2000
-------
Table 3-29. Children's consumption rate (g/kg-day) for consumers only:
individual finfish and shellfish and fish groups
Group
Group E
Group A
Group B
Group C
Group D
Group F (tuna/other
finfish)
All finfish
All shellfish
All seafood
Species
Manila/Littleneck
clams
Horse clams
Butter clams
Geoduck
Cockles
Oysters
Mussels
Moon snails
Shrimp
Dungeness crab
Red rock crab
Scallops
Squid
Sea urchin
Sea cucumber
Consumers Only
N
23
12
6
22
10
10
1
0
17
21
5
8
2
0
0
28
5
25
17
24
31
28
31
Mean
0.128
0.058
0.106
0.158
0.361
0.060
0.026
0.170
0.443
0.046
0.042
0.033
0.300
0.023
0.163
0.055
0.311
0.677
0.886
1.477
SE
0.068
0.032
0.066
0.054
0.233
0.035
—
0.064
0.179
0.011
0.019
0.008
0.128
0.012
0.048
0.019
0.092
0.168
0.299
0.346
Median
0.043
0.009
0.032
0.053
0.078
0.015
—
0.035
0.082
0.051
0.027
0.033
0.112
0.017
0.048
0.033
0.177
0.306
0.363
0.724
Percentile
75%tile
0.066
0.046
0.203
0.230
0.291
0.074
—
0.299
0.305
0.067
0.032
—
0.246
0.043
0.236
0.064
0.336
0.740
0.847
1.983
90%tile
0.200
0.308
—
0.554
2.230
0.336
—
0.621
2.348
—
0.599
0.493
0.140
1.035
2.110
2.466
3.374
Source: The Suquamish Tribe, 2000
3-54
-------
Table 3-30. Fat intake among children based on data from the
Bogalusa Heart Study, 1973-1982 (g/day)
Percentile
Age (vears)
N
Mean
SD
10th
25th
50th
75th
90th
Minimum
Maximum
Total Fat Intake
6 (months)
1
2
3
4
10
13
15
17
125
99
135
106
219
871
148
108
159
37.1
59.1
86.7
91.6
98.6
93.2
107.0
97.7
107.8
17.500
26.000
41.300
38.800
56.100
50.800
53.900
48.700
64.300
18.7
29.1
39.9
50.2
46.0
45.7
53.0
46.1
41.4
25.6
40.4
55.5
63.6
66.8
60.5
69.8
65.2
59.7
33.9
56.1
79.2
82.6
87.0
81.4
90.8
85.8
97.3
46.3
71.4
110.5
114.6
114.6
111.3
130.7
124.0
140.2
60.80
94.40
141.10
153.00
163.30
154.50
184.10
165.20
195.10
3.4
21.6
26.5
32.6
29.3
14.6
9.8
10.0
8.5
107.6
152.7
236.4
232.5
584.6
529.5
282.2
251.3
327.4
Total Animal Fat
6 (months)
1
2
3
4
10
13
15
17
125
99
135
106
219
871
148
108
159
18.400
36.500
49.500
50.100
50.800
54.100
56.200
53.800
64.400
16.000
20.000
28.300
29.400
31.700
39.600
39.800
35.100
48.500
0.7
15.2
20.1
21.3
21.4
20.3
19.8
15.9
15.2
4.2
23.1
28.9
29.1
28.1
30.6
28.5
28.3
30.7
13.9
33.0
42.1
42.9
42.6
45.0
44.8
44.7
51.6
28.4
45.9
66.0
64.4
66.4
64.6
72.8
67.9
86.6
42.50
65.30
81.40
88.90
92.60
97.50
109.40
105.80
128.80
0.0
0.0
10.0
14.1
5.9
0.0
4.7
0.6
2.6
61.1
127.1
153.4
182.6
242.2
412.3
209.6
182.1
230.3
Total Vegetable Fat Intake
6 (months)
1
2
3
4
10
13
15
17
125
99
135
106
219
871
148
108
159
9.200
15.400
19.300
21.100
24.500
23.700
34.300
27.300
25.700
12.800
14.300
16.300
15.500
18.600
21.600
27.400
22.800
21.300
0.6
3.7
3.8
3.9
5.7
4.3
8.4
5.1
4.2
1.2
6.1
7.9
8.6
10.4
9.5
17.9
11.9
11.7
2.8
11.3
14.8
18.7
21.8
18.3
31.2
22.6
20.8
11.6
18.1
26.6
26.6
33.3
30.6
44.6
38.1
32.9
29.40
38.00
42.90
45.20
48.50
49.00
57.50
54.40
47.60
0.0
0.2
0.7
1.0
0.9
0.6
0.0
0.7
0.0
53.2
70.2
96.6
70.4
109.0
203.7
238.3
132.2
141.5
Total Fish Fat Intake
6 (months)
1
2
3
4
10
13
15
17
125
99
135
106
219
871
148
108
159
0.046
0.047
0.036
0.100
2.255
0.292
0.269
0.431
0.465
0.130
0.233
0.229
0.591
31.05
1.452
2.151
1.467
2.010
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.14
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.9
1.9
1.9
4.5
459.2
19.2
25.4
9.5
15.3
Source: Frank etal, 1986
3-55
-------
Table 3-31. Fat intake among children based on data from the
Bogalusa Heart Study, 1973-1982 (g/kg-day)
Percentile
Age (Years)
N
Mean
SD
10th
25th
50th
75th
90th
Minimum
Maximum
Total Fat Intake
6 (months)
1
2
3
4
10
13
15
17
125
99
132
106
218
861
147
105
149
4.940
6.120
6.980
6.400
6.050
2.700
2.280
1.730
1.770
2.320
2.750
3.340
2.670
3.660
1.520
1.300
0.840
1.020
2.410
3.030
3.370
3.610
2.880
1.230
1.030
0.840
0.690
3.280
4.110
4.450
4.560
3.960
1.680
1.470
1.180
0.920
4.670
5.660
6.150
5.500
5.240
2.350
1.990
1.540
1.620
6.190
7.470
8.560
8.160
6.970
3.320
2.800
2.140
2.240
7.970
9.530
11.940
9.930
9.980
4.540
3.810
3.130
3.100
0.390
2.270
2.140
2.180
2.030
0.330
0.210
0.150
0.160
13.160
16.380
18.690
16.730
38.210
13.860
10.190
4.730
6.230
Total Animal Fat
6 (months)
1
-)
3
4
10
13
15
17
Total Vegetable Vi
6 (months)
1
2
3
4
10
13
15
17
125
99
132
106
218
861
147
105
149
it Intake
125
99
132
106
218
861
147
105
149
2.430
3.780
3.990
3.500
3.120
1.560
1.190
0.950
1.040
1.237
1.594
1.561
1.474
1.492
0.685
0.748
0.490
0.439
2.130
2.120
2.310
2.010
2.050
1.160
0.860
0.620
0.770
1.794
1.550
1.381
1.066
1.153
0.638
0.790
0.397
0.359
0.080
1.700
1.730
1.560
1.260
0.550
0.400
0.320
0.260
0.079
0.401
0.299
0.277
0.356
0.127
0.161
0.086
0.071
0.600
2.370
2.290
2.070
1.730
0.840
0.590
0.540
0.510
0.160
0.630
0.647
0.603
0.617
0.257
0.381
0.225
0.175
2.030
3.390
3.360
3.130
2.640
1.280
0.940
0.810
0.830
0.354
1.169
1.134
1.359
1.208
0.516
0.606
0.436
0.353
3.740
4.900
5.220
4.180
4.040
1.920
1.590
1.250
1.380
1.558
1.868
2.037
1.963
2.059
0.863
0.931
0.653
0.597
5.470
6.480
6.690
6.050
5.380
2.830
2.280
1.900
1.970
4.076
3.784
3.504
2.958
2.827
1.440
1.248
0.904
0.908
0.000
0.000
0.670
0.900
0.390
0.000
0.080
0.010
0.050
0.000
0.022
0.057
0.077
0.061
0.019
0.000
0.010
0.000
8.990
13.640
13.400
13.140
15.430
10.790
5.190
3.070
4.150
8.199
7.610
8.474
5.047
7.315
4.244
8.603
2.226
2.128
Total Fish Fat Intake
6 (months)
1
0
3
4
10
13
15
17
125
99
132
106
218
861
147
105
149
0.006
0.005
0.003
0.007
0.148
0.009
0.005
0.008
0.008
0.018
0.026
0.018
0.042
2.034
0.047
0.036
0.028
0.033
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.021
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.127
0.219
0.160
0.341
30.03
0.625
0.405
0.189
0.234
Source: Frank etal, 1986
3-56
-------
Table 3-32. Mean total daily dietary fat intake grouped by age and gender"
Age
(years)
2-1 1 (months)
1-2
3-5
6-11
12-16
16-19
N
871
1231
1647
1745
711
785
Total mean
fat intake
(g/day)
37.52
49.96
60.39
74.17
85.19
100.50
Males
N
439
601
744
868
338
308
Mean fat
intake
(g/day)
38.31
51.74
70.27
79.45
101.94
123.23
Females
N
432
630
803
877
373
397
Mean fat
intake
(g/day)
36.95
48.33
61.51
68.95
71.23
77.46
' Total dietary fat intake includes all fat (i.e., saturated and unsaturated) derived from consumption of foods
and beverages (excluding plain drinking water).
Source: Adapted from CDC, 1994
3-57
-------
Table 3-33. Per capita total dietary intake
Age
(years)
g/day, as
<01
01-02
03-05
06-11
12-19
(g/kg/day,
<01
01-02
03-05
06-11
12-19
Percent
Consuming
consumed)
92.2
100.0
100.0
100.0
100.0
as consumed)
88.0
96.0
93.2
93.4
98.2
Mean
l.OE+03
1.1E+03
l.OE+03
1.1E+03
1.2E+03
1.4E+02
8.4E+01
5.5E+01
3.6E+01
2.0E+01
Adjusted
SE
2.6E+01
1.1E+01
9.9E+00
1.1E+01
1.7E+01
4.6E+00
1.1E+00
7.3E-01
5.1E-01
3.1E-01
Percentile
1st
8.0E+00
3.2E+02
3.4E+02
4.0E+02
2.9E+02
0
0
0
0
0
5th
1.3E+02
5.1E+02
5.0E+02
5.7E+02
4.2E+02
6.9E+00
2.6E+01
O.OE+00
O.OE+00
6.2E+00
10th
3.5E+02
6.2E+02
5.8E+02
6.7E+02
5.6E+02
2.4E+01
3.9E+01
2.6E+01
1.5E+01
8.1E+00
25th
8.4E+02
8.1E+02
7.6E+02
8.3E+02
7.8E+02
l.OE+02
6.0E+01
3.8E+01
2.4E+01
1.2E+01
50th
1.1E+03
1.1E+03
l.OE+03
1.1E+03
1.1E+03
1.4E+02
8.1E+01
5.4E+01
3.4E+01
1.8E+01
75th
1.3E+03
1.3E+03
1.2E+03
1.3E+03
1.5E+03
1.8E+02
l.OE+02
7.0E+01
4.6E+01
2.6E+01
90th
1.6E+03
1.6E+03
1.5E+03
1.7E+03
1.9E+03
2.2E+02
1.3E+02
8.9E+01
6.0E+01
3.5E+01
95th
1.8E+03
1.8E+03
1.7E+03
1.9E+03
2.3E+03
2.4E+02
1.5E+02
l.OE+02
6.9E+01
4.0E+01
99th 100th
2.3E+03 2.5E+03
2.2E+03 2.8E+03
2.1E+03 2.6E+03
2.3E+03 3.6E+03
3.2E+03 9.0E+03
3.2E+02 5.8E+02
1.9E+02 2.6E+02
1.3E+02 1.9E+02
8.9E+01 1.2E+02
5.8E+01 1.2E+02
OJ
oo
Source: Based on U.S. EPA, 2000
-------
Table 3-34. Per capita total intake of major food groups (g/day, as consumed)
Percentile
Food
Group
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat'
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Percent
Consuming
92.2
87.7
33.4
30.1
20.9
67.4
52.4
58.8
30.1
100.0
99.7
97.8
92.5
60.7
99.6
99.3
89.0
93.9
100.0
99.6
99.0
90.8
61.0
99.8
99.4
84.4
95.6
100.0
99.7
99.0
91.6
62.4
99.9
MEAN
l.OE+03
7.9E+02
1.1E+01
3.9E+00
9.6E-01
3.7E+01
6.0E+01
1.1E+02
7.5E-01
1.1E+03
4.8E+02
5.9E+01
1.6E+01
4.9E+00
1.5E+02
1.3E+02
2.5E+02
5.5E+00
l.OE+03
3.9E+02
7.9E+01
1.3E+01
6.1E+00
1.9E+02
1.4E+02
2.1E+02
7.8E+00
1.1E+03
4.3E+02
9.4E+01
1.3E+01
8.9E+00
2.3E+02
Adjusted
SE
2.6E+01
2.4E+01
1.9E+00
1.3E+00
4.2E-01
3.6E+00
5.7E+00
9.0E+00
1.5E-01
1.1E+01
8.3E+00
1.2E+00
7.1E-01
4.7E-01
2.4E+00
2.5E+00
6.4E+00
1.5E-01
9.9E+00
6.3E+00
1.3E+00
7.0E-01
5.4E-01
2.8E+00
2.5E+00
5.5E+00
2.0E-01
1.1E+01
6.7E+00
1.6E+00
7.3E-01
7.9E-01
2.9E+00
1st
8.0E+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
3.2E+02
5.3E+00
O.OE+00
O.OE+00
O.OE+00
1.6E+01
3.9E+00
O.OE+00
O.OE+00
3.4E+02
7.8E+00
O.OE+00
O.OE+00
O.OE+00
4.7E+01
3.4E+00
O.OE+00
O.OE+00
4.0E+02
1.4E+01
2.5E+00
O.OE+00
O.OE+00
5.0E+01
5th
1.3E+02
3.1E+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
5.1E+02
7.0E+01
6.2E+00
O.OE+00
O.OE+00
3.9E+01
1.9E+01
O.OE+00
O.OE+00
5.0E+02
7.4E+01
1.6E+01
O.OE+00
O.OE+00
7.0E+01
2.4E+01
O.OE+00
1.7E-01
5.7E+02
7.6E+01
1.8E+01
O.OE+00
O.OE+00
8.5E+01
10th
3.5E+02
1.3E+02
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
6.2E+02
1.3E+02
1.2E+01
1.7E-01
O.OE+00
5.4E+01
3.4E+01
O.OE+00
6.7E-01
5.8E+02
1.2E+02
2.4E+01
8.3E-02
O.OE+00
8.8E+01
4.0E+01
O.OE+00
l.OE+00
6.7E+02
1.3E+02
2.8E+01
2.1E-01
O.OE+00
1.1E+02
25th
Age < 1 Year
8.4E+02
6.1E+02
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
Ages 1—2 Years
8.1E+02
2.6E+02
2.7E+01
8.1E-01
O.OE+00
8.7E+01
6.6E+01
9.3E+01
1.9E+00
Ages 3—5 Years
7.6E+02
2.2E+02
4.4E+01
7.3E-01
O.OE+00
1.2E+02
7.4E+01
6.2E+01
2.7E+00
Ages 6-11 Years
8.3E+02
2.5E+02
5.1E+01
9.0E-01
O.OE+00
1.5E+02
50th
1.1E+03
8.1E+02
O.OE+00
O.OE+00
O.OE+00
1.4E+01
2.8E+01
6.4E+01
O.OE+00
1.1E+03
4.3E+02
5.2E+01
2.3E+00
1.2E+00
1.3E+02
1.1E+02
2.0E+02
4. 1E+00
l.OE+03
3.6E+02
7.2E+01
1.8E+00
1.7E+00
1.7E+02
1.2E+02
1.6E+02
5.6E+00
1.1E+03
3.9E+02
8.5E+01
2.2E+00
2.4E+00
2.1E+02
75th
1.3E+03
9.9E+02
1.3E+01
6.3E-01
O.OE+00
4.7E+01
1.1E+02
1.9E+02
1.3E+00
1.3E+03
6.5E+02
8.2E+01
2.4E+01
3.9E+00
1.9E+02
1.6E+02
3.6E+02
7.2E+00
1.2E+03
5.1E+02
l.OE+02
2.0E+01
5.0E+00
2.4E+02
1.8E+02
3.1E+02
1.1E+01
1.3E+03
5.8E+02
1.2E+02
6.5E+00
6.1E+00
2.8E+02
90th
1.6E+03
1.3E+03
3.5E+01
2.7E+00
2.5E+00
1.2E+02
1.6E+02
2.9E+02
2.5E+00
1.6E+03
8.9E+02
1.2E+02
4.9E+01
1.1E+01
2.6E+02
2.4E+02
5.4E+02
1.2E+01
1.5E+03
7.2E+02
1.4E+02
4.3E+01
1.4E+01
3.1E+02
2.6E+02
4.7E+02
1.8E+01
1.7E+03
7.7E+02
1.7E+02
4.6E+01
1.9E+01
3.7E+02
95th
1.8E+03
1.5E+03
5.7E+01
3.8E+01
5.0E+00
1.8E+02
1.9E+02
3.5E+02
3.3E+00
1.8E+03
1.1E+03
1.4E+02
7.0E+01
2.4E+01
3.2E+02
3.1E+02
7.1E+02
1.6E+01
1.7E+03
8.3E+02
1.7E+02
6.3E+01
3.4E+01
3.6E+02
3.2E+02
5.6E+02
2.2E+01
1.9E+03
8.6E+02
2.0E+02
6.6E+01
4.4E+01
4.3E+02
99th
2.3E+03
2.0E+03
8.9E+01
7.5E+01
1.3E+01
2.4E+02
3.0E+02
5.6E+02
7.5E+00
2.2E+03
1.4E+03
1.9E+02
1.1E+02
6.9E+01
4.5E+02
4.4E+02
9.2E+02
2.6E+01
2.1E+03
1.2E+03
2.4E+02
1.1E+02
8.0E+01
5.3E+02
4.8E+02
8.4E+02
3.7E+01
2.3E+03
1.2E+03
3.0E+02
1.3E+02
1.3E+02
5.9E+02
100th
2.5E+03
2.1E+03
1.2E+02
8.9E+01
4.3E+01
3.6E+02
7.0E+02
7.5E+02
1.1E+01
2.8E+03
2.0E+03
3.2E+02
1.9E+02
1.7E+02
6.5E+02
7.1E+02
2.1E+03
5.0E+01
2.6E+03
1.7E+03
3.8E+02
2.5E+02
2.0E+02
1.6E+03
7.6E+02
1.9E+03
6.3E+01
3.6E+03
2.7E+03
4.1E+02
2.2E+02
2.1E+02
7.8E+02
-------
Table 3-34. Per capita total intake of major food groups (g/day, as consumed) (continued)
Percentile
Food
Group
Total
Vegetable
Fruit
Fat'
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Percent
Consuming
99.7
77.0
96.9
100.0
98.7
99.1
92.7
64.4
100.0
99.6
61.9
96.7
MEAN
1.7E+02
1.7E+02
1.1E+01
1.2E+03
3.6E+02
1.3E+02
1.8E+01
1.2E+01
2.6E+02
2.4E+02
1.6E+02
1.6E+01
Adjusted
SE
3.1E+00
5.6E+00
2.8E-01
1.7E+01
8.8E+00
2.9E+00
9.5E-01
l.OE+00
4.2E+00
5.1E+00
7.6E+00
4.6E-01
1st
l.OE+01
O.OE+00
O.OE+00
2.9E+02
O.OE+00
2.9E+00
O.OE+00
O.OE+00
3.9E+01
1.8E+01
O.OE+00
O.OE+00
5th
3.6E+01
O.OE+00
7.8E-01
4.2E+02
1.4E+01
2.0E+01
O.OE+00
O.OE+00
7.8E+01
4.8E+01
O.OE+00
9.7E-01
10th 25th
Ages 6-11 Years
5.4E+01 9.1E+01
O.OE+00 3.0E+01
1.6E+00 3.7E+00
Ages 12-19 Years
5.6E+02 7.8E+02
3.2E+01 1.1E+02
3.6E+01 7.0E+01
4.4E-01 1.5E+00
O.OE+00 O.OE+00
1.1E+02 1.6E+02
7.3E+01 1.3E+02
O.OE+00 O.OE+00
2.4E+00 5.3E+00
50th
1.4E+02
1.2E+02
7.7E+00
1.1E+03
2.7E+02
1.2E+02
3.3E+00
3.7E+00
2.3E+02
2.1E+02
8.4E+01
1.1E+01
75th
2.2E+02
2.6E+02
1.4E+01
1.5E+03
5.1E+02
1.7E+02
1.1E+01
1.1E+01
3.4E+02
3.1E+02
2.4E+02
2.0E+01
90th
3.2E+02
4.3E+02
2.4E+01
1.9E+03
7.8E+02
2.5E+02
6.4E+01
2.5E+01
4.4E+02
4.4E+02
4.3E+02
3.7E+01
95th
3.9E+02
5.1E+02
3.0E+01
2.3E+03
l.OE+03
3.0E+02
8.8E+01
6.0E+01
5.3E+02
5.4E+02
6.2E+02
4.9E+01
99th
5.9E+02
8.7E+02
5.2E+01
3.2E+03
1.5E+03
4.4E+02
1.5E+02
1.5E+02
8.4E+02
8.1E+02
9.3E+02
8.5E+01
100th
1.2E+03
1.2E+03
8.2E+01
9.0E+03
2.0E+03
2.1E+03
3.1E+02
3.7E+02
1.7E+03
3.3E+03
2.0E+03
1.3E+02
OJ
o
' Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as meats.
Source: Based on EPA's analysis of the 1994-96 CSFII
-------
Table 3-35. Per capita intake of major food groups (g/kg-day, as consumed)
Food
Group
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fata
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Percent
Consuming
88.0
83.6
32.3
29.0
20.9
64.9
50.1
56.8
29.2
96.0
95.7
94.0
88.8
58.2
95.6
95.4
85.5
90.1
93.2
92.9
92.2
84.5
56.4
93.1
92.7
79.0
89.2
93.4
93.3
92.4
85.3
57.5
93.4
Mean
1.4E+02
1.1E+02
1.1E+00
4.1E-01
1.1E-01
4.1E+00
6.9E+00
1.3E+01
8.3E-02
8.4E+01
3.7E+01
4.4E+00
1.2E+00
3.7E-01
1.1E+01
9.5E+00
1.9E+01
4.2E-01
5.5E+01
2.1E+01
4.1E+00
6.5E-01
3.2E-01
l.OE+01
7.3E+00
1.1E+01
4.2E-01
3.6E+01
1.4E+01
2.9E+00
4.0E-01
2.6E-01
7.2E+00
Adjusted
SE
4.6E+00
4.9E+00
2.0E-01
1.4E-01
4.7E-02
4.2E-01
7.2E-01
1.1E+00
1.7E-02
1.1E+00
7.8E-01
9.4E-02
5.5E-02
3.7E-02
2.0E-01
2.1E-01
5.2E-01
1.2E-02
7.3E-01
4.0E-01
8.0E-02
3.7E-02
3.0E-02
2.0E-01
1.6E-01
3.4E-01
1.2E-02
5.1E-01
2.8E-01
6.0E-02
2.5E-02
2.5E-02
1.2E-01
Percentile
1st
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
5th
6.9E+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
2.6E+01
4.1E-01
O.OE+00
O.OE+00
O.OE+00
1.7E+00
4.7E-01
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
10th 25th
Ages < 1 Year
2.4E+01 l.OE+02
2.5E+00 6.4E+01
O.OE+00 O.OE+00
O.OE+00 O.OE+00
O.OE+00 O.OE+00
O.OE+00 O.OE+00
O.OE+00 O.OE+00
O.OE+00 O.OE+00
O.OE+00 O.OE+00
Ages 1-2 Years
3.9E+01 6.0E+01
6.7E+00 1.8E+01
7.6E-01 1.9E+00
O.OE+00 5.3E-02
O.OE+00 O.OE+00
3.6E+00 6.4E+00
1.9E+00 4.5E+00
O.OE+00 6.4E+00
l.OE-02 1.4E-01
Ages 3-5 Years
2.6E+01 3.8E+01
3.5E+00 l.OE+01
7.7E-01 2.1E+00
O.OE+00 3.0E-02
O.OE+00 O.OE+00
3.7E+00 6.3E+00
1.3E+00 3.4E+00
O.OE+00 2.3E+00
O.OE+00 1.3E-01
Ages 6-11 Years
1.5E+01 2.4E+01
2.2E+00 6.4E+00
5.2E-01 1.4E+00
O.OE+00 2.2E-02
O.OE+00 O.OE+00
2.5E+00 4.3E+00
50th
1.4E+02
l.OE+02
O.OE+00
O.OE+00
O.OE+00
1.6E+00
2.3E+00
7.6E+00
O.OE+00
8.1E+01
3.2E+01
3.8E+00
1.6E-01
8.0E-02
9.8E+00
8.0E+00
1.6E+01
3.1E-01
5.4E+01
1.9E+01
3.8E+00
8.8E-02
6.9E-02
9.2E+00
6.2E+00
8.1E+00
3.0E-01
3.4E+01
1.2E+01
2.5E+00
6.3E-02
5.8E-02
6.7E+00
75th
1.8E+02
1.6E+02
1.4E+00
7.0E-02
O.OE+00
5.4E+00
1.2E+01
2.3E+01
1.4E-01
l.OE+02
5.1E+01
6.2E+00
1.8E+00
2.9E-01
1.4E+01
1.3E+01
2.7E+01
5.5E-01
7.0E+01
2.9E+01
5.6E+00
4.6E-01
2.5E-01
1.3E+01
9.7E+00
1.6E+01
5.9E-01
4.6E+01
1.9E+01
4.0E+00
1.8E-01
1.8E-01
9.4E+00
90th
2.2E+02
2.0E+02
3.9E+00
2.3E-01
3.3E-01
1.3E+01
1.8E+01
3.6E+01
2.6E-01
1.3E+02
7.4E+01
8.9E+00
3.8E+00
7.8E-01
2.1E+01
1.9E+01
4.2E+01
9.1E-01
8.9E+01
4.1E+01
7.8E+00
2.1E+00
6.6E-01
1.8E+01
1.4E+01
2.6E+01
9.5E-01
6.0E+01
2.7E+01
5.6E+00
1.4E+00
4.8E-01
1.3E+01
95th
2.4E+02
2.4E+02
5.9E+00
3.3E+00
5.3E-01
2.0E+01
2.4E+01
4.1E+01
4.0E-01
1.5E+02
9.0E+01
l.OE+01
5.1E+00
1.8E+00
2.5E+01
2.3E+01
5.4E+01
1.2E+00
l.OE+02
4.9E+01
9.4E+00
3.4E+00
1.7E+00
2.1E+01
1.8E+01
3.3E+01
1.3E+00
6.9E+01
3.3E+01
6.8E+00
2.2E+00
1.3E+00
1.6E+01
99th
3.2E+02
3.2E+02
1.1E+01
8.3E+00
1.6E+00
2.7E+01
3.6E+01
6.4E+01
7.2E-01
1.9E+02
1.3E+02
1.5E+01
8.3E+00
4.7E+00
3.5E+01
3.3E+01
7.7E+01
2.2E+00
1.3E+02
6.6E+01
1.3E+01
6.1E+00
4.6E+00
3.4E+01
2.9E+01
5.3E+01
1.8E+00
8.9E+01
4.3E+01
l.OE+01
4.4E+00
4.2E+00
2.0E+01
100th
5.8E+02
5.8E+02
1.2E+01
1.1E+01
4.7E+00
4.0E+01
l.OE+02
1.1E+02
1.7E+00
2.6E+02
1.8E+02
2.4E+01
1.4E+01
1.4E+01
4.8E+01
8.3E+01
1.3E+02
3.3E+00
1.9E+02
9.0E+01
2.1E+01
1.3E+01
9.6E+00
1.2E+02
4.6E+01
1.1E+02
3.1E+00
1.2E+02
8.1E+01
1.8E+01
9.3E+00
6.7E+00
3.6E+01
-------
Table 3-35. Per capita intake of major food groups (g/kg/day, as consumed) (continued)
Food
Group
Total
Vegetable
Fruit
Fat'
Total
Dietary
Dairy
Meat
Egg
Fish
Grain
Vegetable
Fruit
Fat"
Percent
Consuming
Adjusted
SE
Percentile
1st
5th
10th
25th 50th
75th
90th
95th
99th
100th
Ages 6-11 Years
93.2
71.2
90.5
98.2
96.9
97.3
91.0
62.9
98.2
97.9
60.7
95.0
5.3E+00
5.4E+00
3.4E-01
2.0E+01
6.1E+00
2.2E+00
2.9E-01
2.0E-01
4.4E+00
4.0E+00
2.8E+00
2.7E-01
1.2E-01
2.0E-01
l.OE-02
3.1E-01
1.6E-01
4.6E-02
1.5E-02
1.7E-02
8.0E-02
8.5E-02
1.3E-01
8.0E-03
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
6.2E+00
1.7E-01
2.7E-01
O.OE+00
O.OE+00
1.1E+00
6.3E-01
O.OE+00
1.1E-02
1.1E+00
O.OE+00
2.2E-02
A?
8.1E+00
4.1E-01
5.3E-01
6.0E-03
O.OE+00
1.5E+00
1.1E+00
O.OE+00
3.6E-02
2.5E+00 4.3E+00
O.OE+00 3.4E+00
9.8E-02 2.3E-01
;es 12-19 Years
1.2E+01 1.8E+01
1.8E+00 4.5E+00
1.1E+00 1.9E+00
2.4E-02 5.5E-02
O.OE+00 5.5E-02
2.5E+00 3.8E+00
2.1E+00 3.4E+00
O.OE+00 1.4E+00
8.7E-02 1.8E-01
7.1E+00
7.9E+00
4.5E-01
2.6E+01
8.8E+00
2.8E+00
1.8E-01
1.7E-01
5.5E+00
5.1E+00
4.1E+00
3.4E-01
l.OE+01
1.4E+01
8.0E-01
3.5E+01
1.3E+01
3.9E+00
l.OE+00
4.2E-01
7.9E+00
7.4E+00
8.0E+00
6.2E-01
1.4E+01
1.8E+01
1.1E+00
4.0E+01
1.8E+01
4.9E+00
1.4E+00
1.1E+00
9.7E+00
9.3E+00
1.1E+01
8.3E-01
2.1E+01
2.8E+01
1.5E+00
5.8E+01
2.8E+01
7.5E+00
2.5E+00
2.5E+00
1.4E+01
1.5E+01
1.7E+01
1.4E+00
5.2E+01
4.5E+01
3.1E+00
1.2E+02
3.8E+01
2.7E+01
4.7E+00
5.4E+00
3.5E+01
4.2E+01
3.2E+01
1.8E+00
OJ
to
' Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as meats.
Source: Based on EPA's analysis of the 1994-96 CSFII
-------
Table 3-36. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total food intake
Consumers
Food Low-end
WOUP Intake
All
Foods 1.4E+00
Dairy 9.4E-02
Meats O.OE+00
Fish O.OE+00
Eggs O.OE+00
Grains 5.8E-01
Vegetables 4.0E-01
Fruits 3.2E-01
Fats' O.OE+00
%
Mid-range
Intake
%
High-end
Intake
%
Low-end
Intake
Age < 1 Year (g/day, as consumed)
100.0
6.8
0.0
0.0
0.0
41.7
28.7
22.8
0.0
9.9E+02
8.4E+02
4.9E+00
4.6E-01
2.8E+00
2.1E+01
2.6E+01
9.5E+01
5.0E-01
100.0
84.9
0.5
0.0
0.3
2.1
2.6
9.6
0.1
1.8E+03
1.4E+03
7.7E+00
6.0E-01
1.4E+00
6.8E+01
1.1E+02
1.7E+02
7.1E-01
100.0
79.9
0.4
0.0
0.1
3.8
6.1
9.5
0.0
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
Ages 1—2 Years (g/day, as consumed)
Foods 4.8E+02
Dairy 1.6E+02
Meats 4.8E+01
Fish 2.4E+00
Eggs 1.2E+01
Grains l.OE+02
Vegetables 7.4E+01
Fruits 8.0E+01
Fats' 3.7E+00
Foods 4.7E+02
Dairy 1.5E+02
Meats 6.1E+01
Fish 4.1E+00
Eggs l.OE+01
Grains 1.1E+02
Vegetables 8.1E+01
Fruits 5.3E+01
Fats' 4.7E+00
100.0
33.3
10.0
0.5
2.5
21.0
15.3
16.7
0.8
100.0
31.0
12.9
0.9
2.1
24.0
17.0
11.1
1.0
1.1E+03
4.5E+02
5.9E+01
5.6E+00
1.5E+01
1.5E+02
1.2E+02
2.5E+02
5.7E+00
Ages 3—5 Years (j
l.OE+03
4.0E+02
7.8E+01
6.5E+00
1.1E+01
1.9E+02
1.3E+02
1.8E+02
7.0E+00
100.0
42.5
5.6
0.5
1.5
14.5
11.5
23.3
0.5
1.9E+03
9.2E+02
7.0E+01
6.9E+00
2.3E+01
1.8E+02
1.9E+02
4.7E+02
7.5E+00
100.0
49.1
3.7
0.4
1.2
9.8
10.0
25.3
0.4
1.9E+01
6.0E+00
2.0E+00
8.9E-02
6.7E-01
4.2E+00
3.2E+00
2.8E+00
1.6E-01
;/day, as consumed)
100.0
40.0
7.9
0.7
1.1
18.6
13.2
17.9
0.7
1.8E+03
7.2E+02
l.OE+02
l.OE+01
2.5E+01
2.8E+02
2.1E+02
4.4E+02
1.2E+01
100.0
39.9
5.8
0.6
1.4
15.5
11.9
24.4
0.7
6.8E+00
1.8E+00
9.5E-01
4.1E-02
2.0E-01
1.8E+00
1.2E+00
6.9E-01
8.3E-02
Ages 6-11 Years (g/day, as consumed)
Foods 5.4E+02
Dairy 1.6E+02
Meats 7.7E+01
Fish 8.2E+00
Eggs 7.6E+00
100.0
30.1
14.3
1.5
1.4
1.1E+03
3.9E+02
l.OE+02
7.5E+00
1.1E+01
100.0
36.5
9.5
0.7
1.0
1.9E+03
7.8E+02
1.2E+02
1.2E+01
2.0E+01
100.0
39.9
6.1
0.6
1.0
3.8E+00
9.9E-01
5.8E-01
5.3E-02
9.2E-02
%
Age
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Ages
100.0
31.0
11.0
0.0
3.0
22.0
17.0
14.0
1.0
Consumers
Mid-range
Intake
%
High-end
Intake
%
< 1 Year (g/kg/day, as consumed)
1.3E+02
9.6E+01
1.8E+00
1.2E-01
l.OE+00
5.3E+00
7.8E+00
1.6E+01
1.4E-01
100.0
75.2
1.4
0.1
0.8
4.1
6.1
12.2
0.1
2.6E+02
2.4E+02
1.8E-01
2.3E-02
8.0E-03
4.0E+00
6.9E+00
9.6E+00
2.0E-02
100.0
92.1
0.1
0.0
0.0
1.5
2.6
3.7
0.0
1—2 Years (g/kg/day, as consumed)
8.1E+01
3.4E+01
4.8E+00
5.5E-01
1.4E+00
1.1E+01
l.OE+01
1.8E+01
4.4E-01
Ages 3—5 Years (j
100.0
27.1
14.0
0.6
2.9
27.0
17.2
10.1
1.2
5.4E+01
2.2E+01
4.5E+00
3.1E-01
6.4E-01
l.OE+01
7.1E+00
9.1E+00
4.5E-01
100.0
42.5
5.9
0.7
1.7
13.6
12.5
22.6
0.5
1.6E+02
8.3E+01
5.6E+00
5.0E-01
1.6E+00
1.5E+01
1.5E+01
3.8E+01
5.7E-01
100.0
52.3
3.5
0.3
1.0
9.2
9.6
23.7
0.4
;/kg/day, as consumed)
100.0
40.6
8.3
0.6
1.2
18.6
13.1
16.9
0.8
1.1E+02
4.1E+01
6.3E+00
4.6E-01
1.1E+00
1.8E+01
1.3E+01
2.7E+01
6.5E-01
100.0
37.9
5.9
0.4
1.0
16.9
12.0
25.2
0.6
Ages 6-11 Years (g/kg/day, as consumed)
100.0
26.2
15.3
1.4
2.4
3.3E+01
1.3E+01
3.1E+00
2.6E-01
4.5E-01
100.0
39.7
9.2
0.8
1.3
7.2E+01
3.0E+01
4.7E+00
3.6E-01
7.7E-01
100.0
41.4
6.6
0.5
1.1
OJ
oo
-------
Table 3-36. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total food intake (continued)
Consumers
Food
Group
All
Grains
Vegetables
Fruits
Fats'
Low-end
Intake
%
Mid-range
Intake
%
High-end
Intake
%
Low-end
Intake
%
Ages 6-11 Years (g/day, as consumed)
1.4E+02
9.3E+01
4.3E+01
5.7E+00
26.2
17.4
8.1
1.1
2.2E+02
1.7E+02
1.5E+02
9.9E+00
20.3
16.5
14.5
0.9
Ages 12 —19 Years (g/day,
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
4.1E+02
6.2E+01
7.7E+01
6.9E+00
7.3E+00
1.1E+02
1.1E+02
2.8E+01
7.8E+00
100.0
15.1
18.6
1.7
1.8
27.6
26.6
6.8
1.9
1.1E+03
2.9E+02
1.2E+02
8.7E+00
1.7E+01
2.4E+02
2.3E+02
1.4E+02
1.4E+01
100.0
26.8
11.6
0.8
1.6
22.6
21.9
13.5
1.3
3.4E+02
2.8E+02
3.8E+02
1.5E+01
as consumed)
2.4E+03
8.5E+02
2.2E+02
2.2E+01
2.7E+01
4.3E+02
4.4E+02
4.1E+02
2.6E+01
17.5
14.4
19.7
0.8
100.0
35.1
8.9
0.9
1.1
17.9
18.0
17.0
1.1
1.1E+00
7.5E-01
1.3E-01
6.0E-02
5.1E+00
8.7E-01
8.6E-01
8.4E-02
9.9E-02
1.5E+00
1.3E+00
2.4E-01
9.1E-02
30.0
19.7
3.4
1.6
A?
100.0
17.1
17.0
1.7
1.9
29.3
26.5
4.7
1.8
Consumers
Mid-range
Intake %
High-end
Intake
%
Ages 6-11 Years (g/kg/day, as consumed)
7.0E+00 21.0
4.7E+00 13.9
4.4E+00 13.1
3.2E-01 1.0
;es 12—19 Years (g/kg/day,
1.8E+01 100.0
4.7E+00 26.7
2.1E+00 12.1
1.5E-01 0.9
3.0E-01 1.7
4.0E+00 22.5
3.6E+00 20.6
2.5E+00 14.1
2.5E-01 1.4
1.3E+01
9.9E+00
1.3E+01
5.6E-01
as consumed)
4.4E+01
1.6E+01
3.5E+00
3.6E-01
4.0E-01
8.6E+00
7.3E+00
7.5E+00
4.4E-01
17.9
13.8
17.9
0.8
100.0
36.1
7.9
0.8
0.9
19.5
16.6
17.1
1.0
' Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII
-------
Table 3-37. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total meat intake
Consumers
Food
Group
All
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Low-end Mid-range
Tntake
% Tntake
%
High
Tntake
-end
%
Low-end
Tntake
%
Age < 1 Year (g/day, as consumed)
8.0E+02
6.5E+02
O.OE+00
O.OE+00
O.OE+00
8.0E+00
3.5E+01
1.1E+02
O.OE+00
100.0 7.6E+02
80.9 6.5E+02
0.0 O.OE+00
0.0 O.OE+00
0.0 3.5E-01
1.0 8.8E+00
4.3 2.7E+01
13.8 7.0E+01
0.0 8.3E-03
100.0
85.9
0.0
0.0
0.0
1.2
3.5
9.3
0.0
1.3E+03
7.9E+02
5.8E+01
4.6E+00
1.6E+01
l.OE+02
1.4E+02
1.9E+02
2.7E+00
100.0
61.0
4.4
0.4
1.2
7.9
10.4
14.5
0.2
1.2E+02
1.1E+02
O.OE+00
O.OE+00
O.OE+00
1.1E+00
4.3E+00
1.4E+01
O.OE+00
100.0
84.7
0.0
0.0
0.0
0.9
3.4
11.0
0.0
Ages 1—2 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
l.OE+03
5.9E+02
5.9E+00
3.3E+00
l.OE+01
l.OE+02
l.OE+02
2.2E+02
2.4E+00
100.0 l.OE+03
56.9 4.8E+02
0.6 5.2E+01
0.3 5.5E+00
1.0 1.5E+01
9.7 1.4E+02
9.8 1.1E+02
21.6 2.3E+02
0.2 5.4E+00
100.0
45.8
5.0
0.5
1.4
13.6
10.8
22.4
0.5
1.2E+03
4.3E+02
1.5E+02
7.9E+00
2.2E+01
1.7E+02
1.7E+02
2.5E+02
7.9E+00
100.0
35.6
12.5
0.6
1.8
14.3
13.7
20.8
0.7
5.6E+01
3.2E+01
1.6E-01
9.8E-02
4.0E-01
4.7E+00
6.1E+00
1.2E+01
8.4E-02
100.0
57.0
0.0
0.0
1.0
8.0
11.0
22.0
0.0
Ages 3—5 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
9.7E+02
4.0E+02
1.3E+01
6.5E+00
1.2E+01
1.9E+02
1.1E+02
2.4E+02
4.8E+00
100.0 9.6E+02
41.3 3.7E+02
1.4 7.0E+01
0.7 4.6E+00
1.2 1.6E+01
19.6 1.7E+02
10.9 1.4E+02
24.4 1.8E+02
0.5 7.2E+00
100.0
38.8
7.3
0.5
1.6
17.8
14.5
18.7
0.7
1.3E+03
3.7E+02
1.9E+02
7.7E+00
1.9E+01
2.3E+02
1.9E+02
2.3E+02
1.1E+01
100.0
29.9
14.9
0.6
1.5
18.7
14.9
18.7
0.9
1.8E+01
7.9E+00
7.8E-02
1.2E-01
1.4E-01
3.2E+00
1.6E+00
4.7E+00
6.3E-02
Ages 6-11 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
l.OE+03
4.3E+02
1.6E+01
4.7E+00
1.1E+01
100.0 1.1E+03
42.6 4.3E+02
1.6 8.8E+01
0.5 8.7E+00
1.1 1.2E+01
100.0
39.4
8.0
0.8
1.1
1.3E+03
4.3E+02
2.2E+02
8.8E+00
1.5E+01
100.0
32.1
16.7
0.7
1.1
1.3E+01
5.5E+00
5.8E-02
9.7E-02
1.7E-01
100.0
44.6
0.4
0.7
0.8
17.7
9.0
26.5
0.4
Consumers
Mid-range
Tntake
%
High-end
Tntake
%
Age <1 Year (g/kg/day, as consumed)
1.1E+02
l.OE+02
O.OE+00
O.OE+00
3.9E-02
8.1E-01
2.6E+00
7.9E+00
8.0E-04
100.0
89.8
0.0
0.0
0.0
0.7
2.3
7.1
0.0
1.4E+02
8.9E+01
6.2E+00
5.3E-01
1.4E+00
l.OE+01
1.7E+01
1.9E+01
3.0E-01
100.0
61.9
4.3
0.4
1.0
7.2
11.5
13.4
0.2
Ages 1—2 Years (g/kg/day, as consumed)
8.4E+01
3.6E+01
3.9E+00
4.0E-01
1.4E+00
1.1E+01
9.7E+00
2.1E+01
4.3E-01
Ages 3-5 Years (g/kj
5.8E+01
2.3E+01
3.8E+00
4.0E-01
6.6E-01
9.9E+00
7.5E+00
1.2E+01
4.1E-01
100.0
42.9
4.7
0.5
1.7
13.4
11.5
24.7
0.5
l.OE+02
3.9E+01
1.1E+01
7.0E-01
1.4E+00
1.4E+01
1.3E+01
2.0E+01
6.1E-01
100.0
38.9
11.3
0.7
1.4
13.8
13.4
19.9
0.6
;/day, as consumed)
100.0
40.2
6.5
0.7
1.1
17.1
13.0
20.7
0.7
7.5E+01
2.4E+01
l.OE+01
2.8E-01
l.OE+00
1.4E+01
1.1E+01
1.3E+01
6.1E-01
100.0
31.7
13.9
0.4
1.4
18.5
15.3
18.1
0.8
Ages 6-11 Years (g/kg/day, as consumed)
100.0
42.9
0.4
0.8
1.3
3.4E+01
1.3E+01
2.6E+00
2.8E-01
5.0E-01
100.0
38.7
7.5
0.8
1.5
5.2E+01
1.8E+01
7.7E+00
3.0E-01
6.7E-01
100.0
34.8
14.7
0.6
1.3
-------
Table 3-37. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total meat intake (continued)
Consumers
Food
Group
All
Grains
Vegetables
Fruits
Fats'
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
Low-end
Intake
%
Mid-range
Intake
%
High
Intake
-end
%
Low-end
Intake
%
Ages 6-11 Years (g/day, as consumed)
2.2E+02
1.4E+02
1.9E+02
8.0E+00
A?
9.3E+02
3.1E+02
1.9E+01
8.2E+00
1.1E+01
2.2E+02
1.9E+02
1.6E+02
1.2E+01
21.4
13.4
18.6
0.8
2.1E+02
1.8E+02
1.6E+02
1.1E+01
19.6
16.0
14.1
1.0
2.5E+02
2.5E+02
1.6E+02
1.2E+01
18.6
18.3
11.7
0.9
2.8E+00
1.9E+00
2.3E+00
7.8E-02
;es 12—19 Years (g/day, as consumed)
100.0
33.4
2.0
0.9
1.2
23.7
20.0
17.6
1.3
1.1E+03
3.5E+02
1.2E+02
9.6E+00
l.OE+01
2.5E+02
2.2E+02
1.5E+02
1.4E+01
100.0
31.2
10.3
0.9
0.9
22.7
19.3
13.4
1.3
1.7E+03
3.7E+02
3.3E+02
1.7E+01
2.8E+01
3.5E+02
3.8E+02
1.7E+02
2.4E+01
100.0
22.2
19.8
1.0
1.7
21.1
22.7
10.1
1.5
1.3E+01
4.3E+00
2.3E-01
9.5E-02
1.6E-01
3.2E+00
2.5E+00
2.1E+00
1.6E-01
21.7
14.7
17.6
0.6
A?
100.0
33.8
1.8
0.7
1.3
24.9
19.9
16.3
1.2
Consumers
Mid-range
Intake %
High-end
Intake
%
Ages 6-11 Years (g/kg/day, as consumed)
6.9E+00 20.0
5.2E+00 15.2
5.2E+00 15.3
3.3E-01 0.9
;es 12—19 Years (g/kg/day,
2.0E+01 100.0
6.1E+00 30.9
1.9E+00 9.6
2.4E-01 1.2
2.4E-01 1.2
4.4E+00 22.2
3.7E+00 18.8
2.9E+00 14.7
2.7E-01 1.4
9.8E+00
8.7E+00
6.3E+00
4.4E-01
as consumed)
3.0E+01
7.4E+00
5.5E+00
2.7E-01
4.2E-01
6.4E+00
6.2E+00
3.6E+00
3.9E-01
18.9
16.7
12.2
0.8
100.0
24.6
18.2
0.9
1.4
21.2
20.7
11.8
1.3
Oi
Oi
aIncludes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII
-------
Table 3-38. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total meat and dairy intake
Food
Group
All
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Low -end
Intake
%
Consumers
Mid-range
Intake %
Consumers
High-end
Intake
%
Low
Intake
Age <1 Year (g/day, as consumed)
4.2E+01
O.OE+00
O.OE+00
O.OE+00
O.OE+00
3.5E+00
1.1E+01
2.7E+01
O.OE+00
100.0
0.0
0.0
0.0
0.0
8.5
25.7
65.8
0.0
l.OE+03 100.0
7.8E+02 74.9
1.3E+01 1.3
2.0E+00 0.2
6.0E+00 0.6
5.2E+01 4.9
7.1E+01 6.8
1.2E+02 11.2
1.1E+00 0.1
1.7E+03
1.5E+03
5.9E+00
2.6E-01
l.OE+00
3.2E+01
5.1E+01
9.4E+01
3.3E-01
100.0
89.2
0.3
0.0
0.1
1.9
3.0
5.5
0.0
5.6E+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
4.8E-01
1.7E+00
3.4E+00
O.OE+00
Ages 1—2 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
7.2E+02
7.4E+01
4.9E+01
3.7E+00
2.0E+01
1.6E+02
1.2E+02
2.8E+02
4.6E+00
7.0E+02
7.8E+01
5.9E+01
5.9E+00
1.4E+01
1.8E+02
1.3E+02
2.3E+02
6.6E+00
100.0
10.3
6.7
0.5
2.8
22.8
16.9
39.3
0.6
Ages 3—5 Years (j
100.0
11.2
8.4
0.8
2.0
26.1
17.9
32.6
0.9
1.1E+03 100.0
4.2E+02 39.6
6.2E+01 5.8
5.7E+00 0.5
1.6E+01 1.5
1.6E+02 14.8
1.2E+02 11.0
2.8E+02 26.2
5.8E+00 0.5
;/day, as consumed)
9.8E+02 100.0
3.6E+02 37.1
7.5E+01 7.6
7.5E+00 0.8
1.5E+01 1.5
1.8E+02 18.4
1.3E+02 13.3
2.0E+02 20.5
7.5E+00 0.8
1.7E+03
1.1E+03
5.9E+01
4.4E+00
1.5E+01
1.3E+02
1.3E+02
2.2E+02
5.3E+00
100.0
66.4
3.5
0.3
0.9
7.9
7.6
13.0
0.3
3.2E+01
2.4E+00
1.9E+00
7.6E-02
1.1E+00
7.5E+00
5.5E+00
1.3E+01
2.1E-01
-end
%
Mid-range
Intake
%
High-end
Intake
%
Age <1 Year (g/kg/day, as consumed)
100.0
0.0
0.0
0.0
0.0
8.6
29.9
61.5
0.0
Ages
100.0
7.0
6.0
0.0
3.0
24.0
17.0
41.0
1.0
1.3E+02
9.4E+01
1.7E+00
2.2E-01
2.9E-01
5.0E+00
9.2E+00
1.8E+01
8.5E-02
100.0
73.0
1.3
0.2
0.2
3.9
7.1
14.2
0.1
2.5E+02
2.5E+02
3.0E-02
4.3E-03
1.1E-03
7.7E-01
9.6E-01
1.4E+00
6.7E-03
100.0
98.8
0.0
0.0
0.0
0.3
0.4
0.5
0.0
1—2 Years (g/kg/day, as consumed)
8.3E+01
3.2E+01
5.0E+00
3.5E-01
1.3E+00
1.2E+01
1.1E+01
2.2E+01
4.7E-01
Ages 3-5 Years (g/kj
1.6E+03
8.9E+02
8.7E+01
6.7E+00
1.7E+01
2.2E+02
1.5E+02
2.3E+02
8.9E+00
100.0
55.4
5.4
0.4
1.1
13.5
9.4
14.2
0.6
1.3E+01
7.9E-01
8.4E-01
6.8E-02
2.9E-01
3.2E+00
2.4E+00
4.9E+00
1.5E-01
Ages 6-11 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
7.2E+02
8.4E+01
7.2E+01
9.9E+00
1.3E+01
100.0
11.7
10.0
1.4
1.8
1.1E+03 100.0
3.9E+02 36.7
l.OE+02 9.5
6.8E+00 0.6
1.4E+01 1.4
1.8E+03
9.1E+02
1.2E+02
8.6E+00
1.5E+01
100.0
51.2
7.0
0.5
0.8
5.9E+00
4.4E-01
5.7E-01
3.7E-02
1.6E-01
100.0
6.2
6.6
0.5
2.3
25.7
18.9
38.6
1.1
5.5E+01
1.9E+01
4.6E+00
3.5E-01
7.6E-01
1.1E+01
7.8E+00
1.1E+01
4.1E-01
100.0
38.3
6.0
0.4
1.6
14.3
12.7
26.2
0.6
1.5E+02
9.7E+01
4.9E+00
4.0E-01
1.3E+00
1.1E+01
1.2E+01
1.9E+01
4.1E-01
100.0
66.7
3.4
0.3
0.9
7.7
8.0
12.7
0.3
;/day, as consumed)
100.0
34.3
8.4
0.6
1.4
19.4
14.3
20.9
0.8
9.5E+01
5.2E+01
5.5E+00
3.2E-01
8.3E-01
1.3E+01
9.2E+00
1.3E+01
4.5E-01
100.0
54.9
5.9
0.3
0.9
14.1
9.8
13.7
0.5
Ages 6-11 Years (g/kg/day, as consumed)
100.0
7.4
9.6
0.6
2.7
3.5E+01
1.2E+01
3.3E+00
2.5E-01
5.7E-01
100.0
33.7
9.4
0.7
1.6
6.7E+01
3.4E+01
4.6E+00
3.0E-01
6.0E-01
100.0
51.3
6.9
0.5
0.9
-------
Table 3-38. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total meat and dairy intake (continued)
Food
Group
Low -end
Intake
%
Consumers
Mid-range
Intake %
Consumers
High-end
Intake
%
Low
Intake
Ages 6-11 Years (g/day, as consumed)
Grains
Vegetables
Fruits
Fats'
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
1.9E+02
1.7E+02
1.8E+02
9.8E+00
Ages
6.2E+02
3.0E+01
5.6E+01
8.2E+00
2.0E+01
1.8E+02
1.7E+02
1.4E+02
9.9E+00
26.2
23.0
24.6
1.4
2.2E+02 20.9
1.7E+02 15.9
1.5E+02 14.0
9.6E+00 0.9
2.8E+02
2.0E+02
2.2E+02
1.3E+01
16.0
11.5
12.2
0.7
1.6E+00
1.5E+00
1.5E+00
8.5E-02
12-19 Years (g/day, as consumed)
100.0
4.9
9.1
1.3
3.2
28.7
28.2
22.9
1.6
1.1E+03 100.0
2.7E+02 25.0
1.4E+02 13.0
9.3E+00 0.9
1.8E+01 1.6
2.6E+02 24.4
2.3E+02 21.5
1.3E+02 12.2
1.5E+01 1.4
2.2E+03
l.OE+03
2.0E+02
1.3E+01
2.2E+01
3.6E+02
3.3E+02
2.0E+02
2.2E+01
100.0
47.4
9.0
0.6
1.0
16.6
15.2
9.2
1.0
7.9E+00
3.7E-01
6.6E-01
1.3E-01
2.3E-01
2.4E+00
2.1E+00
1.8E+00
1.4E-01
-end
%
Mid
Intake
-range
%
High-end
Intake
%
Ages 6-11 Years (g/kg/day, as consumed)
27.7
26.0
24.7
1.4
Ages
100.0
4.7
8.4
1.6
2.9
30.2
27.3
23.1
1.7
7.7E+00
5.4E+00
5.8E+00
3.6E-01
21.9
15.2
16.5
1.0
1.1E+01
8.1E+00
7.3E+00
5.0E-01
16.6
12.1
11.0
0.8
12-19 Years (g/kg/day, as consumed)
1.8E+01
4.4E+00
2.2E+00
1.9E-01
2.4E-01
4.5E+00
3.6E+00
2.6E+00
2.2E-01
100.0
24.4
12.4
1.0
1.4
25.1
19.9
14.6
1.2
3.9E+01 100.0
1.9E+01
3.3E+00
2.5E-01
3.9E-01
6.7E+00
5.6E+00
4.0E+00
3.7E-01
47.6
8.4
0.6
1.0
17.0
14.3
10.2
0.9
oo
a Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as
meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII
-------
Table 3-39. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total fish intake
Consumers
Food
Group
All
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Low -end
Intake
8.8E+02
6.9E+02
3.6E+00
O.OE+00
1.1E+00
1.4E+01
4.4E+01
1.3E+02
1.3E-01
%
A?
100.0
78.0
0.4
0.0
0.1
1.6
5.0
14.9
0.0
Mid-range
Intake
;e <1 Year (g/day,
8.4E+02
7.0E+02
7.7E+00
O.OE+00
3.2E+00
3.0E+01
4.8E+01
5.3E+01
8.3E-01
%
High-end
Intake
%
Low
Intake
as consumed)
100.0
83.0
0.9
0.0
0.4
3.5
5.7
6.3
0.1
1.2E+03
6.8E+02
3.7E+01
6.7E+00
7.2E+00
9.2E+01
1.4E+02
2.0E+02
2.9E+00
100.0
58.5
3.2
0.6
0.6
7.9
12.0
16.9
0.2
1.3E+02
1.1E+02
4.0E-01
O.OE+00
1.3E-01
1.6E+00
5.6E+00
1.6E+01
1.7E-02
Ages 1-2 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
1.1E+03
4.5E+02
5.5E+01
O.OE+00
1.6E+01
1.6E+02
1.2E+02
3.0E+02
5.2E+00
100.0
41.1
5.0
0.0
1.4
14.4
10.6
27.0
0.5
9.5E+02
4.5E+02
4.7E+01
1.2E+00
1.2E+01
1.3E+02
1.1E+02
1.9E+02
4.5E+00
100.0
48.0
5.0
0.1
1.3
13.7
11.4
20.0
0.5
1.2E+03
4.6E+02
7.4E+01
3.7E+01
1.6E+01
1.6E+02
1.4E+02
2.8E+02
6.7E+00
100.0
39.1
6.3
3.1
1.4
13.5
12.0
24.0
0.6
8.4E+01
3.6E+01
4.0E+00
O.OE+00
1.1E+00
1.2E+01
8.5E+00
2.3E+01
3.8E-01
Ages 3-5 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
1.1E+03
4.1E+02
6.5E+01
O.OE+00
l.OE+01
2.2E+02
1.3E+02
2.3E+02
7.1E+00
100.0
38.7
6.1
0.0
1.0
20.6
11.7
21.2
0.7
9.4E+02
3.5E+02
7.4E+01
1.6E+00
1.2E+01
1.7E+02
1.3E+02
1.8E+02
6.9E+00
100.0
37.7
7.9
0.2
1.3
18.4
14.3
19.5
0.7
1.1E+03
4.0E+02
8.4E+01
4.2E+01
1.4E+01
2.0E+02
1.6E+02
2.2E+02
9.9E+00
100.0
35.7
7.4
3.7
1.3
17.6
14.4
19.2
0.9
5.9E+01
2.2E+01
3.5E+00
O.OE+00
5.6E-01
1.2E+01
6.9E+00
1.2E+01
3.9E-01
Ages 6-11 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Epps
1.1E+03
4.5E+02
9.1E+01
O.OE+00
1.1E+01
100.0
41.6
8.3
0.0
1.0
1.1E+03
4.3E+02
8.0E+01
2.2E+00
1.3F+01
100.0
40.4
7.6
0.2
1.2
1.2E+03
4.2E+02
l.OE+02
5.7E+01
1.6F+01
100.0
34.6
8.4
4.7
1.3
3.7E+01
1.5E+01
3.0E+00
O.OE+00
3.7F-01
-end
%
Consumers
Mid-range
Intake %
High-end
Intake
%
Age <1 Year (g/kg/day, as consumed)
100.0
82.0
0.3
0.0
0.1
1.2
4.2
12.2
0.0
Ages
100.0
43.0
5.0
0.0
1.0
14.0
10.0
27.0
0.0
1.2E+02 100.0
l.OE+02 85.8
7.7E-01 0.7
O.OE+00 0.0
3.7E-01 0.3
3.6E+00 3.0
5.3E+00 4.5
6.5E+00 5.5
1.2E-01 0.1
1.4E+02
8.1E+01
4.3E+00
7.7E-01
7.7E-01
1.1E+01
1.7E+01
2.2E+01
3.3E-01
100.0
59.2
3.1
0.6
0.6
7.8
12.7
15.8
1.3E+02
1-2 Years (g/kg/day, as consumed)
7.8E+01 100.0
3.8E+01 48.7
3.8E+00 4.9
7.9E-02 0.1
9.2E-01 1.2
l.OE+01 12.9
8.7E+00 11.2
1.6E+01 20.7
3.4E-01 0.4
9.4E+01
3.7E+01
6.1E+00
2.8E+00
1.3E+00
1.3E+01
1.1E+01
2.2E+01
5.5E-01
100.0
40.0
6.5
2.9
1.3
13.5
12.1
23.1
0.6
Ages 3-5 Years (g/kg/day, as consumed)
100.0
38.2
6.0
0.0
1.0
21.3
11.8
21.0
0.7
5.5E+01 100.0
2.1E+01 38.2
4.3E+00 7.8
6.2E-02 0.1
5.5E-01 1.0
l.OE+01 18.6
6.9E+00 12.6
1.1E+01 20.9
3.8E-01 0.7
6.4E+01
2.4E+01
4.6E+00
2.2E+00
7.7E-01
1.1E+01
9.3E+00
1.2E+01
5.5E-01
100.0
36.6
7.2
3.5
1.2
17.3
14.5
18.9
0.9
Ages 6-11 Years (g/kg/day, as consumed)
100.0
41.5
8.2
0.0
1.0
3.3E+01 100.0
1.2E+01 37.0
2.8E+00 8.4
5.3E-02 0.2
3.8F-01 1.2
4.3E+01
1.6E+01
3.8E+00
1.7E+00
5.2F-01
100.0
36.5
8.7
3.9
1.2
-------
Table 3-39. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total fish intake (continued)
Consumers
Food
Group
All
Grains
Vegetables
Fruits
Fats'
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
Low -end
Intake
2.1E+02
1.3E+02
1.9E+02
9.6E+00
Ages
1.1E+03
4.1E+02
1.1E+02
O.OE+00
1.4E+01
2.4E+02
2.0E+02
1.5E+02
1.4E+01
%
A?
19.3
11.4
17.5
0.9
Mid-range
Intake
;e <1 Year (g/day,
2.2E+02
1.6E+02
1.5E+02
8.6E+00
%
High-end
Intake
%
Low
Intake
as consumed)
20.5
15.3
13.9
0.8
2.3E+02
1.8E+02
2.0E+02
1.1E+01
18.7
14.6
16.8
0.9
6.9E+00
4.1E+00
6.6E+00
3.2E-01
12-19 Years (g/day, as consumed)
100.0
36.2
9.5
0.0
1.2
21.1
17.9
12.9
1.2
1.1E+03
3.3E+02
1.2E+02
3.4E+00
1.5E+01
2.4E+02
2.1E+02
1.3E+02
1.3E+01
100.0
30.9
11.2
0.3
1.4
22.2
20.0
12.7
1.3
1.4E+03
3.3E+02
1.7E+02
7.5E+01
2.1E+01
2.9E+02
3.1E+02
2.1E+02
2.2E+01
100.0
23.2
11.9
5.2
1.4
20.5
21.7
14.5
1.5
1.9E+01
7.0E+00
1.8E+00
O.OE+00
2.3E-01
4.0E+00
3.4E+00
2.6E+00
2.2E-01
-end
%
Consumers
Mid-range
Intake %
High-end
Intake
%
Age <1 Year (g/kg/day, as consumed)
19.0
11.3
18.1
0.9
Ages
100.0
36.5
9.4
0.0
1.2
20.7
17.6
13.5
1.2
7.0E+00 21.3
5.4E+00 16.6
4.7E+00 14.4
2.9E-01 0.9
8.0E+00
6.4E+00
6.7E+00
3.8E-01
18.5
14.8
15.4
0.9
12-19 Years (g/kg/day, as consumed)
1.8E+01 100.0
5.7E+00 32.0
1.8E+00 10.3
5.4E-02 0.3
2.4E-01 1.3
3.9E+00 21.6
3.4E+00 19.3
2.5E+00 13.9
2.1E-01 1.2
2.5E+01 100.0
6.3E+00
3.0E+00
1.2E+00
3.5E-01
5.5E+00
5.2E+00
3.6E+00
3.7E-01
24.7
11.6
4.8
1.4
21.7
20.3
14.0
1.5
OJ
o
a Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as
meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII
-------
Table 3-40. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total fruit and vegetable intake
Consumers
Food
Group
All
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats'
Low -end
Intake
%
Mid-range
Intake
Age <1 Year (g/day,
6.7E+02
6.7E+02
O.OE+00
O.OE+00
O.OE+00
3.1E+00
O.OE+00
O.OE+00
O.OE+00
100.0
99.5
0.0
0.0
0.0
0.5
0.0
0.0
0.0
8.9E+02
7.2E+02
1.2E+01
6.3E-01
9.4E+00
4.5E+01
4.9E+01
4.9E+01
7.6E-01
%
as consumed)
100.0
81.4
1.3
0.1
1.1
5.1
5.5
5.5
0.1
High-
Intake
end
%
Low-end
Intake
%
Consumers
Mid-range
Intake %
High-end
Intake
%
Age <1 Year (g/kg/day, as consumed)
1.3E+03
7.0E+02
2.1E+01
2.3E+00
7.1E+00
6.4E+01
1.6E+02
3.9E+02
1.2E+00
100.0
51.9
1.5
0.2
0.5
4.7
11.9
29.2
0.1
1.3E+02
1.3E+02
O.OE+00
O.OE+00
O.OE+00
5.5E-01
O.OE+00
O.OE+00
O.OE+00
Ages 1-2 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
7.5E+02
4.7E+02
5.4E+01
4.1E+00
1.5E+01
1.2E+02
5.7E+01
1.7E+01
3.9E+00
7.0E+02
3.9E+02
6.5E+01
5.2E+00
1.1E+01
1.5E+02
5.4E+01
l.OE+01
4.9E+00
100.0
63.5
7.3
0.5
2.0
16.3
7.6
2.3
0.5
Ages 3-5 Years (j
100.0
56.3
9.3
0.7
1.5
22.1
7.8
1.5
0.7
l.OE+03
4.6E+02
6.4E+01
7.5E+00
1.3E+01
1.6E+02
1.2E+02
2.1E+02
5.5E+00
100.0
44.3
6.1
0.7
1.3
15.0
11.5
20.6
0.5
1.6E+03
4.4E+02
6.4E+01
7.8E+00
2.1E+01
1.5E+02
2.0E+02
6.9E+02
6.4E+00
100.0
27.8
4.0
0.5
1.3
9.5
12.7
43.7
0.4
3.4E+01
2.3E+01
2.5E+00
1.5E-01
7.4E-01
5.6E+00
2.1E+00
4.1E-01
1.5E-01
;/day, as consumed)
l.OE+03
3.9E+02
8.2E+01
7.5E+00
1.2E+01
1.9E+02
1.5E+02
1.5E+02
8.1E+00
100.0
39.4
8.3
0.8
1.2
19.4
14.7
15.5
0.8
1.6E+03
4.1E+02
8.4E+01
8.7E+00
2.3E+01
2.1E+02
2.2E+02
6.0E+02
1.1E+01
100.0
26.2
5.4
0.6
1.4
13.4
14.3
38.0
0.7
1.2E+01
7.1E+00
1.1E+00
9.6E-02
1.9E-01
3.1E+00
6.0E-01
3.0E-02
8.2E-02
Ages 6-11 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
7.3E+02
3.7E+02
7.5E+01
9.7E+00
l.OE+01
100.0
51.0
10.3
1.3
1.4
1.1E+03
4.5E+02
l.OE+02
9.8E+00
1.2E+01
100.0
40.4
9.0
0.9
1.1
1.7E+03
4.6E+02
l.OE+02
1.1E+01
1.8E+01
100.0
27.2
6.1
0.7
1.0
6.5E+00
3.2E+00
6.6E-01
3.5E-02
1.3E-01
100.0
99.6
0.0
0.0
0.0
0.4
0.0
0.0
0.0
Ages
100
66
7
0
2
16
6
1
0
1.1E+02 100.0
9.0E+01 84.6
1.1E+00 1.1
6.8E-02 0.1
9.1E-01 0.9
4.2E+00 4.0
4.3E+00 4.1
5.7E+00 5.3
7.9E-02 0.1
1.6E+02
8.1E+01
2.0E+00
2.0E-01
2.5E-01
7.4E+00
2.1E+01
4.3E+01
1.2E-01
100.0
52.0
1.3
0.1
0.2
4.8
13.6
28.0
0.1
1-2 Years (g/kg/day, as consumed)
8.3E+01 100.0
3.8E+01 45.5
5.2E+00 6.2
6.1E-01 0.7
1.2E+00 1.5
1.2E+01 14.7
9.5E+00 11.4
1.6E+01 19.5
3.8E-01 0.5
1.3E+02
3.8E+01
5.1E+00
4.3E-01
1.8E+00
1.3E+01
1.7E+01
5.6E+01
5.2E-01
100.0
29.1
3.9
0.3
1.4
9.9
12.9
42.2
0.4
Ages 3-5 Years (g/kg/day, as consumed)
100.0
57.5
9.2
0.8
1.5
25.1
4.9
0.2
0.7
5.4E+01 100.0
2.2E+01 40.9
4.7E+00 8.7
3.5E-01 0.6
5.0E-01 0.9
l.OE+01 19.0
7.1E+00 13.1
8.6E+00 15.9
4.5E-01 0.8
9.6E+01
2.6E+01
5.0E+00
4.8E-01
1.1E+00
1.3E+01
1.3E+01
3.6E+01
6.0E-01
100.0
26.9
5.3
0.5
1.2
13.9
14.0
37.7
0.6
Ages 6-11 Years (g/kg/day, as consumed)
100.0
50.3
10.2
0.5
2.0
3.5E+01 100.0
1.5E+01 42.7
3.2E+00 9.2
2.4E-01 0.7
3.5E-01 1.0
6.3E+01
1.8E+01
3.9E+00
3.5E-01
7.4E-01
100.0
29.4
6.2
0.6
1.2
-------
Table 3-40. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total fruit and vegetable intake (continued)
Consumers
Food
Group
All
Grains
Vegetables
Fruits
Fats"
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Low-end
Intake
%
Mid-range
Intake
%
High-end
Intake
%
Low-end
Intake
Ages 6-11 Years (g/day, as consumed)
1.8E+02
6.2E+01
8.6E+00
5.2E+00
Ages
6.8E+02
2.9E+02
l.OE+02
5.0E+00
1.3E+01
2.0E+02
6.6E+01
3.3E+00
7 6F+00
25.5
8.5
1.2
0.7
2.4E+02
1.7E+02
1.3E+02
1.1E+01
21.2
15.0
11.5
1.0
2.5E+02
3.0E+02
5.3E+02
1.4E+01
15.0
17.9
31.3
0.9
1.9E+00
3.9E-01
4.1E-02
3.9E-02
12-19 Years (g/day, as consumed)
100.0
42.5
15.2
0.7
1.9
28.5
9.6
0.5
1 1
1.1E+03
3.4E+02
1.3E+02
1.1E+01
1.8E+01
2.6E+02
2.4E+02
7.5E+01
1 6F+01
100.0
31.4
11.7
1.0
1.7
23.7
22.2
6.9
1 5
2.1E+03
4.5E+02
1.8E+02
2.0E+01
2.4E+01
3.6E+02
4.5E+02
5.8E+02
7 5F+01
100.0
21.7
8.7
1.0
1.1
17.1
21.6
27.5
1 7
8.4E+00
3.6E+00
1.3E+00
6.9E-02
1.5E-01
2.4E+00
7.6E-01
4.5E-02
8 6F-07
%
Consumers
Mid-range
Intake
%
High-end
Intake %
Ages 6-11 Years (g/kg/day, as consumed)
29.6
6.0
0.6
0.6
Ages
100.0
43.2
15.0
0.8
1.8
28.5
9.1
0.5
1 0
7.1E+00
4.8E+00
3.9E+00
3.1E-01
20.5
13.8
11.1
0.9
l.OE+01 16.2
1.1E+01 17.2
1.8E+01 28.6
4.9E-01 0.8
12-19 Years (g/kg/day, as consumed)
1.8E+01
6.1E+00
2.3E+00
2.0E-01
2.7E-01
4.3E+00
4.0E+00
1.1E+00
76F-01
100.0
32.8
12.2
1.1
1.5
23.2
21.8
6.0
1 4
3.8E+01 100.0
8.5E+00 22.6
2.9E+00 7.7
3.3E-01 0.9
4.3E-01 1.1
6.8E+00 18.1
7.8E+00 20.7
l.OE+01 27.7
47F-01 1 1
OJ
to
1 Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods
such as meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII
-------
Table 3-41. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total dairy intake
Consumers
Food
Group
All
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
Low -end
Intake
2.2E+01
O.OE+00
O.OE+00
O.OE+00
O.OE+00
2.5E+00
5.8E+00
1.3E+01
O.OE+00
%
100.0
0.0
0.0
0.0
0.0
11.7
26.9
61.4
0.0
Mid-range
Intake
Age <1 Year (g/day,
l.OE+03
7.8E+02
1.4E+01
1.8E+00
4.4E+00
5.1E+01
6.9E+01
1.3E+02
9.2E-01
%
High-end
Intake
%
Low
Intake
as consumed)
100.0
74.4
1.4
0.2
0.4
4.9
6.6
12.0
0.1
1.7E+03
1.5E+03
5.9E+00
2.6E-01
l.OE+00
3.2E+01
5.1E+01
9.4E+01
3.3E-01
100.0
89.2
0.3
0.0
0.1
1.9
3.0
5.5
0.0
2.5E+00
O.OE+00
O.OE+00
O.OE+00
O.OE+00
1.1E-01
7.6E-01
1.6E+00
O.OE+00
Ages 1-2 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
7.4E+02
6.5E+01
6.8E+01
4.3E+00
2.4E+01
1.7E+02
1.4E+02
2.7E+02
5.8E+00
100.0
8.8
9.1
0.6
3.2
22.8
18.4
36.4
0.8
1.1E+03
4.2E+02
6.5E+01
6.5E+00
1.7E+01
1.5E+02
1.1E+02
2.8E+02
5.6E+00
100.0
39.7
6.1
0.6
1.6
14.3
10.4
26.6
0.5
1.6E+03
1.1E+03
5.0E+01
4.5E+00
1.5E+01
1.3E+02
1.2E+02
2.1E+02
5.2E+00
100.0
67.2
3.1
0.3
0.9
7.8
7.4
13.0
0.3
3.3E+01
1.9E+00
2.8E+00
7.4E-02
1.2E+00
8.0E+00
6.3E+00
1.3E+01
2.5E-01
Ages 3-5 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Eggs
Grains
Vegetables
Fruits
Fats"
7.0E+02
6.6E+01
8.3E+01
5.3E+00
1.6E+01
1.8E+02
1.3E+02
2.2E+02
6.7E+00
100.0
9.4
11.9
0.8
2.2
25.8
18.4
30.7
1.0
9.8E+02
3.6E+02
8.6E+01
5.9E+00
9.5E+00
1.8E+02
1.4E+02
1.8E+02
7.1E+00
100.0
36.7
8.8
0.6
1.0
18.8
14.7
18.7
0.7
1.6E+03
9.0E+02
7.5E+01
6.2E+00
1.6E+01
2.1E+02
1.5E+02
2.2E+02
8.5E+00
100.0
56.8
4.7
0.4
1.0
13.2
9.2
14.1
0.5
1.3E+01
4.8E-01
1.6E+00
l.OE-01
3.3E-01
3.4E+00
2.6E+00
4.5E+00
1.6E-01
Ages 6-11 Years (g/day, as consumed)
Foods
Dairy
Meats
Fish
Epps
7.3E+02
7.1E+01
l.OE+02
l.OE+01
1.4E+01
100.0
9.7
14.0
1.4
2.0
l.OE+03
3.9E+02
9.2E+01
7.4E+00
1.2E+01
100.0
38.0
9.0
0.7
1.2
1.7E+03
9.2E+02
9.9E+01
7.4E+00
1.2E+01
100.0
52.6
5.7
0.4
0.7
7.3E+00
2.3E-01
1.2E+00
5.9E-02
1.4E-01
-end
%
Consumers
Mid-range
Intake %
High-end
Intake
%
Age <1 Year (g/kg/day, as consumed)
100.0
0.0
0.0
0.0
0.0
4.6
30.4
65.0
0.0
Ages
100
6
8
0
4
24
19
39
1
1.3E+02 100.0
9.4E+01 73.4
1.9E+00 1.5
3.1E-01 0.2
3.0E-01 0.2
4.8E+00 3.8
8.9E+00 7.0
1.8E+01 13.8
1.1E-01 0.1
2.5E+02
2.5E+02
3.0E-02
4.3E-03
1.1E-03
7.7E-01
9.6E-01
1.4E+00
6.7E-03
100.0
98.8
0.0
0.0
0.0
0.3
0.4
0.5
0.0
1-2 Years (g/kg/day, as consumed)
8.2E+01 100.0
3.2E+01 38.7
4.8E+00 5.9
5.3E-01 0.7
1.1E+00 1.3
1.2E+01 14.6
l.OE+01 12.4
2.1E+01 26.0
4.1E-01 0.5
1.4E+02
9.8E+01
4.1E+00
3.2E-01
1.2E+00
1.1E+01
1.1E+01
1.9E+01
3.8E-01
100.0
67.6
2.8
0.2
0.9
7.6
7.8
12.9
0.3
Ages 3-5 Years (g/kg/day, as consumed)
100.0
3.7
12.1
0.8
2.5
25.5
19.9
34.4
1.2
5.3E+01 100.0
1.9E+01 35.5
4.1E+00 7.8
2.9E-01 0.5
5.9E-01 1.1
9.5E+00 17.9
7.8E+00 14.7
1.1E+01 21.6
4.1E-01 0.8
9.4E+01
5.2E+01
4.7E+00
3.4E-01
8.9E-01
1.3E+01
9.3E+00
1.3E+01
4.5E-01
100.0
55.4
5.0
0.4
0.9
13.9
9.9
13.9
0.5
Ages 6-11 Years (g/kg/day, as consumed)
100.0
3.2
16.0
0.8
1.9
3.3E+01 100.0
1.2E+01 36.4
2.9E+00 8.8
2.1E-01 0.6
4.5E-01 1.4
6.6E+01
3.5E+01
3.8E+00
3.6E-01
5.5E-01
100.0
52.9
5.9
0.5
0.8
OJ
oo
-------
Table 3-41. Per capita intake of total foods and major food groups, and percent of total food intake for
individuals with low-end, mid-range, and high-end total dairy intake (continued)
Consumers
Food Low-end
woup Intake
All
Grains 1.9E+02
Vegetables 1.7E+02
Fruits 1.6E+02
Fats' 1.1E+01
Ages
Foods 6.9E+02
Dairy 1.3E+01
Meats 1.2E+02
Fish 1.1E+01
Eggs 1.4E+01
Grains 2.0E+02
Vegetables 1.8E+02
Fruits 1.4E+02
Fats' 9.7E+00
%
Mid-range
Intake
%
High-
Intake
end
%
Low-
Intake
Age 6-11 Years (g/day, as consumed)
26.3
22.8
22.4
1.5
2.1E+02
1.5E+02
1.4E+02
1.1E+01
20.9
14.9
14.2
1.0
2.9E+02
1.9E+02
2.2E+02
1.3E+01
16.3
10.9
12.7
0.7
2.0E+00
1.9E+00
1.8E+00
1.2E-01
12-19 Years (g/day, as consumed)
100.0
2.0
17.0
1.6
2.1
28.1
26.8
20.8
1.4
1.1E+03
2.7E+02
1.6E+02
l.OE+01
1.7E+01
2.6E+02
2.5E+02
1.6E+02
1.3E+01
100.0
23.9
13.9
0.9
1.5
22.8
22.0
13.8
1.2
2.1E+03
1.1E+03
1.4E+02
1.1E+01
2.0E+01
3.4E+02
2.8E+02
1.8E+02
2.0E+01
100.0
51.6
6.9
0.6
1.0
16.4
13.7
8.9
1.0
8.9E+00
1.4E-01
1.5E+00
1.5E-01
2.2E-01
2.4E+00
2.4E+00
2.0E+00
1.2E-01
end
%
Consumers
Mid-range
Intake
%
High-end
Intake
%
Age 6-11 Years (g/kg/day, as consumed)
27.0
25.3
24.2
1.6
Ages
100.0
1.6
17.3
1.7
2.4
26.7
26.6
22.3
1.4
7.0E+00
4.8E+00
5.3E+00
3.2E-01
21.3
14.6
16.0
1.0
1.1E+01
7.7E+00
7.2E+00
4.7E-01
16.4
11.8
11.0
0.7
12-19 Years (g/kg/day, as consumed)
1.8E+01
4.4E+00
2.1E+00
1.2E-01
3.0E-01
4.5E+00
3.7E+00
2.6E+00
2.2E-01
100.0
24.5
11.7
0.7
1.7
25.2
20.5
14.5
1.2
3.8E+01
1.9E+01
2.4E+00
2.3E-01
3.1E-01
6.5E+00
4.9E+00
3.8E+00
3.4E-01
100.0
50.9
6.5
0.6
0.8
17.2
13.0
10.0
0.9
a Includes added fats such as butter, margarine, dressings and sauces, vegetable oil, etc.; does not include fats eaten as components of other foods such as meats.
Source: Based on U.S. EPA analysis of 1994-96 CSFII.
-------
Table 3-42. Weighted (w) and unweighted (uw) number of observations
(individuals) for NFCS data used in analysis of food intake
All Regions
Age (years)
< 1
1-2
3-5
6-11
12-19
w
2,814,000
5,699,000
8,103,000
16,711,000
20,488,000
uw
156
321
461
937
1084
Northeast
w
545,000
1,070,000
1,490,000
3,589,000
4,445,000
uw
29
56
92
185
210
Midwest
w
812,000
1,757,000
2,251,000
4,263,000
5,490,000
uw
44
101
133
263
310
South
w
889,000
1,792,000
2,543,000
5,217,000
6,720,000
uw
51
105
140
284
369
West
w
568,000
1,080,000
1,789,000
3,612,000
3,833,000
uw
32
59
95
204
195
Source: Based on U.S. EPA analyses of the 1987/1988 NFCS
3-75
-------
Table 3-43. Consumer-only intake of homegrown foods (g/kg-day), all regions combined"
Age
(Years}
N
w
Percentile
uw
Percent
Consuming
Mean
SE
PI
PS
P10
P25
P50
P75
P90
P95
P99
P10
Homegrown Fruits
1-2
3-5
6-11
12-19
360,000
550,000
1,044,000
1.189.000
23
34
75
67
6.32
6.79
6.25
5.80
8.74E+00
4.07E+00
3.59E+00
1.94E+00
3.10E+00
1.48E+00
6.76E-01
3.66E-01
9.59E-01
l.OOE-02
l.OOE-02
8.74E-02
1.09E+00
l.OOE-02
1.91E-01
1.27E-01
1.30E+00
3.62E-01
4.02E-01
2.67E-01
1.64E+00
9.77E-01
6.97E-01
4.41E-01
3.48E+00
1.92E+00
1.31E+00
6.61E-01
7.98E+00
2.73E+00
3.08E+00
2.35E+00
1.93E+01
6.02E+00
1.18E+01
6.76E+00
6.06E+01
8.91E+00
1.58E+01
8.34E+00
6.06E+01
4.83E+01
3.22E+01
1.85E+01
6.06E+0
4.83E+0
3.22E+0
1.85E+0
Homegrown Vegetables
1-2
3-5
6-11
12-19
951,000
1,235,000
3,024,000
3.293.000
53
76
171
183
16.69
15.24
18.10
16.07
5.20E+00
2.46E+00
2.02E+00
1 .48E+00
8.47E-01
2.79E-01
2.54E-01
1.35E-01
2.32E-02
O.OOE+00
5.95E-03
O.OOE+00
2.45E-01
4.94E-02
l.OOE-01
6.46E-02
3.82E-01
3.94E-01
1.60E-01
1.45E-01
1.23E+00
7.13E-01
4.00E-01
3.22E-01
3.27E+00
1.25E+00
8.86E-01
8.09E-01
5.83E+00
3.91E+00
2.21E+00
1.83E+00
1.31E+01
6.35E+00
4.64E+00
3.71E+00
1.96E+01
7.74E+00
6.16E+00
6.03E+00
2.70E+01
1.06E+01
1.76E+01
7.71E+00
2.70E+0
1.28E+0
2.36E+0
9.04E+0
Home Produced Meats
1-2
3-5
6-11
12-19
276,000
396,000
1,064,000
1.272.000
22
26
65
78
4.84
4.89
6.37
6.21
3.65E+00
3.61E+00
3.65E+00
1.70E+00
6.10E-01
5.09E-01
4.51E-01
1.68E-01
3.85E-01
8.01E-01
3.72E-01
1.90E-01
9.49E-01
8.01E-01
6.52E-01
3.20E-01
9.49E-01
1.51E+00
7.21E-01
4.70E-01
1.19E+00
2.17E+00
1.28E+00
6.23E-01
2.66E+00
2.82E+00
2.09E+00
1.23E+00
4.72E+00
3.72E+00
4.71E+00
2.35E+00
8.68E+00
7.84E+00
8.00E+00
3.66E+00
l.OOE+01
9.13E+00
1.40E+01
4.34E+00
1.15E+01
1.30E+01
1.53E+01
6.78E+00
1.15E+0
1.30E+0
1.53E+0
7.51E+0
Home Causht Fish
1-2
3-5
6-11
12-19
82,000
142,000
382,000
346,000
6
11
29
21
1.44
1.75
2.29
1.69
b
b
2.78E+00
1.52E+00
b
b
8.40E-01
4.07E-01
b
b
1.60E-01
1.95E-01
b
b
1.60E-01
1.95E-01
b
b
1.84E-01
1.95E-01
b
b
2.28E-01
1.95E-01
b
b
5.47E-01
3.11E-01
b
b
1.03E+00
9.84E-01
b
b
3.67E+00
1.79E+00
b
b
7.05E+00
4.68E+00
b
b
7.85E+00
6.67E+00
b
b
2.53E+0
8.44E+0
a Data are not provided for intake of home-produced dairy because intake data were not provided for subpopulations for which there were fewer than 20 observations.
b Fewer than 20 observations, w = weighted number of consumers, u = unweighted number of consumers.
Source: Based on EPA's analyses of the 1987/88 NFCS
-------
Table 3-44. Percent weight losses from food preparation
Food group
Meat
Fish
Fruits
Vegetables
a Based on potatoes only.
Source: U.S. EPA, 1997
Mean net cooking loss (°/o) Mean net post-cooking, paring, or preparation loss (°/o)
30 30
32 11
31 25
12 22'
3-77
-------
Table 3-45. Quantity (as consumed) of food groups consumed per eating occasion and the percentage of
individuals using these foods over a 3-day period in a 1997-1978 survey, by age group
Food category
<
1 year old
M/F
Mea
n
SD
1-
2 years old
M/F
Mea SD
n
3-5 years old
M/F
% Mea SD
n
6-8 years olc
M/F
% Mea
n
SD
%
M
Mea
n
9-14yearsold
SD %
F
Mea
n
SD
%
M
Mea
n
15-18 years ols
SD %
F
Mea
n
SD
Fruits and Vegetables
Raw vegetables
White potatoes
Cabbage and coleslaw
Carrots
Cucumbers
Lettuce and tossed salad
Mature onions
Tomatoes
Cooked vegetables
Broccoli
Cabbage
Carrots
Corn, whole kernel
Lima beans
Mixed vegetables
Cowpeas, field peas,
black-eyed peas
Green peas
Spinach
String beans
Summer squash
Sweet potatoes
Tomato juice
Cucumber pickles
Fruits
Grapefruit
Grapefruit juice
Oranges
Orange juice
Apples
Applesauce, cooked
apples
Apple juice
Cantaloupe
Raw peaches
Raw pears
Raw strawberries
18.1
0
0.8
0.6
0
0
0.3
1.0
0.4
21 .7
3.2
1.0
11.4
0.5
16.0
0.9
19.7
0.7
10.8
0
0.2
0
0.6
0.9
20.9
1.7
35.6
19.
0.
1.
1.
0.
72
0
37
63
0
0
21
42
77
71
22
71
81
127
61
26
69
26
82
0
6
0
143
87
122
94
71
125
136
118
56
120
58
0
12
63
0
0
7
27
52
41
17
67
47
64
45
19
47
19
47
0
0
0
44
34
51
51
49
56
0
39
40
30
74.5
3.4
3.4
1.6
16.6
1.4
10.6
5.7
3.2
1 1 .7
25.8
2.4
3.7
2.1
21.8
2.8
25.1
1.3
3.8
0.8
4.6
1.1
1.0
8.1
40.9
23.6
13.6
13.1
1.1
3.5
2.3
1.5
70
33
28
40
30
22
46
55
57
54
56
54
89
63
53
58
48
96
97
147
32
145
156
117
153
105
104
148
68
129
131
87
56
22
25
36
29
18
32
33
48
38
40
38
78
50
36
48
33
63
70
73
26
57
66
45
70
44
65
64
35
48
43
41
76.3
4.9
5.4
3.5
30.4
3.1
15.7
3.8
3.3
8.0
30.1
1.9
3.1
2.5
20.9
3.2
25.4
1.4
3.1
0.9
6.2
1.0
1.2
10.0
41.7
23.8
10.4
8.5
1.5
3.8
2.9
1.2
86
41
38
58
34
19
52
65
77
49
68
49
69
84
61
73
51
97
96
156
38
149
174
134
167
124
126
170
125
128
150
69
62
31
33
50
26
30
44
43
51
31
45
31
40
60
42
53
46
91
50
61
36
56
47
44
73
39
61
65
73
36
57
34
80.7
8.5
9.8
4.1
42.8
3.9
18.3
5.6
3.8
8.7
34.6
1.9
4.0
2 7
22.1
5.1
31.6
1.1
3.2
0.9
8.1
1.5
1.6
12.6
43.7
25.8
14.1
5.5
2.2
4.5
4.0
1.6
100
51
38
68
43
20
55
83
92
59
78
79
82
97
72
93
64
136
99
133
45
158
184
134
178
132
132
193
135
145
163
87
69
31
41
73
33
19
33
50
54
33
41
47
44
57
46
56
38
121
62
48
46
64
52
46
68
41
76
87
76
68
42
44
81.8
9.6
8.6
3.2
45.8
6.0
20.1
4.6
3.9
8.5
32.0
1.8
3.7
2.7
20.9
5.2
31.1
1.2
3.4
1.2
8.6
1.6
1.3
10.7
39.4
22.0
13.6
3.0
2.2
3.5
2.7
1.2
124
60
39
75
54
27
74
96
117
79
95
114
116
109
86
105
75
103
144
159
47
160
194
150
195
146
151
190
165
170
163
95
87
34
36
58
47
20
58
72
79
48
62
133
75
60
52
59
54
50
79
63
50
56
73
51
80
55
107
69
85
77
46
53
77.0
9.3
6.5
4.6
47.5
5.3
21.0
5.1
4.5
8.8
31.0
2.3
3.4
2.3
19.4
3.6
29.4
1.7
2.1
1.0
9.1
2.4
1.5
11.2
41.0
24.5
11.1
4.0
2.5
4.9
3.3
2.2
112
61
33
72
51
26
71
88
121
83
86
101
96
83
102
74
102
134
183
50
153
173
137
188
140
134
204
152
153
161
91
80
40
31
82
43
27
49
55
91
47
45
50
67
46
62
55
56
92
95
59
50
72
49
77
41
82
74
77
68
42
50
81.2
9.8
4.5
3.9
47.7
9.9
24.4
4.3
4.5
28.8
2.6
2.7
3.2
18.1
4.5
29.5
2.1
3.2
2.1
9.9
2.2
1.7
8.9
37.3
16.7
10.2
2.7
2.0
4.0
3.2
1.6
149
77
42
76
61
29
75
100
129
116
141
107
151
112
127
93
155
150
191
45
150
248
158
228
151
171
259
209
205
195
121
112
51
39
64
56
29
56
48
65
48
70
94
60
63
73
80
58
76
75
94
46
68
202
84
116
48
125
180
111
111
219
63
77.2
9.5
5.5
6.3
49.0
7.9
24.3
4.1
4.3
7.0
24.5
1.8
1.8
2.4
16.9
3.0
24.8
1.2
3.3
2.2
8.5
2.3
2.2
9.4
36.6
19.1
7.7
3.1
2.5
3.3
1.4
1.9
116
66
39
62
57
25
66
106
119
71
94
91
124
163
96
108
83
121
166
194
58
159
210
142
208
142
146
236
189
142
167
82
86
41
35
64
49
26
44
55
81
46
59
78
80
10
0
62
64
51
78
84
84
71
57
66
51
81
46
73
13
9
11
3
66
57
45
oo
-------
Table 3-45. Quantity (as consumed) of food groups consumed per eating occasion and the percentage of
individuals using these foods over a 3-day period in a 1997-1978 survey, by age group (continued)
Food category
< 1 year old
M/F
% Mean
SD
1-2 years old
M/F
% Mean
SD
3-5 years old
M/F
% Mean
SD
6-8 years old
M/F
% Mean
SD
M
Mean
9- 14 years old
SD %
F
Mean
SD
M
Mean
15-18 years ols
SD %
F
Mean SD
Grain Products
Yeast Breads
Pancakes
Waffles
Tortillas
Cakes and Cupcakes
Cookies
Pies
Doughnuts
Crackers
Popcorn
Pretzels
Corn-based Salty Snacks
Pasta
Rice
Cooked Cereals
Ready-to-Eat Cereals
Meat3
Beef
Pork
Lamb
Veal
Poultry
Chicken
Turkey
Dairy Products
Eggs
Butter
Margarine
Milk'
Cheese0
17.6
3.0
0.6
0.8
1.6
11.9
0.5
0.8
13.8
0.1
0.7
0.6
3.4
4.3
16.3
68.7
23.2
15.6
10.1
2.6
3.2
18.2
15.6
5.1
17.7
5.2
8.5
89.0
6.1
20
39
30
16
53
15
53
36
10
72
4
8
58
53
116
13
58
56
66
52
54
60
62
53
49
6
5
170
25
11
27
13
7
37
13
30
22
9
0
4
2
42
42
82
11
42
41
44
29
37
38
39
34
30
4
4
71
21
88.0
12.2
3.4
3.9
17.4
46.3
4.7
6.6
38.1
5.7
3.2
6.6
14.1
20.9
33.1
68.0
78.2
60.1
44.2
1.4
1.2
42.2
38.8
4.4
61.3
29 2
43.8
96.9
35.9
28
59
56
26
51
21
88
47
14
9
18
24
82
81
149
23
53
64
37
72
80
73
73
73
59
7
6
179
31
16
50
45
11
38
15
50
26
14
12
18
20
59
50
87
14
40
38
36
46
28
44
43
59
27
6
6
80
19
95.1
12.7
5.7
5.1
25.3
48.1
7.1
8.6
32.8
8.5
3.1
8.6
14.7
22.2
26.0
75.8
82.8
65.5
46.0
0.6
1.6
42.6
39.3
4.5
55.2
28.7
46.1
97.0
37.0
36
76
69
36
61
25
106
54
18
12
21
27
99
95
177
29
66
79
47
90
75
90
92
74
66
9
8
198
31
17
52
41
16
45
22
48
28
20
11
20
22
58
58
97
17
Meat
46
43
44
59
33
50
50
39
34
10
8
83
17
97.2
11.9
5.9
4.7
34.4
53.2
8.1
10.9
26.2
9.5
3.3
10.3
14.5
23.4
21.3
76.8
Poultry
84.6
67.2
46.7
0.5
2.0
45.1
41.4
5.7
48.5
31.7
42.9
98.5
35.3
40
96
69
55
66
28
116
60
20
14
25
29
116
120
198
33
19
59
45
29
42
21
58
30
19
9
21
26
74
77
104
19
96.9
13.5
5.2
4.0
36.4
44.4
10.2
12.0
22.1
9.6
4.1
9.9
14.0
18.9
19.5
69.8
49
118
87
74
80
36
133
67
24
18
29
33
162
149
223
41
28
72
62
31
56
36
55
39
24
17
25
29
102
86
126
28
96.4
10.7
4.1
4.3
35.2
43.1
10.6
12.9
22.1
9.1
3.5
11.3
14.5
22.4
17.3
64.0
44
101
80
66
77
32
129
62
20
17
30
32
145
138
212
36
23
89
68
33
55
29
62
36
16
15
26
30
89
77
107
21
96.2
9.8
3.5
3.4
31.0
37.9
13.6
13.2
18.0
6.1
2.9
8.3
11.2
20.9
14.3
50.4
59
161
125
100
93
45
144
91
32
20
52
46
198
195
259
49
35
110
70
48
71
50
66
74
29
20
50
44
133
117
132
31
93.7
9.8
2.4
4.0
26.5
34.9
9 2
12.9
19.6
7.8
3.1
10.7
10.8
19.0
12.1
43.7
44 2
121 9
79 5
69 3
80 5
31 2
126 4
63 3
23 2
18 2
25 1
34 2
158 9
160 8
229 10
37 2
and Dairy Products
82
97
57
139
115
103
106
74
70
10
9
227
35
55
52
49
86
72
56
55
44
37
11
8
89
23
87.1
69.0
48.8
0.9
1.5
44.3
39.8
6.5
49.1
32.4
44.8
97.4
31.2
103
124
68
171
124
131
136
103
85
12
12
265
39
71
66
65
80
75
75
77
56
47
15
12
125
22
84.2
68.2
47.0
0.7
1.5
44.0
39.6
6.2
44.3
30.9
40.7
95.1
34.9
94
111
64
127
96
112
115
90
75
10
11
242
35
69
70
57
68
46
58
57
54
40
9
12
103
23
87.9
70.3
56.1
0.5
1.5
43.8
38.9
7.5
52.3
32.4
41.4
93.2
39.0
123
152
79
56
70
53
60
20
101
14
16
314
46
90
87
75
81
87
85
87
68
49
12
14
164
30
82.6
65.9
46.2
1.0
2.1
43.7
39.5
6.2
44.4
32.0
38.6
88.0
39.8
102 7
123 7
68 6
112 4
131 6
123 6
128 7
89 4
79 4
13 1
11
244 11
37 2
a Meat includes beef, pork, lamb, and veal.
b Milk includes fluid milk, milk beverages, and milk-based infant formulas.
c Cheese - natural and processed cheese.
Source: Pao et al., 1982 (based on 1977-78 NFCS data)
-------
Table 3-46. Mean moisture content of selected food groups expressed as
percentages of edible portions
Food
Fruit
Apples - dried
Apples -
Apples -juice
Applesauce
Apricots
Apricots - dried
Bananas
Blackberries
Blueberries
Boysenberries
Cantaloupes - unspecified
Casabas
Cherries - sweet
Crabapples
Cranberries
Cranberries - juice cocktail
Currants (red and white)
Elderberries
Grapefruit
Grapefruit - juice
Grapefruit - unspecified
Grapes - fresh
Grapes - juice
Grapes - raisins
Honeydew melons
Kiwi fruit
Kumquats
Lemons - juice
Lemons - peel
Lemons - pulp
Limes -juice
Limes - unspecified
Loganberries
Mulberries
Nectarines
Oranges - unspecified
Peaches
Pears - dried
Pears - fresh
Pineapple
Pineapple -juice
Plums
Quinces
Raspberries
Strawberries
Tangerine - juice
Tangerines
Watermelon
Vegetables
Alfalfa sprouts
Artichokes - globe and French
Artichokes - Jerusalem
Moisture Content (%)
Raw Cooked
31.76
83.93*
86.35
31.09
74.26
85.64
84.61
85.90
89.78
91.00
80.76
78.94
86.54
85.00
83.95
79.80
90.89
90.00
90.89
81.30
84.12
15.42
89.66
83.05
81.70
90.73
81.60
88.98
90.21
88.26
84.61
87.68
86.28
86.75
87.66
26.69
83.81
86.50
83.80
86.57
91.57
88.90
87.60
91.51
91.14
84.38
78.01
84.13*
84.46**
87.93
88.35*
86.62*
85.56*
86.59*
84.95*
90.10*
92.46*
92.52*
87.49*
64.44*
86.47*
83.51*
85.53
85.20
89.97*
87.00*
89.51*
86.50
Comment
Sulfured; *Without added sugar
*With skin; ** Without skin
Canned or bottled
*Unsweetened
*Canned juice pack with skin
Sulfured; *Without added sugar
*Frozen unsweetened
Frozen unsweetened
* Canned, juice pack
Bottled
* Canned unsweetened
Pink, red, white
American type (slip skin)
Canned or bottled
Seedless
* Canned or bottled
* Canned or bottled
All varieties
* Canned juice pack
Sulfured; *Without added sugar
* Canned juice pack
* Canned juice pack
Canned
*Frozen unsweetened
* Canned sweetened
*Canned juice pack
Boiled, drained
3-80
-------
Table 3-46. Mean moisture content of selected food groups expressed as
percentages of edible portions (continued)
Food
Vegetables (continued)
Asparagus
Bamboo shoots
Beans - dry
Beans - dry, blackeye peas (cowpeas)
Beans - dry, hyacinth (mature seeds)
Beans - dry, navy (pea)
Beans - dry, pinto
Beans - lima
Beans - snap, Italian, green, yellow
Beets
Beets - tops (greens)
Broccoli
Brussel sprouts
Cabbage - Chinese/celery,
including bok choy
Cabbage - red
Cabbage - savoy
Carrots
Cassava (yucca blanca)
Cauliflower
Celeriac
Celery
Chili peppers
Chives
Cole slaw
Collards
Corn - sweet
Cress - garden, field
Cress - garden
Cucumbers
Dandelion - greens
Eggplant
Endive
Garlic
Kale
Kohlrabi
Lambsquarter
Leeks
Lentils - whole
Lettuce - iceberg
Lettuce - romaine
Mung beans (sprouts)
Mushrooms
Mustard greens
Okra
Onions
Onions - dehydrated or dried
Parsley
Parsley roots
Parsnips
Peas (garden) - mature seeds, dry
Peppers - sweet , garden
Potatoes (white) - peeled
Moisture Content (%)
Raw Cooked
92.25
91.00
66.80
87.87
79.15
81.30
70.24
90.27
87.32
92.15
90.69
86.00
95.32
91.55
91.00
87.79
68.51
92.26
88.00
94.70
87.74
92.00
81.50
93.90
75.96
89.40
89.40
96.05
85.60
91.93
93.79
58.58
84.46
91.00
84.30
83.00
67.34
95.89
94.91
90.40
91.81
90.80
89.58
90.82
3.93
88.31
88.31
79.53
88.89
92.77
78.96
92.04
95.92
71.80
86.90
76.02
93.39
67.17
89.22
90.90
89.13
90.20
87.32
95.55
93.60
92.00
87.38
92.50
92.30
95.00
92.50*
95.72
69.57
92.50
92.50
89.80
91.77
91.20
90.30
88.90
90.80
68.70
93.39
91.08
94.46
89.91
92.24
77.72
88.91
94.70
75.42
Comment
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
* Canned solids and liquid
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Stir-fried
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Baked
5-81
-------
Table 3-46. Mean moisture content of selected food groups expressed as
percentages of edible portions (continued)
Food
Vegetables (continued)
Potatoes (white) - whole
Pumpkin
Radishes - roots
Rhubarb
Rutabagas - unspecified
Salsify (oyster plant)
Shallots
Soybeans - sprouted seeds
Spinach
Squash - summer
Squash - winter
Sweetpotatoes (including yams)
Swiss chard
Tapioca - pearl
Taro - greens
Taro - root
Tomatoes - juice
Tomatoes - paste
Tomatoes - puree
Tomatoes - raw
Tomatoes - whole
Towelgourd
Turnips - roots
Turnips - tops
Water chestnuts
Yambean - tuber
_ Grains
Barley - pearled
Corn - grain - endosperm
Corn - grain - bran
Millet
Oats
Rice - rough - white
Rye - rough
Rye - flour - medium
Sorghum (including milo)
Wheat - rough - hard white
Wheat - germ
Wheat - bran
Wheat - flour - whole grain
_Meat
"Beef
Beef liver
Chicken (light meat)
Chicken (dark meat)
Duck - domestic
Duck - wild
Goose - domestic
Ham - cured
Horse
Lamb
Lard
Pork
Rabbit - domestic
Turkey
Moisture Content (%)
Raw Cooked
83.29
91.60
94.84
93.61
89.66
77.00
79.80
69.05
91.58
93.68
88.71
72.84
92.66
10.99
85.66
70.64
93.95
93.95
93.85
91.87
91.07
73.46
89.15
10.09
10.37
3.71
3.71
8.22
11.62
10.95
9.85
9.20
9.57
11.12
9.89
10.27
71.60
68.99
74.86
75.99
73.77
75.51
68.30
66.92
72.63
73.42
0.00
70.00
72.81
71.20
93.69
67.79
90.10
81.00
79.45
91.21
93.70
89.01
71.85
92.65
92.15
63.80
93.90
74.06
87.26
92.40
84.29
93.60
93.20
87.93
68.80
71.41
68.72
63.98
69.11
74.16
Comment
Baked
Boiled, drained
Frozen, cooked with added sugar
Boiled, drained
Boiled, drained
Steamed
Boiled, drained
All varieties; boiled, drained
All varieties; baked
Baked in skin
Boiled, drained
Dry
Steamed
Canned
Canned
Canned
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Boiled, drained
Crude
Crude
Crude
Composite, trimmed, retail cuts
Without skin
Without skin
Roasted
Composite, trimmed, retail cuts
Roasted
Roasted
Roasted
3-82
-------
Table 3-46. Mean moisture content of selected food groups expressed as
percentages of edible portions (continued)
flood
Dairy Products
Eggs
Butter
Cheese
American pasteurized
Cheddar
Swiss
Parmesan, hard
Parmesan, grated
Cream, whipping, heavy
Cottage, lowfat
Colby
Blue
Cream
Yogurt
Plain, lowfat
Plain, with fat
Human milk (estimated from USDA Survey)
Human
Skim
Lowfat (1%)
Moisture Content (u/o) Comment
Raw Cooked
74.57
15.87
39.16 Regular
36.75
37.21
29.16
17.66
57.71
79.31
38.20
42.41
53.75
85.07
87.90 Made from whole milk
87.50 Whole, mature, fluid
90.80
90.80
Source: USDA, 1979-1986
3-83
-------
Table 3-47. Percent moisture content for selected fish species"
Species
Finflsh
Anchovy, European
Bass
Bass, striped
Bluefish
Butterfish
Carp
Catfish
Cod, Atlantic
Cod, Pacific
Croaker, Atlantic
Dolphinfish, mahimahi
Drum, freshwater
Flatfish, flounder and sole
Grouper
Haddock
Halibut, Atlantic and Pacific
Halibut, Greenland
Herring, Atlantic and turbot, domestic species
Herring, Pacific
Mackerel, Atlantic
Mackerel, Jack
Mackerel, King
Mackerel, Pacific and Jack
Mackerel, Spanish
Monkfish
Mullet, striped
Ocean Perch, Atlantic
Perch, mixed species
Pike, northern
Pike, walleye
Moisture Content
(%)
73.37
50.30
75.66
79.22
70.86
74.13
76.31
69.63
76.39
58.81
81.22
75.61
75.92
16.14
81.28
78.03
59.76
77.55
77.33
79.06
73.16
79.22
73.36
79.92
74.25
71.48
77.92
71.69
70.27
72.05
64.16
59.70
55.22
71.52
63.55
53.27
69.17
75.85
70.15
71.67
68.46
83.24
77.01
70.52
78.70
72.69
79.13
73.25
78.92
72.97
79.31
Comment
Raw
Canned in oil, drained solids
Freshwater, mixed species, raw
Raw
Raw
Raw
Raw
Cooked, dry heat
Channel, raw
Channel, cooked, breaded and fried
Atlantic, raw
Canned, solids and liquids
Cooked, dry heat
Dried and salted
Raw
Raw
Cooked, breaded and fried
Raw
Raw
Raw
Cooked, dry heat
Raw, mixed species
Cooked, dry heat
Raw
Cooked, dry heat
Smoked
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Kippered
Pickled
Raw
Raw
Cooked, dry heat
Canned, drained solids
Raw
Canned, drained solids
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
3-84
-------
Table 3-47. Percent moisture content for selected fish species" (continued)
Species
Pollock, Alaska and Walleye
Pollock, Atlantic
Rockfish, Pacific, mixed species
Roughy, orange
Salmon, Atlantic
Salmon, chinook
Salmon, chum
Salmon, coho
Salmon, Pink
Salmon, red and sockeye
Sardine, Atlantic
Sardine, Pacific
Sea Bass, mixed species
Seatrout, mixed species
Shad, American
Shark, mixed species
Snapper, mixed species
Sole, spot
Sturgeon, mixed species
Sucker, white
Sunfish, pumpkinseed
Swordfish
Trout, mixed species
Trout, rainbow
Tuna, light meat
Tuna, white meat
Tuna, bluefish, fresh
Turbot, European
Whitefish, mixed species
Whiting, mixed species
Yellowtail, mixed species
Moisture Content
(%)
81.56
74.06
78.18
79.26
73.41
75.90
68.50
73.17
72.00
75.38
70.77
72.63
65.35
76.35
68.81
70.24
68.72
61.84
59.61
68.30
78.27
72.14
78.09
68.19
73.58
60.09
76.87
70.35
75.95
76.55
69.94
62.50
79.71
79.50
75.62
68.75
71.42
71.48
63.43
59.83
74.51
64.02
69.48
68.09
59.09
76.95
72.77
70.83
80.27
74.71
74.52
Comment
Raw
Cooked, dry heat
Raw
Raw (Mixed species)
Cooked, dry heat (mixed species)
Raw
Raw
Raw
Smoked
Raw
Canned, drained solids with bone
Raw
Cooked, moist heat
Raw
Canned, solids with bone and liquid
Raw
Canned, drained solids with bone
Cooked, dry heat
Canned in oil, drained solids with bone
Canned in tomato sauce, drained solids with bone
Cooked, dry heat
Raw
Raw
Raw
Raw
Cooked, batter-dipped and fried
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Smoked
Raw
Raw
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Canned in oil, drained solids
Canned in water, drained solids
Canned in oil
Canned in water, drained solids
Raw
Cooked, dry heat
Raw
Raw
Smoked
Raw
Cooked, dry heat
Raw
3-85
-------
Table 3-47. Percent moisture content for selected fish species" (continued)
Species
Shellfish
Crab, Alaska king
Crab, blue
Crab, dungeness
Crab, queen
Crayfish, mixed species
Lobster, Northern
Shrimp, mixed species
Spiny Lobster, mixed species
Clam, mixed species
Mussel, blue
Octopus, common
Oyster, eastern
Oyster, Pacific
Scallop, mixed species
Squid
Moisture Content
(%)
79.57
77.55
79.02
79.16
77.43
71.00
79.18
80.58
80.79
75.37
76.76
76.03
75.86
72.56
52.86
77.28
74.07
81.82
63.64
97.70
61.55
63.64
80.58
61.15
80.25
85.14
85.14
64.72
70.28
82.06
78.57
58.44
73.82
78.55
64.54
Comment
Raw
Cooked, moist heat
Imitation, made from surimi
Raw
Canned (dry pack or drained solids of wet pack)
Cooked, moist heat
Crab cakes
Raw
Raw
Raw
Cooked, moist heat
Raw
Cooked, moist heat
Raw
Canned (dry pack or drained solids of wet pack)
Cooked, breaded and fried
Cooked, moist heat
Imitation made from surimi, raw
Raw
Canned, drained solids
Canned, liquid
Cooked, breaded and fried
Cooked, moist heat
Raw
Cooked, moist heat
Raw
Raw
Canned (solids and liquid based) raw
Cooked, breaded and fried
Cooked, moist heat
Raw
Raw
Cooked, breaded and fried
Imitation, made from Surimi
Raw
Cooked, fried
Source: USDA, 1979-1986
3-86
-------
Table 3-48. Percentage lipid content (expressed as percentages of 100
grams of edible portions) of selected meat, dairy, and fish products"
Product
Meats
Beef
Lean only
Lean and fat, 1/4 in. fat trim
Brisket (point half)
Lean and fat
Brisket (flat half)
Lean and fat
Lean only
Pork
Lean only
Lean and fat
Cured shoulder, blade roll, lean and fat
Cured ham, lean and fat
Cured ham, lean only
Sausage
Ham
Ham
Lamb
Lean
Lean and fat
Veal
Lean
Lean and fat
Rabbit
Composite of cuts
Chicken
Meat only
Meat and skin
Turkey
Meat only
Meat and skin
Ground
Dairy
Milk
Whole
Human
Lowfat(l%)
Lowfat (2%)
Skim
Fat %
6.16
9.91
19.24
21.54
22.40
4.03
5.88
9.66
14.95
17.18
20.02
12.07
7.57
38.24
4.55
9.55
5.25
9.52
21.59
20.94
2.87
6.58
6.77
11.39
5.55
8.05
3.08
7.41
15.06
13.60
2.86
4.97
8.02
9.73
6.66
3.16
4.17
0.83
1.83
0.17
Raw
Cooked
Raw
Cooked
Raw
Raw
Raw
Cooked
Raw
Cooked
Unheated
Center slice
Comment
Raw, center, country style
Raw, fresh
Cooked, extra lean (5% fat)
Cooked, (11% fat)
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
Cooked
Raw
3.3% fat, raw or pasteurized
Whole, mature, fluid
Fluid
Fluid
Fluid
5-87
-------
Table 3-48. Percentage lipid content (expressed as percentages of 100 grams
of edible portions) of selected meat, dairy, and fish products" (continued)
Product
Cream
Half and half
Medium
Heavy-whipping
Sour
Butter
Cheese
American
Cheddar
Swiss
Cream
Parmesan—hard
Parmesan—grated
Cottage
Colby
Blue
Provolone
Mozzarella
Yogurt
Fat %
18.32
23.71
35.09
19.88
76.93
29.63
31.42
26.02
33.07
24.50
28.46
1.83
30.45
27.26
25.24
20.48
1.47
8.35
Comment
Table or coffee, fluid
25% fat, fluid
Fluid
Cultured
Regular
Pasteurized
Lowfat, 2% fat
Plain, lowfat
Chicken, whole raw, fresh or frozen
3-S
-------
Table 3-48. Percentage lipid content (expressed as percentages of 100 grams
of edible portions) of selected meat, dairy, and fish products" (continued)
Product
Finflsh
Anchovy, European
Bass
Bass, Striped
Bluefish
Butterfish
Carp
Catfish
Cod, Atlantic
Cod, Pacific
Croaker, Atlantic
Dolphinfish, mahimahi
Drum, freshwater
Flatfish, flounder and Sole
Grouper
Haddock
Halibut, Atlantic and Pacific
Halibut, Greenland
Herring, Atlantic and Turbot, domestic species
Fat %
4.101
8.535
3.273
1.951
3.768
NA
4.842
6.208
3.597
12.224
0.456
0.582
0.584
1.608
0.407
2.701
11.713
0.474
4.463
0.845
1.084
0.756
0.970
0.489
0.627
0.651
1.812
2.324
12.164
7.909
10.140
10.822
16.007
Comment
Raw
Canned in oil, drained solids
Freshwater, mixed species, raw
Raw
Raw
Raw
Raw
Cooked, dry heat
Channel, raw
Channel, cooked, breaded, and fried
Atlantic, raw
Canned, solids and liquids
Cooked, dry heat
Dried and salted
Raw
Raw
Cooked, breaded, and fried
Raw
Raw
Raw
Cooked, dry heat
Raw, mixed species
Cooked, dry heat
Raw
Cooked, dry heat
Smoked
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Kippered
Pickled
3-89
-------
Table 3-48. Percentage lipid content (expressed as percentages of 100 grams
of edible portions) of selected meat, dairy, and fish products" (continued)
Product
Herring, Pacific
Mackerel, Atlantic
Mackerel, Jack
Mackerel, king
Mackerel, Pacific and Jack
Mackerel, Spanish
Monkfish
Mullet, striped
Ocean perch, Atlantic
Perch, mixed species
Pike, Northern
Pike, walleye
Pollock, Alaska and walleye
Pollock, Atlantic
Rockfish, Pacific, mixed species
Roughy, orange
Salmon, Atlantic
Salmon, chinook
Salmon, chum
Salmon, coho
Salmon, pink
Salmon, Red and sockeye
Sardine, Atlantic
Sardine, Pacific
Sea bass, mixed species
Seatrout, mixed species
Shad, American
Shark, mixed species
Snapper, mixed species
Sole, Spot
Sturgeon, mixed species
Sucker, white
Sunfish, pumpkinseed
Swordfish
Trout, mixed species
Trout, rainbow
Fat %
12.552
9.076
15.482
4.587
1.587
6.816
5.097
5.745
NA
2.909
3.730
1.296
1.661
0.705
0.904
0.477
0.611
0.990
0.701
0.929
0.730
1.182
1.515
3.630
5.625
9.061
3.947
3.279
4.922
4.908
6.213
2.845
5.391
4.560
6.697
9.616
10.545
11.054
1.678
2.152
2.618
NA
3.941
12.841
0.995
1.275
3.870
3.544
4.544
3.829
1.965
0.502
3.564
4.569
5.901
2.883
3.696
Comment
Raw
Raw
Cooked, dry heat
Canned, drained solids
Raw
Canned, drained solids
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Raw
Raw (Mixed species)
Cooked, dry heat (mixed species)
Raw
Raw
Raw
Smoked
Raw
Canned, drained solids with bone
Raw
Cooked, moist heat
Raw
Canned, solids with bone and liquid
Raw
Canned, drained solids with bone
Cooked, dry heat
Canned in oil, drained solids with bone
Canned in tomato sauce, drained solids with bone
Cooked, dry heat
Raw
Raw
Raw
Raw
Cooked, batter-dipped and fried
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
Smoked
Raw
Raw
Raw
Cooked, dry heat
Raw
Raw
Cooked, dry heat
3-90
-------
Table 3-48. Percentage lipid content (expressed as percentages of 100 grams
of edible portions) of selected meat, dairy, and fish products" (continued)
Product
Tuna, light meat
Tuna, white meat
Tuna, bluefish, fresh
Turbot, European
Whitefish, mixed species
Whiting, mixed species
Yellowtail, mixed species
Shellfish
Crab, Alaska king
Crab, blue
Crab, dungeness
Crab, queen
Crayfish, mixed species
Lobster, northern
Shrimp, mixed species
Spiny Lobster, mixed species
Clam, mixed species
Mussel, blue
Octopus, common
Oyster, Eastern
Oyster, Pacific
Scallop, mixed species
Squid
Fat %
7.368
0.730
NA
2.220
4.296
5.509
NA
5.051
0.799
0.948
1.216
NA
NA
0.854
0.801
0.910
1.188
6.571
0.616
0.821
0.732
0.939
NA
0.358
1.250
1.421
10.984
0.926
1.102
0.456
0.912
NA
10.098
0.912
1.538
3.076
0.628
1.620
1.620
11.212
3.240
1.752
0.377
10.023
NA
0.989
6.763
Comment
Canned in oil, drained solids
Canned in water, drained solids
Canned in oil
Canned in water, drained solids
Raw
Cooked, dry heat
Raw
Raw
Smoked
Raw
Cooked, dry heat
Raw
Raw
Cooked, moist heat
Imitation, made from surimi
Raw
Canned (dry pack or drained solids of wet pack)
Cooked, moist heat
Crab cakes
Raw
Raw
Raw
Cooked, moist heat
Raw
Cooked, moist heat
Raw
Canned (dry pack or drained solids of wet pack)
Cooked, breaded and fried
Cooked, moist heat
Imitation made from surimi, raw
Raw
Canned, drained solids
Canned, liquid
Cooked, breaded and fried
Cooked, moist heat
Raw
Cooked, moist heat
Raw
Raw
Canned (solids and liquid based) raw
Cooked, breaded and fried
Cooked, moist heat
Raw
Raw
Cooked, breaded and fried
Imitation, made from Surimi
Raw
Cooked, fried
* Based on the lipid content in 100 grams, edible portion. Total Fat Content - saturated, monosaturated and polyunsaturated.
For additional information, consult the USDA nutrient database.
Source: USDA, 1979-1986.
3-91
-------
Table 3-49. Fat content of meat products
Meat Product"
Beef, retail composite, lean only
Pork, retail composite, lean only
Lamb, retail composite, lean only
Veal, retail composite, lean only
Broiler chicken, flesh only
Turkey, flesh only
Total Fat
(g)
8.4
8.0
8.1
5.6
6.3
4.2
Fat
Content (%)
9.9
9.4
9.5
6.6
7.4
4.9
' 3-oz cooked serving (85.05 g).
Source: National Livestock and Meat Board, 1993
5-92
-------
Table 3-50. Summary of recommended values (g/kg-day) for per capita intake
of foods, as consumed
Age
Mean
95th
Percentile
Multiple Percentiles
Study
Total Fruit Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
13.20
19.30
11.00
5.40
2.80
41.20
53.90
32.70
18.00
11.00
see Table 3-35
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
Total Vegetable Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
6.90
9.50
7.30
5.30
4.00
24.20
23.30
18.30
13.50
9.30
see Table 3-35
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
Total Grain Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
4.10
11.20
10.30
7.20
4.40
20.20
24.70
21.10
15.60
9.70
See Table 3-35
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
Total Meat Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
1.10
4.40
4.10
2.90
2.20
5.90
10.20
9.40
6.80
4.90
See Table 3-35
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
Total Dairy Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
111.00
37.50
20.90
13.90
6.10
235.00
90.20
48.80
33.50
17.80
See Table 3-35
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
Total Fish Intake
< 1 year
1-2 years
3-5 years
6-1 1 years
12-19 years
Individual Foods Intake
0.11
0.37
0.32
0.26
0.20
see Table 3-3
0.53
1.79
1.74
1.35
1.10
_
See Table 3-35
_
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
EPA Analysis of CSFII
1994-96 Data
(U.S. EPA, 2000)
3-93
-------
Table 3-50. Summary of recommended values for per capita intake of foods,
as consumed (continued)
Age
Mean
95th
Percentile
Multiple Percentiles
Study
Freshwater Total Fish Intake (General Population)
14 years and under
70.6 mg/kg-day
556 mg/kg-day
See Table 3-6
EPA Analysis of CSFII
1989-91 Data
Marine Fish Intake (General Population)
14 years and under
163 mg/kg-day
894 mg/kg-day
See Table 3-6
EPA Analysis of CSFII
1989-91 Data
Recreational Fish Intake — Freshwater
1-5 years
6-10 years
370 mg/kg-day
280 mg/kg-day
See Table 3- 13
EPA Analysis of West et
al.(1989) data
Native American Subsistence Fish Intake
< 5 years
11 g/kg-day
—
—
CRITFC, 1994
Total Fat Intake
See Tables 3-30
and 3-31
See Tables 3-
30 and 3-31
See Tables 3-30 and 3-
31
Frank et al., 1986
Homeproduced Food Intake
See Table 3-43
See Table 3-43
See Table 3-43
EPA Analysis of
1987/88 NFCS
3-94
-------
Table 3-51. Confidence intake recommendations for various foods,
including fish (general population)
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of variability
Lack of bias in study design
(high rating is desirable)
Measurement error
USDA CSFII survey receives high level of peer
review. EPA analysis of these data has been
peer-reviewed outside the Agency.
CSFII data are publicly available.
Enough information is included to reproduce
results.
Analysis is specifically designed to address
food intake.
Data focuses on the U.S. population.
This is new analysis of primary data.
The most current data publicly available at the
time the analysis was conducted for the
handbook.
Survey is designed to collect short-term data.
Survey methodology was adequate.
Study size was very large and therefore
adequate.
The population studied was the U.S.
population.
Survey was not designed to capture long-term,
day-to-day variability. Short-term distributions
are provided.
Response rate was good.
No measurements were taken. The study relied
on survey data.
High
High
Medium
High
High
High
High
High
Medium confidence for average
values; Low confidence for long-
term percentile distribution
High
High
High
Medium
High
N/A
Other Elements
Number of studies
Agreement between researchers
One for most foods, two for fish; CSFII was the
most recent data set publicly available at the
time the analysis was conducted for the
handbook.
Although the CSFII was the only study
classified as a key study for most foods, the
results are in good agreement with earlier data.
Low
High
Overall Rating
The survey is representative of the U.S.
population. Although only one study
considered key, these data are the most recent
and are in agreement with earlier data. The
approach used to analyze the data was
adequate. However, due to the limitations of
the survey design, estimation of long-term
percentile values (especially the upper
percentiles) is uncertain.
High confidence in the average
Low confidence in the long-term
upper percentiles
3-95
-------
Table 3-52. Confidence intake recommendations for fish consumption,
recreational freshwater angler population
Considerations
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of variability
Lack of bias in study design
(high rating is desirable)
Measurement error
Other Elements
Number of studies
Agreement between researchers
Overall Rating
Rationale Rating
Study is in a technical report and has been
reviewed by EPA.
The original study analyses are reported in a
technical report. Subsequent EPA analyses are
detailed in this handbook.
Enough information is available to reproduce
results.
Study focused on ingestion of fish by the
recreational freshwater angler and family.
The study was conducted in the U.S.
Data are from a primary reference.
The study was conducted between January and
May 1989.
Data were collected for 1 week.
Data presented are from a 1-week recall study
offish consumption. Weight offish consumed
was estimated using approximate weight offish
catch and edible fraction or approximate weight
of fish meal.
Study population was 621 children.
The study was localized to a single state.
Distributions were not generated.
Response rate was 47%.
Weight offish portions were estimated in one
study, fish weight was estimated from reported
fish length in another study.
High
High
High
High
High
High
High
Low
Medium
Medium
Low
High
Medium
Medium
There is one study.
There is only one study. EPA performed an
analysis using these data.
Low
Low
The study is not nationally representative and
not representative of long-term consumption.
Low
3-96
-------
Table 3-53. Summary of fish intake rates among Native American children
(consumers only)
Age
(years)
<5(N= 153)
<5(N = 51)
<6(N = 31)
Mean
25 g/day
0.72 g/kg-day
1 1 g/daya
1.5 g/kg-day
21 g/dayb
Upper Percentile
63.0 g/day (90thpercentile)
73.0 g/day (95th percentile)
1.4 g/kg-day (86thpercentile)
21.0 g/day (86th percentile)
3.4 g/kg-day (90th percentile)
7.3 g/kg-day (95th percentile)
48.0 g/day (90thpercentile)b
103.0 g/day (95th percentile)b
Reference
CRITFC, 1994
Toy etal., 1996
The Suquamish
Tribe, 2000
a Intake rate calculated using the average body weight of 15.2 kg reported in Toy et al. (1996).
b Intake rate calculated using the average body weight for children < 6 years of age (14.1 kg), based
onNHANES III (see Table 11-6).
3-97
-------
Table 3-54. Confidence intake recommendations for fish consumption,
Native American subsistence population
Considerations
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of variability
Lack of bias in study design
(high rating is desirable)
Measurement error
Other Elements
Number of studies
Agreement between researchers
Overall Rating
Rationale
Studies are in technical reports.
Studies are technical reports that are publicly
available
The studies were adequately detailed and
enough information is available to reproduce
results.
Studies focused on fish ingestion among Native
American tribes.
The studies were specific in the U.S.
The studies used primary data.
Data were from 1991-2000.
Data were collected for three studies.
Individual intake was measured directly, but
some respondents provided same information
for the children as for themselves.
The sample population was 204 children < 5
years old for CRIFTC, birth to 5 years for Toy
et al., and < 6 years for the Suquamish Indian
Tribe.
Only two states were represented.
Individual variations were not described.
The response rate was 69, 64, and 77% for
CRIFTC, Suquamish Indian Tribe, and Toy et
al., respectively.
The weight of the fish was estimated for one
study, measured for the other study.
There are three studies.
Studies are tribal-specific.
Rating
Medium
Medium
High
High
High
High
High
High
Low confidence for long term
percentile distribution
Low
Medium
Low
Medium
Medium
Medium
Low - Medium
Medium
Low
3-98
-------
APPENDIX A FOR CHAPTER 3
Calculations Used in the 1994-1996 CSFII Analysis to Correct for Mixtures
Distributions of intake for various food groups were generated for the food/items
groups using the USDA 1994-96 CSFII data set, as described in Sections 9.2.2. and 11.1.2.
However, several of the food categories used did not include meats, dairy products, and
vegetables that were eaten as mixtures with other foods. Thus, adjusted intake rates were
calculated for food items that were identified by USDA (1995) as comprising a significant
portion of grain and meat mixtures. To account for the amount of these foods consumed as
mixtures, the mean fractions of total meat or grain mixtures represented by these food items
were calculated (Table 3A-1) using Appendix C of USDA (1995).
Mean values for all individuals were used to calculate these fractions. These fractions
were multiplied by each individual's intake rate for total meat mixtures or grain mixtures to
calculate the amount of the individual's food mixture intake that can be categorized into one of
the selected food groups. These amounts were then added to the total intakes rates for meats,
grains, total vegetables, tomatoes, and white potatoes to calculate an individual's total intake of
these food groups, as shown in the following example for meats:
TO rTR * T
-------
Table 3A-1. Fraction of grain and meat mixture intake represented by
various food items/groups
Grain Mixtures
total vegetables
tomatoes
white potatoes
total meats
beef
pork
poultry
dairy
total grains
fish
eggs
fat
Meat Mixtures
total vegetables
tomatoes
white potatoes
total meats
beef
pork
poultry
dairy
total grains
fish
eggs
fats
0.2584
0.1685
0.0000
0.0787
0.0449
0.0112
0.0112
0.1348
0.3146
0.0000
0.0112
0.0225
0.3000
0.1111
0.0333
0.3111
0.2000
0.0222
0.0778
0.0556
0.1333
0.0444
0.0111
0.0222
3-100
-------
APPENDIX B FOR CHAPTER 3
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996
USDA CSFII data
Food Product
Food Codes
Major Food Groups
Total dairy
Total meats
Total fish
Eggs
Total grains
Total fruits
Total vegetables
1- Milk and Milk Products
Milk and milk drinks
Cream and cream substitutes
Milk desserts, sauces, and gravies
Cheeses
20- Meat, type not specified
21- Beef
22- Pork
23- Lamb, veal, game, carcass meat
24- Poultry
25- Organ meats, sausages, lunchmeats, meat
spreads
26- Fish, all types
3- Eggs
Eggs
Egg mixtures
Egg substitutes
Eggs baby food
Frozen meals with egg as main ingredient
50- Flour
51- Breads
52- Tortillas
53- Sweets
54- Snacks
55- Breakfast foods
561- Pasta
562- Cooked cereals and rice
57- Ready-to-eat and baby cereals
6- Fruits
Citrus fruits and juices
Dried fruits
Other fruits
Fruits/juices & nectar
Fruit/juices baby food
7- Vegetables (all forms)
White potatoes and Puerto Rican starchy
Dark green vegetables
Deep yellow vegetables
Tomatoes and torn, mixtures
Other vegetables
Vegetables and mixtures/baby food
Vegetables with meat mixtures
411- Beans/legumes
412- Beans/legumes
413- Beans/legumes
414- Soybeans
415- Bean dinners and soups
416- Bean dinners and soups
418- Meatless items
419- Soyburgers
Includes regular fluid milk, human milk, imitation milk
products, yogurt, milk-based meal replacements, and infant
formulas. Also includes the average portion of grain
mixtures (13.48%) and the average portion of meat
mixtures (5.56%) made up by dairy.
Also includes the average portion of grain mixtures (7.87%)
and the average portion of meat mixtures (3 1 . 1 1 %) made up
by meats.
Also includes the average portion of meat mixtures (4.44%)
made up by fish.
Includes baby foods. Also includes the average portion of
grain mixtures (1.12%) and the average portion of meat
mixtures (1.11%) made up by eggs.
Also includes the average portion of grain mixtures
(3 1.46%) and the average portion of meat mixtures
(13.33%) made up by grain.
Includes baby foods.
Includes baby foods; mixtures, mostly vegetables; does not
include nuts and seeds. Also includes the average portion
of grain mixtures (25.84%) and the average portion of meat
mixtures (30%) made up by vegetables.
3-101
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Food Codes
Major Food Groups continued
Total fats
8- Fats (all forms)
Includes butter, margarine, animal fat, sauces, vegetable
oils, dressings, and mayonnaise. Also includes the average
portion of grain mixtures (2.25%) and the average portion
of meat mixtures (2.22%) made up by meats.
Individual Meats
Beef
Pork
Game
Poultry
21- Beef
Beef, no form specified
Beefsteak
Beef oxtails, neckbones, ribs
Roasts, stew meat, corned, brisket, sandwich
Steaks
Ground beef, patties, meatballs
Other beef items
Beef baby food
22- Pork
Pork, nfs; ground dehydrated
Chops
Steaks, cutlets
Ham
Roasts
Canadian bacon
Bacon, salt pork
Other pork items
Pork baby food
233- Game
24- Poultry
Chicken
Turkey
Duck
Other poultry
Poultry baby food
Also includes the average portion of grain mixtures (4.49%)
and the average portion of meat mixtures (20.0%) made up
by beef.
Also includes the average portion of grain mixtures (1.12%)
and the average portion of meat mixtures (2.22%) made up
by pork.
Also includes the average portion of grain mixtures (1.12%)
and the average portion of meat mixtures (7.78%) made up
by poultry.
Individual Grains
Breads
Sweets
Snacks
Breakfast foods
Pasta
Cooked cereals
Rice
5 1- Breads, rolls, muffins, bagel, biscuits, corn
bread
52- tortillas
53- Cakes, cookies, pies, pastries, doughnuts,
breakfast bars, coffee cakes
54- Crackers, salty snacks, popcorn, pretzels
55- Pancakes, waffles, french toast
561- Macaroni, noodles, spaghetti
56200-
56201-
56202-
56203-
56206-
56207-
56208-
56209-
56210-
56204-
56205-
Includes grits, oatmeal, cornmeal mush, millet, etc.
Includes all varieties of rice.
3-102
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product I
Food Codes
Individual Grains continued
Ready-to-eat
cereals
570-
571-
572-
573-
574-
576-
Includes all varieties of ready-to-eat cereals.
Baby cereals
578- Baby cereals
Fruit Categories
Citrus fruits
61- Citrus fruits and juices
6720500 Orange juice, baby food
6723050 Orange/carrot baby juice
63403150 Lime souffle
6721100 Orange-apple-banana juice, baby food
Includes some citrus mixtures.
Other fruits
62- Dried fruits
63- Other fruits
64- Fruit juices and nectars, excluding citrus
671- Fruits, baby
67202- Apple juice, baby
67203- Babyjuices
67204- Babyjuices
67212- Babyjuices
67213- Babyjuices
672300 Apple sweet potato juice
6725- Baby juice
673- Baby fruits
674- Baby fruits
675- Apples with meat
Includes some mixtures (i.e., salads, baby
foods).
Apples
Bananas
6210110 Apples, dried, uncooked
6210115 Apples, dried, uncooked, low sodium
6210120 Apples, dried, cooked, not specifiied as to
sweetener
6210122 Apples, dried, cooked, unsweetened
6210123 Apples, dried, cooked, with sugar
6210130 Apple chips
6310100 Apples, raw
6310111 Applesauce, not specified as to sweetener
6310112 Applesauce, unsweetened
6310113 Applesauce with sugar
6310114 Applesauce with low calorie sweetener
6310115 Applesauce/other fruits
6310121 Apples, cooked or canned with syrup
6310131 Apple, baked not specified as to sweetener
6310132 Apple, baked, unsweetened
6310133 Apple, baked with sugar
6310141 Apple rings, fried
6310142 Apple, pickled
6310150 Apple, fried
634010 Apple/other fruit salad
6340106 Apple, candied
6410101 Apple cider
6410401 Applejuice
6410405 Apple juice with vitamin C
6410409 Applejuice with calcium
6410415 Apple-cherry juice
6410420 Apple-pear juice
6210710 Banana flakes, dehydrated
6210720 Banana chips
63107- Bananas, various
6340199 Banana, chocolate covered
6340201 Banawhip
6420150 Banana nectar
6710503 Banana, baby
6711500 Banana, baby
6410445 Apple-raspberry juice
6410450 Apple-grape juice
6710030 Applesauce, baby toddler
6710100 Apple-raspberry, baby, not specified as to
strained or junior
6710101 Apple-raspberry, baby, strained
6710102 Apple-raspberry, baby, junior
6710200 Applesauce baby foodd., NS as to strained or
junior
6710201 Applesauce baby food, strained
6710202 Applesauce baby food, junior
67104- Applesauce and other fruit, baby
67113- Apples and pears, baby
6720200 Apple juice, baby food
6720300 Apple w/other fruit juice, baby
6720320 Apple-banana juice, baby
6720340 Apple-cherry juice, baby
6720345 Apple-cranberry juice, baby
6720350 Apple-grape juice, baby
6720360 Apple-peach juice, baby
6720370 Apple-prune juice, baby
6723000 Apple-sweet potato juice, baby food
6725005 Applejuice w/lowfat yogurt, baby food
67301- Apples and cranberries w/tapioca, baby
6740407 Apple yogurt dessert, baby, strained
67412- Dutch apple dessert, baby
675- Apples and meat, baby
Includes some mixtures.
6725010 Banana juice with yogurt, baby
67308- Banana, baby
67309- Banana, baby
6740411 Banana apple dessert, baby
6740420 Banana pineapple dessert, baby
67408- Banana, baby
674041- Banana, baby
3-103
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Food Codes
Fruit Categories continued
Peaches
Pears
Strawberries
Other Berries
Exposed Fruits
62116- Dried peaches
63135- Peaches
6412203 Peachjuice
6420501 Peach nectar
62119- Dried pears
63137- Pears
6341201 Pear salad
6421501 Pear Nectar
67109- Pears, baby
6322- Strawberries
6413250 Strawberry juice
6210910 Cranberries, dried
6320- Other berries
6321- Other berries
6322400 Youngberries, raw
6341101 Cranberry salad
621011- Apple, dried
621012- Apple, dried
6210130 Apple chips
62104- Apricot, dried
62108- Currants, dried
6210910 Cranberries, dried
62110- Date, dried
62116- Peaches, dried
62119- Pears, dried
62121- Plum, dried
62122- Prune, dried
62125- Raisins
63101- Apples/applesauce
63102- Wi-apple
63103- Apricots
63 1 1 1- Cherries, maraschino
63112- Acerola
63113- Cherries, sour
63115- Cherries, sweet
63117- Currants, raw
63123- Grapes
6312601 Juneberry
63131- Nectarine
63135- Peach
63137- Pear
63139- Persimmons
63143- Plum
63146- Quince
63147- Rhubarb/Sapodillo
632- Berries
6340101 Apple salad w/dressing (include Waldorf
salad)
6340102 Apple & cabbage salad w/dressing
6340 103 Apple & fruit salad w/dressing
6340106 Apple, candied (include caramel apples)
6340203 Prune whip
6341 101 Cranberry salad, congealed
6341201 Pear salad w/dressing
67108-
6711450
67405-
67413700
6711455
6721200
6412300
67114-
6725020
Peaches, baby
Peaches, dry, baby
Peach cobbler, baby
Peach yogurt dessert, baby
Pears, dry, baby
Pear juice, baby
Pear/white grape/passion fruit juice
Pear/pineapple, baby
Pear/peach juice with yogurt, baby
6410460
64105-
6740430
6710102
67102-
6710400
6710401
6710402
6710407
6710408
6710409
67108-
67109-
6711000
6711300
6711301
6711302
6711450
6711455
67202-
6720340
6720345
6720350
6720360
6720370
6720380
67212-
6723000
6725005
6725020
6730100
6730101
6730102
6730400
6730401
6730402
6730403
6730450
6730501
Blackberry juice
Cranberry juice
Blueberry yogurt dessert, baby
Apple-raspberry, baby, junior
Applesauce, baby
Applesauce & apricots, baby, ns as to str or jr
Applesauce & apricots, baby, strained
Applesauce & apricots, baby, junior
Applesauce w/cherries, baby, strained
Applesauce w/cherries, baby, junior
Applesauce w/cherries, baby, ns str/jr
Peaches, baby
Pears, baby
Prunes, baby
Apples & pears, baby, ns as to str or jr
Apples & pears, baby, strained
Apples & pears, baby, junior
Peaches, baby, dry
Pears, baby, dry
Apple Juice, baby
Apple-cherry juice, baby
Apple-cranberry juice, baby
Apple-grape juice, baby
Apple-peach juice, baby
Apple-prune juice, baby
White Grape Juice, baby
Pear Juice, baby
Apple-sweet potato juice, baby food
Apple juice w/lowfat yogurt, baby food
Pear -peach juice w/lowfat yogurt, baby food
Apples & cranberries w/tapioca, baby, ns str/jr
Apples & cranberries w/tapioca, baby, strained
Apples & cranberries w/tapioca, baby, junior
Plums w/tapioca, baby, ns as to str/jr
Plums w/tapioca, baby, strained
Plums w/tapioca, baby, junior
Plums, bananas & rice, baby, strained
Prunes w/oatmeal, baby, strained
Prunes w/tapioca, baby, strained
Fruit Categories continued
3-104
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Exposed Fruits
continued
Protected Fruits
Food Codes
6341500 Soup, sour cherry
64101- Apple cider
64104- Apple Juice
6410409 Apple juice with calcium
64105- Cranberry Juice
64116- Grape Juice
64122- Peach Juice
6412300 Pear-white-grape-passion fruit juice,
w/added vitamin C
64132- Prune/strawberry juice
6420101 Apricot nectar
64205- Peach nectar
64215- Pear nectar
6710030 Applesauce, baby toddler
6710100 Apple-raspberry, baby, ns as to strained or
junior
6710101 Apple-raspberry, baby, strained
61- Citrus Fr., Juices (incl. cit. juice mixtures)
62107- Bananas, dried
62113- Figs, dried
62114- Lychees/Papayas, dried
62120- Pineapple, dried
62126- Tamarind, dried
63105- Avocado, raw
63 107- Bananas
63109- Cantaloupe, Carambola
63110- Cassaba Melon
63119- Figs
63121- Genip
63125- Guava/Jackfruit, raw
6312650 Kiwi
6312651 Lychee, raw
63 12660 Lychee, cooked
6312665 Loquats, raw
63 127- Honeydew
63129- Mango
63133- Papaya
63134- Passion Fruit
63141- Pineapple
63145- Pomegranate
63148- Sweetsop, Soursop, Tamarind
63149- Watermelon
6340199 Banana, chocolate-covered, w/nuts
6340201 Banana whip
6340205 Fried dwarf banana w/cheese, Puerto Rican
style
63403 15 Lime souffle (include other citrus fruits)
6340801 Guacamole w/tomatoes
6340820 Guacamole w/tomatoes & chile peppers
63490901 Guacamole, nfs
64120- Papaya Juice
6730600 Ciruelas w/tapioca, baby
6730700 Apricots w/tapioca, baby, ns as to str/jr
6730701 Apricots w/tapioca, baby, strained
6730702 Apricots w/tapioca, baby, junior
6740407 Apple yogurt dessert, baby, strained
6740430 Blueberry yogurt dessert, baby, strained
6740455 Cherry cobbler, baby, junior
6740500 Peach cobbler, baby, ns as to str/jr
674050 1 Peach cobbler, baby, strained
6740502 Peach cobbler, baby, junior
6741000 Cherry vanilla pudding, baby
6741200 Dutch apple dessert, baby, ns as to str/jr
6741201 Dutch apple dessert, baby, strained
6741202 Dutch apple dessert, baby, junior
6741370 Peach yogurt dessert, baby, strained
675- Apples & meat
64121- Passion Fruit Juice
64124- Pineapple Juice
64125- Pineapple juice
64133- Watermelon Juice
6420 1 50 Banana Nectar
64202- Cantaloupe Nectar
64203- Guava Nectar
64204- Mango Nectar
64210- Papaya Nectar
64213- Passion Fruit Nectar
64221- Soursop Nectar
6710503 Bananas, baby
67 1 1 500 Bananas, baby, dry
6720500 Orange Juice, baby
6721300 Pineapple Juice, baby
6723050 Orange-carrot juice, baby food
6725010 Banana juice w/lowfat yogurt, baby food
6730800 Bananas w/tapioca, baby, ns as to str/jr
6730801 Bananas w/tapioca, baby, strained
6730802 Bananas w/tapioca, baby, junior
6730900 Bananas & pineapple w/tapioca, baby, ns as to
str/jr
6730901 Bananas & pineapple w/tapioca, baby, strained
6730902 Bananas & pineapple w/tapioca, baby, junior
674041 1 Banana apple dessert, baby food, strained
6740420 Banana pineapple dessert, w/tapioca, baby
6740801 Banana pudding, baby, strained
6740850 Banana yogurt dessert, baby, strained
6741400 Pineapple dessert, baby, ns as to str/jr
6741401 Pineapple dessert, baby, strained
6741402 Pineapple dessert, baby, junior
6741410 Mango dessert w/tapioca, baby
Vegetable Categories
Asparagus
Beets
7510080 Asparagus, raw
75202- Asparagus, cooked
7540101 Asparagus, creamed or with cheese
72101- Beet greens
7510250 Beets, raw
752080- Beets, cooked
752081- Beets, canned
7540501 Beets, Harvard
756010 Asparagus soup
Does not include vegetables with meat
mixtures.
7550021 Beets, pickled
7560110 Beet soup
76403- Beets, baby
Does not include vegetable with meat mixtures.
Vegetable Categories continued
3-105
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Food Codes
Broccoli
Cabbage
Carrots
Corn
Cucumbers
Lettuce
Lima Beans
722- Broccoli (all forms)
7230200 Broccoli soup (include cream of broccoli
soup)
7230210 Broccoli cheese soup, prep w/milk
7230200 Broccoli soup (include cream of broccoli
soup)
7510300 Cabbage, raw
7510400 Cabbage, Chinese, raw
7510500 Cabbage, red, raw
7514100 Cabbage salad or coleslaw
7514110 Cabbage salad or coleslaw, w/apples,
raisins, dress
7514120 Cabbage salad or coleslaw, w/pineapple,
dressing
7514130 Cabbage, Chinese, salad
75210- Chinese Cabbage, cooked
73 10- Carrots (all forms)
73 1 1 140 Carrots in Sauce
7311200 Carrot Chips
735- Carrot soup
7510960 Corn, raw
7521600 Corn, cooked, NS as to color/fat added
7521601 Corn, cooked, NS as to color/fat not added
7521602 Corn, cooked, NS as to color/fat added
7521605 Corn, cooked, NS as to color/cream style
7521607 Corn, cooked, dried
7521610 Corn, cooked, yellow/NS as to fat added
7521611 Corn, cooked, yellow/fat not added
7521612 Corn, cooked, yellow/fat added
7521615 Corn, yellow, cream style
7521616 Corn, cooked, yell. & wh./NS as to fat
7521617 Corn, cooked, yell. & wh./fat not added
7521618 Corn, cooked, yell. & wh./fat added
7521619 Corn, yellow, cream style, fat added
7521620 Corn, cooked, white/NS as to fat added
7521621 Corn, cooked, white/fat not added
7511100 Cucumbers, raw
75142- Cucumber salads
752167- Cucumbers, cooked
7550301 Cucumber pickles, dill
7550302 Cucumber pickles, relish
7550303 Cucumber pickles, sour
7550304 Cucumber pickles, sweet
75113- Lettuce, raw
75143- Lettuce salad with other veg.
7514410 Lettuce, wilted, with bacon dressing
7522005 Lettuce, cooked
4110300 Lima beans, dry, cooked, ns as to added fat
4110301 Lima beans, dry, cooked, fat added
4110302 Lime beans, dry, cooked, no fat added
4121011 Stewed dry lima beans, p. r.
4130104 Lima bean soup
4160104 Lima bean soup
7514050 Broccoli salad w/cauliflower, cheese, bacon, &
dressing
Does not include vegetable with meat mixtures.
7521 1- Green Cabbage, cooked
75212- Red Cabbage, cooked
752130- Savoy Cabbage, cooked
75230- Sauerkraut, cooked
7540701 Cabbage, creamed
755025- Cabbage, pickled or in relish
7560120 Cabbage soup
7560121 Cabbage w/meat soup
Does not include vegetable with meat
mixtures.
76201- Carrots, baby
7620200 Carrots & peas, baby
Does not include vegetable with meat
mixtures.
7521622 Corn, cooked, white/fat added
7521625 Corn, white, cream style
7521630 Corn, yellow, canned, low sodium, NS fat
752163 1 Corn, yellow, canned, low sod., fat not add
7521632 Corn, yellow, canned, low sod., fat added
7521749 Hominy, cooked
752175- Hominy, cooked
7530301 Corn w/peppers, red or green, cooked, no fat
added
7541101 Corn scalloped or pudding
7541102 Corn fritter
7541 103 Corn with cream sauce
7550101 Corn relish
756040- Corn soup
76405- Corn, baby
Does not include vegetable with meat
mixtures.
7550305 Cucumber pickles, fresh
7550307 Cucumber, Kim Chee
7550311 Cucumber pickles, dill, reduced salt
7550314 Cucumber pickles, sweet, reduced salt
756045 1 Cucumber soup, cream of, w/milk
Does not include vegetable with meat
mixtures.
Does not include vegetable with meat mixtures.
7510200 Lima beans, raw
752040- Lima beans, cooked
752041- Lima beans, canned
75301- Beans, lima & corn (succotash)
75402- Lima beans with sauce
Does not include vegetable with meat
mixtures.
Vegetable Categories continued
3-106
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Okra
Peas
Peppers
Pumpkin
Snap Beans
Food Codes
7522000 Okra, cooked, not specified as to fat
7522001 Okra, cooked, fat not added
7522002 Okra, cooked, fat added
7522010 Lufta, cooked (Chinese Okra)
413010- Cowpeas, dry, cooked
413020- Chickpeas, dry, cooked
41303- Split peas, dry, cooked
413035- Stewed green peas
4130403 Peas, dry, cooked w/pork
4130413 Cowpeas, dry, cooked w/pork
4131010 Stewed pigeon peas, p.r.
4131015 Stewed chickpeas, p.r.
4131016 Stewed chickpeas, w/potatoes, p.r.
413 1020 Chickpeas, w/pig's feet, p.r.
413 1021 Chickpeas, w/spanish sausage, p.r.
4131022 Fried chickpeas, p.r.
4131031 Stewed cowpeas, p.r.
4160201 Chunky pea & ham soup
4160202 Garbanzo or chickpea soup
4160203 Split pea & ham soup
4160204 Pea soup, instant type
4160205 Split pea soup
4160206 Pigeon pea asopao
4160207 Split pea soup, can, reduced sodium,
w/water/rts
7512140 Pepper, poblano, raw
7512100 Pepper, hot chili, raw
7512150 Pepper, serrano, raw
7512200 Pepper, raw
7512210 Pepper, sweet green, raw
7512220 Pepper, sweet red, raw
7512400 Pepper, banana, raw
7522600 Pepper, green, cooked, NS as to fat added
7522601 Pepper, green, cooked, fat not added
7522602 Pepper, green, cooked, fat added
7522604 Pepper, red, cooked, NS as to fat added
7522605 Pepper, red, cooked, fat not added
732- Pumpkin (all forms)
733- Winter squash (all forms)
76205- Squash, baby
7510180 Beans, string, green, raw
7520498 Beans, string, cooked, NS color/fat added
7520499 Beans, string, cooked, NS color/no fat
7520500 Beans, string, cooked, NS color & fat
7520501 Beans, string, cooked, green/NS fat
7520502 Beans, string, cooked, green/no fat
7520503 Beans, string, cooked, green/fat
7520511 Beans, str., canned, low sod., green/NS fat
7520512 Beans, str., canned, low sod., green/no fat
7520513 Beans, str., canned, low sod., green/fat
7520600 Beans, string, cooked, yellow/NS fat
7520601 Beans, string, cooked, yellow/no fat
7520602 Beans, string, cooked, yellow/fat
7541450 Okra, fried
7550700 Okra, pickled
Does not include vegetable with meat
mixtures.
4160209 Split pea & ham soup, can, reduced sodium,
w/water/rts
731110- &
731112- Peas & carrots
75 12000 Peas, green, raw
7512775 Snowpeas, raw
75223- Peas, cowpeas, field or blackeye, cooked
75224- Peas, green, cooked
75225- Peas, pigeon, cooked
75231- Snowpeas, cooked
753 15- Peas & corn onions, mushrooms, beans, or
potatoes
7541650 Pea salad
7541660 Pea salad with cheese
75417- Peas, with sauce or creamed
75609- Pea soup
76409- Peas, baby
76411- Peas, creamed, baby
7650200 Peas & brown rice, baby
Does not include vegetable with meat
mixtures.
7522606 Pepper, red, cooked, fat added
7522609 Pepper, hot, cooked, NS as to fat added
7522610 Pepper, hot, cooked, fat not added
752261 1 Pepper, hot, cooked, fat added
7530700 Green peppers & onions, cooked, fat added in
cooking
75 5 1 10 1 Peppers, hot, sauce
75 5 1 102 Peppers, pickled
75 5 1 104 Pepper, hot pickled
755 1 105 Peppers, hot pickled
Does not include vegetable with meat
mixtures.
Does not include vegetable with meat mixtures.
7530205 Beans, green & potatoes, cooked, no fat added
7530206 Beans, green w/pinto beans, cooked, no fat
added
7530207 Beans, green w/spaetzel, cooked, no fat added
7530208 Bean salad, yellow &/or green string beans
7530220 Beans, green string w/onions, ns as to added
fat
7530221 Beans, green string w/onions, fat added
7530250 Beans, green & potatoes, ns as to added fat
753025 1 Beans, green & potatoes, fat added
7540301 Beans, string, green, creamed
7540302 Beans, string, green, w/mushroom sauce
7540401 Beans, string, yellow, creamed
Vegetable Categories continued
5-107
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Snap Beans
continued
Tomatoes
White Potatoes
Dark Green
Vegetables
Deep Yellow
Vegetables
Other Vegetables
Exposed
Vegetables
Food Codes
7530201 Beans, green string w/tomatoes (assume
w/o fat)
7530202 Beans, green string w/onions, cooked, no
fat added
7530203 Beans, green string w/chickpeas, cooked,
no fat added
7530204 Beans, green string w/almonds, cooked, no
fat added
74- Tomatoes and Tomato Mixtures
raw, cooked, juices, sauces, mixtures,
soups,
sandwiches
7 1- White Potatoes and PR Starchy Veg.
baked, boiled, chips, sticks, creamed,
scalloped, au gratin, fried, mashed, stuffed,
puffs, salad, recipes, soups, Puerto Rican
starchy vegetables
72- Dark Green Vegetables
all forms
leafy, nonleafy, dk. gr. veg. soups
73- Deep Yellow Vegetables
all forms
carrots, pumpkin, squash, sweet potatoes, dp.
yellow veg. soups
75- Other Vegetables
all forms
721- Dark Green Leafy Veg.
722- Dark Green Nonleafy Veg.
7230200 Broccoli soup (include cream of broccoli
soup)
7230210 Broccoli cheese soup, prep w/milk
7230500 Escarolesoup
7230600 Watercress broth w/shrimp
7230700 Spinach soup
7230800 Dark-green leafy vegetable soup w/meat,
oriental
7230850 Dark-green leafy vegetable soup, meatless,
oriental
74- Tomatoes and Tomato Mixtures
7510050 Alfalfa Sprouts
7510075 Artichoke, Jerusalem, raw
7510080 Asparagus, raw
75101- Beans, sprouts and green, raw
7510260 Broccoflower, raw
7510275 Brussel Sprouts, raw
7510280 Buckwheat Sprouts, raw
7510300 Cabbage, raw
7510400 Cabbage, Chinese, raw
7510500 Cabbage, Red, raw
7510700 Cauliflower, raw
7510900 Celery, raw
7510950 Chives, raw
7510955 Cilantro, raw
7511100 Cucumber, raw
7550011 Beans, string, green, pickled
7640 100 Beans, green, string, baby
7640 10 1 Beans, green, string, baby, str.
7640 102 Beans, green, string, baby, junior
7640 103 Beans, green, string, baby, creamed
7640 106 Beans, green string, baby
Does not include vegetable with meat
mixtures.
Also includes the average portion of grain mixtures
(16.85%) and the average portion of meat mixtures (i.e.,
11.11 percent) made up by tomatoes.
76420000 Potatoes, baby
Also includes the average portion of meat mixtures (3.33%)
made up by meats.
752170- Eggplant, cooked
752171- Fern shoots
752172- Fern shoots
752173- Flowers of sesbania, squash or lily
7521801 Kohlrabi, cooked
75219- Mushrooms, cooked
75220- Okra/lettuce, cooked
75 14800 Cob salad w/dressing
7520060 Algae, dried
75201- Artichoke, cooked
75202- Asparagus, cooked
75203- Bamboo shoots, cooked
752049- Beans, string, cooked
75205- Beans, green, cooked/canned
75206- Beans, yellow, cooked/canned
75207- Bean Sprouts, cooked
752085- Breadfruit
752087- Broccoflower, cooked
752090- Brussel Sprouts, cooked
75210- Cabbage, Chinese, cooked
75211- Cabbage, green, cooked
75212- Cabbage, red, cooked
752130- Cabbage, savoy, cooked
75214- Cauliflower
75215- Celery, Chives, Christophine (chayote)
752167- Cucumber, cooked
Vegetable Categories continued
5-108
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
I
Food Codes
Exposed 7511120
Vegetables 7511200
continued 75113-
7511500
7511900
7512100
75122-
7512400
7512750
7512775
75128-
7513210
7514050
7514100
7514110
7514120
7514130
7514150
75142-
75143-
7514410
7514500
7514600
7514700
7531600
7531601
7531602
7540050
7540101
75403-
75404-
7540601
7540701
75409-
75410-
75412-
75413-
75414-
754180-
7541822
7550011
7550051
7550201
755025-
7550301
7550302
7550303
7550304
7550305
7550307
7550308
7550311
Vegetable Categories continued
Eggplant, raw
Kohlrabi, raw
Lettuce, raw
Mushrooms, raw
Parsley
Pepper, hot chili
Peppers, raw
Pepper, banana, raw
Seaweed, raw
Snowpeas, raw
Summer Squash, raw
Celery Juice
Broccoli salad w/cauliflower, cheese,
bacon, dressing
Cabbage or cole slaw
Cabbage salad or coleslaw w/apples/raisins,
dressing
Cabbage salad or coleslaw w/pineapple,
dressing
Chinese Cabbage Salad
Celery with cheese
Cucumber salads
Lettuce salads
Lettuce, wilted with bacon dressing
Seven-layer salad (lettuce, mayo, cheese,
egg, peas)
Greek salad
Spinach salad
Squash, summer & onions, cooked, no fat
added
Zucchini w/tom sauce, cooked, no fat added
in cooking
Squash, summer & onions, cooked, fat
added
Artichokes, stuffed
Asparagus, creamed or with cheese
Beans, green with sauce
Beans, yellow with sauce
Brussel Sprouts, creamed
Cabbage, creamed
Cauliflower, creamed
Celery/Chiles, creamed
Eggplant, fried, with sauce, etc.
Kohlrabi, creamed
Mushrooms, Okra, fried, stuffed, creamed
Squash, baked, fried, creamed, etc.
Christophine, creamed
Beans, pickled
Celery, pickled
Cauliflower, pickled
Cabbage, pickled
Cucumber pickles, dill
Cucumber pickles, relish
Cucumber pickles, sour
Cucumber pickles, sweet
Cucumber pickles, fresh
Cucumber, Kim Chee
Eggplant, pickled
Cucumber pickles, dill, reduced salt
7522116 Palm Hearts, cooked
7522121 Parsley, cooked
75226- Peppers, pimento, cooked
75230- Sauerkraut, cooked/canned
75231- Snowpeas, cooked
75232- Seaweed
75233- Summer Squash
7530201 Beans, green string w/tomatoes (assume w/o
fat)
7530202 Beans, green string w/onions, no fat added
7530203 Beans, green string w/chickpeas, cooked, no
fat added
7530204 Beans, green string w/almonds, cooked, no fat
added
7530205 Beans, green & potatoes, cooked, no fat added
7530206 Beans, green w/pinto beans, cooked, no fat
added
7530207 Beans, green w/spaetzel, cooked, no fat added
7530208 Bean salad, yellow &/or green string beans
7530220 Beans, green string w/onions, ns as to added
fat
7530221 Beans, green string w/onions, fat added
7530250 Beans, green & potatoes, ns as to added fat
7530251 Beans, green & potatoes, fat added
7530601 Eggplant in torn sauce, cooked, no fat added
7530700 Green peppers & onions, cooked, fat added in
cooking
7550314 Cucumber pickles, sweet, reduced salt
7550500 Mushrooms, pickled
7550700 Okra, pickled
75510- Olives
7551101 Peppers, hot
75 51102 Peppers, pickled
75 51104 Peppers, hot pickled
7551301 Seaweed, pickled
7553500 Zucchini, pickled
756010- Asparagus soup
756012- Cabbage soup
756020- Cauliflower soup, cream of, w/milk
756030- Celery soup
7560451 Cucumber soup, cream of, w/milk
756046- Gazpacho
75607- Mushroom soup
7561201 Zucchini soup, cream of, prep w/milk
7564700 Seaweed soup
76102- Dark Green Veg., baby
76401- Beans, baby (excl. most soups & mixtures)
7660400 Broccoli & chicken, baby, strained
7661150 Green beans & turkey, baby, strained
7731601 Stuffed cabbage w/meat, p.r. (repollo relleno
con carne)
7731651 Stuffed cabbage w/meat & rice, Syrian dish,
puerto rican style
7731660 Eggplant and meat casserole
7756301 Puerto rican stew (sancocho)
Does not include vegetable with meat
mixtures.
5-109
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
I
Food Codes
Protected
Vegetables
411-, 412-,
413- Beans and lentils
414- Soy products
415-, 416-Bean meals
7185-,
7190- Plantains soups etc.
732- Pumpkin
733- Winter Squash
7510200 Lima Beans, raw
7510550 Cactus, raw
7510960 Corn, raw
7512000 Peas, raw
7520070 Aloe vera juice
752040- Lima Beans, cooked
752041- Lima Beans, canned
7520829 Bitter Melon
752083- Bitter Melon, cooked
7520950 Burdock
752131- Cactus
752160- Corn, cooked
752161- Corn, yellow, cooked
752162- Corn, white, cooked
752163- Corn, canned
7521749 Hominy
752175- Hominy
75223- Peas, cowpeas, field or blackeye, cooked
75224- Peas, green, cooked
75225- Peas, pigeon, cooked
75301- Succotash
7531500 Peas & corn, cooked, ns as to added fat
7531501 Peas & corn, cooked, no fat added
7531502 Peas & corn, cooked, fat added
7531510 Peas & onions, cooked, ns as to added fat
7531511 Peas & onions, cooked, fat not added
7531512 Peas & onions, cooked, fat added
7531521 Peas w/mushrooms, cooked, no fat added
7531525 Cowpeas w/snap beans, cooked, no fat added
in cooking
7531530 Peas & potatoes, cooked, no fat added in
cooking
75402- Lima Beans with sauce
75411- Corn, scalloped, fritter, with cream
7541650 Pea salad
7541660 Pea salad with cheese
75417- Peas, with sauce or creamed
7550101 Corn relish
7560401 Corn soup, cream of, w/milk
7560402 Corn soup, cream of, prepared w/water
7560900 Pea soup, nfs
7560901 Pea soup, prep w/milk
7560802 Pea soup, prepared w/water
7560905 Pea soup, prepared w/water, low sodium
7560906 Pea soup, prepared w/lowfat milk
76205- Squash, yellow, baby
76405- Corn, baby
76409- Peas, baby
76411- Peas, creamed, baby
7650200 Peas and brown rice, baby
7720121 Green plantain w/cracklings, p.r. (Mofongo)
7720511 Ripe plantain fritters, p.r. (Pionono)
7720561 Ripe plantainmeat pie, p.r. (Pinon)
Does not include vegetable with meat mixtures.
Root Vegetables
710-, 711-, 712-, 713-, 714-, 715-, 716-, 717-,
7180-, 1793-, 7194-, 7195-, 7196-,
7198- White Potatoes and Puerto Rican St. Veg.
7310- Carrots
7311140 Carrots in s auce
7311200 Carrot chips
734- Sweet potatoes
7510250 Beets, raw
7511150 Garlic, raw
7511180 Jicama (yambean), raw
7511250 Leeks, raw
75117- Onions, raw
7512500 Radish, raw
7512700 Rutabaga, raw
7512900 Turnip, raw
752080- Beets, cooked
752081- Beets, canned
7521362 Cassava
7521740 Garlic, cooked
7521771 Horseradish
7521840 Leek, cooked
7521850 Lotus root
752210- Onions, cooked
7522110 Onions, dehydrated
752220- Parsnips, cooked
75227- Radishes, cooked
75228- Rutabaga, cooked
75229- Salsify, cooked
75234- Turnip, cooked
75235- Water Chestnut
7540501 Beets, harvard
75415- Onions, creamed, fried
7541601 Parsnips, creamed
7541810 Turnips, creamed
7550021 Beets, pickled
7550309 Horseradish
7551201 Radishes, pickled
7553403 Turnip, pickled
7560110 Beet soup (borscht)
7560501 Leek soup, cream of, prep w/milk
7560503 Leek soup, made from dry mix
7560801 Onion soup, cream of, prep w/milk
7560803 Onion soup, cream of, canned, undiluted
7560810 Onion soup, french
7560820 Onion soup, made from dry mix
7560830 Onion soup, dry mix, not reconstituted
76201- Carrots, baby
76209- Sweet potatoes, baby
76403- Beets, baby
7642000 Potatoes, baby
7660200 Carrots & beef, baby, strained
7712101 Fried stuffed potatoes, p.r. (Rellenos de papas)
7712111 Potato & ham fritters, p.r. (frituras de papa y
jamon)
7714101 Potato chicken pie, p.r. (Pastelon de polio)
7723021 Cassava pasteles, p.r. (Pasteles de yuca)
7723051 Cassava pie stuffed w/crab meat, p.r.
7725011 Stuffed tannier fritters, p.r. (Alcapurrias)
7725071 Tannier fritters, p.r. (Frituras de yautia)
Does not include vegetable with meat mixtures.
Fat Categories
3-110
-------
Table 3B-1. Food codes and definitions used in analysis of the 1994-1996 USDA
CSFII data (continued)
Food Product
Food Codes
Animal Fat
Butter
Dressing
Margarine
Mayonnaise
Sauce
Vegetable Oil
81201- Bacon grease
81202- Lard
812032- Shortening, animal
8133011 Lard
811005- Butter
81101- Butter
81105- Butter
81204- Clarified butter
8132200 Honey butter
83100-
83101-
83102-
83103-
83104-
83105-
83106-
8311-
83200-
83201-
81102-
81103-
81104-
81106-
83204-
83107-
83108-
8 130 1- Lemon butter sauce
81302- Sauces, various
81312- Tartar sauce
8 1203 1- Shortening, vegetable
81324- Lechithin
8133021 Adobo fresco
82101- Vegetable oil
82102- Corn oil
82103- Cottonseed & flax seed oil
83202-
83203-
83205-
83206-
83207-
83208-
83209-
83210-
83220-
82104- Olive oil
82105- Peanut, rapeseed, & canola oil
82106- Saffloweroil
82107- Sesame oil
82108- Soy and sunflower oil
82109- Wheat germ oil
5-111
-------
APPENDIX C FOR CHAPTER 3
Sample Calculation of Mean Daily Fat Intake Based on CDC (1994) Data
CDC (1994) provided data on the mean daily total food energy intake (TFEI) and the
mean percentages of TFEI from total dietary fat grouped by age and gender. The overall mean
daily TFEI was 2,095 kcal for the total population, and 34% (or 82 g) of their TFEI was from
total dietary fat (CDC, 1994). Based on this information, the amount of fat per kcal was
calculated as shown in the following example.
0.34 x 2,095 - x X = 82
day day day
.-. X = 0.12
kcal
where 0.34 is the fraction of fat intake, 2,095 is the total food intake, and X is the conversion
factor from kcal/day to g-fat/day.
Using the conversion factor shown above (i.e., 0.12 g-fat/kcal) and the information on the
mean daily TFEI and percentage of TFEI for the various age/gender groups, the daily fat intake
was calculated for these groups. An example of obtaining the grams of fat from the daily TFEI
(1,591 kcal/day) for children ages 3-5 years and their percent TFEI from total dietary fat
(33%) is as follows:
1,591 x 0.33 x 0.12
day kcal day
3-112
-------
APPENDIX D FOR CHAPTER 3
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data
Food
Product
Household Code/Definition
Individual Code
Major Food Groups
Total fruits
Fresh Fruits
citrus
other vitamin-C rich
other fruits
Commercially canned fruits
Commercially frozen fruits
Canned ruit fjuice
Frozen fruit juice
Aseptically packed fruit juice
Fresh fruits
Includes baby foods
6- Fruits
citrus fruits and juices
dried fruits
other fruits
fruits/juices & nectar
fruit/juices baby food
Includes baby foods
Total
vegetables
48- Potatoes, sweetpotatoes
49- Fresh vegetables
dark green
deep yellow
tomatoes
light green
other
511 - Commercially canned vegetables
521- Commercially frozen vegetables
531- Canned vegetable juice
532- Frozen vegetable juice
537- Fresh vegetable juice
538- Aseptically packed vegetable juice
541- Dried vegetables
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures/dinners
7- Vegetables (all forms)
white potatoes & PR starchy
dark green vegetables
deep yellow vegetables
tomatoes and torn, mixtures
other vegetables
veg. and mixtures/baby food
veg. with meat mixtures
Includes baby foods; mixtures, mostly vegetables
Total meats
44- Meat
beef
pork
veal
lamb
mutton
goat
game
lunch meat
mixtures
451- Poultry
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
20- Meat, type not specified
21- Beef
22- Pork
23- Lamb, veal, game, carcass meat
24- Poultry
25- Organ meats, sausages, lunchmeats, meat spreads
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks; includes baby foods
Total dairy
40- Milk equivalent
fresh fluid milk
processed milk
cream and cream substitutes
frozen desserts with milk
cheese
dairy-based dips
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners
1- Milk and milk products
milk and milk drinks
cream and cream substitutes
milk desserts, sauces, and gravies
cheeses
Includes regular fluid milk, human milk, imitation milk products,
yogurt, milk-based meal replacements, and infant formulas
Total fish
452- Fish, shellfish
various species
fresh, frozen, commercial, dried
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners
26- Fish, shellfish
various species and forms
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks
3-113
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods
White
Potatoes
4811- White potatoes, fresh
4821- White potatoes, commercially canned
4831- White potatoes, commercially frozen
4841- White potatoes, dehydrated
4851- White potatoes, chips, sticks, salad
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners
71 - White potatoes and PR starchy veg.
baked, boiled, chips, sticks, creamed, scalloped, au
gratin, fried, mashed, stuffed, puffs, salad, recipes,
soups, Puerto Rican starchy vegetables
Does not include vegetables soups; vegetable mixtures; or
vegetable with meat mixtures
Peppers
4913-Green/red peppers, fresh
5111201 Sweet green peppers, commercially canned
5111202 Hot chili peppers, commercially canned
5211301 Sweet green peppers, commercially frozen
5211302 Green chili peppers, commercially frozen
5211303 Red chili peppers, commercially frozen
5413112 Sweet green peppers, dry
5413113 Red chili peppers, dry
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners
7512100 Pepper, hot chili, raw
7512200 Pepper, raw
7512210Pepper, sweet green, raw
7512220 Pepper, sweet red, raw
2600 Pepper, green, cooked, NS as to fat added
2601 Pepper, green, cooked, fat not added
2602 Pepper, green, cooked, fat added
2604 Pepper, red, cooked, NS as to fat added
2605 Pepper, red, cooked, fat not added
2606 Pepper, red, cooked, fat added
2609 Pepper, hot, cooked, NS as to fat added
2610 Pepper, hot, cooked, fat not added
2611 Pepper, hot, cooked, fat added
7551101 Peppers, hot, sauce
7551102 Peppers, pickled
(does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures)
Onions
4953-Onions, garlic, fresh
onions
chives
garlic
leeks
5114908 Garlic pulp, raw
5114915 Onions, commercially canned
5213722 Onions, commercially frozen
5213723 Onions with Sauce, commercially frozen
5413103 Chives, dried
5413105 Garlic flakes, dried
5413110 Onion flakes, dried
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners
7510950 Chives, raw
7511150 Garlic, raw
7511250 Leek, raw
7511701 Onions, young green, raw
7511702 Onions, mature
7521550 Chives, dried
7521740 Garlic, cooked
7522100 Onions, mature cooked, NS as to fat added
7522101 Onions, mature cooked, fat not added
7522102 Onions, mature cooked, fat added
7522103 Onions, pearl cooked
7522104 Onions, young green cooked, NS as to fat
7522105 Onions, young green cooked, fat not added
7522106 Onions, young green cooked, fat added
7522110Omon, dehydrated
7541501 Onions, creamed
75415 02 Onion rings
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
3-114
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods continued
Corn
4956- Corn, fresh
5114601 Yellow corn, commercially canned
5114602 White corn, commercially canned
5114603 Yellow creamed corn, commercially canned
5114604 White creamed corn, commercially canned
5114605 Corn on cob, commercially canned
5114607 Hominy, canned
5115306 Low sodium corn, commercially canned
5115307 Low sodium cr. Corn, commercially canned
5213501 Yellow corn on cob, commercially frozen
5213502 Yellow corn off cob, commercially frozen
5213503 Yell, corn with sauce, commercially frozen
5213504 Corn with other veg., commercially frozen
5213505 White corn on cob, commercially frozen
5213506 White Corn off Cob, commercially frozen
5213507 Wh. corn with sauce, commercially frozen
5413104 Corn, dried
5413106 Hominy, dry
5413603 Corn, instant baby food
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby food
7510960 Corn, raw
7521600 Corn, cooked, NS as to color/fat added
7521601 Corn, cooked, NS as to color/fat not added
7521602 Corn, cooked, NS as to color/fat added
7521605 Corn, cooked, NS as to color/cream style
7521607 Corn, cooked, dried
7521610 Corn, cooked, yellow/NS as to fat added
7521611 Corn, cooked, yellow/fat not added
7521612 Corn, cooked, yellow/fat added
7521615 Corn, yellow, cream style
7521616 Corn, cooked, yell. & wh./NS as to fat
7521617 Corn, cooked, yell. & wh./fat not added
7521618 Corn, cooked, yell. & wh./fat added
7521619 Corn, yellow, cream style, fat added
7521620 Corn, cooked, white/NS as to fat added
7521621 Corn, cooked, white/fat not added
7521622 Corn, cooked, white/fat added
7521625 Corn, white, cream style
7521630 Corn, yellow, canned, low sodium, NS fat
7521631 Corn, yell., canned, low sod., fat not add
7521632 Corn, yell., canned, low sod., fat added
Hominy, cooked
Hominy, cooked
7541101 Corn scalloped or pudding
7541102 Corn fritter
Corn with cream sauce
Corn relish
Corn, baby
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures; includes baby food
7521749
752175-
Apples
Apples, fresh
1 Applesauce with sugar, commercially canned
2 Applesauce without sugar, comm. canned
3 Apple pie filling, commercially canned
4- Apples, applesauce, baby/jr., comm. canned
5 Apple pie filling, Low Gal., comm. canned
1 Apple slices, commercially frozen
1 Apple juice, canned
2 Apple juice, baby, Comm. canned
1 Apple juice, comm. frozen
2 Apple juice, home frozen
1 Apple juice, aseptically packed
1 Apple juice, fresh
1 Apples, dried
5 baby food; except mixtures
6210110 Apples, dried, uncooked
6210115 Apples, dried, uncooked, low sodium
6210120 Apples, dried, cooked, NS as to sweetener
6210122 Apples, dried, cooked, unsweetened
6210123 Apples, dried, cooked, with sugar
6310100 Apples, raw
6310111 Applesauce, NS as to sweetener
6310112 Applesauce, unsweetened
6310113 Applesauce with sugar
6310114 Applesauce with low calorie sweetener
6310121 Apples, cooked or canned with syrup
6310131 Apple, baked NS as to sweetener
6310132 Apple, baked, unsweetened
6310133 Apple, baked with sugar
6310141 Apple rings, fried
6310142 Apple, pickled
6310150 Apple, fried
6340101 Apple, salad
6340106 Apple, candied
6410101 Apple cider
6410401 Applejmce
6410405 Apple juice with vitamin C
6710200 Applesauce baby fd., NS as to str. or jr.
6710201 Applesauce baby food, strained
6710202 Applesauce baby food, junior
6720200 Apple juice, baby food
Includes baby food; except mixtures
3-115
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods continued
Tomatoes
4931- Tomatoes, fresh
5113- Tomatoes, commercially canned
5115201 Tomatoes, low sodium, commercially canne
5115202 Tomato sauce, low sodium, comm. canned
5115203 Tomato paste, low sodium, comm. canned
5115204 Tomato puree, low sodium, comm. canned
5311- Canned tomato juice and tomato mixtures
5321- Frozen tomato juice
5371- Fresh tomato juice
5381102 Tomato juice, aseptically packed
5413115 Tomatoes, dry
5614- Tomato soup
5624- Condensed tomato soup
5654- Dry tomato soup
Does not include mixtures, and ready-to-eat dinners
74- Tomatoes and tomato mixtures
raw, cooked, juices, sauces, mixtures, soups,
sandwiches
Snap Beans
4943- Snap or wax Beans, fresh
5114401 Green or snap beans, commercially canned
5114402 Wax or yellow beans, commercially canned
5114403 Beans, baby/jr., commercially canned
5115302 Green beans, low sodium, comm. canned
5115303 Yell, or wax beans, low sod., comm. canned
5213301 Snap or green beans, comm. frozen
5213302 Snap or green w/sauce, comm. frozen
5213303 Snap or green beans w/other veg., comm. fr.
5213304 Sp. or gr. Beans w/other veg./sc., comm. fr.
5213305 Wax or yell, beans, comm. frozen
Does not include soups, mixtures, and ready-to-eat dinners;
includes baby foods
7510180 Beans, string, green, raw
0498 Beans, string, cooked, NS color/fat added
0499 Beans, string, cooked, NS color/no fat
0500 Beans, string, cooked, NS color & fat
0501 Beans, string, cooked, green/NS fat
0502 Beans, string, cooked, green/no fat
0503 Beans, string, cooked, green/fat
0511 Beans, str., canned, low sod.,green/NS fat
0512 Beans, str., canned, low sod.,green/no fat
0513 Beans, str., canned, low sod.,green/fat
0600 Beans, string, cooked, yellow/NS fat
0601 Beans, string, cooked, yellow/no fat
75 0602 Beans, string, cooked, yellow/fat
7540301 Beans, string, green, creamed
7540302 Beans, string, green, w/mushroom sauce
7540401 Beans, string, yellow, creamed
7550011 Beans, string, green, pickled
7640100 Beans, green, string, baby
7640101 Beans, green, string, baby, str.
7640102 Beans, green, string, baby, junior
7640103 Beans, green, string, baby, creamed
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures; includes baby foods
441- Beef
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
21- Beef
beef, nfs
beef steak
beef oxtails, neckbones, ribs
roasts, stew meat, corned, brisket, sandwich steaks
ground beef, patties, meatballs
other beef items
beef baby food
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks; includes baby food
3-116
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods continued
Pork
442- Pork
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
22- Pork
pork, nfs; ground dehydrated
chops
steaks, cutlets
ham
roasts
Canadian bacon
bacon, salt pork
other pork items
pork baby food
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks; includes baby food
Game
445- Variety meat, game
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
233- Game
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks
Poultry
451- Poultry
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
24- Poultry
chicken
turkey
duck
other poultry
poultry baby food
Excludes meat, poultry, and fish with non-meat items; frozen plate
meals; soups and gravies with meat, poultry and fish base; and
gelatin-based drinks; includes baby food
Eggs
46- Eggs (fresh equivalent)
fresh
processed eggs, substitutes
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
3- Eggs
eggs
egg mixtures
egg substitutes
eggs baby food
froz. meals with egg as main ingred.
Includes baby foods
Broccoli
4912- Fresh broccoli (and home canned/froz.)
5111203 Broccoli, comm. canned
52112- Comm. frozen broccoli
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
722- Broccoli (all forms)
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
Carrots
Fresh carrots (and home canned/froz.)
Comm. canned carrots
Carrots, low sodium, comm. canned
Comm. rozen carrots
Comm. canned carrot juice
Carrot juice fresh
Carrots, dried baby food
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
7310- Carrots (all forms)
7311140 Carrots in sauce
7311200 Carrot chips
76201- Carrots, baby
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures; includes baby foods except
mixtures
Pumpkin
4922- Fresh pumpkin, winter squash (and home
canned/froz.)
51122- Pumpkin/squash, baby or junior, comm. Canned
52122- Winter squash, comm. frozen
5413504 Squash, dried baby food
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
732- Pumpkin (all forms)
733- Winter squash (all forms)
76205- Squash, baby
Does not include vegetable soups; vegetables mixtures; or
vegetable with meat mixtures; includes baby foods
3-117
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods continued
Asparagus
Lima Beans
Cabbage
Lettuce
Okra
Peas
4941- Fresh asparagus (and home canned/froz.)
5114101 Comm. canned asparagus
5115301 Asparagus, low sodium, comm. canned
52131- Comm. frozen asparagus
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
4942- Fresh lima and fava beans (and home canned/froz.)
511 4204 Comm. canned mature lima beans
5114301 Comm. canned green lima beans
511 5304 Comm. canned low sodium lima beans
52132- Comm. frozen lima beans
541 1 1 - Dried lima beans
541 1306 Dried fava beans
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures; does not
include succotash
4944- Fresh cabbage (and home canned/froz.)
4958601 Sauerkraut, home canned or pkgd
5114801 Sauerkraut, comm. canned
5114904 Comm. canned cabbage
5114905 Comm. canned cabbage (no sauce; incl. baby)
5115501 Sauerkraut, low sodium., comm. canned
5312102 Sauerkraut juice, comm. canned
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
4945- Fresh lettuce, french endive (and home canned/froz.)
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
4946- Fresh okra (and home canned/froz.)
5114914 Comm. canned Okra
5213720 Comm. frozen Okra
5213721 Comm. frozen Okra with Oth. Veg. & Sauce
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
4947- Fresh peas (and home canned/froz.)
511 47- Comm canned peas (incl. baby)
5115310 Low sodium green or English peas (canned)
5 1 1 53 14 Low sod. blackeye, Gr. or imm. peas (canned)
5114205 Blackeyed peas, comm. canned
52134- Comm. frozen peas
5412- Dried peas and lentils
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
7510080 Asparagus, raw
75202- Asparagus, cooked
7540101 Asparagus, creamed or with cheese
Does not include vegetable soups; vegetables mixtures, or
vegetable with meat mixtures
7510200 Lima beans, raw
752040- Lima beans, cooked
752041- Lima beans, canned
75402- Lima beans with sauce
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures; does not include succotash
7510300 Cabbage, raw
7510400 Cabbage, chmese, raw
7510500 Cabbage, red, raw
7514100 Cabbage salad or coleslaw
7514130 Cabbage, chmese, salad
75210- Chinese cabbage, cooked
7521 1- Green cabbage, cooked
75212- Red cabbage, cooked
752130- Savoy cabbage, cooked
75230- Sauerkraut, cooked
7540701 Cabbage, creamed
755025- Cabbage, pickled or in relish
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
75 1 1 3- Lettuce, raw
75 143- Lettuce salad with other veg.
7514410 Lettuce, wilted, with bacon dressing
7522005 Lettuce, cooked
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
7522000 Okra, cooked, NS as to fat
7522001 Okra, cooked, fat not added
7522002 Okra, cooked, fat added
7522010 Lufta, cooked (Chinese Okra)
7541450 Okra, fried
7550700 Okra, pickled
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
7512000 Peas, green, raw
7512775 Snowpeas, raw
75223- Peas, cowpeas, field or blackeye, cooked
75224- Peas, green, cooked
75225- Peas, pigeon, cooked
75231- Snowpeas, cooked
7541650 Pea salad
7541660 Pea salad with cheese
75417- Peas, with sauce or creamed
76409- Peas, baby
7641 1- Peas, creamed, baby
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures; includes baby foods except
mixtures
3-118
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Individual Foods continued
Cucumbers
Beets
Strawberries
Other
Berries
Peaches
4952- Fresh cucumbers (and home canned/froz.)
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
4954- Fresh beets (and home canned/froz.)
51145- Comm. canned beets (incl. baby)
511 5305 Low sodium beets (canned)
5213714 Comm. frozen beets
5312104 Beetjmce
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
5022- Fresh strawberries
5122801 Comm. canned strawberries with sugar
5122802 Comm. canned strawberries without sugar
5122803 Canned strawberry pie filling
5222- Comm. frozen Strawberries
Does not include ready -to-eat dinners; includes baby foods
except mixtures
5033- Fresh berries other than strawberries
5 22804 Comm. canned blackberries with sugar
5 22805 Comm. canned blackberries without sugar
5 22806 Comm. canned blueberries with sugar
5 22807 Comm. canned blueberries without sugar
5 22808 Canned blueberry pie filling
5 22809 Comm. canned gooseberries with sugar
5 22810 Comm. canned gooseberries without sugar
5 22811 Comm. canned raspberries with sugar
5 22812 Comm. canned raspberries without sugar
5 22813 Comm. canned cranberry Sauce
5 22815 Comm. canned cranberry-Orange Relish
52233- Comm. Frozen berries (not strawberries)
5332404 Blackberry juice (home and comm. canned)
54231 14 Dried berries (not strawberries)
Does not include ready -to-eat dinners; includes baby foods
except mixtures
5036- Fresh peaches
51224- Comm. canned Peaches (incl. baby)
5223601 Comm. frozen Peaches
5332405 Home canned Peach Juice
5423105 Dried peaches (baby)
5423106 Dried peaches
Does not include ready -to-eat dinners; includes baby foods
except mixtures
75 1 1 1 00 Cucumbers, raw
75142- Cucumber salads
752167- Cucumbers, cooked
7550301 Cucumber pickles, dill
7550302 Cucumber pickles, relish
7550303 Cucumber pickles, sour
7550304 Cucumber pickles, sweet
7550305 Cucumber pickles, fresh
7550307 Cucumber, Kim Chee
755031 1 Cucumber pickles, dill, reduced salt
7550314 Cucumber pickles, sweet, reduced salt
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
7510250 Beets, raw
752080- Beets, cooked
752081- Beets, canned
7540501 Beets, harvard
7550021 Beets, pickled
76403- Beets, baby
Does not include vegetable soups; vegetable mixtures
or
vegetable with meat mixtures; includes baby foods except
mixtures
6322- Strawberries
641 3250 Strawberry juice
Includes baby food; except mixtures
6320- Other berries
6321- Other berries
6341 101 Cranberry salad
6410460 Blackberry juice
64105- Cranberry juice
includes baby food; except mixtures
62 1 1 6- Dried peaches
63135- Peaches
6412203 Peach juice
6420501 Peach nectar
67108- Peaches,baby
6711450 Peaches, dry, baby
Includes baby food; except mixtures
3-119
-------
Pears
5037- Fresh pears
51225- Comm. canned pears (incl. baby)
5332403 Comm. canned pear juice, baby
5362204 Fresh pear juice
5423107 Dried pears
Does not include ready-to-eat dinners; includes baby foods
except mixtures
62119- Dried pears
63137- Pears
6341201 Pear salad
6421501 Pear nectar
67109- Pears, baby
67 1 1 45 5 Pears, dry, baby
Includes baby food; except mixtures
3-120
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Exposed/Protected Fruits/Veg
Exposed
Fruits
5022-
5023101
5023401
5031-
5033-
5034-
5036-
5037-
50381-
5038305
50384-
50386-
50387-
5038805
5038901
51221-
51222-
51223-
51224-
51225-
51228-
5122903
5122904
5122905
5122906
5122907
5122911
5122912
5122913
5122914
5222-
52231-
52233-
52234-
52236-
52239-
53321-
53322-
5332402
5332403
5332404
5332405
53421-
5342201
5342202
5352101
5352201
5362101
5362202
5362203
5362204
5362205
5421-
5422-
5423101
5423102
5423103
5423104
5423105
Individual Code
;etables, Root Vegetables
Strawberries, fresh
Acerola, fresh
Currants, fresh
Apples/applesauce, fresh
Berries other than strawberries, fresh
Cherries, fresh
Peaches, fresh
Pears, fresh
Apricots, nectarines, loquats, fresh
Dates, fresh
Grapes, fresh
Plums, fresh
Rhubarb, fresh
Persimmons, fresh
Sapote, fresh
Apples/applesauce, canned
Apricots, canned
Cherries, canned
Peaches, canned
Pears, canned
Berries, canned
Grapes with sugar, canned
Grapes without sugar, canned
Plums with sugar, canned
Plums without sugar, canned
Plums, canned, baby
Prunes, canned, baby
Prunes, with sugar, canned
Prunes, without sugar, canned
Raisin pie filling
Frozen strawberries
Apples slices, frozen
Berries, frozen
Cherries, frozen
Peaches, frozen
Rhubarb, frozen
Canned apple juice
Canned grape juice
Canned prune juice
Canned pear juice
Canned blackberry juice
Canned peach juice
Frozen grape juice
Frozen apple juice, comm. fr.
Frozen apple juice, home fr.
Apple juice, asep. packed
Grape juice, asep. packed
Apple juice, fresh
Apricot juice, fresh
Grape juice, fresh
Pear juice, fresh
Prune juice, fresh
Dried prunes
Raisins, currants, dried
Dry apples
Dry apricots
Dates without pits
Dates with pits
Peaches, dry, baby
62101-
62104-
62108-
62110-
62116-
62119-
62121-
62122-
62125-
63101-
63102-
63103-
63111-
63112-
63113-
63115-
63117-
63123-
6312601
63131-
63135-
63137-
63139-
63143-
63146-
63147-
632-
64101-
64104-
64105-
64116-
64122-
64132-
6420101
64205-
64215-
67102-
67108-
67109-
6711450
6711455
67202-
6720380
67212-
Apple, dried
Apricot, dried
Currants, dried
Date, dried
Peaches, dried
Pears, dried
Plum, dried
Prune, dried
Raisins
Apples/applesauce
Wi-apple
Apricots
Cherries, maraschino
Acerola
Cherries, sour
Cherries, sweet
Currants, raw
Grapes
Juneberry
Nectarine
Peach
Pear
Persimmons
Plum
Quince
Rhubarb/sapodillo
Berries
Apple cider
Apple juice
Cranberry juice
Grape juice
Peach juice
Prune/strawberry uice
Apricot nectar
Peach nectar
Pear nectar
Applesauce, baby
Peaches, baby
Pears, baby
Peaches, baby, dry
Pears, baby, dry
Apple juice, baby
White grape juice, baby
Pear juice, baby
Includes baby foods/juices except mixtures; excludes
fruit mixtures
5-121
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Exposed/Protected Fruits/Veg
Exposed
Fruits
continued
Protected
Fruits
5423106
5423107
5423114
5423115
Individual Code
;etables, Root Vegetables continued
Peaches, dry
Pears, dry
Berries, dry
Cherries, dry
Includes baby foods
501-
5021-
5023201
5023301
5023601
5023701
5023801
5032-
5035-
50382-
5038301
5038302
5038303
5038304
50385-
5038801
5038902
5038903
5038904
5038905
5038906
5038907
5121-
51226-
5122901
5122902
5122909
5122910
5122915
5122916
5122917
5122918
5122920
5122921
5122923
5122924
52232-
52235-
52237-
5331-
53323-
5332408
5332410
5332501
5341-
5342203
5351-
5352302
5361-
5362206
5362207
5362208
5362209
5423108
5423109
5423110
Citrus fruits, fresh
Cantaloupe, fresh
Mangoes, fresh
Guava, fresh
Kiwi, fresh
Papayas, fresh
Passion Fruit, fresh
Bananas, plantains, fresh
Melons other than cantaloupe, fresh
Avocados, fresh
Figs, fresh
Figs, cooked
Figs, home canned
Figs, home frozen
Pineapple, fresh
Pomegranates, fresh
Cherimoya, fresh
Jackfruit, fresh
Breadfruit, fresh
Tamarind, fresh
Carambola, fresh
Longan, fresh
Citrus, canned
Pineapple, canned
Figs with sugar, canned
Figs without sugar, canned
Bananas, canned, baby
Bananas and Pineapple, canned, baby
Litchis, canned
Mangos with sugar, canned
Mangos without sugar, canned
Mangos, canned, baby
Guava with sugar, canned
Guava without sugar, canned
Papaya with sugar, canned
Papaya without sugar, canned
Bananas, frozen
Melon, frozen
Pineapple, frozen
Canned citrus juices
Canned pineapple juice
Canned papaya juice
Canned mango juice
Canned papaya concentrate
Frozen citrus juice
Frozen pineapple juice
Citrus and citrus blend juices, asep. packed
Pineapple juice, asep. packed
Fresh citrus and citrus blend juices
Papaya juice, fresh
Pineapple-coconut Juice, fresh
Mango juice, fresh
Pineapple juice, fresh
Pineapple, dry
Papaya, dry
Bananas, dry
61-
62107-
62113-
62114-
62120-
62126-
63105-
63107-
63109-
63110-
63119-
63121-
63125-
6312650
6312651
6312660
63127-
63129-
63133-
63134-
63141-
63145-
63148-
63149-
64120-
64121-
64124-
64133-
6420150
64202-
64203-
64204-
64210-
64213-
64221-
6710503
6711500
6720500
6721300
Citrus fr., juices (incl. cit. juice mixtures)
Bananas, dried
Figs, dried
Lychees/Papayas, dried
Pineapple, dried
Tamarind, dried
Avocado, raw
Bananas
Cantaloupe, carambola
Cassaba melon
Figs
Genip
Guava/jackfruit, raw
Kiwi
Lychee, raw
Lychee, cooked
Honey dew
Mango
Papaya
Passion fruit
Pineapple
Pomegranate
Sweetsop, soursop, tamarind
Watermelon
Papaya juice
Passion fruit juice
Pineapple juice
Watermelon juice
Banana nectar
Cantaloupe nectar
Guava nectar
Mango nectar
Papaya nectar
Passion fruit nectar
Soursop nectar
Bananas, baby
Bananas, baby, dry
Orange juice, baby
Pineapple Juice, baby
Includes baby foods/juices except mixtures; excludes fruit
mixtures
5-122
-------
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Exposed/Protected Fruits/Vegetables, Root Vegetables continued
Protected
Fruits
continued
Exposed
Vegetables
5423111 Mangos, dry
5423117 Litchis, dry
5423118 Tamarind, dry
5423119 Plantain, dry
Includes baby foods
491-
493-
4941-
4943-
4944-
4945-
4946-
49481-
49483-
4951-
4952-
4955-
4958103
4958111
4958112
4958113
4958114
4958118
4958119
4958120
4958200
4958201
4958202
4958203
4958402
4958403
4958504
4958506
4958508
4958601
5111-
5113-
5114101
51144-
5114704
5114801
5114901
5114902
5114903
5114904
5114905
5114906
5114907
5114913
5114914
5114918
5114920
5114923
Fresh dark green vegetables
Fresh tomatoes
Fresh asparagus
Fresh beans, snap or wax
Fresh cabbage
Fresh lettuce
Fresh okra
Fresh artichokes
Fresh Brussel sprouts
Fresh celery
Fresh cucumbers
Fresh cauliflower
Fresh kohlrabi
Fresh Jerusalem artichokes
Fresh mushrooms
Mushrooms, home canned
Mushrooms, home frozen
Fresh eggplant
Eggplant, cooked
Eggplant, home frozen
Fresh Summer squash
Summer squash, cooked
Summer squash, home canned
Summer squash, home frozen
Fresh bean sprouts
Fresh alfalfa sprouts
Bamboo shoots
Seaweed
Tree fern, fresh
Sauerkraut
Dark green Vegetables (all are exposed)
Tomatoes
Asparagus, comm. canned
Beans, green, snap, yellow, comm. canned
Snow Peas, comm. canned
Sauerkraut, comm. canned
Artichokes, comm. canned
Bamboo Shoots, comm. canned
Bean Sprouts, comm. canned
Cabbage, comm. canned
Cabbage, comm. canned, no sauce
Cauliflower, comm. canned, no sauce
Eggplant, comm. canned, no sauce
Mushrooms, comm. canned
Okra, comm. canned
Seaweeds, comm. canned
Summer squash, comm. canned
Chinese or celery cabbage, comm. canned
721-
722-
74-
7510050
7510075
7510080
75101-
7510275
7510280
7510300
7510400
7510500
7510700
7510900
7510950
7511100
7511120
7511200
75113-
7511500
7511900
7512100
75122-
7512750
7512775
75128-
7513210
7514100
7514130
7514150
75142-
75143-
7514410
7514600
7514700
7520600
75201-
75202-
75203-
752049-
75205-
75206-
75207-
752085-
752090-
75210-
75211-
75212-
Dark green leafy Veg.
Dark green nonleafy Veg.
Tomatoes and tomato mixtures
Alfalfa sprouts
Artichoke, Jerusalem, raw
Asparagus, raw
Beans, sprouts and green, raw
Brussels sprouts, raw
Buckwheat Sprouts, raw
Cabbage, raw
Cabbage, Chinese, raw
Cabbage, red, raw
Cauliflower, raw
Celery, raw
Chives, raw
Cucumber, raw
Eggplant, raw
Kohlrabi, raw
Lettuce, raw
Mushrooms, raw
Parsley
Pepper, hot chili
Peppers, raw
Seaweed, raw
Snowpeas, raw
Summer squash, raw
Celery juice
Cabbage or cole slaw
Chinese cabbage Salad
Celery with cheese
Cucumber salads
Lettuce salads
Lettuce, wilted with bacon dressing
Greek salad
Spinach salad
Algae, dried
Artichoke, cooked
Asparagus, cooked
Bamboo shoots, cooked
Beans, string, cooked
Beans, green, cooked/canned
Beans, yellow, cooked/canned
Bean sprouts, cooked
Breadfruit
Brussels sprouts, cooked
Cabbage, Chinese, cooked
Cabbage, green, cooked
Cabbage, red, cooked
Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
5-123
-------
Food
Product
Household Code/Definition
Individual Code
Exposed/Protected Fruits /Vegetables, Root Vegetables continued
Protected
Vegetable
4922- Fresh pumpkin, winter squash
4942- Fresh lima beans
4947- Fresh peas
49482- Fresh soy beans
4956- Fresh corn
4958303 Succotash, home canned
4958304 Succotash, home frozen
4958401 Fresh cactus (prickly pear)
4958503 Burdock
4958505 Bitter melon
4958507 Horseradish tree pods
51122- Comm. Canned pumpkin and squash (baby)
51142- Beans, comm. canned
51143- Beans, lima and soy, comm. canned
51146- Corn, comm. canned
5114701 Peas, green, comm. canned
5114702 Peas, baby, comm. canned
5114703 Peas, blackeye, comm. canned
5114705 Pigeon peas, comm. canned
5114919 Succotash, comm. canned
5115304 Lima beans, canned, low sod.
5115306 Corn, canned, low sod.
5115307 Creamed corn, canned, low sod.
511531- Peas and beans, canned, low sod.
52122- Winter squash, comm. froz.
52132- Lima beans, comm. froz.
5213401 Peas, gr., comm. froz.
5213402 Peas, gr., with sauce, comm. froz.
5213403 Peas, gr., with other veg., comm. froz.
5213404 Peas, gr., with other veg., comm. froz.
5213405 Peas, blackeye, comm froz.
5213406 Peas, blackeye, with sauce, comm froz.
52135- Corn, comm. froz.
5213712 Artichoke hearts, comm. froz.
5213713 Baked beans, comm. froz.
5213717 Kidney beans, comm. froz.
5213724 Succotash, comm. froz.
5411- Dried beans
5412- Dried peas and lentils
5413104 Dry corn
5413106 Dry hominy
5413504 Dry squash, baby
5413603 Dry creamed corn, baby
Does not include soups, sauces, gravies, mixtures, and ready-to-
eat dinners; includes baby foods except mixtures
732- Pumpkin
733- Winter Squash
7510200 Lima Beans, raw
7510550 Cactus, raw
7510960 Corn, raw
7512000 Peas, raw
7520070 Aloe vera juice
752040- Lima Beans, cooked
752041- Lima Beans, canned
7520829 Bitter Melon
752083- Bitter Melon, cooked
7520950 Burdock
752131- Cactus
752160- Corn, cooked
752161- Corn, yellow, cooked
752162- Corn, white, cooked
752163- Corn, canned
7521749 Hominy
752175- Hominy
75223- Peas, cowpeas, field or blackeye, cooked
75224- Peas, green, cooked
75225- Peas, pigeon, cooked
75301- Succotash
75402- Lima Beans with sauce
75411- Corn, scalloped, fritter, with cream
7541650 Pea salad
7541660 Pea salad with cheese
75417- Peas, with sauce or creamed
7550101 Corn relish
76205- Squash, yellow, baby
76405- Corn, baby
76409- Peas, baby
76411- Peas, creamed, baby
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
3-124
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Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
Exposed/Protected Fruits/Vegetables, Root Vegetables continued
Rooted
Vegetable
48- Potatoes, Sweetpotatoes
4921- Fresh carrots
4953- Fresh onions, garlic
4954- Fresh beets
4957- Fresh turnips
4958101 Fresh celenac
495 8 1 02 Fresh horseradish
4958104 Fresh radishes, no greens
4958105 Radishes, home canned
4958106 Radishes, home frozen
495 8 1 07 Fresh radishes, with greens
4958108 Fresh salsify
495 8 1 09 Fresh rutabagas
49581 10 Rutabagas, home frozen
495 8115 Fresh parsnips
49581 16 Parsnips, home canned
49581 17 Parsnips, home frozen
4958502 Fresh lotus root
4958509 Ginger root
4958510 Jicama, including yambean
51121- Carrots, comm. canned
51145- Beets, comm. canned
5114908 Garlic pulp, comm. canned
5114910 Horseradish, comm. prep.
5114915 Onions, comm. canned
5114916 Rutabagas, comm. canned
5114917 Salsify, comm. canned
5114921 Turnips, comm. canned
5114922 Water chestnuts, comm. canned
51151- Carrots, canned, low sod.
5115305 Beets, canned, low sod.
5115502 Turnips, low sod.
52121- Carrots, comm. froz.
5213714 Beets, comm. froz.
5213722 Onions, comm. froz.
5213723 Onions, comm. froz., with sauce
5213725 Turnips, comm. froz.
5312103 Canned carrot juice
53 1 21 04 Canned beet juice
5372102 Fresh carrot juice
5413105 Dry garlic
5413110 Dry onion
5413502 Dry carrots, baby
5413503 Dry sweet potatoes, baby
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures
71-
7310-
7311140
7311200
734-
7510250
7511150
7511180
7511250
75117-
7512500
7512700
7512900
752080-
752081-
7521362
7521740
7521771
7521850
752210-
7522110
752220-
75227-
75228-
75229-
75234-
75235-
7540501
75415-
7541601
7541810
7550021
7550309
7551201
7553403
76201-
76209-
76403-
White Potatoes and Puerto Rican St. Veg.
Carrots
Carrots in sauce
Carrot chips
Sweetpotatoes
Beets, raw
Garlic, raw
Jicama (yambean), raw
Leeks, raw
Onions, raw
Radish, raw
Rutabaga, raw
Turnip, raw
Beets, cooked
Beets, canned
Cassava
Garlic, cooked
Horseradish
Lotus root
Onions, cooked
Onions, dehydrated
Parsnips, cooked
Radishes, cooked
Rutabaga, cooked
Salsify, cooked
Turnip, cooked
Water chestnut
Beets, harvard
Onions, creamed, fried
Parsnips, creamed
Turnips, creamed
Beets, pickled
Horseradish
Radishes, pickled
Turnip, pickled
Carrots, baby
Sweetpotatoes, baby
Beets, baby
Does not include vegetable soups; vegetable mixtures; or
vegetable with meat mixtures
USDA Subcategories
Dark Green
Vegetables
491 - Fresh dark green vegetables
5 1 1 1 - Comm. Canned dark green veg.
511 54- Low Sodium Dark green veg.
521 1- Comm. frozen dark green veg.
5413111 Dry parsley
541 3112 Dry green peppers
541 3113 Dry red peppers
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures /dinners;
excludes vegetable juices and dried vegetables
72-
Dark green vegetables
all forms
leafy, nonleafy, dk. gr. veg. soups
5-125
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Table 3D-1. Food codes and definitions used in analysis of the 1987-88
USDA NFCS data (continued)
Food
Product
Household Code/Definition
Individual Code
USDA Subcategories continued
Deep Yellow
Vegetables
Other
Vegetables
Citrus Fruits
Other
Fruits
492- Fresh Deep Yellow Vegetables
5112- Comm. Canned Deep Yellow Veg.
51151- Low Sodium Carrots
5212- Comm. Frozen Deep Yellow Veg.
5312103 Carrot Juice
54135- Dry Carrots, Squash, Sw. Potatoes
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures /dinners;
excludes vegetable juices and dried vegetables
494- Fresh Light Green Vegetables
495- Fresh Other Vegetables
5114- Comm. Canned Other Veg.
51153- Low Sodium Other Veg.
51155- Low Sodium Other Veg.
5213- Comm. Frozen Other Veg.
5312102- Sauerkraut Juice
5312104- Beet Juice
5411- Dried Beans
5412- Dried Peas, Lentils
541310- Dried Other Veg.
5413114- Dry Seaweed
5413603- Dry Cr. Corn, baby
Does not include soups, sauces, gravies, mixtures, and ready -to-
eat dinners; includes baby foods except mixtures /dinners;
excludes vegetable juices and dried vegetables
501- Fresh Citrus Fruits
5121 Comm. Canned Citrus Fruits
5331 Canned Citrus and Citrus Blend Juice
5341 Frozen Citrus and Citrus Blend Juice
5351 Aseptically Packed Citrus and Citr. Blend Juice
5361 Fresh Citrus and Citrus Blend Juice
Includes baby foods; excludes dried fruits
62- Fresh Other Vitamin C-Rich Fruits
503- Fresh Other Fruits
5122- Comm. Canned Fruits Other than Citrus
5222- Frozen Strawberries
5332- Frozen Other than Citr. or Vitamin C-Rich Fr.
5333- Canned Fruit Juice Other than Citrus
5352- Frozen Juices Other than Citrus
5362- Aseptically Packed Fruit Juice Other than Citr.
542- Fresh Fruit Juice Other than Citrus Dry Fruits
Includes baby foods; excludes dried fruits)
73- Deep Yellow Vegetables
all forms
carrots, pumpkin, squash, sweetpotatoes, dp. yell. veg.
soups
75- Other Vegetables
all forms
61- Citrus Fruits and Juices
6720500 Orange Juice, baby food
6720600 Orange-Apricot Juice, baby food
6720700 Orange-Pineapple Juice, baby food
6721 10 Orange- Apple-Ban ana Juice, baby food
Excludes dried fruits
543- Dried Fruits
63 Other Fruits
64 Fruit Juices and Nectars Excluding Citrus
671 Fruits, baby
67202 Apple Juice, baby
67203 Baby Juices
67204 Baby Juices
67212 Baby Juices
67213 Baby Juices
673 Baby Fruits
674 Baby Fruits
3-126
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APPENDIX E FOR CHAPTER 3
Statistical Notes
Estimates based on small cell sizes may tend to be less statistically reliable than
estimates based on larger cell sizes. Cell size refers to the unweighted number of individuals
in a given sex- age group or demographic group. The guidelines (listed below) for determining
when a cell size is small take into account the average design effect for the survey. The design
effect results from the complex sample design and from the procedures used to weight the data.
When the design effect is 1.00, its effect on accuracy is negligible; a larger design effect
implies a greater effect on variance. The guidelines derive from a policy statement
(FASEB/LSRO 1995) that specifies the use of a broadly calculated design effect. In that role a
variance inflation factor is being used. Variance inflation factors used to generate the
estimates in this table set were calculated on individuals 19 years of age and under; they are as
follows:
Day-1, CSFII 1994- 96, 1998-2.24
2-day, CSFII 1994- 96, 1998 - 2.50
In the tables, estimates that may tend to be less statistically reliable are flagged with a
footnote directing the reader to this appendix. The rules used for flagging estimates are listed
below, and tables to which each rule applies are identified.
1. An estimated mean is flagged when it is based on a cell size of less than 30 times the
average design effect or when its coefficient of variation (CV) is equal to or greater than
30%. The CV is the ratio of the estimated standard error of the mean to the estimated
mean, expressed as a percentage.
Rule 1 has been applied to data in Tables 3-28, 3-30, 3-32, 3-34, 3-36, 3-38, and 3-40 to
flag estimates that should be used with caution. It applies to mean nutrient intakes, mean
food intakes, and means expressed as percentages, such as mean intakes of nutrients
expressed as percentages of Recommended Dietary Allowances and percentages of
nutrients from foods eaten as snacks.
3-127
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2. An estimated proportion (percent) that falls above 25% and below 75% is flagged when
it is based on a cell size of less than 30 times the average design effect or when the CV is
equal to or greater than 30%.
3. An estimated proportion of 25% or lower or 75% or higher is flagged when the smaller
of np and n(l- p) is less than 8 times the average design effect, where "n" is the cell size
on which the estimate is based and "p" is the proportion expressed as a fraction.
Rules 2 and 3 have been applied to data in Tables 3-29, 3-31, 3-33, 3-35, 3-37, 3-39, and
3-41 to flag estimates that should be used with caution.
3-128
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REFERENCES FOR CHAPTER 3
CDC (Centers for Disease Control). (1994) Dietary fat and total food-energy intake. Third national health and
nutrition examination survey, phase 1, 1988-91. Morbidity and mortality weekly report 43(7)118-125.
Cresanta, JL; Farris, RP; Croft, JB; et al. (1988) Trends in fatty acid intakes of 10-year-old children, 1973-1982. J
Am Dietetic Assoc 88:178-184.
CRITFC (Columbia River Inter-Tribal Fish Commission). (1994) A fish consumption survey of the Umatilla, Nez
Perce, Yakama and Warm Springs tribes of the Columbia River Basin. Technical Report 94-3. CRIFTC:
Portland, OR.
Frank, GC; Webber, LS; Farris, RP; et al. (1986) Dietary databook: quantifying dietary intakes of infants,
children, and adolescents, the Bogalusa heart study, 1973-1983. National Research and Demonstration Center -
Arteriosclerosis, Louisiana State University Medical Center, New Orleans, LA.
Goldman, L. (1995) Children: unique and vulnerable. Environmental risks facing children and recommendations
for response. Environ Health Perspect 103(6): 13-17.
National Livestock and Meat Board. (1993) Eating in America today: a dietary pattern and intake report. National
Livestock and Meat Board, Chicago, IL.
Nicklas, TA. (1995) Dietary studies of children: the Bogalusa heart study experience. J Am Dietetic Assoc
95:1127-1133.
Nicklas, TA; Webber, LS; Srinivasan, SR; et al. (1993) Secular trends in dietary intakes and cardiovascular risk
factors in 10-y-old children: the Bogalusa heart study (1973-1988). Am J of Clin Nutr 57:930-937.
Pao, EM; Fleming, KH; Guenther, PM; et al. (1982) Foods commonly eaten by individuals: amount per day and
per eating occasion. Home Economics Report No. 44. U.S. Department of Agriculture. Washington, DC.
SAS Institute, Inc. (1990) SAS procedures guide, ver. 6, 3rd ed. SAS Institute, Inc.: Cary, NC.
The Suquamish Tribe. (2000) Fish consumption survey of the Suquamish Indian Tribe of the Port Madison Indian
Reservation, Puget Sound Region. The Suquamish Tribe: Suquamish, WA.
Tippett, KS; Cypel, YS (eds). (1997) Design and operation: the continuing survey of food intakes by individuals
and the diet and health knowledge survey, 1994-96. Nationwide Food Surveys Report No. 96-1, 197 pp. U.S.
Department of Agriculture, Agricultural Research Service, Washington, DC.
Toy, KA; Polissar, NL; Liao, S; et al. (1996) A fish consumption survey of the Tulalip and Squaxin Island Tribes
of the Puget Sound Region. Tulalip Tribes, Department of Environment, Margsville, WA.
Tsang, AM; Klepeis, NE. (1996) Results tables from a detailed analysis of the National Human Activity Pattern
Survey (NHAPS) response. Draft Report prepared for the U.S. Environmental Protection Agency by Lockheed
Martin, Contract No. 68-W6-001, Delivery Order No. 13.
USDA(U.S. Department of Agriculture). (1975) Food yields summarized by different stages of preparation.
Agricultural Handbook No. 102. U.S. Department of Agriculture, Agriculture Research Service, Washington, DC.
USD A. (1979-1986) Agricultural Handbook No. 8. U.S. Department of Agriculture, Washington, DC.
3-129
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USD A. (1987-1988) Dataset: Nationwide Food Consumption Survey 1987/88 Household Food Use. 1987/88
NFCS Database. U.S. Department of Agriculture, Washington, DC.
USD A. (1992) Changes in food consumption and expenditures in American households during the 1980s.
Statistical Bulletin No. 849. U.S. Department of Agriculture, Washington, DC.
USD A. (1993) Food and nutrient intakes by individuals in the United States, 1 Day, 1987-88. Nationwide Food
Consumption Survey 1987-88, NFCS Report No. 87-1-1. U.S. Department of Agriculture, Washington, DC.
USD A. (1994) Food consumption and dietary levels of households in the United States, 1987-88. Report No.
87-H-l. U.S. Department of Agriculture, Agricultural Research Service, Washington, DC.
USD A. (1995) Food and nutrient intakes by individuals in the United States, 1 day, 1989-91. NFS Report No. 91-
2. U.S. Department of Agriculture, Agricultural Research Service, Washington, DC.
USD A. (1998) 1994-96 Continuing Survey of Food Intakes by Individuals (CSFII) and 1994-96 Diet and Health
Knowledge Survey (DKHS). CD-ROM. U.S. Department of Agriculture, Agricultural Research Service.
Available from the National Technical Information Service, Springfield, VA.
USDA. (1999) Food and nutrient intakes by children 1994-96, 1998: Table Set 17. U.S. Department of
Agriculture. Food Surveys Research Group, Beltsville Human Nutrition Research Center, Agricultural Research
Service, Beltsville, MD.
U.S. EPA (U.S. Environmental Protection Agency). (1996) Daily average per capita fish consumption estimates
based on the combined USDA 1989, 1990 and 1991 continuing survey of food intakes by individuals (CSFII)
1989-91 data, vol. I and II. Preliminary Draft Report. Office of Water, Washington, DC.
U.S. EPA. (1997) Exposure factors handbook. EPA/600/P-95/002F. Office of Research and Development,
Washington, DC.
U.S. EPA. (2000) CSFII analysis of food intake. Draft report prepared for U.S. EPA, Office of Research and
Development, National Center for Environmental Assessment by Versar, Inc.
West, PC; Fly, MJ; Marans, R; et al. (1989) Michigan sport anglers fish consumption survey: a report to the
Michigan Toxic Substance Control Commission. Michigan Department of Management and Budget Contract No.
87-20141.
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4. DRINKING WATER INTAKE
4.1. INTRODUCTION
Drinking water is a potential source of human exposure to toxic substances among
children. Contamination of drinking water may occur by, for example, percolation of toxics
through the soil to ground water that is used as a source of drinking water, runoff or discharge to
surface water that is used as a source of drinking water, intentional or unintentional addition of
substances to treat water (e.g., chlorination), and leaching of materials from plumbing systems
(e.g., lead). Estimating the magnitude of the potential dose of toxicants from drinking water
requires information on the quantity of water consumed. The purpose of this section is to
describe key published studies that provide information on drinking water consumption among
children (section 4.2) and to provide recommendations of consumption rate values that should be
used in exposure assessments (section 4.3).
EPA's current default drinking water intake rate uses the quantity for infants (individuals
of 10 kg body mass or less) and children is 1 L/day (U.S. EPA, 1980, 1991). This rate includes
drinking water consumed in the form of juices and other beverages containing tapwater. The
National Academy of Sciences has estimated that daily consumption of water may vary with
levels of physical activity and fluctuations in temperature and humidity (NAS, 1977). It is
reasonable to assume that children engaging in physically demanding activities or living in
warmer regions may have higher levels of water intake.
Two studies cited in this chapter have generated data on drinking water intake rates. In
general, these sources support EPA's use of 1 L/day as an upper-percentile tapwater intake rate
for children under 10 years of age. The studies have reported intake rates for direct and indirect
ingestion of water. Direct intake is defined as direct consumption of water as a beverage;
indirect intake includes water added during food preparation but not water intrinsic to purchased
foods. Data for consumption of various sources (i.e., community water supply, bottled water,
and other sources) are also presented. For the purposes of exposure assessments involving site-
specific contaminated drinking water, intake rates based on the community supply are most
appropriate. Given the assumption that bottled water and other purchased foods and beverages
are widely distributed and less likely to contain source-specific water, the use of total water
intake rates may overestimate the potential exposure to toxic substances present only in local
water supplies; therefore, tapwater intake of community water rather than total water intake is
emphasized in this section.
4-1
-------
The studies on drinking water intake that are currently available are based on short-term
survey data. Although short-term data may be suitable for obtaining mean intake values that are
representative of both short- and long-term consumption patterns, upper-percentile values may
be different for short-term and long-term data because more variability generally occurs in short-
term surveys. It should also be noted that most available drinking water surveys are based on
recall. This may be a source of uncertainty in the estimated intake rates because of the
subjective nature of this type of survey technique.
The distribution of water intakes is usually lognormal. Instead of presenting only the
lognormal parameters, the actual percentile distributions are presented in this handbook, usually
with a comment on whether or not they are lognormal. To facilitate comparisons between
studies, the mean and the 90th percentiles are given for all studies where the distribution data are
available. With these two parameters, along with information about which distribution is being
followed, one can calculate, using standard formulas, the geometric mean and the geometric
standard deviation and hence any desired percentile of the distribution. Before doing such a
calculation one must be sure that one of these distributions adequately fits the data.
Other studies based on older data were presented in Exposure Factors Handbook (U.S.
EPA, 1997a).
4.2. DRINKING WATER INTAKE STUDIES
4.2.1. U.S. EPA, 2000a
EPA used data from a USD A survey from 1994 through 1996 to estimate drinking water
ingestion rates by the U.S. population. The Continuous Study of Food Intakes by Individuals
(CSFII) is a continuing survey of food consumption habits in the U.S. More than 15,000 persons
responded to the study conducted between 1994 and 1996 on what they ate and drank over two
nonconsecutive days (USDA, 1998). EPA used the drinking water ingestion data to derive
estimates of consumption rates by age groups, gender, water source, and vulnerable subsets of
the population (i.e., lactating and pregnant women) (U.S. EPA, 2000a). The ingestion rates are
expressed in both volume (mL) per day per person and volume per kilogram (kg) body weight
per day. The purpose of the report was to provide data to assist in estimating human health risks
from the ingestion of contaminated or potentially contaminated drinking water.
In the study, EPA reported that community water (i.e., tapwater from the public water
supply) accounts for approximately 75% of the mean ingested water. The total water
consumption consists of community water supply, bottled water, other sources, and missing
sources. Other sources include household wells or cisterns or a spring, either household or
4-2
-------
community. In addition to these sources, the data also distinguish between direct and indirect
water consumption. Direct consumption is water consumed directly from the tap, whereas
indirect consumption is water added during final food or beverage preparation in the home or
food establishment (e.g., restaurants, school cafeterias). Indirect water does not include water
added by the food manufacturer during food processing. Table 4-1 provides the estimates for the
mean total direct and indirect water consumption by water source for 1994 to 1996 per person
combined for all ages. The estimates also include consumption rates for the 90th percentile and
the 95th percentile plus the upper and lower bounds for each percentile.
Table 4-2 shows the estimated total direct and indirect water ingestion by all sources by
broad age groups and percentiles. Tables 4-3 and 4-4 show daily community water consumption
rate estimates by fine and broad age groups in units of mL/day and mL per mass of body weight
per day. Tables 4-5 and 4-6 present data for bottled water ingestion. Water consumption rates
for other sources of water are compiled in Tables 4-7 and 4-8. The data are broken down into
multiple population subsets including children's age groups: less than 1 year, 1 to 10 years, and
11 to 19 years. The data show that although the total quantity of water ingested (mL/kg/day)
decreases with age, the quantity consumed per unit mass of body weight (mL/person-day)
increases. For instance, Table 4-4 shows that the mean community water consumption is
342 mL/child/day for under 1 year, 400 mL/child/day for 1 to 10 years, and 683 mL/child/day
for 11 to 19 years. The consumption as a function of unit mass, however, is 46 mL/kilogram-day
for under 1 year, 19 mL/kg-day for 1 to 10 years, and 12 mL/kg-day for 11 to 19 years. The
significance of this finding is that although children may encounter lower overall doses, the
younger, vulnerable ages (i.e., infants) have significantly higher dose rates per unit of body
weight.
These two sources comprise nearly one-quarter of total water consumption. The trend in
the data is similar to that shown for community water consumption; that is, the younger ages
consume less of these sources of water, but the quantity consumed per unit mass of body weight
increases as age decreases. Missing water sources have not been included in the summary of
water sources because of negligible quantity. Missing water sources comprise only about 1% of
water consumption.
The data collected from the CSFII study for the USDA have both strengths and
limitations. The strengths lie in the design of the survey in that it was intended to collect a
statistically representative sample of the U.S. population (i.e., to obtain data from a sufficiently
large sample set), and the survey was sufficiently specific in detailing types of food and drink.
The large size of the sample population (> 15,000 total and 6000 children) enhances the
4-3
-------
precision and accuracy of the estimates for the overall population and population subsets. The
survey was conducted on nonconsecutive days, which improves the variance over consecutive
days of consumption. In addition, the survey was administered such that an interviewer went
through the data collection process for the initial day to show the participants the proper
response methodology. The second day of the survey was reported by the participant. The
survey also represents the most up-to-date water consumption information, and it incorporated
sufficient parameters to differentiate sources of water, ages, gender, weight, and vulnerable
populations.
The limitations of the survey involve the short duration of the study and some of the data
reporting methods. The short duration (2 nonconsecutive days)—although an advantage over 2
consecutive days—diminishes the precision of an individual's water ingestion rate. The mean
for an individual can easily be skewed for numerous reasons. The large number of the sample
population would hopefully contribute to greater accuracy, but the precision may still be low.
The data reporting did not always support variance estimation for some reported population
subsets. The means differences could not always be statistically tested except for the larger
population subsets. Therefore, the reported differences were derived empirically instead of
statistically.
4.2.2. U.S. EPA, 2000b
U.S. EPA (2000b) presented a system of procedures to fit distributions to selected data
from the draft Exposure Factors Handbook (EFH) (U.S. EPA, 1996). The system was based on
EPA's Guiding Principles for Monte Carlo Analysis (U.S. EPA, 1997b). The system was
applied to the dataset of total tapwater intake reported in Table 3-7 of the EFH; the data
summaries analyzed by U.S. EPA (2000b) consist of nine estimated percentiles for total daily
tapwater intake in mL/kg-day. Only the values for infants, children, and teens are reported here.
The statistical methodology recommended by U.S. EPA (2000b) incorporates: (1) a
dataset and its underlying experimental design, (2) a family of models, and (3) an approach to
inference (e.g., estimation, assessment of fit, and uncertainty analysis). The system used a
12-model hierarchy, with the most general model being a five-parameter generalized F
distribution with a point mass at zero. The point mass at zero represents the proportion of
nonconsuming or nonexposed individuals. As described in U.S. EPA (2000b), the 12 models of
the generalized F hierarchy were fit to each of the three tapwater datasets (i.e., three age groups
of children) using three different estimation criteria: maximum likelihood estimation (MLE),
minimum chi-square estimation, and weighted least squares (WLS). The Pearson chi-square
4-4
-------
tests and likelihood ratio tests of goodness-of-fit (GOF) were used. Tables 4-9 and 4-10 present
chi-square values and associated p-values for chi-square GOF tests, respectively. U.S. EPA
(2000b) noted that in each case the null hypothesis tested is that the data arose from the given
type of model and that a low p-value casts doubt on the null hypothesis. The GenFS model that
appears to fit most of the datasets is the five-parameter generalized F distribution with a point
mass at zero (U.S. EPA, 2000b). According to Table 4-9, the gamma model provides the best fit
(smallest chi-square) of the two-parameter models to the data for each individual age groups.
Table 4-11 presents the results of the statistical modeling of tapwater data using five-
parameter generalized F and two-parameter gamma, lognormal, and Weibull models. The
authors noted that the infants group does not contain results from the five-parameter generalized
F because the selected model had infinite variance. For the gamma and Weibull models, there
was little difference between the three estimation criteria, and the MLE performed best overall;
for the lognormal model, results from the WLS estimation criterion are shown in addition to the
MLE.
U.S. EPA (2000b) recommends use of the gamma model with parameters estimated by
MLE if a two-parameter model for tapwater consumption is used. U.S. EPA (2000b) also noted
that the five-parameter generalized F distribution could be used for sensitivity analyses, and the
age effect seems sufficiently strong to justify the use of separate age groups in risk assessment.
4.3. RECOMMENDATIONS
The studies described in this section were used in selecting recommended drinking water
(tapwater) consumption rates for children. A summary of the recommended values for drinking
water intake rates is presented in Table 4-12. The intake rates generally increase with age, and
the data are consistent across ages for the studies.
A characterization of the overall confidence in the accuracy and appropriateness of the
recommendations for drinking water is presented in Table 4-13. Exposure Factors Handbook
(U.S. EPA, 1997a) gave this factor a medium confidence rating. However, the confidence score
of the overall recommendations has been increased to high for this report because of the addition
of the newer EPA study (U.S. EPA, 2000a).
4-5
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Table 4-1. Estimated direct and indirect community total water ingestion by source for U.S. populationa
Source
Community
water supply
Bottled water
Other sources
Missing sources
All sources
Mean"
Estimate
927
161
128
16
1232
Lower
bound
902
147
101
13
1199
Upper
bound
951
176
155
20
1265
90th percentileb
Estimate
2016
591
343
0
2341
Lower
bound
1991
591
305
0
2308
Upper
bound
2047
632
360
0
2366
95th percentileb
Estimate
2544
1036
1007
0
2908
Lower
bound
2485
1006
947
0
2840
Upper
bound
2576
1065
1074
0
2960
a Estimates are based on 2-day averages for nonconsecutive days, sample size = 15,303.
b Units are mL/person/day; 90% CI.
Source: USDA Continuing Survey of Food Intakes by Individuals (1994-1996), in U.S. EPA, 2000a
-------
Table 4-2. Estimate of total direct and indirect water ingestion, all sources by broad age category
for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<1
1-10
11-19
359
3980
1641
484
528
907
0
4
0
0
75
118
0
133
219
124
254
395
449
444
715
747
710
1188
949
1001
1780
1182
1242
2185
l,645a
1891
3805
Quantity (mL/kg-day)
<1
1-10
11-19
359
3980
1641
67
25
16
0
0
0
0
4
2
0
6
4
16
12
7
57
21
13
101
33
20
156
49
30
170
64
39
218a
98
64
a Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), inUSEPA 2000a
-------
Table 4-3. Estimate of direct and indirect community water ingestion by fine age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
199
160
1834
1203
943
816
825
280
412
313
420
453
594
760
Oa
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
22
29
27
25
0
36
74
133
139
181
201
35
322
236
330
355
435
540
552
712
469
591
671
801
1030
861
884
691
917
978
1365
1610
945b
l,101b
942
1165
1219
1722
2062
l,286b
l,645b
1,358
l,902b
l,914b
2,541b
3,830b
Quantity (mL/kg-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
191
153
1752
1113
879
790
816
47
45
23
21
15
12
12
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
1
1
1
0
0
4
6
6
5
4
3
5
36
17
16
11
9
9
90
79
33
29
21
17
16
139
103
51
44
32
26
25
170b
122b
67
64
39
34
32
217b
169b
109b
91b
60b
54b
61b
oo
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according
to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII),
inUSEPA2000a
-------
Table 4-4. Estimate of direct and indirect community water ingestion by broad age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<1
1-10
11-19
344
3744
1606
342
400
683
Oa
0
0
0
0
0
0
12
26
0
118
191
173
302
473
652
571
937
878
905
1533
1040
1118
1946
1438b
1731
3671
Quantity (mL/kg-day)
<1
1-10
11-19
344
3744
1606
46
19
12
0
0
0
0
0
0
0
0
1
0
5
3
19
15
9
82
27
16
127
42
26
156
56
33
205b
91
59
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), inUSEPA 2000a
-------
Table 4-5. Estimate of direct and indirect bottled water ingestion by fine age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
199
160
1834
1203
943
816
825
110
113
62
73
76
100
130
Oa
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
38
5
0
0
0
0
0
519
496
235
279
271
344
468
809
727b
411
521
497
679
867
1045b
1006b
820
915b
917b
1415b
1775b
Quantity (mL/kg-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
191
153
1752
1113
879
790
816
20
14
5
4
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
6
2
0
0
0
0
0
81
51
17
13
8
7
7
152a
92a
30
24
14
13
12
170b
125b
61
49b
26b
27b
28b
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), in USEPA (2000a)
-------
Table 4-6. Estimate of direct and indirect bottled water ingestion by broad age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<1
1-10
11-19
359
3980
1641
111
71
116
Oa
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
0
0
522
264
414
793
472
764
1083b
906
1648
Quantity (mL/kg-day)
<1
1-10
11-19
344
3744
1606
17
3
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
0
0
76
12
7
123
22
13
169b
49
28
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), inUSEPA 2000a
-------
Table 4-7. Estimate of direct and indirect other water ingestion by fine age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
199
160
1834
1203
943
816
825
18
30
35
43
67
106
77
Oa
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
23
8
32
206
341
234
86a
202a
295
322
554
800
552
468b
554b
710
83 Ob
1049b
1811"
1411b
Quantity (mL/kg-day)
<0.5
0.5-0.9
1-3
4-6
7-10
11-14
15-19
191
153
1752
1113
879
790
816
3
O
O
2
2
2
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
2
2
7
7
4
15a
24a
21
15
18
16
9
86b
63b
48
42b
37b
36b
21b
to
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), inUSEPA 2000a
-------
Table 4-8. Estimate of direct and indirect other water ingestion by broad age category for U.S. children
Age
(years)
Sample Size
Mean
Percentile
1th
5th
10th
25th
50th
75th
90th
95th
99th
Quantity (mL/person-day)
<1
1-10
11-19
359
3980
1641
23
50
90
Oa
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
103
286
148
405
666
556b
920
1710
Quantity (mL/kg-day)
<1
1-10
11-19
344
3744
1606
3
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
5
5
21
18
11
66b
43
29
a Denotes zero.
b Sample size was insufficient for minimum reporting requirements according to "Third Report on Nutritional Monitoring in the U.S. (1994-96)."
Source: 1994-96 USDA Survey of Food Intakes by Individuals (CSFII), inUSEPA 2000a
-------
Table 4-9. Chi-square GOF statistics for 12 models", tapwater data, based on maximum likelihood method of
parameter estimation
Age
group
(years)
Infants
Children
(1-10)
Teens
(11-19)
CHI
Gam2
19.8
84.5
89.5
CHI
Log2
26.6
315
606
CHI
Tic2
39.4
295
557
CHI
Wei2
20.6
198
125
CHI
GgamS
18.1
84.7
81.4
CHI
GenF4
10.6
40.3
38.4
CHI
Gam3
19.8
46.6
23.4
CHI
Log3
13.7
129
286
CHI
Tic3
10.8
195
377
CHI
Wei3
20.6
198
110
CHI
Ggam4
18.1
27.5
23.1
CHI
GenFS
8.10
15.2
7.88
a Prefix indicates model type; Gam = gamma, Log = lognormal, Tic = log-logistic, Wei = Weibull, Ggam = generalized gamma, GenF = generalized F;
suffix indicates number of free or adjustable parameters.
Source: U.S. EPA, 2000b
Table 4-10. P-values for chi-square GOF tests of 12 models", tapwater data
Age Group
(years)
Infants (< 1)
Children (1-10)
Teens (11-19)
PGOF
Gam2
0.001
0.000
0.000
PGOF
Log2
0.000
0.000
0.000
PGOF
Tic2
0.000
0.000
0.000
PGOF
Wei2
0.000
0.000
0.000
PGOF
Ggam3
0.000
0.000
0.000
PGOF
GenF4
0.005
0.000
0.000
PGOF
Gam3
0.000
0.000
0.001
PGOF
Log3
0.003
0.000
0.000
PGOF
Tic3
0.013
0.000
0.000
PGOF
Wei3
0.000
0.000
0.000
PGOF
Ggam4
0.000
0.000
0.000
PGOF
GenFS
0.013
0.004
0.096
' Prefix indicates model type; Gam = gamma, Log = lognormal, Tic = log-logistic, Wei = Weibull, Ggam = generalized gamma, GenF = generalized F;
Model suffix indicates number of free or adjustable parameters.
Source: U.S. EPA, 2000b
-------
Table 4-11. Results of statistical modeling of tapwater data (intake rates in dL/kg-day) using
5-parameter generalized F and 2-parameter gamma, lognormal and Weibull models
Source3
N
P01
P05
P10
P25
P50
P75
P90
P95
P99
MEAN
SDEV
CHIDF"
PGOFC
Infants (< 1 year)d
data
gammle
weimle
logmle
logwls
403
0.010
0.050
0.100
0.250
0.252
0.260
0.227
0.216
0.500
0.526
0.526
0.561
0.559
0.750
0.702
0.699
0.735
0.738
0.900
0.908
0.906
0.903
0.908
0.950
0.951
0.950
0.937
0.942
0.990
0.996
0.996
0.984
0.986
0.435
0.448
0.447
0.470
0.462
0.425
0.410
0.412
0.548
0.512
40.945
50.145
60.660
60.974
0.0006
0.0004
0.0000
0.0000
Children (1-10 years)
data
gammle
gf5mle
weimle
logmle
logwls
5605
0.010
0.010
0.004
0.000
0.011
0.000
0.050
0.047
0.052
0.024
0.070
0.036
0.100
0.106
0.118
0.091
0.134
0.113
0.250
0.250
0.263
0.266
0.264
0.288
0.500
0.495
0.492
0.529
0.474
0.532
0.750
0.752
0.738
0.765
0.721
0.750
0.900
0.900
0.895
0.895
0.894
0.878
0.950
0.952
0.953
0.943
0.959
0.929
0.990
0.989
0.993
0.984
0.997
0.977
0.355
0.356
0.355
0.356
0.355
0.366
0.229
0.234
0.224
0.250
0.218
0.286
30.792
120.07
270.18
280.34
450.07
0.0044
0.0000
0.0000
0.0000
0.0000
Teens (11-19 years)
data
gfSmle
gammle
weimle
logmle
logwls
5801
0.010
0.010
0.002
0.006
0.000
0.000
0.050
0.048
0.046
0.061
0.017
0.032
0.100
0.103
0.110
0.122
0.076
0.108
0.250
0.253
0.274
0.267
0.270
0.303
0.500
0.498
0.511
0.487
0.544
0.548
0.750
0.747
0.740
0.725
0.768
0.747
0.900
0.953
0.947
0.957
0.942
0.920
0.950
0.953
0.947
0.957
0.942
0.920
0.990
0.989
0.989
0.995
0.981
0.968
0.182
0.182
0.182
0.182
0.182
0.189
0.108
0.110
0.111
0.106
0.119
0.144
10.969
120.79
170.86
450.35
860.56
0.0962
0.0000
0.0000
0.0000
0.0000
a Data = data summary, gammle = gamma model using maximum likelihood estimation (MLE), weimle = Weibull modeling use MLE, logmle = lognormal=
model using MLE, logwls = lognormal model using weighted least square (WLS), gf5mle = five parameter generalized F with a point mass at zero.
b CHIDF is the value of the chi-square statistic divided by its degrees of freedom.
0 PGOF is the p-value for model goodness-of-fit, based on the chi-square test.
d The infants group does not contain results for gf5mle because the selected model had infinite variance.
Source: U.S. EPA, 2000b
-------
Table 4-12. Summary of recommended community drinking water intake rates
Age Group/
Population
<1 year3
1-3 yearsa
1-10 years3
11-19 years3
Mean
0.34 L/day
46 mL/kg-day
0.31 L/day
23 mL/kg-day
0.40 L/day
19 mL/kg-day
0.68 L/day
12 mL/kg-day
Percentiles
50th
0.1 7 L/day
19 mL/kg-day
0.24
17 mL/kg-day
0.30 L/day
1 5 mL/kg-day
0.47 L/day
9 mL/kg-day
90th
0.88 L/day
127 mL/kg-day
0.69 L/day
51 mL/kg-day
0.90 L/day
42 mL/kg-day
1.5 L/day
26 mL/kg-day
95th
1.0 L/day
156 mL/kg-day
0.94 L/day
67 mL/kg-day
1.1 L/day
56 mL/kg-day
1.9 L/day
33 mL/kg-day
Multiple
Tables 4-4
Table 4-3
Table 4-4
Tables 4-4
Fitted
Distributions
Table 4-1 lb
Table 4-1 lb
Table 4-1 lb
a Source: U.S. EPA, 2000a
b Source: U.S. EPA, 2000b
-------
Table 4-13. Confidence in tapwater intake recommendations
Considerations
Rationale
Rating
Study Elements
Level of peer
review
Accessibility
Reproducibility
Focus on factor of
interest
Data pertinent to
U.S.
Primary data
Currency
Adequacy of data
collection period
Validity of
approach
Study size
Representativeness
of the population
Characterization of
variability
Lack of bias in
study design (high
rating is desirable)
Measurement error
U.S. EPA (2000) had thorough expert panel review. Other
reports presented are published in scientific journals.
The monograph is available from the sponsoring agency;
the other is library-accessible.
Methods are well-described.
The studies are directly relevant to tapwater. In addition,
U.S. EPA (2000) included consumption for other drinking
water sources
See "representativeness" below.
The three monographs used recent primary data (less than
1 week) on recall of intake.
Data collected for USD A (1998) and used in U.S. EPA
(2000) are current.
These are 1- to 3 -day intake data. However, long term
variability may be small. Their use as a chronic intake
measure can be assumed.
The approach was competently executed.
U.S. EPA (2000) had sufficient sample populations (i.e.,
6000) for the study.
The U.S. EPA (2000) survey was validated as
demographically representative.
The full distributions were given in the main study.
Bias was not apparent.
No physical measurements were taken. The method relied
on recent recall of standardized volumes of drinking water
containers and was not validated.
High
High
High
High
NA
High
High
Medium
High
High
High
High
High
Medium
Other Elements
Number of studies
Agreement
between
researchers
There were two key studies for the recommendations.
This agreement was good.
High
High
4-17
-------
Overall Rating The data are excellent and current. High
REFERENCES FOR CHAPTER 4
NAS (National Academy of Sciences). (1977) Drinking water and health, vol. 1. National Academy of Sciences,
National Research Council, Washington, DC.
USDA (U.S. Department of Agriculture). (1998) 1994-1996 Continuing survey of food intakes and 1994-1996 diet
and health survey.
U.S. EPA (U.S. Environmental Protection Agency). (1980) Water quality criteria documents; availability. Federal
Register 45(231):79318-79379.
U.S. EPA. (1991) National primary drinking water regulations; final rule. Federal Register 56(20):3526-3597.
U.S. EPA. (1996) Exposure factors handbook. Office of Research and Development, National Center for
Environmental Assessment, Washington, DC. SAB Review Draft. EPA/600/P-95/002Ba.
U.S. EPA. (1997a) Exposure factors handbook. Office of Research and Development, Washington, DC.
EPA/600/P-95/002F.
U.S. EPA. (1997b) Guiding principles for monte carlo analysis. Risk assessment forum. Office of Research and
Development, EPA/630/R-97/001.
U.S. EPA. (2000a) Estimated per capita water ingestion in the United States. Office of Science and Technology,
Office of Water, Washington, DC.
U.S. EPA. (2000b) Options for development of parametric probability distributions for exposure factors, U.S.
Environmental Protection Agency, Office of Research and Development, Washington, DC, EPA/600/R-00/058.
4-18
-------
5. SOIL INGESTION AND PICA
5.1. INTRODUCTION
The ingestion of soil is a potential source of human exposure to toxicants. The potential
for exposure to contaminants via this source is greater for children because they are more likely
to ingest more soil than do adults as a result of behavioral patterns present during childhood.
Inadvertent soil ingestion among children may occur through the mouthing of objects or hands.
Mouthing behavior is considered to be a normal phase of childhood development. Deliberate
soil ingestion is defined as pica and is considered to be relatively uncommon. Because normal,
inadvertent soil ingestion is more prevalent and data for individuals with pica behavior are
limited, this section focuses primarily on normal soil ingestion that occurs as a result of
mouthing or unintentional hand-to-mouth activity.
Several studies have been conducted to estimate the amount of soil ingested by children.
Most of the early studies attempted to estimate the amount of soil ingested by measuring the
amount of dirt present on children's hands and making generalizations based on behavior. More
recently, soil intake studies have been conducted using a methodology that measures trace
elements in feces and soil that are believed to be poorly absorbed in the gut. These
measurements are used to estimate the amount of soil ingested over a specified time period. The
available studies on soil intake are summarized in the following sections. Recommended intake
rates are based on the results of key studies presented in Exposure Factors Handbook (U.S. EPA,
1997) and summarized here. Relevant information on the prevalence of pica and intake among
individuals exhibiting pica behavior is also presented.
5.2. SOIL INTAKE STUDIES
5.2.1. Binder et al., 1986
Binder et al. (1986) used a tracer technique modified from a method previously used to
measure soil ingestion among grazing animals to study the ingestion of soil among children 1 to
3 years of age who wore diapers. The children were studied during the summer of 1984 as part
of a larger study of residents living near a lead smelter in East Helena, MT. Soiled diapers were
collected over a 3-day period from 65 children (42 males and 23 females), and composited
samples of soil were obtained from the children's yards. Both excreta and soil samples were
analyzed for aluminum, silicon, and titanium. These elements were found in the soil but were
thought to be poorly absorbed in the gut and to have been present in the diet only in limited
quantities. This made them useful tracers for estimating soil intake. Excreta measurements were
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obtained for 59 of the children. Soil ingestion by each child was estimated on the basis of each
of the three tracer elements using a standard assumed fecal dry weight of 15 g/day and the
following equation:
f x F
r-r !=e 1
Ti,e = o (5-1)
i,e
where:
T; e = estimated soil ingestion for child "i" based on element "e" (g/day);
f; e = concentration of element "e" in fecal sample of child "i" (mg/g);
F; = fecal dry weight (g/day); and
S; e = concentration of element "e" in child "i"'s yard soil (mg/g).
The analysis assumed that (1) the tracer elements were neither lost nor introduced during sample
processing, (2) the soil ingested by children originates primarily from their own yards, and (3)
absorption of the tracer elements by children occurred in only small amounts. The study did not
distinguish between ingestion of soil and housedust nor did it account for the presence of the
tracer elements in ingested foods or medicines.
The arithmetic mean quantity of soil ingested by the children was estimated to be 181
mg/day (range 25 to 1324), based on the aluminum tracer; 184 mg/day (range 31 to 799), based
on the silicon tracer; and 1834 mg/day (range 4 to 17,076), based on the titanium tracer (Table 5-
1). The overall mean soil ingestion estimate, based on the minimum of the three individual
tracer estimates for each child, was 108 mg/day (range 4 to 708). The median values were 121,
136, and 618 mg/day for aluminum, silicon, and titanium, respectively. The 95th percentile
values for aluminum, silicon, and titanium were 584, 578, and 9590 mg/day, respectively. The
95th percentile value, based on the minimum of the three individual tracer estimates for each
child, was 386 mg/day.
The authors were not able to explain the difference between the results for titanium and
for the other two elements, but they speculated that unrecognized sources of titanium in the diet
or in the laboratory processing of stool samples may have accounted for the increased levels.
The frequency distribution graph of soil ingestion estimates based on titanium shows that a
group of 21 children had particularly high titanium values (> 1000 mg/day). The remainder of
the children showed titanium ingestion estimates at lower levels, with a distribution more
comparable to that of the other elements.
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The advantages of this study are that a relatively large number of children were studied
and tracer elements were used to estimate soil ingestion. However, the children studied may not
be representative of the U.S. population, and the study did not account for tracers ingested via
foods or medicines. Also, the use of an assumed fecal weight instead of actual fecal weights
may have biased the results of this study. Finally, because of the short-term nature of the survey,
soil intake estimates may not be entirely representative of long-term behavior, especially at the
upper end of the distribution of intake.
5.2.2. Clausing et al., 1987
Clausing et al. (1987) conducted a soil ingestion study with Dutch children using a tracer
element methodology similar to that of Binder et al. (1986). Aluminum, titanium, and
acid-insoluble residue (AIR) contents were determined for fecal samples from children aged 2 to
4 years who attended a nursery school and for samples of playground dirt at that school.
Twenty-seven daily fecal samples were obtained over a 5-day period for the 18 children
examined. Using the average soil concentrations present at the school and assuming a standard
fecal dry weight of 10 g/day, soil ingestion was estimated for each tracer. Eight daily fecal
samples were also collected from six hospitalized, bedridden children. These children served as
a control group, representing children who had very limited access to soil.
The average quantity of soil ingested by the school children in this study was as follows:
230 mg/day (range 23 to 979) for aluminum, 129 mg/day (range 48 to 362) for AIR, and 1430
mg/day (range 64 to 11,620) for titanium (Table 5-2). As in the Binder et al. (1986) study, a
fraction of the children (6/19) showed titanium values well above 1000 mg/day, with most of the
remaining children showing substantially lower values. Based on the Limiting Tracer Method
(LTM), mean soil intake was estimated to be 105 mg/day, with a population standard deviation
of 67 mg/day (range 23 to 362). Use of the LTM assumed that "the maximum amount of soil
ingested corresponded with the lowest estimate from the three tracers" (Clausing et al., 1987).
Geometric mean soil intake was estimated to be 90 mg/day on the assumption that the maximum
amount of soil ingested cannot be higher than the lowest estimate for the individual tracers.
Mean (arithmetic) soil intake for the hospitalized children was estimated to be 56
mg/day, based on aluminum (Table 5-3). For titanium, three of the children had estimates well
in excess of 1000 mg/day, with the remaining three children in the range of 28 to 58 mg/day.
Using the LTM method, the mean soil ingestion rate was estimated to be 49 mg/day, with a
population standard deviation of 22 mg/day (range 26 to 84). The geometric mean soil intake
rate was 45 mg/day. The data on the hospitalized children suggest a major nonsoil source of
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titanium for some children and may suggest a background nonsoil source of aluminum.
However, conditions specific to hospitalization (e.g., medications) were not considered. AIR
measurements were not reported for the hospitalized children. Assuming that the tracer-based
soil ingestion rates observed in the hospitalized children actually represent background tracer
intake from dietary and other nonsoil sources, mean soil ingestion by nursery school children
was estimated to be 56 mg/day, based on the LTM (i.e., 105 mg/day for nursery school children
minus 49 mg/day for hospitalized children).
The advantages of this study are that the investigators evaluated soil ingestion among two
populations of children that had differences in access to soil, and they corrected soil intake rates
based on background estimates derived from the hospitalized group. However, a smaller number
of children was used in this study than in the Binder et al. (1986) study, and these children may
not be representative of the U.S. population. Tracer elements in foods or medicines were not
evaluated. Also, intake rates derived from this study may not be representative of soil intake
over the long-term because of the short-term nature of the study. In addition, one of the factors
that could affect soil intake rates is hygiene (e.g., hand-washing frequency). Hygienic practices
can vary across countries and cultures and may be more stringently emphasized in a more
structured environment such as child care centers in The Netherlands and other European
countries than in child care centers in the United States.
5.2.3. Calabrese et al., 1989
Calabrese et al. (1989) studied soil ingestion among children using the basic tracer design
developed by Binder et al. (1986). However, in contrast to the Binder study, eight tracer
elements—aluminum, barium, manganese, silicon, titanium, vanadium, yttrium, and
zirconium—were analyzed instead of only three (aluminum, silicon, and titanium). A total of 64
children between the ages of 1 and 4 years were included in the study. These children were all
selected from the greater Amherst, MA area and were predominantly from two-parent
households where the parents were highly educated. The study was conducted over 8 days
during a 2-week period and included the use of a mass-balance methodology in which—in
addition to soil and dust samples collected from the child's home and play area—duplicate
samples of food, beverages, medicines, and vitamins were collected and analyzed on a daily
basis,. Fecal and urine samples were also collected and analyzed for tracer elements.
Toothpaste, which is low in tracer content, was provided to all participants.
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In order to validate the mass-balance methodology used to estimate soil ingestion rates
among the children and to determine which tracer elements provided the most reliable data on
soil ingestion, known amounts of soil (300 mg over 3 days and 1500 mg over 3 days) containing
eight tracers were administered to six adult volunteers (three males and three females). Soil
samples and feces samples from these adults and duplicate food samples were analyzed for tracer
elements to calculate recovery rates of tracer elements in soil. On the basis of the adult
validation study, the investigators confirmed that the tracer methodology could adequately detect
tracer elements in feces at levels expected to correspond with soil intake rates in children. They
also found that aluminum, silicon, and yttrium were the most reliable of the eight tracer elements
analyzed. The standard deviation of recovery of these three tracers was the lowest, and the
percentage of recovery was closest to 100%. The recovery of these three tracers ranged from
120 to 153% when 300 mg of soil had been ingested over a 3-day period and from 88 to 94%
when 1500 mg soil had been ingested over a 3-day period (Table 5-4).
Using the three most reliable tracer elements, the mean soil intake rate for the children,
adjusted to account for the amount of tracer found in food and medicines, was estimated to be
153 mg/day based on aluminum, 154 mg/day based on silicon, and 85 mg/day based on yttrium
(Table 5-5). Median intake rates were somewhat lower (29 mg/day for aluminum, 40 mg/day for
silicon, and 9 mg/day for yttrium). Upper-percentile (95th) values were 223 mg/day for
aluminum, 276 mg/day for silicon, and 106 mg/day for yttrium. Similar results were observed
when soil and dust ingestions were combined (Table 5-5). Intake of soil and dust was estimated
using a weighted ingestion for one child in the study and ranged from approximately 10 to
14 g/day during the second week of observation. Average soil ingestion for this child was 5 to 7
mg/day, based on the entire study period.
The advantages of this study are that intake rates were corrected for tracer concentrations
in foods and medicines and that the methodology was validated using adults. Also, intake was
observed over a longer time period in this study than in earlier studies, and the number of tracers
used was larger than for other studies. A relatively large population was studied, but it may not
be entirely representative of the U.S. population because it was selected from a single location.
5.2.4. Davis et al., 1990
Davis et al. (1990) also used a mass-balance/tracer technique to estimate soil ingestion
among children. In this study, 104 children between the ages of 2 and 7 years were randomly
selected from a three-city area in southeastern Washington State. The study was conducted over
a 7-day period, primarily during the summer. Daily soil ingestion was evaluated for aluminum,
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silicon, and titanium by collecting and analyzing soil and house dust samples, feces, urine, and
duplicate food samples. In addition, information on dietary habits and demographics was
collected in an attempt to identify behavioral and demographic characteristics that influence soil
intake rates among children. The amount of soil ingested on a daily basis was estimated using
the following equation:
sije = ir(pwf + DWp^) x 2Efi + SEJ - (M, x
where:
S; e = soil ingested for child "i" based on tracer "e" (g);
DWf = feces dry weight (g);
DW = feces dry weight on toilet paper (g);
Ef = tracer amount in feces (i-ig/g);
Eu = tracer amount in urine (i-ig/g);
DWfd = food dry weight (g);
Efd = tracer amount in food (i-ig/g); and
Esoil = tracer concentration in soil (i-ig/g).
The soil intake rates were corrected by adding the amount of tracer in vitamins and medications
to the amount of tracer in food and adjusting the food quantities, feces dry weights, and tracer
concentrations in urine to account for missing samples.
Soil ingestion rates were highly variable, especially those based on titanium. Mean daily
soil ingestion estimates were 38.9 mg/day for aluminum, 82.4 mg/day for silicon, and
245.5 mg/day for titanium (Table 5-6). Median values were 25 mg/day for aluminum, 59
mg/day for silicon, and 81 mg/day for titanium. The investigators also evaluated the extent to
which differences in tracer concentrations in house dust and yard soil impacted estimated soil
ingestion rates. The value used in the denominator of the mass balance equation was
recalculated to represent a weighted average of the tracer concentration in yard soil and house
dust based on the proportion of time the child spent indoors and outdoors. The adjusted mean
soil/dust intake rates were 64.5 mg/day for aluminum, 160.0 mg/day for silicon, and 268.4
mg/day for titanium. Adjusted median soil/dust intake rates were 51.8 mg/day for aluminum,
112.4 mg/day for silicon, and 116.6 mg/day for titanium. It was also observed that the following
demographic characteristics were associated with high soil intake rates: male sex, nonwhite
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racial group, low income, operator/laborer as the principal occupation of the parent, and city of
residence. However, none of these factors was predictive of soil intake rates when tested using
multiple linear regression.
The advantages of this study are that soil intake rates were corrected on the basis of the
tracer content of foods and medicines and that a relatively large number of children was
sampled. Also, demographic and behavioral information was collected for the survey group.
However, although the sample was relatively large sample, the children were all from a single
area of the U.S. and may not be representative of the U.S. population as a whole. The study was
conducted over a 1-week period during the summer and may not be representative of long-term
(i.e., annual) patterns of intake.
5.2.5. Van Wijnen et al., 1990
In a study by Van Wijnen et al. (1990), soil ingestion among Dutch children ranging in
age from 1 to 5 years was evaluated using a tracer element methodology similar to that used by
Clausing et al. (1987). Van Wijnen et al. (1990) measured three tracers (titanium, aluminum,
and AIR) in soil and feces and estimated soil ingestion based on the LTM. An average daily
feces weight of 15 g dry weight was assumed. A total of 292 children attending day care centers
were sampled during the first of two sampling periods and 187 children were sampled in the
second sampling period; 162 of these children were sampled during both periods (at the
beginning and near the end of the summer of 1986). In addition, total of 78 children vacationing
at campgrounds and 15 hospitalized children were sampled. The mean values for three groups
were 162 mg/day for children in day care centers, 213 mg/day for campers, and 93 mg/day for
hospitalized children.
The authors also reported geometric mean LTM values, because soil intake rates were
found to be skewed and the log transformed data were approximately normally distributed.
Geometric mean LTM values were estimated to be 111 mg/day for children in day care centers,
174 mg/day for children vacationing at campgrounds (Table 5-7), and 74 mg/day for hospitalized
children (70-120 mg/day, based on the 95% confidence limits of the mean). AIR was the
limiting tracer in about 80 percent of the samples. Among children attending day care centers,
soil intake was also found to be higher when the weather was good (< 2 days/wk precipitation)
than when the weather was bad (> 4 days/wk precipitation (Table 5-8).
The authors suggest that the mean LTM value for hospitalized infants represents
background intake of tracers and should be used to correct the soil intake rates based on LTM
values for other sampling groups. Using mean values, corrected soil intake rates were 69 mg/day
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(162 mg/day minus 93 mg/day) for day care children and 120 mg/day (213 mg/day minus 93
mg/day) for campers. Corrected geometric mean soil intake was estimated to range from 0 to 90
mg/day with a 90th percentile value of 190 mg/day for the various age categories within the
daycare group and 30 to 200 mg/day with a 90th percentile value of 300 mg/day for the various
age categories within the camping group.
The advantage of this study is that soil intake was estimated for three different
populations of children: one expected to have high intake, one expected to have "typical" intake,
and one expected to have low or background-level intake. Van Wijnen et al. (1990) considered
the tracer measurements for the hospitalized cihildred as background levels and used the average
of these values to correct soil intake rates for children in daycare centers and in campgrounds.
Among the limitations of this study is that tracer concentrations in food and medicine were not
evaluated. Also, the population of children studied was relatively large, but it may not be
representative of the U.S. population. The study was conducted over a relatively short time
period; thus, estimated intake rates may not reflect long-term patterns, especially at the high end
of the distribution. An additional limitation is that values were not reported element-by-element,
which would be the preferred way of reporting. Furthermore, one of the factors that could affect
soil intake rates is hygiene (e.g., hand-washing frequency). Hygienic practices can vary across
countries and cultures and may be more stringently emphasized in a more structured
environment such as child care centers in The Netherlands and other European countries than in
child care centers in the United States.
5.2.6. Stanek and Calabrese, 1995a
Stanek and Calabrese (1995a) presented a methodology that links the physical passage of
food and fecal samples to construct daily soil ingestion estimates from daily food and fecal trace-
element concentrations. The investigators reanalyzed soil ingestion data for children obtained
from the Amherst study (Calabrese et al., 1989). In the Amherst study, soil ingestion
measurements were made over a period of 2 weeks for a nonrandom sample of 64 children (ages
1-4 years) living adjacent to an academic area in western Massachusetts. During each week,
duplicate food samples were collected for 3 consecutive days and fecal samples were collected
for 4 consecutive days for each subject. The total amount of each of eight trace elements present
in the food and fecal samples were measured. The eight trace elements were aluminum, barium,
manganese, silicon, titanium, vanadium, yttrium, and zirconium.
-------
Stanek and Calabrese (1995a) expressed the amount of trace element in food input or
fecal output as a "soil equivalent," which was defined as the amount of the element in average
daily food intake (or average daily fecal output) divided by the concentration of the element in
soil. A lag period of 28 hours between food intake and fecal output was assumed for all
respondents. Day 1 for the food sample corresponded to the 24-hour period from midnight on
Sunday to midnight on Monday of a study week; day 1 of the fecal sample corresponded to the
24-hour period from noon on Monday to noon on Tuesday. On the basis of these definitions, the
food soil equivalent was subtracted from the fecal soil equivalent to obtain an estimate of soil
ingestion for a trace element. A daily "overall" ingestion estimate was constructed for each child
as the median of trace element values remaining after tracers falling outside of a defined range
around the overall median were excluded. Additionally, estimates of the distribution of soil
ingestion projected over a period of 365 days were derived by fitting lognormal distributions to
the "overall" daily soil ingestion estimates.
Table 5-9 presents the estimates of mean daily soil ingestion intake per child (mg/day)
for the 64 study participants. (The authors also presented estimates of the median values of daily
intake for each child. For most risk assessment purposes the child mean values, which are
proportional to the cumulative soil intake by the child, are needed instead of the median values.)
The approach adopted in this paper led to changes in ingestion estimates from those presented in
Calabrese et al. (1989).
Specifically, among elements that may be more useful for estimating ingestion, the mean
estimates decreased for aluminum (153 mg/day to 122 mg/day) and silicon (154 mg/day to 139
mg/day) but increased for titanium (218 mg/day to 271 mg/day) and yttrium (85 mg/day to 165
mg/day). The "overall" mean estimate from this reanalysis was 179 mg/d. Table 5-9 presents
the empirical distribution of the the "overall" mean daily soil ingestion estimates for the 8-day
study period (not based on lognormal modeling). The estimated intake, based on the "overall"
estimates, is 45 mg/day or less for 50% of the children and 208 mg/day or less for 95% of the
children. The upper percentile values for most of the individual trace elements are somewhat
higher. Next, estimates of the respondents soil intake averaged over a period of 365 days were
presented, based on the lognormal models fit to the daily ingestion estimates (Table 5-10). The
estimated median value of the 64 respondents' daily soil ingestion averaged over a year is 75
mg/day, wheras the 95th percentile is 1751 mg/day.
A strength of this study is that it attempts to make full use of the collected data through
estimation of daily ingestion rates for children. The data are then screened to remove less-
consistent tracer estimates and the remaining values are aggregated. Individual daily estimates
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of ingestion will be subject to larger errors than are weekly average values, particularly since the
assumption of a constant lag time between food intake and fecal output may be not be correct for
many subject days. The aggregation approach used to arrive at the "overall" ingestion estimates
rests on the assumption that the mean ingestion estimates across acceptable tracers provides the
most reliable ingestion estimates. The validity of this assumption depends on the particular set
of tracers used in the study and is not fully assessed.
In developing the 365-day soil ingestion estimates, data that were obtained over a short
period of time (as is the case with all available soil ingestion studies) were extrapolated over a
year. The 2-week study period may not reflect variability in tracer element ingestion over a year.
Although the authors attempted to address this through lognormal modeling of the long-term
intake, new uncertainties were introduced through the parametric modeling of the limited subject
day data. Also, the sample population size of the original study was small and site-limited, and,
therefore, is not representative of the U.S. population. Study mean estimates of soil ingestion,
such as the study mean estimates presented in Table 5-9, are substantially more reliable than any
available distributional estimates.
5.2.7. Stanek and Calabrese, 1995b
Stanek and Calabrese (1995b) recalculated ingestion rates that were estimated in three
previous mass-balance studies (Calabrese et al., 1989, and Davis et al., 1990, for children's soil
ingestion and Calabrese et al., 1990, for adult soil ingestion) using the Best Tracer Method
(BTM). This method allows for the selection of the most recoverable tracer for a particular
subject or group of subjects. The selection process involves ordering trace elements for each
subject, based on food/soil (F/S) ratios. These ratios are estimated by dividing the total amount
of the tracer in food by the tracer concentration in soil. The F/S ratio is small when the tracer
concentration in food is considerably smaller compared as with the tracer concentration in soil.
A small F/S ratio is desirable, because it lessens the impact of transit time error (the error that
occurs when fecal output does not reflect food ingestion due to fluctuation in gastrointestinal
transit time) in the soil ingestion calculation. Because the recoverability of tracers can vary
within any group of individuals, the BTM uses a ranking scheme of F/S ratios to determine the
best tracers for use in the ingestion rate calculation. To reduce biases that may occur as a result
of sources of fecal tracers other than food or soil, the median of soil ingestion estimates based on
the four lowest F/S ratios was used to represent soil ingestion among individuals.
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For children, the investigators used data on eight tracers from Calabrese et al. (1989) and
data on three tracers from Davis et al. (1990) to estimate soil ingestion rates. The median of the
soil ingestion estimates from the lowest four F/S ratios from the Calabrese et al. (1989) study
most often included aluminum, silicon, titanium, yttrium, and zirconium. The mean soil
ingestion rate was 132 mg/day and the median was 33 mg/day based on the median of soil
ingestion estimates from the best four tracers. The 95th percentile value was 154 mg/day. These
estimates are based on data for 128 subject-weeks for the 64 children in the Calabrese et al.
(1989) study. For the 101 children in the Davis et al. (1990) study, the mean soil ingestion rate
was 69 mg/day and the median soil ingestion rate was 44 mg/day. The 95th percentile estimate
was 246 mg/day. These data are based on the three tracers (aluminum, silicon, and titanium)
from the Davis et al. (1990) study. When the Calabrese et al. (1989) and Davis et al. (1990)
studies were combined, soil ingestion was estimated to be 113 mg/day (mean), 37 mg/day
(median), and 217 mg/day (95th percentile), using the BTM.
This study provides a reevaluation of previous studies. Its advantages are that it
combines data from two studies for children, one from California and one from Massachusetts,
which increases the number of observations. It also corrects for biases associated with the
differences in tracer metabolism. The limitations associated with the data used in this study are
the same as those described in the summaries of the Calabrese et al. (1989), Davis et al. (1990),
and Calabrese et al. (1990) studies.
5.2.8. Thompson and Burmaster, 1991
Thompson and Burmaster (1991) developed parameterized distributions of soil ingestion
rates for children on the basis of a reanalysis of the key study data collected by Binder et al.
(1986). In the Binder et al. study, an assumed fecal weight of 15 g/day was used. Thompson
and Burmaster reestimated the soil ingestion rates from the Binder et al. study using the actual
rather than the assumed stool weights of the study participants. Because the actual stool weights
averaged only 7.5 g/day, the soil ingestion estimates presented by Thompson and Burmaster
(1991) are approximately one-half of those reported by Binder et al. (1986).
Table 5-11 presents the distribution of estimated soil ingestion rates calculated by
Thompson and Burmaster (1991) based on the three tracers elements (aluminum, silicon, and
titanium), and on the arithmetic average of soil ingestion based on aluminum and silicon. The
mean soil intake rates were 97 mg/day for aluminum, 85 mg/day for silicon, and 1004 mg/day
for titanium. The 90th percentile estimates were 197 mg/day for aluminum, 166 mg/day for
silicon, and 2105 mg/day for titanium. Based on the arithmetic average of aluminum and silicon
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for each child, mean soil intake was estimated to be 91 mg/day, and 90th percentile intake was
estimated to be 143 mg/day.
The investigators tested the hypothesis that soil ingestion rates based on the adjusted
Binder et al. (1986) data for aluminum, silicon, and the average of these two tracers were
lognormally distributed. The distribution of soil intake based on titanium was not tested for
lognormality, because titanium may be present in food in high concentrations and the Binder et
al. study did not correct for food sources of titanium (Thompson and Burmaster, 1991). Although
visual inspection of the distributions for aluminum, silicon, and the average of these tracers all
indicated that they may be lognormally distributed, statistical tests indicated that only silicon and
the average of the silicon and aluminum tracers were lognormally distributed. Soil intake rates
based on aluminum were not lognormally distributed. Table 5-11 also presents the lognormal
distribution parameters and underlying normal distribution parameters (i.e., the natural
logarithms of the data) for aluminum, silicon, and the average of these two tracers. According to
the authors, "the parameters estimated from the underlying normal distribution are much more
reliable and robust" (Thompson and Burmaster, 1991).
The advantages of this study are that it provides percentile data and defines the shape of
soil intake distributions. However, the number of data points used to fit the distribution was
limited. In addition, the study did not generate "new" data. Instead, it provided a reanalysis of
previously reported data using actual fecal weights. No corrections were made for tracer intake
from food or medicine, and the results may not be representative of long-term intake rates
because the data were derived from a short-term study.
5.2.9. Sedman and Mahmood, 1994
Sedman and Mahmood (1994) used the results of two of the key children's tracer studies
(Calabrese et al. 1989; Davis et al. 1990) to determine estimates of average daily and lifetime
soil ingestion in young children. In the two key studies, the intake and excretion of a variety of
tracers were monitored, and concentrations of tracers in soil adjacent to the children's dwellings
were determined (Sedman and Mahmood, 1994). From a mass balance approach, estimates of
soil ingestion in these children were determined by dividing the excess tracer intake (the quantity
of tracer recovered in the feces in excess of the measured intake) by the average concentration of
tracer in soil samples from each child's dwelling. Sedman and Mahmood (1994) adjusted the
mean estimates of soil ingestion in children for each tracer (Y) from both studies to reflect that
of a 2-year-old child using the following equation:
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Yi = xe(-O.H2*yr) (5.3)
where:
Y; = adjusted mean soil ingestion (mg/day)
x = a constant
yr = average age (2 years)
The average ages of the children in the two key studies were 2.4 years in Calabrese et al.
(1989) and 4.7 years in Davis et al. (1990). The mean of the adjusted levels of soil ingestion for
a 2-year-old child was 220 mg/kg for the Calabrese et al. (1989) study and 170 mg/kg for the
Davis et al. (1990) study (Sedman and Mahmood, 1994). From the adjusted soil ingestion
estimates, based on a normal distribution of means, the mean estimate for a 2-year-old child was
195 mg/day, and the overall mean of soil ingestion and the standard error of the mean was
53 mg/day (Sedman and Mahmood, 1994). Based on uncertainties associated with the method
employed, the authors recommended a conservative estimate of soil ingestion in young children
of 250 mg/day. Based on the 250 mg/day ingestion rate in a 2-year-old child, an average daily
soil ingestion over a lifetime was estimated to be 70 mg/day. The lifetime estimates were
derived using the equation presented above that describes changes in soil ingestion with age.
5.2.10. Calabrese and Stanek, 1995
Calabrese and Stanek (1995) explored the sources and magnitude of positive and
negative errors in soil ingestion estimates for children on a subject-week and trace element basis.
They identified possible sources of positive errors to be
• ingestion of high levels of tracers before the study starts and low ingestion during
study period, resulting in over estimation of soil ingestion; and
• ingestion of element tracers from a nonfood or nonsoil source during the study
period.
Possible sources of negative bias were identified as
• tracers in ingested food not being captured in the fecal sample due either to slow lag
time or to not having a fecal sample available on the final study day, and
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• sample measurement errors that result in diminished detection of fecal tracers but
not in soil tracer levels.
The authors developed an approach that attempted to reduce the magnitude of error in the
individual trace element ingestion estimates. Results from a previous study conducted by
Calabrese et al. (1989) were used to quantify these errors, based on the following criteria: (1) a
lag period of 28 hours was assumed for the passage of tracers ingested in food to the feces (this
value was applied to all subject-day estimates), (2) daily soil ingestion rate was estimated for
each tracer for each 24-hour day a fecal sample was obtained, (3) the median tracer-based soil
ingestion rate for each subject-day was determined, and (4) negative errors due to missing fecal
samples at the end of the study period were also determined. Also, upper- and lower-bound
estimates were determined, based on criteria formed using an assumption of the magnitude of the
relative standard deviation presented in Stanek and Calabrese (1995a). Daily soil ingestion rates
for tracers that fell beyond the upper and lower ranges were excluded from subsequent
calculations, and the median soil ingestion rates of the remaining tracer elements were
considered the best estimate for that particular day. The magnitude of positive or negative error
for a specific tracer per day was derived by determining the difference between the value for the
tracer and the median value.
Table 5-12 presents the estimated magnitude of positive and negative error for six tracer
elements in the children's study (conducted by Calabrese et al., 1989). The original mean soil
ingestion rates ranged from a low of 21 mg/day based on zirconium to a high of 459 mg/day
based on titanium (Table 5-12). The adjusted mean soil ingestion rate after correcting for
negative and positive errors ranged from 97 mg/day based on yttrium to 208 mg/day based on
titanium (Table 5-12). Calabrese and Stanek (1995) concluded that correcting for errors at the
individual level for each tracer element provides more reliable estimates of soil ingestion.
This study is valuable for providing additional understanding of the nature of potential
errors in trace element-specific estimates of soil ingestion. However, the operational definition
used for estimating the error in a trace element estimate was the observed difference of that
tracer from a median tracer value. Specific identification of sources of error or direct evidence
that individual tracers were indeed in error was not developed. Corrections to individual tracer
means were then made according to how much values for that tracer differed from the median
values. This approach is based on the hypothesis that the median tracer value is the most
accurate estimate of soil ingestion, and the validity of this assumption depends on the specific set
of tracers used in the study and need not be correct. The approach used for the estimation of
5-14
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daily tracer intake is the same as in Stanek and Calabrese (1995a), and some limitations of that
approach are mentioned in the review of that study.
5.2.11. Calabrese et al., 1997
Calabrese et al. (1997) estimated soil ingestion rates for children residing on a Superfund
site using a mass-balance methodology in which eight tracer elements (aluminum, barium,
manganese, silicon, titanium, vanadium, yttrium, and zirconium) were analyzed. The
methodology used in this study is very similar to the one used by in Calabrese et al. (1989). As
in the Calabrese et al. (1989) study, 64 children ages 1-4 years were selected for this study and
were predominantly from two-parent households. This stratified simple random sample of
children was selected from the Anoconda, MT area. Thirty-six of the 64 children were male, and
the children ranged in age from 1 to 4 years, with approximately an equal number of children in
each age group. The study was conducted for 7 consecutive days during a 2-week period in
September. Duplicate samples of meals, beverages, and over- the-counter medicines and
vitamins were collected over the 7-day period, along with fecal samples. In addition, soil and
dust samples were collected from the children's homes and play areas. Toothpaste containing
nondetectable levels of the tracer elements, with the exception of silica, was provided to all of
the children. Infants were provided with baby cornstarch, diaper rash cream, and soap, which
were found to contain low levels of tracer elements.
As in Calabrese et al. (1989), an additional study was conducted in which the identical
mass-balance methodology used to estimate soil ingestion rates among children was used on
adults in order to validate that soil ingestion could be detected. Known amounts of soil were
administered to 10 adults (5 males, 5 females) from western Massachusetts over a period of 28
days. Each adult ingested for 7 consecutive days (1) no soil during week 1, (2) 20 mg of
sterilized soil during week 2, (3) 100 mg of sterilized soil during week 3, and (4) 500 mg of
sterilized soil during week 4. Soil samples were previously characterized and were of sufficient
concentration to be detected in the analysis of fecal samples. Duplicate food and fecal samples
were collected every day during each study week and analyzed for eight tracer elements
(aluminum, silicon, titanium, cerium, lanthanum, neodymium, yttrium, and zirconium). It was
found that ingestion of soil from 20 to 500 mg/day could be detected in a reliable manner.
The investigators estimated soil ingestion by each tracer element using BTM, which
allows for the selection of the most recoverable tracer for a particular group of subjects (Stanek
and Calabrese, 1995b). In this case, barium, manganese, and vanadium were dropped, as they
were found to be poor performing tracers. The median soil ingestion estimates for the four best
5-15
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trace elements, based on F/S ratios for the 64 children using aluminum, silicon, titanium, yttrium,
and zirconium are presented (Table 5-13). The best estimate was calculated by taking the
median of the best four trace elements. Based on the soil ingestion estimate for the best tracer,
the mean soil ingestion rate was 66 mg/day, and the median was 20 mg/day. The 95th percentile
value was 282 mg/day. Using the median of the four best tracers, the mean was 7 mg/day and
the 95th percentile was 160 mg/day. These results are lower than the soil ingestion estimates
obtained by Stanek and Calabrese (1995a). Calabrese et al. (1997) believe that this may be due
to the fact that the families of the children who participated in this study were aware that they
lived on an EPA Superfund site and thus may have limited their children's exposure to soil.
There was no statistically significant difference found in soil ingestion estimates by gender or
age. There was also no significant difference in soil ingestion by housing or yard characteristics
(i.e., porch, deck, door mat, etc.) or between children with or without pets.
The median dust ingestion estimates for the four best tracer elements using aluminum,
silicon, titanium, yttrium, and zirconium are also presented (Table 5-14). The estimate is based
on food/dust ratios for the 64 Anaconda children. The mean dust ingestion rate, based on the
best tracer, was 127 mg/day, and the 95th percentile rate was 614 mg/day.
The advantages of this study were the use of a consecutive 7-day study period rather than
two periods of 3 and 4 days as in Stanek and Calabrese (1995a), the use of the BTM, the use of
an expanded adult validation study that used 10 volunteers rather than 6 as in Calabrese et al.
(1989), and the use of a dietary education program to reduce food tracer input and variability.
However, the data presented in this study are from a single 7-day period during September,
which may not reflect soil ingestion rates for other months or time periods. In addition, the
study displayed a net residual negative error, which may have resulted in underestimated soil
ingestion rates. The authors believe that this error did not likely affect the median by more than
40 mg/day.
5.3. SOIL PICA
5.3.1. Prevalence
The scientific literature define pica as "the repeated eating of non-nutritive substances"
(Feldman, 1986). For the purposes of this handbook, pica is defined as an deliberately high soil
ingestion rate. Numerous published articles have reported on the incidence of pica among
various populations. However, most of these articles describe pica for substances other than soil,
including sand, clay, paint, plaster, hair, string, cloth, glass, matches, paper, feces, and various
other items. These articles indicate that the pica occurs in approximately half of all children
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between the ages of 1 and 3 years (Sayetta, 1986). The incidence of deliberate ingestion
behavior in children has been shown to differ among different subpopulations. The incidence
rate appears to be higher for black children than for white children. Approximately 30% of
black children aged 1 to 6 years are reported to have deliberate ingestion behavior, compared
with 10 to 18% of white children in the same age group (Danford, 1982). There does not appear
to be any differences in the incidence rates for males or females (Kaplan and Sadock, 1985).
Lourie et al. (1963) states that the incidence of pica is higher among children in lower
socioeconomic groups (50 to 60%) than in higher income families (about 30%). Deliberate soil
ingestion behavior appears to be more common in rural areas (Vermeer and Frate, 1979).
A higher rate of pica has also been reported for pregnant women and individuals with poor
nutritional status (Danford, 1982). In general, deliberate ingestion behavior is more frequent and
more severe in mentally retarded children than in children in the general population (Behrman
and Vaughan, 1983; Danford, 1982; Forfar and Arneil, 1984; Illingworth, 1983; Sayetta, 1986).
It should be noted that the pica statistics cited above apply to the incidence of general
pica and not soil pica. Information on the incidence of soil pica is limited, but it appears that soil
pica is less common. A study by Vermeer and Frate (1979) showed that the incidence of
geophagia, earth eating, was about 16% among children from a rural black community in
Mississippi. However, geophagia was described as a cultural practice in the community
surveyed and may not be representative of the general population. Average daily consumption
of soil was estimated to be 50 g/day. Bruhn and Pangborn (1971) reported the incidence of pica
for "dirt" among 91 nonblack, low-income families of migrant agricultural workers in California
to be 19% in children, 14% in pregnant women, and 3% in nonpregnant women. However,
"dirt" was not clearly defined. On the basis of data from the five key tracer studies (Binder et
al., 1986; Clausing et al., 1987; Van Wijnen et al., 1990; Davis et al., 1990; and Calabrese et al.,
1989), only one child out of the more than 600 children involved in all of these studies ingested
an amount of soil significantly greater than the range for other children. Although these studies
did not include data for all populations and were representative of short-term ingestions only, it
can be assumed that the incidence rate of deliberate soil ingestion behavior in the general
population is low. However, it is incumbent upon users to select the appropriate value for their
specific study population.
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5.3.2. Pica Among Children
Information on the amount of soil ingested by children with abnormal soil ingestion
behavior is limited. However, some evidence suggests that a rate on the order of 10 g/day may
not be unreasonable.
5.3.2.1. Calabreseetal.,1991
Calabrese et al. (1991) estimated that upper range soil ingestion values may range from
approximately 5 to 7 g/day. This estimate was based on observations of one pica child among
the 64 children who participated in the study. In the study, a 3.5-year-old female exhibited
extremely high soil ingestion behavior during 1 of the 2 weeks of observation. Intake ranged
from 74 mg/day to 2.2 g/day during the first week of observation and from 10.1 to 13.6g/day
during the second week of observation (Table 5-15). These results are based on mass-balance
analyses for seven tracer elements (aluminum, barium, manganese, silicon, titanium, vanadium,
and yttrium) of the eight used. Intake rates based on zirconium werr significantly lower, but the
authors indicated that this may have "resulted from a limitation in the analytical protocol."
5.3.2.2. Calabrese and Stanek, 1992
Using a methodology that compared differential element ratios, Calabrese and Stanek
(1992) quantitatively distinguished outdoor soil ingestion from indoor dust ingestion in a soil
pica child. This study was based on a previous mass-balance study (Calabrese et al., 1991) in
which a 3.5-year-old child ingested 10-13 g/day of soil over the second week of a 2-week soil
ingestion study. Also, the previous study used a soil tracer methodology with eight tracers
(aluminum, barium, manganese, silicon, titanium, vanadium, yttrium, and zirconium). The
reader is referred to Calabrese et al. (1989) for a detailed description and results of the soil
ingestion study.
Table 5-16 presents tracer ratios of soil, dust, and residual fecal samples in the soil pica
child. The authors reported that there was a maximum total of 28 pairs of tracer ratios based on
eight tracers. However, only 19 pairs of tracer ratios were available for quantitative evaluation,
as shown in Table 5-16. Of these 19 pairs, nine fecal tracer ratios fell within the boundaries for
soil and dust (Table 5-16). For these nine tracer soils, an interpolation was performed to
estimate the relative contribution of soil and dust to the residual fecal tracer ratio. The other 10
fecal tracer ratios that fell outside the soil and dust boundaries were concluded to be 100% of the
fecal tracer ratios from soil origin. Also, the nine residual fecal samples within the boundaries
revealed that a high percentage (71-99%) of the residual fecal tracers were estimated to be of
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soil origin. Therefore, the authors concluded that the predominant proportion of the fecal tracers
was from outdoor soil and not from indoor dust origin.
In conducting a risk assessment for 2,3,7,8-tetrachlorodibenzo-/?-dioxin (TCDD), U.S.
EPA (1984) used 5 g/day to represent the soil intake rate for pica children. The Centers for
Disease Control (CDC) also investigated the potential for exposure to TCDD through the soil
ingestion route. CDC used a value of 10 g/day to represent the amount of soil that a child with
deliberate soil ingestion behavior might ingest (Kimbrough et al., 1984). These values are
consistent with those observed by Calabrese et al. (1991).
5.3.2.3. Calabrese and Stanek, 1993
Calabrese and Stanek (1993) reviewed a study by Wong (1988) that attempted to estimate
the amount of soil ingested by two groups of children. Wong studied a total of 52 children in
two government institutions in Jamaica. The younger group (from the Glenhope Place of Safety)
contained 24 children with an average age of 3.1 years (ranging from 0.3 to 7.6 years). The
older group (from the Reddies Place of Safety) contained 28 children with an average age of 7.2
years (ranging from 1.8 to 14 years). Fecal samples were obtained from the subject children, and
the amount of silicon in dry feces was measured in order to quantify soil ingestion.
An unspecified number of daily fecal samples were collected from a hospital control
group of 30 children with an average age of 4.8 years (ranging from 0.3 to 12 years). Dry feces
were observed to contain 1.45% silicon, or 14.5 mg of silicon per 1 g of dry feces. This amount
of silicon in dry feces was assumed to be representative of the typical background amount of
silicon from dietary sources only. Observed quantities of silicon greater than 1.45% were then
assumed to be from soil ingestion.
The amount of soil ingested was calculated by using the standard soil ingestion
estimation formula (Binder et al. 1986). One fecal sample was collected from each subject per
month over the 4-month study period.
For the 28 children in the older group, soil ingestion was estimated to be 58 mg/day,
based on the mean minus one outlier, and 1520 mg/day, based on the mean of all the children.
The outlier was a child with an estimated average soil ingestion rate of 41 g/day over the 4
months. Of the 28 children in the group, 7 had an average soil ingestion of > 100 mg/day,
4 of > 200 mg/day, and 1 of > 300 mg/day; 8 showed no indication of soil ingestion for any
month.
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Estimated average soil ingestion in the younger group of children was higher. The mean
soil ingestion of all the children was 470 ± 370 mg/day. Due to some sample losses, of the 24
children studied, only 15 subjects had samples for each of the 4 months. Of the 24 children, 14
had an average soil ingestion of < 100 mg/day, 10 of > 100 mg/day, 5 of > 600 mg/day, and 4 of
> 1000 mg/day; 5 showed no indication of soil ingestion for any month.
Over the entire 4-month study period, 9 of 84 total samples (or 10.5%) showed soil
ingestion estimates of > 1 g/day (pica behavior). Of the 52 children studied, 6 displayed soil
pica behavior. The estimated soil ingestion for each of these subjects is shown in Table 5-17.
For the younger group of children, 5 of 24 (or 20.8%) displayed soil pica behavior on at least one
occasion. A high degree of daily variability in soil ingestion was observed among the six
children who exhibited pica behavior. As shown in Table 5-17, three of six children (#11, 12,
and 22) showed soil pica on only 1 of 4 days. The other three (#14, 18, and 27) ingested
> 1.0 g/day on 2 of 4, on 3 of 4, and on 4 of 4 days, respectively. Subject #27 displayed a high
degree of soil pica, ranging from 3.7 to 60.6 g/day of soil ingestion; however, it was indicated
that this child was mentally retarded, whereas the other pica children were considered to have
normal mental capabilities.
Sources of uncertainty or error in this study include differences between the hospital
study group (the background control) and the two study groups, lack of information on the
dietary intake of silicon for the studied children, use of a single fecal sample, and loss of fecal
samples. The use of a single soil tracer may also introduce error, because there may be other
sources from which the tracer could originate. For example, some toothpastes have extremely
high concentrations of silicon, and children could ingest significant quantities of toothpaste.
Additionally, tracers could be found in indoor dust that children may ingest. However, despite
these uncertainties, the results are important in that they indicate that soil pica is not a rare
occurrence in younger children.
5.4. RECOMMENDATIONS
The studies described in this section were used to recommend values for soil intake
among children. Estimates of the amount of soil ingested by children are summarized in Table
5-18, and the recommended values are presented in Table 5-19. The mean values range from 39
mg/day to 271 mg/day, with an average of 138 mg/day for soil ingestion and 193 mg/day for soil
and dust ingestion. Results obtained using titanium as a tracer in the Binder et al. (1986) and
Clausing et al. (1987) studies were not considered in the derivation of this recommendation,
because these studies did not take into consideration other sources of the element in the diet,
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which for titanium seems to be significant. Therefore, these values may overestimate the soil
intake. One can note that this group of mean values is consistent with the 200 mg/day value that
EPA programs have used as a conservative mean estimate.
Taking into consideration that the highest values were seen with titanium, which may
exhibit greater variability than the other tracers, and the fact that the Calabrese et al. (1989)
study included a pica child, 100 mg/day is the best estimate of the mean for children under 6
years of age. However, because the children were studied for short periods of time and the
prevalence of pica behavior is not known, excluding the pica child from the calculations may
underestimate soil intake rates. It is plausible that many children may exhibit some pica
behavior if studied for longer periods of time. Over the period of study, upper-percentile values
ranged from 106 mg/day to 1432 mg/day, with an average of 358 mg/day for soil ingestion and
790 mg/day for soil and dust ingestion. Rounding to one significant figure, the recommended
upper percentile soil ingestion rate for children is 400 mg/day. However, because the period of
study was short, these values are not estimates of usual intake.
Data on soil ingestion rates for children who deliberately ingest soil are also limited. An
ingestion rate of 10 g/day is a reasonable value for use in acute exposure assessments, based on
the available information. It should be noted, however, that this value is based on only one pica
child observed in the Calabrese et al. (1989) study, where the intake ranged from 10 to 14 g/day
during the second week of observation. In addition, a statistical designation is not assigned to
this value.
It should be noted that these recommendations are based on studies that used different
survey designs and populations. For example, some of the studies considered food and nonfood
sources of trace elements, whereas others did not. In other studies, soil ingestion estimates were
adjusted to account for the contribution of house dust to this estimate. Despite these differences,
the mean and upper-percentile estimates reported for these studies are relatively consistent. The
confidence rating for soil intake recommendations is presented in Table 5-20. It is important,
however, to understand the various uncertainties associated with these values.
First, individuals were not studied for sufficient periods of time to obtain a good estimate
of the usual intake. Therefore, the values presented in this section may not be representative of
long term exposures. Second, the experimental error in measuring soil ingestion values for
individual children is also a source of uncertainty. For example, incomplete sample collection
of both input (i.e., food and nonfood sources) and output (i.e., urine and feces) is a limitation for
some of the studies conducted. In addition, an individual's soil ingestion value may be
artificially high or low depending on the extent to which a mismatch between input and output
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occurs due to individual variation in the gastrointestinal transit time. Third, the degree to which
the tracer elements used in these studies are absorbed in the human body is uncertain. Accuracy
of the soil ingestion estimates depends on how good this assumption is. Fourth, there is
uncertainty with regard to the homogeneity of soil samples and the accuracy of parents'
knowledge about their child's playing areas. Fifth, all the soil ingestion studies presented in this
section, with the exception of Calabrese et al. (1989), were conducted during the summer, when
soil contact is more likely.
Although the recommendations presented below are derived from studies that were
mostly conducted in the summer, exposure during the winter months when the ground is frozen
or snow covered should not be considered as zero. Exposure during these months, although
lower than in the summer months, would not be zero because some portion of the house dust
comes from outdoor soil.
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Table 5-1. Estimated daily soil ingestion based on aluminum, silicon,
and titanium concentrations
Estimation
method
Aluminum
Silicon
Titanium
Minimum
Soil ingestion (mg/day)
Mean ± SD
181 ±203
184 ± 175
1834 ±3091
108 ± 121
Median
121
136
618
88
Range
25-1324
31-799
4-17,076
4-708
95th percentile
584
578
9590
386
Geometric mean
128
130
401
65
Source: Binder etal., 1986
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Table 5-2. Calculated soil ingestion by nursery school children
Child
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Arithmetic
Mean
N
L3
L14
L25
L5
L13
L27
L2
L17
L4
Lll
L8
L21
L12
L16
L18
L22
LI
L6
L7
L9
L10
L15
L19
L20
L23
L24
L26
Soil ingestion (mg/day)
Calculated
from Ti
103
154
130
131
184
142
124
670
246
2,990
293
313
1,110
176
11,620
11,320
3,060
624
600
133
354
2,400
124
269
1,130
64
184
1,431
Calculated
from Al
300
211
23
—
103
81
42
566
62
65
—
—
693
—
—
77
82
979
200
—
195
—
71
212
51
566
56
232
Calculated
from AIR
107
172
—
71
82
84
84
174
145
139
108
152
362
145
120
—
96
111
124
95
106
48
93
274
84
—
—
129
Limiting
Tracer
103
154
23
71
82
81
42
174
62
65
108
152
362
145
120
77
82
111
124
95
106
48
71
212
51
64
56
105
Ti =titanium
Al = aluminum
AIR = acid-insoluble residue
Source: Adapted from Clausing et al., 1987
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Table 5-3. Calculated soil ingestion by hospitalized, bedridden children
Child
1
2
3
4
5
6
Arithmetic Mean
Sample
G5
G6
Gl
G2
G8
G3
G4
G7
Soil ingestion (mg/day)
Calculated from Ti
3290
4790
28
6570
2480
28
1100
58
2293
Calculated from Al
57
71
26
94
57
77
30
38
56
Limiting
Tracer
(mg/day)
57
71
26
84
57
28
30
38
49
Ti = titanium
Al = aluminum
Source: Adapted from Clausing et al, 1987
Table 5-4. Mean and standard deviation percentage recovery of eight
tracer elements
Tracer Element
Aluminum
Barium
Manganese
Silicon
Titanium
Vanadium
Yttrium
Zirconium
Percentage recovered
300 mg soil ingested
Mean ± SD
152.8 ±107.5
2304.3 ±4533.0
1177.2 ± 1341.0
139.3 ± 149.6
251. 5 ±316.0
345.0 ±247.0
120.5 ±42.4
80.6 ±43.7
1500 mg soil ingested
Mean ± SD
93. 5 ± 15.5
149.8 ±69.5
248.3 ± 183.6
91.8 ± 16.6
286.3 ±380.0
147.6 ±66.8
87.5 ± 12.6
54.6 ±33.4
Source: Adapted from Calabrese et al., 1989
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Table 5-5. Soil and dust ingestion estimates for children ages 1-4 yearsa (mg/day)
Tracer Element
Aluminum
Soil
Dust
Soil/dust combined
Silicon
Soil
Dust
Soil/dust combined
Yttrium
Soil
Dust
Soil/dust combined
Titanium
Soil
Dust
Soil/dust combined
N
64
64
64
64
64
64
62
64
62
64
64
64
Intake (mg/day)a
Mean ± SD
153 ±852
3 17 ±1,272
154 ±629
154 ±693
964 ±6848
483 ±3 105
85 ±890
62 ±687
65 ±717
218±1150
163 ±659
170 ±691
Median
29
31
30
40
49
49
9
15
11
55
28
30
95th percentile
223
506
478
276
692
653
106
169
159
1432
1266
1059
Maximum
6837
8462
4929
5,549
54,870
24,900
6,736
5,096
5,269
6,707
3,354
3,597
' Corrected for Tracer Concentrations in Foods
Source: Adapted from Calabrese et al. (1989)
Table 5-6. Average daily soil ingestion values based on aluminum, silicon,
and titanium as tracer elements3
Element
Aluminum
Silicon
Titanium
Minimum
Maximum
Soil ingestion (mg/day)
Mean ± SE
38.9 ± 14.4
82.4 ± 12.2
245.5 ± 119.7
38.9 ± 12.2
245.5 ± 119.7
Median
25.3
59.4
81.3
25.3
81.3
Rangeb
279.0-904.5
-404.0-534.6
-5820.8-6182.2
-5820.8
6182.2
a Excludes three children who did not provide any samples (N=101).
b Negative values occurred as a result of correction for nonsoil sources of the tracer elements.
Source: Adapted from Davis etal., 1990
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Table 5-7. Geometric mean (GM) and standard deviation (GSD) LTM values
for children at daycare centers and campgrounds (mg/day)
Age
(years)
< 1
l-<2
2-<3
3-4
4-<5
All girls
All boys
Total
Sex
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Girls
Boys
Daycare Centers
N
3
1
20
17
34
17
26
29
1
4
86
72
162a
GMLTM
81
75
124
114
118
96
111
110
180
99
117
104
111
GSD LTM
1.09
1.87
1.47
1.74
1.53
1.57
1.32
1.62
1.70
1.46
1.60
Campgrounds
N
3
5
4
8
6
8
19
18
36
42
78b
GMLTM
207
312
367
232
164
148
164
136
179
169
174
GSD LTM
1.99
2.58
2.44
2.15
1.27
1.42
1.48
1.30
1.67
1.79
1.73
a Age and/or sex not registered for eight children.
b Age not registered for seven children.
Source: Adapted from Van Wijnen et al., 1990
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Table 5-8. Estimated geometric mean (GM) LTM values of children
attending daycare centers according to weather category, age, and
sampling period (mg/day)
Weather
Category
Bad
(> 4 days/wk
precipitation)
Reasonable
(2-3 days/wk
precipitation)
Good
(< 2 days/wk
precipitation)
Age
(years)
< 1
l-<2
2-<3
4-<5
< 1
l-<2
2-<3
3-<4
4-<5
< 1
l-<2
2-<3
3-<4
4-<5
First sampling period
n
3
18
33
5
4
42
65
67
10
Estimated GM
LTM Value
94
103
109
124
102
229
166
138
132
Second sampling period
n
O
33
48
6
1
10
13
19
1
Estimated GM
LTM Value
67
80
91
109
61
96
99
94
61
Source: Van Wijnen et al. (1990)
5-28
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Table 5-9. Per child distribution of average (mean) daily soil ingestion estimates by trace for 64 children3
Category
Mean
25th percentile
50th percentile
75th percentile
90th percentile
95th percentile
Maximum
Soil ingestion (mg/day)
Overall
(N = 64)
179
10
45
88
186
208
7,703
Al
(N = 64)
122
10
19
73
131
254
4,692
Ba
(N = 33)
655
28
65
260
470
518
17,991
Mn
(N = 19)
1,053
35
121
319
478
17,374
17,374
Si
(N = 63)
139
5
32
94
206
224
4,975
Ti
(N = 56)
271
8
31
93
154
279
12,055
V
(N = 52)
112
8
47
177
340
398
845
Y
(N = 61)
165
0
15
47
105
144
8,976
Zr
(N = 62)
23
0
15
41
87
117
208
to
VO
a For each child, estimates of soil ingestion were formed on days 4-8 and the mean of these estimates was then evaluated for each child.
The values in the "overall" column correspond to percentiles of the distribution of these means over the 64 children. When specific
trace elements were not excluded via the relative standard deviation criteria, estimates of soil ingestion based on the specific trace element
were formed for 108 days for each subject. The mean soil ingestion estimate was again evaluated. The distribution of these means for
specific trace elements is shown.
Al = aluminum
Ba = barium
Mn = manganese
Si = silicon
Ti = titanium
V = vanadium
Y = yttrium
Zr = zirconium
Source: Stanek and Calabrese, 1995a
-------
Table 5-10. Estimated distribution of individual mean daily soil ingestion based
on data for 64 subjects projected over 365 days"
Category
Range
50th percentile (median)
90th percentile
95th percentile
Soil ingestion (mg/day)
l-2268b
75
1190
1751
a Based on fitting a lognormal distribution to model daily soil ingestion values.
b Subject with pica excluded.
Source: Stanek and Calabrese, 1995a
5-30
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Table 5-11. Summary statistics and parameters for distributions of estimated
soil ingestion by tracer element"
Mean
Minimum
10th percentile
20th percentile
30th percentile
40th percentile
Median
60th percentile
70th percentile
80th percentile
90th percentile
Maximum
Soil ingestion (mg/day)
Aluminum
97
11
21
33
39
43
45
55
73
104
197
1,201
Silicon
85
10
19
23
36
52
60
65
79
106
166
642
Titanium
1,004
1
3
22
47
172
293
475
724
1,071
2,105
14,061
Meanb
91
13
22
34
43
49
59
69
92
100
143
921
Lognormal Distribution Parameters
Median
Standard deviation
Arithmetic mean
45
169
97
60
95
85
—
—
59
126
91
Underlying normal distribution parameters
Mean
Standard deviation
4.06
0.88
4.07
0.85
—
4.13
0.80
a Using Binder et al. (1986) data with actual fecal weights.
b Arithmetic average of soil ingestion based on aluminum and silicon.
Source: Thompson and Burmaster, 1991
5-31
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Table 5-12. Positive/negative error (bias) in soil ingestion estimates in the Calabrese
et al. (1989) mass-balance study—effect on mean soil ingestion estimate (mg/day)a
Tracer
element
Aluminum
Silicon
Titanium
Vanadium
Yttrium
Zirconium
Lack of
Fecal
Sample on
Final
Study Day
14
15
82
66
8
6
Other
Causesb
11
6
187
55
26
91
Total
Negative
Error
25
21
269
121
34
97
Total
Positive
Error
43
41
282
432
22
5
Net
Error
+18
+20
+13
+311
-12
-92
Original
Mean
153
154
218
459
85
21
Adjusted
Mean
136
133
208
148
97
113
a How to read table: for example, aluminum as a soil tracer displayed both negative and positive error. The
cumulative total negative error is estimated to bias the mean estimate by 25 mg/day downward. However,
aluminum has positive error biasing the original mean upward by 43 mg/day. The net bias in the original
mean was 18 mg/day positive bias. Thus, the original 156 mg/day mean for aluminum should be corrected
downward to 136 mg/day.
b Values indicate impact on mean of 128-subject-weeks in milligrams of soil ingested per day.
Source: Calabrese and Stanek, 1995
5-32
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Table 5-13. Soil ingestion estimates for the median of best four tracer elements based on food/soil ratios
for 64 Anaconda children using aluminum, silicon, titanium, yttrium, and zirconium
Category
Median of best four
Best tracer
Second best
Third best
Fourth best
Soil ingestion (mg/day)
Min
-101.3
-53.4
-115.9
-170.5
-298.3
P5
-91.0
-24.4
-62.1
-88.9
-171.0
P10
-53.8
-14.4
-48.6
-67.0
-131.9
SP25
-38.0
2.2
-26.6
-52.0
-74.7
P50
-2.4
20.1
1.5
-18.8
-29.3
SP75
26.8
68.9
38.4
25.6
0.2
P90
73.1
223.6
119.5
154.7
74.8
P95
159.8
282.4
262.3
376.1
116.8
Max
380.2
609.9
928.5
1293.5
139.1
Mean
6.8
65.5
33.2
31.2
-34.6
SD
74.5
120.3
144.8
199.6
79.7
Source: Calabrese et al., 1997
Table 5-14. Dust ingestion estimates for the median of best four trace elements based on food/dust
ratios for 64 Anaconda children using aluminum, silicon, titanium, yttrium, and zirconium
Category
Median of best four
Best tracer
Second best
Third best
Fourth best
Soil ingestion (mg/day)
Min
-261.5
-377.0
-239.8
-375.7
-542.7
P5
-186.2
-193.8
-147.2
-247.5
-365.6
P10
-152.7
-91.0
-137.1
-203.1
-277.7
SP25
-69.5
-20.8
-59.1
-81.7
-161.5
P50
-5.5
26.8
7.6
-14.4
-55.1
SP75
62.8
198.1
153.1
49.4
52.4
P90
209.2
558.6
356.4
406.5
277.3
P95
353.0
613.6
409.5
500.5
248.8
Max
683.9
1499.4
1685.1
913.2
6120.5
Mean
16.5
127.2
82.7
25.5
81.8
SD
160.9
299.1
283.6
235.9
840.3
Source: Calabrese et al., 1997
-------
Table 5-15. Daily soil ingestion estimation in a soil-pica child by tracer and by week
Tracer
element
Aluminum
Barium
Manganese
Silicon
Titanium
Vanadium
Yttrium
Zirconium
Estimated soil ingestion (mg/day)
Weekl
74
458
2,221
142
1,543
1,269
147
86
Week 2
13,600
12,088
12,341
10,955
11,870
10,071
13,325
2,695
Source: Calabrese etal., 1991
5-34
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Table 5-16. Ratios of soil, residual fecal, and dust samples in the soil pica child
Tracer Pairs
1 . Manganese/Titanium
2. Barium/Titanium
3. Silicon/Titanium
4. Vanadium/Titanium
5. Aluminum/Titanium
6. Yttrium/Titanium
7. Manganese/Yttrium
8 . B arium/ Yttrium
9. Silicon/Yttrium
1 0 . Vanadium/ Yttrium
1 1 . Aluminum/Yttrium
12. Manganese/ Aluminum
13. Barium/ Aluminum
14. Silicon/Aluminum
15. Vanadium/ Aluminum
16. Silicon/Vanadium
17. Manganese/Silicon
18. Barium/Silicon
19. Manganese/Barium
Ratio
Soil
208.368
187.448
148.117
14.603
18.410
8.577
24.293
21.854
17.268
1.702
2.146
11.318
10.182
8.045
0.793
10.143
1.407
1.266
1.112
Fecal
215.241
206.191
136.662
10.261
21.087
9.621
22.373
21.432
14.205
1.067
2.192
10.207
9.778
6.481
0.487
13.318
1.575
1.509
1.044
Dust
260.126
115.837
7.490
17.887
13.326
5.669
45.882
20.432
1.321
3.155
2.351
19.520
8.692
0.562
1.342
0.419
34.732
15.466
2.246
Estimated Residual
Fecal Tracers of Soil
Origin as Predicted
by Specific Tracer
Ratios (%)
87
100
92
100
100
100
100
71
81
100
88
100
73
81
100
100
99
83
100
Source: Calabrese and Stanek. 1992
5-35
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Table 5-17. Daily variation of soil ingestion by children displaying soil pica
in Wong (1988)
Child subject number
Month
Estimated soil ingestion
(mg/day)
Glenhope Place of Study
11
12
14
18
22
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
1
2
3
4
55
1,447
22
40
0
0
7,924
192
1,016
464
2,690
898
30
10,343
4,222
1,404
0
—
5,341
0
Reddles Place of Study
27
1
2
3
4
48,314
60,692
51,422
3,782
Source: Calabrese and Stanek, 1993
5-36
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Table 5-18. Summary of estimates of soil ingestion by children
Soil ingestion (mg/day)
Mean
Al
181
230
39
64.5a
153
154a
122
133C
69-120c
66b
196a
Si
184
82
160a
154
483a
139
AIR
129
Ti
245.5
268.4a
218.0
170.0b
271.0
Y
85
65a
165
Average: 138 (soil)
193 (soil and dust combined)
Upper percentile
Al
584
223
478a
254
217b
280b
994a
Si
578
276
653a
224
Ti
1,432
l,059a
279
Y
106
159a
144
References
Binder et al., 1986
Clausing etal., 1987
Davis etal., 1990
Calabrese et al., 1989
Stanek and Calabrese, 1995a
Stanek and Calabrese, 1995b
Van Wijnen et al., 1990
Calabrese et al., 1997
358 (soil)
790 (soil and dust combined)
a Soil and dust combined
b Best tracer method
0 Limited tracer method; corrected value
Al = aluminum
Si = silicon
AIR = acid-insoluble residue
Ti = titanium
Y = yttrium
Table 5-19. Summary of recommended values for soil ingestion
Population
Children (age 1-6 years)
Pica child
Soil ingestion
Mean
100 mg/day a
lOg/day
Upper Percentile
400 mg/day
a 200 mg/day may be used as a conservative estimate of the mean (see text).
b Study period was short; therefore, these values are not estimates of usual intake.
0 To be used in acute exposure assessments. Based on only one pica child (Calabrese et al., 1989).
5-37
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Table 5-20. Confidence in soil intake recommendation
Considerations
Rationale
Rating
Study Elements
Level of peer
review
Accessibility
Reproducibility
Focus on factor of
interest
Data pertinent to
U.S.
Primary data
Currency
Adequacy of data
collection period
Validity of
approach
Study size
Representativeness
of the population
Characterization of
variability
Lack of bias in
study design (high
rating is desirable)
Measurement error
All key studies are from peer-reviewed literature.
Papers are widely available from peer-review journals.
Methodology used was presented, but results are difficult to reproduce.
The focus of the studies was on estimating soil intake rate by children;
studies did not focus on intake rate by adults.
Two of the key studies focused on Dutch children; other studies used
children from specific areas of the U.S.
All the studies were based on primary data.
Studies were conducted after 1980.
Children were not studied long enough to fully characterize day-to-day
variability.
The basic approach is the only practical way to study soil intake, but
refinements are needed in tracer selection and matching input with
outputs. The more recent studies corrected the data for sources of the
tracers in food. There are, however, some concerns about absorption of
the tracers into the body and lag time between input and output.
The sample sizes used in the key studies were adequate for children.
However, only a few adults have been studied.
The study population may not be representative of the U.S. in terms of
race, socioeconomics, and geographical location; studies focused on
specific areas; two of the studies used Dutch children.
Day-to-day variability was not very well characterized.
The selection of the population studied may introduce some bias in the
results (i.e., children near a smelter site, volunteers in nursery school,
Dutch children).
Errors may result due to problems with absorption of the tracers in the
body and mismatching inputs and outputs.
High
High
Medium
High (for children)
Low (for adults)
Medium
High
High
Medium
Medium
Medium (for children)
Low (for adults)
Low
Low
Medium
Medium
Other Elements
Number of studies
Agreement
between researchers
Overall Rating
There are seven key studies.
Despite the variability, there is general agreement among researchers on
central estimates of daily intake for children.
Studies were well designed; results were fairly consistent; sample size
was adequate for children and very small for adults; accuracy of
methodology is uncertain; variability cannot be characterized due to
limitations in data collection period. Insufficient data to recommend
upper-percentile estimates for both children and adults.
High
Medium
Medium (for children;
long-term central
estimate)
Low (for adults)
Low (for upper
percentile)
5-38
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REFERENCES FOR CHAPTER 5
Binder, S; Sokal, D; Maughan, D. (1986) Estimating soil ingestion: the use of tracer elements in estimating the
amount of soil ingested by young children. Arch Environ Health 41(6):341-345.
Behrman, LE; Vaughan, VC, III. (1983) Textbook of pediatrics. Philadelphia, PA: W.B. Saunders.
Bruhn, CM; Pangborn, RM. (1971) Reported incidence of pica among migrant families. J Am Diet Assoc
58:417-420.
Calabrese, EJ; Stanek, EJ. (1992) Distinguishing outdoor soil ingestion from indoor dust ingestion in a soil pica
child. Regul Toxicol Pharmacol 15:83-85.
Calabrese, EJ; Stanek, EJ. (1993) Soil pica: not a rare event. J Environ Sci Health. A28(2):373-384.
Calabrese, EJ; Stanek, EJ. (1995) Resolving intertracer inconsistencies in soil ingestion estimation. Environ Health
Perspect 103(5):454^56.
Calabrese, EJ; Pastides, H; Barnes, R; et al. (1989) How much soil do young children ingest: an epidemiologic
study. In: Petroleum Contaminated Soils. Chelsea, MI: Lewis Publishers, pp. 363-397.
Calabrese, EJ; Stanek, EJ, III; Gilbert, C; et al. (1990) Preliminary adult soil ingestion estimates: results of a pilot
study. Reg ToxPharm 12:88-95.
Calabrese, EJ; Stanek, EJ; Gilbert, CE. (1991) Evidence of soil-pica behavior and quantification of soil ingested.
Hum Exp Toxicol 10:245-249.
Calabrese, EJ; Stanek, EJ; Pekow, P; et al. (1997) Soil ingestion estimates for children residing on a Superfund site.
Ecotoxicol Environ Saf. 36:258-268.
Clausing, P; Brunekreef, B; Van Wijnen, JH. (1987) A method for estimating soil ingestion by children. Int Arch
Occup Environ Health (W. Germany) 59(l):73-82.
Danford, DC. (1982) Pica and nutrition. Annu Rev Nutr 2:303-322.
Davis, S; Waller, P; Buschbon, R; et al. (1990) Quantitative estimates of soil ingestion in normal children between
the ages of 2 and 7 years: population based estimates using aluminum, silicon, and titanium as soil tracer elements.
Arch Environ Hlth 45:112-122.
Feldman, MD. (1986) Pica: current perspectives. Psychosomatics 27(7):519-523.
Forfar, JO; Arneil, GC. (eds.) (1984) Textbook of paediatrics, 3rd ed. London: Churchill Livingstone.
Illingworth, RS. (1983) The normal child. New York: Churchill Livingstone.
Kaplan, HI; Sadock, BJ. (1985) Comprehensive textbook of psychiatry flV. Baltimore, MD: Williams and Wilkins.
Kimbrough, R; Falk, H; Stemr, P; et al. (1984) Health implications of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD)
contamination of residential soil. J Toxicol Environ Health 14:47-93.
5-39
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Lourie, RS; Layman, EM; Millican, FK. (1963) Why children eat things that are not food. Children 10:143-146.
Sayetta, RB. (1986) Pica: an overview. Am Fam Physician 33(5): 181-185.
Sedman, R; Mahmood, RS. (1994) Soil ingestion by children and adults reconsidered using the results of recent
tracer studies. Air and Waste 44:141-144.
Stanek, EJ; Calabrese, EJ. (1995a) Daily estimates of soil ingestion in children. Environ Health Perspect
103(3):276-285.
Stanek, EJ; Calabrese, EJ. (1995b) Soil ingestion estimates for use in site evaluations based on the best tracer
method. Hum Ecolog Risk Assess 1:133-156.
Thompson, KM; Burmaster, DE. (1991) Parametric distributions for soil ingestion by children. Risk Anal
11:339-342.
U.S. EPA (U.S. Environmental Protection Agency). (1984) Risk analysis of TCDD contaminated soil. Office of
Health and Environmental Assessment, Washington, DC. EPA 600/8-84-031.
U.S. EPA. (1997) Exposure factors handbook. Office of Research and Development, Washington, DC. EPA/600/
P-95/002F.
Van Wijnen, JH; Clausing, P; Brunekreff, B. (1990) Estimated soil ingestion by children. EnvironRes 51:147-162.
Vermeer, DE; Frate, DA. (1979) Geophagia in rural Mississippi: environmental and cultural contexts and nutritional
implications. Am J Clin Nutr 32:2129-2135.
Wong, MS. (1988) The role of environmental and host behavioural factors in determining exposure to infection with
ascaris lumbricoldes and trichuris trichlura. Ph.D. Thesis, Faculty of Natural Sciences, University of the West
Indies.
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6. OTHER NONDIETARY INGESTION FACTORS
6.1. INTRODUCTION
Young children (i.e., ages 6 months through approximately 4 years) have the potential for
exposure to toxic substances through nondietary ingestion pathways other than soil ingestion
(e.g., ingesting pesticide residues that have been transferred from treated surfaces to the hands or
objects that are mouthed). These children have an urge to mouth objects or their fingers in
exploring their environment, as a sucking reflex and as a habit (Groot et al., 1998). Exposure via
this route may exceed that of other routes of ingestion (i.e., food, pica, drinking water, breast
milk) and dermal exposure, because nondietary ingestion may result in higher ingestion rates of
contaminated material (Weaver et al., 1998). This exposure route is also difficult to model,
because there is little literature or research on mouthing behavior (Reed et al., 1999) and little
information on the susceptibility of children to toxic substances (Weaver et al., 1998).
Mouthing behavior includes all activities in which objects, including fingers, are touched
by the mouth or put into the mouth except for eating and drinking and includes licking, sucking,
chewing, and biting (Groot et al., 1998). Children's contact with surfaces is intermittent and
nonuniform over different parts of the body and the nature of the mouthing itself is intermittent
and nonuniform, also making this pathway difficult to model (Zartarian et al., 1997).
Children exhibit large differences in mouthing behavior (Groot et al., 1998). Infants are
born with a sucking reflex for breast-feeding, and within a few months, they begin to use sucking
or mouthing as a means to explore their surroundings. Children will use both sucking and
licking to explore their environment. Sucking also becomes a means of comfort when a child is
tired or upset. In addition, teething normally causes substantial mouthing behavior—sucking or
chewing—to alleviate discomfort in the gums. Each child is different, and large differences
occur even between children in the same family.
Where mouthing becomes critical in exposure to potentially toxic substances is the small
child's proximity to and behavior around potentially contaminated sources. Children play close
to the ground and are constantly licking their fingers or mouthing toys or objects. As a result,
mouthing becomes a potentially significant exposure route. Children can ingest more toxic
constituents through this behavior than from dietary ingestion or inhalation because they may
place wet, sticky fingers on potentially contaminated surfaces, and more toxic constituents may
adhere than if the fingers were dry (Gurunathan et al., 1998).
6-1
-------
Gurunathan et al. (1998) estimated that young children spend as much as 90% of their
days inside, so exposure to contaminants that may infiltrate the home through the vapor phase
(e.g., volatile organic compounds [VOCs] and semi-vocs [SVOCs]) may be of concern. This
may be a significant pathway of exposure to SVOCs, because these compounds can be deposited
on surfaces in the home or become absorbed onto plastic toys or settle onto stuffed animals,
where they can serve as reservoirs for toxic constituents (Gurunathan et al., 1998).
Few studies have investigated this potential exposure route. The shortage of research and
data may be due to the difficulty in observing very young children and the labor-intensive effort
in gathering the data (Hubal et al., 2000). The applicable research efforts use two general
approaches to gather data: real-time hand recording, in which trained persons observe a child and
manually record information on a survey sheet or score sheet, and videotaping, in which trained
videographers tape a child's activities and subsequently extract the pertinent data manually or
with computer software (Hubel et al., 2000).
Some researchers express mouthing behavior in terms of frequency of occurrence (e.g.,
contacts per hour, contacts per minute). Others express mouthing behavior as a rate in units of
minutes per hour of mouthing time. Both approaches have their use in exposure assessments.
The former approach is more appropriate when studying children's behavior during various
microactivities. The latter, however, is more useful when studying children's behavior during
macroactivities. Macroactivities can be described by a child's general activities, such as
sleeping, watching television, playing, and eating. Microactivities refer to the specific behavior
a child is engaged in, such as hand-to-surf ace contacts and hand-to-mouth behavior (Hubel et al.,
2000). Time spent in various macroactivities in several microenvironments (e.g., indoors at
home) is presented in Chapter 9.
6.2. STUDIES RELATED TO NONDIETARY INGESTION
6.2.1. Davis, 1995
In 1992, the Fred Hutchinson Cancer Research Center, under a Cooperative Agreement
with EPA, conducted a study to estimate children's soil intake rates and collect mouthing
behavior data. Originally, the study was designed with two primary purposes: (1) to describe
and quantify the distribution of soil ingestion values in a group of children under the age of 5
who exhibit behaviors that would be likely to result in the ingestion of larger than normal
amounts of soil, and (2) to assess and quantify the degree to which soil ingestion varies among
children according to season of the year (summer vs. winter). The study was conducted during
the first 4 months of 1992 and included 92 children from the Tri-Cities area in Washington State.
6-2
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The children were volunteers among a group selected through random digit dialing and their
ages ranged between 0 and 48 months. The study was conducted during a 7-day period.
Because there was no standard methodology to study mouthing behavior, a pretest and a
series of pilot studies were conducted to examine various aspects of the methodology. As a
result of the pilot studies, it was determined that although parents could be taught to conduct
observations using the instrument, the resulting ranking of children according to degree of
mouthing behavior did not correspond very well to the rankings based on observations of the
same children by trained staff observers. Therefore, using parents' observations to select a group
with high mouthing activity was not deemed appropriate. Funding constraints made it
impractical to continue with the original design of screening a large number of children and
conducting field work during two different times of the year.
The Davis (1995) research recognizes that mouthing behavior is intermittent. Therefore,
a method called "interval method" of observation was used. This method measures both
frequency and duration of the behavior. Under this method, children were observed during 15
second intervals, during which the mouthing behavior was recorded. Based on the types of
behaviors observed in the testing of the instrument, two mouthing behaviors were selected for
the full study: (1) tongue contacts object, and (2) object in mouth. In addition, four other
behaviors were included in an attempt to better describe the types of behaviors that would likely
result in soil ingestion: (1) hand touches ground, (2) child is repulsed by object in mouth and
tries to get it out, (3) other person stops child's contact with object, and (4) child is out of sight
or view. To further characterize potential exposures to soil associated with the two types of
mouthing behaviors, six object categories were included to be used along with the behaviors: (1)
hand, finger, or thumb; (2) other body parts, including toes, feet, arms; (3) natural materials,
including dirt, sand, rocks, leaves; (4) toys and other objects, including books, utensils, keys; (5)
surfaces, including, window sills, floor, furniture, carpet; and (6) food or drink. An additional
code was added to indicate whether an object was swallowed by the child. The type of activity
the child was engaged in during the observation period was also recorded. In addition to
mouthing behavior data, information on how long the child spent indoors and outdoors each day
and the general types of outdoor settings in which the child played was collected.
Mouthing behavior data were collected during a 4-day period. Both trained observers
and one parent observed the children to record mouthing behavior data. Trained observers
recorded mouthing behavior data for 1 hour during active play time, and the parent recorded
mouthing behavior data for the first 15 minutes of that hour.
-------
The basic measure of each type of mouthing activity derived from the observation form
was the percent of time spent in that activity. This measure was defined as the percentage of the
total number of intervals observed that indicate such an activity took place. If there was no
activity in an interval, that interval was excluded. For tabulating the object categories, multiple
instances of the same object in a single interval were counted only once in that interval.
Multiple instances of different objects in a single interval were counted separately under each
object category.
Based on the mouthing behavior data collected in this study, EPA calculated that during
the period of observation (assumed to be 1 hour) the average mouthing activity was 6.2 minutes
and the average tongue activity was 0.70 minutes. It is important to note that these values are
based on 1 hour of observation. In order to estimate the overall mouthing activity in a day, one
would have to make some assumptions about the amount of time a child is involved in active
play time in a day. These values may also be underestimates, because they assume that all the
children in the study were observed for 1 hour on each of the 4 days. If this were true, each child
would have a total of 960 intervals of observations (i.e., 3600 seconds x intervals/15 seconds x
4 days). The data show that the number of intervals of observation ranged from 80 to 840. It
can be concluded that some children were either observed for less than 1 hour or less than 4
days.
In order to compare the values estimated by Groot et al. (1998), whose work also used
time as a basis for measuring mouthing activity, it is necessary to multiply the Davis (1995)
hourly estimate by an estimate of how long the children were awake during the day. According
to Davis (1995) children ages 0 to 48 months are awake approximately 8.9 hours per day. Based
upon this estimate, the Davis (1995) findings translate into about 55 min/day of mouthing
activity and 6 min/day of tongue activity. The 55 minutes compares favorably to the 37 minutes
and 44 minutes estimated by Groot et al. (1998) for 3- to 6-month and 6- to 12-month-old
children, respectively, but it is significantly above the 16.4 minutes and 9.3 minutes estimated
for the 12- to 18-month and 18- to 36-month-old children, respectively.
EPA also analyzed the mouthing behavior data for 86 children (43 males and 43 females)
from the Davis (1995) study. Six children from the original sample size of 92 were excluded
from the analysis because no age information was provided. Total mouthing behavior included
both mouth and tongue contacts with hands, other body parts, surfaces, natural objects, and toys.
Eating events were excluded from the analysis. Statistical analysis was undertaken to determine
whether significant differences existed between age and gender. Model results showed that there
were no associations between mouthing frequency and gender. However, a clear relationship
6-4
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was observed between mouthing frequency and age. Two distinct groups could be identified:
children younger than 24 months and children older than 24 months. Children than 24 months
exhibited the highest frequency of mouthing behavior, with 81 ± 7 contacts/hr (28 subjects, 69
observations). On the other hand, children older than 24 months exhibited a lower frequency of
mouthing behavior, with 42 ± 4 contacts/hr (44 subjects, 117 observations). These results
suggest that as children grow older, they are less likely to put objects into their mouths.
This study has both strengths and weaknesses. The strengths are that it incorporates
more children (92) in the sample population than any of the other studies in the reviewed
literature. In addition, the research is very detailed in defining the parameters and variables
associated with mouthing behavior. The research also gathered information over 4 days,
whereas most of the other studies involved only 1 or 2 days of observation.
Although the research included the largest sample population of the reviewed literature,
92 sample points is still a small number, considering the wide variability associated with
mouthing in children. The random nature in which the population was selected probably
provides a representative population of the northwest U.S. but not the national population in
general. The interval time of 15 seconds would also appear to be small and potentially easily
skewed for those children observed less than an hour. In addition, most other studies used
observation times of 15 minutes to continuous observation throughout waking hours.
6.2.2. Groot et al., 1998
In this study, Groot et al. (1998) examined the mouthing behavior of infants and young
children between the ages of 3 and 36 months in the Netherlands. The study was actually part of
a larger effort to determine whether PVC toys softened with phthalate could pose health risks to
children from mouthing. As part of the effort, the investigators asked parents to observe their
children and gather information that could be used to estimate how often children engage in
mouthing and the duration spent mouthing during a day. Parents were asked to observe their
children 10 times per day for 15-minute intervals (i.e., 150 minutes total per day) for 2 days and
measure mouthing with a stopwatch. In total, 36 parents participated in the study and 42
children were observed by their parents. For the study, a distinction was made to differentiate
between toys meant for mouthing (e.g., pacifiers, teething rings) and those not meant for
mouthing. The time a child spent mouthing a dummy (e.g., pacifier) was not included in the
time recorded.
Although the sample size was relatively small, the results provide a first-order estimate
on mouthing times during a day. Table 6-1 compiles the mouthing times from the study. The
6-5
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results show wide variation. The standard deviation in all four age categories, except the 3- to 6-
month old children, exceeds the mean time estimated for mouthing during a day. The large
standard deviations is not unexpected, given the vast behavioral differences from child to child
and the small sample size of the study. The overall trend of the data, however, may be accurate
in that it shows that as the children age, the time spent mouthing decreases. The 3- to 6-month-
old children were estimated to mouth 37 min/day and the 6- to 12-month-old 44 min/day. After
12 months, the estimated mouthing time dropped quickly to 16 min/day for the 12- to 18-month-
old and 9 min/day for the 18- to 36-month-old children.
The study has several limitations that have an impact on the usability of the data. The
initial drawback concerns the small size of the study. The authors acknowledged this
shortcoming and recommended further study using a larger sample population. In addition, the
area where the study was performed consisted primarily of people with higher education and
most of the parents in the study were in this category. The study attempted to recruit persons of
lower education and socioeconomic levels, but these persons chose not to participate in the study
after recruitment. Therefore, the results do not reflect data from the full spectrum of the
population. The study also recorded only the time spent mouthing and not the number of times
that mouthing occurred, and it did not differentiate the types of objects mouthed. In addition,
children were observed for a period of 2 consecutive days, which may not reflect long-term
behavior. The study may not be representative of the U.S. population.
6.2.3. Reed et al., 1999
In this study, Reed et al. (1999) used videotaping to quantify the frequency and type of
contacts children have during the course of an hour. The contacts included numerous categories:
hand to clothing, hand to dirt, hand to hand, hand to mouth, hand to object, object to mouth, hand
to smooth surface (e.g., counter tops, table tops), hand to textured surface (e.g., stuffed animal).
A total of 30 children were observed in this study. Children were observed in both day care (20
children 3-6 years old) and residential (10 children 2-5 years old) settings. Parents and day care
providers were also asked to complete questionnaires describing the behavior of the children. In
addition, the study also differentiated between the usage of right and left hands.
Over the course of the research, the investigators found that the behavior of children in
day care residential settings was similar except for the contact rate of hand to smooth surfaces.
Children in residential settings had higher contact rates with smooth surfaces than did children in
day care centers. The results of the study are compiled in Table 6-2. The highest contacts were
with objects (123 contacts/hr), smooth surfaces (84 contacts/hr), and other (83 contacts/hr). The
6-6
-------
two lowest contact rates were hand to mouth (9.5 contacts/hr) and object to mouth (16.3
contacts/hr). Because the contact rates of hand to objects and smooth surfaces are high, these
results indicate that the fingers would appear to provide a continual dose per hand-to-mouth
contact because of constant touching of potentially contaminated surfaces. Pesticides and other
SVOCs are partitioned between the vapor and deposited phases (e.g., on dust or absorbed on a
plastic toy or stuffed animal) such that a child's fingers, especially if wet from mouthing, will
continually be acquiring doses of these types of constituents (Gurunathan et al., 1998).
The investigators also noted that children acted equally on their environment with both
hands with the exception of object-to-mouth behavior. Therefore, the compiled data are reported
as combined right-hand and left-hand data. The object-to-mouth behavior showed a strong
preference for the right hand over the left hand for nearly all children. The preference ratio for
the right hand over the left hand for this category was 6.8 to 1.
The advantage of this study is that it incorporates a wide variety of contacts that small
children have—not just the hand to mouth or object to mouth. This information allows assessors
to identify areas or surfaces that may serve as sources for toxic constituent transfer. This is
especially important for exposure to SVOCs such as pesticides (e.g., chlorpyrifos) that have an
affinity for absorption onto dust particles and plastic toys and into the polyurethane foam that is
used in many stuffed animals (Gurunathan et al., 1998). Another strength of this study is the
agreement it shows with earlier work by Zartarian et al. (1998) for the hand-to-mouth contacts.
Some of the shortcomings are the small sample size study and the lack of comment as to the
representativeness of the sample population to the U.S. population. The authors acknowledged
the weakness in regard to the sample size and recommended further work with a larger
population. The study makes no mention of the representativeness of the sample population nor
does it address the need for a representative population for any additional study.
6.2.4. Zartarian et al., 1997
Zartarian et al. (1997) conducted a pilot study of four children of farm workers to
investigate the applicability of using videotaping for gathering information related to children's
interaction with their environment. The evaluation of the videotaping included observation of
the children's contact frequency and duration with objects in their environment, duration spent in
different locations, activity levels, and frequency distributions. As such, the research was not
specifically intended to gather data for nondietary ingestion; however, the activities used to
evaluate the use of videotaping provide data were for dermal and nondietary exposure.
6-7
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Four Mexican-American farm worker children—two girls and two boys between the ages
of 2.5 and 4.2 years—were videotaped for 33 hours using hand-held cameras over the course of a
single day in 1993. The videotaping gathered information on detailed micro-activity patterns of
children to be used to evaluate software for videotaped activities and translation training
methods. The data were also reported by type of object/surface and by left or right hand.
The investigators presented the data for their observations on a per-child and per-hand
basis. The data suggest that the U.S. EPA (1997) estimate of hand-to-mouth contact of 1.56
contacts/hr may significantly underestimate the number of contacts per hour for young children.
None of the children had average contact frequencies for either hand individually lower than 3
contacts/hr for hand-to-mouth contact, and the investigators estimated the average as 9
contacts/hr. As also reported by Reed et al. (1999), the most frequently contacted objects were
toys and hard (i.e., smooth) surfaces. The average contact time with objects was only 2 to 3
seconds; therefore questionnaires and diaries would be insufficient for gathering that level of
activity, according to the authors.
This study has several weaknesses. The sample population is very small, only four
children; however, the work was reported as a pilot study, completely acknowledging that
further work was necessary. The effort was intended to evaluate only the methodology of
collecting observations; thus, the data are not presented in a format that can be used to support
other research or supply recommended estimates for contact frequency. This study may not
reflect long-term behavior. In addition, the sample population is not representative of the U.S.
population in general.
6.2.5. Stanek et al., 1998
Stanek et al. (1998) presented a methodology that links mouthing behavior among
children to the prevalence of ingestion of nonfat items. Soil ingestion data were collected via
face-to-face interviews over a period of 3 months from parents or guardians of 533 children ages
1 to 6 years who attended well-visits in western Massachusetts. Three clinics participated in this
study during the months of August, September, and October, 1992: Kaiser Permanente's clinic in
Amherst, a private clinic associated with the Cooley Dickinson Hospital in Northampton, and the
BayState Medical Center clinic in Springfield. Participants were questioned about the frequency
of 28 mouthing behaviors of the children over the past month in addition to exposure time (e.g.,
time outdoors, play in sand or dirt) and children's characteristics (e.g., teething). Response
categories of the clinic questionnaire corresponded to daily, at least weekly, at least monthly, and
never.
6-8
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The authors expressed the mouthing rate for each child as the sum of rates for responses
to four questions on mouthing specific outdoor objects. Regression models with variables in a
stepwise manner identified factors related to high outdoor mouthing rates. The authors first
considered variables that indicated opportunity for exposure, then subjects' characteristics (e.g.,
teething) and environmental factors, and finally, concurrent reported behaviors.
Table 6-3 presents the prevalence of nonfood ingestion/mouthing behaviors by age as the
percentage of children whose parents reported the behavior in the past month. Outdoor soil
mouthing behavior prevalence was found to be higher than indoor dust mouthing prevalence, but
both behaviors had highest prevalence among 1-year-old children and dropped quickly among
children 2 years old and older. The investigators conducted principal component analyses on the
responses to four questions relating to ingesti on of outdoor objects (Table 6-3) in an attempt to
characterize variability. Responses were converted to mouthing rates per week using values of
0, 0.25, 1, and 7 for responses of never, monthly, weekly, and daily ingesti on. Outdoor
ingestion/mouthing rates were 4.73 per week for children 1 year of age and 0.44 per week for
children 2-6 years of age. The frequency with which children played in sand/dirt was estimated
as a measure of potential exposure; 71% of the children were reported to play in sand or dirt at
least weekly and 45% were reported as playing in the sand or dirt daily. Children who played in
the sand or dirt were found to have higher outdoor object ingestion/mouthing rates. Thus,
children with higher direct exposure to sand or dirt were more likely to ingest or mouth on
outdoor objects. The authors found similar results when comparing the time spent outdoors to
reported outdoor ingestion and mouthing rates. Sixty-five percent of 1 year-old children were
reported to spend less than 3 hour per day outdoors, whereas 42 percent of children 2-6 years old
spend less than 3 hours per day outdoors.
Table 6-4 presents average outdoor mouthing rates by age and sand/dirt play frequency.
The authors presented the data for children by quartiles according to their general mouthing rates
and applied linear regression models fit to general mouthing rates. They found a significant
slope for all groups but one and thus demonstrated that outdoor mouthing behavior increased
with higher quartiles and that rates of increase depended on age and sand/soil play exposure.
A strength of this study is that it focuses on the prevalence of specific behaviors to
quantify soil mouthing or ingestion among healthy children. The results might have important
health implications, as they show that 1 year-old children with high general levels of mouthing
behavior have the potential for high-risk soil ingestion.
A limitation associated with this study is that the data are based on recall behavior from
the summer previous to the interview. Extrapolation to other seasons may be difficult. In
6-9
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addition, data were collected for children in western Massachusetts and they were available only
for the healthy children who were present for well-visits.
6.3. RECOMMENDATIONS
Due to the paucity of available research data, mouthing frequency data should be used
with caution. Table 6-5 summarizes the studies on mouthing behavior that were described in this
chapter. Table 6-6 summarizes the results of these studies. Table 6-7 summarizes the
recommended values for mouthing frequency. As mentioned earlier, the studies presented used
different units for reporting mouthing behavior. If the assessor is interested in estimating
exposures during macroactivities, then the total amount of time engaged in mouthing behavior
may be the unit of interest. Groot et al. (1998) is the only published study that presents data for
infants. These data, as well as those of the Davis (1995) study, show that mouthing behavior
decreases as children age. Data from both Groot et al. (1998) and Davis (1995) for children
between 3 to 60 months ranged from 9 min/day to 55 min/day, with a weighted average of 46
min/day.
If the assessor is interested in estimating exposures to various microactivities, then the
number of contacts with hands or objects per unit of time may be the unit of interest. Both Reed
et al. (1999) and Zartarian et al. (1997) studied hand-to-mouth behavior. Although there are
uncertainties with the results of these two studies due to sample size, they are fairly consistent.
Based on these two studies, a value of 9 contacts/hr seems to be a reasonable average estimate of
hand-to-mouth behavior for ages 24-72 months. Reed et al. (1999) estimated a 90th percentile
hand-to-mouth behavior of 20.1 contacts/hr. In the same study Reed et al. examined object-to-
mouth frequency. On the basis of Reed et al. (1999) and the analysis of the Davis (1995) data,
mean total mouthing behavior, including hand-to-mouth as well as objects, ranged from 26
contacts/hr (i.e., 9.5 (hand-to-mouth) + 16.3 (object-to-mouth)) to 76 contacts/hr, with a
weighted average of 49 contacts/hour for ages 10-72 months.
The frequency of finger-to-mouth contact (9.5 contacts/hr) greatly exceeds the 1.56
contacts/hr for fingers to mouth suggested by EPA in its guidance for calculating exposure to
pesticides (U.S. EPA, 1997). The estimate of 9.5 contacts/hr is close to the 9 contacts/hr
estimated by Zartarian et al. (1997) in a study conducted using video taping, as reported by Reed
et al. (1999). The agreement of the two studies suggests that EPA's value of 1.56 contacts/hr
may significantly underestimate the nondietary exposure route. Table 6-8 presents the
confidence ratings for the recommended values.
6-10
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Table 6-1. Extrapolated total mouthing times minutes per daya
Age (months)
3-6
6-12
12-18
18-36
No. Children
5
14
12
11
Mouthing time
(min/day)b
Mean ± SD
36.9± 19.1
44 ± 44.7
16.4 ± 18.2
9.3 ±9.8
Minimum
14.5
2.4
0.0
0.0
Maximum
67.0
171.5
53.2
30.9
a The object most mouthed in all age groups was the fingers except for the 6-12 month group, which
mostly mouthed on toys.
b Time awake.
Source: Grootetal., 1998
Table 6-2. Frequency of contacts
Variable
Clothing
Dirt
Hand
Hand to mouth
Object
Object to mouth
Other
Smooth surface
Textured surface
Contacts per hour
Mean
66.6
11.4
21.1
9.5
122.9
16.3
82.9
83.7
22.1
Medium
65.0
0.3
14.2
8.5
118.7
3.6
64.3
80.2
16.3
Minimum
22.8
0.0
6.3
0.4
56.2
0.0
8.3
13.6
0.2
Maximum
129.2
146.3
116.4
25.7
312.0
86.2
243.6
190.4
68.7
90th Percentile
103.3
56.4
43.5
20.1
175.8
77.1
199.6
136.9
52.2
Source: Reed etal, 1999
6-11
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Table 6-3. Prevalence of non-food ingestion/mouthing behaviors by agea
Non-food
ingestion/mouthing
Age (years)
Prevalence
1
N = 171
2
N = 70
3
N = 93
4
N = 82
5
N = 90
6
N = 22
All
N = 528
Outdoor "soil" mouthing/ingestion
Sand, stones
Grass, leaves,
flowers
Twigs, sticks,
woodchips
Soil, dirt
Dust, lint,
dustballs
Plaster, chalk
Paintchips,
splinters
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
54
36
17
48
34
16
42
29
12
38
24
11
14
7
2
8
5
2
6
2
0
26
10
0
16
7
0
23
7
0
21
7
0
4
1
0
10
3
0
0
0
0
19
6
2
24
14
2
13
9
0
5
3
1
2
1
0
3
0
0
0
0
0
9
2
1
13
4
1
13
5
1
7
2
0
0
0
0
2
1
1
4
1
0
7
4
1
9
6
1
11
7
0
3
1
1
0
0
0
3
0
0
1
0
0
9
5
5
5
0
0
5
0
0
9
9
0
5
0
0
5
0
0
0
0
0
27
16
6
26
16
6
23
14
4
18
10
4
6
3
1
5
2
1
3
1
0
General mouthing of objects
Other toys
Paper, cardboard,
tissues
Teething toys
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
88
82
63
71
54
28
65
55
53
44
27
37
23
9
29
16
64
42
20
32
20
8
15
9
44
26
9
23
12
5
4
1
42
28
7
18
7
2
o
6
i
23
9
5
14
9
5
9
9
62
49
30
41
28
13
29
22
6-12
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Table 6-3. Prevalence of non-food ingestion/mouthing behaviors by agea (continued)
Non-food
ingestion/mouthing
Teething toys
(continued)
Crayons,
pencils,
erasers
Blankets, cloth
Shoes, Footware
Clothing
Other items
Crib, chairs,
furniture
Age (years)
Prevalence
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
1
N = 171
44
56
41
19
51
42
29
50
42
20
49
39
25
41
35
22
37
26
13
2
N = 70
6
54
37
17
21
17
11
23
10
1
34
24
7
30
26
11
11
9
3
3
N = 93
6
46
25
4
26
17
9
8
3
0
37
23
11
30
24
15
8
3
1
4
N = 82
0
50
27
6
22
18
13
7
2
0
43
28
9
23
15
7
10
5
1
5
N = 90
0
41
26
4
22
14
7
2
1
0
26
16
6
21
10
6
4
2
0
6
N = 22
9
36
27
18
14
14
5
5
5
0
27
14
14
27
14
5
5
0
0
All
N = 528
17
50
32
12
32
25
16
22
16
7
39
27
14
31
23
14
17
11
5
Sucking of fingers, etc.
Suck
fingers/thumb
Suck feet or toes
Use pacifier
Suck hair
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
67
60
44
37
23
8
24
22
20
1
1
41
27
21
14
4
1
9
9
6
o
5
3
43
31
22
12
3
0
6
5
5
8
2
57
43
26
11
2
1
2
2
1
9
2
39
31
24
3
1
0
2
2
1
10
4
41
18
14
0
0
0
5
0
0
5
5
52
41
30
18
9
3
11
10
9
5
2
6-13
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Table 6-3. Prevalence of non-food ingestion/mouthing behaviors by agea (continued)
Non-food
ingestion/mouthing
Suck hair
(continued)
Age (years)
Prevalence
% Daily
1
N = 171
1
2
N = 70
1
3
N = 93
1
4
N = 82
0
5
N = 90
2
6
N = 22
0
All
N = 528
1
"Disgusting" object mouthing/ingestion
Soap, detergent,
shampoo
Plastic, plastic
wrap
Cigarette butts,
tobacco
Matches
Insect
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
48
37
15
32
22
7
16
10
4
6
2
1
5
2
0
34
27
14
19
11
4
6
4
0
4
3
0
1
0
0
24
14
3
8
3
1
5
4
1
1
1
0
2
1
1
17
11
2
7
4
0
4
1
1
4
1
0
4
4
2
9
6
0
9
4
1
3
2
1
1
1
0
2
2
2
9
9
0
0
0
0
5
5
0
0
0
0
0
0
0
29
21
8
17
10
3
8
5
2
4
2
0
3
2
1
Other ingestion and behaviors
Toothpaste
Chew gum
Bite nails
Play in sand/dirt
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
% > Monthly
% > Weekly
% Daily
63
60
52
18
10
3
8
5
2
62
57
42
97
94
87
56
40
17
26
23
7
76
64
39
92
91
86
76
60
18
31
24
12
85
77
43
94
93
93
76
60
13
29
20
9
96
88
55
93
92
89
91
69
21
33
26
10
88
81
52
86
86
82
100
68
36
59
45
14
73
68
45
84
82
77
58
43
14
24
18
7
78
71
45
' Parents reported the behavior in the past month.
Source: Staneketal., 1998
6-14
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Table 6-4. Average outdoor object mouthing scores for children by age,
frequency of sand/dirt play, and general mouthing quartiles
General mouthing
score quartiles (mean)
lstquartile(1.5)
2nd quartile (9.7)
3rd quartile (19.6)
4th quartile (3 5. 6)
Slope based on general
mouthing quartile score
SE
Score
1-year-olds
Frequency of sand/dirt play
> Daily
Mean
0.1
0.7
1.3
3.6
N
19
14
33
35
0.110
0.052
Daily
Mean
2.8
3.9
10.5
14.0
N
16
11
22
23
0.340
0.060
2- to 6-year-olds
Frequency of sand/dirt play
> Daily
Mean
0.1
0.3
0.2
0.5
N
139
27
19
2
0.007
0.021
Daily
Mean
0.5
0.8
1.8
1.5
N
117
28
21
4
0.054
0.019
Source: Staneketal., 1998
Table 6-5. Summary of studies on mouthing behavior
Study
Grootetal., 1998
Reedetal., 1999
Zartarian et al, 1997
Davis, 1995
Staneketal., 1998
Population Size
42
30
4
92
355
Population and time studied
3-36 months in Netherlands
children from well educated parents
20 children 3-6 years
10 children 2-5 years
Day care and residential settings
2. 5-4. 2 years
children of farm workers
10-60 months
Washington State
1-6 years
Private medical clinic in
Springfield, MA
6-15
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Table 6-6. Summary of mouthing frequency data
Age
(months)
3-6
6-12
12-18
18-36
2-6 years
2.5-4.2
years
10-60
<24
>24
Mouthing frequency
37 min/day
44 min/day
16 min/day
9 min/day
9.5 contacts/hr (hand to mouth)
16.3 contacts/hr (object to mouth)
9 contacts/hr
55 min/day
8 1 ± 7 contacts/hr
42 ± 4 contacts/hr
Population size
5
14
12
11
30
4
92
28
44
Reference
Grootetal., 1998
Reedetal., 1999
Zartarian, 1997
EPA analysis of
Davis, 1995
Table 6-7. Summary of recommended values for mouthing behavior
Age
(months)
3-60
24-72
10-72
Mean Mouthing
Frequency/Time
46 min/day
Hand-to-Mouth
9 contacts/hr
20 contact/hr
Object-to-Mouth
16.3 contacts/hr
49 contacts/hr
(total mouthing)
Reference
Weighted average from Groot et al., 1998 and Davis, 1995
Hand-to-mouth is the weighted average from
al., 1997 and Reed et al., 1999
Zartarian et
90th percentile, Reed et al., 1999
Object-to-mouth based on Reed et al., 1999
Weighted average from Reed et al., 1999 and
of Davis, 1995
EPA analysis
6-16
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Table 6-8. Confidence in mouthing behavior recommendations
Considerations
Rationale
Rating
Study Elements
Peer Review
Accessibility
Reproducibility
Focus on factor of
Interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data
collection period
Validity of Approach
Representativeness of
the population
Characterization of
variability
Lack of bias in study
design
Measurement error
Three of the studies are from peer-reviewed journals, one is
from a contractor's report to EPA.
Studies in journals have wide circulation. Contractor's report
available only through EPA.
Cannot reproduce the data unless raw data are provided.
Studies focused on mouthing behavior as well as other hand
contacts.
Studies were conducted in the U.S.
Analyses were done on primary data. EPA did the analysis of
the raw data from Davis, (1995).
Recent studies were evaluated.
Data were collected for a period of several days, not enough to
represent seasonal variations.
Measurements were made by observation methods. Both
surveys and videotaping were used. Videotaping techniques
may be more reliable but resource intensive.
An effort was made to consider age and gender (in the Davis
study), but sample size was too small.
An effort was made to consider age and gender; data for infants
are fairly limited.
Subjects were selected from volunteers.
Measuring children's behavior is difficult and somewhat
subjective and depends on the experience of the observer.
Medium
Medium
Medium
High
High
High
High
Medium
Medium
Low
Low
Medium
Medium
Other Elements
Number of studies
Agreement between
researchers
Overall Rating
Four studies were evaluated
There is general agreement among the researchers.
Although there are four studies, they have very small sample
sizes; variability in the population cannot be assessed. Variation
in behavior due to seasons cannot be evaluated. Measuring
children's behavior is difficult.
Medium
High
Low
6-17
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REFERENCES FOR CHAPTER 6
Davis, S. (1995) Soil ingestion in children with pica. Final Report. EPA Cooperative Agreement CR 816334-01.
Office of Research and Development, Washington, DC.
Groot, M; Lekkerkerk, M; Steenbekkers, L. (1998) Mouthing behavior of young children: an observational study.
H&C onderzoeksraport 3.
Gurunathan, S; Robson, M; Freeman, N; et al. (1998) Accumulation of chloropyrifos on residential surfaces and toys
accessible to children. Environ Health Perspect 106(1):9-16.
Hubal, EA; Sheldon, LS; Burke, JM; et al. (2000) Children's exposure assessment: a review of factors influencing
children's exposure, and the data available to characterize and assess that exposure. Environmental Health
Perspectives Vol. 108, No 6; pp 475-486, June 2000.
Reed, K; Jimenez, M Freeman, N; Lioy, P. (1999) Quantification of children's hand and mouthing activities through
a videotaping methodology. J Expo Anal Environ Epidemiol 9:513-520.
Stanek, EJ;Calabrese, EJ; Mundt, K; et al. (1998) Prevalence of soil mouthing/ingestion among healthy children
aged 1 to 6. J Soil Contamin 7(2):227-242.
U.S. EPA (U.S. Environmental Protection Agency). (1997) Standard operating procedures (SOPs) for residential
exposure assessment. Office of Pesticide Programs, Washington, DC.
Weaver, V; Buckley, T; Groopman, J. (1998) Approaches to environmental exposure assessment in children.
Environ Health Perspect 106(3): 827-831.
Zartarian, VG; Ferguson, AC; Leckie, JO. (1997) Quantified dermal activity data from a four-child pilot field study.
J Expo Anal Environ Epimemiol 7(4):543-553.
6-18
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7. INHALATION ROUTE
7.1. INTRODUCTION
This chapter presents data and recommendations for inhalation rates that can be used to
assess children's exposure to contaminants in air. Children may be more highly exposed to
environmental toxicants through inhalation routes than are adults. Infants and young children
have a higher resting metabolic rate and rate of oxygen consumption per unit body weight than
do adults because they have a larger cooling surface per unit body weight and because they are
growing rapidly. The oxygen consumption of a resting infant aged between 1 week and 1 year is
7 mL/kg body weight per minute. The rate for an adult under the same conditions is 3-5 mL/kg
per minute (WHO, 1986). Thus, on a body-weight basis, the volume of air passing through the
lungs of a resting infant is twice that of a resting adult under the same conditions and, therefore,
twice as much of any chemical in the atmosphere could reach the lungs of an infant. The
recommended inhalation rates for children are summarized in section 7.3.
7.2. INHALATION RATE STUDIES
7.2.1. Linn et al., 1992
Linn et al. (1992) conducted a study that estimated the inhalation rates for "high-risk"
subpopulation groups exposed to ozone in their daily activities in the Los Angeles area. The
population surveyed consisted of several panels of both adults and children. The panels
consisting of children included Panel 2: 17 healthy elementary school students (5 males and 12
females, ages 10-12 years); Panel 3: 19 healthy high school students (7 males and 12 females,
ages 13-17 years); Panel 6: 13 young asthmatics (7 males and 6 females, ages 11-16 years).
An initial calibration test was conducted, followed by a training session. Finally, a field
study that involved the subjects collecting their own heart rate and diary data was conducted.
During the calibration tests, ventilation rate (VR), breathing rate, and heart rate (HR) were
measured simultaneously at each exercise level. From the calibration data, an equation was
developed using linear regression analysis to predict VR from measured.
In the field study, each subject recorded in diaries their daily activities, change in
locations (indoors, outdoors, or in a vehicle), self-estimated breathing rates during each
activity/location, and time spent at each activity/location. Healthy subjects recorded their HR
once every 60 seconds. Asthmatic subjects recorded their diary information once every hour
using a Heart Watch. Subjective breathing rates were defined as slow (walking at their normal
pace), medium (faster than normal walking), and fast (running or similarly strenuous exercise).
7-1
-------
Table 7-1 presents the calibration and field protocols for self-monitoring of activities for each
subject panel.
Table 7-2 presents the mean VR, the 99th percentile VR, and the mean VR at each
subjective activity level (slow, medium, fast). The mean VR and the 99th percentile VR were
derived from all HR recordings (that appeared to be valid) without considering the diary data.
Each of the three activity levels was determined from both the concurrent diary data and HR
recordings by direct calculation or regression. The authors reported that the diary data showed
that most individuals spent most of their time (in a typical day) indoors at slow activity level.
During slow activity, asthmatic subjects had higher VRs than did healthy subjects, (Table 7-2).
The authors also reported that in every panel the predicted VR correlated significantly with the
subjective estimates of activity levels.
A limitation of this study is that calibration data may overestimate the predictive power
of HR during actual field monitoring. The wide variety of exercises in everyday activities may
result in greater variation of the VR-HR relationship than was calibrated. Another limitation is
the small sample size of each subpopulation surveyed. An advantage of this study is that diary
data can provide rough estimates of ventilation patterns that are useful in exposure assessments.
Another advantage is that inhalation rates were presented for both healthy and asthmatic
children.
7.2.2. Spier et al., 1992
Spier et al. (1992) investigated the activity patterns of 17 elementary school students
(10-12 years old) and 19 high school students (13-17 years old) in suburban Los Angeles from
late September to October (oxidant pollution season). Calibration tests were conducted in
supervised outdoor exercise sessions. The exercise sessions consisted of 5 minutes each of rest,
slow walking, jogging, and fast walking. HR and VR were measured during the last 2 minutes
of each exercise. Individual VR and HR relationships for each individual were determined by
fitting a regression line to HR values and log VR values. Each subject recorded his or her daily
activities, change in location, and breathing rates in diaries for 3 consecutive days. Self-
estimated breathing rates were recorded as slow (slow walking), medium (walking faster than
normal), and fast (running). HR was recorded once per minute during the 3 days with a Heart
Watch. VR values for each self-estimated breathing rate and activity type were estimated from
the HR recordings by employing the VR and HR equation obtained from the calibration tests.
The data presented in Table 7-3 represent HR distribution patterns and corresponding
predicted VR for each age group during hours spent awake. At the same self-reported activity
7-2
-------
levels for both age groups, inhalation rates were higher for outdoor activities than for indoor
activities. The total number of hours spent indoors was higher for high school students
(21.2 hours) than for elementary school students (19.6 hours). The converse was true for
outdoor activities: 2.7 hours for high school students and 4.4 hours for elementary school
students (Table 7-4). Based on the data presented in Tables 7-3 and 7-4, the average activity-
specific inhalation rates for elementary school students (10-12 years old) and high school
students (13-17 years old) were calculated and are presented in Table 7-5. For elementary
school students, the average daily inhalation rates (based on indoor and outdoor locations) are
15.8 m3/day for light activities, 4.62 m3/day for moderate activities, and 0.98 m3/day for heavy
activities. For high school students, the daily inhalation rates for light, moderate, and heavy
activities are estimated to be 16.4 m3/day, 3.1 nrVday, and 0.54 m3/day, respectively (Table 7-5).
A limitation of this study is the small sample size. The results may not be representative
of all children in these age groups. Another limitation is that the accuracy of the self-estimated
breathing rates reported by younger age groups is uncertain, which may affect the validity of the
data set generated. An advantage of this study is that inhalation rates were determined for
children and adolescents. These data are useful in estimating exposure for the younger
population.
7.2.3. Adams, 1993
Adams (1993) conducted research to accomplish two main objectives: (1) identification
of mean and ranges of inhalation rates for various age/gender cohorts and specific activities, and
(2) derivation of simple linear and multiple regression equations that could be used to predict
inhalation rates through other measured variables: breathing frequency and oxygen
consumption. A total of 160 subjects participated in the primary study. For children, there were
two age-dependent groups: children 6 to 12.9 years old, and adolescents 13 to 18.9 years old.
An additional 40 children from 6 to 12 years old and 12 young children from 3 to 5 years old
were identified as subjects for pilot testing purposes.
Resting protocols conducted in the laboratory for all age groups consisted of three phases
(25 minutes each) of lying, sitting, and standing. The phases were categorized as resting and
sedentary activities. Two active protocols—moderate (walking) and heavy (jogging/ running)
phases—were performed on a treadmill over a progressive continuum of intensities made up of
6-minute intervals at three speeds, ranging from slow to moderately fast. All protocols involved
measuring VR, FIR, breathing frequency, and oxygen consumption. Measurements were taken
-------
in the last 5 minutes of each phase of the resting protocol and the last 3 minutes of the 6-minute
intervals at each speed designated in the active protocols.
In the field, all children completed spontaneous play protocols; the older adolescent
population (16-18 years) completed car driving and riding, car maintenance (males), and
housework (females) protocols.
During all activities in either the laboratory or the field protocols, IR for the children's
group revealed no significant gender differences. Therefore, the inhalation rate data presented in
Appendix Tables 7A-1 and 7A-2 were categorized by activity type (lying, sitting, standing,
walking, and running) for young children and children (no gender). These categorized data from
the appendix tables are summarized as inhalation rates in Tables 7-6 and 7-7. The laboratory
protocols are shown in Table 7-6. Table 7-7 presents the mean inhalation rates by group and for
moderate activity levels in field protocols. Data were not provided for the light and sedentary
activities because the group did not perform for this protocol or the number of subjects was too
small for appropriate comparisons. Accurate predictions of inhalation rates across all population
groups and activity types were obtained by including body surface area (BSA), HR, and
breathing frequency in multiple regression analysis. The authors calculated BSA from measured
height and weight using the equation:
BSA = Height^0-425) x Weight^0-425) x 71.84 (7-1)
A limitation associated with this study is that the population does not represent the
general U.S. population. Also, the classification of activity types (i.e., laboratory and field
protocols) into activity levels may bias the inhalation rates obtained for various age/gender
cohorts. The estimated rates were based on short-term data and may not reflect long-term
patterns.
7.2.4. Layton, 1993
Layton (1993) presented a new method for estimating metabolically consistent inhalation
rates for use in quantitative dose assessments of airborne radionuclides. Generally, the approach
for estimating the breathing rate for a specified time frame was to calculate a time-weighted-
average of ventilation rates associated with physical activities of varying durations. However, in
this study, breathing rates were calculated on the basis of oxygen consumption associated with
energy expenditures for short (hours) and long (weeks and months) periods of time, using the
following general equation to calculate energy-dependent inhalation rates:
VE = E x H x VQ (7-2)
7-4
-------
where:
VE = ventilation rate (L/min or m3/hr);
E = energy expenditure rate; (kilojoules [KJ]/min or megajoules [MJ]/hr;
H = volume of oxygen (at standard temperature and pressure, dry air consumed in the
production of 1 KJ of energy expended (L/KJ or m3/MJ)]; and
VQ= ventilatory equivalent (ratio of minute volume [L/min] to oxygen uptake [L/min]
unitless.
Layton used two alternative approaches to estimate daily chronic (long-term) inhalation
rates for different age/gender cohorts of the U.S. population using this methodology.
7.2.4.1. First Approach
Inhalation rates were estimated by multiplying average daily food energy intakes for
different age/gender cohorts, volume of oxygen (H), and ventilatory equivalent (VQ), as shown
in the equation above. The average food energy intake data (Table 7-8) were based on
approximately 30,000 individuals and were obtained from the USDA 1977-78 Nationwide Food
Consumption Survey (USDA-NFCS). The food energy intakes were adjusted upwards by a
constant factor of 1.2 for all individuals 9 years and older. This factor compensated for a
consistent bias in the USDA-NFCS that was attributed to under reporting of the foods consumed
or the methods used to ascertain dietary intakes. Layton used a weighted average oxygen uptake
of 0.05 L O2/KJ, which was determined from data reported in the 1977-78 USDA-NFCS and the
second National Health and Nutrition Examination Survey (NHANES II). The survey sample
for NHANES II was approximately 20,000 participants. A VQ of 27 used was calculated as the
geometric mean of VQ data that were obtained from several studies.
The inhalation rate estimation techniques are shown in footnote (a) of Table 7-9.
Table 7-9 presents the daily inhalation rate for each age/gender cohort. The highest daily
inhalation rates were 10 m3/day for children between the ages of 6 and 8 years, 17 nrVday for
males between 15-18 years, and 13 m3/day for females between 9 and 11 years. Inhalation rates
were also calculated for active and inactive periods for the various age/gender cohorts.
The inhalation rate for inactive periods was estimated by multiplying the basal metabolic
rate (BMR) times H times VQ. BMR was defined as "the minimum amount of energy required
to support basic cellular respiration while at rest and not actively digesting food" (Layton, 1993).
The inhalation rate for active periods was calculated by multiplying the inactive inhalation rate
by the ratio of the rate of energy expenditure during active hours to the estimated BMR. This
7-5
-------
ratio is presented as F in Table 7-9. The data for active and inactive inhalation rates are also
presented in Table 7-9. For children, inactive and active inhalation rates ranged between 2.35
and 5.95 nrVday and 6.35 and 13.09 nrVday, respectively.
7.2.4.2. Second Approach
Inhalation rates were calculated by multiplying the BMR of the population cohorts times
the ratio of total daily energy expenditure to daily BMR times H times VQ. The BMR data
obtained from the literature were statistically analyzed, and regression equations were developed
to predict BMR from body weights of various age/gender cohorts. The statistical data used to
develop the regression equations are presented in Appendix Table 7A-3. The data obtained from
the second approach are presented in Table 7-10. Inhalation rates for children (6 months to 10
years) ranged from 7.3-9.3 m3/day for male and 5.6 to 8.6 m3/day for female children; for older
children (10-18 years), the rates were 15 nrVday for males and 12 m3/day for females. These
rates are similar to the daily inhalation rates obtained using the first approach. Also, the inactive
inhalation rates obtained from the first approach are lower than the inhalation rates obtained
using the second approach. This may be attributed to the BMR multiplier employed in the
equation of the second approach to calculate inhalation rates.
Inhalation rates were also obtained for short-term exposures for various age/gender
cohorts and five energy-expenditure categories (rest, sedentary, light, moderate, and heavy).
BMRs were multiplied by the product of the metabolic equivalent times H times VQ. The data
obtained for short-term exposures are presented in Table 7-11.
The major strengths of this study are that it obtains similar results using two different
approaches to estimate inhalation rates in different age groups and that the populations are large
and consist of men, women, and children. Explanations for differences in results due to
metabolic measurements, reported diet, or activity patterns are supported by observations
reported by other investigators in other studies. Major limitations of this study are (1) the
estimated activity pattern levels are somewhat subjective, (2) the explanation that activity pattern
differences are responsible for the lower level obtained with the metabolic approach (25%)
compared with the activity pattern approach is not well supported by the data, and (3) different
populations were used in each approach, which may have introduced error.
7.2.5. Rusconi et al., 1994
Rusconi et al. (1994) examined a large number of infants and children in order to
determine the reference values for respiratory rate in children aged 15 days to 3 years. Previous
7-6
-------
discrepancies in methodologies and results and lack of age-related reference values for the first
years of life prompted the investigators to obtain normal reference values for respiratory rate
from a sufficient number of subjects. They assessed 618 infants and children (336 males and
282 females) ages 15 days to 3 years old who did not have respiratory infections or any severe
disease. Of the 618, a total of 309 were in good health and in daycare centers; while the other
309 were seen in hospitals or outpatients.
Respiratory rates were recorded twice, 30 to 60 minutes apart, listening to breath sounds
for 60 seconds with a stethoscope, when the child was awake and calm and when the child was
sleeping quietly (sleep not associated with any spontaneous movement, including eye
movements or vocalizations) (Table 7-12). The children were assessed for 1 year in order to
determine the repeatability of the recordings, to compare respiratory rate counts obtained by
stethoscope and by observation, and to construct reference percentile curves by age in a large
number of subjects.
The authors plotted the differences between respiratory rate counts determined by
stethoscope at 30- to 60-minute intervals against the mean count in awake and asleep subjects.
The standard deviation of the differences between the two counts was 2.5 and 1.7 breaths/min,
respectively, when the infants and children were awake and when they were asleep. This
standard deviation yielded 95% repeatability coefficients of 4.9 breaths when the infants and
children were awake and 3.3 breaths when they were asleep.
In both awake and asleep states, the respiratory rate counts determined by stethoscope
were found to be higher than those obtained by observation. The mean difference was 2.6 and
1.8 breaths per minute, respectively, in awake and asleep states. The mean respiratory rate
counts were significantly higher in infants and children at all ages when awake and calm than
when asleep. A decrease in respiratory rate with increasing age was seen in awake and asleep
infants and children. A scatter diagram of respiratory rate counts by age in awake subjects and
during sleep showed that the pattern of respiratory rate decline with age was similar in both
states, but it was much faster in the first few months of life. The authors constructed centile
curves by first log-transforming the data and then applying a second-degree polynomial curve,
which allowed excellent fitting to observed data. Figures 7-1 and 7-2 show smoothed percentiles
by age in awake and asleep subjects, respectively.
The authors suggested that the differences between the reported respiratory rates in
healthy infants and children might be due to various factors, including the number of infants
studied, the period of time of counting, the method of counting, and the state of the infant. The
variability of respiratory rate among subjects was higher in the first few months of life, which
7-7
-------
may be attributable to biological events that occur during these months, such as maturation of the
neurologic control of breathing and changes in lung and chest wall compliance and lung
volumes.
An advantage of this study is that it provides distribution data for respiratory rate for
children ages < 2 months to 36 months old. These data are not for the U.S.; however, U.S.
distributions were not available.
7.3. RECOMMENDATIONS
The recommended inhalation rates for children are based on the studies described in this
chapter. Different survey designs and populations were used in these studies. Excluding the
Layton (1993) and Rusconi et al., (1994) studies, the population surveyed in all of the studies
described in this report were limited to the Los Angeles area. This regional population may not
represent the general U.S. population and may result in biases. However, these studies were
selected as the basis for recommended inhalation rates, based on other aspects of the study
design.
The selection of inhalation rates to be used for exposure assessments depends on the age
of the exposed population and the specific activity levels of this population during various
exposure scenarios. The confidence ratings and recommended inhalation rates are presented in
Tables 7-13 and 7-14, respectively. Based on the results from Layton (1993), the recommended
daily inhalation rate for infants (children less than 1 year old), during long-term dose
assessments is 4.5 m3/day. For children 1-2 years old, 3-5 years old, and 6-8 years old, the
recommended daily inhalation rates are 6.8 m3/day, 8.3 m3/day, and 10 m3/day, respectively.
Recommended values for children ages 9-11 years are 14 m3/day for males and 13 nrVday for
females. The recommended values for children aged 12-14 years and 15-18 years are also
shown in Table 7-14.
Recommended short-term inhalation rates for children ages 18 years and under are
summarized in Table 7-14. The short-term inhalation rates were calculated by averaging the
inhalation rates for each activity level from the various key studies (Table 7-15). The
recommended average hourly inhalation rates are 0.3 m3/hr during rest, 0.4 m3/hr for sedentary
activities, 1.0 m3/hr for light activities, 1.2 m3/hr for moderate activities, and 1.9 m3/hr for heavy
activities. The recommended short-term exposure data also include infants (under 1 year).
-------
Table 7-1. Calibration and field protocols for self-monitoring of activities,
grouped by subject panels
Panel
Panel 2: Healthy elementary
school students; 5 males,
12 females, ages 10-12 years
Panel 3 : Healthy high school
students; 7 males, 12 females,
ages 13-17 years
Panel 6: Young asthmatics; 7
males, 6 females, ages 11-16
years
Calibration Protocol
20-minute outdoor calibration study
including rest, slow walking,
jogging, and fast walking
20-minute outdoor calibration study
including rest, slow walking,
jogging, and fast walking
Laboratory exercise tests on bicycles
and treadmills
Field Protocol
Saturday, Sunday, and Monday
(school day) in early autumn; heart
rate recordings and activity diary
during waking hours and during sleep.
Same as Panel 2; however, no heart
rate recordings during sleep for most
subjects.
Summer monitoring for 2 successive
weeks, including two controlled
exposure studies with few or no
observable respiratory effects.
Source: Linnetal., 1992
Table 7-2. Subject panel inhalation rates by mean ventilation rate, upper
percentiles, and self-estimated breathing rates
Panel
Healthy
2 - Elementary school students
3 - High school students
Asthmatics
6 - Elementary and high school
Students
Na
17
19
13
Inhalation Rate (m3/hr)
Mean VR
(m3/hr)
0.90
0.84
1.20
99th
Percentile VR
1.98
2.22
2.40
Mean VR at Activity Levels
(m3/hr)b
Slow
0.84
0.78
1.20
Medium
0.96
1.14
1.20
Fast
1.14
1.62
1.50
a Number of individuals in each survey panel.
b Some subjects did not report medium and/or fast activity. Group means were calculated from individual
means (i.e., give equal weight to each individual who recorded any time at the indicated activity level).
Source: Linnetal., 1992
7-9
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Table 7-3. Distribution of predicted inhalation rates by location and activity levels
for elementary and high school students who participated in the survey
Age
years
10-12
13-17
Student
Elementary
school
(N=17)
High
school
(N=19)
Location
Indoors
Outdoors
Indoors
Outdoors
Activity
level
Slow
Medium
Fast
Slow
Medium
Fast
Slow
Medium
Fast
Slow
Medium
Fast
Recorded
timea
49.6
23.6
2.4
8.9
11.2
4.3
70.7
10.9
1.4
8.2
7.4
1.4
Inhalation Rates (m3/hr)
Mean±
SD
0.84 ±0.36
0.96 ± 0.42
1.02 ±0.60
0.96 ±0.54
1.08 ±0.48
1.14 ±0.60
0.78 ±0.36
0.96 ± 0.42
1.26 ±0.66
0.96 ±0.48
1.26 ±0.78
1.44± 1.08
Percentile
Rankings15
1st
0.18
0.24
0.24
0.36
0.24
0.48
0.30
0.42
0.54
0.42
0.48
0.48
50th
0.78
0.84
0.84
0.78
0.96
0.96
0.72
0.84
1.08
0.90
1.08
1.02
99.9th
2.34
2.58
3.42
4.32
3.36
3.60
3.24
4.02
6.84C
5.28
5.70
5.94
a Recorded time averaged about 23 hours per elementary school student and 33 hours per high school student,
over 72-hour periods.
b Geometric means closely approximated 50th percentiles; geometric standard deviations were 1.2-1.3 for
heart rate and 1.5-1.8 for ventilation rate.
0 Highest single value.
Source: Spier etal, 1992
Table 7-4. Average hours spent per day in a given location and activity
level for elementary and high school students
Students
Elementary school,
ages 10 12 years
(N=17)
High school, ages
13 17 years
(N=19)
Location
Indoors
Outdoors
Indoors
Outdoors
Activity Level
Slow
16.3
2.2
19.5
1.2
Medium
2.9
1.7
1.5
1.3
Fast
0.4
0.5
0.2
0.2
Total time spent
(hrs/day)
19.6
4.4
21.2
2.7
Source: Spier etal., 1992
7-10
-------
Table 7-5. Distribution patterns of daily inhalation rates for elementary and high
school students grouped by activity leveP
Students
Elementary
school, ages
10-12 years
(N - 17)
High school,
ages 13-17 years
(N=19)
Location
Indoors
Outdoors
Indoors
Outdoors
Activity typeb
Light
Moderate
Heavy
Light
Moderate
Heavy
Light
Moderate
Heavy
Light
Moderate
Heavy
Mean inhalation
rate0 (m3/day)
13.70
2.80
0.40
2.10
1.84
0.57
15.20
1.40
0.25
1.15
1.64
0.29
Percentile Rankings
1st
2.930
0.700
0.096
0.790
0.410
0.240
5.850
0.630
0.110
0.500
0.620
0.096
50th
12.71
2.44
0.34
1.72
1.63
0.48
14.04
1.26
0.22
1.08
1.40
0.20
99.9th
38.14
7.48
1.37
9.50
5.71
1.80
63.18
6.03
1.37
6.34
7.41
1.19
a Generated using data from Tables 7-3 and 7-4
b For this report, activity type presented in Table 7-2 was redefined as light activity for slow, moderate
activity for medium, and heavy activity for fast.
0 Daily inhalation rate was calculated by multiplying the hours spent at each activity level (Table 7-4) by
the corresponding inhalation rate (Table 7-3).
Source: Adapted from Spier et al., 1992
7-11
-------
Table 7-6. Summary of average inhalation rates by age group and activity
levels for laboratory protocols
Age Group
Young children
(3-5.9 years)
Average inhalation rate (m3/hr)
Children
(6-12.9 years)
average inhalation rate (m3/hr)
Activity Level
Resting"
0.37
0.45
Sedentary15
0.40
0.47
Light'
0.65
0.95
Moderate*1
DNPf
1.74
Heavy6
DNPf
2.23
a Resting defined as lying (see Appendix Table 7A-1 for original data).
b Sedentary defined as sitting and standing (see Appendix Table 7A-1 for original data).
0 Light defined as walking at speed level 1.5-3.0 mph (see Appendix Table 7A-1 for original data).
d Moderate defined as fast walking (3.3-4.0 mph) and slow running (3.5-4.0 mph) (see Appendix Table 7A-1
for original data).
e Heavy defined as fast running (4.5 - 6.0 mph) (see Appendix Table 7A-1 for original data).
f Group did not perform this protocol or N was too small for appropriate mean comparisons. All young
children did not run.
Source: Adapted from Adams, 1993
Table 7-7. Summary of average inhalation rates by age group and activity
levels in field protocols
Age Group
Young children (3-5.9 years)
average inhalation rate (m3/hr)
Children (6-1 2.9 years)
average inhalation rate (m3/hr)
Moderate activity a
0.68
1.07
a Moderate activity was defined as mowing (males); wood working (males); yard work (males); and play (children)
(see Appendix Table 7A-2 for original data).
Source: Adams, 1993
7-12
-------
Table 7-8. Comparisons of estimated basal metabolic rates (BMR) with
average food-energy intakes for individuals sampled in the 1977-1978 NFCS
Cohort/Age
(years)
Body weight
(kg)
BMRa
MJd1
kcal d'1
EFD
MJd1
kcal d'1
Ratio
EFD/BMR
Children
>1
1-2
3-5
6-8
7.6
13.0
18.0
26.0
1.74
3.08
3.69
4.41
416
734
881
1053
3.32
5.07
6.14
7.43
793
1209
1466
1774
1.90
1.65
1.66
1.68
Males
9-11
12-14
15-18
36.0
50.0
66.0
5.42
6.45
7.64
1293
1540
1823
8.55
9.54
10.80
2040
2276
2568
1.58
1.48
1.41
Females
9-11
12-14
15-18
36.0
49.0
56.0
4.91
5.64
6.03
1173
1347
1440
7.75
7.72
7.32
1849
1842
1748
1.58
1.37
1.21
1 Calculated from the appropriate age and gender-based BMR equations given in Appendix Table 7A-3.
MJ d"1 = - mega joules/day.
kcal d"1 = kilo calories/day.
Source: Layton, 1993
7-13
-------
Table 7-9. Daily inhalation rates calculated from food-energy intakes
Cohort/Age
(years)
Children
<1
1-2
3-5
6-8
Males
9-11
12-14
15-18
Females
9-11
12-14
15-18
L
1
2
3
3
3
3
4
3
3
4
Daily Inhalation Ratea
(m3/day)
4.5
6.8
8.3
10.0
14.0
15.0
17.0
13.0
12.0
12.0
Sleep
(hours)
11
11
10
10
9
9
8
9
9
8
MET value
A"
.9
.6
.7
.7
.9
.8
.7
.9
.6
.5
Fc
2.7
2.2
2.2
2.2
2.5
2.2
2.1
2.5
2.0
1.7
Inhalation Rates
Inactive11
(m3/day)
2.35
4.16
4.98
5.95
7.32
8.71
10.31
6.63
7.61
8.14
Active"
(m3/day)
6.35
9.15
10.96
13.09
18.30
19.16
21.65
16.58
15.20
13.84
' Daily inhalation rate was calculated by multiplying the EFD values (see Table 7-10) by H x VQ x
(m3 1,000 L'1) for subjects under 9 years of age and by 1.2 x H x VQ x (m3 1,000 L'1) (for subjects 9 years
of age and older (see text for explanation), where EFD = food energy intake (Kcal/day) or (MJ/day), H =
oxygen uptake = 0.05 LO2/KJ or 0.21 LO2/Kcal, and VQ = ventilation equivalent = 27 = geometric mean
of VQs (unitless).
' For individuals 9 years of age and older, A was calculated by multiplying the ratio for EFD/BMR (unitless)
(Table 7-10) by the factor 1.2 (see text for explanation).
: F = (24A - S)/(24 - S) (unitless), ratio of the rate of energy expenditure during active hours to the estimated
BMR (unitless), where S = number of hours spent sleeping each day.
1 Inhalation rate for inactive periods was calculated as BMR x H x VQ x (d 1,440 min') and for active periods
by multiplying inactive inhalation rate by F (See footnote f); BMR values are from Table 7-10, where BMR =
basal metabolic rate (MJ/day) or (kg/hr).
L = number of years for each age cohort.
MET = metabolic equivalent.
Source: Layton, 1993
7-14
-------
Table 7-10. Daily inhalation rates obtained from the ratios of total energy
expenditure to basal metabolic rate (BMR)
Gender/Age
(years)
Males
0.5 - <3
3-<10
10-<18
Females
0.5 - <3
3-<10
10-<18
Body weight3
(kg)
14
23
53
11
23
50
BMR"
(MJ/day)
3.4
4.3
6.7
2.6
4.0
5.7
VQ
27
27
27
27
27
27
Ac
1.6
1.6
1.7
1.6
1.6
1.5
H
(m3O2/MJ)
0.05
0.05
0.05
0.05
0.05
0.05
Inhalation Rate, VE
(m3/day)d
7.3
9.3
15.0
5.6
8.6
12.0
a Body weight was based on the average weights for age/gender cohorts in the U.S. population.
b BMRs were calculated using the respective body weights and BMR equations (see
Appendix Table 7A-3).
The values of the BMR multiplier (EFD/BMR) for those 18 years and older were derived from the Basiotis
et al. (1989) study: Male = 1.59, Female = 1.38. For males and females under 10 years old, the mean BMR
multiplier used was 1.6. For males and females aged 10to< 18 years, the mean values for A given in
Table 7-11 for 12-14 years and 15-18 years, age brackets for males and females were used: male =1.7 and
female =1.5.
d Inhalation rate = BMR x A x H x VQ, where VQ = ventilation equivalent and H = oxygen uptake.
Source: Layton, 1993
7-15
-------
Table 7-11. Inhalation rates for short-term exposures
MET (BMR
Multiplier)
Gender/age
(years)
Males
0.5 -<3
3-<10
10-<18
Females
0.5 -<3
3-<10
10-<18
Weight
(kg)a
14
23
53
11
23
50
BMR"
(MJ/day)
3.40
4.30
6.70
2.60
4.00
5.70
Inhalation Rate (m3/hr)f>g by activity level
1
Rest
0.19
0.24
0.38
0.14
0.23
0.32
1.2
Sedentary
0.23
0.29
0.45
0.17
0.27
0.38
2C
Light
0.38
0.49
0.78
0.29
0.45
0.66
4"
Moderate
0.78
0.96
1.50
0.60
0.90
1.26
10e
Heavy
1.92
2.40
3.78
1.44
2.28
3.18
a Body weights were based on average weights for age/gender cohorts of the U.S. population.
b The BMRs for the age/gender cohorts were calculated using the respective body weights and the BMR
equations (Appendix Table 7A-3).
c Range =1.5-2.5.
d Range = 3-5.
e Range = >5-20.
f The inhalation rate was calculated by multiplying BMR (MJ/day) x H (0.05 L/KJ) x MET x VQ (27) x
(d/1,440 min).
g Original data were presented in L/min. Conversion to nf/hr was obtained as follows:
60 min
hr
m
L
1000L min
Source: Layton, 1993
7-16
-------
Table 7-12. Mean, median, and SD of respiratory rate according to waking or
sleeping in 618 infants and children grouped in classes of age (breaths/minute)
Age (months)
<2
2-<6
6-<12
12-<18
18-<24
24 - <30
30-36
N
104
106
126
77
65
79
61
Respiratory rate (breaths/min)
Awake
Mean + SD
48.0 + 9.1
44.1 + 9.9
39.1 + 8.5
34.5 + 5.8
32.0 + 4.8
30.0 + 6.2
27.1+4.1
Median
47
42
38
34
32
30
28
Asleep
Mean + SD
39.8 + 8.7
33.4 + 7.0
29.6 + 7.0
27.2 + 5.6
25.3+4.6
23.1+4.6
21.5 + 3.7
Median
39
32
28
26
24
23
21
Source: Rusconi et al., 1994
7-17
-------
Table 7-13. Confidence in inhalation rate recommendations
Considerations
Study Elements
Peer Review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection period
Validity of approach
Representativeness of the population
Characterization of variability
Lack of bias in study design
Measurement error
Other Elements
Number of studies
Agreement between researchers
Overall Rating
Rationale
Studies are from peer-reviewed journal articles and an
EPA peer-reviewed report.
Studies in journals have wide circulation.
EPA reports are available from the National Technical
Information Service.
Information on questionnaires and interviews were not
provided.
Studies focused on ventilation rates and factors
influencing them.
Studies conducted in the U.S. Distribution data are
foreign data.
Both data collection and reanalysis of existing data
occurred.
Recent studies were evaluated.
Effort was made to collect data over time.
Measurements were made by indirect methods.
An effort has been made to consider age and gender, but
not systematically. Sample size was too small.
An effort has been made to address age and gender, but
not systematically.
Subjects were selected randomly from volunteers and
measured in the same way.
Measurement error is well documented by statistics, but
procedures measure factor indirectly.
Five key studies and seven relevant studies were
evaluated.
There is general agreement among researchers using
different experimental methods.
Several studies exist that attempt to estimate inhalation
rates according to age, gender, and activity.
Rating
High
High
Medium
High
High
Medium
High
High
Medium
Medium
High
High
Medium
High
Medium
7-18
-------
Table 7-14. Summary of recommended values for inhalation'
Population
(years)
Mean
Long-term exposures
Infants
<1
Children
1-2
3-5
6-8
9-11
Males
Females
12-14
Males
Females
15-18
Males
Females
4.5 mVday
6.8 nfVday
8.3 mVday
10 mVday
14 mVday
13 mVday
15 mVday
12 mVday
17 mVday
12 mVday
Short-term exposures
Children (18 and under)
Rest
Sedentary Activities
Light Activities
Moderate Activities
Heavy Activities
0.3 nrVhr
0.4 nrVhr
LOrnVhr
1.2m3/hr
1.9m3/hr
' No data were available for the upper percentile.
7-19
-------
Table 7-15. Summary of arithmetic mean (nrVhr) of children's inhalation
rates by activity level for short-term exposure studies
Rest
0.4
—
0.2
—
—
Sedentary
0.4
—
0.3
—
—
Light
0.8
—
0.5
1.8
0.8
Moderate
—
0.9
1.0
2.0
1.0
High
—
—
2.5
2.2
11.0
Adams, 1993 (lab protocols)
Adams, 1993 (field protocols)
Layton, 1993 (short-term data)
Spier et al., 1992 (10-12 yrs)
Linn et al., 1992 (10-12 yrs)
7-20
-------
70
60
50
I 30
20
10 U
0 3 6 9 12 15 18 21 24 27 30 33 36
Age (months)
Figure 7-1. 5th, 10th, 25th, 50th, 75th, 90th, and 95th smoothed centiles by age in awake
subjects.
70!
60
50
0 40
2
I30
* 20
10
0 3 6 9 12 15 18 21 24 27 30 33 36
Age (months)
Figure 7-2. 5th, 10th, 25th, 50th, 75th, 90th, and 95th smoothed centiles by age in asleep
subjects.
7-21
-------
Appendix A for Chapter 7: Ventilation Rates
Table 7A-1. Mean minute ventilation (Ve, L/min) by group and activity for
laboratory protocols
Activity
Lying
Sitting
Standing
Walking
Running
1.500 mph
1.875mph
2.000 mph
2.250 mph
2. 500 mph
3. 000 mph
3. 300 mph
4.000 mph
3. 500 mph
4.000 mph
4.500 mph
5. 000 mph
6.000 mph
Young Children"
6.19
6.48
6.76
10.25
10.53
DNPb
11.68
DNP
DNP
DNP
DNP
DNP
DNP
DNP
DNP
DNP
Children
7.51
7.28
8.49
DNPb
DNP
14.13
DNP
15.58
17.79
DNP
DNP
26.77
31.35
37.22
DNP
DNP
a Young Children, males and females, 3-5.9 years old; Children, males and females, 6-12.9 years old; Adult
Females, adolescent, young to middle-aged, and older adult females; Adult Males, adolescent, young to
middle-aged, and older adult males.
b Group did not perform this protocol or N was too small for appropriate mean comparisons.
Source: Adams, 1993
Table 7A-2. Mean minute ventilation (Ve, L/min) by group and activity
for field protocols
Activity"
Play
Young Children"
11.31
Children
17.89
a Activities for which groups did not perform the protocol or N was too small for appropriate mean
comparisons were car driving, car riding, yardwork, housework, car maintenance, mowing, and woodworking.
b Young children, males and females, 3-5.9 years old; Children, males and females, 6-12.9 years old.
Source: Adams, 1993
7-22
-------
Table 7A-3. Statistics of the age/gender cohorts used To develop regression
equations for predicting basal metabolic rates (BMR)
Gender/ Age
(years)
Males
Under 3
3 to < 10
0 to < 18
Females
Under 3
3 to < 10
10 to < 18
BMR
MJd1
1.51
4.14
5.86
1.54
3.85
5.04
±SD
0.918
0.498
1.171
0.915
0.493
0.780
cv
0.61
0.12
0.20
0.59
0.13
0.15
Body
weight
(kg)
6.6
21.0
42.0
6.9
21.0
38.0
N
162
338
734
137
413
575
BMR equation3
0.249 bw- 0.127
0.095 bw + 2.110
0.074 bw + 2.754
0.244 bw- 0.130
0.085 bw + 2.033
0.056 bw + 2.898
r
0.95
0.83
0.93
0.96
0.81
0.80
' Body weight (bw) in kg
CV = Coefficient of variation (SD/mean)
r = coefficient of correlation
Source: Layton, 1993
7-23
-------
REFERENCES FOR CHAPTER 7
Adams, WC. (1993) Measurement of breathing rate and volume in routinely performed daily activities. Final
Report. California Air Resources Board (CARB) Contract No. A033-205. June 1993. 185 pgs.
Basiotis, PP; Thomas, RG; Kelsay, JL; et al. (1989) Sources of variation in energy intake by men and women as
determined from one year's daily dietary records. AmJClinNutr 50:448-453.
Layton, DW. (1993) Metabolically consistent breathing rates for use in dose assessments. Health Physics
64(l):23-36.
Linn, WS; Shamoo, DA; Hackney, JD. (1992) Documentation of activity patterns in "high-risk" groups exposed to
ozone in the Los Angeles area. In: Proceedings of the Second EPA/AWMA Conference on Tropospheric Ozone,
Atlanta, Nov. 1991. pp. 701-712. Air and Waste Management Assoc., Pittsburgh, PA.
Rusconi, F; Castagneto, M; Garliardi, L; et al. (1994) Reference values for respiratory rate in the first 3 years of life.
Pediatrics (3) 350-355.
Spier, CE; Little, DE; Trim, SC; et al. (1992) Activity patterns in elementary and high school students exposed to
oxidant pollution. J Exp Anal Environ Epidemiol 2(3):277-293.
WHO (World Health Organization). (1986) Principles for evaluating health risks from chemicals during infancy and
early childhood: the need for a special approach. Environmental Health Criteria 59, WHO, International Programme
on Chemical Safety. Geneva, Switzerland.
7-24
-------
8. DERMAL ROUTE
8.1. INTRODUCTION
Children may be more highly exposed to environmental toxicants through dermal routes
than are adults. For instance, children often play and crawl on contaminated surfaces and are
more likely to wear less clothing than do adults. These factors result in higher dermal contact
with contaminated media. In addition, children have a higher body surface area relative to body
weight. In fact, the surface area-to-body weight (SA/BW) ratio for newborn infants is more than
two times greater than that for adults (Cohen-Hubal et al., 1999).
Dermal exposure can occur during a variety of activities in different environmental media
and microenvironments (U.S. EPA, 1992a, b). These include:
• Water (e.g., bathing, washing, swimming);
• Soil (e.g., outdoor recreation, gardening, construction);
• Sediment (e.g., wading, fishing);
• Liquids (e.g., use of commercial products);
• Vapors/fumes (e.g., use of commercial products); and
• Indoors (e.g., touching carpets, floors, countertops).
The major factors that must be considered when estimating dermal exposure are the
chemical concentration in contact with the skin, the extent of skin surface area exposed, the
duration of exposure, the absorption of the chemical through the skin, the internal dose, and the
amount of chemical that can be delivered to a target organ (i.e., biologically effective dose) (see
Figure 8-1). A detailed discussion of these factors can be found in Guidelines for Exposure
Assessment (U.S. EPA, 1992a).
This chapter focuses on measurements of body surface areas and dermal adherence of
soil to the skin. Dermal Exposure Assessment: Principles and Applications (U.S. EPA, 1992b),
provides detailed information concerning dermal exposure assessment using a stepwise guide in
the exposure assessment process.
8.2. SURFACE AREA
8.2.1. Background
The total surface area of skin exposed to a contaminant must be determined using
measurement or estimation techniques before conducting a dermal exposure assessment.
8-1
-------
Depending on the exposure scenario, estimates of the surface area for the total body or a specific
body part can be used to calculate the contact rate for the pollutant. This section presents
estimates for total body surface area and for body parts and presents information on the
application of body surface area data.
8.2.2. Measurement Techniques
Coating, triangulation, and surface integration are direct measurement techniques that
have been used to measure total body surface area and the surface area of specific body parts.
Consideration has been given for differences due to age, gender, and race. The results of the
various techniques are summarized in Development of Statistical Distributions or Ranges of
Standard Factors Used in Exposure Assessments (U.S. EPA, 1985).
The coating method consists of coating either the whole body or specific body regions
with a substance of known or measured area. Triangulation consists of marking the area of the
body into geometric figures, then calculating the figure areas from their linear dimensions.
Surface integration is performed by using a planimeter and adding the areas.
The triangulation measurement technique developed by Boyd (1935) has been found to
be highly reliable. It estimates the surface area of the body using geometric approximations that
assume that parts of the body resemble geometric solids. More recently, Popendorf and
Leffmgwell (1976) and Haycock et al. (1978) have developed similar geometric methods that
assume that body parts correspond to geometric solids, such as the sphere and cylinder. A linear
method proposed by Dubois and Dubois (1916) is based on the principle that the surface areas of
the parts of the body are proportional rather than equal to the surface area of the solids they
resemble.
In addition to direct measurement techniques, several formulae have been proposed to
estimate body surface area from measurements of other major body dimensions (i.e., height and
weight) (U.S. EPA, 1985). Generally, the formulae are based on the principles that body density
and shape are roughly the same and that the relationship of surface area to any dimension may be
represented by the curve of central tendency of their plotted values or by the algebraic
expression for the curve. A discussion and comparison of formulae to determine total body
surface area are presented in the appendix to Chapter 8.
8-2
-------
8.2.3. Body Surface Area Studies
8.2.3.1. Costeff,1966
Costeff (1996) developed an empirical formula for calculating the surface area of
children. Earlier formulae encompassed derivation for a regular solid and then changing
exponents and coefficients or using empirical formulae that bear no relation to the geometric
problem. Costeff s empirical formula is the following:
4W + 7
SA = — (8-1)
W + 90
where:
SA = surface area (m2);
Constants = 4, 7, and 90; and
W = weight (kg).
This simple formula applies to the weight range between 1.5 and 100 kg. According to the
author, this formula is recommended for calculating basal metabolic rate and other physiological
indices.
8.2.3.2. U.S. EPA, 1985
U.S. EPA (1985) analyzed the direct surface area measurement data of Gehan and
George (1970) using the Statistical Processing System (SPS) software package of Buhyoff et al.
(1982). For their analysis, Gehan and George (1970) selected 401 measurements made by Boyd
(1935) that were complete for surface area, height, weight, and age. Boyd (1935) had reported
surface area estimates for 1114 individuals using coating, triangulation, or surface integration
methods (U.S. EPA, 1985).
U.S. EPA (1985) used SPS to generate equations to calculate surface area as a function of
height and weight. These equations were then used to calculate body surface area distributions
of the U.S. population using the height and weight data obtained from the National Health and
Nutrition Examination Survey (NHANES) II and the computer program QNTLS of Rochon and
Kalsbeek(1983).
The equation proposed by Gehan and George (1970) was determined by U.S. EPA (1985)
to be the best choice for estimating total body surface area. However, the paper by Gehan and
George (1970) gave insufficient information to estimate the standard error about the regression.
-------
Therefore, U.S. EPA (1985) used the 401 direct measurements of children and adults and
reanalyzed the data using the formula of Dubois and Dubois (1916) and SPS to obtain the
standard error.
Regression equations were developed for specific body parts using the Dubois and
Dubois (1916) formula and using the surface area of various body pars provided by Boyd (1935)
and Van Graan (1969) in conjunction with SPS. Equations to estimate the body part surface area
of children were not developed because of insufficient data.
The percentile estimates for total surface area of male and female children presented in
Tables 8-1 and 8-2 were calculated using the total surface area regression equation and
NHANES II height and weight data and using QNTLS. Estimates are not included for children
younger than 2 years old because NHANES height data are not available for this age group. For
children, the error associated with height and weight cannot be assumed to be zero because of
their relatively small sizes. Therefore, the standard errors of the percentile estimates cannot be
estimated, because it cannot be assumed that the errors associated with the exogenous variables
(height and weight) are independent of those associated with the model; there are insufficient
data to determine the relationship between these errors.
Measurements of the surface area of children's body parts are summarized as a
percentage of total surface area in Table 8-3. Because of the small sample size, the data cannot
be assumed to represent the average percentage of surface area by body part for all children.
Note that the proportion of total body surface area contributed by the head decreases from
childhood to adulthood, whereas the proportion contributed by the leg increases.
8.2.3.3. Phillips et al, 1993
Phillips et al. (1993) observed a strong correlation (0.986) between body surface area and
body weight and studied the effect of using these factors as independent variables in the lifetime
average daily dose (LADD) equation. The authors concluded that, because of the correlation
between these two variables, the use of SA/BW ratios in human exposure assessments is more
appropriate than treating these factors as independent variables. Direct measurement (coating,
triangulation, and surface integration) data from the scientific literature were used to calculate
SA/BW ratios for two age groups of children (infants ages 0 to 2 years and children ages 2.1 to
17.9 years). These ratios were calculated by dividing body surface areas by corresponding body
weights for the 401 individuals analyzed by Gehan and George (1970) and summarized by U.S.
EPA (1985). Distributions of SA/BW ratios were developed, and summary statistics were
calculated for the two age groups and the combined data set.
8-4
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Summary statistics for the two children's age groups are presented in Table 8-4. The
shapes of these SA/BW distributions were determined using D'Agostino's test. The results
indicate that the SA/BW ratios for infants are lognormally distributed. The SA/BW ratios for
children were neither normally nor lognormally distributed. According to Phillips et al. (1993),
SA/BW ratios should be used to calculate LADDs by replacing the body surface area factor in
the numerator of the LADD equation with the SA/BW ratio and eliminating the body weight
factor in the denominator of the LADD equation.
The effect of gender and age on SA/BW distribution was also analyzed by classifying the
401 observations by gender and age. Statistical analyses indicated no significant differences
between SA/BW ratios for males and females. SA/BW ratios were found to decrease with
increasing age.
8.2.3.4. Wong et al., 2000
Wong et al. (2000) conducted telephone surveys to gather information on children's
activity patterns as related to dermal contact with soil during outdoor play on bare dirt or mixed
grass and dirt surfaces. This second Soil Contact Survey (SCS-II) was a follow-up to an initial
Soil Contact Survey (SCS-I) conducted in 1996, which primarily focused on assessing adult
behavior related to dermal contact with soil and dust (Garlock et al., 1999). As part of SCS-I,
information was gathered on the behavior of children under the age of 18 years; however, the
questions were limited to clothing choices and the length of time after soil contact to hand
washing. Results obtained for children from SCS-I were not reported in Garlock et al. (1999),
but some of the collected information is summarized in Wong et al. (2000).
For SCS-I, the population size of children sampled was 211. Older children (those
between the ages of 5 and 17 years) were questioned regarding participation in "gardening and
yardwork," "outdoor sports," and "outdoor play activities." For children less than 5 years old,
"outdoor play activities" occurring on a playground or yard with "bare dirt or mixed grass and
dirt" surfaces were noted. An effort was also made to determine the clothing worn during these
play activities during warm weather months (April though October). For both groups of
children, information was gathered concerning hand washing, bathing, and clothes changing
habits after soil contact activities, but these results are not reported in Wong et al. (2000).
The results of SCS-I indicated that most children wore short pants, a dress or skirt, short
sleeve shirts, no socks, and leather or canvas shoes during the outdoor play activities of interest.
Using data from Anderson et al. (1985), percentages of total body surface area associated with
specific body parts were estimated (Table 8-5). Then exposed skin surface areas for children
8-5
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under age 5 were estimated per clothing item as well as for all clothing items worn together
during warm weather outdoor play (Table 8-6). Faces and hands were assumed to be exposed
under all conditions, with the face having a constant surface area fraction of 5% and the hands
6%.
8.2.4. Application of Body Surface Area Data
For swimming and bathing scenarios, past exposure assessments have assumed that 75%
to 100% of the skin surface is exposed (U.S. EPA, 1992b). Central and upper-percentile values
for children should be derived from Table 8-1 or Table 8-2.
Unlike in exposure to liquids, clothing may or may not be effective in limiting the extent
of exposure to soil. The children clothing scenarios are presented below.
Central tendency mid-range: Child wears long-sleeved shirt, pants, and shoes. The
exposed skin surface is limited to the head and hands. Table 8-3 can be used to
determine the skin surface area, depending on the age group of interest.
Upper percentile: Child wears short-sleeved shirt, shorts, and shoes. The exposed skin
surface is limited to the head, hands, forearms, and lower legs. Table 8-3 can be used to
determine the skin surface, area depending on the age group of interest.
The clothing scenarios presented above suggest that roughly 10% to 25% of the skin area may be
exposed to soil. Because some studies have suggested that exposure can occur under clothing,
the upper end of this range was selected in Dermal Exposure Assessment: Principles and
Applications (U.S. EPA, 1992b) for deriving defaults. Default values for children can be derived
by multiplying the 50th and 95th percentiles of the total surface area by 0.25 for the ages of
interest.
When addressing soil contact exposures, assessors may want to refine estimates of
surface area exposed on the basis of seasonal conditions. For example, in moderate climates, it
may be reasonable to assume that 5% of the skin is exposed during the winter, 10% during the
spring and fall, and 25% during the summer.
These estimates of exposed skin area should be useful to assessors using the traditional
approach of multiplying the soil adherence factor by exposed skin area to estimate the total
amount of soil on skin.
8-6
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8.3. SOIL ADHERENCE TO SKIN
This section presents soil adherence data specific to activity and body part and is
designed to be combined with the total surface area of that body part. No reduction of body part
area is made for clothing coverage using this approach. Thus, assessors who adopt this
approach, should not use the defaults presented above for soil exposed skin area. Rather, they
should use Table 8-3 to estimate surface areas of specific body parts.
8.3.1. Background
Soil adherence to the surface of the skin is a required parameter for calculating dermal
dose when the exposure scenario involves dermal contact with a chemical in soil. A number of
studies have attempted to determine the magnitude of dermal soil adherence. These studies are
described in detail in U.S. EPA (1992b). This section summarizes recent studies that estimate
soil adherence to skin for use as exposure factors.
8.3.2. Soil Adherence to Skin Studies
8.3.2.1. Kissel etal, 1996a
Kissel et al. (1996a) conducted soil adherence experiments using five soil types
(descriptors) obtained locally in the Seattle, WA, area: sand (211), loamy sand (CP), loamy sand
(85), sandy loam (228), and silt loam (72). All soils were analyzed by hydrometer (settling
velocity) to determine composition. Clay content ranged from 0.5 to 7.0%. Organic carbon
content, determined by combustion, ranged from 0.7 to 4.6%. Soils were dry-sieved to obtain
particle size ranges of < 150, 150-250, and > 250 |im. For each soil type, the amount of soil
adhering to an adult female hand, using both sieved and unsieved soils, was determined by
measuring the difference in soil sample weight before and after the hand was pressed into a pan
containing the test soil. Loadings were estimated by dividing the recovered soil mass by total
hand area, although loading occurred primarily on only one side of the hand. Results showed
that, generally, soil adherence to hands could be directly correlated with moisture content, and
inversely correlated with particle size and was independent of clay content or organic carbon
content.
8.3.2.2. Kissel etal, 1996b
Further experiments were conducted by Kissel et al. to estimate soil adherence associated
with various indoor and outdoor activities: greenhouse gardening, tae kwon do karate, soccer,
3-7
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rugby, reed gathering, irrigation installation, truck farming, and playing in mud (Kissel et al.,
1996b). Several of the activities studied by involved children, as shown in Table 8-7.
A summary of field studies by activity, gender, age, field conditions, and clothing worn is
presented in Table 8-7. The subjects' body surfaces (forearms, hands, lower legs for all sample
groups; faces, and/or feet pairs in some sample groups) were washed before and after the
monitored activities. Paired samples were pooled into single ones. Mass recovered was
converted to loading using allometric models of surface area. Geometric means for soil
adherence by activity and body region are presented in Table 8-8. The results presented are
based on direct measurement of soil loading on the surfaces of skin before and after activities
that may be expected to have soil contact (Kissel et al., 1996b). The results indicate that the rate
of soil adherence to the hands is higher than for other parts of the body.
8.3.2.3. Holmes et al.,1999
Holmes et al. (1999) collected pre- and post-activity soil loadings on various body parts
of individuals within groups engaged in various occupational and recreational activities. These
groups included children at a daycare center (Daycare kids) and playing indoors in a residential
setting (Indoor kids). This study was conducted as a follow-up to previous field sampling of soil
adherence on individuals participating in various activities (Kissel et al., 1996b). For this round
of sampling, soil loading data were collected using the same methods used and described in
Kissel et al. (1996b). Information regarding the groups of children studied and their observed
activities is presented in Table 8-9.
The daycare children studied were all at one location, and measurements were taken on 3
different days. The children freely played both indoors in the house and outdoors in the
backyard. The backyard was described as having a grass lawn, shed, sand box, and wood chip
box. In this setting, the children engaged in typical activities, including playing with toys and
with each other, wrestling, sleeping, and eating. The number of children within each day's
group and the clothing worn are described in Table 8-10.
The five children measured on the first day were washed first thing in the morning to
establish a preactivity level. They were next washed at noon to determine the postactivity soil
loading for the morning (Daycare kids No. la). The same children were washed once again at
the close of the day for measurement of soil adherence from the afternoon play activities
(Daycare kids No. Ib).
-------
For the second observation day (Daycare kids No. 2), postactivity data were collected for
five children. All the activities on this day occurred indoors. For the third daycare group
(Daycare kids No. 3), four children were studied.
On two separate days, children playing indoors in a home environment were monitored.
The first group (Indoor kids No. 1) had four children and the second group (Indoor kids No. 2)
had six children. The play area was described by the authors as being primarily carpeted. The
clothing worn by the children within each day's group is described in Table 8-10.
The geometric means and standard deviations of the postactivity soil adherence for each
group of children and for each body part are summarized in Table 8-11. According to the
authors, variations in the soil loading data from the daycare participants reflect differences in the
weather and access to the outdoors.
An advantage of this study is that it provides a supplement to the soil loading data
collected in a previous round of studies (Kissel et al., 1996b). Also, the data support the
assumption that hand loading can be used as a conservative estimate of soil loading on other
body surfaces for the same activity. The activities studied represent normal child play both
indoors and outdoors, as well as for different combinations of clothing. The small number of
participants is a disadvantage of this study. Also, the children studied and the activity setting
may not be representative of the U.S. population.
8.3.2.4. Kissel et al, 1998
In this study, Kissel et al. (1998) measured dermal exposure to soil from staged activities
conducted in a greenhouse. A fluorescent marker was mixed in soil so that soil contact for a
particular skin surface area could be identified. The subjects, which included a group of
children, were video-imaged under a long-wave ultraviolet (UV) light before and after soil
contact. In this manner, soil contact on hands, forearms, lower legs, and faces was assessed by
the presence of fluorescence. In addition to fluorometric data, gravimetric measurements for
preactivity and postactivity were obtained from the different body parts examined.
The studied group of children played for 20 minutes in a soil bed of varying moisture
content that represented wet and dry soils. For wet soils, combinations of both long sleeves and
long pants and short sleeves and short pants were tested. Children only wore short sleeves and
short pants during play in the dry soil. Clothing was laundered after each trail. Thus, a total of
three trials with children were conducted. The parameters describing each of these trials are
summarized in Table 8-12.
8-9
-------
Before each trial, each child was washed in order to obtain a preactivity or background
gravimetric measurement. Preactivity data are shown in Table 8-13. Body part surface areas
were calculated using Anderson et al. (1985) for the range of heights and weights of the study
participants.
For wet soil, postactivity fluorescence results indicated that the hand had a much higher
fractional coverage than other body surfaces (see Figure 8-2). No fluorescence was detected on
the forearms or lower legs of the children dressed in long sleeves and pants.
As shown in Figure 8-3, postactivity gravimetric measurements showed higher soil
loading on hands and much lower amounts on other body surfaces, as was observed with the
fluorescence data. According to the authors, the relatively low loadings observed on nonhand
body parts may have been a result of the limited area of contact rather than lower localized
loadings. A geometric mean dermal loading of 0.7 mg/cm2 was found on the children's hands
following play in wet soil. Mean loadings were lower on hands in the dry soil trial and on lower
legs, forearms, and faces in both the wet and dry soil trials. Higher loadings were observed for
all body surfaces with the higher moisture content soils.
This report is valuable in showing soil loadings from soils of different moisture content
and providing evidence that dermal exposure to soil is not uniform for various body surfaces.
There is also some evidence from this study demonstrating the protective effect of clothing.
Disadvantages of the study include a small number of study participants and a short activity
duration. Also, no information is provided on the ages of the children involved in the study.
8.4. RECOMMENDATIONS
8.4.1. Body Surface Area
Body surface area estimates are based on direct measurements. Re-analysis of data
collected by Boyd (1935) by several investigators (Gehan and George, 1970; U.S. EPA, 1985;
Murray and Burmaster, 1992; Phillips et al., 1993) constitutes much of this literature. Methods
are highly reproducible and the results widely accepted. The representativeness of these data to
the general population is somewhat limited, because variability due to race or gender have not
been systematically addressed.
The recommendations for body surface area for children are summarized in Table 8-14.
These recommendations are based on U.S. EPA (1985) and Phillips et al. (1993). Table 8-15
presents the confidence ratings for various aspects of the recommendations for body surface
area. The U.S. EPA (1985) study is based on generally accepted measurements that enjoy
widespread usage, it summarizes and compares previous reports in the literature, it provides
8-10
-------
statistical distributions for adults, and it provides data for total body surface area and body parts
by gender for children. The results are based on selected measurements from the original data
collected by Boyd (1935). The Phillips et al. (1993) analyses are based on direct-measurement
data that provide distributions of body surface area to calculate LADD. The results are
consistent with previous efforts to estimate body surface area. Analyses are also based on
measurements selected from the original measurements made by Boyd (1935), and data were not
analyzed for specific body parts.
8.4.2. Soil Adherence to Skin
Recommendations for the rate of soil adherence to the skin are based on data collected by
Kissel et al. (1996a, b) for specific activities. The experimental design and measurement
methods used by Kissel et al. are straightforward and reproducible, but it should be noted that the
controlled experiments and field studies are based on a limited number of measurements, and
specific situations were selected to assess soil adherence to skin. Consequently, variation due to
individuals, protective clothing, temporal, or seasonal factors remains to be studied in more
detail. Therefore, caution is required in the interpretation and application of these results for
exposure assessments.
In consideration of these general observations and the recent data from Kissel et al.
(1996a, b), changes are needed to past EPA recommendations, which used one adherence value
to represent all soils, body parts, and activities. One approach would be to select the activity
from Table 8-7 that best represents the exposure scenario of concern and use the corresponding
adherence value from Table 8-8. Although this approach represents an improvement, it still has
shortcomings. For example, it is difficult to decide which activity in Table 8-8 is most
representative of a typical residential setting involving a variety of activities. It may be useful to
combine these activities into general classes of low, moderate, and high contact. In the future, it
may be possible to combine activity-specific soil adherence estimates with survey-specific soil
adherence estimates with survey-derived data on activity frequency and duration to develop
overall average soil contact rates. EPA is sponsoring research to develop such an approach.
As this information becomes available, updated recommendations will be issued.
Table 8-8 provides the best estimates available on activity-specific adherence values, but
they are based on limited data. Therefore, they have a high degree of uncertainty, so
considerable judgment must be used when selecting them for an assessment. The confidence
ratings for various aspects of this recommendation are summarized in Table 8-16. Insufficient
data are available to develop a distribution or a probability function for soil loadings.
8-11
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Past EPA guidance has recommended assuming that soil exposure occurs primarily to
exposed body surfaces, and it used typical clothing scenarios to derive estimates of exposed skin
area. The approach recommended above for estimating soil adherence addresses this issue in a
different manner. This change was motivated by two developments. First, there has been
increased acceptance that soil and dust particles can get under clothing and be deposited on skin.
Second, recent studies of soil adherence measured soil on entire body parts (whether or not they
were covered by clothing) and averaged the amount of soil adhering to skin over the area of the
entire body part. The soil adherence levels resulting from these new studies must be combined
with the surface area of the entire body part (not merely unclothed surface area) to estimate the
amount of contaminant on skin. An important caveat, however, is that this approach assumes
that clothing in the exposure scenario of interest matches the clothing in the studies used to
derive these adherence levels, such that the same degree of protection provided by clothing can
be assumed in both cases. If clothing differs significantly between the studies reported here and
the exposure scenarios under investigation, considerable judgment is needed to adjust either the
adherence level or the surface area assumption.
The dermal adherence value represents the amount of soil on the skin at the time of
measurement. Assuming that the amount measured on the skin represents its accumulation
between washings and that people wash at least once per day, these adherence values can be
interpreted as daily contact rates (U.S. EPA, 1992b). However, this is not recommended,
because the residence time of soils on skin has not been studied. Instead, it is recommended that
these adherence values be interpreted on an event basis (U.S. EPA, 1992b).
8-12
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Table 8-1. Total body surface area of male children in m2a
Age
(years)b
2<3
3<4
4<5
5<6
6<7
7<8
8<9
9<10
10<11
IK 12
12<13
13<14
14<15
15<16
16<17
17<18
3<6
6<9
9<12
12<15
15<18
Percentile
5th
0.527
0.585
0.633
0.692
0.757
0.794
0.836
0.932
1.010
1.000
1.110
1.200
1.330
1.450
1.550
1.540
0.616
0.787
0.972
1.190
1.500
10th
0.544
0.606
0.658
0.721
0.788
0.832
0.897
0.966
.040
.060
.130
.240
.390
.490
.590
.560
0.636
0.814
1.000
1.240
1.550
15th
0.552
0.620
0.673
0.732
0.809
0.848
0.914
0.988
.060
.120
.200
.270
.450
.520
.610
.620
0.649
0.834
1.020
1.270
1.590
25th
0.569
0.636
0.689
0.746
0.821
0.877
0.932
.000
.100
.160
.250
.300
.510
.600
.660
.690
0.673
0.866
1.070
1.320
1.650
50th
0.603
0.664
0.731
0.793
0.866
0.936
1.000
1.070
1.180
1.230
1.340
1.470
1.610
1.700
1.760
1.800
0.728
0.931
1.160
1.490
1.750
75th
0.629
0.700
0.771
0.840
0.915
0.993
1.060
1.130
1.280
1.400
1.470
1.620
1.730
1.790
1.870
1.910
0.785
1.010
1.280
1.640
1.860
85th
0.643
0.719
0,796
0.864
0.957
.010
.120
.160
.350
.470
.520
.670
.780
.840
.980
.960
0.817
1.050
1.360
1.730
1.940
90th
0.661
0.729
0.809
0.895
1.010
1.060
1.170
1.250
1.400
1.530
1.620
1.750
1.840
1.900
2.030
2.030
0.842
1.090
1.420
1.770
2.010
95th
0.682
0.764
0.845
0.918
1.060
1.110
1.240
1.290
1.480
1.600
1.760
1.810
1.910
2.020
2.160
2.090
0.876
1.140
1.520
1.850
2.110
a Lack of height measurements for children < 2 years in NHANES II precluded calculation of surface
areas for this age group.
b Estimated values calculated using NHANES II data.
Source: U.S. EPA, 1985
8-13
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Table 8-2. Total body surface area of female children m2 a
A fff\
Age
(year)"
2<3
3<4
4<5
5<6
6<7
7<8
8<9
9<10
10<11
IK 12
12<13
13<14
14<15
15<16
16<17
17<18
3<6
6<9
9<12
12<15
15<18
5th
0.516
0.555
0.627
0.675
0.723
0.792
0.863
0.897
0.981
1.060
1.130
1.210
1.310
1.380
1.400
1.420
0.585
0.754
0.957
1.210
1.400
10th
0.532
0.570
0.639
0.700
0.748
0.808
0.888
0.948
1.01
1.09
1.19
1.28
1.34
1.49
1.46
1.49
0.610
0.790
0.990
1.27
1.44
15th
0.544
0.589
0.649
0.714
0.770
0.819
0.913
0.969
.050
.120
.240
.320
.390
.430
.480
.510
0.630
0.804
1.030
1.300
1.470
25th
0.557
0.607
0.666
0.735
0.791
0.854
0.932
.010
.100
.160
.270
.380
.450
.470
.530
.560
0.654
0.845
1.060
1.370
1.510
Percentile
50th
0.579
0.649
0.706
0.779
0.843
0.917
1.000
1.060
1.170
1.300
1.400
1.480
1.550
1.570
1.600
1.630
0.711
0.919
1.160
1.480
1.600
75th
0.610
0.688
0.758
0.830
0.914
0.977
1.050
1.140
1.290
1.400
1.510
1.590
1.660
1.670
1.690
1.730
0.770
1.000
1.310
1.610
1.700
85th
0.623
0.707
0.777
0.870
0.961
1.020
1.080
1.220
1.340
1.500
1.620
1.670
1.740
1.720
1.790
1.800
0.808
1.040
1.380
1.680
1.760
90th
0.637
0.721
0.794
0.902
0.989
1.060
1.110
1.310
1.370
1.560
1.640
1.750
1.760
1.760
1.840
1.840
0.831
1.070
1.430
1.740
1.820
95th
0.653
0.737
0.820
0.952
.030
.130
.180
.410
.430
.620
.700
.860
.880
.830
.910
.940
0.879
1.130
1.560
1.820
1.920
a Lack of height measurements for children < 2 years in NHANES II precluded calculation of surface
areas for this age group.
b Estimated values calculated using NHANES II data.
Source: U.S. EPA, 1985
8-14
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Table 8-3. Percentage of total body surface area by body part for children
Age
(years)
<1
1<2
2<3
3<4
4<5
5<6
6<7
7<8
8<9
9<10
10<11
IK 12
12<13
13<14
14<15
15<16
16<17
17<18
Na
M:F
2:0
1:1
1:0
0:5
1:3
1:0
0:2
1:0
1:0
1:0
1:0
Percent of Total
Head
Mean
18.20
16.50
14.20
13.60
13.80
13.10
12.00
8.74
9.97
7.96
7.58
Min-Max
18.2-18.3
16.5-16.5
13.3-14.0
12.1-15.3
11.6-12.5
Trunk
Mean
35.70
35.50
38.50
31.90
31.50
35.10
34.20
34.70
32.70
32.70
31.70
Min-Max
34.8-36.6
34.5-36.6
29.9-32.8
30.5-32.4
33.4-34.9
Arms
Mean
13.70
13.00
11.80
14.40
14.00
13.10
12.30
13.70
12.10
13.10
17.50
Min-Max
12.4-15.1
12.8-13.1
14.2-14.7
13.0-15.5
11.7-12.8
Hands
Mean
5.300
5.680
5.300
6.070
5.700
4.710
5.300
5.390
5.110
5.680
5.130
Min-Max
5.21-5.39
5.57-5.78
5.83-6.32
5.15-6.62
5.15-5.44
Legs
Mean
20.60
23.10
23.20
26.80
27.80
27.10
28.70
30.50
32.00
33.60
30.80
Min-Max
18.2-22.9
22.1-24.0
26.0-28.6
26.0-29.3
28.5-28.8
Feet
Mean
6.540
6.270
7.070
7.210
7.290
6.900
7.580
7.030
8.020
6.930
7.280
Min-Max
6.49-6.59
5.84-6.70
6.80-7.88
6.91-8.10
7.38-7.77
oo
' N = number of subjects; M:F = male-to-female ratios.
Source: U.S. EPA, 1985
-------
Table 8-4. Descriptive statistics for surface area/body weight (SA/BW) ratios (m2/kg)
Age
(years)
0-2
2.1-17.9
Mean
0.0641
0.0423
Range
(min-max)
0.0421-0.1142
0.0268-0.0670
SDa
0.0114
0.0076
SE"
7.84e-4
1.05e-3
Percentiles
5th
0.0470
0.0291
10th
0.0507
0.0328
25th
0.0563
0.0376
50th
0.0617
0.0422
75th
0.0719
0.0454
90th
0.0784
0.0501
95th
0.0846
0.0594
Source: Phillips et al., 1993
oo
-------
Table 8-5. Clothing choices and assumed body surface areas exposed
Clothing
Long pants
Short pants
Long sleeves
Short sleeves
No shirt (males)
Halter (females)
High socks
Low socks
No socks
Shoes
No shoes or sandals
Gloves
No gloves
Hat or no hat
Maximum exposure
Area assumed exposed
Lower 1A of thigh and upper 1A of lower leg
Forearms
3/4 trunk and arms
1/2 trunk and arms
1/4 lower leg
bottom half lower leg
feet
hands
1/3 head for face
% of total body surface area
M
0
13
0
6
38
n/a
0
3
6
0
7
0
6
5
75
F
0
13
0
6
n/a
30
0
3
6
0
7
0
6
5
67
Source: Wong etal, 2000
Table 8-6. Estimated skin surface exposed during warm weather outdoor
play for children under age 5 (based on SCS-I data)
N
Mean
Median
S.D.
Skin area exposed (% of total) based on expressed choice of clothing
Pants
41.0
12.8
13.0
1.0
Shirt sleeves
43.0
6.6
6.0
2.7
Socks
42.0
4.4
5.3
1.7
Shoes
43.0
3.0
3.5
3.2
Hat"
43.0
5.0
5.0
0.0
All clothing
41.0
32.0
30.5
6.0
' Face was assumed to always be exposed.
Source: Wong et al, 2000
8-17
-------
Table 8-7. Summary of field studies
Activity
Month
Event
duration
(hours)
M/F
Nb
Age
(years)
Conditions
Clothing
Indoor
Tae kwon do
Indoor kids
No. 1
Indoor kids
No. 2
Daycare kids
No. la
Daycare kids No.
Ib
Daycare kids
No.2b
Daycare kids
No. 3
Feb
Jan
Feb
Aug
Aug
Sept
Nov
1.50
2.00
2.00
3.50
4.00
8.00
8.00
6/1
3/1
4/2
5/1
5/1
4/1
3/1
7
4
6
6
6
5
4
8-42
6-13
3-13
1-6.5
1-6.5
1-4
1^.5
Carpeted floor
Playing on carpeted
floor
Playing on carpeted
floor
Indoors: linoleum
surface; outdoors:
grass, bare earth,
barked area
Indoors: linoleum
surface; outdoors:
grass, bare earth,
barked area
Indoors, low napped
carpeting, linoleum
surfaces
Indoors: linoleum
surface, outside: grass,
bare earth, barked area
All in long sleeved, long
pants martial arts uniform,
sleeves rolled back, barefoot
3 of 4 short pants, 2 of 4
short sleeves, socks, no shoes
5 of 6 long pants, 5 of 6 long
sleeves, socks, no shoes
4 of 6 long pants, 4 of 6 shofl
sleeves, shoes
4 of 6 long pants, 4 of 6 shofl
sleeves, no shoes
4 of 5 long pants, 3 of 5 long
sleeves, all barefoot for part
of the day
All long pants, 3 of 4 long
sleeves, socks and shoes
Outdoor
Soccer No. 1
Gardeners No. 1
Archeologists
Kids in mud
No. 1
Kids in mud
No. 2
Nov
Aug
July
Sept
Sept
0.67
4.00
11.50
0.17
0.33
8/0
1/7
3/4
5/1
5/1
8
8
7
6
6
13-15
16-35
16-35
9-14
9-14
Half grass-half bare
earth
Weeding, pruning,
digging a trench
Digging with trowel,
screening dirt, sorting
Lake shoreline
Lake shoreline
6 of 8 long sleeves, 4 of 8
long pants, 3 of 4 short pants
and shin guards
6 of 8 long pants, 7 of 8 shofl
sleeves, 1 sleeveless, socks,
shoes, intermittent use of
gloves
6 of 7 short pants,all short
sleeves, 3 no shoes or socks,
2 sandals
All in short sleeve T-shirts,
shorts, barefoot
All in short sleeve T-shirts,
shorts, barefoot
b Activities were confined to the house
Sources: Kissel et al., 1996b; Holmes et al., 1996
8-18
-------
Table 8-8. Geometric mean (Geometric Standard Deviation) of soil adherence
by activity and body region
Activity
N
Postactivity dermal soil loadings (mg/cm2)
Hands
Arms
Legs | Faces
Feet
Indoor
Tae kwon do
Indoor kids No. 1
Indoor kids No. 2
Daycare kids No. la
Daycare kids No. Ib
Daycare kids No. 2
Daycare kids No. 3
7
4
6
6
6
5
4
0.0063
(1.9)
0.0073
(1.9)
0.0140
(1.5)
0.1100
(1.9)
0.1500
(2.1)
0.0730
(1.6)
0.0360
(1.3)
0.0019
(4.1)
0.0042
(1.9)
0.0041
(2.0)
0.0260
(1.9)
0.0310
(1.8)
0.0230
(1.4)
0.0120
(1.2)
0.0020
(2.0)
0.0041
(2.3)
0.0031
(1.5)
0.0300
(1.7)
0.0230
(1.2)
0.0110
(1.4)
0.0140
(3.0)
0.0022
(2.1)
0.0120
(1.4)
0.0091
(1.7)
0.0790
(2.4)
0.1300
(1.4)
0.0440
(1.3)
0.0053
(5.1)
Outdoor
Soccer No. 1
Gardeners No. 1
Archeologists
Kids in mud No. 1
Kids in mud No. 2
8
8
7
6
6
0.1100
(1.8)
0.2000
(1.9)
0.1400
(1.3)
35.0000
(2.3)
58.0000
(2.3)
0.0110
(2.0)
0.0500
(2.1)
0.0410
(1.9)
11.0000
(6.1)
11.0000
(3.8)
0.0310
(3.8)
0.0720
0.0280
(4.1)
36.0000
(2.0)
9.5000
(2.3)
0.012
(1.5)
0.058
(1.6)
0.050
(1.8)
0.1700
0.2400
(1.4)
24.0000
(3.6)
6.7000
(12.4)
Sources: Kissel et al., 1996b; Holmes et al, 1996
8-19
-------
Table 8-9. Summary of groups assayed in round 2 of field measurements
Activity
Daycare kids No. la
Day care kids No. Ib
Daycare kids No. 2
Daycare kids No. 3
Indoor kids No. 1
Indoor kids No. 2
Month
Aug
Aug
Sept
Nov
Jan
Feb
Event
duration (hours)
3.5
4.0
8.0
8.0
2.0
2.0
M/F
5/1
5/1
4/1
3/1
3/1
4/2
N
6
6
5
4
4
6
Ages
1-6.5
1-6.5
1-4
1^.5
6-13
3-13
Source: Holmes et al., 1999
Table 8-10. Attire for individuals within children's groups studied
Activity
Daycare kids No. la
Daycare kids No. Ib
Daycare kids No. 2
Daycare kids No. 3a
Indoor kids No. 1
Indoor kids No. 2
N
6
6
5
4
4
6
Pants
Long
4
4
4
4
1
5
Short
2
2
1
0
3
1
Sleeves
Long
1
1
2
3
2
5
Short
5
5
o
6
i
2
1
Socks
High
1
1
NA
0
0
0
Low
5
5
NA
4
4
6
Shoes
Low leather or canvas
shoes: 6
Barefoot: 3
Low leather or canvas
shoes: 3
Barefoot: 2
Shoes/socks l/i day and
barefoot !/2day: 3
Low shoes: 4
No shoes (socks only): 4
No shoes (socks only): 6
' All children wore jackets when engaged in outdoor activities.
NA = Not Available: 3 children wore socks for 1A day in the morning but no specific information is
provided on the type of socks worn.
Source: Holmes et al., 1999
8-20
-------
Table 8-11. Geometric mean (geometric standard deviation) of round 2
post-activity loadings
Activity
Daycare kids No. la
Day care kids No. Ib
Daycare kids No. 2
Daycare kids No. 3
Indoor kids No. 1
Indoor kids No. 2
Na
4
6
6
6
5
4
Postactivity Dermal Soil Loadings (mg/cm2)
Hands
0.1100
(1.9)
0.1500
(2.1)
0.0730
(1.6)
0.0360
(1.3)
0.0073
(1.9)
0.0140
(1.5)
Forearms
0.0260
(1.9)
0.0310
(1.8)
0.0230
(1.4)
0.0120
(1.2)
0.0042
(1.9)
0.0041
(2.0)
Lower legs
0.0300
(1.7)
0.0230
(1.2)
0.0110
(1.4)
0.0140
(3.0)
0.0041
(2.3)
0.0031
(1.5)
Feet"
0.0790
(2.4)
0.1300
(1.4)
0.0440
(1.3)
0.0053
(5.1)
0.0120
(1.4)
0.0091
(1.7)
a Number of data points for specific non-hand body parts may deviate slightly.
b Children's feet rather than faces were washed in order to reduce the chance of a child's refusal to participate.
Source: Holmes et al., 1999
Table 8-12. Summary of controlled greenhouse trials, children playing
Activity
Playing
Age (years)
8-12
Duration (min)
20
Soil moisture (%)
17-18
16-18
3-4
Clothing
L
S
S
N
4
9
5
M/F
3/1
5/4
3/2
L = long sleeves and long pants.
S = short sleeves and short pants.
Source: Kissel etal., 1998
8-21
-------
Table 8-13. Preactivity loadings recovered from greenhouse trial children
volunteers
Area
Hands
Forearms
Lower legs
Face
N
12
12
12
12
Body part surface area
(cm2)
420-798
584-932
1206-2166
388-602
Geometric mean (jig/cm2)
(95% CI)
9.4
(5.4-15.8)
3.4
(2.3-5.2)
1.0
(0.7-1.5)
0.8
(0.5-1.5)
Source: Kissel etal., 1998
Table 8-14. Summary of recommended values for skin surface area
Surface Area
Whole body
Body parts
Central Tendency
See Table 8-1 and 8-4
for 50th percentile
See Table 8-3
Upper Percentile
See Tables 8-1, 8-2, and 8-4
See Table 8-3
Multiple Percentiles
See Tables 8-1, 8-2, and 8-4
See Table 8-3
8-22
-------
Table 8-15. Confidence in body surface area measurement recommendations
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Representativeness of the
population
Characterization of variability
Lack of bias in study design
Measurement error
Studies were from peer-reviewed journal articles.
EPA report was peer-reviewed before distribution.
The journals used have wide circulation.
EPA report available from the National Technical
Information Service.
Experimental methods are well described.
Experiments measured skin area directly.
Experiments conducted in the U.S.
Re-analysis of primary data in more detail by two
different investigators.
Neither rapidly changing nor controversial area;
estimates made in 1935 deemed to be accurate and
subsequently used by others.
Not relevant to exposure factor; parameter not time
dependent.
Approach used by other investigators; not challenged
in other studies.
Not statistically representative of U.S. population.
Individual variability due to age, race, or gender not
studied.
Objective subject selection and measurement
methods used; results reproduced by others with
different methods.
Measurement variations are low; adequately
described by normal statistics.
High
High
High
High
High
Low
Low
NA
High
Medium
Low
High
Low/Medium
Other Elements
Number of studies
Agreement among researchers
Overall Rating
One experiment; two independent re-analyses of this
data set.
Consistent results obtained with different analyses
but from a single set of measurements.
This factor can be directly measured. It is not subject
to dispute. Influences of age, race, or gender have
not been detailed adequately in these studies.
Medium
Medium
Medium
-------
Table 8-16. Confidence in soil adherence to skin recommendations
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection
period
Validity of approach
Representativeness of the
population
Characterization of variability
Lack of bias in study design
Measurement error
Studies were from peer-reviewed journal articles.
Articles were published in widely circulated journals.
Reports clearly describe experimental method.
The goal of the studies was to determine soil
adherence to skin.
Experiments were conducted in the U.S.
Experiments directly measured soil adherence to
skin; exposure and dose of chemicals in soil were
measured indirectly or estimated from soil contact.
New studies were presented.
Seasonal factors may be important but have not been
studied adequately.
Skin-rinsing technique is a widely employed
procedure.
Studies were limited to Washington State and may
not be representative of other locales.
Variability in soil adherence is affected by many
factors, including soil properties, activity, and
individual behavior patterns.
The studies attempted to measure soil adherence in
selected activities and conditions to identify
important activities and groups.
The experimental error is low and well controlled.
High
High
High
High
High
High
High
Medium
High
Low
Low
High
High
Other Elements
Number of studies
Agreement among researchers
Overall Rating
The experiments were controlled as they were
conducted by a few laboratories; activity patterns
were studied by only one laboratory.
Results from key study were consistent with earlier
estimates from relevant studies and assumptions but
are limited to hand data.
Data are limited; therefore it is difficult to extrapolate
from experiments and field observations to general
conditions. Application of results to other similar
activities may be subject to variation.
Medium
Medium
Medium
8-24
-------
Biologically
Effective
Dose
Potential Applied ,.
Exposure , Dose ^- Dose X.^
^^ 1^
Internal
Dose
A-X^ T
Metabolism
Effect
Skin
Uptake
Figure 8-1. Schematic of dose and exposure: dermal route.
Source: U.S. EPA, 1992a
8-25
-------
Hands
Lower legs/short pants
Forearms/short sleeves
Faces -
•)»X^I*X*X*X*I*X*X*X*X*I*x"x*t*!*?*?1'.
X-HH
Adult [3
Child
20 40 60 80
Percent Fluorescing
100
Figure 8-2. Skin coverage as determined by fluorescence versus body part for adults
transplanting plants and for children playing in wet soils.
Source: Kissel, 1998
Figure 8-3. Gravimetric loading versus body part for adult transplanting plants in wet soil
10
o
"So
E
too
41
ea
o
-J
t— H
'3
on
0.1-
0.01-
0.001
IT
!I li
1 x
1
1
>
o
-T I
1 ?t'
adult
child, wet
child, dry
' *J
- 1
X
^
O
Hands
Legs
Arms
Faces
and for children playing in wet and dry soils.
Source: Kissel, 1998
8-26
-------
Appendix A for Chapter 8: Formulae for Total Body Surface Area
Most formulae for estimating surface area (SA) relate height to weight to surface area. The
following formula was proposed by Gehan and George (1970):
SA = KW2/3 (8A-1)
where:
SA = surface area in m2;
W = weight in kg; and
K = constant.
Although the above equation has been criticized because human bodies have different
specific gravities and because the surface area per unit volume differs for individuals with
different body builds, it gives a reasonably good estimate of surface area.
SA = a0HalWa2 (8A-2)
A formula published in 1916 that still finds wide acceptance and use is that of Dubois and
Dubois. Their model can be written:
where:
SA = surface area in m2;
H = height in cm; and
W = weight in kg.
The values of a0 (0.007182), al (0.725), and a2 (0.425) were estimated from a sample of
only nine individuals for whom surface area was directly measured. Boyd (1935) stated that the
Dubois and Dubois formula was considered a reasonably adequate substitute for measuring
surface area. Nomograms for determining surface area from height and mass presented in
Volume I of the Geigy Scientific Tables (Lentner, 1981) are based on the Dubois and Dubois
formula. In addition, a computerized literature search conducted for this report identified several
8-27
-------
articles written in the last 10 years in which the Dubois and Dubois formula was used to estimate
body surface area.
Boyd (1935) developed new constants for the DuBois and DuBois model, based on
231 direct measurements of body surface area found in the literature. These data were limited to
measurements of surface area by coating methods (122 cases), surface integration (93 cases), and
triangulation (16 cases). The subjects were Caucasians of normal body build for whom data on
weight, height, and age (except for exact age of adults) were complete. Resulting values for the
constants in the Dubois and Dubois model were a0 = 0.01787, ax = 0.500, and a2 = 0.4838. Boyd
also developed a formula based exclusively on weight, which was inferior to the Dubois and
Dubois formula based on height and weight.
Gehan and George proposed another set of constants for the Dubois and Dubois model.
The constants were based on a total of 401 direct measurements of surface area, height, and
weight of all postnatal subjects listed in Boyd (1935). The methods used to measure these
subjects were coating (163 cases), surface integration (222 cases), and triangulation (16 cases).
Gehan and George (1970) used a least-squares method to identify the values of the constants.
The values of the constants chosen are those that minimize the sum of the squared percentage
errors of the predicted values of surface area. This approach was used because the importance of
an error of 0.1 m2 depends on the surface area of the individual. Gehan and George used the 401
observations summarized in Boyd (1935) in the least-squares method. The following estimates
of the constants were obtained: a0 = 0.02350, ax = 0.42246, and a2 = 0.51456. Hence, their
equation for predicting SA is:
SA = 0.02350 H°-42246Wa51456 (8A-3)
or in logarithmic form:
InSA = -3.75080 + 0.422461nH + 0.514561nW (8A-4)
where:
SA = surface area in m2;
H = height in cm; and
W = weight in kg.
8-28
-------
This prediction explains more than 99% of the variations in surface area among the 401
individuals measured (Gehan and George, 1970).
The equation proposed by Gehan and George was determined by EPA as the best choice
for estimating total body surface area (U.S. EPA, 1985). However, the paper by Gehan and
George gave insufficient information to estimate the standard error about the regression.
Therefore, the 401 direct measurements of children and adults (i.e., Boyd, 1935) were reanalyzed
in U.S. EPA (1985) using the formula of Dubois and Dubois (1916) and the Statistical
Processing System software package to obtain the standard error.
The Dubois and Dubois (1916) formula uses weight and height as independent variables
to predict total body SA, and can be written as:
or in logarithmic form:
where:
Sa; = surface area of the i-th individual (m2);
H; = height of the i-th individual (cm);
W; = weight of the i-th individual (kg);
a0, al3 and a2 = parameters to be estimated; and
a = a random error term with mean zero and constant variance.
Using the least=squares procedure for the 401 observations, the following parameter
estimates and their standard errors were obtained:
a0 = -3.73(0.18), a, = 0.417(0.054), a2 = 0.517(0.022)
The model is then:
SA^aQHW (8A-5)
SA = 0.0239 Ha417W°-517 (8A-7)
ln(SAY = InaQ +a^lnH- + a2lnW- + Ine- (8A-6)
8-29
-------
or in logarithmic form:
lnSA = 3.73+0.4171nH + 0.5171nW (8A-8)
with a standard error about the regression of 0.00374. This model explains more than 99% of the
total variation in surface area among the observations and is identical to two significant figures
with the model developed by Gehan and George (1970).
When natural logarithms of the measured surface areas are plotted against natural
logarithms of the surface predicted by the equation, the observed surface areas are symmetrically
distributed around a line of perfect fit, with only a few large percentage deviations. Only five
subjects differed from the measured value by 25% or more. Because each of the five subjects
weighed less than 13 pounds, the amount of difference was small. Eighteen estimates differed
from measurements by 15 to 24%. Of these, 12 weighed less than 15 pounds each, 1 was
overweight (5 feet 7 inches, 172 pounds), 1 was very thin (4 feet 11 inches, 78 pounds), and 4
were of average build. Because the same observer measured surface area for these four subjects,
the possibility of some bias in measured values cannot be discounted (Gehan and George 1970).
Gehan and George (1970) also considered separate constants for different age groups:
less than 5 years old, 5 years old to less than 20 years old, and greater than 20 years old. The
different values for the constants are presented in Table 8A-1.
The surface areas estimated using the parameter values for all ages were compared to
surface areas estimated by the values for each age group for subjects at the 3rd, 50th, and
97th percentiles of weight and height. Nearly all differences in surface area estimates were less
than 0.01 m2, and the largest difference was 0.03 m2 for an 18-year-old at the 97th percentile.
The authors concluded that there is no advantage in using separate values of a0, al3 and a2 by age
interval.
Haycock et al. (1978), working without knowledge of the work by Gehan and George
(1970), developed values for the parameters a0, al3 and a2 for the Dubois and Dubois model.
Their interest in making the Dubois and Dubois model more accurate resulted from their work in
pediatrics and the fact that Dubois and Dubois (1916) included only one child in their study
group—a severely undernourished girl who weighed only 13.8 pounds at age 21 months.
Haycock et al. used their own geometric method for estimating surface area from 34 body
measurements for 81 subjects. Their study included newborn infants (10 cases), infants
(12 cases), children (40 cases), and adult members of the medical and secretarial staffs of
two hospitals (19 cases). The subjects all had grossly normal body structure, but the sample
8-30
-------
Table 8A-1. Estimated Parameter Values for Different Age Intervals
Age group
All ages
< 5 years old
> 5 - <20 years old
> 20 years old
N
401
229
42
30
a0
0.02350
0.02667
0.03050
0.01545
Values
»i
0.42246
0.38217
0.35129
0.54468
»2
0.51456
0.53937
0.54375
0.46336
included subjects of widely varying physique, ranging from thin to obese. Black, Hispanic, and
white children were included in the sample.
The values of the model parameters were solved for the relationship between surface area
and height and weight by multiple regression analysis. The least=squares best fit for this
equation yielded the following values for the three coefficients: a0 = 0.024265, al = 0.3964, and
a2 = 0.5378. The result was the following equation for estimating surface area:
= 0.024265H03964W'
0.3964-IA70.5378
expressed logarithmically as:
InSA = lnO.024265 + 0.39641nH + 0.53781nW
(8A-9)
(8 A-10)
The coefficients for this equation agree remarkably well with those obtained by Gehan and
George (1970) for 401 measurements.
George et al. (1979) agreed that a model more complex than the model of Dubois and
Dubois for estimating surface area is unnecessary. Based on samples of direct measurements by
Boyd (1935) and Gehan and George (1970) and samples of geometric estimates by Haycock
et al. (1978), these authors obtained parameters for the Dubois and Dubois model that are
different than those originally postulated in 1916. The Dubois and Dubois model can be written
logarithmically as:
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InSA = Ina0 + a^nH + a2lnW (8A-11)
The values for a0, al3 and a2 obtained by the various authors discussed in this section are
shown in Table 8A-2.
Table 8A-2. Summary of surface area parameter values for the Dubois and Dubois
model
Author
Dubois and Dubois, 1916
Boyd, 1935
Gehan and George, 1970
Haycock et al., 1978
N
9
231
401
81
a»
0.007184
0.01787
0.02350
0.024265
Values
»i
0.725
0.500
0.42246
0.3964
a2
0.425
0.4838
0.51456
0.5378
The agreement between the model parameters estimated by Gehan and George (1970)
and Haycock et al. (1978) is remarkable in view of the fact that Haycock et al. were unaware of
the previous work. Haycock et al. used an entirely different set of subjects and used geometric
estimates of surface area rather than direct measurements. It has been determined that the Gehan
and George model is the formula of choice for estimating total surface area of the body because
it is based on the largest number of direct measurements.
Sendroy and Cecchini (1954) proposed a graphical method for creating a nomogram
whereby surface area could be read from a diagram relating height and weight to surface area.
However, they did not give an explicit model for calculating surface area. The graph was
developed empirically based on 252 cases, 127 of which were from the 401 direct measurements
reported by Boyd (1935). In the other 125 cases, the surface area was estimated using the linear
method of Dubois and Dubois (1916). Because the Sendroy and Cecchini method is graphical, it
is inherently less precise and less accurate than the formulae of other authors discussed above.
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REFERENCES FOR CHAPTER 8
Anderson, E; Browne, N; Duletsky, S; et al. (1985) Development of statistical distributions or ranges of standard
factors used in exposure assessments. U. S. Environmental Protection Agency Office of Health and Environmental
Assessment, Washington, DC. NTIS PB85-242667.
Boyd, E. (1935) The growth of the surface area of the human body. Minneapolis, MN: University of Minnesota
Press.
Buhyoff, GJ; Rauscher, HM; Hull, RB; et al. (1982) User's manual for statistical processing system (version 3C. 1).
Southeast Technical Associates, Inc.
Cohen-Hubal, EA; Sheldon, LS; Burke, JM; et al. (1999) Children's exposure assessment: a review of factors
influencing children's exposure, and the data available to characterize and assess that exposure. U.S. Environmental
Protection Agency, National Exposure Research Laboratory, Research Triangle Park, NC.
Costeff, H. (1966) A simple empirical formula for calculating approximate surface area in children. Arch Dis Child
41:651-683.
Dubois, D; Dubois, EF. (1916) A formula to estimate the approximate surface area if height and weight be known.
Arch of Intern Med 17:863-871.
Gehan, E; George, GL. (1970) Estimation of human body surface area from height and weight. Cancer Chemother
Rep 54(4):225-235.
Garlock TJ; Shirai, JH; Kissel, JC. 1999) Adult responses to a survey of soil contact related behaviors. J Expo Anal
Environ Epidemiol 1999: 9:134-142.
George, SL; Gehan, EA; Haycock, GB; et al. (1979) Letters to the editor. J Fed 94(2):342.
Haycock, GB; Schwartz, GJ; Wisotsky, DH. (1978) Geometric method for measuring body surface area:
a height-weight formula validated in infants, children, and adults. J Pedatr 93(l):62-66.
Holmes, KK; Kissel, JC; Richter, KY. (1996) Investigation of the influence of oil on soil adherence to skin. J Soil
Contam5(4):301-308.
Holmes, KK, Jr.; Shirai, JH; Richter, KY; et al. (1999) Field measurement of dermal loadings in occupational and
recreational activities. Environ Res, Section A, 80:148-157.
Kissel, J; Richter, K; Duff, R; et al. (1996a) Factors affecting soil adherence to skin in hand-press trials. Bull
Environ Contam Toxicol 56:722-728.
Kissel, J; Richter, K; Fenske, R. (1996b) Field measurements of dermal soil loading attributable to various activities:
implications for exposure assessment. Risk Anal 16(1): 116-125.
Kissel, JC; Shirai, JH; Richter, K.Y., and R.A. Fenske. (1998) Investigation of dermal contact with soil in controlled
trials. J Soil Contam 7(6):737-752.
Lentner, C. (ed). (1981) Geigy Scientific Tables, Nomograms for determination of body surface area from height and
mass. CIBA-Geigy Corporation, West Caldwell, NJ; pp. 226-227.
Murray, DM; Burmaster, DE. (1992) Estimated distributions for total surface area of men and women in the United
States. J Expos Anal Environ Epidemiol 3(4):451-462.
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Phillips, LJ; Fares, RJ; Schweer, LG. (1993) Distributions of total skin surface area to body weight ratios for use in
dermal exposure assessments. J Expos Anal Environ Epidemiol 3(3):33 1-338.
Popendorf, WJ; Leffingwell, JT. (1976) Regulating OP pesticide residues for farmworker protection.
In: Residue Review 82. New York, NY: Springer- Verlag New York, Inc., 1982; pp. 125-201.
Rochon, J; Kalsbeek, WD. (1983) Variance estimation from multi-stage sample survey data: the jackknife repeated
replicate approach. Presented at 1983 SAS Users Group Conference, New Orleans, LA, January 1983.
Sendroy, J; Cecchini, LP. (1954) Determination of human body surface area from height and weight. J Appl Physiol
U.S. EPA (U. S. Environmental Protection Agency). (1985) Development of statistical distributions or ranges of
standard factors used in exposure assessments. Office of Research and Development, Office of Health and
Environmental Assessment, Washington, DC. EPA 600/8-85-010. Available from: NTIS, Springfield, VA.
PB85-242667.
U.S. EPA. (1992a) Guidelines for exposure assessment. FR 57:104:22888-22938. May 29, 1992.
U.S. EPA. (1992b) Dermal exposure assessment: principles and applications. Washington, DC: Office of Research
and Development, Office of Health and Environmental Assessment/OHEA. U.S. EPA/600/8-9-91.
U.S. EPA. (1996) Analysis of the National Human Activity Pattern Survey (NHAPS) Respondents from a
Standpoint of Exposure assessment. Office of Research and Development, Washington, DC, EPA/600/R-96/074.
Van Graan, CH. (1969) The determination of body surface area. S African J Lab Clin Med-(suppl) 8-2-69.
Wong, E; Shirai, JH; Gay lock, TJ; et al. (2000) Adult proxy responses to a survey of children's dermal soil contact
activities. J Expo Anal Environ Epidmiol.
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9. ACTIVITY FACTORS
9.1. INTRODUCTION
As a consequence of a child's immaturity and small stature, certain activities and
behaviors specific to children place them at higher risk to certain environmental agents (Chance
and Harmsen, 1998). Individual or group activities are important determinants of potential
exposure, because toxic chemicals introduced into the environment may not cause harm to a
child until an activity is performed that subjects the child to contact with those contaminants. An
activity or time spent will vary on the basis of, for example, culture, hobbies, location, gender,
age, and personal preferences. It is difficult to accurately collect/record data for a child's
activity patterns (Hubal et al., 2000). Children engage in more contact activities than do adults:
therefore, a much wider distribution of activities need to be considered when assessing children's
exposure (Hubal et al., 2000). Behavioral patterns, preferred activities, and developmental
stages result in different exposures for children than for adults (Chance and Harmsen, 1998).
This section summarizes data on how much time children spend participating in various
activities in various microenvironments and on the frequency of performing various activities.
These data cover a wide scope of activities and populations, which are arranged by age group
when such data are available.
9.2. ACTIVITY PATTERNS
This section briefly describes published time-use studies that provide information on
time-activity patterns of children in the U.S. For a detailed description of the studies, the reader
is referred to Exposure Factors Handbook (U.S. EPA, 1997).
9.2.1. Timmer et al., 1985
Timmer et al. (1985) conducted a study using the data obtained on children's time use
from a 1981-1982 panel study. A total of 922 children between the ages of 3 and 17 years
participated in the survey, which used a time diary and a standardized interview. The time diary
involved the children's reporting their activities beginning at 12:00 a.m. the previous night, the
duration and location of each activity, the presence of another individual, and whether they were
performing other activities at the same time. The standardized interview was administered to
the children to gather information about their psychological, intellectual (using reading
comprehension tests), and emotional well-being; their hopes and goals; their family
environment; and their attitudes and beliefs.
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The mean time spent performing major activities on weekdays and weekends by age and
sex and type of day is presented in Table 9-1. On weekdays, the children spent about 40% of
their time sleeping, 20% in school, and 10% eating, washing, dressing, and performing other
personal activities (Timmer et al., 1985). The data in Table 9-1 indicate that girls spent more
time than boys performing household work and personal care activities and less time playing
sports. Also, the children spent most of their free time watching television.
Table 9-2 presents the mean time children spent during weekdays and weekends
performing major activities by five different age groups. The significant effects of each variable
(i.e., age and sex) are also shown. Older children spent more time performing household and
market work, studying, and watching television and less time eating, sleeping, and playing. The
authors estimated that, on average, boys spent 19.4 hours a week and girls spent 17.8 hours per
week watching television.
A limitation associated with this study is that it was conducted in 1981, and there is the
potential that activity patterns in children may have changed significantly from 1981 to the
present. Thus, application of these data for current exposure assessment may bias results.
Another limitation is that the data do not provide overall annual estimates of children's time use,
because data were collected only during the time of the year when children attended school and
not during school vacation.
EPA estimated the total time indoors and outdoors using the Timmer et al. data.
Activities performed indoors were assumed to include household work, personal care, eating,
sleeping, attending school, studying, attending church, watching television, and engaging in
household conversations. The average times spent in these indoor activities and half the time
spent in each activity that could have occurred either indoors or outdoors (e.g., market work,
sports, hobbies, art activities, playing, reading, and other passive leisure) were summed. Table
9-3 shows the results of this analysis by age groups and time of the week.
9.2.2. Robinson and Thomas, 1991
Robinson and Thomas (1991) reviewed and compared data from the 1987-88 California
Air Resources Board (CARB) time-activity study for California residents and from a similar
1985 national study, American's Use of Time. Both studies used the diary approach data. Time-
use patterns were collected for individuals 12 years and older. Telephone interviews based on
the random-digit-dialing procedure were conducted for 1762 and 2762 respondents for the
CARB study and the national study, respectively. Data categorized for children (18 years and
younger) were not provided in Robinson and Thomas (1991).
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In addition, Robinson and Thomas (1991) defined a set of 16 microenvironments on the
basis of the activity and location codes employed in the two studies. The mean duration of time
spent in three location categories is presented in Table 9-4. Respondents spent most of their time
indoors: 1255 and 1279 min/day in the CARB study and the national study, respectively.
Table 9-5 presents the mean duration of time and standard mean error for
16 microenvironments, grouped by total sample population and gender. Also included is the
mean time spent by respondents who reported participating in each activity ("doers"). Table 9-5
shows that in both studies males spent more time in work locations, in automobiles and other
vehicles, and in autoplaces (garages) and engaging in physical activities at outdoor sites.
In contrast, females spent more time cooking, engaging in other kitchen activities, performing
other chores, and shopping. The same trends also occurred on a per-participant basis.
Table 9-6 shows the mean time spent in various microenvironments by time of week
(weekday or weekend) in both studies. Generally, respondents spent most of their time during
the weekends in restaurants/bars (CARB study), motor vehicles, outdoor activities,
social-cultural settings, leisure/communication activities, and sleeping. Microenvironmental
differences by age are presented in Table 9-7.
One of the limitations associated with the Robinson and Thomas (1991) study is that the
CARB survey was performed in California only. Therefore, if applied to other populations, the
data set may be biased. In addition, the studies were conducted in 1980s and may bias exposure
assessment results when used for current exposure assessments. Another limitation is that time
distribution patterns were not provided for both studies, and the data are based on short-term
studies.
9.2.3. Wiley et al., 1991
The California children's activity pattern survey design (Wiley et al., 1991) provided
estimates of the time children spent in various activities and locations (microenvironments) on a
typical day. A total of 1200 children under the age of 12 years were included in the study. The
average times spent participating in the 10 activity categories are presented in Table 9-8. Also
included in this table are the detailed activity, including its code, with the highest mean duration
of time; the percentage of respondents who reported participating in any activity (percent doing);
and the mean, median, and maximum time duration for "doers." The activity category with the
highest time expenditure was personal care (794 mins/day, 13.2 hrs/day), with night sleep being
the detailed activity with the highest average minutes. The activity category "don't know" had a
duration of about 2 min/day, and only 4% of the respondents reported missing activity time.
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Table 9-9 presents the mean time spent in the 10 activity categories by age and gender.
Differences in activity patterns for boys and girls tended to be small. Table 9-10 presents the
mean time spent in the 10 activity categories grouped by seasons and California regions. There
were seasonal differences for 5 activity categories: personal care, educational activities,
social/entertainment, recreation, and communication/ passive leisure. Time expenditure
differences in various regions of the state were minimal for childcare, work-related activities,
shopping, personal care, education, social life, and recreation.
Table 9-11 presents the distribution of time across six location categories. The
participation rates (percent) of respondents; the mean, median, and maximum time for "doers";
and the detailed location with the highest average time expenditure are shown. The largest
amount of time spent was at home (1078 min/day); 99% of respondents spent time at home
(1086 min/participant/day). Tables 9-12 and 9-13 show the average time spent in the six
locations grouped by age and gender and by season and region, respectively. There are age
differences in time expenditure in educational settings (Table 9-12). There are no differences in
time expenditure at the six locations by regions, and time spent in school decreased in the
summer months as compared to other seasons (Table 9-13).
Table 9-14 shows the average potential exposure time children spent in proximity to
tobacco smoke, gasoline fumes, and gas oven fumes. The sampled children spent more time
closer to tobacco smoke (77 min/day) than to gas oven fumes (11 min/day) or to gasoline fumes
(2 min/day).
EPA estimated the total time indoors and outdoors using the data from the Wiley et al.
(1991) study. Activities performed indoors were assumed to include household work, child care,
personal needs and care, education, and communication and passive leisure. The average times
spent in these indoor activities and half the time spent in each activity that could have occurred
either indoors or outdoors (i.e., work-related, goods/services, organizational activities,
entertainment/social, don't know/not coded) were summed. Table 9-15 summarizes the results
of this analysis by age groups.
9.2.4. U.S. EPA, 1992
U.S. EPA (1992) addressed the variables of exposure time, frequency, and duration
needed to calculate dermal exposure as related to activity. The reader is referred to the
document for a detailed discussion of these variables for soil- and water-related activities. The
suggested values that can be used for dermal exposure are presented in Table 9-16. Limitations
of this study are that the values are based on small data sets and a limited number of studies.
9-4
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These data are not representative for children in specific age group categories. An advantage is
that it presents default values for frequency and duration for use in exposure assessments when
specific data are not available.
9.2.5. Tsang and Klepeis, 1996
The National Human Activity Pattern Survey (NHAPS) conducted by EPA is the largest
and most current human activity pattern survey available (Tsang and Klepeis, 1996). A total of
9386 individuals of all ages participated in the study. Data were collected on duration and
frequency of selected activities and of the time spent in selected microenvironments. In addition,
demographic information was collected for each respondent to allow for statistical summaries to
be generated according to specific subgroups of the U.S. population (e.g., gender, age, race,
employment status, census region, season). The participants' responses were weighted
according to geographic, socioeconomic, time/season, and other demographic factors to ensure
that results were representative of the U.S. population.
Tables 9-17 through 9-47 provide data from the NHAPS study. Tables 9-17 through 9-31
present data on the amount of time spent in selected activities and/or the corresponding
distribution data, when available. The data are for age groups 1-17 years old only, where data
are available.
Table 9-17 presents number of showers per day by age of respondents. The data
show that the majority of respondents took a shower one or two times a day.
Table 9-18 shows time spent taking a shower and time spent in the shower room
immediately after showering. Most of the respondents spent 10-20 minutes taking a
shower and in the shower room after showering.
Table 9-19 shows the percentile data for the same activity shown in Table 9-16. The
50th percentile value is 10 minutes for showering and 5 minutes for time spent after
showering was complete. The 90th percentile values vary across age groups and
range from 30-35 minutes and 10-15 minutes for time spent showering and in the
bathroom after showering, respectively.
Table 9-20 presents total time (minutes) spent in the shower or bathtub and in the
bathroom immediately after a shower or bath. The majority of respondents spent
10-20 minutes in the shower or bathtub and approximately 10 minutes in the
bathroom afterwards.
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• Table 9-21 presents the percentile data for the same activity shown in Table 9-18.
The 50th percentile values range from 15 to 20 minutes and from 2 to 5 minutes for
taking a shower or bath and time spent in the bathroom after the bath, respectively.
• Table 9-22 provides a range of the number of times the respondents washed their
hands in a day. Most washed their hands 3-5 times a day.
• Table 9-23 presents statistics data for the number of minutes per day spent working or
being near excessive dust in the air. For age groups 1-11 years old, the 50th
percentile data indicate that approximately 75 min/day was spent in air with excessive
dust.
• Table 9-24 provides data for the frequency of starting a motor vehicle in a garage or
carport and starting with the garage door closed.
• Table 9-25 provides data for the range of min/day spent playing on sand, gravel, dirt,
or grass and playing when fill dirt was present.
• Table 9-26 provides the percentile data for the same activity shown in Table 9-25.
• Table 9-27 presents data for time (min/day) spent playing on the grass by number of
respondents. The majority of respondents spent more than 120 min/day in this
activity.
• Table 9-28 presents percentile data for the same activity shown in Table 9-27. The
50th percentile rate is 60 min/day for all age groups.
• Table 9-29 provides number of times/month respondents swam in a freshwater
swimming pool. The majority of respondents did so 1 or 2 times/month.
• Table 9-30 provides percentile data for the same activity shown in Table 9-29. The
50th percentile values are 42.5 min/month for age group 1-4 years and 60 min/month
for age groups 5-11 and 12-17 years.
• Table 9-31 presents the range of the average amount of time (min/month) actually
spent in the water by swimmers. The majority of swimmers spent an average of 50 to
60 min/month in the water.
Tables 9-32 through 9-45 provide statistics for 24-hour cumulative time (minimum,
mean, maximum) spent in or in the presence of selected activities. The minimum is the
minimum number of minutes spent in the activity. The mean is the mean 24-hour cumulative
number of minutes spent by doers. The maximum is the maximum number of minutes spent in
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the activity. The percentiles are the percentage of doers below or equal to the given number of
minutes.
• Table 9-32 provides number of minutes spent playing indoors and playing outdoors.
• Table 9-33 provides number of minutes spent sleeping/napping in a day.
• Table 9-34 presents data for time spent attending full-time school.
• Table 9-35 provides data for time spent in active sports and for time spent in
sports/exercise.
• Table 9-36 presents data for time spent in outdoor recreation and walking.
• Table 9-37 provides data for time spent bathing.
• Table 9-38 presents statistics for minutes eating or drinking.
• Table 9-39 provides data for time spent indoors at school and in a restaurant.
• Table 9-40 provides information for time spent outdoors on school
grounds/playgrounds and at a pool/river/lake.
• Table 9-41 provides information on time spent at home in the kitchen, bathroom, and
bedroom, and indoors in a residence (all rooms).
• Table 9-42 presents data for time spent traveling inside a vehicle.
• Table 9-43 provides data for time spent outdoors (outside the residence) and outdoors
at locations other than near a residence, such as parks, golf courses, or farms.
• Table 9-44 provides information for time spent in malls, grocery stores, and other
stores.
• Table 9-45 presents data for minutes spent with smokers present.
Advantages of the NHAPS data set are that it is representative of the U.S. population and
it has been adjusted to be balanced geographically, seasonally, and for day/time. Also, it is
representative of all ages and gender, and it is race specific. A disadvantage of the study is that
for ages 1-17, the N is small for most activities. In addition, means cannot be calculated for time
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spent over 60, 120, and 181 minutes in selected activities. Therefore, actual time spent at the
high end of the distribution for these activities cannot be captured.
9.2.6. Funk et al., 1998
Funk et al. (1998) used the data from the California Air Resources Board (CARB) study
to determine distributions of exposure time by tracking the time spent participating in daily at-
home and at-school activities for male and female children and adolescents. CARB performed
two studies from 1987 to 1990; the first was focused on adults and adolescents (12-17 years
old), and the second focused on children (6-11 years old) (Funk et al., 1998). The targeted
groups were noninstitutionalized, English-speaking Californians who had a telephone in their
residence. Individuals were contacted by telephone and asked to account for every minute
within the previous 24 hours, including the amount of time spent on an activity and the location
of the activity. The surveys were conducted on different days of the week as well as different
seasons of the year.
Using the location descriptors provided in the CARB study, Funk et al. (1998) assigned
the activities into two groups, "at home" (any activity at principal residence) and "away." Each
activity was assigned to one of three ventilation levels: low, moderate, or high. Resting
activities were placed in the low-ventilation-level group; moderate-exertion activities were
assigned to the moderate group; activities requiring high levels of physical exertion were placed
in the high group. Ambiguous activities were assigned to moderate-ventilation levels. Among
the adolescents and children studied, means were determined for the aggregate age groups, as
shown in Table 9-46.
Funk et al. used several statistical methods, such as chi-square, Kolmogorov-Smirnov,
and Anderson-Darling, to determine whether the time spent in an activity group had a known
distribution. All the activities identified in the CARB study were assigned to the three
ventilation levels. Most of the activities performed by children were in the low- to moderate-
ventilation-level groups, as shown in Table 9-47.
The aggregate time periods spent at home in each activity are shown in Table 9-48.
Aggregate time spent at home performing different activities was compared between genders.
No significant differences were found between adolescent male and females in any of the
activity groups (Table 9-49). In children ages 6-11 years differences were found between
gender and age at the low-ventilation levels. In the moderate-ventilation level there were
significant differences between two age groups (6-8 years and 9-11 years) and gender (Table 9-
50).
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Large proportions of the respondents in the study did not participate in high-ventilation-
level activities; discrete distributions were used to characterize high-ventilation-activity groups
(Funk et al., 1998). Lognormal distribution best described the time spent by children at high
ventilation levels.
9.2.7. Hubal et al., 2000
Hubal et al. (2000) reviewed available data, including activity patterns data, to
characterize and assess environmental exposures for children. The EPA National Exposure
Research Laboratory's Consolidated Human Activity Database (CHAD), which contains data
from several studies on human activities, was reviewed. CHAD contains 4300 person-days of
information and 3009 person-days of microactivity data for 2640 children under 12 years (Table
9-51). Specific examples of the type of microactivity data available for children are shown in
Tables 9-52 and 9-53. The number of hours spent in various microenvironments are shown in
Table 9-52 and time spent in various activities indoors at home in Table 9-53.
CHAD contains approximately
"... 140 activity codes and 110 location codes, but the data generally are
not available for all activity locations for any single respondent. In fact, not
all of the codes were used for most of the studies. Even though many codes
are used in macroactivity studies, many of the activity codes do not
adequately capture the richness of what children actually do. They are much
too broadly defined and ignore many child-oriented behaviors. Thus, there is
a need for more and better-focused research into children's activities."(Hubal
et al., 2000).
9.2.8. Wong et al., 2000
Wong et al. (2000) conducted telephone surveys to gather information on children's
activity patterns as related to dermal contact with soil during outdoor play on bare dirt or mixed
grass and dirt surfaces. This study, the second Soil Contact Survey (SCS-II), was a follow-up to
the initial Soil Contact Survey (SCS-I) conducted in 1996, which focused primarily on assessing
adult behavior related to dermal contact with soil and dust (Garlock et al., 1999). As part of
SCS-I, information was gathered on the behavior of children under the age of 18 years; however,
the questions were limited to clothing choices and the length of time between soil contact and
hand washing. For SCS-II, questions were posed to further define children's outdoor activities
and hand washing and bathing frequency. For both soil contact surveys, households were
randomly phoned in order to obtain nationally representative results. The adult respondents were
9-9
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questioned as surrogates for one randomly chosen child under the age of 18 residing within the
household.
In SCS-II, of 680 total adult respondents with a child in their household, 500 (73.5%)
reported that their child played outdoors on bare dirt or mixed grass and dirt surfaces (identified
as "players"). Those children that reportedly did not play outdoors ("nonplayers") were
typically very young (< 1 year) or relatively older (> 14 years). Of the 500 children that played
outdoors, 497 played outdoors in warm weather months (April through October), and 390 were
reported to play outdoors during cold weather months (November through March). These results
are presented in Table 9-54. The frequency (days/wk), duration (hrs/day), and total hours per
week spent playing outdoors were determined for those children identified as "players" (Table 9-
55). The responses indicated that children spend a relatively high percentage of time outdoors
during the warmer months and a lesser amount of time in cold weather. The median play
frequency reported was 7 days/wk in warm weather and 3 days/wk in cold weather. Median play
duration was 3 hrs/day in warm weather and 1 hr/day during cold weather months.
Adult respondents were then questioned as to how many times per day their child washed
his/her hands and how many times the child bathed or showered per week during both warm and
cold weather months. This information provided an estimate of the time between skin contact
with soil and removal of soil by washing (i.e., exposure time). Hand washing and bathing
frequencies for child players are reported in Table 9-56. Hand washing was found to occur a
median of four times per day during both warm and cold weather months. The median
frequency for baths and showers was estimated to be seven times per week for both warm and
cold weather.
Based on reported household incomes, the respondents sampled in SCS-II tended to have
higher incomes than the general population. This may be explained by the fact that phone
surveys cannot sample households that do not have telephones. Additional uncertainty or error
in the study results may have occurred due to the use of surrogate respondents. Adult
respondents were questioned regarding children's activities that may have occurred in prior
seasons, introducing the chance of recall error. In some instances, a respondent did not know the
answer to a question or refused to answer. The information in Tables 9-57 and 9-58 was
extracted from the National Human Activity Pattern Survey (NHAPS) (Tsang and Klepeis,
1996). Table 9-57 compares mean play duration data from SCS-II to similar activities identified
in NHAPS. The number of times per day a child washed his or her hands was presented in both
SCS-II and NHAPS follow-up survey B and is shown in Table 9-57. Corresponding information
for bathing frequency data collected from SCS-II was not collected in NHAPS. As indicated in
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Tables 9-57 and 9-58, where comparison is possible, NHAPS and SCS-II results showed
similarities in observed behaviors.
9.3. RECOMMENDATIONS
Assessors are commonly interested in a number of specific types of time-use data,
including time/frequencies for bathing, showering, gardening, residence time, indoor versus
outdoor time, swimming, occupational tenure, and population mobility. Recommendations for
each of these are discussed below. The confidence in the recommendations for activity patterns
is presented in Table 9-59.
9.3.1. Recommendations for Activity Patterns
This chapter presents several studies that provide data on activity patterns. Table 9-60
summarizes information on the various studies. Recommendations for selected activities
commonly used in exposure assessments and known to increase exposure to certain chemicals
are provided below. These activities are time spent indoors versus outdoors, showering,
swimming, residential time spent indoors and outdoors, and time spent playing on sand or gravel
and on grass.
9.3.1.1. Time Spent Indoors Versus Outdoors
Assessors often require knowledge of the amount of time individuals spend indoors
versus outdoors. Ideally, this issue would be addressed on a site-specific basis because the times
are likely to vary considerably, depending on climate, residential setting (i.e., rural vs. urban),
personal traits (e.g., age, health), and personal habits.
Activities can vary significantly with differences in age. Table 9-61 summarizes the
studies that present information on time spent indoors and outdoors. Of these studies, Timmer et
al. (1985), in addition to being a national study, presents the data for a more comprehensive set
of age groupings for children. This study presented data on time spent in various activities for
boys and girls ages 3-17 years and focused on activities performed indoors, such as household
work, personal care, eating, sleeping, attending school, studying, attending church, watching
television, and engaging in household conversations. The average times spent in each activity
and half the times spent in each activity that could have occurred indoors or outdoors were
summed. The results are presented in Table 9-62 for various age groups. Although there is good
agreement between Robinson and Thomas (1991) and Timmer et al. (1985), the
9-11
-------
recommendations are based on the Timmer study (Table 9-61), because it provides data for
younger children.
9.3.1.2. Showering
The recommended showering frequency of one shower per day is based on the NHAPS
data summarized in Table 9-17. This table shows that 341 of the 451 total participants indicated
taking at least one shower the previous day.
Recommendations for showering duration are based on Tsang and Klepeis (1996). A
recommended value for average showering time is 10 minutes (Table 9-18), based on
professional judgment.
9.3.1.3. Swimming
Data for swimming frequency is taken from the NHAPS Study (Tsang and Klepeis,
1996). A total of 241 of the 653 participants who answered "yes" to the question "in the past
month, did you swim in a freshwater pool?", were ages 1-17 years (see Table 9-29). The
recorded number of times respondents swam in the past month ranged from 1 to 60, with the
greatest number of respondents reporting that they swam one time per month. Thus, the
recommended swimming frequency is one event per month. The recommended swimming
duration, 60 minutes per swimming event, is based on the NHAPS distribution shown on Table
9-30. Sixty minutes is based on an average of the 50th percentile values. The 90th percentile
value is 180 minutes per swimming event (based on one event per month) and the 99th percentile
value is 181 minutes. The 99th percentile value indicates that more than 180 minutes were
spent. This number refers to time swimming at a swimming pool, not necessarily at a lake, river,
etc.
9.3.1.4. Residential Time Spent Indoors and Outdoors
The recommendations for average and 95th percentile time spent indoors at one's
residence for children 1-17 years old is 18 hrs/day and 24 hrs/day, respectively. These numbers
are based on the NHAPS data summarized in Table 9-41 for number of minutes spent indoors in
a residence (all rooms). Table 9-41 presents the distributions of time spent indoors at one's
residence for age groups 1-4, 5-11, and 12-17 years. The total average time spent indoors at
one's residence and elsewhere for children 3-17 years old is 19 hrs/day, based on Timmer et al.
(1985). These data are summarized in Table 9-3. Table 9-3 presents mean values for time spent
indoors by weekday and weekend for age groups 3-5, 6-8, 9-11, 12-14, and 15-17 years.
9-12
-------
The average and 95th percentile time spent outdoors at one's residence for children 1-17
years old is 3 hrs/day and 8 hrs/day, respectively, based on the NHAPS data shown on Table 9-
43. Table 9-43 presents distributions of time spent outdoors at one's residence for age groups
1-4, 5-11, and 12-17 years. The total average time spent outdoors for children 3-17 years old is
2 hours, based on the data from Timmer et al. (1985) (Table 9-3).
9.3.1.5. Playing on Sand or Gravel and on Grass
The recommended value for time spent playing on sand or gravel is 60 min/day. This
value is based on the NHAPS data shown in Table 9-25. This recommendation is based on
professional judgement. The data in Table 9-25 show that the majority of respondents are
captured in the 0-0 min/day category. For the remaining time categories, the majority of
respondents are captured in the 50-60 min/day category.
The recommended average value for time spent playing on grass is 60 min/day, based on
the 50th percentile data shown in Table 9-28 and the 50-60 min/day category data in Table 9-27.
9.3.2. Summary of Recommended Activity Factors
Table 9-62 summarizes the recommended activity pattern factors presented in this section
and the studies that provided data on the specific activities. The type of activities include indoor
activities, outdoor activities, taking a shower, swimming, time spent playing on sand or gravel,
and time spent playing on grass.
9-13
-------
Table 9-1. Mean time spent performing major activities grouped by age,
sex and type of day
Activity
Market work
Household
work
Personal care
Eating
Sleeping
School
Studying
Church
Visiting
Sports
Outdoors
Hobbies
Art activities
Playing
TV
Reading
Household
conversations
Other passive
leisure
NAa
Time
accounted for
by activities
above (%)
Age (3-11 years)
Time (mins/day)
Weekdays
Boys
(N=118)
16
17
43
81
584
252
14
7
16
25
10
3
4
137
117
9
10
9
22
94
Girls
(N=lll)
0
21
44
78
590
259
19
4
9
12
7
1
4
115
128
7
11
14
25
92
Weekends
Boys
(N=118)
7
32
42
78
625
—
4
53
23
33
30
3
4
177
181
12
14
16
20
93
Girls
(N=lll)
4
43
50
84
619
—
9
61
37
23
23
4
4
166
122
10
9
17
29
89
Age (12-17 years)
Time (mins/day)
Weekdays
Boys
(N=77)
23
16
48
73
504
314
29
o
J
17
52
10
7
12
37
143
10
21
21
14
93
Girls
(N=83)
21
40
71
65
478
342
37
7
25
37
10
4
6
13
108
13
30
14
17
92
Weekends
Boys
(N=77)
58
46
35
58
550
—
25
40
46
65
36
4
11
35
187
12
24
43
10
88
Girls
(N=83)
25
89
76
75
612
—
25
36
53
26
19
7
9
24
140
19
30
33
4
89
' NA = Unknown
Source: TimmeretaL 1985
9-14
-------
Table 9-2. Mean time spent in major activities grouped by type of day for
five different age groups
Activity
Market work
Personal care
Household
work
Eating
Sleeping
School
Studying
Church
Visiting
Sports
Outdoor
activities
Hobbies
Art activities
Other passive
leisure
Playing
TV
Reading
Being read to
NA
Weekday time (mins/day)
Age (years)
3-5
—
41
14
82
630
137
2
4
14
5
4
0
5
9
218
111
5
2
30
6-8
14
49
15
81
595
292
8
9
15
24
9
2
4
1
111
99
5
2
14
9-11
8
40
18
73
548
315
29
9
10
21
8
2
o
6
2
65
146
9
0
23
12-14
14
56
27
69
473
344
33
9
21
40
7
4
3
6
31
142
10
0
25
15-17
28
60
34
67
499
314
33
3
20
46
11
6
12
4
14
108
12
0
7
Weekend time (mins/day)
Age (years)
3-5
—
47
17
81
634
—
1
55
10
o
5
8
1
4
6
267
122
4
3
52
6-8
4
45
27
80
641
—
2
56
8
30
23
5
4
10
180
136
9
2
7
9-11
10
44
51
78
596
—
12
53
13
42
39
3
4
7
92
185
10
0
14
12-14
29
60
72
68
604
—
15
32
22
51
25
8
7
10
35
169
10
0
4
15-17
48
51
60
65
562
—
30
37
56
37
26
3
10
18
21
157
18
0
9
Significant
effects3
A, S, AxS
(F>M)
A, S, AxS
(F>M)
A
A
A
A
A (Weekend
only)
A, S (M>F)
A
A, S (M>F)
A, S, AxS
(M>F)
A
A
A
' Effects are significant for weekdays and weekends, unless otherwise specified A = age effect, /><0.05, for both
weekdays and weekend activities; S = sex effect, p<0.05; F>M, M>F = females spend more time than males or
vice versa; and AxS = age by sex interaction, p<0.05.
Source: Timmeretal., 1985
9-15
-------
Table 9-3. Mean time spent indoors and outdoors grouped by age and
time of the week
Age
(years)
3-5
6-8
9-11
12-14
15-17
Time indoors
Weekday
(hrs/day)
19.4
20.7
20.8
20.7
19.9
Weekend
(hrs/day)
18.9
18.6
18.6
18.5
17.9
Time outdoors
Weekday
(hrs/day)
2.5
1.8
1.3
1.6
1.4
Weekend
(hrs/day)
3.1
2.5
2.3
1.9
2.3
Source: Adapted from Timmer et al, 1985
Table 9-4. Mean time spent at three locations for both CARB and national
studies (ages 12 years and older)
Location
Indoors
Outdoors
In vehicle
Total time spent
Time (mins/day)
CARB
(N = 1762)a
1255b
86C
98C
1440
SE
28
5
4
National
(N = 2762)a
1279b
74C
87C
1440
SE
21
4
2
a Weighted number; national sample population was weighted to obtain a ratio of 46.5 males and 53.5 females
in equal proportion for each day of the week and for each quarter of the year.
b Difference between the mean values for the CARB and national studies is not statistically significant.
0 Difference between the mean values for the CARB and national studies is statistically significant at the 0.05 level.
Source: Robinson and Thomas, 1991
9-16
-------
Table 9-5. Mean time spent in various microenvironments grouped by total population and gender
(12 years and over) in the national and CARB data
Microenvironment
Auto places
Restaurant/bar
In vehicle
In vehicle/other
Physical/outdoors
Physical/indoors
Work/study -residence
Work/study -other
Cooking
Other activities/kitchen
Chores/child
Shop/errand
Other/outdoors
Social/cultural
Leisure-eat/indoors
Sleep/indoors
National data
Time (mins/day) (SE)
N =
1284a
Male
5 (1)
22 (2)
92 (3)
1 (1)
24 (3)
11 (1)
17 (2)
221 (10)
14 (1)
54 (3)
88 (3)
23 (2)
70 (6)
71 (4)
235 (8)
491 (14)
"Doer"b
Male
90
73
99
166
139
84
153
429
35
69
89
56
131
118
241
492
N =
1478a
Female
1 (0)
20 (2)
82 (3)
1 (0)
11 (2)
6 (1)
15 (2)
142 (7)
52 (2)
90 (4)
153 (5)
38 (2)
43 (4)
75 (4)
215 (7)
496(11)
"Doer"
Female
35
79
94
69
101
57
150
384
67
102
154
74
97
110
224
497
N =
2762a
Total
3 (0)
21 (1)
87 (2)
1 (0)
17 (2)
8 (1)
16 (1)
179 (6)
34 (1)
73 (2)
123 (3)
31 (1)
56 (4)
73 (3)
224 (5)
494 (9)
"Doer"
Total
66
77
97
91
135
74
142
390
57
88
124
67
120
118
232
495
CARB data
Mean duration (mins/day) (SE)
N =
867a
Male
31 (8)
45 (4)
105 (7)
4 (1)
25 (3)
8 (1)
14 (3)
213(14)
12 (1)
38 (3)
66 (4)
21 (3)
95 (9)
47 (4)
223 (10)
492 (17)
"Doer""
Male
142
106
119
79
131
63
126
398
43
65
75
61
153
112
240
499
N =
895a
Female
9 (2)
28 (3)
85 (4)
3 (2)
8 (1)
5 (1)
11 (2)
156(11)
42 (2)
60 (4)
134 (6)
41 (3)
44 (4)
59 (5)
251(10)
504(15)
"Doer"
Female
50
86
100
106
86
70
120
383
65
82
140
78
82
114
263
506
N =
1762a
Total
20 (4)
36 (3)
95 (4)
3 (1)
17 (2)
7 (1)
13 (2)
184 (9)
27 (1)
49 (2)
100 (4)
31 (2)
69 (5)
53 (3)
237 (7)
498(12)
"Doer"
Total
108
102
111
94
107
68
131
450
55
74
109
70
117
112
250
501
VO
a Weighted number.
b Doer = Respondents who reported participating in each activity/location or spent time in microenvironments.
Source: Robinson and Thomas, 1991
-------
Table 9-6. Mean time spent in various microenvironments by type of day for the CARB and
national surveys (sample population ages 12 years and older)
Microenvironment
Auto places
Restaurant/bar
In vehicle/internal combustion
In vehicle/other
Physical/outdoors
Physical/indoors
Work/study-residence
Work/study-other
Cooking
Other activities/kitchen
Chores/child
Shop/errand
Other/outdoors
Social/cultural
Leisure-eat/indoors
Sleep/indoors
Weekday
Time (SE)
(mins/day)
CARB
(N=1259)b
21 (5)
29 (3)
90 (5)
3 (1)
14 (2)
7 (1)
14 (2)
228(11)
27 (2)
51 (3)
99 (5)
30 (2)
67 (6)
42 (3)
230 (9)
490 (14)
National
(N=1973)c
3 (1)
20 (2)
85 (2)
1 (0)
15 (2)
8 (1)
16 (2)
225 (8)
35 (2)
73 (3)
124 (4)
30 (2)
51 (4)
62 (3)
211 (6)
481 (10)
Time for "Doer"a
(mins/day)
CARB
108
83
104
71
106
64
116
401
58
76
108
67
117
99
244
495
National
73
73
95
116
118
68
147
415
57
87
125
63
107
101
218
483
Weekend
Time (SE)
(mins/day)
CARB
(N=503)b
19 (4)
55 (6)
108 (8)
5 (3)
23 (3)
7 (1)
10 (2)
74(11)
27 (2)
44 (3)
103 (7)
35 (4)
74 (7)
79 (7)
256 (12)
520 (20)
National
(N=789)b
3 (1)
23 (2)
91 (6)
0 (0)
23 (4)
9 (2)
15 (3)
64 (6)
34 (2)
73 (4)
120 (5)
35 (3)
67 (7)
99 (6)
257(11)
525 (17)
Time for "Doer"a
(mins/day)
CARB
82
127
125
130
134
72
155
328
60
71
114
81
126
140
273
521
National
62
84
100
30
132
80
165
361
55
90
121
75
132
141
268
525
VO
I
oo
a Doer = Respondent who reported participating in each activity/location or spent time in microenvironments.
b Weighted number
Source: Robinson and Thomas, 1991
-------
Table 9-7. Mean time spent in various microenvironments by age groups for the national and
CARB surveys
Microenvironment
Auto places
Restaurant/bar
Invehicle/internal combustion
Invehicle/other
Physical/outdoors
Physical/indoors
Work/study-residence
Work/study-other
Cooking
Other activities/kitchen
Chores/child
Shop/errands
Other/outdoors
Social/cultural
Leisure -eat/indoors
Sleep/indoors
National Data
Time (mins/day) (SE)
Age 12-17
years
N=340 a
2 (1)
9 (2)
79 (7)
0 (0)
32 (8)
15 (3)
22 (4)
159 (14)
11 (3)
53 (4)
91 (7)
26 (4)
70 (13)
87 (10)
237 (16)
548 (31)
"Doer""
73
60
88
12
130
87
82
354
40
64
92
68
129
120
242
551
Age 18-24
years
N=340
7 (2)
28 (3)
103 (8)
1 (1)
17 (4)
8 (2)
19 (6)
207 (20)
18 (2)
42 (3)
124 (9)
31 (4)
34 (4)
100 (12)
181(11)
511(26)
"Doer"
137
70
109
160
110
76
185
391
39
55
125
65
84
141
189
512
CARB Data
Time (mins/day) (SE)
Age 12-17
years
N=183 a
16 (8)
16 (4)
78(11)
1 (0)
32 (7)
20 (4)
25 (5)
196 (30)
3 (1)
31 (4)
72(11)
14 (3)
58 (8)
63 (14)
260 (27)
557 (44)
"Doer""
124
44
89
19
110
65
76
339
19
51
77
50
78
109
270
560
Age 18-24
years
N=250
16 (4)
40 (8)
111(13)
3 (1)
13 (3)
5 (2)
30(11)
201 (24)
14 (2)
31 (5)
79 (8)
35 (7)
80 (15)
65 (10)
211(19)
506 (30)
"Doer"
71
98
122
60
88
77
161
344
40
55
85
71
130
110
234
510
VO
VO
a All Ns are weighted numbers.
b Doer = Respondents who reported participating in each activity/location or spent time in microenvironments.
Source: Robinson and Thomas, 1991
-------
Table 9-8. Mean time children ages 12 years and under spent in 10 major activity categories for
all respondents
Activity
Work-related15
Household
Childcare
Goods/services
Personal needs and carec
Educationd
Organizational activities
Entertain/social
Recreation
Communication/passive
leisure
Don't know/not coded
All activities6
Mean time
(mins/day)
10
53
<1
21
794
110
4
15
239
192
2
1441
%
Doing
25
86
<1
26
100
35
4
17
92
93
4
Time for "doers"
(mins/day)a
Mean
39
61
83
81
794
316
111
87
260
205
41
Median
30
40
30
60
770
335
105
60
240
180
15
Maximum
405
602
290
450
1440
790
435
490
835
898
600
Detailed activity with highest avg.
minutes
(code)
Eating at work/school/daycare (06)
Travel to household (199)
Other child care (27)
Errands (3 8)
Night sleep (45)
School classes (50)
Attend meetings (60)
Visiting with others (75)
Games (87)
TV use (91)
—
VO
to
o
a "Doers" indicate the respondents who reported participating in each activity category.
b Includes eating at school or daycare, an activity not grouped under the "education activities" (codes 50-59, 549).
0 Personal care includes night sleep and daytime naps, eating, travel for personal care.
d Education includes student and other classes, homework, library, travel for education.
e Column total may not sum to 1440 due to rounding error.
Source: Wiley et al., 1991
-------
Table 9-9. Mean time children spent in 10 major activity categories grouped by age and gender
Activity
Work-related
Household
Childcare
Goods/Services
Personal needs and carea
Education13
Organizational activities
Entertainment/social
Recreation
Communication/passive leisure
Don't know/not coded
All activities0
Sample sizes (unweighted Ns)
Time (mins/day)
Boys
0-2
yrs
4
33
0
20
914
60
1
3
217
187
1
1440
172
3-5
yrs
9
45
0
22
799
67
3
15
311
166
4
1441
151
6-8
yrs
14
55
0
19
736
171
7
5
236
195
1
1439
145
9-11
yrs
12
65
1
14
690
138
6
34
229
250
1
1440
156
0-11
yrs
10
48
< 1
19
792
106
4
13
250
197
2
1442
624
Girls
0-2
yrs
5
58
0
22
906
41
6
5
223
171
3
1440
141
3-5
yrs
12
44
0
25
816
95
1
16
255
173
1
1438
151
6-8
yrs
11
51
0
23
766
150
4
9
238
189
< 1
1441
124
9-11
yrs
10
76
4
22
701
176
6
36
194
213
3
1441
160
0-11
yrs
10
57
1
23
797
115
4
17
228
186
2
1440
576
VO
to
a Personal needs and care includes night sleep and daytime naps, eating, travel for personal care.
b Education includes student and other classes, homework, library, travel for education.
0 The column totals may differ from 1440 due to rounding error.
Source: Wiley etal., 1991
-------
Table 9-10. Mean time children ages 12 years and under spent in 10 major activity categories grouped by
seasons and regions
Activity
Work-related
Household
Childcare
Goods/Services
Personal needs and care3
Education13
Organizational activities
Entertainment/social
Recreation
Communication/passive leisure
Don't know/not coded
All activities0
Sample sizes (unweighted)
Time (mins/day)
Season
Winter
(Jan-Mar)
10
47
< 1
19
799
124
3
14
221
203
< 1
1442
318
Spring
(Apr-June)
10
58
1
17
774
137
5
12
243
180
2
1439
204
Summer
(July-Sept)
6
53
<1
26
815
49
5
12
282
189
3
1441
407
Fall
(Oct-Dec)
13
52
< 1
23
789
131
3
22
211
195
< 1
1441
271
All
Seasons
10
53
<1
21
794
110
4
15
239
192
2
1441
1200
Region of California
So.
Coast
10
45
< 1
20
799
109
2
17
230
206
1
1440
224
Bay
Area
10
62
<1
21
785
115
6
10
241
190
1
1442
263
Rest of
State
8
55
1
23
794
109
6
16
249
175
3
1439
713
All
Regions
10
53
<1
21
794
110
4
15
239
192
2
1441
1200
VO
to
to
a Personal needs and care includes night sleep and daytime naps, eating, travel for personal care.
b Education includes student and other classes, homework, library, travel for education.
0 The column totals may not be equal to 1440 due to rounding error.
Source: Wiley etal., 1991
-------
Table 9-11. Mean time children ages 12 years and under spent in six major
location categories for all respondents
Location
Home
School/childcare
Friend's/other's house
Stores, restaurants, shopping
places
In-transit
Other locations
Don't know/not coded
All locations
Time
(mins/day)
1078
109
80
24
69
79
<1
1440
%
Doing
99
33
32
35
83
57
1
Time for "doers" (mins/day)
Mean
1086
330
251
69
83
139
37
Median
1110
325
144
50
60
105
30
Maximum
1440
1260
1440
475
1111
1440
90
Detailed location with
highest average time
Home - bedroom
School or daycare facility
Friend's/other's house -
bedroom
Shopping mall
Traveling in car
Park, playground
—
Source: Wiley etal, 1991
Table 9-12. Mean time children spent in six location categories grouped
age and gender
by
Location
Home
School/childcare
Friend's/other's house
Stores, restaurants, shopping places
In-transit
Other locations
Don't know/not coded
All locations3
Sample sizes (unweighted)
Time (mins/day)
Boys
0-2
yrs
1157
86
67
21
54
54
<1
1439
172
3-5
yrs
1134
88
73
25
62
58
<1
1440
151
6-8
yrs
1044
144
77
22
61
92
<1
1439
145
9-11
yrs
1020
120
109
15
62
114
<1
1440
156
All
Boys
1094
108
80
21
59
77
<1
1439
624
Girls
0-2
yrs
1151
59
56
23
76
73
<1
1438
141
3-5
yrs
1099
102
47
35
88
68
<1
1440
151
6-8
yrs
1021
133
125
27
53
81
<1
1440
124
9-11
yrs
968
149
102
26
93
102
<1
1440
160
All
Girl
s
1061
111
80
28
79
81
<1
1440
576
1 The column totals may not sum to 1440 due to rounding error.
Source: Wiley etal., 1991
9-23
-------
Table 9-13. Mean time children spent in six location categories grouped by season and region
Location
Home
School/childcare
Friend's/other's house
Stores, restaurants, shopping places
In-transit
Other locations
Don't know/not coded
All locations3
Sample sizes (unweighted Ns)
Time (mins/day)
Season
Winter
(Jan-Mar)
1091
119
69
22
75
63
<1
1439
318
Spring
(Apr-June)
1042
141
75
21
75
85
<1
1439
204
Summer
(July-Sept)
1097
52
108
30
60
93
<1
1440
407
Fall
(Oct-Dec)
1081
124
69
24
65
76
<1
1439
271
All
Seasons
1078
109
80
24
69
79
<1
1439
1200
Region of California
So.
Coast
1078
113
73
26
71
79
<1
1439
224
Bay
Area
1078
103
86
23
73
76
<1
1440
263
Rest of
State
1078
108
86
23
63
81
<1
1440
713
All
Regions
1078
109
80
24
69
79
<1
1439
1200
VO
to
' The column totals may not sum to 1440 due to rounding error.
Source: Wiley etal., 1991
-------
Table 9-14. Mean time children spent in proximity to three potential exposures grouped by all respondents,
age, and gender
Potential Exposure
Tobacco smoke
Gasoline fumes
Gas oven fumes
Sample sizes (unweighted Ns)
Time (mins/day)
All
Children
77
2
11
l,166a
Boys
0-2
yrs
115
2
10
168
3-5
yrs
75
1
15
148
6-8
yrs
66
1
12
144
9-11
yrs
66
4
11
150
All
Boys
82
2
12
610
Girls
0-2
yrs
77
1
12
140
3-5
yrs
68
1
10
147
6-8
yrs
71
O
10
122
9-11
yrs
74
1
7
147
All
Girls
73
1
10
556
VO
to
' Respondents with missing data were excluded.
Source: Wiley et al., 1991
Table 9-15. Mean time spent indoors and outdoors grouped by age
Age (years)
0-2
3-5
6-8
9-11
Time indoors (hrs/day)
20.0
18.8
19.7
19.9
Time outdoors (hrs/day)
4.0
5.2
4.4
4.1
Source: Adapted from Wiley et al., 1991
-------
Table 9-16. Range of recommended defaults for dermal exposure factors
Factor
Event time and
frequency"
Exposure
duration (years)
Water Contact
Bathing
Central
10 min/event
1 event/day
350 days/yr
9
Upper
15 min/event
1 event/day
350 days/yr
30
Swimming
Central
0.5 hr/event
1 event/day
5 days/yr
9
Upper
1.0 hr/event
1 event/day
150 days/yr
30
Soil Contact
Central
40 events/yr
9
Upper
350 events/yr
30
1 Bathing event time is presented to be representative of baths as well as showers.
Source: U.S. EPA, 1992
Table 9-17. Number of times taking a shower by number of respondents
Age
(years)
1-4
5-11
12-17
N
41
140
270
Times/day
0
*
*
*
1
30
112
199
2
9
26
65
3
1
1
6
4
*
*
*
5
*
*
*
8
*
*
*
10
*
*
*
11:1-0+
*
*
*
"Don't Know"
1
1
*
Note: * Signifies missing data.
Source: Tsang and Klepeis, 1996
9-26
-------
Table 9-18. Time spent taking a shower and spent in the shower room after taking a shower by the number of
respondents
Age
1-4
5-11
12-17
N
41
140
270
Time in shower (mins)
0-10
13
60
94
10-20
14
52
104
20-30
10
18
40
30-40
1
3
13
40-50
b
2
9
50-60
2
4
7
60-613
—
—
1
Time in shower room (mins)
0-10
5
9
17
10-20
31
110
206
20-30
3
14
29
30-40
1
3
10
40-50
*
*
3
50-60
1
*
2
60-6
la
*
1
1
to
a A value of 61 for number of minutes signifies that more than 60 minutes were spent.
b Signifies missing data.
Source: Tsang and Klepeis, 1996
Table 9-19. Time spent taking a shower and spent in the shower room immediately after showering
Age
(years)
N
Percen tiles3
1"
2»a
5th
10th
25*
50th
75th
91"
95*
98th
99th
100th
Time Spent Taking a Shower (mins)
1^
5-11
12-17
40
139
268
5
3
5
5
4
5
5
5
5
5
5
7
5
10
10
10
15
15
17.5
20.0
25.0
30
30
35
50
40
45
60
60
60
60
60
60
60
60
61b
1"
2»d
5th
10th
25*
50th
75*
91"
95*
98th
99th
100th
0
0
0
0
0
0
0
0
0
0
1
1
1
2
3
5
5
5
10
10
10
15
15
20
20
20
30
45
30
40
45
30
52
45
60
61b
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b A value of 61 for number of minutes signifies that more than 60 minutes were spent.
Source: Tsang and Klepeis, 1996
-------
Table 9-20. Total time spent in the shower or bathtub and in the bathroom
immediately after by number of respondents
Age
(years)
Na
Minutes/Bath
0-0
0-10
10-20
20-30
30-40
40-50
50-60
70-80
80-90
90-100
100-110
110-120
121-121
Total time spent altogether in the shower or bathtub by the number of respondents
l^\
5-11
12-17
198
265
239
*
35
64
78
84
107
96
50
66
46
2
3
5
13
7
5
7
7
8
1
2
*
1
2
*
1
1
*
*
1
*
4
2
1
*
1
*
Time spent in the bathroom immediately following a shower or bath by the number of respondents
1-4
5-11
12-17
198
265
239
59
33
17
123
198b
165
12
23
34
*
3
16
1
1
1
1
*
3
*
1
2
*
*
*
*
*
*
*
*
*
*
*
*
*
1
*
*
*
*
a N = Doer sample size in specified range of number of minutes spent.
b A value of 121 for number of minutes signifies that more than 120 minutes were spent.
—Signifies missing data.
Source: Tsang and Klepeis, 1996
Table 9-21. Total number of minutes spent altogether in the shower or
bathtub and spent in the bathroom immediately following a shower or bath
Age (years)
Percentiles"
N"
1
2
5
10
25
50
75
90
95
98
99
100
Total number of minutes spent altogether in the shower or bathtub (mins/bath)
Age (years)
Age (years)
Age (years)
1-4
5-11
12-17
198
263
239
1
4
4
5
5
4
5
5
5
10
10
7
15
13
10
20
20
15
30
30
30
45
30
30
60
60
45
120
90
60
120
120
60
120
121C
120
Number of minutes spent in the bathroom immediately following a shower or bath (mins/bath)
Age (years)
Age (years)
Age (years)
1-4
5-11
12-17
196
260
238
0
0
0
0
0
0
0
0
0
0
0
2
0
2
5
2
5
5
5
10
10
10
15
20
15
15
30
20
30
45
35
35
45
45
120
60
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = Doer sample size.
0 A value of 121 for number of minutes signifies that more than 120 minutes were spent.
Source: Tsang and Klepeis, 1996
9-28
-------
Table 9-22. Number of times hands were washed at specified daily
frequencies by the number of respondents
Age (years)
1-4
5-11
12-17
Na
263
348
326
Number of times/day
0-0
15
5
6
1-2
62
61
46
3-5
125
191
159
6-9
35
48
64
10-19
11
21
30
20-29
2
4
7
30+
3
2
2
"Don't Know"
10
15
9
J Sum of individual Ns may not equal total because of missing data.
Source: Tsang and Klepeis, 1996
Table 9-23. Number of minutes per day spent working or being near
excessive dust in the air (mins/day)
Age
(years)
1-4
5-11
12-17
N
22
50
52
Percentiles"
1st
0
0
0
^nd
0.0
0.5
1.0
5th
0
2
2
10th
2
4
5
25th
5
15
5
50th
75
75
20
75th
121b
121
120
90th
121
121
121
95th
121
121
121
98th
121
121
121
99th
121
121
121
100th
121
121
121
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b A value of 121 signifies that more than 120 minutes were spent.
Source: Tsang and Klepeis, 1996
9-29
-------
Table 9-24. Number of times per day a motor vehicle was started in a garage or carport and started with the
garage door closed by number of respondents
Age
(years)
1-4
5-11
12-17
Na
111
150
145
Automobile or motor vehicle was started
in a garage or carport (times/day)
1-2
68
93
86
3-5
39
49
42
6-9
2
6
12
10+
2
—
1
"Don't Know"
2
4
Motor vehicle was started with
garage door closed
1-2
99
141
127
3-5
8
6
9
6-9
2
—
4
10+
—
1
"Don't Know"
2
o
3
4
1 Sum of individual Ns may not equal total N because of missing data.
—Signifies missing data.
Source: Tsang and Klepeis, 1996
oo
o
-------
Table 9-25. Number of minutes spent playing on sand, gravel, dirt, or grass
by number of respondents
Age
(years)
Na
Minutes per day
0-0
0-10
10-20
20-30
30-40
40-50
50-60
70-80
80-90
90-100
110-120
121"
Spent playing on sand or gravel
1-4
5-11
12-17
216
200
41
115
96
23
15
11
1
9
12
2
15
14
4
2
—
—
3
5
—
15
25
3
1
1
—
5
2
—
1
1
7
6
o
J
16
20
3
Spent playing in outdoors on sand, gravel, dirt, or grass when fill dirt was present
1-4
5-11
12-17
216
200
41
118
103
19
14
14
o
5
10
8
2
13
15
7
1
—
—
4
1
—
18
17
4
4
1
1
—
—
7
9
2
16
17
—
a Sum of individual Ns may not equal total N because of missing data.
b A value of 121 signifies that more than 120 minutes were spent.
—Signifies missing data.
Source: Tsang and Klepeis, 1996
Table 9-26. Number of minutes spent playing in sand, gravel, dirt or grass by
percentiles
Age
(years)
Nb
Percentile"
1st
•^nd
5th
10th
25th
50th
75th
90th
95th
98th
99th
100th
Number of minutes per day spent playing on sand or gravel
1-4
5-11
12-17
203
193
40
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0
3.0
0.0
30
60
45
120
121
120
121C
121
121
121
121
121
121
121
121
121
121
121
Number of minutes per day spent playing on sand, gravel, dirt, or grass when fill dirt was present
1-4
5-11
12-17
205
185
38
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0.0
0.0
0.5
30
30
30
120
120
60
121
121
120
121
121
120
121
121
120
121
121
120
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 A value of 121 for number of minutes signifies that more than 120 minutes were spent.
Source: Tsang and Klepeis, 1996
9-31
-------
Table 9-27. Number of minutes per day spent playing on grass in a day
by the number of respondents
Age
(years)
1-4
5-11
12-17
Na,b
216
200
41
Minutes per day
0-0
24
24
5
0-10
19
10
1
10-20
21
10
2
20-30
25
19
8
30-40
1
2
—
40-50
4
3
1
50-60
35
38
8
60-70
—
1
—
70-80
1
—
—
80-90
8
8
1
90-
100
—
1
—
100-
110
1
—
—
110-
120
18
20
8
121-
121C
49
49
5
a Sum of individual Ns may not equal total N because of missing data.
b N = doer sample size.
0 A value of 121 for number of minutes signifies that more than 120 minutes were spent. Percentiles are the
percentage of doers below or equal to a given number of minutes. Refused = respondent refused to answer.
—Signifies missing data.
Source: Tsang and Klepeis, 1996
Table 9-28. Number of minutes spent playing on grass by percentile
Age
(years)
1-4
5-11
12-17
Nb
206
185
39
Percentile3
1st
0
0
0
<^nd
0
0
0
5th
0
0
0
10th
0
0
0
25th
15
30
30
50th
60
60
60
75th
120
121
120
90th
121C
121
121
95th
121
121
121
98th
121
121
121
99th
121
121
121
100th
121
121
121
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 A value of 121 for number of minutes signifies that more than 120 minutes were spent.
Source: Tsang and Klepeis, 1996
9-32
-------
Table 9-29. Number of times swimming in a month in freshwater swimming pool by the number of respondents
Age
(years)
l^\
5-11
12-17
Na
63
100
84
Times/Month
1
11
16
21
2
14
15
13
3
7
7
7
4
3
9
4
5
3
6
8
6
4
4
4
7
1
2
2
8
3
4
3
9
1
1
10
4
7
8
11
—
12
2
5
1
13
1
14
1
15
2
11
2
16
2
18
1
20
2
3
4
23
—
24
1
25
2
26
1
28
—
29
1
30
2
5
2
31
—
32
1
40
—
42
—
45
—
50
1
60
1
DK
1
' Sum of individual Ns may not equal total N because of missing data.
— Signifies missing data.
Source: Tsang and Klepeis, 1996
-------
Table 9-30. Number of minutes spent swimming in a month in freshwater
swimming pool by percentile
Age
(years)
1-4
5-11
12-17
Nb
60
95
83
Percentiles"
1st
3
2
4
<^nd
3
o
J
5
5th
7.5
20.0
15.0
10th
15
30
20
25th
20
45
40
50th
42.5
60.0
60.0
75th
120
120
120
90th
180C
180
180
95th
181
181
181
98th
181
181
181
99th
181
181
181
100th
181
181
181
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 A value of 181 for number of minutes signifies that more than 180 minutes were spent.
Source: Tsang and Klepeis, 1996
Table 9-31. Range of the average amount of time actually spent in the water
by swimmers by number of respondents
Age (years)
1-4
5-11
12-17
NM>
63
100
84
Minutes per month
0-10
5
3
3
10-20
12
2
7
20-30
12
12
10
30^10
1
5
2
40-50
4
4
6
50-60
8
25
15
60-70
_
—
—
70-80
_
—
1
80-90
2
7
8
90-100
_
—
1
110-120°
7
16
14
150-150
1
2
4
180-180
3
11
6
181-181
5
8
6
a Sum of individual Ns may not equal total N because of missing data.
b N = doer sample size.
0 Values of 120, 150, and 180 for number of minutes signify that 2 hours, 2.5 hours, and 3 hours, respectively,
were spent.
—Signifies missing data.
Source: Tsang and Klepeis, 1996
9-34
-------
Table 9-32. Statistics for twenty-four hour cumulative number of minutes spent playing indoors and
outdoors by percentiles
Age
(years)
Nb,c
Mean
SD
SE
Min
Max
Percentiles'
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent in indoor playing
1-4
5-11
12-17
11
11
4
130.000
93.600
82.500
80.200
64.300
45.000
24.200
19.400
22.500
15
30
30
270
195
120
15
30
30
60
30
45
115
60
90
180
175
120
255
180
120
270
195
120
270
195
120
270
195
120
Nb,c
Mean
SD
SE
Min
Max
Percentiles'
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent in outdoor playing
4
9
1
83.250
148.333
15.000
89.660
144.265
—
44.830
48.088
—
15
5
15
210
360
15
15
5
15
20
55
15
54
60
15
146.5
280.0
15.0
210
360
15
210
360
15
210
360
15
210
360
15
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
— Signifies missing data.
Source: Tsang and Klepeis, 1996
Table 9-33. Statistics for twenty-four hour cumulative number of minutes spent sleeping/napping by percentiles
Age
(years)
1-4
5-11
12-17
N"
499
702
588
Meanc
732.363
625.058
563.719
SD
124.328
100.656
110.83
SE
5.5657
3.7990
4.5706
Min
270
120
150
Max
1320
1110
1015
Percentiles'
5th
540
480
395
25th
655
570
484
50th
720
630
550
75th
810
680
630
90th
900
725
705
95th
930
780
750
98th
1005
840
810
99th
1110
875
900
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
-------
Table 9-34. Statistics for twenty-four hour cumulative number of minutes
spent attending full-time school by percentiles
Age
(years)
\-4
5-11
12-17
Nb
56
297
271
Meanc
365.036
387.811
392.28
SD
199.152
98.013
84.986
SE
26.6128
5.6873
5.1625
Min
20
60
10
Max
710
645
605
Percentiles3
5th
30
170
200
25th
172.5
360.0
375.0
50th
427.5
390.0
405.0
75th
530
435
435
90th
595
485
460
95*
628
555
485
98th
665
600
510
99th
710
630
555
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
Table 9-35. Statistics for twenty-four hour cumulative number of minutes
spent in active sports and for time spent in sports/exercise by percentiles
Age
(years)
Nb
Meanc
SD
SE
Statistics for 24-hour cumulative number of minutes spent
1-4
5-11
12-17
105
247
215
115.848
148.870
137.460
98.855
126.627
124.516
9.6472
8.0571
8.4919
Statistics for 24-hour cumulative number of minutes spent
1-4
5-11
12-17
114
262
237
118.982
153.496
134.717
109.170
130.580
122.228
10.2247
8.0673
7.9396
Min
Max
Percentiles"
5th | 25th | 50th | 75th | 90th | 95th | 98th | 99th
in active sports
10
2
5
630
975
1065
30 45 90 159 250 330 345 390
20 60 120 188 320 390 510 558
15 60 110 180 265 375 470 520
in sports/exercise11
10
2
5
670
975
1065
25 45 90 159 250 330 390 630
20 60 120 200 330 415 525 580
15 60 110 179 265 360 470 520
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
d Includes active sports, exercise, hobbies.
Source: Tsang and Klepeis, 1996
9-36
-------
Table 9-36. Statistics for twenty-four hour cumulative number of minutes
spent in outdoor recreation and spent walking by percentiles
Age
(years)
N"
Meanc
SD
SE
Min
Max
Percentiles3
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent in outdoor recreation
1-4
5-11
12-17
13
21
27
166.5400
206.1400
155.0700
177.0600
156.1700
128.2800
49.1090
34.0780
24.6870
15
30
5
630
585
465
15
60
5
30
90
60
130
165
135
180
245
225
370
360
420
630
574
420
630
585
465
630
585
465
24-hour cumulative number of minutes spent walking
1-4
5-11
12-17
58
155
223
24.3276
18.2129
25.8341
26.3268
21.0263
32.3753
3.4569
1.6889
2.1680
1
1
1
160
170
190
2
1
2
10
5
6
15
10
15
35
25
30
60
40
60
60
60
100
70
65
135
160
100
151
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
Table 9-37. Statistics for twenty-four hour cumulative number of minutes
spent in bathing" by percentiles
Age
(years)
1-4
5-11
12-17
Nc
330
438
444
Meand
29.9727
25.7511
23.1216
SD
19.4226
35.3164
18.7078
SE
1.0692
1.6875
0.8878
Min
1
1
1
Max
170
690
210
Percentilesb
5th
10
5
5
25th
15
15
10
50th
30
20
18
75th
31
30
30
90th
54.5
45.0
45.0
95th
60
60
60
98th
85
60
65
99th
90
75
90
a Includes baby and child care, personal care services, washing and personal hygiene (bathing, showering, etc.).
b Percentiles are the percentage of doers below or equal to a given number of minutes.
0 N = doer sample size.
d Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
9-37
-------
Table 9-38. Statistics for twenty-four hour cumulative number of minutes
eating or drinking by percentiles
Age
(years)
1-4
5-11
12-17
Nb
492
680
538
Mean0
93.4837
68.5412
55.8587
SD
52.8671
38.9518
34.9903
SE
2.3834
1.4937
1.5085
Min
2
5
2
Max
345
255
210
Percentiles3
5th
20
15
10
25th
60
40
30
50th
90
65
50
75th
120
90
75
90th
160
120
105
95th
190.0
142.5
125.0
98th
225
165
150
99th
270
195
170
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
Table 9-39. Statistics for twenty-four hour cumulative number of minutes
spent indoors at school and indoors at a restaurant by percentiles
Age
(years)
Nb
Meanc
SD
SE
Min
Max
Percentiles"
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent indoors at school
1-4
5-11
12-17
43
302
287
288.465
396.308
402.551
217.621
109.216
125.512
33.187
6.285
7.409
5
5
15
665
665
855
10
170
120
60
365
383
269
403
420
500
445
450
580
535
500
595
565
565
665
625
710
665
640
778
24-hour cumulative number of minutes spent indoors at a restaurant
1-4
5-11
12-17
61
84
122
62.705
56.690
69.836
47.701
38.144
78.361
6.1075
4.1618
7.0945
4
5
2
330
180
455
10
10
10
35
30
30
55
45
45
85
85
65
115
120
165
120
120
250
130
140
325
330
180
360
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
9-38
-------
Table 9-40. Statistics for twenty-four hour cumulative number of minutes
spent outdoors on school grounds/playground, at a park/golf course, and at a
pool/river/lake by percentiles
Age
(years)
Nb
Mean0
SD
SE
Min
Max
Percentiles"
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent outdoors on school grounds/playground
1-4
5-11
12-17
9
64
76
85.000
88.016
78.658
61.084
95.638
88.179
20.3600
11.9600
10.1200
10
5
3
175
625
570
10
10
5
30
30
25
65.0
60.0
55.0
140.0
120.0
105.0
175
170
165
175
220
225
175
315
370
175
625
570
24-hour cumulative number of minutes spent outdoors at a park/golf course
1-4
5-11
12-17
21
54
52
149.857
207.556
238.462
176.250
184.496
242.198
38.4609
25.1068
33.5869
21
25
15
755
665
1065
25
35
15
50
70
60
85.0
125.0
147.5
150.0
275.0
337.5
360
555
590
425
635
840
755
660
915
755
665
1065
24-hour cumulative number of minutes spent outdoors at a pool/river/lake
1-4
5-11
12-17
14
29
22
250.571
175.448
128.318
177.508
117.875
94.389
47.441
21.889
20.124
90
25
40
630
390
420
90
30
58
130
60
60
168.0
145.0
82.5
370.0
293.0
210.0
560
365
225
630
375
235
630
390
420
630
390
420
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers. Min = minimum number of minutes.
Source: Tsang and Klepeis, 1996
9-39
-------
Table 9-41. Statistics for twenty-four hour cumulative number of minutes
spent at home in the kitchen bathroom, bedroom, and in a residence
(all rooms) by percentiles
Age
(years)
Nb
Meanc
SD
SE
Min
Max
Percentiles3
5th
25th
50th
75th
90th
95th
98th
99th
24-hour cumulative number of minutes spent at home in the kitchen
1-4
5-11
12-17
335
477
396
73.719
60.468
55.02
54.382
52.988
58.111
2.9712
2.4262
2.9202
5
1
1
392
690
450
15
10
5
30.0
30.0
15.0
60
50
36
100
75
65
140.0
120.0
125.0
180.0
150.0
155.0
225
180
240
240
235
340
24-hour cumulative number of minutes spent in the bathroom
1-4
5-11
12-17
328
490
445
35.939
30.9673
29.0517
46.499
38.609
32.934
2.5675
1.7442
1.5612
1
1
1
600
535
547
10
5
5
15.0
15.0
15.0
30
27
20
40
35
35
60.0
52.5
60.0
75.0
60.0
65.0
125
100
90
270
200
100
24-hour cumulative number of minutes spent at home in the bedroom
1-4
5-11
12-17
488
689
577
741.988
669.144
636.189
167.051
162.888
210.883
7.562
6.2055
8.7792
30
35
15
1440
1440
1375
489
435
165
635.0
600.0
542.0
740
665
645
840
740
750
930.0
840.0
875.0
990.0
915.0
970.0
1095
1065
1040
1200
1140
1210
24-hour cumulative number of minutes spent indoors in a residence (all rooms)
1-4
5-11
12-17
498
700
588
1211.64
1005.13
969.5
218.745
222.335
241.776
9.8022
8.4035
9.9707
270
190
95
1440
1440
1440
795
686
585
1065
845.0
811.5
1260
975
950
1410
1165
1155
1440
1334
1310
1440.0
1412.5
1405.0
1440
1440
1440
1440
1440
1440
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
9-40
-------
Table 9-42. Statistics for twenty-four hour cumulative number of minutes
spent traveling inside a vehicle by percentiles
Age
(years)
1-4
5-11
12-17
Nb
335
571
500
Mean0
68.116
71.033
81.530
SD
75.531
77.620
79.800
SE
4.1267
3.2483
3.5687
Min
1
1
1
Max
955
900
790
Percentilea
5th
10
10
10
25th
30
25
30
50th
47
51
60
75th
85
90
100
90th
150.0
140.0
165.5
95th
200.0
171.0
232.5
98th
245
275
345
99th
270
360
405
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers. Min = minimum number of minutes.
Max = maximum number of minutes.
Source: Tsang and Klepeis, 1996
Table 9-43. Statistics for twenty-four hour cumulative number of minutes
spent outdoors (outside the residence) and outdoors other than near a
residence or vehicle, such as parks, golf courses, or farms by percentiles
Age
(years)
Nb
Mean0
SD
SE
Min
Max
Percentile"
5th
25th
50th
75th
90th
95th
98th
99th
Statistics for 24-hour cumulative number of minutes spent outdoors (outside the residence)
1-4
5-11
12-17
201
353
219
195.652
187.564
135.260
163.732
158.575
137.031
11.5488
8.4401
9.2597
o
5
4
1
715
1250
720
30
20
5
75
80
35
135
150
100
270
265
190
430
365
300
535
479
452
625
600
545
699
720
610
Statistics for 24-hour cumulative number of minutes spent outdoors other than near a residence or
vehicle such as parks, golf courses, or farms
1-4
5-11
12-17
54
159
175
164.648
171.340
156.903
177.340
177.947
174.411
24.1330
14.1120
13.1840
1
5
5
980
1210
1065
10
15
10
60
55
45
120
115
100
175
221
210
370
405
385
560
574
570
630
660
735
980
725
915
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers. Min = minimum number of minutes.
Max = maximum number of minutes.
Source: Tsang and Klepeis, 1996
9-41
-------
Table 9-44. Statistics for twenty-four hour cumulative number of minutes
spent in malls, grocery stores, or other stores by percentiles
Age
(years)
1-4
5-11
12-17
Nb
110
129
140
Meanc
90.036
77.674
88.714
SD
77.887
68.035
101.360
SE
7.4263
5.9901
8.5666
Min
5
O
1
Max
420
320
530
Percentile"
5th
10
5
5
25th
40
30
20
50th
65
60
45
75th
105.0
110.0
123.5
90th
210.0
180.0
222.5
95th
250.0
225.0
317.5
98th
359
255
384
99th
360
280
413
a Percentiles are the percentage of doers below or equal to a given number of minutes.
b N = doer sample size.
0 Mean = Mean 24-hour cumulative number of minutes for doers.
Source: Tsang and Klepeis, 1996
9-42
-------
Table 9-45. Statistics for twenty-four hour cumulative number of minutes spent with smokers present
by percentiles
Age
(years)
1-4
5-11
12-17
N
155
224
256
Mean
366.56
318.07
245.77
SD
324.46
314.02
243.61
SE
26.062
20.981
15.226
Min
5
1
1
Max
1440
1440
1260
Percentile
5th
30
25
10
25th
90
105
60
50th
273
190
165
75th
570
475
360
90th
825
775
595
95th
1010
1050
774
98th
1140
1210
864
99th
1305
1250
1020
Table 9-46. Gender and age groups
Gender-age group
Adolescents
Children3
Subgroup
Males
Females
Young males
Young females
Old males
Old females
N
98
85
145
124
156
160
Age
(years)
12-17
12-17
6-8
6-8
9-11
9-11
' Children under the age of 6 are excluded for the present study (too few responses in CARD study).
Source: Funk etal, 1998
-------
Table 9-47. Assignment of at-home activities to ventilation levels for children
Low
Moderate
Watching child care
Night sleep
Watch personal care
Homework
Radio use
TV use
Records/tapes
Reading books
Reading magazines
Reading newspapers
Letters/writing
Other leisure
Homework/watch TV
Reading/TV
Reading/listen music
Paperwork
Outdoor cleaning
Food preparation
Metal clean-up
Cleaning house
Clothes care
Car/boat repair
Home repair
Plant care
Other household
Pet care
Baby care
Child care
Helping/teaching
Talking/reading
Indoor playing
Outdoor playing
Medical child care
Washing, hygiene
Medical care
Help and care
Meals at home
Dressing
Visiting at home
Hobbies
Domestic crafts
Art
Music/dance/drama
Indoor dance
Conservations
Painting room/home
Building fire
Washing/dressing
Outdoor play
Playing/eating
Playing/talking
Playing/watch TV
TV/eating
TV/something else
Reading book/eating
Read magazine/eat
Read newspaper/eat
Source: Funk etal., 1998
9-44
-------
Table 9-48. Aggregate time spent (mins/day) at home in activity groups by
adolescents and children"
Activity Group
Low
Moderate
High
High participants0
Adolescents
Mean
789
197
1
43
SD
230
131
11
72
Children
Mean
823
241b
O
58
SD
153
136
17
47
a Time spent engaging in all activities embodied by ventilation level category (mins/day).
b Significantly different from adolescents (p<0.05).
0 Represents time spent at home by individuals participating in high-ventilation levels.
Source: Funk etal, 1998
Table 9-49. Comparison of mean time (mins/day) spent at home by gender
(adolescents)
Activity Group
Low
Moderate
High
Males
Mean
775
181
2
SD
206
126
16
Females
Mean
804
241
0
SD
253
134
0
Source: Funk etal., 1998
9-45
-------
Table 9-50. Comparison of mean time (mins/day) spent at home by gender
and age for children"
Activity Group
Low
Moderate
High
High participants0
Males
6-8 Years
Mean
806
259
3
77
SD
134
135
17
59
9-11 Years
Mean
860
198
7
70
SD
157
111
27
54
Females
6-8 Years
Mean
828
256
1
68
SD
155
141
9
11
9-11 Years
Mean
803
247
2
30
SD
162
146
10
23
a Time spent engaging in all activities embodied by ventilation level category (mins/day).
b Participants in high-ventilation level activities.
Source: Funk etal., 1998
9-46
-------
Table 9-51. Number of person-days/individuals for children in CHAD
database"
Age Group
0 year
0-6 months
6-12 months
1 year
12- 18 months
18-24 months
2 years
3 years
4 years
5 years
6 years
7 years
8 years
9 years
10 years
1 1 years
Total
All Studies
223/199
259/238
317/264
278/242
259/232
254/227
237/199
243/213
259/226
229/195
224/199
227/206
3009/2640
California15
104
50
54
97
57
40
112
113
91
98
81
85
103
90
105
121
1200
Cincinnati0
36/12
15/5
21/7
31/11
81/28
54/18
41/14
40/14
57/19
45/15
49/17
51/17
38/13
32/11
556/187
NHAPS-Air
39
64
57
51
64
52
59
57
51
42
39
44
619
NHAPS-
Water
44
67
67
60
63
64
40
56
55
46
42
30
634
a The number of person-days of data are the same as the number of individuals for all studies except for
the Cincinnati study. Because up to 3 days of activity pattern data were obtained from each participant
in this study, the number of person-days of data is approximately three times the number of individuals.
CHAD (Consolidated Human Activity Database) is available at www.epa.gov/chadnetl/.
b Wiley etal. (1991)
c Johnson (1989).
NHAPS = National Human Activity Pattern study
Source: Hubaletal., 2000
9-47
-------
Table 9-52. Number of hours per day children spent in various
microenvironments, by age: average ± SD (percent of children reporting
> 0 hours in microenvironment)
Age
(years)
0
1
2
3
4
5
6
7
8
9
10
11
Indoors at Home
19.6 ±4.3 (99)
19.5 ±4. 1(99)
17.8 ±4.3 (100)
18.0 ±4.2 (100)
17.3 ±4.3 (100)
16.3 ± 4.0 (99)
16.0 ± 4.2 (98)
15.5 ±3. 9 (99)
15.6 ±4. 1(99)
15.2 ±4.3 (99)
16.0 ± 4.4 (96)
14.9 ±4.6 (98)
Outdoors at
Home
1.4 ±1.5 (20)
1.6 ±1.3 (35)
2.0 ±1.7 (46)
2.1 ±1.8 (48)
2.4 ±1.8 (42)
2.5 ±2. 1(52)
2.6 ± 2.2 (48)
2.6 ± 2.0 (48)
2.1 ±2.5 (44)
2.3 ±2.8 (49)
1.7 ±1.9 (40)
1.9 ±2.3 (45)
Indoors at
School
3.5 ±3.7 (2)
3.4 ±3.8 (5)
6.2 ±3.3 (9)
5.7 ±2.8 (14)
4.9 ±3.2 (16)
5.4 ±2.5 (39)
5. 8 ±2.2 (34)
6.3 ± 1.3 (40)
6.2 ±1.1 (41)
6.0 ±1.5 (39)
5.9 ±1.5 (39)
5.9 ±1.5 (41)
Outdoors at
Park
1.6 ±1.5 (9)
1.9 ±2.7 (10)
2.0 ± 1.7(17)
1.5 ±0.9 (17)
2.3 ± 1.9 (20)
1.6 ±1.5 (28)
2.1 ±2.4 (32)
1.5 ± 1.0 (28)
2.2 ± 2.4 (37)
1.7 ±1.5 (34)
2.2 ± 2.3 (40)
2.0 ±1.7 (44)
In Vehicle
1.2 ±1.0 (65)
1.1 ±0.9 (66)
1.2 ±1.5 (76)
1.4 ±1.9 (73)
1.1 ±0.8 (78)
1.3 ±1.8 (80)
1.1 ±0.8 (79)
1.1 ±1.1 (77)
1.3 ±2.1 (82)
1.2 ±1.2 (76)
1.1 ±1.1 (82)
1.6 ±1.9 (74)
Source:Hubaletal., 2000
9-48
-------
Table 9-53. Average number of hours per day children spent doing various
macroactivities while indoors at home, by age (percent of children reporting
> 0 hours for microenvironment/macroactivity)
Age
(years)
0
1
2
3
4
5
6
7
8
9
10
11
Eating
1.9 (96)
1.5 (97)
1.3 (92)
1.2 (95)
1.1 (93)
1.1 (95)
1.1 (94)
1.0 (93)
0.9 (91)
0.9 (90)
1.0 (86)
0.9 (89)
Sleeping or
napping
12.6 (99)
12.1 (99)
11.5(100)
11.3(99)
10.9 (100)
10.5 (98)
10.4 (98)
9.9 (99)
10.0 (96)
9.7 (96)
9.6 (94)
9.3 (94)
Shower
or bathe
0.4 (44)
0.5 (56)
0.5 (53)
0.4 (53)
0.5 (52)
0.5 (54)
0.4 (49)
0.4 (56)
0.4(51)
0.5 (43)
0.4 (43)
0.4 (45)
Playing
games
4.3 (29)
3.9 (68)
2.5 (59)
2.6 (59)
2.6 (54)
2.0 (49)
1.9 (35)
2.1 (38)
2.0 (35)
1.7 (28)
1.7 (38)
1.9 (27)
Watching TV
or listening to
radio
1.1 (9)
1.8(41)
2.1 (69)
2.6(81)
2.5 (82)
2.3 (85)
2.3 (82)
2.5 (84)
2.7 (83)
3.1(83)
3.5 (79)
3.1 (85)
Reading,
writing, doing
homework
0.4 (4)
0.6(19)
0.6 (27)
0.8 (27)
0.7(31)
0.8(31)
0.9 (38)
0.9 (40)
1.0 (45)
1.0 (44)
1.5 (47)
1.1 (47)
Thinking,
relaxing,
passive
3.3 (62)
2.3 (20)
1.4(18)
1.0(19)
1.1(17)
1.2(19)
1.1(14)
0.6 (10)
0.7 (7)
0.9 (17)
0.6 (10)
0.6 (10)
Source: Hubaletal., 2000
Table 9-54. Respondents with children and those reporting outdoor playa
activities in both warm and cold weather
Survey
Population
SCS-II base
SCS-II
oversample
Total
N respondents
with children
197
483
680
Child
playersa
N
128
372
500
%
65.0
77.0
73.5
Child
nonplayers
N
69
111
180
%
35.0
23.0
26.5
Warm-
weather
playerb
N
127
370
497
Cold-
weather
player
N
100
290
390
Player
in both
seasons
%
50.8
60.0
57.4
a "Play" and "player" refer specifically to participation in outdoor play on bare dirt or mixed grass and dirt.
b Does not include three "Don't know/refused" responses regarding warm-weather play.
9-49
-------
Table 9-55. Play frequency and duration for all child players (from SCS-II data)
by percentiles
Percentile
N
5th percentile
50th percentile
95th percentile
Cold weather
Frequency
(days/wk)
372
1
3
7
Duration
(hrs/day)
374
1
1
4
Total
(hrs/wk)
373
1
5
20
Warm weather
Frequency
(days/wk)
488
2
7
7
Duration
(hrs/day)
479
1
3
8
Total
(hrs/wk)
480
4
20
50
Table 9-56. Hand washing and bathing frequency for all child players
(from SCS-II data) by percentiles
Percentile
N
5th percentile
50th percentile
95th percentile
Cold weather
Hand washing
(times/day)
329
2
4
10
Bathing
(times/wk)
388
2
7
10
Warm weather
Hand washing
(times/day)
433
2
4
12
Bathing
(times/wk)
494
3
7
14
Table 9-57. NHAPS and SCS-II play duration3 comparison
Survey
Population
NHAPS
SCS-II
Mean play duration (min/day)
Cold weather
114
102
Warm weather
109
206
Total
223
308
X2 test"
pO.OOOl
a Selected previous day activities in NHAPS, average day outdoor play on bare dirt or mixed grass and dirt
in SCS-II.
' 2x2 chi-square test for contingency between NHAPS and SCS-II.
9-50
-------
Table 9-58. Comparison of NHAPS and SCS-II hand-washing frequencies
Survey
Population
NHAPS
SCS-II
NHAPS
SCS-II
Season
Cold
Cold
Warm
Warm
Percent reporting by frequency (times/day)
0
O
1
O
0
1-2
18
16
18
12
3-5
51
50
51
46
6-9
17
11
15
16
10-19
7
7
7
10
20-29
1
1
2
1
30+
1
0
1
0
"Don't
know"
O
15
4
13
X2 test3
p=0.060
p=0.001
2x2 chi-square test for contingency between NHAPS and SCS-II.
9-51
-------
Table 9-59. Confidence in activity patterns recommendations
Considerations
Rationale
Rating
Time spent indoors vs. outdoors
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of
interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data-
collection period
Validity of approach
Study size
Representativeness of
the population
Characterization of
variability
Lack of bias in study
design (high rating is
desirable)
Measurement error
The study received high level of peer review.
The study is widely available to the public.
The reproducibility of these studies is left to question. Evidence has shown
that activities have tended to shift over the past decade since the study was
published due to economic conditions and technological developments, etc.
Thus, it is assumed there would be differences in results. However, if data
were reanalyzed in the same manner, the results are expected to be the same.
The study focused on general activity patterns.
The study focused on the U.S. population.
Data were collected via questionnaires and interviews.
The studies were published in 1985 (data were collected 1981-1982).
Households were sampled four times during 3 -month intervals from
February to December 1981.
A 24-hour recall time diary method was used to collect data.
The sample population was 922 children between the ages of 3 and 17
years.
The study focused on activities of children.
Variability was characterized by age, gender, day of the week, location of
activities, and various age categories for children.
Biases noted were sampled during time when children were in school
(activities during vacation time are not represented); activities in the 1980's
may be different than they are now.
Measurement or recording error may occur because the diaries were based
on recall (in most cases a 24-hour recall).
High
High
Medium
High
High
High
Medium
High
High
High
High
Medium
Medium
Medium
Other Elements
Number of studies
Agreement between
researchers
Overall Rating
Two
Difficult to compare due to varying categories of activities and the unique
age distributions found within each study.
High
Not
Ranked
Medium
9-52
-------
Table 9-59. Confidence in activity patterns recommendations (continued)
Considerations
Rationale
Rating
Time spent showering
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data-collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of
variability
Lack of bias in study design
(high rating is desirable)
Measurement error
The study received high level of peer review.
Currently, raw data are available only to EPA. It is not known when
data will be publicly available.
Results are reproducible.
The study focused specifically on time spent showering.
The study focused on the U.S. general population.
The study was based on primary data.
The study was published in 1996.
The data were collected between October 1992 and September 1994.
The study used a valid methodology and approach that collected
information from 24-hour diaries as well as information on temporal
conditions and demographic data such as geographic location and
socioeconomic status for various U.S. subgroups.
Study consisted of 9386 total participants of all ages; 450 respondents
ages 1-17 years were included in this category.
The data were representative of the U.S. population.
The study provides a distribution on showering duration.
The study includes distributions for showering duration. Study is based
on short-term data.
Measurement or recording error may occur because diaries were based
on 24-hour recall.
High
Low
High
High
High
High
High
High
High
Medium-
Low
High
High
High
Medium
Other Elements
Number of studies
Agreement between
researchers
Overall Rating
One; the study was a national study.
The recommendation is based on the data (presented in ranges) from
only one study (NHAPS), but it is a widely accepted study. The
recommended value was selected on the basis of professional judgment
because the data were presented as a range (10-20 minutes).
Low
Low-
Medium
Medium
Shower frequency
9-53
-------
Table 9-59. Confidence in activity patterns recommendations (continued)
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data-collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of
variability
Lack of bias in study design
(high rating is desirable)
Measurement error
The study received high level of peer review.
Currently, raw data are available only to EPA. It is not known when
data will be publicly available.
Results can be reproduced or methodology can be followed and
evaluated, provided comparable economic and social conditions exists.
The survey collected information on duration and frequency of selected
activities and time spent in selected microenvironments.
The data represent the U.S. population.
The study was based on primary data.
The study was published in 1996.
The data were collected between October 1992 and September 1994.
The study used a valid methodology and approach that collected
information from 24-hour diaries as well as information on temporal
conditions and demographic data such as geographic location and
socioeconomic status for various U.S. subgroups. Responses were
weighted according to these demographic data.
The study consisted of 9386 total participants of all age groups; 451
respondents ages 1-17 years participated in this category.
Studies were based on the U.S. population.
The study provided data that varied across geographic region, race,
gender, employment status, educational level, day of the week, seasonal
conditions, and medical conditions of respondent.
Study is based on short-term data.
Measurement or recording error may occur because diaries were based
on 24-hour recall.
High
Low
High
High
High
High
High
High
High
Medium-
low
High
High
Medium
Medium
Other Elements
Number of studies
Agreement between
researchers
One; the study was based on one primary national study.
Recommendation was based on only one study.
Overall Rating
Low
Not
Ranked
Medium
Time spent swimming
9-54
-------
Table 9-59. Confidence in activity patterns recommendations (continued)
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data-collection
period
Validity of approach
Study size
Representativeness of the
population
Characterization of
variability
Lack of bias in study design
(high rating is desirable)
Measurement error
Study received high level of peer review.
Currently, raw data are available only to EPA. It is not known when
data will be publicly available.
Results can be reproduced or methodology can be followed and
evaluated, provided comparable economic and social conditions exists.
The survey collected information on duration and frequency of selected
activities and time spent in selected microenvironments. It addresses
only time swimming at a swimming pool.
The data represent the U.S. population.
The study was based on primary data.
The study was published in 1996.
The data were collected between October 1992 and September 1994.
The study used a valid methodology and approach that collected
information from 24-hour diaries as well as information on temporal
conditions and demographic data such as geographic location and
socioeconomic status for various U.S. subgroups. Responses were
weighted according to these demographic data.
The study consisted of 9386 total participants of all age groups; 238
respondents aged 1-17 years participated in this category.
Studies were based on the U.S. population.
The study provided data that varied across geographic region, race,
gender, employment status, educational level, day of the week, seasonal
conditions, and medical conditions of respondent.
The study includes distributions for swimming duration. Study is based
on short-term data.
Measurement or recording error may occur because diaries were based
on 24-hour recall.
High
Low
High
Medium
High
High
High
High
High
Low
High
High
Medium
Medium
Other Elements
Number of studies
Agreement between
researchers
One; the study was based on one primary national study.
Recommendation was based on only one study.
Overall Rating
Low
Not
Ranked
Medium
Time spent playing on sand, gravel, or grass
9-55
-------
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data-collection
period
Validity of approach
Study size
Representativeness of
the population
Characterization of
variability
Lack of bias in study design
(high rating is desirable)
Measurement error
The study received high level of peer review.
Currently, raw data are available only to EPA. It is not known when
data will be publicly available.
Results can be reproduced or methodology can be followed and
evaluated provided comparable economic and social conditions exists.
The survey collected information on duration and frequency of selected
activities and time spent in selected micro-environments.
The data represent the U.S. population.
The study was based on primary data.
The study was published in 1996.
The data were collected between October 1992 and September 1994.
The study used a valid methodology and approach that collected
information from 24-hour diaries as well as information on temporal
conditions and demographic data such as geographic location and
socioeconomic status for various U.S. subgroups. Responses were
weighted according to these demographic data.
The study consisted of 9386 total participants of all age groups; 457
respondents aged 1-17 years participated in this category.
The studies were based on the U.S. population.
The study provided data that varied across geographic region, race,
gender, employment status, educational level, day of the week, seasonal
conditions, and medical conditions of respondent.
The study includes distributions for bathing duration. Study is based on
short-term data.
Measurement or recording error may occur because diaries were based
on 24-hour recall.
High
Low
High
High
High
High
High
High
High
Medium-
low
High
High
Medium
Medium
Other Elements
Number of studies
Agreement between
researchers
One; the study was based on one primary national study.
Recommendation was based on only one study. Recommendations
based on 50% time spent playing in grass.
Overall Rating
Low
Not
Ranked
Medium
Table 9-60. Summary of activity pattern studies
9-56
-------
Study
Timmer et al.,
1985
Robinson and
Thomas, 1991
Wiley et al.,
1991
Tsang and
Klepeis, 1996
Funk et al.,
1998
Hubal et al.
2000
Age
Groups
(years)
3-5, 6-8,
9-11,
12-14,
15-17
12-adults
0-2, 3-5,
6-8, 9-1 1
1-4,5-11,
12-17
6-11,
12-17
0, 1,2,3,
4, 5, 6, 7,
8, 9, 10,
11
Sample Size
922
1762 (California)
2762 (national)
1200
Varies with age
groups and
activities
768
2640
Population
National
California and
national
California
National
California
Based on Wiley
etal., 1991;
Johnson, 1989;
and Tsang and
Klepeis, 1996
Activities
18 microenvironments
16 microenvironments
10 microenvironments
23 microenvironments
Activities grouped into
low-, medium-, and
high-ventilation levels
Activities grouped into
indoors at home, indoors
at school, outdoors at
home, outdoors at part,
and in vehicle
9-57
-------
Table 9-61. Summary of mean time spent indoors and outdoors from several
studies
Age
(years)
3-5
6-8
9-11
12-14
15-17
12 and older
0-2
3-5
6-8
9-11
1-4
5-11
12-17
Time Indoors
(hrs/day)a
19.0
20.0
20.0
20.0
19.0
21 (National)
21 (California)
20.0
18.8
19.7
19.9
—
—
Time Outdoors
(hrs/day)a
2.8 (National)
2.2
1.8
1.8
1.9
1.2 (National)
1.4 (California)
4 (California)
5.2
4.4
4.1
6.0
6.0
5.0
Study
Timmer et al., 1985
Robinson and Thomas,
1991
Wiley etal., 1991
Tsang and Klepeis, 1996
' Mean of weekday and weekend rounded up to two significant figures.
9-58
-------
Table 9-62. Summary of recommended values for activity factors
Type
Value
Study
Time indoors
At residence
Ages (years)
1-4
5-11
12-17
Mean (hrs)
20
17
16
95th percentile
24
24
23
Tsang and Klepeis, 1996
Use values in Table 9-41 for other percentile data.
Total time indoors
Ages (years) Mean (hrs)
3-5 19
6-8 20
9-11 20
12-14 20
15-17 19
Mean is mean of weekday and weekend rounded up
to two significant figures. Use Table 9-3 for mean
values spent indoors weekend and weekday.
Timmeretal., 1985
Time
outdoors
At residence
Ages (years)
1-4
5-11
12-17
Mean (hrs)
3
3
2
95th percentile
Tsang and Klepeis, 1996
Use values in Table 9-43 for other percentile data.
Total time outdoors
Ages (years) Mean (hrs)
3-5 3
6-8 2
9-11 2
12-14 2
15-17 2
Mean is mean of weekday and weekend rounded up
to one significant figure. Use Table 9-3 for mean
values spent outdoors weekend and weekday.
Timmeretal., 1985
9-59
-------
Table 9-62. Summary of recommended values for activity factors (continued)
Type
Taking
shower
Swimming
Playing on
sand or gravel
Playing on
grass
Value
10 min/day shower duration
1 shower event/day
1 event/mo
60 mins/event
60 mins/day
60 mins/day
Study
Tsang and Klepeis, 1996
Tsang and Klepeis, 1996
Tsang and Klepeis, 1996
Tsang and Klepeis, 1996
Tsang and Klepeis, 1996
9-60
-------
REFERENCES FOR CHAPTER 9
Chance, WG; Harmsen, E. (1998) Children are different: environmental contaminants and children's health. Can J
Public Health 89 (suppl): 59-513.
Funk, L; Sedman, R; Beals, JAJ; et al. (1998) Quantifying the distribution of inhalation exposure in human
populations: distributions of time spent by adults, adolescents, and children at home, at work, and at school. Risk
Anal 18(l):47-56.
Garlock, TJ; Shirai JH; Kissel, JC. (1999) Adult responses to a survey of soil contact-related behaviors. J Expo Anal
Environ Epidemiol 1(2): 134-142.
Hubal, EA; Sheldon, LS; Burke, JM; et al. (2000) Children's exposure assessment: a review of factors influencing
children's exposure and the data available to characterize and assess that exposure. U.S. Environmental Protection
Agency, National Exposure Research Laboratory, Research Triangle Park, NC.
Johnson, T. (1989) Human activity patterns in Cincinnati, Ohio. Palo Alto, CA: Electric Power Research Institute.
Robinson, JP; Thomas, J. (1991) Time spent in activities, locations, and microenvironments: a California-National
Comparison Project report. U.S. Environmental Protection Agency, Environmental Monitoring Systems Laboratory,
Las Vegas, NV.
Timmer, SG; Eccles, J; O'Brien, K. (1985) How children use time. In: Juster, FT; Stafford, FP; eds. Time, goods, and
well-being. University of Michigan, Survey Research Center, Institute for Social Research, Ann Arbor, MI, pp.
353-380.
Tsang, AM; Klepeis, NE. (1996) Results tables from a detailed analysis of the National Human Activity Pattern
Survey (NHAPS) response. Draft report prepared for the U.S. Environmental Protection Agency by Lockheed Martin,
Contract No. 68-W6-001, Delivery Order No. 13.
U.S. EPA (U.S. Environmental Protection Agency). (1992) Dermal exposure assessment: principles and applications.
Office of Health and Environmental Assessment, Washington, DC. EPA No. 600/8-91-01 IB. Interim Report.
U.S. EPA. (1997) Exposure factors handbook. National Center for Environmental Assessment, Office of Research
and Development, Washington, DC. EPA/600/P-95/002Fa,b,c.
Wiley, JA; Robinson, JP; Cheng, Y; et al. (1991) Study of children's activity patterns. California Environmental
Protection Agency, Air Resources Board Research Division. Sacramento, CA.
Wong, EY; Shirai, JH; Garlock, TJ; et al. (2000) Adult proxy responses to a survey of children's dermal soil contact
activities. J Expos Anal Environ Epidemiol 10(6):509-517.
9-61
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10. CONSUMER PRODUCTS
10.1. BACKGROUND
Consumer products may contain toxic or potentially toxic chemical constituents to which
children may be exposed. For example, methylene chloride and other solvents and carriers are
common in consumer products and may prompt health concerns. Potential pathways of exposure
to consumer products or chemicals released from consumer products during use can occur via
ingestion, inhalation, and dermal contact. This chapter presents information on the amount of
product used, the frequency of use, and the duration of use for various consumer products
typically found in households.
Limited data are available on consumer product use for the general population and
especially for children. Children can be in environments where household consumer products
such as cleaners, solvents, and paints are used, and they can be passively exposed to chemicals in
these products. Table 10-1 provides a list of household consumer products that are commonly
found in some U.S. households. It should be noted that these are 1987 data, and consumer use of
some of the products listed (e.g., aerosol products) may be limited. The studies presented in the
following sections represent readily available surveys for which data were collected on the
frequency and duration of use and the amount of use of cleaning products, painting products,
household solvent products, cosmetics and other personal care products, household equipment,
pesticides, and tobacco. The reader is referred to Exposure Factors Handbook (U.S. EPA, 1997)
for a more detailed presentation for use of consumer products for the general population.
10.2. CONSUMER PRODUCTS USE STUDIES
10.2.1. Tsang and Klepeis, 1996
EPA collected information for the general population on the duration and frequency of
selected activities and the time spent in selected microenvironments via 24-hour diaries. More
than 9000 individuals from all age groups in 48 contiguous states participated in this National
Human Activity Pattern Survey (NHAPS). The survey was conducted between October 1992
and September 1994. Individuals were asked to categorize their 24-hour routines (diaries) and/or
answer follow-up exposure questions that were related to exposure events. Data were collected
on the basis of selected socioeconomic (gender, age, race, education, etc.) and geographic
(census region, state, etc.) factors and time/season (day of week, month). Data were collected
for a maximum of 82 possible microenvironments and 91 different activities (Tsang and Klepeis,
1996).
10-1
-------
As part of the survey, data were also collected on the duration and frequency of use of
selected consumer products by parents who had children in various age groups. These data are
presented in Tables 10-2 through 10-6 for age groups 1-4, 5-11, and 12-17 years. Distribution
data are presented for selected percentiles, where possible. Other data are presented in ranges of
time spent in an activity (e.g., working with or near a product being used) or in ranges for the
number of times an activity involving a consumer product was performed. Total N denotes the
number of respondents for that specific activity category. The data in this document are for age
groups 1-17 years old only, where data are available.
The advantages of the NHAPS is that the data were collected for a large number of
individuals and are representative of the U.S. general population. However, means cannot be
calculated for consumers who spent more than 60 or 120 minutes (depending on the activity) in
an activity using a consumer product. Therefore, a good estimate of the high consumer activities
cannot be captured.
10.3. RECOMMENDATIONS
Due to the large range and variation among consumer products and their exposure
pathways, it is not feasible to specify recommended exposure values as had been done in other
chapters of this handbook. The user is referred to the contents and references in Chapter 16 of
Exposure Factors Handbook (U.S. EPA, 1997) to derive appropriate exposure factors and review
its associated recommendations.
10-2
-------
Table 10-1. Consumer products commonly found in some U.S. households"
Consumer Product Category
Cosmetics hygiene products
Household furnishings
Garment conditioning products
Household maintenance products
Consumer Product
Adhesive bandages
Bath additives (liquid)
Bath additives (powder)
Cologne/perfume/aftershave
Contact lens solutions
Deodorant/antiperspirant (aerosol)
Deodorant/antiperspirant (wax and liquid)
Depilatories
Facial makeup
Fingernail cosmetics
Hair coloring/tinting products
Hair conditioning products
Hair sprays (aerosol)
Lip products
Mouthwash/breath freshener
Sanitary napkins and pads
Shampoo
Shaving creams (aerosols)
Skin creams (non-drug)
Skin oils (non-drug)
Soap (toilet bar)
Sunscreen/suntan products
Talc/body powder (non-drug)
Toothpaste
Waterless skin cleaners
Carpeting
Draperies/curtains
Rugs (area)
Shower curtains
Vinyl upholstery, furniture
Anti-static spray (aerosol)
Leather treatment (liquid and wax)
Shoe polish
Spray starch (aerosol)
Suede cleaner/polish (liquid and aerosol)
Textile waterproofing (aerosol)
Adhesive (general) (liquid)
Bleach (household) (liquid)
Bleach (see laundry)
Candles
Cat box litter
Charcoal briquets
Charcoal lighter fluid
Drain cleaner (liquid and powder)
Dishwasher detergent (powder)
Dishwashing liquid
Fabric dye (DIY)
Fabric rinse/softener (liquid)
10-2
-------
Table 10-1. Consumer products commonly found in some U.S. households"
(continued)
Consumer Product Category
Consumer Product
Household maintenance products
(continued)
Fabric rinse/softener (powder)
Fertilizer (garden) (liquid)
Fertilizer (garden) (powder)
Fire extinguishers (aerosol)
Floor polish/wax (liquid)
Food packaging and packaged food
Furniture polish (liquid)
Furniture polish (aerosol)
General cleaner/disinfectant (liquid)
General cleaner (powder)
General cleaner/disinfectant (aerosol and pump)
General spot/stain remover (liquid)
General spot/stain remover (aerosol and pump)
Herbicide (garden-patio) (Liquid and aerosol)
Insecticide (home and garden) (powder)
Insecticide (home and garden) (aerosol and pump)
Insect repellent (liquid and aerosol)
Laundry detergent/bleach (liquid)
Laundry detergent (powder)
Laundry pre-wash/soak (powder)
Laundry pre-wash/soak (liquid)
Laundry pre-wash/soak (aerosol and pump)
Lubricant oil (liquid)
Lubricant (aerosol)
Matches
Metal polish
Oven cleaner (aerosol)
Pesticide (home) (solid)
Pesticide (pet dip) (liquid)
Pesticide (pet) (powder)
Pesticide (pet) (aerosol)
Pesticide (pet) (collar)
Petroleum fuels (home) (liquid and aerosol)
Rug cleaner/shampoo (liquid and aerosol)
Rug deodorizer/freshener (powder)
Room deodorizer (solid)
Room deodorizer (aerosol)
Scouring pad
Toilet bowl cleaner
Toiler bowl deodorant (solid)
Water-treating chemicals (swimming pools)
Home building/improvement products (DIY)
Adhesives, specialty (liquid)
Ceiling tile
Caulks/sealers/fillers
Dry wall/wall board
Flooring (vinyl)
House Paint (interior) (liquid)
House Paint and Stain (exterior) (liquid)
Insulation (solid)
Insulation (foam)
10-4
-------
Table 10-1. Consumer products commonly found in some U.S. households"
(continued)
Consumer Product Category
Consumer Product
Home building/improvement products (DIY)
(continued)
Paint/varnish removers
Paint thinner/brush cleaners
Patching/ceiling plaster
Roofing
Refmishing products (polyurethane, varnishes, etc.)
Spray paints (home) (aerosol)
Wall paneling
Wall paper
Wall paper glue
Automobile-related products
Antifreeze
Car polish/wax
Fuel/lubricant additives
Gasoline/diesel fuel
Interior upholstery/components, synthetic
Motor oil
Radiator flush/cleaner
Automotive touch-up paint (aerosol)
Windshield washer solvents
Personal materials
Clothes/shoes
Diapers/vinyl pants
Jewelry
Printed material (colorprint, newsprint, photographs)
Sheets/towels
Toys (intended to be placed in the mouth)
a A subjective listing, based on consumer use profiles.
x > DIY = Do It Yourself.
Source: U.S. EPA, 1987
10-5
-------
Table 10-2. Number of minutes per day spent in activities working with or near
household cleaning agents such as scouring powders or ammonia
Age groups
(years)
1-4
5-11
12-17
Na
21
26
41
Percentiles
1st
0
1
0
2nd
0
1
0
5th
0
2
0
10th
0
2
0
25th
5
3
2
50th
10
5
5
75th
15
15
10
90th
20
30
40
95th
30
30
60
98th
121b
30
60
99th
121b
30
60
100th
121b
30
60
a N is the doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.
b A value of 121 for number of minutes signifies that more than 120 minutes were spent.
Source: Tsang and Klepeis, 1996
Table 10-3. Number of minutes per day spent using any microwave oven
Age groups
(years)
5-11
12-17
Na
62
141
Percentiles
1st
0
0
I"*1
0
0
5th
0
0
10th
1
1
25th
1
2
50th
2
3
75th
5
5
90th
10
10
95th
15
15
98th
20
30
99th
30
30
100th
30
60
n = doer sample size; percentiles are the percentage of doers below or equal to a given number of minutes.
Source: Tsang and Klepeis, 1996
Table 10-4. Number of respondents using a humidifier at home
Age group
(years)
1-4
5-11
12-17
N
111
88
83
Frequency
Almost every
day
33
18
21
3-5 times a
week
16
10
7
1-2 times a
week
7
12
5
1-2 times a
month
53
46
49
Don't Know
2
2
1
Source: Tsang and Klepeis, 1996
10-6
-------
Table 10-5. Number of respondents reporting that pesticides were applied by a
professional at their home by number of times applied over a 6-month period"
Age group
(years)
1-4
5-11
12-17
N
113
150
143
Number of times applied
0
60
84
90
1-2
35
37
40
3-5
11
10
5
6-9
6
18
6
10+
1
1
*
Don't Know
*
*
2
a Applied to eradicate insects, rodents, or other pests.
* = Missing data.
Source: Tsang and Klepeis, 1996
Table 10-6. Number of respondents reporting that pesticides were applied
by the consumer at home by number of times applied over a 6-month period
Age group
(years)
1-4
5-11
12-17
M
113
150
143
Number of times applied
0
46
50
45
1-2
46
70
64
3-5
15
24
21
6-9
3
1
5
10+
3
4
8
Don't Know
*
1
*
Applied to eradicate insects, rodents, or other pests
* = Missing Data.
Source: Tsang and Klepeis, 1996
10-7
-------
REFERENCES FOR CHAPTER 10
Tsang, AM; Klepeis, NE. (1996) Results tables from a detailed analysis of the National Human Activity Pattern
Survey (NHAPS) response. Draft Report prepared for the U.S. Environmental Protection Agency by Lockheed
Martin, Contract No. 68-W6-001, Delivery Order No. 13.
U.S. EPA (U.S. Environmental Protection Agency). (1997) Exposure Factors Handbook, National Center for
Environmental Assessment, Office of Research and Development, Washington, DC EPA/600/P-95/002FC.
10-8
-------
11. BODY WEIGHT STUDIES
11.1. INTRODUCTION
An average daily dose is a dose that is typically normalized to the average body weight of
the exposed population. If exposure occurs only during childhood years, the average child body
weight during the exposure period should be used to estimate risk (U.S. EPA, 1989).
The purpose of this section is to describe key published studies on body weight for
children in the general U.S. population, as described in Exposure Factors Handbook (U.S. EPA,
1997). Recommended values are based on the results of these studies.
11.2. BODY WEIGHT STUDIES
11.2.1. Hamill et al., 1979
A National Center for Health Statistics (NCHS) Task Force that included academic
investigators and representatives from the Centers for Disease Control Nutrition Surveillance
Program selected, collated, integrated, and defined appropriate data sets to generate growth
curves for the ages of birth to 36 months (Hamill et al., 1979). The percentile curves were
developed to assess the physical growth of children in the U.S. They are based on accurate
measurements made on large, nationally representative samples of children. Smoothed
percentile curves were derived for body weight by age. Curves were developed for boys and for
girls. The data used to construct the curves were provided by the Pels Research Institute, Yellow
Springs, OH. These data came from an ongoing longitudinal study where anthromopetric data
from direct measurements are collected regularly from participants (-1000) in various areas of
the U.S. The NCHS used advanced statistical and computer technology to generate the growth
curves. Table 11-1 presents the percentiles of weight-by-sex and age. Figures 11-1 and 11-2
present weight-by-age percentiles for boys and for girls ages birth to 36 months, respectively.
Limitations of this study are that mean body weight values were not reported, and the data are
more that 15 years old. However, this study does provide body weight data for infants younger
than 6 months old.
11.2.2. National Center for Health Statistics, 1987
Statistics on anthropometric measurements, including body weight, for the U.S.
population were collected by NCHS through the second National Health and Nutrition
Examination Survey (NHANES II). NHANES II was conducted on a nationwide probability
sample of 27,801 persons ages 6 months to 74 years from the civilian, noninstitutionalized
11-1
-------
population of the U.S. A total of 20,322 individuals in the sample were interviewed and
examined—a response rate of 73.1%. The survey began in February 1976 and was completed in
February 1980. The sample was selected so that certain subgroups thought to be at high risk of
malnutrition (persons with low incomes, preschool children, and the elderly) were oversampled.
The estimates were weighted to reflect national population estimates. The weighting was
accomplished by inflating examination results for each subject by the reciprocal of selection
probabilities adjusted to account for those who were not examined and post-stratifying by race,
age, and sex.
The NHANES II collected standard body measurements of sample subjects, including
height and weight, that were made at various times of the day and in different seasons of the
year. This technique was used because an individual's weight may vary between winter and
summer and may fluctuate with recency of food and water intake and other daily activities.
Percentile data for children, by age, are presented in Table 11-2 for males and in Table 11-3 for
females.
11.2.3. Burmaster and Crouch, 1997
Burmaster and Crouch (1997) performed data analysis to fit normal and lognormal
distributions to the body weights of females and males at age 9 months to 70 years. Exposure
Factors Handbook (U.S. EPA, 1997) used a pre-published version of this paper. The numbers
reported in Tables 11-4 and 11-5 vary slightly from those reported in Exposure Factors
Handbook.
Data used in this analysis were from NHANES II, which included 27,801 persons 6
months to 74 years of age in the U. S. The NHANES II data had been statistically adjusted for
nonresponse and probability of selection and stratified by age, sex, and race to reflect the entire
U.S. population prior to reporting. Burmaster and Crouch conducted exploratory and
quantitative data analyses and fit normal and lognormal distributions to percentiles of body
weights of children, teens, and adults as a function of age. Cumulative distribution functions
were plotted for female and male body weights on both linear and logarithmic scales.
The maximum likelihood estimation to fit lognormal distributions to the data. Linear and
quadratic regression lines were fitted to the data. A number of goodness-of-fit measures were
conducted on data generated. The investigations found that lognormal distributions gave strong
fits to the data for each sex across all age groups. Statistics for the lognormal probability plots
for children ages 9 months to 20 years are presented in Tables 11-4 and 11-5. These data can be
11-2
-------
used for further analyses of body weight distribution (i.e., application of Monte Carlo analysis).
The reader is referred to the original study for a more detailed description.
11.2.4. U.S. EPA, 2000
EPA's Office of Water has estimated body weights for children by age and gender using
data from NHANES III, which was conducted from 1988 to 1994. NHANES III collected body
weight data for approximately 15,000 children between the ages of 2 months and 17 years.
Table 11-6 presents the body weight estimates by age and gender. Table 11-7 shows the body
weight estimates for the infants under the age of 3 months and/or younger; Figures 11-3 and 11-4
compare the body weights (mean and median) of males and females of various age groups,
respectively.
The limitations of these data are that the data were not available for infants younger than
2 months old, and the data are roughly 6-12 years old. With the upward trends in body weight
from NHANES II (1976-1980) to NHANES III, which may still be valid, the data in Tables 11-6
and 11-7 may underestimate current body weights. Adjustment factors may be needed to update
the estimates from 1988-1994 data to 2000. However, the data are national in scope and
represent the general children's population.
11.3. RECOMMENDATIONS
The recommended values for body weight are summarized in Table 11-8. Table 11-9
presents the confidence ratings for body weight recommendations.
For infants (birth to 6 months), appropriate values for body weight may be selected from
Table 11-1. These data (percentile only) are presented for male and female infants.
For children, appropriate mean values for weights may be selected from Tables 11-7 and
11-8. If percentile values are needed, these data are presented in Table 11-2 for male children
and in Table 11-3 for female children.
-------
Table 11-1. Smoothed percentiles of weight by sex and age: statistics from NCHS
and data from Fels Research Institute
Sex and Age
Male
Birth
1 month
3 months
6 months
9 months
12 months
18 months
24 months
30 months
36 months
Female
Birth
1 month
3 months
6 months
9 months
12 months
18 months
24 months
30 months
36 months
Smoothed Percentile"
5th
10th
25th
50th
75th
90th
95th
Weight in kilograms
2.54
3.16
4.43
6.20
7.52
8.43
9.59
10.54
11.44
12.26
2.36
2.97
4.18
5.79
7.00
7.84
8.92
9.87
10.78
11.60
2.78
3.43
4.78
6.61
7.95
8.84
9.92
10.85
11.80
12.69
2.58
3.22
4.47
6.12
7.34
8.19
9.30
10.26
11.21
12.07
3.00
3.82
5.32
7.20
8.56
9.49
10.67
11.65
12.63
13.58
2.93
3.59
4.88
6.60
7.89
8.81
10.04
11.10
12.11
12.99
3.27
4.29
5.98
7.85
9.18
10.15
11.47
12.59
13.67
14.69
3.23
3.98
5.40
7.21
8.56
9.53
10.82
11.90
12.93
13.93
3.64
4.75
6.56
8.49
9.88
10.91
12.31
13.44
14.51
15.59
3.52
4.36
5.90
7.83
9.24
10.23
11.55
12.74
13.93
15.03
3.82
5.14
7.14
9.10
10.49
11.54
13.05
14.29
15.47
16.66
3.64
4.65
6.39
8.38
9.83
10.87
12.30
13.57
14.81
15.97
4.15
5.38
7.37
9.46
10.93
11.99
13.44
14.70
15.97
17.28
3.81
4.92
6.74
8.73
10.17
11.24
12.76
14.08
15.35
16.54
1 Smoothed by cubic-spline approximation.
Source: Hamill et al., 1979
11-4
-------
Table 11-2. Weight in kilograms for males 6 months to 19 years of age by sex and age, U.S. population 1976-1980"
Age
6-11 months
1 years
2 years
3 years
4 years
5 years
6 years
7 years
8 years
9 years
10 years
1 1 years
12 years
1 3 years
14 years
1 5 years
16 years
17 years
1 8 years
1 9 years
N
179
370
375
418
404
397
133
148
147
145
157
155
145
173
186
184
178
173
164
148
Mean
(kg)
9.4
11.8
13.6
15.7
17.8
19.8
23.0
25.1
28.2
31.1
36.4
40.3
44.2
49.9
57.1
61.0
67.1
66.7
71.1
71.7
SD
1.3
1.9
1.7
2.0
2.5
3.0
4.0
3.9
6.2
6.3
7.7
10.1
10.1
12.3
11.0
11.0
12.4
11.5
12.7
11.6
Percentile
5th
7.5
9.6
11.1
12.9
14.1
16.0
18.6
19.7
20.4
24.0
27.2
26.8
30.7
35.4
41.0
46.2
51.4
50.7
54.1
55.9
10th
7.6
10.0
11.6
13.5
15.0
16.8
19.2
20.8
22.7
25.6
28.2
28.8
32.5
37.0
44.5
49.1
54.3
53.4
56.6
57.9
15th
8.2
10.3
11.8
13.9
15.3
17.1
19.8
21.2
23.6
26.0
29.6
31.8
35.4
38.3
46.4
50.6
56.1
54.8
60.3
60.5
25th
8.6
10.8
12.6
14.4
16.0
17.7
20.3
22.2
24.6
27.1
31.4
33.5
37.8
40.1
49.8
54.2
57.6
58.8
61.9
63.8
50th
9.4
11.7
13.5
15.4
17.6
19.4
22.0
24.8
27.5
30.2
34.8
37.3
42.5
48.4
56.4
60.1
64.4
65.8
70.4
69.5
75th
10.1
12.6
14.5
16.8
19.0
21.3
24.1
26.9
29.9
33.0
39.2
46.4
48.8
56.3
63.3
64.9
73.6
72.0
76.6
77.9
85th
10.7
13.1
15.2
17.4
19.9
22.9
26.4
28.2
33.0
35.4
43.5
52.0
52.6
59.8
66.1
68.7
78.1
76.8
80.0
84.3
90th
10.9
13.6
15.8
17.9
20.9
23.7
28.3
29.6
35.5
38.6
46.3
57.0
58.9
64.2
68.9
72.8
82.2
82.3
83.5
86.8
95th
11.4
14.4
16.5
19.1
22.2
25.4
30.1
33.9
39.1
43.1
53.4
61.0
67.5
69.9
77.0
81.3
91.2
88.9
95.3
82.1
1 Includes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.
Source: National Center for Health Statistics, 1987
-------
Table 11-3. Weight in kilograms for females 6 months to 19 years of age by sex and age, U.S. population 1976-1980"
Age
6-11
months
1 years
2 years
3 years
4 years
5 years
6 years
7 years
8 years
9 years
10 years
1 1 years
12 years
13 years
14 years
15 years
16 years
17 years
18 years
19 years
N
177
336
336
366
396
364
135
157
123
149
136
140
147
162
178
145
170
134
170
158
Mean
(kg)
8.8
10.8
13.0
14.9
17.0
19.6
22.1
24.7
27.9
31.9
36.1
41.8
46.4
50.9
54.8
55.1
58.1
59.6
59.0
60.2
SD
1.2
1.4
1.5
2.1
2.4
3.3
4.0
5.0
5.7
8.4
8.0
10.9
10.1
11.8
11.1
9.8
10.1
11.4
11.1
11.0
Percentile
5th
6.6
8.8
10.8
11.7
13.7
15.3
17.0
19.2
21.4
22.9
25.7
29.8
32.3
35.4
40.3
44.0
44.1
44.5
45.3
48.5
10th
7.3
9.1
11.2
12.3
14.3
16.1
17.8
19.5
22.3
25.0
27.5
30.3
35.0
39.0
42.8
45.1
47.3
48.9
49.5
49.7
15th
7.5
9.4
11.6
12.9
14.5
16.7
18.6
19.8
23.3
25.8
29.0
31.3
36.7
40.3
43.7
46.5
48.9
50.5
50.8
51.7
25th
7.9
9.9
12.0
13.4
15.2
17.2
19.3
21.4
24.4
27.0
31.0
33.9
39.1
44.1
47.4
48.2
51.3
52.2
52.8
53.9
50th
8.9
10.7
12.7
14.7
16.7
19.0
21.3
23.8
27.5
29.7
34.5
40.3
45.4
49.0
53.1
53.3
55.6
58.4
56.4
57.1
75th
9.4
11.7
13.8
16.1
18.4
21.2
23.8
27.1
30.2
33.6
39.5
45.8
52.6
55.2
60.3
59.6
62.5
63.4
63.0
64.4
85th
10.1
12.4
14.5
17.0
19.3
22.8
26.6
28.7
31.3
39.3
44.2
51.0
58.0
60.9
65.7
62.2
68.9
68.4
66.0
70.7
90th
10.4
12.7
14.9
17.4
20.2
24.7
28.9
30.3
33.2
43.3
45.8
56.6
60.5
66.4
67.6
65.5
73.3
71.6
70.1
74.8
95th
10.9
13.4
15.9
18.4
21.1
26.6
29.6
34.0
36.5
48.4
49.6
60.0
64.3
76.3
75.2
76.6
76.8
81.8
78.0
78.1
' Includes clothing weight, estimated as ranging from 0.09 to 0.28 kilogram.
Source: National Center for Health Statistics, 1987
-------
Table 11-4. Statistics for probability plot regression analyses female's body
weights 6 months to 20 years of age
Age midpoint
(years)
0.75
1.50
2.50
3.50
4.50
5.50
6.50
7.50
8.50
9.50
10.50
11.50
12.50
13.50
14.50
15.50
16.50
17.50
18.50
19.50
Lognormal probability plots
linear curve
^
2.16
2.38
2.56
2.69
2.83
2.98
3.10
3.19
3.31
3.46
3.57
3.71
3.82
3.92
3.99
4.00
4.05
4.08
4.07
4.10
<
0.145
0.129
0.112
0.136
0.134
0.164
0.174
0.174
0.156
0.214
0.199
0.226
0.213
0.215
0.187
0.156
0.167
0.165
0.147
0.149
1 [i2 and o2 correspond to the mean and standard deviation, respectively, of the lognormal distribution
of body weight (kg).
Source: Burmasteretal., 1997
11-7
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Table 11-5. Statistics for probability plot regression analyses male's body
weights 6 months to 20 years of age
Age midpoint (years)
0.75
1.50
2.50
3.50
4.50
5.50
6.50
7.50
8.50
9.50
10.5
11.5
12.5
13.5
14.5
15.5
16.5
17.5
18.5
19.5
Lognormal probability plots
linear curve
^
2.23
2.46
2.60
2.75
2.87
2.98
3.13
3.21
3.33
3.43
3.59
3.69
3.78
3.88
4.02
4.09
4.20
4.19
4.25
4.26
°/
0.132
0.119
0.120
0.114
0.133
0.138
0.145
0.151
0.181
0.165
0.195
0.252
0.224
0.215
0.181
0.159
0.168
0.167
0.159
0.154
' [i2 and o2 correspond to the mean and standard deviation, respectively, of the lognormal distribution
of body weight (kg).
Source: Burmasteretal., 1997
11-8
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Table 11-6. Body weight estimates (in kilograms) by age and gender, U.S. population 1988-1994
Age
2-6 months
7-12 months
1 year
2 years
3 years
4 years
5 years
6 years
7 years
8 years
9 years
10 years
1 1 years
12 years
1 3 years
14 years
1 5 years
16 years
17 years
1 and older
1-3 years
1-14 years
15^4 years
Sample Size
1,020
1,072
1,258
1,513
1,309
1,284
1,234
750
736
711
770
751
754
431
428
415
378
427
410
31,311
4,080
12,344
10,393
Population
1,732,702
1,925,573
3,935,114
4,459,167
4,317,234
4,008,079
4,298,097
3,942,457
4,064,397
3,863,515
4,385,199
3,991,345
4,270,211
3,497,661
3,567,181
4,054,117
3,269,777
3,652,041
3,719,690
251,097,002
12,711,515
56,653,796
118,430,653
Male and Female
Median
7.4
9.4
11.3
13.2
15.3
17.2
19.6
21.3
25.0
27.4
31.8
35.2
40.6
47.2
53.0
56.9
59.6
63.2
65.1
66.5
13.2
24.9
70.8
Mean
7.4
9.4
11.4
12.9
15.1
17.1
19.4
21.7
25.5
28.1
32.7
35.6
41.5
46.9
55.1
61.1
62.8
65.8
67.5
64.5
13.1
29.9
73.5
Male
Median
7.6
9.7
11.7
13.5
15.5
17.2
19.7
21.5
25.4
27.2
32.0
35.9
38.8
48.1
52.6
61.3
62.6
66.6
70.0
73.9
13.4
25.1
77.5
Mean
7.7
9.7
11.7
13.1
15.2
17.0
19.3
22.1
25.5
28.4
32.3
36.0
40.0
49.1
54.5
64.5
66.9
69.4
72.4
89.0
13.4
30.0
80.2
Female
Median
7.0
9.1
10.9
13.0
15.1
17.3
19.6
20.9
24.1
27.9
31.1
34.3
43.4
45.7
53.7
53.7
57.1
56.3
60.7
80.8
13.0
24.7
63.2
Mean
7.0
9.1
11.0
12.5
14.9
17.2
19.4
21.3
25.6
27.9
33.0
35.2
42.8
48.6
55.9
57.9
59.2
61.6
62.2
80.3
12.9
29.7
67.3
Source: U.S. EPA, 2000
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Table 11-7. Body weight estimates by age, U.S. population 1988-19943
Age
2 months
3 months
3 months and
older
Sample Size
243
190
433
Population
408,837
332,823
741,660
Male and Female
Median
6.3
7.0
6.6
Mean
6.3
6.9
6.6
95% CI
6.1-6.4
6.7-7.1
6.4-6.7
' Data not available for infants younger than 2 months.
Source: U.S. EPA, 2000
Table 11-8. Summary of recommended values for body weight
Population
Children
Infants
Mean
See Tables 11 -6 and 11 -7
See Tables 11 -6 and 11 -7
Upper Percentile
See Tables 11-2 and 11-3
See Table 11-7
Multiple Percentiles
See Tables 11-2 and 11-3
See Table 11-1
11-10
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Table 11-9. Confidence in body weight recommendations
Considerations
Rationale
Rating
Study Elements
Level of peer review
Accessibility
Reproducibility
Focus on factor of interest
Data pertinent to U.S.
Primary data
Currency
Adequacy of data collection period
Validity of approach
Study size
Representativeness of the population
Characterization of variability
Lack of bias in study design (high
rating is desirable)
Measurement error
NHANES III was the major source for central tendency values. This analysis has
not yet been published. Percentile data are based on NHANES II and published by
the National Center for Health Statistics (1987) and Hamill et al. (1979). Both of
these studies received a high level of peer review.
The National Center for Health Statistics (1987) study and Hamill et al. (1979) are
available to the public. U.S. EPA (2000) is available upon request.
Results can be reproduced by analyzing NHANES II data, NHANES III data, and
the Eels Research Institute data.
The studies focused on body weight, the exposure factor of interest.
The data represent the U.S. population.
The primary data were generated from NHANES II and III data and Eels studies;
thus these data are secondary.
The data were collected between 1976 and 1980 for Hamill et al. (1979) and
NHANES II. U.S. EPA (2000) covered the years 1988-1994.
The NHANES II study included data collected over a period of 4 years. Body
weight measurements were taken at various times of the day and at different seasons
of the year. NHANES III study included data collected over a 7-year period.
Direct body weights were measured for both studies. For NHANES II, subgroups at
risk for malnutrition were oversampled. Weighting was accomplished by inflating
examination results for those not examined and were stratified by race, age, and sex.
The Fels data are from an ongoing longitudinal study where the data are collected
regularly.
The sample size consisted of 28,000 persons for NHANES II. Hamill et al. (1979)
noted that the data set was large. NHANES III study included 12,344 children 1-14
years of age.
Data collected focused on the U.S. population for both studies.
All studies characterized variability regarding age and sex. Additionally, NHANES
II characterized race (for Blacks, Whites and total populations) and sampled persons
with low income.
There are no apparent biases in the study designs for NHANES II. The study design
for collecting the Fels data was not provided.
For NHANES II and III, measurement error should be low because body weights
were performed in a mobile examination center using standardized procedures and
equipment. Also, measurements were taken at various times of the day to account
for weight fluctuations as a result of recent food or water intake. Hamill et al.
(1979) reported that study data are based on accurate direct measurements from an
ongoing longitudinal study.
Low for
central
tendency;
high for
percentiles
High
High
High
High
Medium
High for
central
tendency;
medium for
percentiles
High
High
High
High
High
Medium-
high
High
Other Elements
Number of studies
Agreement between researchers
There are three studies.
There is consistency among the two studies.
Overall Rating
High
High
High
11-11
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I
(5
o
c
3
Q.
(0
0
9 12 15 18 21 24 27 30 33 36
Figure 11-1. Weight by age percentiles for girls ages birth to 36 months.
Source: Hamilletal., 1979
11-12
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2.
(Q
3
o
c
3 6 9 12 15 18 21 24 27 30 33 36
Age in Months
Figure 11-2. Weight by age percentiles for boys ages birth to 36 months.
Source: Hamilletal., 1979
11-13
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•male •"•"famate |
80.0
-------
•mala
90.0
1 1 1 1 1 1 1 1 1 1 1 1 1
-------
Burmaster, DE; Crouch, EAC. (1997) Lognormal distributions for body weight as a function of age for males and
females in the United States, 1976-1980. Risk Anal. 17(4):499-505.
Hamill, PW; Drizd, TA; Johnson, CL; et al. (1979) Physical growth: National Center for Health Statistics
Percentiles. Am J Clin Nutr 32:607-609.
National Center for Health Statistics. (1987) Anthropometric reference data and prevalence of overweight, United
States, 1976-80. Data from the National Health and Nutrition Examination Survey, Series 11, No. 238. U.S.
Department of Health and Human Services, Public Health Service, National Center for Health Statistics, Hyattsville,
MD. DHHS Publication No. (PHS) 87-1688.
U.S. EPA (U.S. Environmental Protection Agency). (1989) Risk assessment guidance for Superfund, vol I: human
health evaluation manual. U.S. Environmental Protection Agency, Office of Emergency and Remedial Response,
Washington, DC. EPA/540/1-89/002.
U.S. EPA. (1997) Exposure factors handbook. Office of Research and Development, Washington, DC. EPA/600-P-
95/002F.
U.S. EPA. (2000) Memorandum: Bodyweight estimates onNHANES III data, revised, Contract 68-C-99-242, Work
Assignment 0-1 from Bob Clickner, Westat Inc. to Helen Jacobs, U.S. EPA dated March 3, 2000.
11-16
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12. LIFETIME
12.1. INTRODUCTION
The length of a typical life is potentially a factor to consider when evaluating cancer risk,
because the standard approach for carcinogens has been to calculate the dose estimate averaged
over a "typical" lifetime of 70 years.
Because this typical life span value is found in the denominator of the dose equation for
cancer, adjusting this value to a shorter typical lifetime could result in a higher estimate of
lifetime average daily dose if assessing an exposure that is fixed (i.e., not a situation of a
constant lifetimes such that changing the expected lifespan would similarly change both the
numerator and denominator and therefore would have no net effect), and conversely, adjusting
this value to a longer life expectancy when assessing an exposure with a fixed duration could
produce a lower lifetime average daily dose estimate. If the same estimates of lifetime risk per
average lifetime dose are then used, the result would be counterintuitive, given that the longer a
person lives, the higher his/her chances could be of developing a chronic disease, or a disease
that often has long latency period, such as cancer.
Children have more years of future life than do adults. Therefore, they have more time to
develop any chronic diseases that might be triggered by early environmental exposures.
Diseases initiated by chemical hazards require several decades to develop, and early childhood
exposure to certain carcinogens or toxicants is more likely to lead to disease than the same
exposures later in life (NRDC, 1997). In addition, recent data published by the U.S. Census
Bureau indicate that overall life expectancy in the U.S. has increased. For example, the
projected life expectancy for children born in 2000 is almost 4% longer than that of children
born in 1980.
12.2. RECOMMENDATIONS
Although it is standard practice to calculate lifetime average daily doses for carcinogens
using a standard assumption of a 70-year lifespan, a different approach may be appropriate when
considering effects of childhood exposures if children are more sensitive than adults.
Specifically, it may not be appropriate to average childhood exposures over a full lifetime,
because this implies that childhood exposure is equivalent to full-life exposure at a lower rate
(U.S. EPA, 1999).
Recent data show that the U.S. human population lives longer than 70 years. However,
because there are some issues that need to be addressed regarding methodology for estimating
12-1
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environmental exposures and risks to children, EPA is not ready to suggest a change in the
current default value of 70 years life expectancy when calculating the lifetime average daily
dose. Issues regarding estimation of cancer risks to children are under discussion in the
revisions to the Cancer Guidelines. In addition, issues related to life expectancy and less-than-
lifetime exposures have been recognized by the RfD Technical Panel, the Harmonization
Technical Panel of the Risk Assessment Forum. Guidance on these issues will be developed in
the future.
12-2
-------
REFERENCES FOR CHAPTER 12
Monro, A. (1992) What is an appropriate measure of exposure when testing drugs for carcinogenicity in rodents?
Toxicol Appl Pharmacol 112:171-181.
NRDC (Natural Resources Defense Council). (1997) Our children at risk: the 5 worst environmental threats to their
health.
U.S. Census Bureau. (1999) Statistical abstracts of the United States. Washington, DC.
U.S. EPA. (U.S. Environmental Protection Agency). (1999) Guidelines for carcinogen risk assessment, review draft,
National Center for Environmental Assessment, Office of Research and Development, Washington, DC.
NCEA-F-0644.
12-2
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