SEPA
United States
Environmental Protection
Agency
Office of Research and
Washington, D.C.
EPA/600/R-15/169 | December 2015 | www.epa.gov/research
Issue Paper on Physiological and
Behavioral Changes in Pregnant
and Lactating Women and Available
Exposure Factors I
Development
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EP A/600/R-15/169
December 2015
Final
www.epa.gov/ncea
Issue Paper on Physiological and Behavioral Changes
in Pregnant and Lactating Women and
Available Exposure Factors
National Center for Environmental Assessment
Office of Research and Development
U.S. Environmental Protection Agency
Washington, DC 20460
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DISCLAIMER
This final 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.
Preferred citation:
U.S. Environmental Protection Agency (EPA). (2015) Issue Paper on Physiological and Behavioral Changes
in Pregnant and Lactating Women and Available Exposure Factors. National Center for Environmental Assessment,
Washington, D.C.; EPA/600/R-15/169. Available from the National Technical Information Service, Springfield, VA
and online at http://www.epa.gov/ncea.
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CONTENTS
CONTENTS iii
LIST OF TABLES v
LIST OF FIGURES vii
LIST OF ABBREVIATIONS AND ACRONYMS viii
PREFACE x
AUTHORS, CONTRIBUTORS, AND REVIEWERS xi
EXECUTIVE SUMMARY xiii
1. INTRODUCTION 1-1
1.1. BACKGROUND 1-1
1.2. OBJECTIVES AND SCOPE 1-6
1.3. METHODS 1-7
1.4. ORGANIZATION 01 THE REPORT 1-8
1.5. INTENDED AUDIENCE 1-8
2. DEFINING THE PREGNANT AND I .ACTATING LIFESTAGE 2-1
3. PHYSIOLOGICAL CHANGES DURING PREGNANCY AND LACTATION 3-1
3.1. CARDIOVASCULAR SYSTEM AND HEMATOLOGICAL SYSTEMS 3-1
3.2. RESPIRATORY SYSTEM 3-2
3.2.1. Pulmonary Function, Lung Volume, and Capacities 3-2
3.2.2. Respiration 3-3
3.3. RENAL SYSTEM 3-5
3.4. SKELETAL SYSTEM 3-6
3.5. NEUROLOGICAL SYSTEM 3-7
3.6. DIGESTIVE/GASTROINTESTINAL SYSTEM 3-8
3.7. ENDOCRINE SYSTEM 3-10
3.7.1. Placenta 3-10
3.7.2. Thyroid Function 3-11
3.7.3. Parathyroid Function 3-13
3.7.4. Hypothalamic-Pituitary-Adrenal Axis 3-13
3.7.5. Glucose and Carbohydrate Metabolism 3-15
3.7.6. Protein and Lipid Metabolism 3-16
3.7.7. Metabolic Adjustments 3-18
3.7.8. Total Body Water Metabolism 3-20
3.8. INTEGUMENTARY SYSTEM 3-21
3.9. WEIGHT CHANGES 3-21
4. BEHAVIORAL ADAPTATIONS AND PSYCHOLOGICAL CHANGES DURING
PREGNANCY AND LACTATION 4-1
4.1. ADAPTATIONS 4-2
4.1.1. Smoking 4-2
4.1.2. Caffeine Consumption 4-4
4.1.3. Alcohol Use 4-4
in
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CONTENTS (continued)
4.1.4. Other Adaptations 4-6
4.2. DEPRESSION 4-6
4.2.1. Prevalence of Depression 4-6
4.2.2. Drug Treatment for Depression 4-7
4.2.3. Factors Impacting Depression in Pregnancy and Lactation 4-7
4.3. STRESS, ANXIETY, IRRITABILITY, SLEEP, AND FATIGUE 4-8
4.4. CHANGES IN DIETARY BEHAVIORS 4-9
4.4.1. Nutritional Needs 4-9
4.4.2. Energy Requirements 4-11
4.4.3. Cravings and Aversions 4-13
5. EXPOSURE FACTORS FOR PREGNANT/LACTATING WOMEN 5-1
5.1. WATER INTAKE 5-1
5.2. DIETARY INTAKE 5-7
5.3. NONDIETARY INTAKE (PICA) 5-24
5.4. INHALATION RATES 5-27
5.5. ACTIVITY FACTORS AND CONSUMER PRODUCT USE 5-31
5.6. BODY WEIGHT 5-35
6. DATA GAPS 6-1
7. REFERENCES 7-1
APPENDIX. COMPARISON OF COMMODITY CONSUMPTION PATTERNS OF
PREGNANT AND NONPREGNANT WOMEN OF CHILDBEARING AGE A-l
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LIST OF TABLES
ES-1. Physiological changes during pregnancy by organ system xv
ES-2. Physiological changes during lactation by organ system xix
1 -1. Changes during the first trimester (weeks 1-12) 1-2
1-2. Changes during the second trimester (weeks 13-27) 1-3
1 -3. Changes during the third trimester (weeks 28-40) 1-5
2-1. U.S. pregnancy rates for 2008, by age and race (pregnancies per 1,000 women) 2-1
2-2. Percentage of women breastfeeding in 2007 by maternal age and race 2-3
3-1. Changes in lung volumes and capacities during pregnancy 3-4
4-1. Comparison of cigarette, alcohol, and illicit drug use among pregnant and
nonpregnant women, aged 15-44 years, based on the 2009/2010 NSDUH 4-3
4-2. Recommended number of servings for three caloric intake levels for pregnant
women 4-12
5-1. Tapwater and total fluid intake among pregnant, lactating, and control women,
based on a 1977-1978 dietary survey (mL/day) 5-2
5-2. Water ingestion rates by pregnancy status (L/day) for a population of women in
Colorado 5-3
5-3. Principal sources of drinking water at home for a population of women in
Colorado (%) 5-4
5-4. Water ingestion rates of pregnant, lactating, and nonpregnant nonlactating U.S.
women aged 15-44 years, community water and (total water from all sources),
based on 1994-1996 and 1998 CSFII data 5-5
5-5. Per capita intake of major food groups: U.S. pregnant and nonpregnant women of
child-bearing age (13 to 49 years), based on NHANES 2003-2008 (g/kg-day) 5-9
5-6. Consumer-only intake of major food groups: U.S. pregnant and nonpregnant
women of child-bearing age (13 to 49 years), based on NHANES 2003-2008
(g/kg-day) 5-11
5-7. Per capita and consumer-only intake of individual foods: U.S. pregnant and
nonpregnant women of child-bearing age (13 to 49 years), based on NHANES
2003-2008 (g/kg-day) 5-12
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LIST OF TABLES (continued)
5-8. Percentage of women who ate each type of fish during the prior month and their
weekly intake, based on a FDA/CDC study 5-21
5-9. Mean number of local fish meals consumed per year by time period and selected
characteristics for all respondents (Mohawk, N= 97; Control, N= 154), based on
a New York Health Department study 5-23
5-10. Frequency of pica behavior among low-income women in Georgia (N = 410) 5-25
5-11. Items ingested by low-income Mexican-born women who practiced pica during
pregnancy in California (N= 46) 5-26
5-12. Simulated inhalation rates of prepregnant, pregnant, and postpartum lactating
underweight women, aged 11 to 55 years 5-28
5-13. Simulated inhalation rates of prepregnant, pregnant, and postpartum lactating
normal-weight women, aged 11 to 55 years 5-29
5-14. Simulated inhalation rates of prepregnant, pregnant, and postpartum lactating
overweight/obese women, aged 11 to 55 years 5-30
5-15. Mean (95% CI) time spent in various activities (hours/day), by trimester among a
population of Canadian women 5-31
5-16. Activities associated with exposure to water, by pregnancy status in a population
of women in Colorado 5-33
5-17. Percentage of a population of pregnant minority women residing in New York (N
= 186) who reported use of selected personal care products over a 48-hour survey
period 5-35
5-18. Weight gained during pregnancy, and pre- and postpregnancy body weight (kg),
among a population of Michigan women (N= 110) 5-36
5-19. Weight gained during pregnancy (kg), for populations of underweight, normal-
weight, overweight, and obese women in California who had good pregnancy
outcomes (N= 4,218) 5-38
5-20. Estimated body weight (kg) of pregnant women—NHANES (1999-2006) 5-39
5-21. Mean ± SD prepregnancy weight, pregnancy weight gain, and postpartum weight
loss (kg) among a population of Louisiana women (N= 56) 5-40
5-22. Mean ± SD changes in maternal body weight in populations of breastfeeding and
formula feeding California women 5-41
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LIST OF FIGURES
3-1. Relative changes in maternal thyroid function during pregnancy 3-12
5-1. Ratios of mean consumer-only intake for pregnant women to that of nonpregnant
women in the United States, based on NHANES 2003-2008 5-17
5-2. Weight of breastfeeding (•, N= 26) and formula-feeding (A, N= 27) women
during the first 24 months postpartum 5-41
5-3. Predicted weight-retention curves over time for four lactation practices 5-42
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LIST OF ABBREVIATIONS AND ACRONYMS
AAP
American Academy of Pediatrics
ACOG
American College of Obstetricians and Gynecologists
ACTH
Adrenocorticotropic hormone
AT SDR
Agency for Toxic Substances and Disease Registry
BMD
bone mineral density
BMI
body mass index
BMR
basal metabolic rate
CDC
Centers for Disease Control and Prevention
CHAPS
Canadian Human Activity Pattern Study
CI
confidence interval
CO
cardiac output
CrCl
creatinine clearance rate
CRH
corticotropin releasing hormone
CSFII
Continuing Survey of Intake by Individuals
DBP
diastolic blood pressure
EPA
U.S. Environmental Protection Agency
eRPF
effective renal plasma flow
ERV
expiratory reserve volume
FDA
U.S. Food and Drug Administration
FRC
functional residual capacity
GFR
glomerular filtration rate
GPx
glutathione peroxidase
hCG
human chorionic gonadotropin
HDL
high-density lipoprotein
HPA
hypothalamic-pituitary-adrenal axis
hPL
human placental lactogen
HR
heart rate
IC
inspiratory capacity
IOM
Institute of Medicine
IRV
inspiratory reserve volume
LDL
low-density lipoproteins
NCEA
National Center for Environmental Assessment
NHANES
National Health and Nutrition Examination Survey
NIH
National Institutes of Health
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NMCM
National Institute for Health Care Management
NSDUH
National Survey on Drug Use and Health
ORD
Office of Research and Development
PDIR
physiological daily inhalation rate
RDA
recommended daily allowance
RR
respiratory rate
RV
residual volume
SAMHSA
Substance Abuse and Mental Health Services Administration
SBP
systolic blood pressure
SD
standard deviation
SE
standard error
SIP
Share of Intake Panel
T3
triiodothyronine
T4
thyroxine
TBG
thyroid-binding globulin
TLC
total lung capacity
TSH
thyroid-stimulating hormone
TT3
total triiodothyronine
USD A
U.S. Department of Agriculture
USDHHS
U.S. Department of Health and Human Services
VC
vital capacity
VLDL
very-low-density lipoproteins
WHO
World Health Organization
WIC
Women, Infant, and Children
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PREFACE
The Exposure Factors Program of the U.S. Environmental Protection Agency's (EPA's)
Office of Research and Development (ORD) has three main goals: (1) provide updates to the
Exposure Factors Handbook (U.S. EPA, 2011) and the Child-Specific Exposure Factors
Handbook (U.S. EPA. 2008); (2) identify exposure factors data gaps and needs in consultation
with clients; and (3) develop companion documents to assist clients in the use of exposure
factors data. The activities under each goal are supported by and respond to the needs of the
various EPA program offices and others. This issue paper provides summaries of physiological
and behavioral changes cited in published literature (through December 2013) that may impact a
woman's exposure or susceptibility to environmental contaminants during periods of pregnancy
and lactation. Additionally, more recent targeted searches of the literature have been conducted
to supplement this paper in response to peer-review comments. This paper also summarizes
available exposure factors that may be used in an exposure assessment specific to pregnant and
lactating women and current data gaps.
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AUTHORS, CONTRIBUTORS, AND REVIEWERS
The National Center for Environmental Assessment (NCEA), Office of Research and
Development (ORD) was responsible for the preparation of this report. Jacqueline Moya served
as the Task Order Manager, providing overall direction and technical assistance, and is a
contributing author. A preliminary draft was prepared by Battelle under contract with EPA
(contract number EP10H000132).
AUTHORS
EPA
Jacqueline Moya
Linda Phillips
Laurie Schuda
Battelle, Columbus, OH
Jessica D. Sanford
Maureen A. Wooton
Anne Gregg
DOCUMENT PRODUCTION
Vicki Soto, EPA
CONTRIBUTORS
Bayazid H. Sarkar, James Nguyen, and David J. Miller of EPA, Office of Chemical
Safety and Pollution Prevention, Health Effects Division made important contributions by
conducting food consumption analysis based on the NHANES/WWEIA 2003-2008 survey and
comparing dietary consumption patterns for pregnant versus nonpregnant females of child
bearing age. This analysis is included as an appendix to this report (see Appendix).
EPA REVIEWERS
Matt Crowley, Office of Pesticide Programs
Michael Firestone, Office of Children's Health Protection
Bayazid Sarkar, Office of Pesticide Programs
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Michael Wright, National Center for Environmental Assessment, Office of Research and
Development
under EPA Work
contract was managed
Juleen Lam, Ph.D.
University of California, San Francisco
San Francisco, CA 94143
P. Barry Ryan, Ph.D.
Rollins School of Public Health, Emory University
Atlanta, Georgia 30322
Erika F. Werner, M.D.
Alpert Medical School, Brown University
Providence, RI 02906
CONTRACTOR SUPPORT
Highlight Technologies, LLC, Fairfax, VA
Kathleen Bland
Michael Gallagher
Dan Heing
Debbie Kleiser
Ashley Price
CACI International, Inc., Arlington, VA
Thomas Schaffner
Linda Tackett
Lisa Walker
EXTERNAL REVIEWERS
The external reviewers were independently selected by Versar, Inc.
Assignment No. 42, Contract No. EP-C-12-045. The external peer-review
by Jacqueline Moya.
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EXECUTIVE SUMMARY
This report summarizes published literature on the physiological and behavioral changes
that occur during pregnancy and subsequent lactation period. It also covers related exposure
factors and includes relevant literature published through December 2013. Exposure factors are
factors related to human behavior and characteristics that help determine an individual's
exposure to an agent (U.S. EPA. 2011). More recent targeted searches of the literature have also
been conducted to supplement the report in response to peer-review comments.
This issue paper addresses pregnancy- and lactation-related physical and behavioral
changes that may affect a pregnant or lactating woman's exposure or susceptibility to
environmental contaminants. The female body undergoes a variety of physiological changes to
nurture the fetus and to produce milk for breastfeeding postpartum. These changes can affect a
woman's body systems and behaviors in ways that differentiate her from women in the general
population. Thus, pregnancy and lactation are unique lifestages in which women are potentially
vulnerable to different environmental exposures. Although EPA recognizes the potential
vulnerabilities of the fetus from maternal exposures, the focus of this issue paper is on the
exposures to the pregnant or lactating woman.
Specific risk factors and their possible association with pregnancy outcomes or
interventions are not discussed. Likewise, any potential effects that physiological or behavioral
changes can have on the pregnant or lactating woman, the fetus, the infant, or later in life among
individuals exposed in utero or via breastfeeding, are not the focus of this issue paper. Whenever
possible, this issue paper links physiological and behavioral changes during pregnancy and
lactation with the potential for experiencing differential exposures by this population. However,
some of the associations between physiological or behavioral changes and exposures are not
known and therefore not presented.
This issue paper is organized into four major sections: (1) physiological
changes, (2) behavioral adaptations and psychological changes, (3) exposure factors, and (4) data
gaps. The section on physiological factors is organized by organ system. Behavioral changes
include both voluntary adaptations, such as eliminating or reducing the use of caffeine or
alcohol, and psychological changes that may affect behavior (such as depression) that can occur
as a result of pregnancy. The exposure factors section is organized by the various factors (e.g.,
water intake, dietary intake, nondietary intake). The data gaps section summarizes areas where
information is limited or lacking.
Physiological changes occurring during pregnancy and lactation are summarized in
Tables ES-1 and ES-2, respectively, according to organ system. They include changes in the
cardiovascular, respiratory, renal, skeletal, digestive, endocrine, and integumentary systems,
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where available. Some of the physiological changes during pregnancy and lactation may alter
the internal dose and toxic response from environmental contaminants, resulting in health risks
that are different from those of the general population. For example, the increased blood plasma
volume and protein binding that occur during pregnancy can affect the volume distribution of
chemicals in the pregnant woman's body (Anderson. 2005). Hormonal changes during
pregnancy and lactation can affect the woman's appetite and food intake (Gabbe et al.. 2007).
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Table ES-1. Physiological changes during pregnancy by organ system
Measurement
Definition
Change in pregnancy
Cardiovascular system
Cardiac output (CO)
Volume of blood being pumped by
the heart per unit of time.
Increased 18-33%
Total blood volume
Combination of plasma and red
blood cells.
Increased 30-45%
Oxygen consumption
The amount of oxygen consumed
by the tissues of the body.
Increased 20-40% and up to
60% during labor
Heart rate
The number of heart beats per unit
of time.
Increased 10-20% by week 32
of gestation
Respiratory system
Respiratory rate (RR)
Number of breaths per minute.
Unchanged
Vital capacity (VC)
Maximum amount of air that can
be forcibly expired after maximum
inspiration (IC + ERV).
Unchanged
Inspiratory capacity (IC)
Maximum amount of air that can
be inspired from resting expiratory
level (TV + IRV).
Increased 5-10%
Tidal volume (TV)
Amount of air inspired and expired
with normal breath.
Increased 30-40%
Inspiratory reserve
volume (IRV)
Maximum amount of air that can
be inspired at end of normal
inspiration.
Unchanged
Functional residual
capacity (FRC)
Amount of air in lungs at resting
expiratory level (ERV + RV).
Decreased 20%
Expiratory reserve volume
(ERV)
Maximum amount of air that can
be expired from resting expiratory
level.
Decreased 15-20%
Residual volume (RV)
Amount of air in lungs after
maximum expiration.
Decreased 20-25%
Total lung capacity (TLC)
Total amount of air in lungs at
maximal inspiration (VC + RV).
Decreased 5%
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Table ES-1. Physiological changes during pregnancy by organ system
(continued)
Measurement
Definition
Change in pregnancy
Renal system
Glomerular filtration rates
(GFR)
Flow rate of filtered fluid through
the kidney.
Increased 19-40%
Creatinine clearance rate
(CrCl)
Volume of blood plasma cleared
of creatinine per unit time.
Increased 26-58%
Effective renal plasma
flow (eRPF)
Amount of plasma flowing to the
parts of the kidney that function in
the production of urine.
Increased 31-50%
Skeletal system
Bone mineral density
(BMD)
The amount of minerals, such as
calcium, per square centimeter of
bones.
Reversible bone loss during
pregnancy and lactation
Bone turnover
The continuous process of
removal and replacement of bone.
Decreased during the third
trimester and lactation
Neurological system
Adrenocorti cotropi c
hormone (ACTH)
Produced by the pituitary gland
and its key function is to stimulate
the production and release of
Cortisol.
Increased 89% from the 7th to
the 37th week gestation
Cortisol
Produced by the adrenal cortex.
Becomes elevated in response to
physical or psychological stress.
Increased 141%
Beta-endorphins
Produced by the pituitary gland in
response to pain, trauma, exercise,
or other forms of stress.
Decreased during pregnancy
and increased during labor
Digestive/Gastrointestinal system
Leptin
Hormone produced by fat cells
and affects body weight regulation
by suppressing appetite and
burning fat stored in adipose
tissue.
Increased from the second to
the third trimester
Intestinal tone and
motility
Relaxation of the muscles and
transit time.
Decreased
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Table ES-1. Physiological changes during pregnancy by organ system
(continued)
Measurement
Definition
Change in pregnancy
Digestive/Gastrointestinal system
Appetite
Desire to satisfy the body's need
for food.
Appetite may increase or
decrease; generally there is an
increase in caloric intake by
200 kcal/day.
Endocrine system
Placenta volume
Volume of the temporary organ
that forms in the lining of the
uterus and provides nourishment
to the fetus.
Increased volume with
gestation from 134 ± 58 mL at
14 weeks to 659 ± 103 mL at
40 weeks
Thyroid function
Function of a gland in the neck
that secretes hormones that
regulate growth and metabolism.
Hormones include total thyroxine
(T4); total triiodothyronine (TT3);
thyroid-binding globulin (TBG);
thyroid-stimulating hormone
(TSH).
T4 and TT3 increased during
the first trimester peaking at
mid gestation; TBG increased
during the first trimesters and
peaks at 12 to 14 weeks
gestation; TSH decreased
temporarily during the first
trimester and remains stable
through the third trimester.
Hypothal ami c-Pituitary-
Adrenal Axis (HPA)
Three glands of the endocrine
system (i.e., pituitary, thyroid, and
adrenal) that regulate body
processes including energy storage
and expenditures.
Increased estrogen,
aldosterone,
deoxycorticosterone,
corticosteroid-binding
globulin, Cortisol (2.5 times
higher than nonpregnant
women), and free Cortisol,
testosterone, androstenedione,
prolactin (10 times higher at
term); decreased
dehydroepiandrosterone,
dehydroepiandrosterone-
sulfate, follicle-stimulating
hormone, luteinizing hormone,
growth hormone; pituitary
gland increase in size by 36%
at term.
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Table ES-1. Physiological changes during pregnancy by organ system
(continued)
Measurement
Definition
Change in pregnancy
Endocrine system
Glucose and carbohydrate
metabolism
The process by which sugars and
carbohydrates are used in the body
to produce energy.
Decreased glucose levels of
10% in early pregnancy;
increased insulin as well as
insulin resistance; 50-80%
reduction in insulin sensitivity
by the third trimester.
Protein and lipid
metabolism
The decomposition and synthesis
of protein and lipids in the body.
Decreased protein catabolism;
increased generation of
glycocerol, fatty acids, and
ketones; increased total
cholesterol (50-60%), low
density lipoprotein (LDL)
(50-60%), high density
lipoprotein (HDL), very low
density lipoproteins (VLDLS)
and triglycerides; increased
alpha- and gamma-
tocopherols, lycopene, and
beta-carotene; decreased
retinol; increased total
saturated fatty acids;
unchanged n-9 fatty acids;
decreased n-6 fatty acids.
Metabolic adjustments
Adaptive changes in the body's
metabolism.
Increased energy expenditures;
preferential use of
carbohydrates; basal metabolic
rate, sleeping metabolic rate,
and minimal sleeping
metabolic rate is 15-26%
higher during pregnancy.
Total water metabolism
Changes in the water content in
the human body.
Increased 45%
Integumentary system
Surface area
Body surface area calculated as a
function of height and weight.
Increased
Sources: Abdulialil et al. (2012): Gabbe et al. (2007): Crapo (1996).
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Table ES-2. Physiological changes during lactation by organ system
Measurement
Change during lactation
Cardiovascular system
Systolic blood pressure, diastolic blood
pressure, and heart rate
Lower in lactating than nonlactating women
Endocrine system
Estrogen and progesterone
Decrease levels of estrogens and progesterone
that result in the onset of milk production
Parathyroid
Increase of parathyroid hormone during
lactation to meet calcium demands
Prolactin
Increase of prolactin, which plays a vital role
in the initiation and maintenance of lactation.
It remains elevated throughout the first 12
months postpartum
Oxytocin
Oxytocin levels increase to stimulate let down
of milk; low oxytocin levels are associated
with mood disorders postpartum
Metabolic adjustments
Recommended increase of caloric intake by
500 kcal/day for the first 6 months of lactation
and 400 kcal/day after the sixth month
Energy expenditures and sleeping metabolic
rates
Levels are higher in lactating and nonlactating
women
Skeletal system
Bone turnover
Reversible bone loss to provide adequate
calcium for milk production
BMD
Temporary decrease between 3-9%
Neurological system
ACTH and Cortisol
Levels are lower in lactating than nonlactating
women when responding to stress
Sleep
Altered sleeping patterns to accommodate
lactation schedule
Sources: Picciano (2003): Stuebe et al. (2012): Blvtonet al. (2002): Groeret al. (2013): Butte et al. (1999).
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Assessing exposures to pregnant and lactating women requires information about
exposure factors for this potentially susceptible lifestage. These include water and food intake,
inhalation rates, nondietary ingestion rates, time spent at various locations and activities,
consumer products use, and body weight.
In general, lactating women ingest more water than pregnant women, nonpregnant
women, and nonlactating women (Forssen et al.. 2009; Kahn and Stralka. 2008; Zender et al..
2001; Ershow et al.. 1991). An EPA analysis of data from the 2003-2008 National Health and
Nutrition Examination Survey (NHANES) found that differences in per capita food intake rates
for pregnant women were statistically significant from those of nonpregnant women for some of
the major food groups including: total fruits, vegetables, dairy, and grain and several individual
food categories (i.e., banana, cabbage, citrus, corn, cucurbits, leafy vegetables, peaches, root and
tuber vegetables, stalk and stem vegetables, stone fruits, tropic fruits, and white potatoes) (Sarkar
and Nguyen. 2013) (see Appendix). On a per capita basis, there was an increase in consumption
of total fruits, vegetables, dairy, and grain of 69, 13, 43, and 12%, respectively. There was also a
statistically significant increase in consumption of the individual foods except for cabbage, leafy
vegetables, and stalk and stem vegetables.
Some pregnant women experience cravings for nonfood substances. This behavior,
known as pica, is characterized by the intentional ingestion of materials such as dirt, clay,
cigarette ashes, ice, freezer frost, flour, baking soda or powder, cornstarch, powdered milk, and
other materials (Cooksey, 1995; Bronstein and Dollar, 1974). Pregnant and lactating women
who engage in this behavior may be exposed to environmental contaminants present in soil and
other materials. Data on nondietary intake among pregnant women are very limited and have
focused on specific minority populations, the incidence of the behavior, and types of materials
ingested, but very few authors have reported on the amounts consumed. Among the studies
reporting the amounts of materials consumed (mostly clay or "dirt"), ingestion rates ranged from
1-1,650 g/day (Kutalek et al.. 2010; Klitzman et al.. 2002; Smulian et al.. 1995; Ferguson and
Keaton. 1950). Gavrelis et al. (2011) found that the prevalence of pica behavior among pregnant
women was twice that of nonpregnant women. There are no data on the prevalence of the
behavior or on the amount ingested among lactating women.
Daily inhalation rates for normal-weight women are approximately 18-41% higher
during pregnancy and 23-39% during postpartum (Brochu et al.. 2006). Data on activity factors
and use of consumer products are limited for pregnant and lactating women. Furthermore, the
studies on activity patterns and consumer products have focused on specific geographical
locations or minority populations and were based on small sample sizes. On the other hand,
information on body weight gained during pregnancy and lost during the postpartum period is
generally more readily available. The U.S. national mean body weight of pregnant women
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averaged over the three trimesters is 75 kg. The average body weight from the same data set for
all women is 71 kg (U.S. EPA. 2011). Studies during the postpartum period relate to the effects
of lactation on body weight changes.
Although data are available on physiological and behavioral changes as a result of
pregnancy or lactation, the direct link between these changes and the potential for experiencing
differential exposures is not well understood and is a significant data gap. Most of the
physiological information found relates to pregnancy, while the information on lactating women
is more limited. Exposure factors data for pregnant and lactating women are also somewhat
limited. Some of the studies were conducted on a small scale or on a certain geographical area
or ethnic/socioeconomic group, and may not be generalizable to other pregnant and lactating
women. Exposure factors for which data are available include: water and food intake (for
pregnant women only), inhalation rates, and body weight. There are no data with regard to food
intake by lactating women. Information regarding activity patterns and the frequency and use of
consumer products is an area in which research is needed. In addition, the role that race, age,
ethnicity, geographical location, and socioeconomic factors plays in the variability with regard to
these exposure factors for this lifestage is not well understood. More importantly, additional
analyses to understand whether the differences between exposure factors for pregnant and
lactating women and those of the general population of women are significant in terms of
exposure and risk have not been conducted.
The information summarized in this issue paper was obtained from various sources and
presents the findings of the individual study authors. It is not intended to provide an exhaustive
review of all possible physiological and behavioral changes that occur during pregnancy and
lactation. Instead, it provides an introduction to the topic of pregnancy- and lactation-related
changes and related exposure factors, potentially serves as a precursor to investigations into how
these changes may alter environmental exposures for this potentially susceptible lifestage, and
may inspire research in areas identified as data gaps.
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1. INTRODUCTION
1.1. BACKGROUND
Physiological and behavioral changes occur in women during pregnancy and lactation,
and these changes can lead to different environmental exposures than for those in the general
population. A number of metabolic functions are altered to provide for the demands of the fetus.
For instance, changes in metabolic rates cause an increase in nutritional demands during
pregnancy and lactation, which may result in corresponding increases in exposures. Differences
in exposure may also result from differences in food choices. Behavioral adaptations during
pregnancy and lactation may also affect environmental exposures. For example, pregnant and
lactating women may eliminate their use of coffee, cigarettes, and alcohol, thereby reducing their
exposure to chemicals in these products.
Tables 1-1 through 1-3 provide lists of the various changes that may occur during each
trimester of pregnancy, as presented in Bonillas and Feehan (2008). The tables illustrate the
wide variety of physiological changes that occur throughout pregnancy. Not all of the changes
listed in Tables 1-1 through 1-3 are relevant to potential differences in environmental exposures
in this lifestage compared to nonpregnant women, and are not discussed in detail in this issue
paper. There are, however, a number of changes that are potentially significant with regard to
exposures or that may increase or decrease susceptibility among this population, and these
changes are discussed in further detail in this issue paper.
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Table 1-1. Changes during the first trimester (weeks 1-12)
Change
Explanation for change
Missed period
Hormones secreted by the blastocyst (after burrowing into the
endometrial lining) take control of the menstrual cycle.
Nausea and vomiting
Due to rapidly increasing levels of the hormone, human chorionic
gonadotropin (hCG); nausea tends to peak around the same time as
levels of hCG.
Sensitivity to odors
Due to high levels of the hormone estrogen.
Fatigue
Occurs due to higher levels of the hormone progesterone, allowing
the body to focus its energy on sustaining the pregnancy.
Breast enlargement
Due to increased levels of estrogen, the mammary glands begin to
enlarge in preparation for breastfeeding.
Breast tenderness
The enlargement of the mammary glands causes the breasts to
become tender.
Darkening of the areola
The pigmented areas around each breast's nipple darken due to
increased levels of progesterone and estrogen (this is believed to
help the newborn find the breast at birth).
Areola increases in size
Due to increased hormone levels (and believed to help the newborn
find the breast at birth).
Mood swings
Partly due to surges in hormones; characterized by change in
emotional stability and irritability.
Expanding uterus
(womb)
The placenta produces progesterone, which relaxes the muscles of
the uterus so they can stretch as the pregnancy progresses.
Source: Bonillas and Feehan (2008).
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Table 1-2. Changes during the second trimester (weeks 13-27)
Change
Explanation for change
Slower digestion
High levels of progesterone slow down the contractions of the
esophagus and intestine, thus slowing down digestion.
Constipation
Due to a slower digestion.
Hemorrhoids
Due to constipation, increased blood volume and vascular
congestion.
Heartburn
The placenta produces progesterone, which relaxes the valve that
separates the esophagus from the stomach, allowing gastric acids to
seep back up, causing an unpleasant burning sensation.
Backaches
Due to the expanding uterus affecting posture.
Pinching of sciatic nerve
Nerve in the hip/buttock area gets pinched because of pressure
exerted on it by the expanding uterus.
Facial skin changes
Dark patches appear on the face due to hormonal changes.
Increased frequency in
urination
Due to increased blood flow to the kidneys and pressure from the
weight of the pregnancy on the bladder.
Edema
Swelling of the ankles, hands, and face, due to fluid retention.
Expanding uterus
Due to progesterone, which in turn, relaxes the muscles of the uterus
so they can stretch as the pregnancy progresses.
Abdominal enlargement
Due to the progression of the pregnancy, the uterus expands into the
abdominal cavity.
Increase in blood
volume
Due to the need for extra blood flow to the uterus.
Heart growth
Due to the body needing to supply more blood for the growing fetus
and placenta.
Quickening
Feeling fetal movements for the first time.
Stretch marks
Due to the expanding abdomen, breasts, legs, buttocks.
Sweating
Due to hormonal changes, increased effort on physical activities due
to the expanding uterus, and the fetus beginning to radiate body heat.
Difficulty in sleeping
Due to fetal movements or frequent urination at night.
Leukorrhea
Higher levels of estrogen increase blood flow to the vagina, which, in
turn, increases the release of a white-colored, odorless vaginal
discharge (sign of a healthy vagina).
Hair growth
Due to hormone stimulation of hair follicles on the head, arms, legs,
and face.
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Table 1-2. Changes during the second trimester (weeks 13-27) (continued)
Change
Explanation for change
Dry, itchy skin
Particularly on the abdomen as the skin continues to grow and stretch
due to the expanding uterus.
"Linea nigra"
A dark line running from the pubic bone up the center of the
abdomen to the ribs, which is caused by the increase in hormones.
Source: Bonillas and Feehan (2008).
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Table 1-3. Changes during the third trimester (weeks 28-40)
Change
Explanation for change
Heart rotates
Takes place to make room for expanding uterus, which pushes other
organs up as well.
Varicose veins
Swollen/bluish veins that bulge near the surface of the skin, usually
behind the legs. As the uterus grows, it puts pressure on the large
vein on the right side of the body, which in turn, increases pressure
on the veins in the legs, making the veins swell from the extra
pressure to return the blood from the extremities to the heart (as they
work against gravity).
Heartburn
The growing fetus crowds the abdominal cavity, pushing the
stomach acids back up into the esophagus.
Hemorrhoids
Due to constipation.
Leg cramps
Believed to be due to lack of calcium in the body.
Shortness of breath
Due to the expanding uterus pushing up against the diaphragm.
Braxton-Hicks
contractions
Usually painless uterine contractions that help the uterus prepare for
birth.
Increased frequency in
urination
Due to increased blood flow to the kidneys and pressure from the
weight of the pregnancy on the bladder.
Stretch marks
Due to the expanding abdomen, breasts, thighs, and buttocks.
Dry, itchy skin
Particularly on the abdomen as the skin continues to grow and
stretch due to the expanding uterus.
Naval protrusion
(bellybutton sticking out)
Due to the expanding abdominal cavity.
Colostrum
Yellow, watery fluid produced by the mammary glands. Colostrum
contains large amounts of antibodies that help protect the mucous
membranes in the throat, lungs, and intestines of the infant. White
blood cells are also present in large numbers and begin protecting
the infant from harmful bacteria and viruses. Beneficial bacteria are
also established in the digestive tract of an infant when colostrum is
ingested.
Estrogen
A pregnant woman will have more estrogen in her body during the
9 months of pregnancy than a woman who never gets pregnant will
have in her entire lifetime.
Progesterone
By the end of the pregnancy, levels of this hormone will increase
seven times its normal levels during pregnancy.
Source: Bonillas and Feehan (2008).
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1.2. OBJECTIVES AND SCOPE
The primary purpose of this issue paper is to provide a summary of information from the
published literature related to physiological and behavioral changes during pregnancy and
lactation that may alter a woman's exposure or susceptibility to environmental contaminants.
Available data on exposure factors for this lifestage and current data gaps are also summarized.
Exposure factors are factors related to human behavior and characteristics that help determine an
individual's exposure to an agent (e.g., water intake, food intake, inhalation rates) (U.S. EPA.
2011V
The scope of this issue paper is not on chemical-specific exposures or risk factors and
their possible association with pregnancy outcomes or interventions. Therefore, potential effects
that physiological or behavioral changes can have on the pregnant or lactating woman, the fetus,
the infant, or later in life among individuals exposed in utero or via breastfeeding are not
discussed. Not all women will experience pregnancy symptoms in the same way, and some
physiological and behavioral changes may impact different women to differing degrees. For
example, some women develop gestational diabetes, depression, or nausea but others do not.
Other maternal factors such as gravidity, parity (number of previous childbirths), and having had
previous adverse reproductive outcomes may be related to certain behavioral and physiologic
changes.
Although other aspects of vulnerability may affect a pregnant/lactating woman's response
to environmental exposures (e.g., access to health care, chronic health conditions), these are not
the focus of this issue paper. The term vulnerability here refers to differences in risk resulting
from the combination of both intrinsic differences in susceptibility and extrinsic social stress
factors (e.g., low socioeconomic status, crime and violence, lack of community resources,
crowding, access to health care, education, poverty, segregation, geography, etc.). Susceptibility
refers to differences in risk resulting from variation in both toxicity response (sensitivity) and
exposure (as a result of gender, lifestage, and behavior). The term sensitivity refers to
differences in toxic response resulting from toxicodynamics differences and/or toxicokinetics
differences. These differences can arise due to numerous biological factors such as lifestage
(windows of enhanced sensitivity), genetic polymorphisms, gender, disease status, nutritional
status, etc.
In some cases, this issue paper links physiological and behavioral changes during
pregnancy and lactation with the potential for experiencing differential exposures by this
population. Some associations between physiological or behavioral changes and exposures are
apparent; others are not. For instance, it is known that hormonal changes are responsible for
changes in the women's appetite and food intake (Gabbe et al.. 2007). In contrast, as an example
of a not-so-obvious association, the changes in insulin sensitivity during pregnancy have been
1-6
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positively correlated with dietary intake of fat (Chen et al.. 2003). However, fat intake rates for
pregnant and lactating women are not available. Some changes may not be relevant with regard
to susceptibility or environmental exposures (e.g., hair growth that occurs as a result of hormone
stimulation of hair follicles). Therefore, this type of information is not included. Some of the
physiological and behavioral changes occurring during pregnancy and lactation might not
necessarily affect the exposure received, but rather such changes can increase susceptibility and
alter the internal dose. For this reason, they are included in this issue paper. For example, an
increase in blood volume and cardiac output due to hormonal and metabolic changes maximizes
the delivery of respiratory gases to the maternal and fetal tissues (Gabbe et al„ 2007; Heidemann
and McClure. 2003; Ciliberto and Marx. 1998).
Information for various demographic groups is presented where available. Racial and
ethnic categories used throughout this issue paper were the ones used by the original authors. No
attempt was made to use consistent categories, since definitions may vary across studies.
Information was presented by trimester where available. Inconsistencies in the presentation of
data are mainly a result of data limitations.
1.3. METHODS
A targeted search of published literature was conducted through December 2013 using
19 databases via DIALOG and PubMed. The search terms included: pregnant, pregnancy
(including trimesters), lactation, lactating, postpartum, physiological change, change in
physiology, behavior/behaviour, environmental toxicant, environmental exposures,
environmental factor, environmental risk, and activity pattern. These terms were combined with
the requirement that the article also have the terms risk, expose, or exposure. Other targeted
searches were conducted to supplement the report in response to external review comments. The
lower limit on the years of the literature searched was determined by each individual database.
Relevant articles included those that pertained to physiological or behavioral factors in any or all
of the three trimesters of pregnancy or the lactation period. In addition, supplementary
background information on basic obstetric science and physiology were integrated into some of
the sections of this issue paper. Although studies on the U.S. population were preferred, some
studies for other populations were included when data were limited or to supplement information
presented. Recent articles were favored over older literature. Articles that only contained
information on health effects or pregnancy outcomes and no exposure data or physiological data
were not deemed relevant.
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1.4. ORGANIZATION OF I II I REPORT
This issue paper is organized into six main sections: (1) introduction, (2) defining the
pregnant and lactating lifestage, (3) physiological changes during pregnancy and lactation,
(4) behavioral adaptations and psychological changes during pregnancy and lactation,
(5) exposure factors for pregnant and lactating women, and (6) exposure factors data gaps.
Section 3 is organized according to the various organ systems, and Section 4 is organized
according to general categories of adaptations and behavioral changes. Section 5 summarizes
data for several exposure factor categories (e.g., water intake, dietary intake, inhalation rates,
activity factors, consumer product use, and body weight).
1.5. INTENDED AUDIENCE
This report is intended for use by exposure and risk assessors both within and outside the
EPA as a resource of information on physiological and behavioral changes that may be important
to consider when assessing exposures to pregnant and lactating women. It may be used by
scientists and other interested parties to inspire research in areas where data gaps have been
identified.
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2. DEFINING THE PREGNANT AND LACTATING LIFESTAGE
Approximately 60 million women of reproductive age live in the United States. In the
Centers for Disease Control and Prevention (CDC) reports on pregnant women, reproductive age
for women is most often defined as ages 15 to 44 years (Ventura et al., 2012). In reality,
reproductive age begins at the onset of menses, when pregnancy can occur, and continues until
menopause when menses ends and pregnancy is no longer possible. Because the age of menses
onset and end can vary among women and populations, reproductive age can begin earlier than
15 years or end later than 44 years. However, since this is the historical age range used in
discussions of reproductive age, this range was used in the literature search, but note that these
age ranges vary among the studies cited in this issue paper.
Approximately 10% of U.S. women between the ages of 15 and 44 years become
pregnant annually. In 2008 there were almost 6.6 million pregnancies (105.5 pregnancies per
1,000 women aged 15-44 years) in the United States, of which 4.2 million resulted in live births
(Ventura et al.. 2012). Pregnancy rates vary by age and race (see Table 2-1).
Table 2-1. U.S. pregnancy rates for 2008, by age and race (pregnancies per
1,000 women)
Population group
pregnancy
outcome
Total
Age (years)
Under
15
15-19
20-24
25-29
30-34
35-39
40-44
All races
All pregnancies
105.5
1.4
69.8
163.0
167.9
141.2
78.5
18.8
Live births
68.1
0.6
40.2
101.8
115.0
99.4
46.8
10.6
White non-Hispanic
All pregnancies
87.5
0.5
44.8
124.2
149.8
132.5
71.0
16.2
Live births
60.5
0.2
26.7
82.8
109.7
100.8
45.2
9.6
Black non-Hispanic
All pregnancies
144.3
3.8
121.6
261.6
216.2
157.7
81.1
21.3
Live births
70.8
1.4
60.4
131.5
108.8
75.3
36.3
9.3
Hispanic
All pregnancies
136.9
2.2
111.5
229.5
197.1
149.2
87.2
23.9
Live births
92.7
1.1
70.3
154.1
142.3
105.3
54.0
14.0
Source: Ventura et al. (2012).
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The CDC reports that in 2001, approximately one-half of pregnancies in the United States
were unplanned (Finer and Henshaw. 2006). This information is important in that it indicates
that while physical changes in these pregnancies would proceed as in all pregnancies, changes in
behaviors related to prenatal care would likely not precede pregnancy in most women, but would
begin at diagnosis of the pregnancy, at the earliest. For example, consumption of prenatal
vitamins or cessation of smoking may not occur prior to pregnancy, but at some point later in
pregnancy or during lactation, so any associated environmental exposures may be similarly
variable in the population.
Race may also be a potential factor in pregnancy- and lactation-related environmental
exposures of women of reproductive age in the United States. The average U.S. woman is
expected to have 3.2 pregnancies in her lifetime at current pregnancy rates; non-Hispanic black
and Hispanic women are expected to have 4.3 and 4.0 pregnancies respectively, compared with
2.7 for non-Hispanic white women (Ventura et al.. 2012). Therefore, if pregnancy and/or
lactation carries susceptibilities to certain environmental exposures, then being pregnant more
frequently increases these susceptibilities for certain racial groups than for others. For example,
if African-American women have more pregnancies on average, then this population may be at
increased risk for certain pregnancy- and/or lactation-related environmental exposures.
In a similar manner, age can also impact pregnancy-related exposures to environmental
contaminants, since certain age groups have higher pregnancy rates. From 1990 to 2008, there
was a reported 40% drop in the teenage pregnancy rate, reaching a historic low of
69.8 pregnancies per 1,000 women aged 15-19 years in 2008 (Ventura et al., 2012). Rates for
younger teenagers (ages 15-17 years) declined more relative to older teenagers (ages
17-19 years). The estimated pregnancy rate for U.S. women aged 15-44 years was
105.5 pregnancies per 1,000 women. The highest pregnancy rates were for women
aged 25-29 years, at 167.9 per 1,000 in 2008, followed closely by women aged 20-24 years, at
163.0 per 1,000. Pregnancy rates for women aged 30-34 years was 141.2 per 1,000 (Ventura et
al.. 2012).
Provided that there are no health concerns after delivery, an increasing number of
mothers breastfeed. The American Academy of Pediatrics (AAP) reaffirms their
recommendation of exclusive breastfeeding for approximately the first 6 months of life and
supports the continuation of breastfeeding for the first year and beyond if desired by the mother
and child (AAP, 2012, 2005). Breastfeeding rates increased between 2008 and 2010; 74.6% of
mothers breastfed their infants in the early postpartum timeframe in 2008 and 16.5% of mothers
breastfed their infants over a similar timeframe in 2010 (CDC, 2013). At 6 and 12 months
postpartum, 49% and 27%, respectively, continued to breastfeed (CDC, 2013). Breastfeeding
rates vary with the mother's age and other sociodemographic factors. Breastfeeding data
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stratified by sociodemographic characteristics for 2008 or later have not been released by CDC.
The percentage of mothers that breastfeed varies with age, race, and postpartum time (see
Table 2-2). The highest percentages of breastfeeding are for mothers 30 years and older and for
non-Hispanic white mothers (CDC. 2012).
Table 2-2. Percentage of women breastfeeding in 2007 by maternal age and
race
Age or race
Breastfeeding
ever
Breastfeeding
at 6 months
postpartum
Breastfeeding
at 12 months
postpartum
Age (years)
<20
59.7
22.2
10.7
20-29
69.7
33.4
16.1
>30
79.3
50.5
27.1
Race
Non-Hispanic white
76.2
44.7
23.3
Non-Hispanic black
58.1
27.5
12.5
Hispanic
72.8
41.9
21.5
Source: CDC (2012).
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3. PHYSIOLOGICAL CHANGES DURING PREGNANCY AND LACTATION
The following sections present a summary of physiological changes women can
experience during pregnancy and lactation. Most of the information available and presented in
this section relates to changes during pregnancy. Very limited data have been found on
physiological changes in lactating women. Information is organized according to the human
body organ systems.
3.1. CARDIOVASCULAR SYSTEM AND HEMATOLOGICAL SYSTEMS
As early as 5 weeks gestation, significant physiological changes and adaptive
mechanisms occur in the maternal cardiovascular and hematological systems. These changes,
such as increased cardiac output and increased blood volume, are a result of hormonal and
metabolic changes that maximize delivery of respiratory gases, nutrients, and metabolites to
maternal and fetal tissues (Gabbe et al.. 2007; Heidemann and McClure. 2003; Ciliberto and
Marx. 1998). Many of these changes influence susceptibility to environmental contaminants in
pregnant women due to the impacts that these changes can have on physiological activities such
as duration of action of exogenous chemicals in the blood stream, blood flow patterns, and other
pharmacokinetic factors. The mean ± standard deviation (SD) cardiac output in liters/hour
increases from the prepregnancy value of 301 ± 65 to 354 ± 76, 386 ± 75, 400 ± 79, and 391 ± 79
at 10, 20, 36, and 38 weeks of gestation, respectively (Abdulialil et al.. 2012). This represents an
increase of 18-33% from weeks 10-38. Total blood volume, which is a combination of plasma
and red blood cells, increases 30-45% during pregnancy. This increase occurs rapidly until mid-
pregnancy, more slowly during the latter half, and plateaus or decreases slightly to term
(Blackburn, 2007). During pregnancy, there is also a progressive increase in resting oxygen
consumption, which is a reflection of the metabolic needs of the mother and the fetus. Oxygen
consumption reaches its peak increase of 20-30% by term (Blackburn. 2007). In addition,
maternal heart rate (HR) increases progressively during pregnancy, by an average of 10-20 beats
per minute (10-20% increase) by 32 weeks gestation (Blackburn. 2007).
In a study of 45 postpartum women (22 breastfeeding; 23 formula feeding), there was a
statistically significant decline in systolic blood pressure (SBP) and HR. This decline was
statistically significantly lower in the breastfeeding group than in the formula-feeding group,
even after adjusting for body mass index (BMI) (Groer et al.. 2013). Diastolic blood pressure
(DBP) was also lower for the breastfeeding group.
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3.2. RESPIRATORY SYSTEM
Due to a combination of hormonal fluctuations and mechanical factors that affect the
physical configuration of the thoracic cage, both anatomical and physiological changes occur in
the maternal respiratory system during pregnancy. These changes, which may or may not affect
airborne exposures, include changes in pulmonary function, lung volume and capacities, and
respiration. Inhalation rates and oxygen consumption increase during gestation to meet the
metabolic demands during pregnancy. Inhalation rates by pregnant and lactating women are
discussed in Section 5.4.
3.2.1. Pulmonary Function, Lung Volume, and Capacities
Respiratory parameter changes during pregnancy reported in the literature include
increases in tidal volume, minute ventilation, respiratory frequency, inspiratory drive, inspiratory
capacity, respiratory resistance, and occlusion pressure and decreases in respiratory tract
conductance, peak expiratory flow rates, and expiratory reserve volume (Harirah et al.. 2005;
Kolarzyk et al.. 2005; Chhabra et al.. 1988). The mechanical pressure from the enlarging uterus
causes an upward displacement of the diaphragm by up to 4 cm as gestation progresses. Total
lung capacity, however, is reduced only slightly (about 5%) because of compensating increases
that occur in chest diameter and a flaring of the ribs from hormone-induced relaxation of the
ligaments between the ribs and sternum (Gabbe et al.. 2007; Ciliberto and Marx. 1998).
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Despite observed changes in certain lung capacity measures, overall pulmonary muscle
function and respiratory rates are not generally observed to be affected by pregnancy
(e.g., maximum inspiratory and expiratory pressures are unchanged). Also, spirometric
measurements assessing bronchial flow (e.g., forced vital capacity) are generally reported to be
unaltered, suggesting compensations that facilitate stability of airway function during pregnancy
(Gabbe et al.. 2007; Kolarzyk et al.. 2005; Brancazio et al.. 1997; Weinberger et al.. 1980).
However, spirometric measurements have been observed to vary in pregnant women when
taking into account additional factors such as trimester, position (e.g., sitting, standing), and
body mass. The fact that some values (e.g., respiratory resistance) increase during pregnancy,
while others (e.g., vital capacity) remain the same is thought to reflect the effect of the
autonomous nervous system on the respiratory tract (Kolarzyk et al.. 2005).
Peak expiratory flow rate, expiratory reserve volume, and vital capacity have been shown
to be affected by maternal position (e.g., sitting, standing) at the time the measurement is made
(Harirah et al.. 2005; Chhabra et al.. 1988). Correlations have been observed between BMI
(measured before pregnancy) and the magnitude of increases in minute ventilation, inspiratory
drive, and occlusion pressure across all trimesters (Kolarzyk et al.. 2005). These alterations in
the pulmonary function and lung volume of pregnant women may affect the disposition of air
pollutants in the respiratory tract.
3.2.2. Respiration
The amount of air breathed in or out during normal respiration (i.e., tidal volume) is
influenced by hormonal changes in pregnant women. Increasing levels of progesterone during
pregnancy drive a state of chronic hyperventilation, which has been observed to increase tidal
volume by up to 30-40% at 8 weeks gestation (Gabbe et al.. 2007) (see Table 3-1).
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Table 3-1. Changes in lung volumes and capacities during pregnancy
Measurement
Definition
Change in
pregnancy
Respiratory rate (RR)
Number of breaths per minute.
Unchanged
Vital capacity (VC)
Maximum amount of air that can be
forcibly expired after maximum
inspiration (IC + ERV).
Unchanged
Inspiratory capacity (IC)
Maximum amount of air that can be
inspired from resting expiratory level
(TV + IRV).
Increased 5-10%
Tidal volume (TV)
Amount of air inspired and expired
with normal breath.
Increased 30-40%
Inspiratory reserve volume (IRV)
Maximum amount of air that can be
inspired at end of normal inspiration.
Unchanged
Functional residual capacity (FRC)
Amount of air in lungs at resting
expiratory level (ERV + RV).
Decreased 20%
Expiratory reserve volume (ERV)
Maximum amount of air that can be
expired from resting expiratory level.
Decreased 15-20%
Residual volume (RV)
Amount of air in lungs after
maximum expiration.
Decreased 20-25%
Total lung capacity (TLC)
Total amount of air in lungs at
maximal inspiration (VC + RV).
Decreased 5%
Source: Crapo (1996).
Progesterone-induced hyperventilation and concurrent increases in tidal volume also lead
to an overall parallel rise in minute ventilation, despite a stable respiratory rate
(Minute ventilation = Tidal volume x Respiratory rate). As the minute volume increases, a
concurrent increase in oxygen uptake and consumption occurs, with maternal oxygen
consumption typically observed to be 20-40% greater in pregnant women due to the oxygen
requirements of the fetus, the placenta, and maternal organs and up to 60% greater during labor
due to the exaggerated cardiac and respiratory work load (Ciliberto and Marx. 1998). This rise
in minute volume also ultimately increases alveolar oxygen (Gabbe et al.. 2007).
Oxygen consumption at rest in pregnant women ranges from 249-331 mL/minute and
from 191-254 mL/minute in nonpregnant women (Abduljalil et al., 2012). Early in pregnancy,
the arterial oxygen increases (106-108 mmHg); however, due to the enlarging uterus, a slight
decrease in arterial oxygen (101-104 mmHg) is observed by the third trimester (Gabbe et al..
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2007). Oxygen depletion has also been cited as a possible physiological cause of the fatigue
frequently observed in the first trimester of pregnancy (Poole. 1986). Inhalation rates for
pregnant and lactating women are presented in Section 5.4.
3.3. RENAL SYSTEM
Several changes occur in the renal system during pregnancy. The kidneys enlarge in size
and their weight increases by approximately 30% due to increased renal vasculature, interstitial
volume, and urinary dead space (Abdulialil et al.. 2012; Gabbe et al.. 2007). Changes are also
observed as the maternal anatomy to accommodate the growing fetus. These changes decrease
the capacity of the bladder and increase the frequency of urinary incontinence. Frequent
urination is also caused by increased blood flow to the kidneys and increased pressure on the
bladder from the weight of the pregnancy (Bonillas and Feehan. 2008). The increased urination
may have affect the elimination of chemicals from the body. Nocturia, or excessive urination at
night, is also common during pregnancy as water retained during the day is excreted at night
when the woman is in the recumbent position (Chesley and Sloan, 1964).
The glomerular filtration rates (GFR), defined as the flow rate of filtered fluid through
the kidneys, and creatinine clearance rate (CrCl), the volume of blood plasma cleared of
creatinine per unit time, increase throughout the pregnancy (Abdulialil et al.. 2012; Gabbe et al..
2007). The increase in glomerular filtration rates and creatinine clearance can affect the
elimination of chemicals from the body (Hebert. 2013). Effective renal plasma flow (eRPF), the
amount of plasma flowing to the parts of the kidney that function in the production of urine,
increases during early pregnancy, but decreases towards term (Abdulialil et al.. 2012). The
mean ± SD in mL/minute of glomerular filtration rates increases from a prepregnancy value of
114 ± 28 to 136 ± 32, 156 ± 26, 160 ± 26, and 156 ± 42 at 10, 16, 26, and 36 weeks of gestation,
respectively (Abdulialil et al.. 2012) and represents an increase ranging from 19% to 40% in
early pregnancy. The mean ± SD in mL/minute of creatinine clearance increases from a
prepregnancy value of 98.3 ± 14.4 to 126 ± 20, 155 ± 28, 152 ± 39, and 124 ± 34 at 12, 26, 33,
and 37 weeks of gestation, respectively (Abdulialil et al., 2012), representing an increase ranging
from 26%) to 58%>. The mean ± SD in L/hour of effective renal plasma flow increases from a
prepregnancy value of 32.3 ± 6.4 to 44.5 ± 6.1, 48.4 ± 8.8, 47.8 ± 12.5, and 42.3 ± 11.2 at 7, 16,
26, and 36 weeks of gestation, respectively (Abdulialil et al„ 2012), representing an increase
ranging from 31%> to 50%>. These changes along with many other physiological changes are
attributed to causing the energy depletion and fatigue during the first trimester (Poole, 1986).
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3.4. SKELETAL SYSTEM
There are two primary measures of the skeletal state. The first is bone mineral density
(BMD) and the other is bone turnover. BMD is a point measurement taken at different bones of
the skeleton. Bone turnover is a flux measurement incorporating both calcium absorption and
depletion in bone. BMD is measured as the amount of minerals, such as calcium, per square
centimeter of bone. Both pregnancy and lactation result in reversible bone loss, caused by the
need to provide the adequate calcium for the developing fetus and for milk production (Mjaller et
al.. 2012; Gabbe et al.. 2007; Ritchie et al.. 1998; Krebs et al.. 1997). Hormone levels inherently
affect bone turnover while a woman is supporting a fetus (Holmberg-Marttila et al.. 2003).
These changes in bone depletion and absorption may permit the release to the blood system of
any pollutants (e.g., lead) that may have been deposited in the bones. The majority of the
literature addresses BMD during and after lactation. More specifically, while lactating, women
experience an increase in bone absorption with a larger decrease in bone deposition resulting in a
net loss or higher bone turnover (Osterloh and Kelly. 1999).
The process of calcium absorption in the small intestine and bone turnover in the skeleton
both affect the overall BMD throughout pregnancy and into the postpartum period. Therefore,
calcium metabolism and its relationship with BMD during pregnancy and lactation is also a
highly studied area. It is generally accepted and supported in the literature that absorption and
urinary excretion of calcium increase during the second trimester, whereas bone turnover
increases during the third trimester and lactation (O'Brien et al.. 2006; Silva et al.. 2005; Yoon et
al.. 2000; Kolthoff et al.. 1998; Cross et al.. 1995; Cole et al.. 1987). During the third trimester,
the fetal demand on calcium is at its peak due to bone calcification (More et al.. 2001). For
active women, there is some evidence to indicate that changes in BMD during pregnancy and
lactation may represent changes in mechanical stress as a result of weight gain, changes in
posture and/or activity, or some other factor specific to this population (Drinkwater and Chesnut.
1991).
The majority of BMD loss is during the first 5 months of lactation. Between
5-12 months postpartum, there is no further BMD loss (Karlsson et al., 2001). Once
menstruation resumes, BMD recovers. Bone recovery back to prepregnancy level appears to be
modulated slightly by lactation habits and hormonal status (Holmberg-Marttila et al.. 2003;
Holmberg-Marttila et al.. 2000; Laskev and Prentice. 1999). The length of lactation, maternal
age (Holmberg-Marttila et al.. 2003). and ovarian dysfunction (Kalkwarf. 2004; Honda et al..
1998) are positively correlated with increased bone turnover during lactation. Higher parity,
longer history of previous lactation (Holmberg-Marttila et al.. 2003). and resumption of
menstruation (Holmberg-Marttila et al.. 2000; Laskev and Prentice. 1999) are related to bone
density recovery.
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Further evidence indicates that when a woman has dual demands of lactation and a
subsequent pregnancy, she is not at risk for failure to recover the bone loss (Sowers et al..
1995b). By 12 to 24 months postpartum, regardless of lactation practice, most women have
regained their prepregnancy BMD fPolatti et al.. 1999; Kalkwarf and Specker. 1995; Matsumoto
et al.. 1995; Sowers et al.. 1995a; Sowers et al.. 1993). No associations have been detected
between bone loss and calcium intake, physical activity, body size (Sowers et al.. 1995a; Sowers
et al.. 1993). weight changes, or initial bone density (Kolthoff et al.. 1998).
Pregnant women may be at risk years after exposure to lead due to calcium mobilization
from bone when calcium demand increases in pregnancy (Alba et al„ 2012). A case study of a
woman exposed to lead at levels of 145 (j,g/dL for 7 years prior to pregnancy, showed that
measured blood lead levels tripled to 81 (j,g/dL within 5 months after giving birth (Riess and
Halm. 2007). Two studies found limited evidence supporting the hypothesis of lead mobilization
from bone during lactation (Moline et al.. 2000; Osterloh and Kelly. 1999). while others found
that breastfeeding practices and maternal bone lead were good predictors of blood lead levels
(Tellez-Roio et al.. 2002). The blood lead concentration is shown to be highest 3-6 months after
parturition (Gulson et al.. 2004; Gulson et al.. 2003). This potential risk of lead exposure to the
woman and the breastfeeding infant is associated with very low (one-half to two-thirds the daily
recommended requirements) calcium intakes (Gulson et al.. 2004; Gulson et al.. 1999). It has
also been shown that foods high in calcium may have a protective effect against the
accumulation of lead in bone (Hernandez-Avila et al„ 1996). Calcium supplementation has a
limited benefit inhibiting lead mobilization from bone during lactation (Gulson et al„ 2004), but
low calcium dietary intake is an indicator for higher bone lead mobilization.
3.5. NEUROLOGICAL SYSTEM
Neuroendocrine processes are significantly altered during pregnancy. Associations have
been made between prenatal psychosocial stress, social support, and personality variables with
neuroendocrine parameters (plasma levels of adrenocorticotropic hormone [ACTH],
beta-endorphin, and Cortisol) (Wadhwa et al„ 1996). ACTH is produced by the pituitary gland
and the hormone's primary function is to stimulate the production and release of Cortisol. More
information about ACTH and pituitary function is found in Section 3.7.4. Cortisol is produced
by the adrenal cortex in response to physical or psychological stress. Mean Cortisol levels also
increase during pregnancy from 12.5 ±1.3 (J,g/dL in nonpregnant women to 30.1 ± 6.6 (J,g/dL
(Gabbe et al.. 2007). a 141% increase. Levels of beta-endorphin, a neurochemical produced in
the pituitary gland in response to pain, trauma, exercise, or stress, are statistically significantly
lower during pregnancy than in the nonpregnant state (Goebelsmann et al.. 1984). The levels are
at their lowest during the second trimester of pregnancy. Beta-endorphin levels rise dramatically
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during early and advanced labor. The sympathetic nervous system, which is responsible for
regulating an individual's "fight or flight" response is dampened due to diminished blood
pressure responses while female reproductive hormones are elevated (Matthews and Rodin.
1992). Responses to stress in postpartum women who did not lactate indicated increased
sympathetic and decreased parasympathetic nervous system activity. However, the lactating
counterparts did not have attenuated physiological or anxiety responses (Altemus et al.. 2001).
These changes may alter a woman's behavior (e.g., eating patterns, time spent at various
activities at different locations) and her chances for exposure to environmental chemicals.
Additional information on stress and anxiety is found in Section 4.3.
Cholinesterase is one of many important enzymes needed for the proper functioning of
the nervous system. Some studies have shown that serum cholinesterase activity changes during
pregnancy. A study conducted by Evans et al. (1988) examined serum cholinesterase activity in
44 women before, during, and after pregnancy. Some women showed a decline in cholinesterase
activity after conception, with no return towards preconception values before delivery. Other
women exhibited a decline in cholinesterase activity accompanied by a partial or complete return
to preconception values before delivery. A few women displayed either no discernible decline or
increased cholinesterase activity during gestation. The differences are potentially age-related as
the continuous decrease in cholinesterase activity occurred in the youngest group of women, the
decrease followed by an increase occurred in the intermediate age group, and no decrease at all
was seen in the oldest group, although none of the age-related differences were statistically
significant.
3.6. DIGESTIVE/GASTROINTESTINAL SYSTEM
It is common during pregnancy and lactation for changes to occur in the amount,
frequency, and choices of food consumed. Increased or decreased appetite during pregnancy and
changes in taste may be induced by estrogen and progesterone, which are present at elevated
levels during pregnancy (Faas et al., 2010). Generally, appetite increases during pregnancy
result in the average consumption of an additional 200 kcal/day. Fat storage and appetite are
regulated by free and bound leptin, respectively. Leptin is a pregnancy-related hormone that
regulates appetite and metabolism, and it is usually produced in adipose tissue. Since pregnancy
is generally associated with increased appetite, it is likely that a leptin-resistant state develops
during pregnancy allowing for an increase in food intake (Augustine et al.. 2008). Leptin has
also been found to control energy expenditures and body mass accumulations (see Section 3.7.6).
In general, leptin has been observed to be higher in pregnant women than nonpregnant
women and may increase progressively through pregnancy or after delivery (Teppa et al.. 2000;
Lage et al.. 1999; Lin. 1999; Butte et al.. 1997). Free leptin tends to increase from the first to the
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second trimester and then remains the same for the rest of the pregnancy (Gabbe et al.. 2007).
Bound leptin that affects metabolism, and therefore appetite, increases from the second to the
third trimester (Widiaia et al.. 2000). Leptin serum level concentrations in pregnant women
during the first trimester were reported to range from 14.3 ± 1.4-14.7 ± 0.7 ng/mL (Lage et al..
1999; Lin. 1999). Second trimester leptin levels ranged between 16.3 ±1.3 and 18.3 ± 0.6
ng/mL (Lin. 1999). The highest leptin levels were observed during the third trimester, with
values ranging from 21.1 ± 1.4 to 33.8 ±4.1 ng/mL (Teppa et al.. 2000; Lin. 1999). Leptin
plasma levels reach a peak at gestation weeks 35-41 (Lin, 1999). Lin (1999) reported a positive
correlation of these levels with a BMI in 65 women (r = 0.65, p < 0.001). Leptin values in
nonpregnant women have been reported to range from 9.1 ± 0.6-16.5 ± 0.9 ng/mL, with the
highest values observed in women with higher BMIs. Leptin serum level concentrations were
48.1 ± 5.6 ng/mL in patients with preeclampsia in a study of 18 healthy, 18 preeclamptic, and
18 never-pregnant women (Teppa et al.. 2000). Preeclampsia is a pregnancy-related condition
that usually develops after the 20th week of pregnancy and is marked by high blood pressure,
edema in the hands and feet, and protein in the urine.
Obese pregnant women have been found to have statistically significant changes in
several gastrointestinal hormones affecting food intake, such as acylated ghrelin, peptide YY,
and cholecystokinin, a possible explanation for the enhanced appetite and increased food intake
in these individuals (Sodowski et al.. 2007). Ghrelin concentrations may change with increased
adiposity and may play a role in body weight postpartum (Larson-Meyer et al„ 2010). However,
neither ghrelin nor peptide YY were shown to affect appetite and body weight regulation during
lactation in the cohort of women in the study.
A common digestive complaint during pregnancy is nausea and vomiting, also commonly
referred to as "morning sickness." Approximately 70% of pregnant women suffer from nausea
in the first trimester, about 10-25% report it continuing into the second trimester, and 1-3%
develop into severe cases that persist throughout pregnancy. The most severe form can lead to
significant weight loss, excess ketones in the blood, or electrolyte imbalances. Food eating
patterns may change in order to cope with this condition. For example, women may eat more
frequent and smaller meals throughout the day, eat bland foods that are easier to digest, consume
foods that are high in protein, drink more fluids, and avoid high-fat foods (March of Dimes.
2013a; Gabbe et al.. 2007). Data on dietary intake by pregnant women are presented in
Section 5.2.
The stomach also changes in tone and motility, likely due to progesterone- and
estrogen-induced relaxation of the smooth muscle (Shah et al.. 2001). Anywhere from 30-50%)
of women report an increase in gastric reflux and indigestion due to increased hormone levels
and the physical compression of the stomach from the enlarging uterus (Gabbe et al.. 2007).
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During pregnancy, there is also an uncommon symptom called ptyalism, which is popularly
believed to be the inability of the nauseated woman to swallow saliva and can result in a loss of
1 to 2 L of saliva per day (Gabbe et al.. 2007).
Pregnancy is credited with causing slower digestion due to a slowdown in esophageal and
intestinal contractions (Bonillas and Feehan, 2008). The increase in progesterone levels during
pregnancy produces a relaxation of the muscles that results in a decrease in intestinal tone and
motility (Blackburn. 2007). The decrease in intestinal motility leads to an increase in the
absorption of nutrients such as calcium and iron, as well as other substances (Blackburn, 2007).
The small intestines and colon have a higher rate of water and sodium absorption and a slower
rate of mobility, which can lead to constipation (Parry et al.. 1970). Slowed digestion may result
in longer residence time of contaminated food in pregnant women, and therefore, increased
uptake of ingested environmental contaminants.
3.7. ENDOCRINE SYSTEM
A pregnant woman experiences a multitude of hormonal changes throughout pregnancy.
These hormones direct various changes in the woman's body systems and processes that
primarily function to support the fetus during its different stages of development. The following
discussion of pregnancy- and lactation-related changes in the endocrine system is divided into
separate sections that include: the placenta, thyroid function, parathyroid function,
hypothalamic-pituitary-adrenal axis, glucose and carbohydrate metabolism, protein and lipid
metabolism, metabolic adjustments, and total body water metabolism.
3.7.1. Placenta
The placenta is a temporary endocrine organ developed during pregnancy that has the
primary functions of nourishing the fetus as well as eliminating fetal waste materials. The
placenta becomes the main source of progesterone during the second and third trimester
(Abdulialil et al.. 2012). Other hormones produced by the placenta include human chorionic
gonadotropin, human placental lactogen, estrogen, and leptin (Blackburn. 2008; Lin. 1999).
These hormones play a critical role in many metabolic and endocrine changes during pregnancy.
For example, human chorionic gonadotropin alters maternal protein, carbohydrate, and fat
metabolism (Blackburn. 2008). During pregnancy, the placenta also synthesizes
corticotropin-releasing hormone (CRH), which may modulate important physiological aspects of
labor, glucose transport to the placenta and the fetus, and the psychological mood of the mother
(Thomson, 2013). CRH has been found to be stimulated by Cortisol (Sirianni et al., 2004) (see
Section 3.5).
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The placenta also secretes leptin, which is a pregnancy-related hormone that regulates
appetite and metabolism, as discussed in Section 3.6. Comparisons to age- and BMI-matched
nonpregnant women, suggest that placental production of leptin is one of the major sources of
leptin to maternal circulation (Butte et al.. 1997).
The size of the placenta increases with gestational time. The mean ± SD placenta
volumes in mL are 134 ± 58, 254 ± 62, 460 ± 173, 593 ± 90, and 659 ± 103 at 14, 20, 30, 36, and
40 weeks of gestation (Abdulialil et al.. 2012).
3.7.2. Thyroid Function
During pregnancy, there are alterations in maternal thyroid morphology, histology, and
laboratory indices, although pregnant women generally retain normal thyroid function. The
thyroid is a gland in the neck that secretes hormones that regulate growth and metabolism. The
thyroid gland may increase in size, but if adequate iodine intake is maintained the size changes
may be extremely small to none. The thyroid continues to synthesize and secrete thyroid
hormone actively during pregnancy (see Figure 3-1). During the first trimester, total thyroxine
(T4) and total triiodothyronine (TT3) levels begin to increase. However, T4 and TT3 peak at the
end of the second trimester due to the increased production of thyroid-binding globulin (TBG),
which also begins in the first trimester and plateaus at 12 to 14 weeks. Thyroid-stimulating
hormone (TSH) concentrations decrease temporarily in the first trimester, but then return to
prepregnancy levels by the end of the first trimester and remain stable through the second and
third trimester (Burrow et al.. 1994). The temporary decrease in TSH and increase in T4 during
the first trimester are attributed to the thyrotropic effects of human chorionic gonadotropin (hCG;
higher hCG levels suppress more TSH). TSH and hCG are structurally very similar, but the
exact role of hCG in maternal thyroid function is not well understood (Gabbe et al.. 2007).
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TBG
Total T,
hCG
Free T4
TSH
20
30
40
10
Week of pregnancy
Figure 3-1. Relative changes in maternal thyroid function during pregnancy.
Source: Burrow et al. (1994). From The New England Journal of Medicine, Burrow et al., Maternal and
Fetal Thyroid Function, 331, 1072. Copyright © 1994 Massachusetts Medical Society. Reprinted with
permission from Massachusetts Medical Society.
The effect of gestation on women with hypothyroidism (insufficient production of
thyroid hormone) has been investigated. In a retrospective study of data from 167 pregnancies
on women with hypothyroidism, the median T4 dose (jag) used to supplement low thyroid status
was observed to increase significantly during pregnancy (first trimester: 100; second
trimester: 125, p < 0.001; and third trimester: 150, p < 0.001) (Idris et al.. 2005). Exposure to
certain exogenous chemicals may further inhibit iodine uptake (e.g., perchlorate) (Leung et al..
2010).
Studies have suggested that women with hormone concentrations even in the lower
euthyroid range (i.e., normal thyroid function) may be at greater risk of developing postpartum
depressive symptoms (Pedersen et al.. 2007). Both statistically significantly higher T3-resin
uptake and marginally lower total T4 concentrations have been observed at 38 weeks of
pregnancy in women with higher postpartum depression ratings. Further, mean antenatal T4
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concentrations and free T4 indices were statistically significantly and negatively correlated with
mean depression scores during postpartum weeks 2-6, 14-18, and 20-24 in a study of
31 women (Pedersen et al.. 2007).
3.7.3. Parathyroid Function
Changes in parathyroid hormone levels (produced by the parathyroid glands to maintain
the body's calcium level) during pregnancy have also been studied. Parathyroid hormone
promotes the absorption of calcium from the bones. Research has suggested that parathyroid
hormone-related protein produced in the fetoplacental unit, the breast, or both, can reach the
maternal circulation. Parathyroid hormone levels increase during pregnancy and lactation to
meet the calcium demands of the mother and the growing fetus (Ardawi et al.. 1997). Higher
demands for calcium may impact the mother's skeletal system (see Section 3.4). In a study of
40 healthy nonpregnant women, 90 healthy pregnant women (30 in each trimester), and
140 postpartum women (74 breastfeeding, 33 mixed feeding, 33 bottle feeding) plasma and
umbilical cord (in 24 women) levels of parathyroid hormone-related protein were measured
(Hirota et al., 1997). Mean plasma level of parathyroid hormone-related protein increased
throughout pregnancy and was statistically significantly higher in the third trimester (increasing
from 1.06 ± 0.19 pmol/L in the first trimester to 1.17±0.16 pmol/L in the third trimester), and
was closely associated with the degree of breastfeeding at 1 month postpartum. The umbilical
venous blood also contained statistically significantly higher levels of parathyroid
hormone-related protein than was in maternal circulation.
3.7.4. Hypothalamic-Pituitary-Adrenal Axis
Hypothalamic-pituitary-adrenal axis (HPA) changes in pregnant and lactating women
affect a number of body systems. HPA consist of three glands (hypothalamus, pituitary, adrenal)
of the endocrine system that regulate body processes including energy storage and expenditures.
Although the adrenal glands themselves do not increase in size significantly during pregnancy,
the area within the glands that produces glucocorticoids (zona fasciculata) expands. Changes in
maternal adrenocortical function during pregnancy include increased serum levels of
aldosterone, deoxycorticosterone, corticosteroid-binding globulin, Cortisol, and free Cortisol.
Specifically, the plasma corticosteroid-binding globulin concentration doubles by the end of the
6th month of gestation and there is an exponential increase in corticotropin-releasing hormone,
which is produced by the placenta and fetal membranes, during the third trimester.
Corticotropin-releasing hormone, in turn, triggers the production of ACTH in the pituitary.
Levels of ACTH increase during pregnancy from 12.1 ± 5.8 pg/mL during the 7th to 9th week of
gestation to 22.9 ±1.0 pg/mL by the 36th to the 37th week, representing an increase of 89%
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(Klimek. 2005). These increases together stimulate elevations in Cortisol (see Section 3.5)
(Gabbe et al.. 2007). Deoxycorticosterone levels also increase by mid-gestation and peak in the
third trimester. Regarding the androgenic steroids, dehydroepiandrosterone and
dehydroepiandrosterone-sulfate levels are decreased due to increased metabolic processing. In
addition, maternal concentrations of testosterone are slightly higher during pregnancy due to an
elevation in sex-binding hormone, and androstenedione is higher due to increased synthesis
(Gabbe et al.. 2007).
The alteration of maternal neuroendocrine processes during pregnancy also has
implications for neuroendocrine responsivity to exogenous conditions and psychosocial factors
(Wadhwa et al.. 1996) (see Section 3.5). A study of 54 pregnant women was conducted to assess
the association between prenatal psychosocial factors and stress-related neuroendocrine
parameters (Wadhwa et al.. 1996). The psychosocial factors were strongly associated with the
maternal-placental-fetal neuroendocrine parameters, both in magnitude and specificity. In
addition, a combination of the maternal psychosocial and sociodemographic factors measured
during pregnancy accounted for 36% of the variance in ACTH, 22% of the variance in the
ACTH-beta-endorphin disregulation index, 13% of the variance in Cortisol, and 3% of the
variance in beta-endorphin (Wadhwa et al.. 1996). More information about stress and anxiety
during pregnancy is found in Section 4.3.
The pituitary gland also undergoes changes during pregnancy, increasing in size by up to
36% at term due to a proliferation of prolactin-producing (lactotroph) cells in the anterior
pituitary. Lactotroph proliferation results in increases in serum prolactin production, which
functions to prepare the breast for lactation. Prolactin levels begin to increase at 5 to 8 weeks
gestation and continue to increase until reaching levels up to 10 times higher at term.
Postpartum, the prolactin levels return to normal within 3 months in nonlactating women, while
it may take several months (with intermittent episodes of hyperprolactinemia) in women who are
nursing. Other pituitary hormone levels that change dramatically throughout gestation include
follicle-stimulating hormone, luteinizing hormone, and growth hormone. Maternal
follicle-stimulating hormone and luteinizing hormone decrease to undetectable levels as a result
of feedback inhibition from the elevated levels of estrogen, progesterone, and inhibin. Growth
hormone levels are also suppressed because of the action of placental growth hormone variant on
the hypothalamus and pituitary (Gabbe et al.. 2007).
HPA responses to stress may also be suppressed in lactating women. For instance, in a
study of 10 lactating and 10 nonlactating women between 7 and 18 weeks postpartum, plasma
ACTH, Cortisol, glucose, and basal norepinephrine responses to physical exercise (20 minutes on
treadmill) were considerably attenuated in lactating women (Altemus et al.. 1995). In another
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study, however, lactation was observed to have little effect on the HPA responses to
psychological stress (Altemus et al.. 2001).
In 24 lactating women, 13 postpartum nonlactating women, and 14 healthy control
women in the early follicular phase of the menstrual cycle, ACTH, Cortisol, heart rate, diastolic
blood pressure, systolic blood pressure, and subjective anxiety ratings were all statistically
significantly elevated in response to psychological stress (Trier Social Stress Test). However,
there were no differences among the three groups in any of these responses to the stress. The
only difference observed in the postpartum lactating women was a persistently lower systolic
blood pressure and higher cardiac vagal tone than the nonlactating women in response to stress,
suggesting enhanced vagal control of cardiac reactivity in lactating women (Altemus et al..
2001). For more information regarding stress and anxiety during pregnancy and lactation see
Section 4.3.
3.7.5. Glucose and Carbohydrate Metabolism
Substantial physiologic changes in carbohydrate metabolism, which is the process by
which sugars and carbohydrates are used in the body to produce energy, occur during pregnancy
to allow for the continuous transport of glucose to the fetus and placenta. In early pregnancy, the
release of insulin is increased, causing a 10% reduction of glucose levels and enhanced
lipogenesis (fat storage) in pregnant women.
As pregnancy progresses, hyperinsulinemia (insulin resistance) develops after the first
trimester, resulting in a 50-80% reduction in insulin sensitivity by the third trimester (Gabbe et
al.. 2007; Paramsothy and Knopp. 2005). Insulin resistance functions to allow for glucose
competition between the maternal tissues to favor the fetus (Paramsothy and Knopp. 2005).
Insulin resistance causes further changes in maternal regulation of blood glucose levels,
including hypoglycemia (low blood glucose) during fasting and hyperglycemia (excessive blood
glucose) after meals. The average fasting glucose levels in pregnant versus nonpregnant women
was 73.1 mg/dL and 79.7 mg/dL, respectively (O'Sullivan and Mahan, 1966). The release of
insulin also increases progressively through pregnancy, peaking in the third trimester. In healthy
pregnancies, the exaggerated response in insulin production and greater glucose fluctuations
from the fasting to the postfeeding state glucose generally function to maintain glucose
homeostasis (Gabbe et al.. 2007). Although the physiological causes of the insulin resistance are
not well understood, they may be influenced by hormonal factors such as human placental
lactogen, Cortisol, progesterone, and estrogen (Paramsothy and Knopp. 2005).
Longitudinal changes in various measures of carbohydrate metabolism have been
observed in association with pregnancy. Changes in pancreatic beta function and metabolic
clearance rates of insulin were evaluated in seven women with normal glucose tolerance and nine
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women with abnormal glucose tolerance prior to conception, and in early (12-14 weeks) and late
(34-36 weeks) gestation (Catalano et al.. 1998a). There were progressive alterations in insulin
kinetics with advancing gestation, including statistically significant increases in basal insulin,
C-peptide concentrations and the metabolic clearance rate of insulin. No statistically significant
differences between the women with normal and abnormal glucose tolerance were observed.
These changes in insulin kinetics are partly responsible for pregnancy-related hyperinsulinemia
and support the unique role of pregnancy on maternal carbohydrate metabolism. Changes in
energy expenditure and body composition as a function of altered carbohydrate metabolism
during pregnancy were also investigated in 6 women with normal glucose tolerance and
10 women with abnormal glucose tolerance before conception, and in early (12 to 14 weeks) and
late (34 to 36 weeks) gestation (Catalano et al.. 1998b). Increases in basal oxygen utilization,
resting energy expenditure, and carbohydrate oxidation with increasing gestational age were
observed. Overall, observed changes in maternal fat mass and basal oxygen consumption
correlated inversely with changes in insulin sensitivity during pregnancy (Catalano et al.. 1998b).
Normal pregnancy is also associated with increased antioxidant enzyme activity, and
there appear to be ethnic differences in antioxidant responses and dietary fat intake (Chen et al..
2003). Glutathione peroxidase (GPx) activity (one of the most important antioxidant enzymes in
humans), measures of insulin resistance (fasting serum insulin, plasma glucose, and C-peptide),
and dietary fat intake were measured in 408 normotensive nondiabetic pregnant women at
16 weeks and during the third trimester of pregnancy (Chen et al„ 2003). Increases in GPx
activity and insulin resistance were observed between the first and third trimesters, with overall
GPx activity also being positively correlated with the dietary intake of fat and polyunsaturated
fatty acids, suggesting a potential link between antioxidant defenses, insulin resistance, and
dietary fat intake. In addition, African-Americans had statistically significant higher GPx
activity, dietary fat, and polyunsaturated fatty acid intake than Hispanics and Caucasians (Chen
et al.. 2003). In contrast to these findings of decreased glucose tolerance in the developed world,
in a study of 58 nondiabetic pregnant African women in Tanzania, Lutale et al. (1993) found that
women in an urban African setting showed little change in glucose tolerance during pregnancy.
3.7.6. Protein and Lipid Metabolism
Protein and lipid metabolism is the decomposition and synthesis of protein and lipids in
the body. By the third trimester of pregnancy, the increase in glucose and amino acid uptake by
the fetus results in a metabolic shift from predominantly carbohydrate to predominantly fat
utilization (Butte et al.. 1999). Protein catabolism is also decreased during pregnancy, resulting
in the preferential use of fat stores to fuel metabolism. This lipolysis in turn results in increased
generation of glycerol, fatty acids, and ketones for gluconeogenesis and fuel metabolism (i.e.,
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hyperlipidemia and hyperketonemia) (Gabbe et al.. 2007). Changes in protein and lipid
metabolism may result in changes in appetite.
Increases in maternal plasma cholesterol and triglycerides from the first to the third
trimester of gestation, in conjunction with free fatty acids increases from the first trimester
through the third trimester to delivery, suggest an enhanced lipolytic activity during pregnancy
CHerrem et al.. 2004). In addition, plasma levels of alpha- and gamma-tocopherols, lycopene,
and beta-carotene also increase with gestation while retinol levels decline with gestational time.
Finally, the proportion of total saturated fatty acids increases with gestation and total n-9 fatty
acids remain stable throughout pregnancy whereas total n-6 fatty acids decline CHerrem et al.,
2004).
Blood plasma levels of lipids (fats/triglycerides, fatty acids, cholesterol) and lipoproteins
(i.e., low-density lipoproteins [LDLs], high-density lipoproteins [HDLs], and very-low-density
lipoproteins [VLDLs]) increase in pregnancy in a study of 19 pregnant women in Sweden. By
full term, triglyceride levels may increase by up to two to three times (levels of 200 to
300 mg/dL are considered normal), and total cholesterol and LDLs may increase by 50-60%
(Salameh and Mastrogiannis. 1994). After temporarily rising in the first 28 weeks of pregnancy,
HDL levels decrease in late gestation, reaching levels that are generally about 15% higher than
nonpregnant levels by term. By 8 weeks postpartum, triglyceride levels return to prepregnancy
levels (including during lactation). In contrast, cholesterol and LDL levels remain elevated.
Mechanisms causing the pregnancy-induced changes in lipids are hypothesized to be related to
the elevated levels of estrogen, progesterone, and human placental lactogen (hPL) (Gabbe et al.,
2007).
Pregnancy-associated changes in blood concentrations of lipids, lipoproteins, and
apolipoproteins have been investigated in several studies. Desove et al. (1987) conducted a
longitudinal study to investigate correlations between hormones and lipid/lipoprotein levels
during pregnancy and postpartum. Concentrations of plasma lipids and lipo- and apolipoproteins
were measured in 24 nonpregnant and 42 pregnant women. Insulin concentrations were constant
during pregnancy until week 25 and then increased for the duration of the pregnancy. Plasma,
beta-estradiol, progesterone, and hPL as well as plasma lipid levels rose continuously during
gestation. Apolipoproteins Al, All, and B concentrations increased until weeks 25, 28, and 32,
respectively, and then remained constant until term. LDL cholesterol reached maximum levels
at week 36. HDL cholesterol exhibited a triphasic behavior, with maximum levels at week 25, a
fall until week 32, and then maintenance of the level until term. Time series analysis revealed
positive correlations with beta-estradiol, progesterone, and hPL (Desove et al.. 1987).
Mazurkiewicz et al. (1994) measured fasting serum concentrations of total cholesterol,
triglyceride, LDL cholesterol, HDL cholesterol, apolipoproteins Al, All, and B, and
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lipoprotein(a). These parameters were measured in 178 women with normal glucose tolerance in
the second and third trimesters of pregnancy and in a control group of 58 nonpregnant women of
similar age. Pregnant women had statistically significantly higher concentrations of total
cholesterol, triglyceride, LDL cholesterol, HDL cholesterol, and apolipoproteins (AI, All, and B)
than the control women. Also, the ratio of apolipoprotein B to apolipoprotein AI was
statistically significantly higher in the pregnant women than in the controls, but the total
cholesterol-HDL cholesterol ratio was not statistically significantly different.
The relationship between recreational physical activity and plasma lipid concentrations in
early pregnancy was investigated in 925 normotensive, nondiabetic pregnant women averaging
32 years of age from Washington State (Butler et al.. 2004). Mean triglyceride concentration
was 12.7 mg/dL lower in women performing any physical activity versus none. Mean total
cholesterol was also reduced in women with the highest levels of physical activity, although
there was no association observed between physical activity and HDL cholesterol. There was
also a consistent linear relationship across levels of physical activity measures for triglyceride
and total cholesterol, suggesting that habitual physical activity may attenuate disruptions in
blood lipid levels (dyslipidemia) frequently observed during pregnancy (Butler et al.. 2004).
3.7.7. Metabolic Adjustments
In general, metabolic adjustments characterized by increased energy expenditure and
preferential use of carbohydrates are observed during pregnancy and lactation to support fetal
growth and milk synthesis (Butte and King. 2005; Butte et al.. 2004; Butte et al.. 1999;
Blackburn and Calloway. 1976). These are adaptive changes in the body's metabolism and may
result in altered appetite. Energy expenditure also, in part, increases during pregnancy because
of the metabolic contribution of the uterus and fetus and the increased work by the maternal heart
and lungs after adjusting for free fat mass, fat mass, and energy balance (Butte et al.. 1999).
Related to these metabolic changes, maternal fat stores increase to a peak late in the second
trimester and then decline for the remainder of gestation as a result of mobilization to support the
rapidly growing fetus (Widjaja et al., 2000).
In a study of energy expenditures in 76 women (40 lactating, 36 nonlactating) at
37 weeks gestation and 3 and 6 months postpartum, total energy expenditure and its components
(basal metabolic rate, sleeping metabolic rate, and minimal sleeping metabolic rate) were
15-26% higher during pregnancy than postpartum (Butte et al.. 1999). During the postpartum
period, total energy expenditure and sleeping metabolic rate were higher in lactating than in
nonlactating women. Butte et al. (1999) suggested that total energy expenditure and its
components (basal metabolic rate, sleeping metabolic rate, and minimal sleeping metabolic rate)
correlated positively with fasting serum insulin, insulin-like growth factor I, fatty acids, leptin,
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norepinephrine, epinephrine, and dopamine. In addition, elevated respiratory quotients and
preferential utilization of carbohydrates were observed to occur during pregnancy and continue
through lactation, which was considered to be consistent with the preferential use of glucose by
the fetus and mammary glands (Butte et al.. 1999).
Optimal energy requirements of pregnant and lactating women are not fully known. In
part, some of this uncertainty is due to variability in energy use possibly related to
pregnancy-associated fat deposition and reductions in physical activity (Butte et al.. 2004).
Pregnancy-related energy adaptations of 63 women (17 underweight with a low BMI, 34 with a
normal BMI, and 12 overweight with a high BMI) were estimated at 0, 9, 22, and 36 weeks of
pregnancy and at 27 weeks postpartum. Basal metabolic rate (BMR) generally increased
gradually throughout pregnancy at a mean rate of 10.7 ± 5.4 kcal/gestational-week, although
there was notable variability between individuals in the study (e.g., some individuals had
decreased BMR initially before increases began). The recommended increase in energy intake
for lactating women is 500 kcal/day during the first six months of lactation and 400 kcal/day
after the sixth month (Picciano. 2003).
In the normal BMI group, energy requirements increased negligibly in the first trimester,
by 350 kcal/day in the second trimester, and by 500 kcal/day in the third trimester. In addition,
because there was a slight decrease in activity energy expenditure, total energy expenditure
increased by only 5.2 ± 12.8 kcal/gestational week. There were also statistically significant
differences in pregnancy-associated metabolic responses associated with BMI. For example, in
the normal BMI group, BMR increased by 2% in the first trimester, 7% in the second trimester,
and 28% in the third trimester, whereas in the high-BMI group, the increase in BMR was greater
(7, 16, and 38% in the first, second, and third trimesters, respectively), consistent with that
group's greater gestational weight gain (Butte et al.. 2004).
Butte and King (2005) calculated that the estimated total energy cost of pregnancy for
women with a mean gestational weight gain of 12.0 kg, was 76,670-77,625 kcal. This energy is
distributed as 90; 287; and 466 kcal/day, for the first, second, and third trimesters, respectively.
Other research has suggested that energy expenditure in pregnant women may be seriously
underestimated if energy cost figures do not take into account level of fitness and rate of
recovery/oxygen uptake in the calculations (Blackburn and Calloway. 1976).
Several studies have specifically examined energy requirements and expenditures during
lactation, which in general causes substantial energy stress for the woman (Spaaij et al„ 1994).
A study of 24 Dutch women before pregnancy and 2 months postdelivery was conducted to
investigate whether any of the three components of energy metabolism (metabolic rate at rest,
following a meal, and following exercise) show signs of metabolic adaptation in the lactating
women. The women were from the middle to upper socioeconomic stratum, nonsmokers with an
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average age of 29.8 years. The women in the study showed signs of metabolic adaptation during
lactation (Spaaii etal.. 1994). Resting metabolic rate and postprandial metabolic rate increased
similarly during lactation relative to prepregnancy, and metabolic rates measured after cycling
exercise did not change significantly. Accordingly, the researchers suggest that there are no
statistically significant changes in diet- and work-induced thermogenesis or metabolic efficiency
during lactation (Spaaii et al.. 1994).
In a population of 40 healthy, but rural and poor, lactating Filipino women, energy intake,
energy expenditure, and body composition were measured throughout the first 6 months of
lactation (Guillermo-Tuazon et al., 1992). Energy intakes at 6 and 30 weeks of lactation were
244 and 185 kcal/day, respectively. These values were significantly higher (p < 0.05) than in
early pregnancy. Energy intakes decreased slightly between 6 weeks and 30 weeks of lactation
from 2,213 ± 489 to 2,073 ± 566 kcal/day. Basal metabolic rates also remained unchanged
throughout lactation in this study.
Leptin (see Section 3.6), an adipose-derived hormone that plays a key role in regulating
energy intake and energy expenditure (as well as hypothesized roles in maternal and fetal fat
mass changes during pregnancy), has received particular attention in studies of metabolic
changes during pregnancy. Leptin inhibits appetite by acting on receptors in the hypothalamus.
3.7.8. Total Body Water Metabolism
Total body water increases gradually during pregnancy. The total body water content
increase by the end of pregnancy is considered one of the most major changes in pregnancy
(Gabbe et al.. 2007). This increase is the result of water in the fetus, the placenta, amniotic fluid,
enlargement of reproductive organs, increased blood volume, expanded adipose tissue, and
increase in inter- and extracellular water (Gabbe et al.. 2007; Hytten et al.. 1966). Abdulialil et
al. (2012) conducted a meta-analysis of available total body water data. The analysis shows that
the mean ± SD of total body water in liters increases from 31.67 ± 4.60 before pregnancy to
35.22 ± 1.65, 40.14 ± 7.55, and 46.00 ± 5.5 0 at 12, 25, and 40 weeks of gestation, respectively,
an overall increase of 45% from prepregnancy to the 40th week of gestation (Abdulialil et al.,
2012). Approximately 3.5 L is accounted for by the water content of the fetus, placenta, and
amniotic fluid at term (Gabbe et al.. 2007). The expansions of the maternal blood volume by
1,500 to 1,600 mL, plasma volume by 1,200 to 1,300 mL, and red blood cells by 300 to 400 mL
account for additional water (Theunissen and Parer. 1994). The remainder is attributed to
extravascular fluid, intracellular fluid in the uterus and breasts, and expanded adipose tissue
(Gabbe et al.. 2007).
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3.8. INTEGUMENTARY SYSTEM
The integumentary system is the body system consisting of the skin and its associated
structures, such as the hair, nails, sweat glands, and sebaceous glands. Blood flow to the
maternal skin increases during pregnancy, particularly in the extremities (Abduljalil et al., 2012).
Increased blood flow during pregnancy may not affect percutaneous absorption through normal
skin, but may affect the absorption rate of the skin that has been stripped of its outside layer
(stratum corneum) (U.S. EPA. 1992). During pregnancy, the combined effect of endocrine,
metabolic, mechanical, and blood flow alterations in the body cause a woman's skin to undergo
substantial changes. Most of the changes are cosmetic in nature and are therefore not harmful or
associated with risks to the mother or developing fetus (Gabbe et al.. 2007). Most of the
complaints of pregnancy-related skin changes can be expected to resolve or improve postpartum
(Gabbe et al.. 2007). Surface area of the skin, which is calculated as a function of height and
weight, increases during pregnancy due to weight gain. Increased surface area and increased
permeability may be important considerations for exposure to waterborne contaminants.
3.9. WEIGHT CHANGES
Differences in the quantity of food consumed by pregnant or lactating women may lead
to weight loss or gain. Resistance to free leptin, a pregnancy-related hormone (see Section 3.6),
is related to an increase in BMI during the middle of the pregnancy (Widiaia et al.. 2000). In a
study of 630 women from Galicia, Spain, study researchers concluded that leptin increases may
be responsible for the postpartum weight gain observed in some women (Lage et al.. 1999).
The differences in body weight can affect the potential dose received by the pregnant or
lactating mother. While weight gain is a topic largely discussed and monitored in the three
trimesters of pregnancy, weight loss is more closely associated with the postpartum lactation
phase. However, while there is a large body of published research on weight gain during
pregnancy, there is considerably less published literature on the issue of weight loss during
pregnancy or the postpartum period. Morning sickness and associated nausea may lead to
weight loss during pregnancy (see Section 4.4.3), and lactation does promote weight loss during
the first year postpartum, particularly if breastfeeding continues for at least 6 months (Dewey et
al.. 1993).
Cohen and Kim (2009) used data from the Behavioral Risk Factor Surveillance System
collected by the CDC to study sociodemographic and behavioral factors associated with
attempting to lose weight during pregnancy. Using data collected from 1996-2003 on
8,036 pregnant women aged 18 to 44, Cohen and Kim (2009) reported that 8.1% of pregnant
women intentionally try to lose weight during pregnancy, and that this behavior was associated
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with maternal age (35-44 years), Hispanic ethnicity, obesity, lower income levels, alcohol
consumption, and mental distress.
The recommended weight gain during pregnancy is between 25 and 35 pounds for
normal weight women (BMI = 19.8 to 26 kg/m2) (Brochu et al.. 2006). which amounts to the
consumption of an additional 200 kcal/day (Cox. 2003). Recommendations for weight gain are
slightly higher (28-40 pounds) in underweight women (BMI <19.8 kg/m2) (Brochu et al.. 2006)
and slightly lower (15-25 pounds) in obese women (BMI > 26 kg/m2) (Brochu et al.. 2006). As
the fetus gains most of its weight during the last 2 months of pregnancy, it is recommended that
women regulate their weight gain accordingly (Cox, 2003). Increased consumption to
adequately support a pregnancy may affect exposures to environmental contaminants found in
drinking water and food.
Reports indicate that only 30-40% of women actually gain weight within recommended
ranges, while most women have inadequate prenatal weight gain (Hickev. 2000). A review of
the literature to examine demographic, sociocultural, and behavioral factors associated with the
risk of low prenatal weight gain among adult women with low and normal BMIs found that
ethnicity, socioeconomic status, age, education, pregnancy intendedness or wantedness, prenatal
advice, and psychosocial characteristics such as attitude toward weight gain, social support,
depression, stress, anxiety, and self-efficacy may have an impact (Hickev. 2000). However,
Hickey (2000) concluded that further identification and characterization of sociocultural and
behavioral risk factors, along with reproductive and nutritional characteristics, are needed to help
predict which women are most likely to have inadequate prenatal weight gain.
In adolescent pregnant women who are still growing and maturing, it has long been
thought that any maternal statural growth occurring during pregnancy would be insignificant and
have little impact on fetal growth. A study investigating growth and weight gain in this group of
pregnant women found that maternal growth is prevalent and is associated with increased weight
gain during pregnancy (Scholl et al.. 1993). Postpartum measures indicated that growth and
weight gain occurred in the pregnant adolescents even when caloric intakes were equivalent to
pregnant, nongrowing adolescents or mature women. The infant birth weights of growing
pregnant teens were reduced, an indication that fat reserves in growing pregnant adolescents
support the mother's development rather than fetal growth.
Efforts to determine whether there are modifiable behavioral factors that can predict
inadequate and excessive gestational weight gain in the U.S. population found that there are valid
and easily implemented measures of change. Prepregnancy food intake, physical activity, and
cigarette smoking during pregnancy were each statistically significantly and independently
related to gestational weight gain (Olson and Strawderman. 2003). Women who consumed more
or less food during pregnancy than prior to pregnancy had statistically significantly greater and
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less gestational weight gain, respectively, compared to women who maintained similar food
intake patterns during pregnancy as in prepregnancy. Decreased physical activity during
pregnancy was associated with statistically significant greater gestational weight gain.
Consumption of more than one and a half packs of cigarettes per day was associated with
significantly lower gestational weight gain (Olson and Strawderman. 2003).
Major weight gain in pregnancy and subsequent weight retention 1 year postpartum were
associated with the factors of gestational weight gain, postpartum exercise frequency, and food
intake. Economic status also had an impact because lower income women gained more weight
in pregnancy than the recommended amount and were at higher risk for weight retention (Olson
et al.. 2003). In another study investigating the patterns of maternal weight gain in pregnancy,
factors that were associated with statistically significant differences in average weekly weight
gain were parity, BMI, smoking habit, and raised blood pressure (Dawes and Grudzinskas.
1991). Additional research into the factors associated with maternal weight gain patterns found
they vary according to trimester of pregnancy (Abrams et al.. 1995). In a study of
10,418 women in California, the most important predictors of maternal weight gain were found
to be Asian race-ethnicity and age in the first trimester; prepregnancy BMI, parity, and height in
the second trimester; and hypertension, age, and parity in the third trimester (Abrams et al..
1995). Pregnant women, who successfully practiced dietary restraint to maintain a proper weight
prior to pregnancy, reported lower levels of dietary restraint, were less dissatisfied with their
body shape, and showed higher eating self-efficacy than nonpregnant women (Clark and Ogden,
1999).
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4. BEHAVIORAL ADAPTATIONS AND PSYCHOLOGICAL CHANGES
DURING PREGNANCY AND LACTATION
Behavioral changes in pregnant and lactating women differ from physiological changes
in that the physiological changes passively happen to a woman, whereas the behavioral changes
usually involve an active response from the woman. In other words, a woman may have more
control over a behavioral change than she may have over a physiological change that occurs
during pregnancy or lactation. However, the level of control a woman has may vary for different
behavioral factors, with some being more controllable than others. Also, some women may make
more of an effort to actively alter these behaviors than others. For instance, a pregnant woman
may elect to stop smoking, reduce caffeine consumption, or eat a healthier diet to benefit the
health of the fetus. On the other hand, a woman may experience uncontrollable feelings of
depression, stress, fatigue, or irritability during pregnancy and modify her behavior as a result.
These changes in behavior may impact her exposure to environmental contaminants.
While it is possible that a woman will experience most, if not all, of the physiological
changes noted in the previous sections, it is possible that a woman may not undergo many, or
even any, of the possible behavioral changes noted in the following sections. Many of the
behavioral changes noted include those to benefit the health of the pregnant woman and her
child. Recommended behavioral changes are associated with documented risk factors for
adverse pregnancy outcomes; such risks include smoking, low prepregnancy weight, and
inadequate weight gain during pregnancy (Savitz et al.. 2012). There is some overlap between
physiological and behavioral changes, since modifications in behavior may result from any of the
physiological changes described in the preceding section.
Pregnancy or breastfeeding can be strong motivators for changing a woman's behavior
(Ravburn and Phelan. 2008). The following sections describe the various behavioral changes or
recommendations for change found in the literature. It is worth noting that many of the
published articles do not describe a direct link between behavioral changes during pregnancy or
lactation and the potential for exposures to environmental contaminants. However, the
behavioral factors identified are presented as evidence of behavioral modifications commonly
found in pregnant and lactating women. These behavioral modifications may result in exposures
to environmental contaminants that are different in pregnant and lactating women from those of
non-pregnant, non-lactating women. For that reason, the potential behavioral changes common
to this lifestage are worth presenting.
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4.1. ADAPTATIONS
During pregnancy and in the lactation period that follows, women may be advised to
reduce or avoid a number of behaviors that may harm the health of the mother or the developing
fetus. Among the key behaviors to be avoided are smoking, the consumption of caffeine and
alcohol, and illicit drug use.
4.1.1. Smoking
Active smoking and secondhand exposure to smoke while pregnant and after is a concern
because of linkages to premature births, fetal growth restrictions, low birth weight babies,
pregnancy complications, and sudden infant death syndrome, among other health concerns.
Tobacco smoke contains nicotine, carbon monoxide, and thousands of other compounds
(Cal/EPA. 2006). Nicotine and carbon monoxide can cross the placenta and enter fetal tissues
(Jacqz-Aigrain et al.. 2002; Jauniaux et al.. 1999). Quitting smoking and reducing exposure to
second-hand smoke during pregnancy can eliminate exposure to environmental contaminants
contained in the inhaled cigarette smoke as well as reduce the risk of adverse pregnancy
outcomes (Mund et al.. 2013).
In a study designed primarily to assess alcohol use among pregnant women, respondents
were also asked about their current smoking and intended smoking in future pregnancies. When
asked about their smoking behavior, 16% of the respondents indicated that they had smoked
during their last pregnancy, 5% intended to continue smoking if they were planning to become
pregnant, and 4% intended to smoke if they became pregnant in the future (Peadon et al.. 2011).
Based on data from the 2009/2010 National Survey on Drug Use and Health (NSDUH), 16.3%
of pregnant women aged 15-44 years smoked cigarettes compared to 26.7% of nonpregnant
women in the same age group (Behnke and Smith. 2013; SAMHSA. 2011) (see Table 4-1). The
NSDUH is an annual survey of the about 67,000 civilian, noninstitutionalized people in the
United States ages 12 years or older.
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Table 4-1. Comparison of cigarette, alcohol, and illicit drug use among
pregnant and nonpregnant women, aged 15-44 years, based on the 2009/2010
NSDUH
Behavior
Pregnant Women
Nonpregnant Women
Cigarette use
16.3%
26.7%
Alcohol use
10.8%
54.7%
Binge drinking
3.7%
24.6%
Heavy alcohol use
1.0%
5.4%
Illicit drug use
4.4%
10.9%
Source: SAMHS A (2011).
A recent study examining psychosocial factors found that women who were less educated
and unmarried, who were living below the poverty level, whose partners smoked or suggested an
abortion, or who had mental health problems were identified as more likely to be smokers while
pregnant or postpartum (Page et al.. 2012). In a study examining smoking and smoking
cessation behaviors among U.S. pregnant women, sociodemographic correlates of smoking
cessation in pregnancy were investigated (Yu et al., 2002). Four categories of smoking behavior
were analyzed: nonsmoking at last pregnancy, persistent smoking throughout pregnancy,
attempting unsuccessfully to quit during pregnancy, and successfully quitting during pregnancy.
Results of this study showed that the factors most strongly associated with attempts to quit
smoking were Hispanic ethnicity and the combined effect of age and smoking duration. Race
was shown not to impact smoking cessation in a study of prenatal smoking cessation among
U.S. women that found a similar level of spontaneous cessation for black (46.8%) and white
(43.3%) women who quit smoking when they learned they were pregnant (Orr et al.. 2007).
In another study, Hispanic mothers were identified as being more likely to smoke
postpartum than to smoke throughout their pregnancies (Page et al.. 2012). In addition,
socioeconomically disadvantaged women from ethnic minority groups were found to be more
likely to smoke before pregnancy and postpartum (Hawkins et al., 2010). Urban minority
pregnant women were significantly more likely to continue smoking during pregnancy when
they also reported symptoms of depression (Tan et al.. 2011).
A study of U.K. women (Morris et al.. 2008) investigated whether women in their second
or subsequent pregnancy (multigravid) were more or less likely than women pregnant for the
first time (primigravidae) to change their smoking behavior. While in general, women who
reported smoking before pregnancy showed a decreasing trend in continuing to smoke the same
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amount after recognizing their pregnancy, the multigravid smokers were almost 75% more likely
than primigravid smokers to continue to smoke while pregnant with no change in consumption.
However, women who were breastfeeding smoked less than nonbreastfeeding women, and
prolonged breastfeeding was found to reduce the risk of smoking relapse (Lauria et al.. 2012).
4.1.2. Caffeine Consumption
Although there is no general agreement among researchers, some studies concerning
caffeine consumption during pregnancy suggest that reducing caffeine consumption improves
fetal outcome. These studies advise that caffeine consumption should be reduced or avoided
during pregnancy because of a concern with increased pregnancy loss or fetal growth retardation
(Weng et al.. 2008; Klebanoff et al.. 1999; Mills et al.. 1993). However, there are others that
found no evidence to indicate that moderate caffeine use increased the risk of spontaneous
abortion, intrauterine growth retardation, or microcephaly after accounting for other risk factors
(Mills et al.. 1993). Although caffeine consumption itself may or may not affect fetal outcome,
exposure to contaminants may occur if they are present in the source water used to make coffee
or tea (e.g., furan, PAHs, ochratoxin A, cadmium, cobalt) (Guenther et al.. 2010; Houessou et al..
2007; Vargas et al.. 2005; Horwitz and van der Linden. 1974). Thus, reducing or avoiding the
consumption of coffee or tea may reduce a pregnant or lactating woman's exposure to those
environmental contaminants.
The March of Dimes (2012) recommends that women who are pregnant or trying to get
pregnant limit their caffeine intake to 200 mg/day. Knight et al. (2004) used data from the
1999 Share of Intake Panel (SIP), a marketing research program, which contained data for more
than 10,000 caffeinated beverage consumers to estimate caffeine consumption in pregnant and
nonpregnant women. Caffeine consumption among pregnant women averaged 58 mg/day
compared to 91 and 109 mg/day for 20-24 and 25-34-year-old nonpregnant women,
respectively. Coffee was the major source of caffeine consumption. For comparison purposes,
using data from the 1994-96 Continuing Survey of Intake by Individuals (CSFII), USD A
(2000a) estimated caffeine intake to be 143, 209, and 250 mg/day for 20-29- (N= 720), 30-39-
(N= 816), and 40-49-(/V = 902) year-old women, respectively (data were not reported for
pregnant or lactating women).
4.1.3. Alcohol Use
There are a variety of contaminants in alcohol, including aluminum, cadmium, and
ochratoxin A (Battilani et al.. 2006; Lopez et al.. 1998; Mena et al.. 1996). Alcohol consumption
during pregnancy is a concern because of the possible teratogenic effects on the offspring as well
as multiple congenital abnormalities, developmental delays, and behavioral changes (Ornov and
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Ergaz. 2010; Grisso et al.. 1984). Many women who drink alcoholic beverages regularly before
becoming pregnant either reduce their alcohol intake or stop drinking during pregnancy
(Takimoto et al.. 2003). thereby reducing their exposure to contaminants in alcohol. In a study
of drinking behavior, 4.6% of women reported drinking an average of one alcoholic drink per
day by the end of the third trimester of pregnancy, compared with 44% before pregnancy (Day et
al.. 1989). Similar results were found in a U.S. government survey, which indicated that about
13%) of pregnant women drink alcohol during pregnancy and about 3% of pregnant women
report binge drinking (five or more drinks on any one occasion) or frequent drinking (seven or
more drinks per week) (CDC, 2004). Based on data from the 2009/2010 NSDUH, 10.8%> of
pregnant women aged 15-44 years used alcohol compared to 54.7% of nonpregnant women in
the same age group (Behnke and Smith. 2013; SAMHSA. 2011) (see Table 4-1). Only 3.7% of
pregnant women reported binge drinking compared to 24.6% of nonpregnant women. Heavy
alcohol use was reported by 1.0% of pregnant women compared to 5.4% of nonpregnant women.
In a study of pregnancy-related changes in alcohol consumption between black and white
women, white women were more likely to reduce both drinking and binge drinking behavior
during their pregnancies (Morris et al.. 2008). The study population was comprised of
280,126 non-Hispanic white or black women, aged 18-44, selected for the years 2001-2005
from the CDC's Behavioral Risk Factor Surveillance System, a national telephone survey.
Results of the survey showed that pregnant white women averaged 38% fewer drinks and had a
33%) greater reduction in binge drinking than pregnant black women in the study. Groups also
seen to reduce their drinking or binge drinking were pregnant younger women (aged 18-33) and
pregnant women with more than a high school education. Smoking status was the greatest
predictor of drinking behavior for both pregnant and nonpregnant women; pregnant smokers
were more than 2.5 times more likely to drink and more than 4 times as likely to binge drink as
pregnant nonsmokers.
To examine the attitudes and behavior of women regarding alcohol use during pregnancy,
1,103 nonpregnant Australian women of childbearing age were interviewed by telephone
(Peadon et al.. 2011. 2010). The majority of respondents (93%) agreed that alcohol can affect
the fetus, but a small percentage (16%) believed that the effects on the fetus were transient.
Women with higher education levels were more likely to know the effects of alcohol
consumption on the fetus. Of those respondents who had been pregnant in the past, 34% drank
alcohol while they were pregnant and 31% intended to consume alcohol in a future pregnancy.
Education level and knowledge about the effects of alcohol consumption were not associated
with the respondent's attitudes regarding alcohol consumption in pregnancy.
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4.1.4. Other Adaptations
In addition to the key avoidances of smoking, caffeine, and alcohol, pregnant and
lactating women are also cautioned to make other behavioral changes to benefit their health and
the health of their child. Many of these adaptations would serve to reduce a pregnant or lactating
woman's exposure to environmental contaminants. For example, the March of Dimes
recommends that pregnant women avoid marijuana, ecstasy, methamphetamines, physical abuse,
foods that may contain mercury (e.g., certain fish), rodents, lead, solvents, paints, pesticides,
benzene, formaldehyde, and carbon monoxide. They also caution against the use of street drugs,
over-the-counter drugs, prescription drugs, certain dietary supplements, herbal preparations, and
other medications that have not been approved by a doctor who is aware of the impact of
exposure of pregnant women to potentially harmful substances (March of Dimes. 2013b).
SAMHSA (2011) found that illicit drug use was lower among pregnant women than nonpregnant
women of reproductive age. During 2009/2010 an estimated 4.4% of pregnant women aged
15-44 years used illicit drugs compared to 10.9% of nonpregnant women in the same age group.
Illicit drug use was highest (16.2%) among pregnant women aged 15-17 years and lowest
(1.9%) for pregnant women aged 26-44 years (Behnke and Smith, 2013; SAMHSA, 2011) (see
Table 4-1).
In addition to avoidances, the March of Dimes also recommends other adaptations
pregnant women should actively practice to foster a healthy environment for the mother and
child while pregnant and lactating, including the consumption of prenatal vitamins and minerals,
the addition of calcium in the diet, a healthy diet, and exercise.
4.2. DEPRESSION
Depression is not only a disorder in which one feels sad, or depressed, but also is a
condition that can manifest itself in a host of additional symptoms and behavioral changes that
can include appetite disturbance or significant weight change, sleep loss or excessive sleep,
psychomotor agitation/retardation, fatigue or energy loss, feelings of worthlessness or guilt,
impaired thinking or concentration, and suicidal ideation. These symptoms cover a wide range
of behaviors and depression can therefore look vastly different from person to person (APA,
2000). Changes in appetite or behavior as a result of depression may result in either reduced or
increased exposure to environmental contaminants in pregnant and lactating women.
4.2.1. Prevalence of Depression
Each year approximately twice as many women (12.0%) as men (6.6%) suffer from a
depressive disorder (Regier et al.. 1993). According to the American College of Obstetricians
and Gynecologists (ACOG), reproductive-age women have the highest prevalence of major
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depressive disorders; with approximately 1 in 10 women at risk for having major or minor
depression sometime during pregnancy and in the postpartum period (ACOG. 2006). Women
between the ages of 25 and 44 years are at the greatest risk for a major depressive episode, which
is the primary age bracket for childbearing. Approximately, 10-20% of women suffer from
depression during pregnancy or in the first 12 months postpartum (NIHCM 2010). Hormonal
changes during pregnancy, genetics, and psychosocial factors and can trigger depression
(NIHCM 2010). Depressive disorders are the leading cause of disease burden for women aged
15-44 years (WHO, 2008). Unfortunately, major depressive episodes during pregnancy are
often undiagnosed and untreated as illustrated in a large sample (N= 3,472) of pregnant women
screened in obstetric settings, in which 20% had significant symptoms and only 13.8% were
receiving treatment (Marcus et al.. 2003).
4.2.2. Drug Treatment for Depression
Drug treatment during pregnancy and lactation has been shown to be effective in treating
depression. In a study of the prevalence of medication use among pregnant women in Boston
and Philadelphia, Mitchell et al. (2011) found that the use of antidepressants increased from <1%
in 1976-1990 to 7.5% in 2006-2008. Haves et al. (2012) suggests that the prevalence of
medication therapy to treat depression during pregnancy is about 4—10% in the United States and
Canada. Selective serotonin reuptake inhibitors are medications commonly used to treat
depression (ACOG. 2006). and they accounted for the majority of the antidepressants taken by
pregnant women in recent years (Mitchell et al.. 2011). However, given the potential for harm to
the developing fetus, women previously diagnosed with depression prior to pregnancy often
choose to discontinue the use of this medication during pregnancy (NIHCM. 2010).
4.2.3. Factors Impacting Depression in Pregnancy and Lactation
This section provides a summary of the additional studies related to depression that were
found in the literature and that provide information on factors that have been shown to make
some women more prone to depression and the subsequent behavioral changes that occur. There
is a large body of relevant research that addresses changes in maternal health from depression in
pregnant and lactating women. Race/ethnicity, age, and socioeconomic status are found to be
good predictors of maternal depression (NIHCM. 2010). One study found that education,
material deprivation, and subjective social standing were independently associated with all health
measures (Stewart et al.. 2007). After adjusting for all socioeconomic status variables, there
were racial/ethnic disparities remaining in depression rates for all minority groups, and
disparities in self-rated health for Asian/Pacific Islanders. In another socioeconomic-related
study, changes in health status experienced by a multiethnic cohort of women during and after
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pregnancy were characterized (Haas et al.. 2005). Insufficient money for food or housing and
lack of exercise were associated with prevalence of depressive symptoms before, during, and
after pregnancy. The study supports the common finding that depressive symptoms may be
more prevalent during the postpartum period than during pregnancy.
In an investigation of hormones and metabolism on depression in women, a study of
neuroactive ring A-reduced metabolites of progesterone in human plasma during pregnancy
measured elevated levels of 5a-dihydroprogesterone in depressed patients during the latter half
of pregnancy. Mean levels of progesterone metabolites tended to be higher in depressed patients
compared with controls, and this difference reached statistical significance for
5a-dihydroprogesterone both at 27 weeks and at 37 weeks of gestation. A marked rise in all of
the progesterone metabolites was found during pregnancy suggesting that these metabolites may
be involved in the mood changes of pregnancy and early postpartum period (Pearson Murphy et
al.. 20011
In another study investigating the impact of metabolism, the relationship between thyroid
status during late pregnancy and antenatal and postpartum depression scores was studied
(Pedersen et al.. 2007). Thyroid measures were obtained at 32-35, 36, and 37 weeks of
pregnancy in women with normal range thyroid hormone levels. Pregnant women with antenatal
total and free T4 concentrations in the lower euthyroid range may be at greater risk of developing
postpartum depressive symptoms.
Finally, a longitudinal study of women's mental and physical health from pregnancy
through 6 months postpartum was conducted to determine whether health was related to length
of maternity leave by investigating changes in women's mental and physical health around the
time of childbirth (Gierdingen et al.. 1991). The study of first-time mothers revealed that while
many physiological symptoms resolve soon after delivery, there are lingering physical and
emotional symptoms that persist. There was an increase in depressive symptoms for new
mothers from pregnancy to the 6th week postpartum, with a subsequent decline thereafter. In
addition, from pregnancy to the 6th postpartum month, the number of days that mothers were ill
due to infections steadily increased. A significant decline in depressive symptoms was observed
from the prenatal period through the 6th postpartum month for women who did not return to work
during the period of the study.
4.3. STRESS, ANXIETY, IRRITABILITY, SLEEP, AND FATIGUE
The terms "anxiety" and "stress" are sometimes used interchangeably by laymen, but in
the medical community the terms have distinct definitions. Clinically, anxiety is defined as a
feeling of apprehension or fear. In extreme cases, anxiety can manifest itself in behaviors such
as a phobia, avoidance, posttraumatic stress disorder, or obsessive-compulsive disorder (Gabbe
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et al.. 2007). On the other hand, stress can arise from any situation or thought that causes
feelings of frustration, anger, nervousness, or even anxiousness and produces the "fight or flight"
response that can lead to positive adaptive behaviors or negative behaviors such as social
withdrawal, drug or alcohol abuse, overeating, undereating, or angry outbursts. Changes in
appetite or behavior in pregnant and lactating women as a result of anxiety, stress, irritability,
lack of sleep, and fatigue may result in either reduced or increased exposure to environmental
contaminants.
Anxiety can have a number of impacts on pregnant or lactating women. Anxiety is cited
as one of many factors that may potentially influence maternal weight gain. Hickey (2000)
suggested that stress may result in neuroendocrine-mediated alterations in prenatal energy
metabolism that may be responsible, in part, for low weight gain. Lactating women suffer less
anxiety over regulation of food and fluid intake as they tend to be "significantly more calm,"
both before and after meals, than either nonpregnant or nonlactating women, based on subjective
self-ratings (Heck and de Castro. 1993).
4.4. CHANGES IN DIETARY BEHAVIORS
Dietary behaviors may change during pregnancy and lactation as a result of the
nutritional needs, energy requirements, or cravings or aversions of the mother. These dietary
changes may influence environmental exposures for pregnant and lactating women.
4.4.1. Nutritional Needs
Pregnant and lactating women require a wide range of nutrients to support the health of
both the mother and the infant. The U.S. Department of Agriculture (USDA) and
U.S. Department of Health and Human Services (USDHHS) published 2010 Dietary Guidelines
for Americans to assist consumers in selecting the types and amounts of foods that are
appropriate for their age, gender, and activity levels (USDA. 2010). Pregnant or lactating
women have special nutritional needs. An online daily food plan tool was created by USDA to
provide guidance to pregnant and lactating women on specific nutritional needs based on their
age, height, weight, physical activity level, and stage of pregnancy or breastfeeding status
(www.choosemvplate.gov/supertracker-tools/daily-food-plans/moms.html). Data available on
intake rates of various food items by pregnant/lactating women are provided in Section 5.
Generally, nutritional research efforts focus on understanding nutrient intake and the
resulting impacts on women during periods of pregnancy and lactation. A popular nutrient of
interest is calcium. Calcium is used by various systems throughout the body. When a woman
does not get enough calcium from her diet the body mobilizes it from her bones (see
Section 3.4). Over time, this loss may weaken bone and lead to osteoporosis. Because fetal
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growth requires extra calcium to build healthy teeth and bones, pregnancy also places added
calcium demands on women that can increase the leaching of calcium and environmental
pollutants (e.g., lead, cadmium) from bones (WHO. 2013; Alba et al.. 2012). Average calcium
intake during pregnancy has been measured at 1,526 mg/day, 1,622 mg/day during lactation, and
1,756 mg/day in nonpregnant women (Drinkwater and Chesnut 1991). Nonpregnant women in
the study were counseled by a registered dietician to maintain a calcium intake of 1,500 mg/day,
either by increasing intake of dairy products or by adding supplements. Pregnant women in the
study initially followed the same protocol as nonpregnant women, but then followed their
physician's advice regarding calcium intake. Increased dietary calcium intakes have been shown
to improve calcium balance and may minimize bone loss across pregnancy and lactation in
women with habitual intakes of <500 mg calcium/day (O'Brien et al.. 2006).
An adequate diet rich in calcium can also provide protection for women in cases of
exposure to lead (Hernandez-Avila et al.. 1996). A study of bone lead levels in recently
postpartum Mexico City women found that consumption of foods with high calcium content may
protect against the accumulation of lead in bone (Hernandez-Avila et al.. 1996). Low
consumption of milk and cheese, as compared to the highest consumption category (every day),
was associated with an increase in tibia bone lead of 9.7 jag of lead/g of bone mineral
(Hernandez-Avila et al.. 1996). Because there is some evidence that mobilization of lead from
bone may be markedly enhanced during the increased bone turnover of pregnancy and
lactation—potentially resulting in lead exposure to the fetus and the breastfed infant—the
potential for delayed toxicity from bone lead stores remains a significant public health concern.
Vitamin D promotes calcium absorption and is needed for bone growth and remodeling
(NIH. 2014). During pregnancy, adequate Vitamin D levels are needed to meet the demands of
the growing fetus (Specker. 2004). Sufficient levels of Vitamin D in lactating women are needed
to prevent rickets in breastfeeding children (CDC. 2015). The Institute of Medicine's (IOM)
recommended daily allowance (RDA) of Vitamin D for all women, including those who are
pregnant and lactating, is 600 International Units (IU) (15 (j,g/day). For pregnant and lactating
women ages 19-50 years who are at risk of Vitamin D deficiency, the RDA is 1,500-2000 IU
(37.5-50 (j,g/day) (Holick et al.. 2011). Using data from the 2001-2006 National Health and
Nutrition Examination Survey (NHANES), Looker et al. (2011) evaluated the Vitamin D status
of the U.S. population. Approximately 24% were "at risk of inadequacy," 8% were "at risk of
deficiency," and 1% had levels that could possibly be harmful. However, Looker et al. (2011)
also found that pregnant and lactating women were less likely to be Vitamin D deficient than
nonpregnant women. Selenium is another important dietary mineral during pregnancy.
According to ATSDR (2003). "selenium is a biologically active part of a number of important
proteins, particularly enzymes involved in antioxidant defense mechanisms (e.g., glutathione
4-10
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peroxidases), thyroid hormone metabolism (e.g., deiodinase enzymes), and redox control of
intracellular reactions (e.g., thioredoxin reductase)." Deficiency in selenium has been associated
with adverse outcomes of pregnancy (Kantola et al.. 2004). In a study of pregnant Finnish and
Estonian women, selenium concentrations were 10-30% lower at term relative to preconception,
regardless of the significant differences in selenium status among the different mothers (Kantola
et al.. 2004). Based on this decline and observations of higher cord-blood selenium
concentrations than maternal whole blood levels, active transportation of selenium to the fetus is
inferred. Evidence suggests that selenium has an active role in the mother's defense systems
against the toxicity of environmental pollutants and chemical stress, including the constituents of
cigarette smoke (Kantola et al.. 2004).
Iron demand increases during pregnancy as a result of the expanded blood volume and
growth of the fetus, placenta, and other maternal tissues (Mei et al.. 2011). The recommended
daily allowance for iron is 27 mg/day for pregnant women and 10 mg/day for lactating women
(NIH. 2015). Mei et al. (2011) analyzed data from the 2001-2006 NHANES to assess the iron
status of pregnant women in the United States, and found that 18% of pregnant women were iron
deficient, with iron deficiency increasing over the course of pregnancy from approximately 7%
in the first trimester to 30% in the third trimester. Dietary intake of folic acid is also
recommended for women of childbearing age and pregnant women in order to reduce the infant's
risk of spina bifida or other neural tube defects (CDC. 1992). Branum et al. (2013) analyzed folic
acid and iron supplement intake data from 1,296 pregnant women who participated in the
NHANES, 1999-2006. Results indicated that approximately 55-60%) of women in their first
trimester took a folic acid- or iron-containing supplements compared with 76-78%) in their
second trimester and 89%> in their third trimester. Among all pregnant women in the survey that
were taking folic acid supplements (N= 761), the mean supplemental folic acid intake was
817 ± 27.6 (J,g/day (Branum et al.. 2013). Among those taking iron supplements, supplemental
iron intake was 47.7 ± 4.2 mg/day (Branum et al.. 2013). In an earlier study, using data from the
1988-1994 NHANES, Cogswell et al. (2003) found that 72%> of pregnant women and 60%> of
lactating women consumed supplements containing iron, compared to 23%> of nonpregnant,
nonlactating women aged 19-50 years.
4.4.2. Energy Requirements
There is a sizable amount of research on the topic of energy requirements during
pregnancy and lactation. During pregnancy, more calories, protein, and other nutrients are
required for the growth of the fetus, placenta, and uterus (Landau. 1983). According to Fowles
(2006). the USD A recommends no increase of caloric intake during the first trimester, an
increase of 340 kcal/day during the second trimester, and 450 kcal/day during the third trimester.
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In 2000, the USD A recommended the number of servings for each of the food groups in the food
pyramid for three caloric levels (i.e., 1,600 kcal; 2,200 kcal; 2,800 kcal) for pregnant women
(USDA. 2000b) (see Table 4-2). More recently, the USDA replaced the food pyramid with an
interactive tool that allows the user to determine the adequate number of servings of each food
group that the pregnant mother should eat based on a personal profile, which includes
information on age, weight, height, and level of physical activity
(http://www.choosemvplate.gov/pregnancv-breastfeeding/pregnancv-nutritional-needs.htmn.
Table 4-2. Recommended number of servings for three caloric intake levels
for pregnant women
Calorie level (kcal)
Food group
1,600
2,200
2,800
Bread (grain group)
6
9
11
Vegetable group
3
4
5
Fruit group
2
3
4
Milk group
3
3
3
Meat group (ounces)
5
6
7
Source: USDA (2000b).
A U.S. study by Rifas-Shiman et al. (2006) assessed changes in food and nutrient intake
from the first to second trimester of pregnancy. Whereas diet in the first trimester may be more
important to fetal development and differentiation of various organs, maternal diet later in
pregnancy may be important for overall fetal growth as well as for brain development. The
study authors examined individual-level changes in food and nutrient intake from the first to
second trimester of pregnancy. The mean energy intake reported for the first trimester,
2,046 kcal, was similar to the mean intake reported during the second trimester, 2,137 kcal, but
the food and nutrient intakes changed. The foods and energy-adjusted nutrients from foods for
which overall mean intakes increased more than 5% from the first to second trimester were skim
or 1% dairy foods (22%), whole-fat dairy foods (15%), red and processed meat (11%), saturated
fat (6%>) and vitamin D (7%). On the other hand, intake of caffeinated beverages decreased by
about 30%) and alcoholic beverages decreased about 88%>. Mean multivitamin intake increased
by 35%o from the first to second trimester, thereby increasing the total micronutrient intake
(Rifas-Shiman et al.. 2006).
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Dietary evaluations of calories and energy intake have also been performed on women
during pregnancy and lactation. In a study of U.S. pregnant women, the average daily energy
intake was reported as 1,955 kcal or 28.5 kcal/kg for the latter half of gestation (Blackburn and
Calloway. 1976). Pregnant women had a mean protein intake of 1.17 g/kg-day, which
represented 17% of gross energy consumed. Energy intake in lactating women was 30 kcal/kg,
which represented 74% of need when adjusted for milk production. Average energy intake of
nonlactating women was 19 kcal/kg, with protein intake representing 19% of energy consumed
for both groups of lactating and nonlactating women (Blackburn and Calloway, 1976).
A comparison of dietary intake in U.S. women during lactation at 6 weeks postpartum to
intake in two groups of nonpregnant, nonlactating women was performed to determine the
regulation of food and fluid intake in lactating women (Heck and de Castro. 1993). Lactating
women did not differ from body weight-matched, nonlactating controls in their total daily intakes
or their meal patterns, but they did consume a significantly smaller percentage of the
recommended dietary allowances per day than did their nonlactating counterparts. The lack of
compensation in intake for lactating women to meet the caloric demands of lactation either
indicates that the lactating women catabolize weight gained during pregnancy faster than
accounted for in the recommended dietary allowance, or that lactating women increase their
metabolic efficiency (Heck and de Castro. 1993).
4.4.3. Cravings and Aversions
Food cravings and aversions that occur during periods of pregnancy and lactation can
greatly influence the types and amounts of food consumed. One functional hypothesis, known as
the maternal-embryo protection hypothesis, proposes that pregnant women may avoid certain
foods that can contain toxins or pathogens in order to protect themselves or the developing fetus
(Steinmetz et al.. 2012). Alternatively, food cravings during pregnancy have been suggested to
promote maternal intake of beneficial foods containing needed nutrients, or that the foods that
are craved relieve the nausea and vomiting associated with morning sickness (Weigel et al.,
2011).
A study of taste changes across pregnancy evaluated reactions to salty, sweet (sucrose),
sour (citric acid), and bitter (quinine hydrochloride) stimuli in both pregnant women and controls
(Duffy et al„ 1998). Stimuli were evaluated on intensity (scale ranging from "nothing" to
"extremely") and hedonistic value (pleasant or unpleasant). The study authors indicate the taste
intensity and hedonistic changes across pregnancy could serve to support healthy pregnancy
outcomes: increases in bitter intensity in first trimester to protect against ingesting poisons;
changes in salty, sour, and bitter preference later in pregnancy to support ingesting a varied diet
(Duffy et al„ 1998).
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A laboratory study on the taste and specific food consumption changes across the course
of pregnancy found that women in the second trimester had an increased preference for sweet
food, but not salty or nonsweet/nonsalty food, as compared with women during other points in
pregnancy (Bowen. 1992). However, the pregnant women who participated in the study did not
exhibit excessive weight gain. One reason for this may have been that these women were
generally classified as "restrained eaters" and so they may have refrained from daily
consumption of excess sweet foods (Bowen. 1992 ).
The patterns in food choices from prepregnancy through midpregnancy and 2 years
postpartum were investigated along with other factors, including breastfeeding behavior (Olson,
2005). The food choice behaviors evaluated were consuming >2 cups of milk per day,
consuming >3 fruits and vegetables per day, and eating a daily breakfast. The results showed a
significant increase in the proportion of women engaging in these three behaviors during
pregnancy compared to prepregnancy. Approximately 66% of the women breastfed for any
length of time and over 33% of the women breastfed for 1 year or more.
Pregnancy may result in dietary changes among women who diet and/or experience
eating disorders. The impact of pregnancy on eating disorders, dietary habits, and body image
perception was studied in a population of pregnant women with positive and negative histories of
dieting and/or an eating disorder diagnosis (Rocco et al.. 2005). Pregnancy had a protective
effect in the groups of women as their concerns with shape, body attitude, thinness, and daily
worries were reduced in comparison with the obligations to the child (Rocco et al., 2005; Baker
et al., 1999). Another study observed that symptoms of eating disorders (anorexia nervosa,
bulimia nervosa, or binge eating) that lead to weight loss diminish during pregnancy, but return
to baseline levels postpartum (Crow et al., 2008). Factors significantly associated with binge
eating during pregnancy include sexual and physical abuse, anxiety and depression, low
self-esteem and low life satisfaction, smoking, alcohol use, and lack of social support (Knoph
Berg et al., 2011).
Morning sickness during pregnancy can also impact food cravings and aversions. In a
study of the association between morning sickness symptoms and dietary preferences, cravings,
and aversions in pregnant women, Crystal et al. (1999) found that women reported more
aversions during pregnancy than before pregnancy. Also, women with more severe pregnancy
symptoms reported a greater number of aversions both before and during pregnancy than women
with less severe morning sickness.
Some pregnant women may also crave and ingest nonfood substances. This is known as
pica behavior. The term "pica" generally refers to behavior associated with the intentional
ingestion of foreign (i.e., nonfood or nonnutritive) substances (Bronstein and Dollar, 1974). The
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types of materials ingested can include: dirt, clay, cigarette ashes, ice, freezer frost, flour, baking
soda or powder, cornstarch, powdered milk (Cooksev. 1995). or other materials.
Studies have indicated that pica behavior may be more prevalent among pregnant women
than among nonpregnant women, and some researchers have theorized that this behavior may
result from the desire to satisfy cravings or hunger due to poor nutrition, the need to supplement
minerals (e.g., calcium or iron) in the diet, cultural practices, or other physiological needs or
behaviors (Bronstein and Dollar. 1974). Others have suggested that geophagy (a specific type of
pica in which soil or clay are ingested) (ATSDR, 2001) among pregnant women is best explained
as protection against symptoms of gastrointestinal distress and the effects of harmful chemicals,
parasites, and pathogens (Young et al.. 2011; Young. 2010). The behavior has been more
commonly found among socioeconomically disadvantaged women in rural and immigrant
communities, and in women of African heritage (Kim and Nelson. 2012). Pregnant or lactating
women who engage in pica behavior may be exposing themselves to environmental
contaminants present in soil or other nonfood substances that they ingest. Information about
prevalence of this behavior and the amounts of substances ingested is discussed in Section 5.
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5. EXPOSURE FACTORS FOR PREGNANT/LACTATING WOMEN
Exposure factors are factors related to human behavior and characteristics that help
determine an individual's exposure to an agent (U.S. EPA, 2011). For example, these include
water and food intake, inhalation rates, nondietary (e.g., soil) ingestion rates, time spent at
various microenvironments and activities, body weight, and use of consumer products. Due to
the physiological and behavioral changes that occur during pregnancy and lactation, exposure
factors for pregnant and lactating women may be different than those of the general population
of women and they may, in turn, impact the fetus or newborn. The following sections
summarize the available exposure factor data for women during this lifestage. Data for pregnant
women are presented by trimester where available.
5.1. WATER INTAKE
Pregnant and lactating women tend to increase their consumption of water to support the
physiological requirements of the growing fetus and to produce milk. Ershow et al. (1991) used
data from a 1977-1978 national dietary survey to evaluate drinking water intake rates for
pregnant and lactating women. In general, lactating women ingested more water than pregnant
women and pregnant women ingested more water than control women. The 3-day average total
fluid intake rates (mean ± SD) were 1,940 ± 686 mL/day for control women,
2,076 ± 743 mL/day for pregnant women, and 2,242 ± 658 mL/day for lactating women (see
Table 5-1). Tapwater intake rates were also calculated. Mean ± SD tapwater intake rates were
estimated to be 1,157 ± 635 mL/day for control women; 1,189 ± 699 mL/day for pregnant
women; and 1,310 ± 591 mL/day for lactating women (Ershow et al., 1991). Because these rates
are based on data that were collected more than three decades ago, they may not reflect current
tapwater intake rates. For example, because the consumption of bottled water has increased in
the United States since the 1977-1978 survey was conducted, the results are likely to overstate
current consumption patterns for tapwater (Burinaster. 1998).
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Table 5-1. Tapwater and total fluid intake among pregnant, lactating, and
control women, based on a 1977-1978 dietary survey (mL/day)
Pregnant
N= 188
Lactating
N=ll
Control
N= 6,201
Tapwater
Mean ± SD
1,189 ±699
1,310± 591
1,157 ±635
Median
1,063
1,330
1,065
95th percentile
2,424
2,191
2,310
Total Fluid
Mean ± SD
2,076 ± 743
2,242 ± 658
1,940 ±686
Median
2,164
1,928
1,835
95th percentile
3,475
3,353
3,186
N = Number of observations.
SD = Standard deviation.
Source: Ershow et al. (1991).
Zender et al. (2001) conducted a study in Colorado in 1996 and 1997 to compare
tapwater intake among pregnant and nonpregnant women. A total of 71 pregnant and
43 nonpregnant women were recruited from Women, Infant, and Children (WIC) clinics. Nearly
one-half of the pregnant women were in their second trimester, and one-quarter were in each of
the first and third trimesters. Total tapwater intake included tapwater consumed directly as a
beverage and tap water-based cold and hot beverages. Information on the sources of the water
consumed (e.g., tapwater, bottled water, or filtered water) were also collected. Total tapwater
intake was slightly higher for pregnant women (3.4 L/day) than nonpregnant women (3.0 L/day)
(see Table 5-2). The proportions of each principal source of drinking water to total water intake
for pregnant and for nonpregnant women (see Table 5-3) were similar for bottled water (14%
and 12%), filtered water (11% and 16%) and tapwater (75% and 72%). Seventeen percent of
pregnant women reported altering their source of drinking water after they became pregnant.
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Table 5-2. Water ingestion rates by pregnancy status (L/day) for a population of women in Colorado
Percentiles
Percentiles
Characteristic
Mean
SD
25th
50th
75th
Mean
SD
25th
50th
75th
Pregnant (N
= 71)
Nonpregnant (N= 43)
Cold tap water
1.8
1.4
0.9
1.4
2.3
1.3
1.0
0.5
0.9
2.0
E
Cold tapwater-based beverages
1.0
0.8
0.7
0.9
1.4
0.9
0.6
0.4
0.7
1.2
©
X
Hot tapwater-based beverages
0.1
0.2
0.0
0.0
0.2
0.2
0.5
0.0
0.0
0.2
Total tapwater intake
2.9
1.8
1.8
2.3
3.7
2.4
1.2
1.5
2.3
2.9
Pregnant (N
= 36)
Nonpregnant (N= 23)
Cold tapwater
0.7
0.6
0.2
0.4
1.3
1.0
1.2
0.4
0.7
1.4
•-
Cold tapwater-based beverages
0.1
0.3
0.0
0.0
0.0
0.1
0.3
0.0
0.0
0.0
£
Hot tapwater-based beverages
0.1
0.2
0.0
0.0
0.0
0.1
0.1
0.0
0.0
0.0
Total tapwater intake
0.9
0.7
0.4
0.7
1.4
1.2
1.2
0.5
0.9
1.4
Pregnant (N
= 71)
Nonpregnant (N= 43)
Cold tapwater
2.1
1.5
1.1
1.8
2.8
1.8
1.6
0.7
1.5
2.7
-J
<
Cold tapwater-based beverages
1.1
0.8
0.7
0.9
1.4
0.9
0.6
0.4
0.9
1.4
o
H
Hot tapwater-based beverages
0.2
0.3
0.0
0.0
0.2
0.3
0.5
0.0
0.0
0.4
Total tapwater intake
3.4
1.8
2.0
3.0
4.3
3.0
1.7
1.8
2.7
4.1
SD = Standard deviation.
N = Number of observations.
Source: Zender et al. (2001).
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Table 5-3. Principal sources of drinking water at home for a population of
women in Colorado (%)
Pregnant
Nonpregnant
Source of water
N=l\
iV= 43
Tapwater
74.6
72.1
Bottled water
14.1
11.6
Filtered water
11.3
16.3
N = Number of observations.
Source: Zender et al. (2001).
Kahn and Stralka (2008) used data from USDA's 1994-1996 and 1998 Continuing
Survey of Food Intakes by Individuals to estimate drinking water intake. The data were
collected from a total of 70 pregnant women, 41 lactating women, and 2,221 nonpregnant and
nonlactating women aged 15-44 years (TJSDA. 1998). Consumer-only and per capita water
ingestion rates were estimated for both community water only and for all sources of water. The
percentage of consumers was approximately 93% and 83% for pregnant and lactating women,
respectively. Community water was defined as tapwater from a community or municipal water
supply, and all sources as tapwater from the community water supply plus bottled water, water
obtained from wells, springs, and cisterns, and other sources that could not be identified.
Estimates of drinking water intake included direct water ingestion (i.e., as a beverage) and
indirect water ingestion (i.e., water added to foods and beverages during final preparation), but
commercial water added by a manufacturer (i.e., water contained in soda or beer) and intrinsic
water in foods and liquids (i.e., milk and natural undiluted juice) were not included.
Kahn and Stralka (2008) estimated mean and upper percentile intake rates (mL/day and
indexed by body weight in mL/kg-day) for the four groups of women: (1) pregnant, (2) lactating,
(3) nonpregnant and nonlactating, aged 15-44 years, and (4) all women, aged 15-44 years (see
Table 5-4). The mean total water intake was lowest among nonpregnant and nonlactating
women and highest among lactating women. For community water source only, the mean was
lowest among pregnant women and highest among lactating women. Per capita mean and
95th percentile values for drinking water ingestion among pregnant women were 819 mL/day and
2,503 mL/day, respectively. Per capita mean and 95th percentile values for lactating women
were 1,379 mL/day and 3,434 mL/day, respectively.
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Table 5-4. Water ingestion rates of pregnant, lactating, and nonpregnant nonlactating U.S. women aged
15-44 years, community water3 and (total water from all sources), based on 1994-1996 and 1998 CSFII data
Group
N
Mean
95th percentile
mL/day mL/kg-day
mL/day mL/kg-day
Per capitab
Pregnant women
70 (70)
819°(1,318°)
13° (21°)
2,503° (2,674°)
43° (44°)
Lactating women
41 (41)
1,379°(1,806°)
21°(21°)
3,434° (3,767°)
55° (55°)
Nonpregnant and nonlactating women, aged
15 to 44 years
2,221 (2,221)
916 (1,243)
14 (19)
2,575 (2,937)
38 (46)
All women, aged 15 to 44 years
2,332 (2,332)
922 (1,256)
14 (19)
2,605 (2,949)
39 (46)
Consumer-only11
Pregnant women
65 (70)
872°(1,318°)
14° (21°)
2,589° (2,674°)
43° (44°)
Lactating women
34 (41)
1,665° (1,806°)
26° (28°)
3,588° (3,767°)
55° (57°)
Nonpregnant and nonlactating women, aged
15 to 44 years
2,077 (2,203)
976 (1,252)
15(19)
2,614(2,941)
38 (46)
All women, aged 15 to 44 years
2,176 (2,314)
985 (1,265)
15 (20)
2,732 (2,953)
39 (46)
"¦Ingestion rates for combined direct and indirect water from community water supply.
bPer capita intake rates are generated by averaging consumer-only intakes over the entire population (including those individuals that reported no intake).
"Estimates are less statistically reliable based on guidance published in the Joint Policy on Variance Estimation and Statistical Reporting Standards on
NHANES III and CSFII Reports: NHIS/NCHS Analytical Working Group Recommendations (NCHS. 1993).
dConsumer-only intake represents the quantity of water consumed only by individuals that reported consuming water during the survey period.
N= Sample size.
Source: Kalin and Stralka (2008).
-------
In a study of 1,990 pregnant women from three southern cities in the United States, mean
cold tapwater intake increased from prepregnancy (1.5 L/day) though early pregnancy
(1.7 L/day) to mid-pregnancy (1.8 L/day). Mean hot tapwater intake decreased slightly from
prepregnancy (0.18 L/day) to early and mid-pregnancy (0.16 L/day). Bottled water consumption
was essentially the same during early and mid-pregnancy (0.57 and 0.59 L/day, respectively).
The greatest changes in water consumption were reported for cold tapwater for which 80% of the
women reported either increases or decreases in consumption. Thirty-three percent reported
changes (increases or decreases) equal to or greater than 1.0 L/day (Forssen et al., 2009).
A few studies were identified that investigated differences in water intake by pregnant
women in relation to age, employment, income, and ethnicity. Smith et al. (2009) estimated the
amount of water ingested by 39 pregnant women in northern England. There were no
differences in water intake with regard to age, employment status, or income level. However,
the results suggested that pregnant women of South Asian origin (N= 16; including Pakistani
and Indian women) may consume more tapwater than other ethnic groups. Mean tapwater
consumption for pregnant women in the study (N= 39) was 1.8 L/day, and represented 84% of
all fluid intake.
In a study of 34 pregnant women in North Carolina, daily intake of cold tapwater at home
was 1.7 times higher for women employed part-time or less than for those employed full-time
(Shimokura et al.. 1998). Considerably higher levels of cold tapwater consumption were
reported at home versus work in other studies (Forssen et al., 2007; Zender et al., 2001). In a
study of 2,297 pregnant women in three geographical locations of the southern United States,
Forssen et al„ (2007) reported similar daily intake levels of cold tapwater (1.7 L/day) and bottled
water (0.5-0.6 L/day). Among this population, non-Hispanic white women drank 0.4 L/day
more cold tapwater than Hispanic women and 0.3 L/day more than non-Hispanic black women.
Increases in cold tapwater intake during pregnancy were also associated with non-Hispanic
women older than 35 years of age and income level less than $40,000 per year (Forssen et al.,
2009).
The treatment (i.e., filtered or unfiltered) and sources of the water (i.e., bottled water)
consumed by pregnant women have also been studied. Daily intake of cold filtered tap water by
pregnant women increased for those older in age, those who had higher income and education,
and those who were unemployed (Forssen et al„ 2007). A higher proportion of the water
consumed by Hispanic women was bottled. Black and non-Hispanic women drank more of the
water as unfiltered tapwater. Mean bottled water consumption among pregnant women has been
reported as 0.6 L/day (Forssen et al„ 2007) and 0.94 L/day (Kaur et al., 2004).
5-6
-------
5.2. DIETARY INTAKE
EPA's Office of Pesticide Programs estimated food intake rates for pregnant and
nonpregnant women of child-bearing age (13 to 49 years) using data from the NHANES for the
years 2003 to 2008 (Sarkar and Nguyen, 2013) (see Appendix). It should be noted that no
editing was performed to the appendix. A select number of tables and figures from the appendix
were extracted and edited for format and in response to peer review comments before inclusion
in the main report.
NHANES collects data on dietary recall of foods eaten over the previous 24-hour period
on two nonconsecutive days. Two-day data were available for 612 pregnant women and
4,321 women of child-bearing age that were not pregnant during the 2003 to 2008 survey years.
EPA's Food Commodity Intake Database was used to convert the NHANES "as eaten" food
consumption data into consumption of individual food commodities, and the data were weighted
according to sampling weights provided for the years 2003 to 2008 (Sarkar and Nguyen. 2013)
(see Appendix). Food commodities and food groupings were selected consistent with previous
consumption analyses presented in EPA's Exposure Factors Handbook (U.S. EPA, 2011).
Two-day average intake rates were calculated for each survey respondent for the major food
groups (total fruits, total vegetables, total meats, total dairy, total fish, and total grains) and for a
variety of individual food items/groups, and summary statistics were calculated for the pregnant
and nonpregnant women on both a consumer-only and on a per capita basis. Consumer-only
intake is defined as the quantity of foods consumed by the women during the survey period. Per
capita intake represents an average across the entire population of women surveyed, regardless of
whether those individuals reported consumption or not.
Table 5-5 provides summary statistics for per capita intake and Table 5-6 provides data
for consumers only. Mean, standard error, 95th percentile per capita, and consumer-only intake
rates for a variety of individual foods and food groups are provided in Table 5-7. Figure 5-1
depicts the ratios of intake for pregnant women to those of nonpregnant women, based on mean
consumer-only intake rates for these two groups of women in rank order from low to high. For
graphical convenience, these have been split arbitrarily into two groups: those with lower
pregnant to nonpregnant consumption ratios (List 1) and those with higher ratios (List 2). As
shown in Figure 5-1, ratio >1 indicates higher consumption for pregnant females. Statistical
comparisons of mean consumption for pregnant and nonpregnant women were also evaluated
using an alpha level of 0.05. A two sample (unpaired) t-test adapted for complex survey
procedure accounting for sampling weight, stratification, and multistage sampling was used. As
noted in Tables 5-5, 5-6, and 5-7, and Figure 5-1, intake rates for pregnant women were found to
be significantly different from those of nonpregnant women for some of the major food groups
5-7
-------
(i.e., total fruits, total vegetables, total dairy, and total grain) and several individual food
categories.1
'Note that multiple tests have been performed in EPA's analysis of these data, and any putative statistical
differences have not been corrected to account for these. Many of these differences may not be statistically
significant if such adjustments had been made. In addition, differences in mean consumption between pregnant and
not pregnant females of child-bearing age do not necessarily imply differences in overall exposure estimates or
potential risk. Statistical tests for the difference in consumption between pregnant and nonpregnant females were
performed for the mean, and not for the percentiles. In many cases at the upper (and lower) percentiles of the
consumers-only distribution (and particularly for less commonly consumed food commodities or commodity
groupings), there are not adequate numbers of individuals to produce reliable estimates of consumption.
5-8
-------
Table 5-5. Per capita intake of major food groups: U.S. pregnant and nonpregnant women of child-bearing age
(13 to 49 years), based on NHANES 2003-2008 (g/kg-day)
Food group
N
%
Cons.
Mean
SE
Min
Percentiles
Max
jst 5th 10th 25th 50th 75th 90th 95th 99th
Total fruits
Pregnant
612
92.5
1.66a
0.13
b
b
—
—
0.07
0.97
2.65
4.34
5.02
8.1 lb
11.01b
Nonpregnant
4,321
83.6
0.98
0.04
b
—
—
0.00
0.39
1.41
2.83
3.75
6.19
16.67b
Total vegetables
Pregnant
612
100
2.74a
0.14
0.01b
0.23b
0.42
0.95
1.47
2.31
3.52
5.00
6.26
9.57b
18.30b
Nonpregnant
4,321
99.8
2.43
0.06
b
0.05
0.36
0.62
1.16
2.03
3.25
4.69
5.93
8.79
17.06b
Total meats
Pregnant
612
99.3
1.59
0.05
b
0.00b
0.29
0.5
0.89
1.47
2.15
2.94
3.34
4.54b
5.9 lb
Nonpregnant
4,321
98.0
1.53
0.03
b
—
0.14
0.36
0.79
1.30
2.01
2.98
3.54
4.97
12.23b
Total dairy
Pregnant
612
100
5.04a
0.28
0.00b
0.09b
0.43
0.82
2.23
3.88
6.91
10.05
12.52
22.54b
52.68b
Nonpregnant
4,321
99.6
3.53
0.13
b
0.02
0.21
0.43
1.02
2.41
4.90
7.94
10.48
17.16
52.07b
Total fish
Pregnant
612
26.5
0.19
0.03
b
b
0.00
0.55
1.28
2.40b
5.32b
Nonpregnant
4,321
28.7
0.19
0.01
b
0.03
0.68
1.10
2.24
8.64b
Total grains
Pregnant
612
100
2.12a
0.07
0.21b
0.57b
0.79
1.03
1.38
1.94
2.65
3.44
3.94
4.88b
7.76b
Nonpregnant
4,321
99.8
1.90
0.04
b
0.20
0.53
0.75
1.12
1.68
2.44
3.36
3.94
5.66
9.79b
-------
Table 5-5. Per capita intake of major food groups: U.S. pregnant and nonpregnant women of child-bearing age
(13 to 49 years), based on NHANES 2003-2008 (g/kg-day) (continued)
aMean of pregnant female is statistically significantly different from the nonpregnant female; alpha = 0.05 level. Significant differences were NOT evaluated
for percentiles values.
bEstimates are less statistically reliable based on np <8 "Design Effect" guidance published in the Joint Policy on Variance Estimation and Statistical Reporting
Standards on NHANES III and CSFII (NCHS. 1993): where n refers to the sample size and p is the percentile expressed as a fraction.
— = Either no reported per capita consumption at this percentile, or per capita consumption is <0.0001 g/kg body weight.
N = Sample size.
% Cons. = Number of individuals who consumed the food item during the survey period divided by the total number of individuals surveyed x 100.
SE = Standard error.
Source: Sarkar and Nguyen (2013) (see Appendix).
-------
Table 5-6. Consumer-only intake of major food groups: U.S. pregnant and nonpregnant women of
child-bearing age (13 to 49 years), based on NHANES 2003-2008 (g/kg-day)
Food group
N
Mean
SE
Min
Percentiles
Max
1st 5th 10th 25th 50th 75th
90th 95th 99th
Total fruits
Pregnant
558
1.79a
0.14
0.00b
0.00b
0.00b
0.01
0.15
1.10
2.82
4.64
5.37b
8.1 lb
11.01b
Nonpregnant
3,640
1.18
0.05
0.00b
0.00
0.00
0.00
0.09
0.68
1.67
3.16
4.06
6.45
16.67b
Total vegetables
Pregnant
612
2.74a
0.14
0.01b
0.23b
0.42
0.95
1.47
2.31
3.52
5.00
6.26
9.57b
18.30b
Nonpregnant
4,318
2.43
0.06
0.00b
0.05
0.37
0.63
1.16
2.03
3.25
4.69
5.93
8.79
17.06b
Total meats
Pregnant
607
1.60
0.05
0.00b
0.03b
0.32
0.51
0.93
1.48
2.15
2.94
3.34
4.54b
5.91b
Nonpregnant
4,259
1.56
0.03
0.00b
0.01
0.25
0.43
0.82
1.32
2.03
3.01
3.56
4.97
12.23b
Total dairy
Pregnant
612
5.04a
0.28
0.00b
0.09b
0.43
0.82
2.23
3.88
6.91
10.05
12.52
22.52b
52.68b
Nonpregnant
4,310
3.54
0.12
0.00b
0.03
0.22
0.44
1.03
2.41
4.91
7.97
10.51
17.16
52.07b
Total fish
Pregnant
153
0.71
0.12
0.00b
0.00b
0.00b
0.02b
0.18
0.41
1.00
1.59b
2.40b
3.79b
5.32b
Nonpregnant
1,204
0.65
0.04
0.00b
0.00b
0.00
0.00
0.18
0.43
0.86
1.44
1.93
3.45b
8.64b
Total grains
Pregnant
612
2.12a
0.07
0.21b
0.57b
0.79
1.03
1.38
1.94
2.65
3.44
3.94
4.88b
7.76b
Nonpregnant
4,318
1.90
0.04
0.00b
0.20
0.53
0.75
1.12
1.68
2.44
3.36
3.94
5.66
9.79b
aMean of pregnant female is statistically significantly different from the nonpregnant female; alpha = 0.05 level. Significant differences were NOT evaluated for
percentiles values.
bEstimates are less statistically reliable based on np <8 "Design Effect" guidance published in the Joint Policy on Variance Estimation and Statistical Reporting
Standards on NHANES III and CSFII (NCHS. 1993): where n refers to sample size and p is the percentile expressed as a fraction.
N = Sample size.
SE = Standard error.
Source: Sarkar and Neuven (2013) (see Appendix).
-------
Table 5-7. Per capita and consumer-only intake of individual foods: U.S.
pregnant and nonpregnant women of child-bearing age (13 to 49 years),
based on NHANES 2003-2008 (g/kg-day)
Food
Per capita
Consumer-only
%
Cons.
N
Mean
SE
95th
N
Mean
SE
95th
Fruits and vegetables
Apples
Pregnant
38.3
612
0.33
0.05
2.02
245
0.86
0.12
2.75b
Nonpregnant
28.1
4,321
0.24
0.02
1.61
1,181
0.87
0.05
2.70
Bananas
Pregnant
63.4
612
0.34a
0.04
1.78
383
0.53a
0.06
1.97b
Nonpregnant
49.4
4,321
0.20
0.01
1.27
2,259
0.41
0.02
1.75
Beans
Pregnant
46.6
612
0.20
0.02
0.80
319
0.44
0.04
1.16b
Nonpregnant
45.7
4,321
0.18
0.01
0.85
1,964
0.39
0.02
1.21
Berries and small fruits
Pregnant
75.5
612
0.25
0.03
1.25
429
0.33
0.04
1.29b
Nonpregnant
65.7
4,321
0.22
0.01
1.13
2,821
0.33
0.02
1.37
Broccoli
Pregnant
14.6
612
0.08
0.02
0.74
95
0.58
0.08
1.45b
Nonpregnant
17.1
4,321
0.09
0.01
0.67
610
0.54
0.04
1.55
Bulb vegetables
Pregnant
97.5
612
0.19
0.02
0.62
596
0.20
0.02
0.62
Nonpregnant
97.3
4,321
0.16
0.01
0.53
4,206
0.17
0.01
0.54
Cabbage
Pregnant
8.0
612
0.02a
0.00
0.06
79
0.24a
0.04
0.80b
Nonpregnant
12.0
4,321
0.05
0.01
0.26
497
0.39
0.04
1.29
Carrot
Pregnant
46.1
612
0.10
0.01
0.46
292
0.21
0.02
0.65b
Nonpregnant
45.7
4,321
0.11
0.01
0.59
1,850
0.25
0.01
0.91
5-12
-------
Table 5-7. Per capita and consumer-only intake of individual foods: U.S.
pregnant and nonpregnant women of child-bearing age (13 to 49 years),
based on NHANES 2003-2008 (g/kg-day) (continued)
Food
Per capita
Consumer-only
%
Cons.
N
Mean
SE
95th
N
Mean
SE
95th
Fruits and vegetables
Citrus
Pregnant
23.7
612
0.29a
0.06
1.80
156
1.2 la
0.20
3.23b
Nonpregnant
20.5
4,321
0.10
0.01
0.80
877
0.50
0.05
2.05
Corn
Pregnant
99.6
612
0.42a
0.03
1.42
604
0.42a
0.03
1.42
Nonpregnant
95.8
4,321
0.31
0.01
1.13
4,157
0.32
0.01
1.15
Cucumbers
Pregnant
46.4
612
0.14
0.05
0.65
249
0.29
0.10
1.16b
Nonpregnant
43.0
4,321
0.09
0.01
0.50
1,679
0.22
0.01
0.83
Cucurbits
Pregnant
55.6
612
0.48a
0.09
2.93
309
0.87a
0.14
3.84b
Nonpregnant
50.6
4,321
0.27
0.04
1.43
1,990
0.54
0.07
2.27
Fruiting vegetables
Pregnant
99.1
612
0.77
0.04
2.08
601
0.78
0.04
2.08
Nonpregnant
96.0
4,321
0.72
0.02
2.33
4,134
0.75
0.02
2.42
Leafy vegetables
Pregnant
93.4
612
0.46a
0.03
1.58
576
0.49a
0.03
1.64
Nonpregnant
92.8
4,321
0.57
0.02
1.97
3,978
0.61
0.03
2.01
Legume vegetables
Pregnant
95.1
612
0.38
0.07
1.39
597
0.40
0.08
1.47
Nonpregnant
95.2
4,321
0.33
0.02
1.37
4,100
0.35
0.02
1.42
Lettuce
Pregnant
64.2
612
0.24
0.02
0.89
381
0.37a
0.03
1.14b
Nonpregnant
60.0
4,321
0.27
0.01
1.15
2,492
0.46
0.02
1.47
5-13
-------
Table 5-7. Per capita and consumer-only intake of individual foods: U.S.
pregnant and nonpregnant women of child-bearing age (13 to 49 years),
based on NHANES 2003-2008 (g/kg-day) (continued)
Food
Per capita
Consumer-only
%
Cons.
N
Mean
SE
95th
N
Mean
SE
95th
Fruits and vegetables
Onions
Pregnant
97.4
612
0.19
0.02
0.59
595
0.19
0.02
0.60
Nonpregnant
96.5
4,321
0.16
0.01
0.52
4,175
0.16
0.01
0.52
Peaches
Pregnant
54.6
612
0.09a
0.02
0.76
317
0.17
0.04
1.18b
Nonpregnant
44.4
4,321
0.04
0.01
0.14
2,034
0.10
0.01
0.59
Pears
Pregnant
8.9
612
0.05
0.01
0.22
56
0.52
0.11
1.72b
Nonpregnant
7.0
4,321
0.04
0.01
0.11
318
0.60
0.07
2.35b
Peas
Pregnant
13.4
612
0.04
0.01
0.37
101
0.29
0.03
0.80b
Nonpregnant
18.4
4,321
0.05
0.01
0.34
743
0.28
0.02
0.87
Pome fruit
Pregnant
40.9
612
0.37
0.06
2.02
265
0.91
0.11
2.75b
Nonpregnant
31.1
4,321
0.29
0.02
1.86
1,354
0.92
0.04
2.84
Root and tuber vegetables
Pregnant
100
612
1.03a
0.08
2.67
612
1.03a
0.80
2.67
Nonpregnant
99.8
4,321
0.84
0.02
2.36
4,315
0.84
0.02
2.36
Stalk and stem vegetables
Pregnant
23.5
612
0.04
0.01
0.23
131
0.15a
0.03
0.52b
Nonpregnant
21.3
4,321
0.05
0.00
0.26
764
0.21
0.02
0.74
Stone fruits
Pregnant
60.4
612
0.1 T
0.04
1.22
340
0.28
0.06
1.33b
Nonpregnant
47.2
4,321
0.08
0.01
0.53
2,137
0.17
0.02
0.97
5-14
-------
Table 5-7. Per capita and consumer-only intake of individual foods: U.S.
pregnant and nonpregnant women of child-bearing age (13 to 49 years),
based on NHANES 2003-2008 (g/kg-day) (continued)
Per capita
Consumer-only
Food
%
Cons.
N
Mean
SE
95th
N
Mean
SE
95th
Fruits and vegetables
Strawberries
Pregnant
49.2
612
0.09
0.02
0.63
261
0.18
0.04
0.8 lb
Nonpregnant
38.2
4,321
0.08
0.01
0.60
1,532
0.21
0.02
1.09
Tomatoes
Pregnant
96.0
612
0.70
0.04
1.88
578
0.73
0.04
1.93
Nonpregnant
87.5
4,321
0.63
0.02
2.02
3,830
0.72
0.02
2.17
Tropical fruits
Pregnant
72.0
612
0.52a
0.07
2.35
447
0.73a
0.08
3.26b
Nonpregnant
59.4
4,321
0.27
0.02
1.53
2,700
0.45
0.02
1.85
White potatoes
Pregnant
93.0
612
0.65a
0.07
2.04
563
0.70a
0.07
2.04b
Nonpregnant
90.2
4,321
0.48
0.02
1.92
3,868
0.53
0.02
1.98
Meat, fish, and grains
Beef
Pregnant
89.9
612
0.64
0.05
1.78
540
0.71
0.05
2.09b
Nonpregnant
85.7
4,321
0.58
0.02
1.98
3,744
0.67
0.02
2.13
Poultry
Pregnant
80.9
612
0.67
0.05
1.99
488
0.82
0.06
2.05b
Nonpregnant
76.8
4,321
0.67
0.02
2.17
3,414
0.87
0.03
2.40
Pork
Pregnant
85.5
612
0.29
0.02
1.01
529
0.33
0.03
1.12b
Nonpregnant
77.9
4,321
0.28
0.01
1.07
3,397
0.36
0.01
1.19
Finfish
Pregnant
20.9
612
0.12
0.02
0.94
108
0.59
0.10
1.80b
Nonpregnant
22.9
4,321
0.13
0.01
0.90
882
0.59
0.03
1.75
5-15
-------
Table 5-7. Per capita and consumer-only intake of individual foods: U.S.
pregnant and nonpregnant women of child-bearing age (13 to 49 years),
based on NHANES 2003-2008 (g/kg-day) (continued)
Food
Per capita
Consumer-only
%
Cons.
N
Mean
SE
95th
N
Mean
SE
95th
Meat, fish, and grains
Shellfish
Pregnant
12.9
612
0.07
0.02
0.48
75
0.51
0.09
1.79b
Nonpregnant
10.6
4,321
0.05
0.01
0.34
492
0.48
0.05
1.79
Rice
Pregnant
90.1
612
0.22
0.04
0.98
555
0.24
0.04
0.98b
Nonpregnant
86.7
4,321
0.20
0.01
0.86
3,690
0.23
0.01
0.95
Total cereal
Pregnant
100
612
3.02a
0.08
5.71
612
3.02a
0.08
5.71
Nonpregnant
100
4,321
2.80
0.04
5.77
4,320
2.80
0.04
5.77
aMean of pregnant female is statistically significantly different from the nonpregnant female; alpha = 0.05 level.
Significant differences were NOT evaluated for percentiles values.
bEstimates are less statistically reliable based on np <8 "Design Effect" guidance published in the Joint Policy on
Variance Estimation and Statistical Reporting Standards on NHANES III and CSFII (NCHS. 1993): where n refers
to sample size and p is the percentile expressed as a fraction.
N = Sample size.
% Cons. = Number of individuals who consumed the food item during the survey period divided by the total number
of individuals surveyed x 100.
SE = Standard error.
Source: Sarkar and Nguyen (2013) (see Appendix).
5-16
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Mean Ratio
Mean Ratio
Cabbage -
Stalk and Stem Vegetables -
Leafy Vegetables -
Lettuce -
Carrot -
Strawberry -
Pear
Pork
Poultry
Apple -
Pome Fruit -
Fin Fish -
Tomatoes -
Berries and Small Fruits -
Total Meats -
Rice
Fruiting Vegetables -
Beef
Peas -
Shell Fish -
Broccoli
List = 1
-d
o
u
Pregnant/Non-pregnant
List =2
Total Cereal -
A
Total Fish -
~
Total Grain -
A
Total Vegetables -
A
Beans -
~
Legume Vegetables -
~
Onion -
~
Bulb Vegetables -
~
Root and Tuber Vegetables -
A
Banana -
A
White Potatoes -
A
Corn -
A
Cucumbers -
~
Total Daily -
A
Total Fruits -
A
Stone Fnait -
~
Cucurbits -
A
Tropical Fruits -
A
Peaches -
~
Citrus -
A
i
0.5 1
i i i
0 1.5 2.0 2.5
Pregnant/Non-pregnant
| ~ Non-significantly Different A Significantly Different |
| ~ Non-significantly Different A Significantly Different |
Figure 5-1. Ratios of mean consumer-only intake for pregnant women to that of nonpregnant women in the United
States, based on NHANES 2003-2008. Note: List 1 contains the commodities that have lower mean ratio; List 2 contains
the commodities that have slightly higher mean ratio. Food commodities were presented in two graphs for clarity.
Source: Sarkar and Nguyen (2013) (see Appendix).
-------
As mentioned in Section 4, U.S. research related to dietary intake during pregnancy has
focused primarily on nutrient intake (e.g., calcium, folic acid), changes in eating patterns, or
associations between contaminant intake (e.g., mercury in fish) and pregnancy outcomes
(Bloomingdale et al.. 2010; Mirel et al.. 2009; Xue et al.. 2007; Buck et al.. 1997). Thus, limited
data are available on the difference in food intake rates between pregnant and nonpregnant
women. In addition, limited research was found with regard to the role that race and ethnicity, as
well as other demographic variables, play in food choices and amounts of foods consumed. Data
on food consumption by lactating women is extremely limited. Dubowitz et al. (2007)
conducted a study in a multiethnic sample of 662 low-income, postpartum women in the Boston,
MA metropolitan area and found that foreign-born women ate 2.5 more servings of fruits and
vegetables than women born in the United States. Leslie et al. (2012) observed differences in
fruits and vegetables servings between low and high socioeconomic position among
breastfeeding mothers in Melbourne, Australia.
Crozier et al. (2009) collected dietary data using a food frequency questionnaire for
2,270 women in early pregnancy (about 12 weeks); 2,649 women in late pregnancy (about
35 weeks); and 12,572 nonpregnant women in Southampton, U.K. Data on the consumption of
48 foods or food groups were collected. During early pregnancy, intake of 21 foods or food
groups increased, including: white bread, breakfast cereals, cakes and biscuits (cookies),
processed meat, crisps (potato chips), fruits and fruit juices, dried fruit, sweet spreads,
confectionery, and hot chocolate drinks. During late pregnancy, intake of puddings, cream, milk,
cheese, full-fat spread, cooking fats and salad oils, red meat, and soft drinks increased. Intake of
10 foods or food groups, including: rice and pasta, liver and kidney, salad vegetables, other
vegetables, vegetable dishes, nuts, diet cola, tea, and coffee decreased during pregnancy (Crozier
et al.. 2009). These results indicate that there may be differences in food intake rates between
pregnant and nonpregnant women and that they may change across each trimester. However, the
consumption patterns or food choices observed in this study may not be representative of
pregnant women in the United States.
Adequate protein consumption, including consumption of fish, is essential during periods
of pregnancy and lactation. Fish consumption during pregnancy and lactation is particularly of
interest to health officials because fish may contain contaminants that are harmful to the
developing fetus, and the benefits of fish as a healthy choice and a good source for omega-3 fatty
acids and other nutrients essential for fetal neurodevelopment must be weighed against the
potential risks associated with the contaminants. Limited data are available on fish consumption
rates for pregnant and lactating women. Many of the studies found relate to knowledge of fish
advisories by pregnant and lactating women and effectiveness of intervention techniques (Teisl
et al.. 2011; Karouna-Renier et al.. 2008; Scherer et al.. 2008).
5-18
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A study at a WIC clinic was performed to characterize commercial and sport fish
consumption patterns and advisory awareness among 500 ethnically diverse women living in
California's Sacramento-San Joaquin Delta (Silver et al.. 2007). The study included 66 pregnant
women (13%) and 105 breastfeeding mothers (21%). The area was under a state health advisory
limiting consumption of certain Delta fish, to be followed in conjunction with a federal advisory
for commercial and sport fish. Among all women surveyed, 95% of consumed commercial fish
and 32% consumed sport fish. Commercial fish are those caught for profit and may be a more
widely distributed food source than sport fish. Sport fish are those caught as part of a sporting or
recreational activity and not for the purpose of providing a primary source of food (U.S. EPA,
2011). Pregnant women ate less fish overall (geometric mean =16.8 g/day) than nonpregnant
women (geometric mean = 30.0 g/day). The geometric mean fish consumption rate among
breastfeeding mothers was 31.1 g/day. Sport fish consumption among pregnant and lactating
women was 12.8 and 10.2 g/day, respectively (geometric mean). These data may not be
generalizable to the U.S. population because the study was limited to women in WIC clinics in
California.
Xue et al. (2007) estimated mean total fish consumption among 1,024 pregnant mothers
in five Michigan communities. During the first 6 months of pregnancy, fish intake was
estimated to be 19.6 meals/6 months (3.3 meals/month). Similar results were obtained by Gliori
et al. (2006) who surveyed 726 postpartum women in Wisconsin in 2003 to obtain information
about the number of types of fish that women consumed while pregnant and their knowledge
about outreach materials regarding fish consumption during their pregnancy. Eighty-five percent
of the women had consumed fish during the year prior to giving birth. The average consumption
among pregnant women was 3 meals/month. The types of fish most frequently consumed were
tuna and commercially purchased frozen fish, followed by shellfish and sportcaught fish (Gliori
et al.. 2006). In another study, Oken et al. (2003) used food frequency questionnaires completed
by 2,235 pregnant women in eastern Massachusetts, to estimate the number of fish meals
consumed in a month in order to assess changes in consumption habits after implementation of
the 2001 National Mercury Advisory. A decline in fish consumption among pregnant women
was observed following the advisory. The mean number of fish meals consumed per month was
7.7 before the mercury advisory and 6.4 after the mercury advisory (Oken et al.. 2003). A study
of 22 pregnant women in the Boston area assessed women's knowledge of the health effects of
fish consumption during pregnancy and changes in consumption during pregnancy
(Bloomingdale et al., 2010). One-half of the women (11 of 22) reported eliminating
consumption of sushi during pregnancy (Bloomingdale et al., 2010), and others eliminated or
reduced certain fish species from their diets during pregnancy (Bloomingdale et al., 2010). The
5-19
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data from these studies are not based on nationally representative samples and may not be
generalizable to the U.S. population.
The Food and Drug Administration (FDA) and the CDC conducted a study of pregnant
women and new mothers to assess awareness of mercury contamination in food and examined
fish consumption levels across groups (Lando et al.. 2012). The study was conducted from May
through December of 2005. It included 4,902 "single birth" (not expecting twins or other
multiples) pregnant women age 18 or older who were recruited nationally in the third trimester.
The women were also followed postpartum if their gestational time was greater than 35 weeks,
both mother and infant were healthy at birth, and the infant's birth weight was greater than
2.3 kg (5 lbs). The study included a control group of women who were between the ages of
18 and 40 years, were not pregnant, had not had a baby within the past year, and had not already
participated in the study. A food frequency questionnaire developed by the National Cancer
Institute was used to collect intake data using a recall period of 1 month. A total of
1,286 pregnant, 522 postpartum, and 1,349 control group women completed the dietary intake
component of the study. Table 5-8 presents the percentage of women in each group who ate fish
and their daily fish intake levels. Mean fish consumption rates were 12.7, 14.6, and 17.0 g/day
for pregnant, postpartum, and control women, respectively. Awareness about mercury in food
was reported to be 73.3, 74.0, and 58.9% among pregnant, postpartum, and control women,
respectively.
5-20
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Table 5-8. Percentage of women who ate each type of fish during the prior month and their weekly intake,
based on a FDA/CDC study
Daily intake (consumers only) (g)a
Percentage that ate each type of
fish during the prior month
Pregnant
N= 1,018
Postpartum
N= 412
Control
N= 1,121
Type of fish
Pregnant
N =
1,286
Postpartum
N= 522
Control
N =
1,349
25th
50th
75th
25th
50th
75th
25th
50th
75th
Total fish
79.2
78.9
83.1
4.8
7.4
16.4
5.4
10.2
17.0
5.4
12.2
19.3
All canned tuna
53.3
53.3
57.6
2.6
5.4
9.6
2.6
5.4
8.5
2.6
4.8
9.6
Albacore
33.4
33.9
41.1
1.7
3.7
6.2
1.7
2.8
5.7
1.7
3.1
7.4
Not albacore
30.3
32.4
32.1
2.0
4.0
8.2
2.0
4.3
8.2
1.7
3.7
6.5
Fresh tuna
2.4
5.6
8.5
0.9
1.7
4.3
0.9
1.4
3.1
0.9
1.8
3.7
Salmon
16.8
19.5
23.9
1.1
2.3
5.7
2.0
3.3
5.7
1.1
2.6
5.7
Shark, swordftsh, tilefish, king
mackerel
1.1
4.0
4.5
0.9
1.7
2.6
0.9
1.4
2.6
0.6
1.1
2.6
All shellfish
41.7
40.6
52.3
1.7
3.1
6.7
2.0
3.7
8.5
2.3
5.4
11.1
Shrimp
38.3
36.4
48.9
1.4
2.8
6.0
1.7
3.1
6.2
1.7
3.4
6.8
Not shrimp
18.4
19.4
30.1
1.1
2.3
5.4
1.1
2.6
4.8
1.4
3.1
6.5
All other fish (includes fish
sticks)
46.6
46.0
51.9
2.0
4.0
6.2
2.0
4.4
10.1
2.0
4.0
7.1
aDaily intake is based on the number of women who ate each type of fish and therefore sample sizes vary by the type of fish. Weekly intake from Lando et al.
(2012) was converted to daily intake by dividing by 7.
N = Sample size.
Source: Lando et al. (2012).
-------
Limited information is available on fish intake among pregnant Native American women.
The New York State Department of Health (Fitzgerald et al.. 1995) conducted a study among
breastfeeding Mohawk women residing near three industrial sites between 1986 and 1992. A
total of 97 breastfeeding Mohawk women living on the Akwesasne Reservation and
154 breastfeeding white controls living in Warren and Schoharie Counties in NY were included
in the study. Data were collected on fish intake during pregnancy, the year before the pregnancy,
and more than 1 year before the pregnancy (Fitzgerald et al.. 1995). Table 5-9 presents the mean
number of local fish meals consumed by Mohawk and control women per year by time period for
all respondents. Of the 97 Mohawk mothers and 154 control mothers, local fish were consumed
by 82 Mohawk mothers (85%) and 72 control mothers (47%). Annual consumption of local fish
dramatically declined over time for all Mohawk respondents, from 23.4 (over 1 year before
pregnancy) to 9.2 (less than 1 year before pregnancy) to 3.9 (during pregnancy). Data on the
mean number of fish meals consumed per year by time period and selected characteristics (age,
education, cigarette smoking, and alcohol consumption) are also provided in Table 5-9. The
trend of decreased fish consumption during pregnancy was more notable among Mohawk
women 25 to 29 years old and those with some college education.
5-22
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Table 5-9. Mean number of local fish meals consumed per year by time
period and selected characteristics for all respondents3 (Mohawk, N= 97;
Control, N= 154), based on a New York Health Department study
Variable
Time period
During pregnancy
<1 year before pregnancy
>1 year before
pregnancy
Mohawk
Control
Mohawk
Control
Mohawk
Control
All
3.9
7.3
9.2
10.7
23.4b
10.9
Age (years)
<20
7.7
0.8
13.5
13.9
21A
10.4
20 to 24
1.3
5.9
5.7
14.5
20.4
15.9
25 to 29
3.9
9.9
15.5
6.2
25.1
5.4
30 to 34
12.0
7.6
9.5
2.9
12.0
5.6
>34
1.8
11.2
1.8
26.2
52.3
22.1°
Education (years)
<12
6.3
7.9
14.8
12.4
24.7
8.6
12
7.3
5.4
8.1
8.4
15.3
11.4
13 to 15
1.7
10.1
8.0
15.4
29.2
13.3
>15
0.9
6.8
10.7
0.8
18.7
2.1
Cigarette smoking
Yes
3.8
8.8
10.4
13.0
31.6
10.9
No
3.9
6.4
8.4
8.3
18.1
10.8
Alcohol consumption
Yes
4.2
9.9
6.8
13.8
18.0
14.8
No
3.8
6.3d
12.1
4.7s
29.8
2.9f
aLocal fish were consumed by 82 Mohawk mothers (85%) and 72 Control mothers (47%).
bp = <0.001 for Mohawks vs. Controls.
CF (4, 149) = 2.66, p = 0.035 for Age Among Controls.
^(1, 152) = 3.77,/? = 0.054 for Alcohol Among Controls.
eF (1, 152) = 5.20, p = 0.024 for Alcohol Among Controls.
{F (1, 152) = 6.42, p = 0.012 for Alcohol Among Controls.
Note: F (rl. r2) = F statistic with rl and r2 degrees of freedom.
N= Sample size.
Source: Fitzgerald et al. (1995)
5-23
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5.3. NONDIETARY INTAKE (PICA)
Several researchers have attempted to estimate the prevalence and nature of pica behavior
among pregnant women. As noted previously, pica generally refers to behavior associated with
the intentional ingestion of foreign (i.e., nonfood or nonnutritive) substances (Bronstein and
Dollar. 1974). The types of materials ingested can include: dirt, clay, cigarette ashes, ice, freezer
frost, flour, baking soda or powder, cornstarch, powdered milk, or other materials (Cooksev.
1995). Soil pica has been used to refer to the recurrent ingestion of large quantities of soil
(ATSDR. 2001). Geophagy, a special type of pica, involves the deliberate ingestion of earth
materials such as clay.
The practice of geophagy is still widespread in many parts of the world including Asia,
Africa, South America, North America, and parts of Europe (Al-Rmalli et al.. 2010). During
pregnancy, some cultures encourage geophagy as means to obtain essential elements. The
prevalence of geophagy among pregnant and lactating women in Africa has been reported to
range from 29-73% (Kutalek et al.. 2010; Kawai et al.. 2009; Nvaruhucha. 2009; Luoba et al..
2005; Prince et al., 1999). Amounts reported among these African populations have ranged from
1-100 g/day (Kutalek et al„ 2010).
In the United States, studies on the practice of pica and geophagy among pregnant and
lactating women are limited, and the available data on this topic are more than three decades old.
Ferguson and Keaton (1950) surveyed 361 pregnant, predominantly black, low-income women
in Mississippi. Clay eating was reported to be 27% among the black women and 7% among
white women, and ranged from 1 tablespoon/day-1 cup/day (~15-240g/day). Starch eating was
reported to be 41% among the black women and 10% among white women, and the amounts
consumed ranged from 2-3 small lumps-3 boxes (24 ounces) per day (-680 g/day). Hook
(1978) interviewed 250 new mothers in New York about any cravings or aversions for other
foods or nonfood items that may have developed at any time during their pregnancy. Three
women reported eating ice and one woman reported eating chalk from a river clay bank, but no
quantitative data were provided. Bronstein and Dollar (1974) studied 410 pregnant, low-income
women from both urban (N= 201) and rural (A' = 209) areas in Georgia. Women were
interviewed during their initial prenatal visit about food frequency, social and dietary history,
and the presence of pica. A total of 65 women (16%) indicated that they practiced pica (see
Table 5-10). Laundry starch was the substance most commonly ingested, but specific
information on the amount ingested was not provided. Vermeer and Frate (1979) conducted a
similar survey among 142 pregnant black females in rural Mississippi. Forty of these women
(28%>) reported geophagy, and another 27 respondents (19%) reported ingesting other types of
substances including laundry starch, dry powdered milk, and baking soda.
5-24
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Table 5-10. Frequency of pica behavior among low-income women in
Georgia (N= 410)
Rural
Urban
Total
Number
%
Number
%
Number
%
Nonpica
172
82
172
86
345
84
Pica
37
18
28
14
65
16
Total
209
100
201
100
410
100
Chalk
16
42
8
27
24
35
Starch
14
37
17
57
31
46
Clay
3
8
3
10
6
9
Other
5
13
2
7
7
10
N = Sample size.
Source: Bronstein and Dollar (1974). Reprinted with permission.
Cooksev (1995) asked 300 postpartum women at a midwestern hospital about cravings to
eat ice or other nonfood items during their pregnancies. The majority of women in the study
were low-income blacks. Sixty-five percent reported that they had ingested one or more pica
substances during their pregnancy. Freezer ice was one of the items most commonly consumed.
The largest quantities of items ingested on a daily basis were reported to be three to four 8-pound
bags of ice, two to three boxes of cornstarch, two cans of baking powder, one cereal bowl of dirt,
five quarts of freezer frost, and one large can of powdered cleanser.
Smulian et al. (1995) conducted a survey among 125 pregnant women in Muscogee
County, Georgia. Of the 18 women (14%) who reported practicing pica, four acknowledged
eating "white dirt" or "red dirt" (0.5 to 1.0 pounds of dirt or clay per week-roughly
-30-70 g/day). Of the nine women who reported the amounts of substances ingested, six stated
that their ingestion occurred daily and three stated that it occurred three times per week. The
prevalence of the overall pica, was 17.8% among black women, 10.6% among white women, and
0% among the Asian and Hispanic women in the sample, with no significant differences between
pica and nonpica groups with respect to age distribution and race.
Simpson et al. (2000) interviewed 225 Mexican-born women residing in low-income
areas of Ensenada, Mexico (N= 75), and Santa Ana, Bakersfield, and East Los Angeles,
California (N= 150) who were pregnant or had been pregnant within the previous year. Among
the women interviewed in California, 46 (31%) reported pica behavior with ice being the item
5-25
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eaten by the highest percentage of women. Excluding ice, 34 (23%) of the women interviewed
in California reported pica behavior. The items ingested and the number of women reporting
pica for each type of item is shown in Table 5-11. The reasons given for consuming nonfood
items included the following: because of the taste, smell, or texture of the items, for medicinal
purposes, or because of advice from someone, and one woman reported eating clay for religious
reasons. Except for magnesium carbonate, which was reported to have been consumed in
amounts ranging from a quarter of a block to five blocks per day, no specific quantities of pica
substances ingested were provided. Simpson et al. (2000) compared the blocks to approximately
the size of a 35-mm film box (i.e., about 2x1x1 inches).
Table 5-11. Items ingested by low-income Mexican-born women who
practiced pica during pregnancy in California (N = 46)
Item ingested
Number (%) ingesting items
Dirt
11 (24)
Bean stones3
17 (37)
Magnesium carbonate
8(17)
Ashes
5(11)
Clay
4(9)
Ice
18 (39)
Otherb
17 (37)
aLittle clods of dirt found among unwashed beans.
including eggshells, starch, paper, lipstick, pieces of clay pot, and adobe.
N = Number of individuals reporting pica behavior.
Source: Simpson et al. (2000).
Klitzman et al. (2002) interviewed 33 pregnant women with elevated blood lead levels
(i.e., >20 (J,g/dL) in New York City. Thirteen of the 33 women (39%) reported pica behavior
during their pregnancy; 10 reported eating soil, dirt or clay, 2 reported pulverizing and eating
pottery, and 1 reported eating soap. Except for one of the women who reported eating
approximately one quart of dirt daily (-1,650 g/day assuming a soil bulk density of 1.5 g/cm3)
(U.S. EPA. 2002) from her backyard over a 3-month period, no other quantity data were
reported. Using NHANES data for the years 1971-1975 (NHANES I) and 1976-1980
(NHANES II), Gavrelis et al. (2011) conducted an analysis of the prevalence of nondietary
intake among the U.S. population. The prevalence of ingestion of nonfood substances among
5-26
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pregnant females 12 years and older was found to be more than twice (2.5%; 95% confidence
interval [CI]: 0.0-5.6%) that of nonpregnant women (1.0%; 95% CI: 0.7-1.4%) in both
NHANES I and NHANES II.
5.4. INHALATION RATES
Inhalation rates among pregnant women may differ from those of nonpregnant females as
a result of changes in hormone levels, anatomy, activity levels, and body weight. Brochu et al.
(2006) developed physiological daily inhalation rates (PDIRs) for pregnant and lactating females
aged 11 to 55 years. Published data on total daily energy expenditures, and energy costs for
growth, pregnancy, and lactation (breast-energy output and maternal milk-energy synthesis) were
used to estimate rates for underweight, normal-weight, and overweight/obese females in
prepregnancy, at weeks 9, 22, and 36 during pregnancy, and weeks 6 and 27 postpartum.
"Underweight, normal-weight, and overweight/obese [were] defined as those having [body mass
indices] BMIs lower than 19.8 kg/m2, between 19.8 and 26 kg/m2, and greater than 26 kg/m2,
respectively" (Brochu et al., 2006). Brochu et al. (2006) used data for 357 nonpregnant and
nonlactating females and 91 pregnant and breastfeeding females. Monte Carlo simulations were
used to integrate total daily energy requirements of nonpregnant and nonlactating females into
energy costs and weight changes at the 9th, 22nd, and 36th weeks of pregnancy and at the 6th and
27th postpartum weeks. Energetic values in kcal/day and kcal/kg-day were converted into PDIRs
in m3/day and m3/kg-day by using the equation developed by Lavton (1993). Tables 5-12, 5-13,
and 5-14 present the mean and 95th percentile PDIRs in m3/day and m3/kg-day for underweight,
normal-weight, and overweight/obese females, respectively. Daily inhalation rates for normal
weight women are approximately 18-41%) higher during pregnancy and 23-39% higher during
postpartum (Brochu et al.. 2006). For all weight groups, inhalation rates were estimated to be
higher during pregnancy and postpregnancy than before pregnancy, with decreases only evident
among the 27th postpartum weeks time period.
5-27
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Table 5-12. Simulated inhalation rates of prepregnant, pregnant, and
postpartum lactating underweight women, aged 11 to 55 years3
Group
Mean
95th percentile
m3/day
m3/kg-day
m3/day
m3/kg-day
11 to <23 years
12.18
0.277
15.60
0.352
Prepregnancy
12.27
0.276
15.48
0.345
Pregnant, 9th week
17.83
0.385
23.13
0.504
Pregnant, 22nd week
17.98
0.343
23.90
0.455
Pregnant, 36th week
18.68
0.323
25.59
0.452
Postpartum, lactating, 6th week
20.39
0.368
24.82
0.548
Postpartum, lactating, 27th week
20.21
0.383
24.61
0.584
23 to <50 years
13.93
0.264
17.65
0.342
Prepregnancy
13.91
0.264
17.81
0.361
Pregnant, 9th week
20.03
0.366
26.94
0.501
Pregnant, 22nd week
20.15
0.332
27.46
0.452
Pregnant, 36th week
20.91
0.317
28.95
0.439
Postpartum, lactating, 6th week
22.45
0.352
27.68
0.518
Postpartum, lactating, 27th week
22.25
0.364
27.44
0.545
50 to <55 years
12.89
0.249
15.20
0.293
Prepregnancy
12.91
0.249
15.13
0.294
Pregnant, 9th week
18.68
0.347
22.69
0.431
Pregnant, 22nd week
18.84
0.315
23.20
0.401
Pregnant, 36th week
19.60
0.301
25.58
0.404
Postpartum, lactating, 6th week
21.19
0.337
24.53
0.457
Postpartum, lactating, 27th week
21.01
0.349
24.31
0.483
aBased on data for 81 underweight women. Number of simulated women = 5,000.
Source: Brochu et al. (2006).
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Table 5-13. Simulated inhalation rates of prepregnant, pregnant, and
postpartum lactating normal-weight women, aged 11 to 55 years3
Group
Mean
95th percentile
m3/day
m3/kg-day
m3/day
m3/kg-day
11 to <23 years
Prepregnancy
14.55
0.252
18.71
0.339
Pregnant, 9th week
19.99
0.344
25.89
0.468
Pregnant, 22nd week
22.59
0.360
30.75
0.500
Pregnant, 36th week
23.27
0.329
31.07
0.453
Postpartum, lactating, 6th week
23.28
0.342
28.98
0.499
Postpartum, lactating, 27th week
23.08
0.352
28.73
0.527
23 to <50 years
Prepregnancy
13.66
0.222
17.87
0.285
Pregnant, 9th week
19.00
0.308
24.49
0.395
Pregnant, 22nd week
21.36
0.321
28.43
0.433
Pregnant, 36th week
22.14
0.297
29.27
0.399
Postpartum, lactating, 6th week
22.15
0.309
27.53
0.425
Postpartum, lactating, 27th week
21.96
0.317
27.29
0.441
50 to <55 years
Prepregnancy
13.79
0.229
17.02
0.287
Pregnant, 9th week
19.02
0.314
23.38
0.400
Pregnant, 22nd week
21.53
0.330
28.30
0.439
Pregnant, 36th week
22.20
0.303
28.53
0.401
Postpartum, lactating, 6th week
22.31
0.316
26.70
0.434
Postpartum, lactating, 27th week
22.12
0.325
26.47
0.453
aBased on data for 172 normal weight women. Number of simulated women = 5,000.
Source: Brochu et al. (2006).
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Table 5-14. Simulated inhalation rates of prepregnant, pregnant, and
postpartum lactating overweight/obese women, aged 11 to 55 years3
Group
Mean
95th percentile
m3/day
m3/kg-day
m3/day
m3/kg-day
11 to <23 years
16.62
0.206
21.41
0.261
Prepregnancy
16.64
0.207
20.06
0.253
Pregnant, 9th week
25.51
0.302
33.32
0.401
Pregnant, 22nd week
26.10
0.287
34.93
0.391
Pregnant, 36th week
25.71
0.270
34.95
0.377
Postpartum, lactating, 6th week
25.93
0.280
30.53
0.395
Postpartum, lactating, 27th week
25.71
0.285
30.26
0.409
23 to <50 years
15.45
0.186
19.27
0.227
Prepregnancy
15.47
0.186
19.46
0.233
Pregnant, 9th week
23.93
0.274
31.77
0.374
Pregnant, 22nd week
24.44
0.261
33.49
0.360
Pregnant, 36th week
24.15
0.245
34.18
0.360
Postpartum, lactating, 6th week
24.47
0.256
29.43
0.360
Postpartum, lactating, 27th week
24.25
0.260
29.17
0.372
50 to <55 years
15.87
0.184
20.01
0.235
Prepregnancy
15.83
0.184
19.47
0.226
Pregnant, 9th week
24.47
0.272
33.08
0.378
Pregnant, 22nd week
25.02
0.259
35.01
0.363
Pregnant, 36th week
24.46
0.242
34.27
0.351
Postpartum, lactating, 6th week
24.91
0.253
29.75
0.364
Postpartum, lactating, 27th week
24.70
0.257
29.50
0.374
aBased on data for 104 overweight/obese women. Number of simulated women = 5,000.
Source: Brochu et al. (2006).
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5.5. ACTIVITY FACTORS AND CONSUMER PRODUCT USE
Most of the activity pattern studies located in the literature addressed relationships
between physical activity (e.g., exercise) and birth weight or pregnancy outcome (Evenson and
Wen, 2010; Borodulin et al„ 2009; Borodulin et al„ 2008; Mottola and Campbell, 2003). Few
data are available on activity factors that can be used to evaluate the relationship between time
use and exposure to environmental agents among pregnant and lactating women.
Nethery et al. (2009) compared the time-activity patterns among a nonrandom sample of
62 pregnant Canadian women and a comparison group of 103 women in the Canadian Human
Activity Pattern Study (CHAPS). The data were collected in 2005-2006. Changes in
location-based activity patterns (i.e., at or near home, work, other indoor locations, outdoors, car,
bus, walk, bike) were measured over the course of pregnancy. The mean and 95% CI for the
pregnancy cohort and the CHAPS comparison group are provide in Table 5-15.
Table 5-15. Mean (95% CI) time spent in various activities (hours/day), by
trimester among a population of Canadian women
Activity/
location
Pregnant
cohort
N= 62
Pregnant cohort by trimester
CHAPS
comparison
group
N= 103
1st trimester
N= 11
2nd trimester
N = 62
3rd trimester
N=54
At/near
home
16.2
(15.7-16.8)
14.4
(13.3-15.4)
16.1
(15.3-17.0)
16.9
(16.0-17.8)
15.5
(14.7-16.3)
Work
4.2(3.6-4.7)
5.6 (4.4-6.7)
4.3 (3.5-5.1)
3.7 (2.8-4.6)
3.8 (3.0-4.6)
Indoors,
other
1.6(1.3-1.8)
2.2 (0.9-3.6)
1.6(1.3-1.9)
1.4 (1.1-1.7)
2.5 (1.9-3.0)
Outdoors
0.3 (0.2-0.4)
0.0 (0.0-0.0)
0.2 (0.07-0.2)
0.4 (0.2-0.6)
0.6 (0.3-0.8)
Car
0.9(0.7-1.0
1.1 (0.5-1.7)
0.9(0.7-1.1)
0.8 (0.6-1.0)
1.4 (1.1-1.7)
Bus
0.2 (0.2-0.3)
0.3 (0.01-0.5)
0.2(0.1-0.4)
0.2(0.08-0.3)
0.1 (0.1-0.2)
Walk
0.7(0.5-0.8)
0.4 (0.2-0.7)
0.7 (0.6-0.9)
0.6 (0.4-0.8)
0.2 (0.1-0.2)
Bike
0.1 (0.0-0.1)
0.02 (0.0-0.07)
0.06 (0.0-0.1)
0.06 (0.0-0.1)
0.0 (0.0-0.1)
CI = Confidence interval.
N = Sample size.
Source: Netherv et al. (2009).
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In 1996 and 1997, Zender et al. (2001) conducted a study in Colorado to compare
tapwater intake among pregnant and nonpregnant women. Data were also collected on activities
resulting in dermal contact with tapwater (e.g., showering, bathing, swimming, cleaning, etc.). A
total of 71 pregnant and 43 nonpregnant women were recruited from WIC clinics; nearly
one-half of the pregnant women were in their second trimester, and one-quarter were in each of
the first and third trimesters. Most of the women were white and had fewer than 13 years of
education. The average ages were 24 years for pregnant women and 27 years for nonpregnant
women. Approximately one-half of the women worked outside the home, and nearly all the
women used municipal water source in their home. The women were interviewed in person or
by phone and responded to questions about tapwater usage and activities such as frequency and
duration of showering, bathing, and swimming. Table 5-16 shows the statistics for these
activities and others including bathing children or pets and washing dishes, clothes, or cars. The
frequency and duration of showering was similar for pregnant and nonpregnant women, but
pregnant women spent more time bathing than nonpregnant women.
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Table 5-16. Activities associated with exposure to water, by pregnancy status
in a population of women in Colorado
Characteristic
Pregnant (N= 71)
Not pregnant (N= 43)
Showering at home (%)
97.2
100.0
Showers per week3 (mean ± SD)
7.1 ±2.8
7.2 ±2.4
Duration of showers (minutes)
13.9 ± 5.2
13.9 ± 6.0
Bathing at home (%)
50.7
37.2
Baths per weekb (mean ± SD)
3.0 ± 3.5
1.4 ± 1.1
Duration of baths (minutes)
28.8 ± 12.9
41.3 ±30.3
Swimming (%)
25.4
27.9
Swimming per weekc (mean ± SD)
1.3 ± 1.6
0.6 ±0.5
Duration of swimming (minutes)
73.9 ±46.1
80.8 ± 79.3
Bathing children (%)
43.7
81.4
Washing dishes (%)
66.2
76.2
Washing clothes (%)
14.1
21.4
Washing cars (%)
18.3
31.0
Bathing pets (%)
18.3
11.9
a Among women showering at home.
bAmong women bathing at home.
°Among women swimming.
N = Number of observations.
SD = Standard deviation.
Source: Zender et al. (2001).
Bell and Belanger (2012) studied women's residential mobility (i.e., change of residence)
during pregnancy and the potential implications for environmental exposures during pregnancy.
Data from 14 studies on residential mobility among pregnant women were examined for overall
mobility rates and distances moved. Of the 14 studies, 7 were based in the United States, and the
remaining 7 were based in the United Kingdom, Canada, The Netherlands, Norway, and
Australia. The percentage of women who moved during pregnancy ranged from 9-32%
(median = 20%). Bell and Belanger (2012) reported that more moves occurred during the second
trimester of pregnancy, based on the studies that presented data by trimester. Several other
factors were found to affect mothers' residential mobility including age (the probability of
moving generally declined with age), socioeconomic status (mobility was generally higher
5-33
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among women with lower income), marital status (married women were less likely to move), and
parity (rates were generally higher in women with fewer pregnancies). Relationships with
factors such as race, smoking, and alcohol use were more variable. Of the studies that reported
on the distance moved, most distances were short, with median values typically <10 km.
Consumer product use data for pregnant women are also limited. No consumer products
use data were found for lactating women. Just et al. (2010) conducted a survey among
186 women, 18-35 years old, who self-identified as either African-American or Dominican and
had resided in Northern Manhattan or the South Bronx for at least 1 year prior to pregnancy.
The women were part of the Mothers and Newborns cohort study of the Columbia Center for
Children's Environmental Health. The primary objective of the study was to explore
relationships between the use of personal care products and exposure to phthalates. Participants
were included in this study if phthalates were "measured within a week in either a personal air
and/or urine sample collected during the third trimester of pregnancy." Consumer product use
questionnaires were administered to study participants in the third trimester of pregnancy. The
questionnaire was designed to gather information on the total number of uses over the previous
48 hours and the frequency of use during each trimester of pregnancy (>l/day, 1/day, 2-3/week,
1/week,
-------
Table 5-17. Percentage of a population of pregnant minority women residing
in New York (N= 186) who reported use of selected personal care products
over a 48-hour survey period
Personal care product
%
Deodorant
98
Lotion
82
Perfume
41
Liquid soap
29
Hair gel
25
Hair spray
10
Nail polish or polish remover
10
N = Sample size.
Source: Just et al. (2010).
5.6. BODY WEIGHT
Jannev et al. (1997) evaluated body weight among a sample of women in Ann Arbor,
Michigan area. Prepregnancy body weights of 110 women were compared to postpartum
weights at 0.5, 2, 4, 6, 12, and 18 months after parturition. The women ranged in age from
20- 40 years, and most were white (106 whites, 1 Asian-American, and 3 African-Americans).
Data on weight gained during pregnancy, pre- and postpregnancy body weights for the women,
as well as information on weight retained after pregnancy, are provided in Table 5-18. Mean
body weight declined from 67.2 kg at 0.5 months after parturition to 62.4 kg at 12 months after
parturition (see Table 5-18).
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Table 5-18. Weight gained during pregnancy, and pre- and postpregnancy
body weight (kg), among a population of Michigan women (N= 110)
Variable
Mean ± SD (range)
Weight before pregnancy
59.7 ±9.7 (43.1-93.0)
Weight gained during pregnancy
First trimester
3.1 ±2.9 (-4.5-11.3)
Second trimester
6.4 ±3.0 (-6.8-13.6)
Third trimester
6.5 ±2.9 (1.4-18.1)
Total
16.2 ± 5.2 (1.4-35.8)
Postpregnancy weight
0.5 month
67.2 ± 1.0 (47.9-96.4)
2 months
65.5 ± 1.0 (49.3-94.9)
4 months
64.3 ± 1.0 (48.6-3.2)
6 months
63.6 ± 1.0 (47.2-94.2)
12 months
62.4 ± 1.1 (44.4-96.0)
18 months
63.8 ± 1.3 (47.8-98.4)
Retained weight, postpregnancy
0.5 month
7.4 ±0.5 (-6.5-20.8)
2 months
5.8 ±0.4 (-5.1-16.8)
4 months
4.7 ±0.4 (-5.8-15.5)
6 months
3.9 ±0.4 (-7.5-15.1)
12 months
2.5 ±0.5 (-8.3-13.5)
18 months
3.0 ±0.5 (10.1-14.5)
N = Sample size.
SD = standard deviation.
Source: Jannev et al. (1997).
Carmichael et al. (1997) conducted a study in 4,218 California women who had good
pregnancy outcomes between 1980 and 1990 to obtain the distribution of maternal weight gain
by trimester. A good pregnancy outcome was defined as a "vaginal, term (37 or more completed
weeks' gestation) delivery of a live infant of average size for gestational age (i.e., between the
10th and 90th percentiles of gestation specific birthweight, based on data from more than
5-36
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2 million California births) to a mother without diabetes or hypertension." The average age of
the women was 27.7 years. The mean prepregnancy weight for these women was 57.6 kg; 29%
were underweight, 61% were of normal weight, 5% were overweight, and 4% were obese, based
on BMI calculations. The difference between the self-reported prepregnancy weight and the last
measured weight was used to estimate weight gain. Table 5-19 presents the estimated
mean ± SD weight gain for underweight, normal-weight, overweight, and obese women during
the first, second, and third trimesters of pregnancy. The average weight gains for the first,
second, and third trimesters, calculated by averaging the weight gains for the four groups (i.e.,
underweight, normal weight, overweight, and obese), were 1.98 kg, 6.73 kg, and 6.37 kg,
respectively. Based on the prepregnancy weight of 57.6 kg, total body weights for the first,
second, and third trimesters would be 59.6 kg, 66.3 kg, and 72.7 kg, respectively (i.e., calculated
by adding the average weight gain for each trimester to the prepregnancy weight and the
previous trimester weight).
5-37
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Table 5-19. Weight gained during pregnancy (kg), for populations of
underweight, normal-weight, overweight, and obese women in California
who had good pregnancy outcomes (N= 4,218)
Variable
Mean ± SD
first trimester
Underweight
1.92 ±3.06
Normal weight
2.19 ±3.47
Overweight
2.16 ±3.95
Obese
1.65 ±3.94
second trimester
Underweight
7.41 ±2.60
Normal weight
7.54 ±2.86
Overweight
6.63 ±3.12
Obese
5.33 ±3.51
third trimester
Underweight
6.24 ± 2.47
Normal weight
6.63 ±2.73
Overweight
6.37 ±2.86
Obese
6.11 ±3.12
N = Sample size
SD = standard deviation.
Source: Carmichael et al. (1997).
In 2010, EPA analyzed body weight data for 1,248 pregnant women from the 1999-2006
NHANES. After removing a few very large and improbable body weights (i.e., outliers), the
statistically weighted average body weight of all pregnant women was 75 kg (see Table 5-20)
(U.S. EPA 2011). The same data showed the average body weight for all women aged
16-40 years old is 71 kg (U.S. EPA 2011).
5-38
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Table 5-20. Estimated body weight (kg) of pregnant women—NHANES
(1999-2006)3
Percentiles
Trimester
N
Mean
SD
5th
10th
15th
25th
50th
75th
85th
90th
95th
1
204
76
3
48
50
55
60
74
91
98
106
108
2
430
73
1
50
53
57
61
72
83
93
95
98
3
402
80
1
60
63
65
69
77
88
99
104
108
Ref/Dkb
186
69
2
46
52
55
60
65
77
84
87
108
All
1,222
75
1
50
55
59
63
73
85
94
99
107
aDue to a few large weight (>90 kg) respondents with very large sample weights (>18,000 kg), the weighted mean
body weight of first trimester women (76 kg) is larger than that of second trimester women (73 kg).
bRefers to pregnant women who either refused to tell which trimester they were in or did not know, or when data
were missing.
N= Sample size.
SD = standard deviation.
Source: U.S. EPA (2011V
Lactation can also have an effect on body weight. One study conducted in a cohort of
405 Brazilian women suggested that breastfeeding for longer periods of time might contribute to
decreases in postpartum weight retention (Kac et al.. 2004). Brewer et al. (1989) examined
postpartum weight changes in 56 Louisiana women. The women were over 18 years of age
(mean age = 27 years) and were predominantly white, well educated, and middle to upper-middle
income. The women were assigned to one of three groups according to the method of feeding
the infants during the first 6 months of life: exclusive breastfeeding, exclusive formula feeding,
or a combination of breastfeeding and formula feeding. The mothers were weighed in the
hospital 1 to 2 days postpartum and at home at 3 and 6 months after delivery. Although
responses from individual participants varied, overall, there was a steady, significant decline in
weight for all three groups, and the most weight loss occurred during the first 3 months (see
Table 5-21). With the exception of one woman who showed essentially no change in weight and
two women who gained weight, total weight losses ranged from 1.9-20.9 kg over the 6-month
study period for all participants. Weight losses averaged 8.30 kg for the breastfeeding group,
8.19 kg for the formula feeding group, and 7.23 kg for the combination of breastfeeding and
formula. Weight loss after pregnancy may have implications with regard to lipid mobilization
(e.g., contaminants stored in lipid).
5-39
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Table 5-21. Mean ± SD prepregnancy weight, pregnancy weight gain, and
postpartum weight loss (kg) among a population of Louisiana women (N =
56)
Lifestage
group
Prepregnancy
weight
Pregnancy
weight gain
Postpartum weight loss3
0 to 3 3 to 6 0 to 6
months months months
Breastfeeding
59.8 ± 13.1
14.6 ± 5.8
6.75 ± 0.53b 1.29 ± 0.64° 8.30 ± 0.74b
Formula feeding
54.9 ±6.0
16.3 ± 5.7
8.14 ± 0.68b 0.16 ±0.85 8.19±0.96b
Combination
57.3 ± 7.5
14.6 ± 5.0
6.39 ± 0.53b 0.82 ±0.65 7.23 ± 0.73b
"¦Postpartum weight loss measured in reference to the last weight recorded before delivery.
Represents a statistically significant change (p < 0.001).
Represents a statistically significant change (p < 0.05).
N = Sample size.
SD = Standard deviation.
Source: Brewer et al. (1989).
Weight loss during lactation was studied in California women participating in the Davis
Area Research on Lactation Infant Nutrition and Growth study by Dewey et al. (1993). A total
of 46 mothers who breastfed their infants and 39 mothers who fed their infants formula were
included in the study. The mean ages, education levels, pregnancy weight gains, and
prepregnancy weights of the women in the two groups were similar. Infants in the breastfeeding
group received <120 mL/day of other milks until at least 12 months of age, and more than
one-third of the infants were breastfed for longer than 18 months. The mothers' weights were
measured monthly from 1 to 18 months, and at 21 and 24 months postpartum. The results
indicated that weight loss among the two groups of women was similar at 1 month postpartum,
but the breastfeeding women weight losses were statistically significantly greater than that of the
formula-feeding group in subsequent months. At 6 months postpartum, the breastfeeding group
had an average body weight that was approximately 2.8 kg lower than that of the
formula-feeding group (see Figure 5-2). At 12 months the breastfeeding group had a mean body
weight that was 3.2 kg lower than the formula-feeding group. Table 5-22 presents data showing
the differences in weight loss for the two groups of mothers. Over the first 12 months
postpartum, breastfeeding mothers lost 4.4 kg compared to 2.4 kg for the formula-feeding
mothers. Other factors that contributed to greater weight loss were parity and mothers' height.
5-40
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70 1
67 ¦ T # ~
*
e 64 -
H
I
<2 61 -
5 58 - ^-Ti i rriYi j ir*^ "-
55 1 1 1 1 1 1 1
0 3 6 9 12 15 18 21 24
MONTHS POSTPARTUM
Figure 5-2. Weight of breastfeeding (m,N= 26) and formula-feeding
(A.,N= 27) women during the first 24 months postpartum.
From Am. J. Clin. Nutri. (1993; 58; 162-166), American Society for Nutrition. Reprinted with permission.
Source: Dewev et al. (1993).
Table 5-22. Mean ± SD changes in maternal body weight in populations of
breastfeeding and formula feeding California women
Postpartum
Timeframe
Breastfeeding
Formula feeding
N
Body weight change (kg)
N
Body weight change (kg)
1 to 3 months
37
-1.2 ±2.1
27
-1.0 ± 2.3
3 to 6 months
42
-2.2 ±2.2
31
-0.3 ± 1.9
6 to 9 months
40
-0.6 ± 1.5
30
-0.9 ± 1.8
9 to 12 months
35
-0.3 ± 1.5
33
-0.0 ± 1.6
Total (1 to 12 months)
29
-4.4 ±3.4
21
-2.4 ± 5.0
N = Sample size.
SD = Standard deviation.
Source: Dewev et al. (1993).
iTTTrrmTTT
5-41
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Jannev et al. (1997) also evaluated weight loss according to how the women fed their
infants. The women in the study were categorized as fully breastfeeding, partly breastfeeding, or
bottle feeding. Jannev et al. (1997) found that women who bottle fed their infants retained more
weight over time than women who breastfed their infants. Lactating women retained less of their
body weight gained during pregnancy than did nonlactating women. The duration of lactation
practice was found to be a significant predictor of postpartum weight retention over time (Jannev
et al.. 1997). Figure 5-3 depicts weight losses according to breastfeeding practices.
t 1 1 r
10 12 14 16 18 20
Time (mo since parturition)
Figure 5-3. Predicted weight-retention curves over time for four lactation
practices. Bottle-feeding only (•); fully breastfeeding at 2 weeks, partly
breastfeeding at 2 months, and bottle feeding or infant weaned at 4, 6, 12, and
18 months (¦); fully breastfeeding for 6 months and bottle-feeding or infant
weaned at 12 and 18 months (A); and fully breastfeeding for 6 months, partly
breastfeeding for 12 months, and bottle feeding or infant weaned at
18 months (~).
From Am. J. Clin. Nutr. (1997; 66; 1116-1124), American Society for Nutrition. Reprinted with
permission.
Source: Jannev et al. (1997).
5-42
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Another study investigated the effect of protein intake during pregnancy and postpartum
body weight changes (Castro et al.. 2010). Dietary intake information was obtained using a food
frequency questionnaire from a cohort of 421 women in Rio de Janeiro, Brazil at 15 days,
2, 6, and 9 months postpartum. The study found a positive association between postpartum
weight loss and protein intake during pregnancy. The average postpartum weight loss was
0.409 kg/month (±0.12). Women with adequate protein intake (>1.2 g/kg body weight) lost an
additional 0.094 kg/month (±0.04) than women with inadequate protein intake (<1.2 g/kg body
weight) (Castro et al., 2010).
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6. DATA GAPS
This issue paper provides an overview of the physiological and behavioral changes that
occur during pregnancy and lactation, and exposure factors for pregnant and lactating women. It
is not meant to provide a comprehensive review of every aspect of pregnancy and lactation, but
is meant to introduce topics related to women's potential susceptibility to environmental
contaminants as a result of differences in physiology and behavior, summarize key areas that
may be relevant to exposure, and highlight areas where data gaps exist. Not all changes during
pregnancy and lactation would be likely to result in new or different environmental exposures,
but some may increase or decrease exposures to environmental contaminants.
Sections 3 and 4 of this paper present the available literature with respect to physiological
and behavioral changes during pregnancy and lactation. However, the direct link between these
changes and the potential for experiencing differential exposures is not well understood and is a
significant data gap. Most of the physiological information found in the literature relates to
pregnancy, while the information on lactating women is more limited. This may be because
most of the physiological changes occurring during lactation relate to hormonal changes
necessary for the production of milk and the resulting mobilization of calcium from the bones.
Other organ systems may not undergo significant changes. The remainder of this section (see
Section 6) summarizes some of the data gaps pertaining to exposure factors for pregnant and
lactating women and highlights areas where additional information would be useful for
estimating exposures among this potentially susceptible lifestage.
Most of the research on pregnant and lactating women has been conducted for clinical
purposes and is aimed at providing recommendations for pregnant and lactating women in
support of a good pregnancy outcome, or to support the health of the nursing infant. Research to
develop exposure factor data specifically for pregnant women is somewhat limited, but data for
some factors are available (see Section 5). Exposure factor data for lactating women is even
more limited. For factors where data are available, some are based on studies conducted on a
specific geographical location or for a specific demographic group. These data may not be
representative of pregnant and lactating women in other areas of the United States or within
other demographic groups. For instance, several studies are available regarding fish
consumption during pregnancy, but these are limited to specific geographical locations or tribal
groups. The results of these studies may not be generalizable to other populations. Fish
consumption rates may be highly dependent on geographical location, cultural practices, and
other factors. These studies were also primarily conducted to assess knowledge and compliance
with fish advisories rather than intake.
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Some data are available at the national level, but these data are sometimes limited by
sample size. For example, data on consumption of community water are available on a national
scale, but the sample size for pregnant and lactating women is small. The national-scale analysis
of food intake by pregnant women also suffers from small sample size limitations, and data are
not available for lactating women. Some studies report on adaptive behaviors with regard to
food consumption, but do not provide information on amounts of food consumed. Also,
although research is available on the adequacy of the pregnant woman's diet with regard to the
intake of certain nutrients (e.g., calcium, folic acid, iron, vitamin D) needed for a good
pregnancy outcome, data on the quantities of foods is more limited. In addition, the role that
race, age, ethnicity, geographical location, and socioeconomic factors plays in the variability
with regard to these exposure factors for this lifestage is not well understood.
Activity pattern data are limited for pregnant women in the U.S. population, and are
entirely lacking for lactating women. Many of the studies available examined the relationship
between physical activity and pregnancy outcomes, but did not provide information about the
duration and frequency of activities. One study provided information on time spent in a limited
number of activities for a population of pregnant Canadian women. Another study conducted in
the United States reported on activities related to water exposures in a small population of
pregnant women in Colorado. Another study on residential mobility examined the data from
seven studies conducted in the United States that reported on the percentage of pregnant women
who move during pregnancy and the distance moved, but provided no information on residence
time. The use of consumer products by pregnant and lactating women is another area where
research may be warranted. There is some information regarding the percentage of pregnant
women using selected personal products, but no information about the amount of product use
and the frequency of use.
For some behaviors, there are data with regard to the prevalence of the behavior, but
quantitative estimates on the frequency and duration of the activity are not available. For
example, there is some information about the prevalence of pica behavior among pregnant
women, but limited data on the amount of material ingested. In addition, the data on the
prevalence of pica are primarily based on older studies and may not reflect current behaviors.
No data on pica behavior during lactation were found.
For some exposure factors, no data for pregnant and lactating women were available. For
example, no data on skin surface area were found, but these data could be generated from height
and weight data for these pregnant women. While there are some exposure factors data for
pregnant women, data for lactating women are lacking. Factors for which no data are available
for lactating women include: food intake, soil intake, prevalence of pica or geophagy, activity
patterns, and consumer product use.
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More importantly, additional analyses are needed to understand whether differences
between the exposure factors for pregnant and lactating women and those of the general
population of women are significant in terms of exposure and risk. Some of the questions that
need to be answered include:
• Are differences in exposure factor values statistically different (i.e., at the mean or
upper end of the distribution)?
• Would such differences result in statistically relevant differences in exposure and
risk?
• Would risk management decisions differ if based on the pregnant and lactating
women instead of women in the general population?
• When (e.g., for what types of chemicals) would it be appropriate to base
exposure/risk assessments on pregnant and lactating women?
• Are current risk assessment practices and guidances properly accounting for
variations in this potentially susceptible lifestage?
These and other questions remain in terms of how the information in this issue paper may
be applied in the context of the assessment and management of risk.
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APPENDIX. COMPARISON OF COMMODITY
CONSUMPTION PATTERNS OF PREGNANT AND
NONPREGNANT WOMEN OF CHILDBEARING AGE
-------
UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
^ WASHINGTON, D.C. 20460
.Wi
p'
PRO^
OFFICE OF CHEMICAL SAFETY AND
POLLUTION PREVENTION
MEMORANDUM
DATE: April 3, 2013
SUBJECT: Comparison of Commodity Consumption Patterns of Pregnant and Non-Pregnant
Women of Childbearing Age, WWEIA-FCID 2003-08
PC Code: NA DP Barcode: 410152
Decision No.: 476479 Registration No.: GP-33396
Petition No.: NA Regulatory Action: NA
Risk Assessment Type: NA Case No.: NA
TXR No.: NA CAS No.: NA
MRU) No.: NA 40 CFR: NA
FROM: Bayazid H. Sarkar, Mathematical Statistician
and
James Nguyen, Mathematical Statistician
Chemistry and Exposure Branch,
Health Effects Division (7509P)
Ver.Apr.08
THROUGH: David J. Miller, Chief
Chemistry and Exposure Branch,
Health Effects Division (7509P)
TO: Jacqueline Moya, Environmental Engineer
National Center for Environmental Assessment (8623P)
Office of Research and Development
SUMMARY
CEB was requested by ORD's National Center for Environmental Assessment (NCEA) to
examine and compare food commodity consumption patterns of pregnant women and non-
pregnant women of childbearing age. NCEA intends to use this information to update the
Exposure Factors Handbook (EFH) and also intends to release an Issue Paper on exposure
factors for pregnant women later this year. CEB had previously provided updated food
consumption data (based on the NHANES/WWEIA 2003-2006 survey) to NCEA for various
demographic subpopulations, and this data was incorporated into the 2011 EFH issued during the
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fall, 2011. In order to be consistent with our previous analysis and the 2011 EFH data and
previous versions to the EFH and as requested by NCEA, the summary statistics here represent
2-dav averages of those survey respondents who report both days of consumption. Thus, the
mean and various percentile amounts are average values over the two non-consecutive days of
the survey.
At NCEA's request, CEB's new analysis compared dietary consumption patterns for pregnant vs.
non-pregnant females of child bearing age (here, assumed to be ages 13-49). With respect to
overall consumption of fruits and vegetables, we found that pregnant females have a slightly
higher per capita mean intake of total vegetables (2.74 g/kg-day) than non-pregnant females
(2.43 g/kg-day), which is statistically significant. The 95th percentile of per capita consumption
of total vegetables for pregnant females (6.26 g/kg-day) is also higher than that of non-pregnant
females (5.93 g/kg-day). With respect to total fruit consumption, pregnant females on average
consume more total fruits (1.79 g/kg-day consumers only, 1.66 g/kg-day per capita basis) than
non-pregnant females (1.18 g/kg-day consumers only, 0.98 g/kg-day per capita basis), and this
difference is statistically significant. The 95th percentile of per capita consumption of total fruits
for pregnant females (5.02 g/kg-day) is also higher than that of non-pregnant females (3.75 g/kg-
day).
In addition, we found that pregnant females in general in the years examined consumed greater
amounts of certain commodities, including (in particular) citrus, peaches, cucurbits, and tropical
fruits. We also found that pregnant females tended to consume lesser amounts of other
commodities, including less cabbage, stalk and stem vegetables, leafy vegetables, and lettuce.
Note that multiple tests have been performed in our analysis, and any putative statistical
differences have not been corrected to account for this. Many of these differences may not have
been found to be statistically significant if such adjustments had been made. Thus, it is left to the
data user to determine if differences in mean consumption between pregnant and not pregnant
females of child-bearing age are substantive from an exposure assessment viewpoint. It is also
important for the user to understand that differences in consumption in an exposure or risk
assessment context may or may not produce differences in exposure or risk in that increased
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consumption of one commodity or type of commodity may result in reduced consumption of
other commodities/commodity types.
PET ATT,ED ANALYSIS
Overview and Methods
Since OPP has updated the NHANES/WWEIA 2003-2006 data we originally provided to NCEA
to include the 2007-2008 consumption data, our current analysis is based on the NHANES 2003-
2008 data that is now available at www.fcid.foodrisk.org. At NCEA's request, our analysis
focuses on the 2-dav average consumption of food commodities (e.g., apples, bananas) and
associated commodity groupings (e.g., citrus, leafy vegetables, fruiting vegetables) that had been
defined previously in the EFH (available at http://cfpub.epa.gov/ncea/risk/recordisplav.cfm?
deid=236252#tab-3). This was done to be consistent with previous analyses and with previous
versions of the EFH. CEB compared consumption of pregnant women and not-pregnant women,
aged 13 - 49 years, where pregnancy status was defined in the NHANES survey by 'ridexprg2'
variable in the NHANES demographic file.
NHANES/WWEIA collects two days of 24-hour dietary recall on foods using WWEIA
standardized food vocabulary. Because the WWEIA food vocabulary is based on foods as
reported eaten, U.S. EPA's Food Commodity Intake Database (FCID) was used to convert
WWEIA food consumption into consumption of individual food commodities. The intake rates
we calculated represent 2-day averages of intake of all forms of the commodities for pregnant
and non-pregnant women of childbearing age (13-49 years old) who provided two complete
days of 24-hour dietary recall. Two-day average intake rates of the commodities and commodity
groups of interest were first calculated for women of childbearing age. After calculating two-day
average intake for each survey respondent, summary statistics were calculated on both a
2 Ridexprg=l indicates Yes, positive lab pregnancy test or self-reported pregnant at exam; Ridexprg=2 indicates SP
not pregnant at exam.
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consumer-only and on a per capita basis using six-year survey weights and NHANES survey
design variables (i.e., strata and primary sampling unit). See the appendix-3 for the detailed
summary statistics of pregnant and non-pregnant females of child bearing age (13-49 years).
Summary statistics that were calculated for each commodity and commodity group included:
• Number of consumers,
• Percentage of the population reported consumption,
• Mean consumption,
• Standard error,
• Minimum and maximum, and,
• Various selected percentiles ranging from the 1st to the 99th.
SAS version 9.3 was used for statistical analysis. Statistical comparisons of mean consumption
between pregnant and non-pregnant females (each aged 13-49 years old) were also evaluated
using an alpha level of 0.05. Table 1 and Table 2 in Appendix 3 provide the detailed summary
statistics on both consumers only and per capita basis. Appendices 1 and 2 present the plot of
ratios3 of mean to different percentiles for consumption between pregnant and non-pregnant
females within child bearing age (13-49 years old). EPA uses the recommendation based on
Appendix-B of "Analytic And Reporting Guidelines: The Third National Health and Nutrition
Examination Survey, NHANES III (1988-94)" (available at:
http://www.cdc.gov/nchs/data/nhanes/nhanes3/nh3gui.pdf) and "Healthy People 2010 Criteria
for Data Suppression (2002)" (available at: http://www.cdc. gov/nchs/data/statnt/statnt24.pdf) for
determining reliability of tail percentiles.
3 Ratio = statistic of consumption of pregnant female / statistic of consumption of non-pregnant female; ratio > 1
indicates higher consumption for pregnant female.
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RESULTS AND DISCUSSION
This section presents some of the highlights of the dietary consumption of pregnant and non-
pregnant females. Details of the result can be found in Appendix 3.
Pregnant females have a slightly higher per capita mean intake of total vegetables (2.74 g/kg-
day) than non-pregnant female (2.43 g/kg-day), which is statistically significant (see Table 2 in
Appendix 3)4. The 95th percentile of per capita consumption of total vegetables for pregnant
females (6.26 g/kg-day) is also higher than that of non-pregnant females (5.93 g/kg-day) (Table
2, Appendix 3). On average, pregnant females consume more total fruits (1.79 g/kg-day
consumers only, 1.66 g/kg-day per capita basis) than non-pregnant females (1.18 g/kg-day
consumers only, 0.98 g/kg-day per capita basis) (See Table 1 and and Table 2). The 95th
percentile of per capita consumption of total fruits for pregnant females (5.02 g/kg-day) is also
higher than that of non-pregnant females (3.75 g/kg-day) (Table 2).
For some of the individual fruit categories, pregnant women were also found to have higher
commodity consumption than non-pregnant women. As an example, pregnant women have
significantly higher average citrus fruit consumption than non-pregnant women on both a
consumers only (1.21 g/kg-day and 0.50 g/kg-day, respectively) and a per capita basis (0.29
g/kg-day and 0.10 g/kg-day) (Table 1 and Table 2). Figure 9 demonstrates that 95th percentiles
of per capita consumption of citrus and peaches among pregnant females are more than twice
than that of non-pregnant females. In contrast, pregnant females consume less cabbage, stalk
and stem vegetables, leafy vegetables, and lettuce than non-pregnant females at both mean and
upper-end percentile consumptions (i.e., ratio of means and 95th percentiles are less than 1; see
Figure 1, Figure 5, and Figure 9).
4 Multiple testing, meaning simultaneous testing of several hypotheses, can be a concern due to inflating the
combined type I error fhttp://ies.ed.gov/ncee/pubs/20084018/app b.asp). Note that multiple tests have been
performed and the statistical differences cited here have not been corrected for this. Many of these differences may
not have been found to be statistically significant if such adjustments had been made. Thus, it is left to the data user
to determine if differences in mean consumption between pregnant and not pregnant females of child-bearing age
are substantive from an exposure assessment viewpoint.
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Although pregnant females were found to have statistically significantly higher mean
consumption than non-pregnant females for total grains (2.12 g/kg-day vs. 1.90 g/kg-day on a
per capita basis, respectively) (see Table 1 and and Table 2), the ratios of the mean and upper
percentiles are all close to one (Figures 1, 5, 6, 9) which may indicate that the difference may not
be substantial. The average per capita consumption of total dairy for pregnant females is higher
than that of non-pregnant females (5.04 g/Kg-day vs. 3.53 g/Kg-day from Table 2). There was
no statistically significant difference found in total fish and total meat consumption between
pregnant and non-pregnant women.
It is important to consider a number of issues while evaluating consumption by pregnant and
non-pregnant females. For example: whether there are sufficient numbers of individuals in each
group; whether there is a statistically significant difference in the means; whether there are
differences in the upper percentiles (e.g., 75th, 90th, and 95th); how much of a difference is
important from a substantive (exposure assessment) point of view; and how these should be
considered on a combined/synthesized basis. It should be noted that statistical tests for the
difference in consumption between pregnant and non-pregnant females was performed for the
mean, and NOT for the percentiles. In many cases at the upper (and lower) percentiles of the
consumers only distribution (and particularly for less commonly consumed food commodities or
commodity groupings), there are not adequate numbers of individuals to produce reliable
estimates of consumption. While there may be statistically significant consumption differences
between the two groups, this does not necessarily imply there would be differences in exposure
or risk.
A-7
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APPENDICES
Appendix 1: Plots of ratio of Consumption between Pregnant and Non-pregrant
Females (Consumers Only)
Appendix 2: Plots of ratio of Consumption between Pregnant and Non-pregrant
Females (Per Capita)
Appendix 3: Tables of Consumption
Appendix 4: SAS codes
Appendix 5: Reference
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Appendix 1
Plots of ratio of consumption between Pregnant and Non-
Pregnant Females (Consumers Only)
A-9
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Figure 1: Plot of the Mean Ratio in rank order (Consumers only )
Mean Ratio
List = 1
Cabbage -
A
Stalk and Stem Vegetables -
A •
Leafy Vegetables -
A i
Lettuce -
A i
Carrot -
~ i
Strawberry -
O 1
Pear-
D I
Pork -
~ !
Poultry -
~
Apple -
d
Pome Fruit -
~
Fin Fish -
Tomatoes -
Berries and Small Fruits -
"?
Total Meats -
P
Rice -
b
Fruiting Vegetables -
;~
Beef-
:~
Peas -
!~
Shell Fish -
!~
Broccoli -
! ~
0.5
1.0
2.0
2.5
PrcgnantA'on-pregnmit
| ~ Insignificantly' Different A Significantly Different |
Mean Ratio
—
c
List = 2
Total Cereal -
A
Total Fish -
~
Total Grain -
A
Total Vegetables -
A
Beans -
~
Legume Vegetables -
O
Onion -
~
Bulb Vegetables -
~
Root and Tuber Vegetables -
A
Banana -
A
White Potatoes -
A
Com -
A
Cucumbers -
~
Total Dairy -
A
Total Finite -
A
Stone Fruit -
D
Cucurbits -
A
Tropical Fruits -
A
Peaches -
D
Citrus -
A
0.5 1.0 1.5 2.0
Pregnant "Non-pregnant
I ~ Insignificantly Different A Significantly Different I
2.5
-------
Figure 2: Plot of the ratio of 5Q'h percentile in rank order (Consumers only)
>
P50 Ratio
List = 1
Strawberry -
0
Leafy Vegetables -
0 1
Cabbage -
o i
Cucumbers -
o ;
Lettuce -
0
Rice -
0
Stalk and Stem Vegetables -
0!
Fin Fish -
0
Total Fish -
cj
¦3
Carrot -
6
0
2
Peaches -
9
0
U
Poultry -
Apple -
Legume Vegetables -
Beef-
Pome Fruit -
P
p
io
o
io
Total Meats -
!0
Tomatoes -
!0
Total Cereal-
! 0
Total Vegetables -
1°
Pork-
!o
t
l
l I till
5 10 15 20 30
Pregnant Noil-pregnant
|0 Reliable Estimate 1
P50 Ratio
Broccoli
Total Grain ¦
Onion ¦
Fruiting Vegetables
Bulb Vegetables ¦
Beans
Root and Tuber Vegetables
Shell Fish'
White Potatoes
Stone Fruit ¦
Cucurbits
Berries and Small Fruits
Pear •
Total Dairy ¦
Total Fruits ¦
Peas
Com ¦
Tropical Fruits
Citrus
Banana
l.isl 2
1 1 1—TT
10 15 20 30
PregnantNon-pregnant
|Q Reliable Estimate |
-------
Figure 3: Plot of the ratio of 75th percentile in rank order (Consumers only )
>
u
P75 Ratio
List = 1
Strawberry -
O
Pear -
1
+ ;
Cabbage -
i
+ ;
Stalk and Stem Vegetables -
o i
Lettuce -
o :
Leafy Vegetables -
0 i
Cucumbers -
0 i
Carrot -
o :
Poultry -
O !
i
Pome Fruit -
¦
0
Berries and Small Fruits -
0
1
Apple -
-------
Figure 4: Plot of the ratio of 90th percentile in rank order (Consumers only)
>
UJ
P90 Ratio
List -1
Cabbage -
+
Stalk and Stem Vegetables -
+ ;
Carrot -
o ;
Peas -
+ ;
Lettuce -
o i
Pear-
+ i
Strawbeny -
+ !
Beans -
0 i
Lealj' Vegetables -
0 i
Pome Fruit -
+ !
Porfc-
0 i
Apple -
0
Broccoli -
+ ;
Poultry -
0
Beef-
cj
Berrics and Small Fruits -
q
Total Cereal -
6
Total Meals -
d
Legume Vegetables -
<5
Total Grain -
b
Root and Tuber Vegetables -
b
0.5 1.0 1.5 2.0 2.5 3.0
Pregnant "Non-pregnant
O Reliable Estimate + Less Reliable Estimate
P90 Ratio
List = 2
Fruiting Vegetables -
0
Total Vegetables -
0
Banana -
O
Tomatoes -
O
Fin Fish -
+
White Potatoes -
O
Rice -
O
Total Fish -
+
Onion -
O
Bulb Vegetables -
0
Cucumbers -
+
Total Dairy -
0
Shell Fish -
+
Corn-
0
Total Fruits -
0
Tropical Fruits -
o
Stone Fruit -
0
Cucurbits -
0
Citrus -
+
Peaches -
0
h 1 1 1 1 r
0.5 1.0 1.5 2.0 2.5 3.0
Pregnant Non-pregnant
O Reliable Estimate + Less Reliable Estimate
-------
Figure 5: Plot of the ratio of 95th percentile in rank order (Consumers only)
>
4^
P95 Ratio
List = 1
Cabbage -
+
Stalk and Stem Vegetables -
+
Carrot -
+
Pear -
+
Strawberry -
+
Lettuce -
+
Leafy Vegetables -
O
Poultry -
+
Fruiting Vegetables -
0
Tomatoes -
0
Peas -
+
Broccoli -
+
Total Meats -
0
Pork -
+
Berries and Small Fruits -
+
Beans -
+
Pome Fruit -
+
Beef-
Total Cereal -
<5
Total Grain -
Shell Fish -
H 1 1 1 1 1 H
0.50 0.75 1.00 1.25 1 50 1,75 2.00
Pregnant'Non-pregnant
| 0 Reliable Estimate + Less Reliable Estimate]
P95 Ratio
List - 2
Apple -
Rice -
+
White Potatoes -
+
Fin Fish -
+
Legume Vegetables -
O
Total Vegetables -
0
Banana -
+
Root and Tuber Vegetables -
0
Onion -
0
Bulb Vegetables -
0
Total Dairy -
0
Corn -
0
Total Fish -
+
Total Fruits -
+
Stone Fruit -
+
Cucumbers -
+
Citrus -
+
Cucurbits -
+
Tropical Fruits -
+
Peaches -
+
0.50 0.75 1.00 1.25 1.50 1.75 2.00
PregnajifNon-pregnant
| O Reliable Estimate + Less Reliable Estimate]
-------
Appendix 2
Plots of ratio of consumption between Pregnant and Non-
Pregnant Females (Per Capita)
Notes: Plots of ratios of the 50th percentile for per capita were not provided since the values
were zero for several groups of commodities. For the same reason, the ratios for other
percentiles were not plotted for some commodities.
A-15
-------
Figure 6: Plot of the mean ratio in rank order (per capita)
Mean Ratio
List = 1
Cabbage -
A
Peas -
~
Stalk and Stem Vegetables -
~
Lealy Vegetables -
A
Carrot -
~
Lettuce -
~
Broccoli -
~
Finfish -
~
Poultry -
m
Total Fish -
3
Pork -
Total Meats -
~
Starwberry -
~
Rice -
~
Fruiting Vegetables -
~
Total Cereal -
A
Tomatoes -
~
Beef-
O
Pears -
P
A
Total Grain
Total Vegetables -
A
i 1 1 i 1 H
0.5 1.0 1.5 2.0 2.5 3.0
Pregnant Non-pregnant
~ Insignificantly Different A Significantly Different |
Mean Ratio
List = 2
Beans -
~
Berries and Small Fruits -
~
Legume Vegetables -
~
Bulb Vegetables -
~
Onion -
~
Root and Tuber Vegetables -
A
Shellfish -
~
Pome Fruit -
~
Apple -
~
White Potatoes -
A
Com -
A
Total Dairy -
A
Cucumber -
~
Banana -
A
Total Fraits -
A
Cucurbits -
A
Tropical Fruits -
A
Stone Fruits -
A
Peaches -
A
Citrus -
A
~i 1 1 1 1 H
0,5 1.0 1.5 2.0 2.5 3.0
Pregnant "Non-pregnant
| ~ Insignificantly Different A Significantly Different
-------
Figure 7: Plot of the ratio of 75Til percentile in rank order (per capita)
l'7S Ratio
>
-J
-3
=
2
c
U
List = 1
Total Fish -
O
Leafy Vegetables -
0 i
Lettuce -
o;
Carrot -
o:
Poultry -
cb
Cucumber -
0
Total Cereal -
?
Rice -
P
Total Meats -
b
Total Vegetables -
b
Pork -
b
0.01
PrcgnantNon-pregnanl
|Q Reliable Estimate |
II)
P75 Ratio
Total Grain
Tomatoes
Beef
Bulb Vegetables
Fruiting Vegetables
Onion
Berries and Small Fruits
Root and Tuber Vegetables
Legume Vegetables
Beans
Corn
Total Dairy
White Potatoes
Cucurbits
Total Fruits
Peaches
Tropical Fruits -
Stone Fruits
Pome Fruit
Banana
0.01
List = 2
0
P
'O
0
0
o
0
0
o
0
0
o
0
0
0
-I 1
0.1 1
PregnantNon-pregnant
10
O Reliable Estimate
-------
Figure 8: Plot of the ratio of 90th percentile in rank order (per capita)
>
00
P90 Ratio
List - 1
Peas -
O;
Finfish -
a.
Leafy Vegetables
cj
Tola! Pish -
cj
Cucumber -
d
Carrot -
Q
Poultry -
ci
Lettuce -
0
Pork -
?
Total Cereal -
Legume Vegetable- -
6
Broccoli -
?
Total Meats -
9
Beef-
6
Stalk and Stem Vegetables -
6
Total Grain -
0
Root and Tuber Vegetables -
-------
Figure 9: Plot of the ratio of the 95th percentile in rank order (per capita)
>
vo
P95 Ra tio
List 1
Cabbage -
0
Lettuce -
0 ;
Carrot -
o;
Leafy Vegetables -
0
Stalk and Stem Vegetables -
o;
Fruiting Vegetables -
o:
Beef-
o;
Poultry -
d
Beans -
cj
Tomatoes -
d
Total Meats -
0
Pork -
q
Total Cereal -
-------
Appendix 3: Tables of consumption
Table 1: Table of Consumption for Consumers Only (g/kg-day)
Commodity
Pregnancy3
N
Mean
SE
max
P99
p95
p90
p75
p50
p25
p10
p5
p1
min
Total Vegetables
PREGNANT
NOT-PREGNANT
612
4318
2.7409*
2.4342
0.1362
0.0633
18.3032?
17.0561 f
9.5662f
8.7948
6.2623
5.9334
4.9957
4.6928
3.5249
3.2537
2.3098
2.034
1.4698
1.1609
0.9457
0.6292
0.4169
0.3716
0.2254f
0.0524
0.0144f
O.OOOOf
Total Fruits
PREGNANT
NOT-PREGNANT
558
3640
1.7900*
1.1751
0.1424
0.0482
11,0089f
16.6742f
8.1086f
6.4516
5.3692f
4.0614
4.6415
3.1614
2.818
1.67
1.0951
0.6769
0.1481
0.0873
0.0057
0.0039
0.0002f
0.0001
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Apple
PREGNANT
NOT-PREGNANT
245
1181
0.8555
0.8652
0.1174
0.0471
3.6472f
6.9416f
3.1992f
3.6275f
2.7473f
2.7035
2.0214
2.2258
1.3295
1.3295
0.7267
0.6922
0.0669
0.0405
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Banana
PREGNANT
NOT-PREGNANT
383
2259
0.5289*
0.4089
0.0554
0.0243
3.2114f
6.7417f
2.7927 f
2.4066
1.9699f
1.7481
1.3603
1.2702
0.8206
0.7504
0.4039
0.0133
0.0006
0.0000
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Beans
PREGNANT
NOT-PREGNANT
319
1964
0.4348
0.3853
0.039
0.0148
4.9616f
4.0313f
2.2083f
2.1228f
1.1644f
1.2136
0.7963
0.9092
0.6541
0.5279
0.3176
0.2523
0.1294
0.0953
0.0387
0.019
0.0061 f
0.0076
0.0021 f
0.0014f
O.OOOOf
O.OOOOf
Berries and Small Fruits
PREGNANT
NOT-PREGNANT
429
2821
0.3309
0.328
0.0419
0.0177
4.6918f
10.1279f
3.4096f
3.0955
1.2874f
1.3665
0.9203
0.9441
0.3649
0.3752
0.0983
0.0681
0.0098
0.0106
0.0001
0.0001
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Broccoli
PREGNANT
NOT-PREGNANT
95
610
0.5785
0.5422
0.0836
0.0406
3.5508f
3.6304f
3.5508f
3.2844f
1.4480f
1.5446
1.0540f
1.1239
0.7997f
0.7188
0.427
0.3705
0.2331 f
0.1766
0.0884f
0.0713
0.0768f
0.038
0.0436f
0.0120f
0.0183f
0.0019f
Bulb Vegetables
PREGNANT
NOT-PREGNANT
596
4206
0.1988
0.1664
0.0157
0.0056
2.0595f
2.7933f
0.9020f
0.9763
0.6186
0.5396
0.497
0.4062
0.273
0.2294
0.1293
0.1032
0.0466
0.0313
0.0154
0.0071
0.0075
0.0017
0.0014f
0.0002
0.0001 f
O.OOOOf
Cabbage
PREGNANT
NOT-PREGNANT
79
497
0.2362*
0.3867
0.0419
0.0396
2.4063f
3.9186f
2.4063f
3.4557f
0.8008f
1.2931
0.5143f
0.9659
0.3068f
0.4480
0.1323
0.1848
0.0557f
0.0318
0.0214f
0.0122
0.0133f
0.0055
0.0053f
0.0027f
0.0007f
0.0002f
Carrot
PREGNANT
NOT-PREGNANT
292
1850
0.2066
0.2493
0.0213
0.0123
2.2291 f
4.2628f
1.121 Of
1,4977f
0.6540f
0.9064
0.4855
0.635
0.2931
0.3398
0.1277
0.1279
0.0280
0.0374
0.0104
0.0134
0.0086f
0.0054
0.0004f
0.0001 f
O.OOOOf
O.OOOOf
Citrus
PREGNANT
156
1.2084*
0.1988
5.4783f
4.8469f
3.2278f
3.1720f
1.7250
0.9052
0.3252
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
-------
Commodity
Pregnancy3
N
Mean
SE
max
P99
p95
p90
p75
p50
p25
p10
p5
p1
min
NOT-PREGNANT
877
0.5025
0.0474
13.4653f
3.2821 f
2.0508
1.4286
0.7882
0.0572
0.0000
0.0000
0.0000
O.OOOOf
O.OOOOf
Corn
PREGNANT
NOT-PREGNANT
604
4157
0.4228*
0.3194
0.0293
0.011
4.2018f
4.5603f
1,9473f
1.9947
1.4241
1.1525
1.1129
0.8296
0.5854
0.432
0.2783
0.1656
0.0543
0.0342
0.0069
0.0054
0.001
0.0018
0.0001 f
0.0001
O.OOOOf
O.OOOOf
Cucumbers
PREGNANT
NOT-PREGNANT
249
1679
0.2944
0.2187
0.0964
0.0128
3.8437f
3.4979f
3.8437f
1,8698f
1.1579f
0.8307
0.7107f
0.5708
0.2147
0.2627
0.0729
0.096
0.0402
0.0306
0.0224f
0.0078
0.0020f
0.0016
0.0006f
O.OOOOf
O.OOOOf
O.OOOOf
Cucurbits
PREGNANT
NOT-PREGNANT
309
1990
0.8714*
0.5417
0.1366
0.0667
14.6581 f
14.1597f
5.2672f
8.1859
3.8437f
2.2687
2.9325
1.3743
0.9472
0.4491
0.2019
0.1401
0.053
0.0455
0.0276
0.0123
0.0105f
0.0034
0.0011f
0.0000
O.OOOOf
O.OOOOf
Fruiting Vegetables
PREGNANT
NOT-PREGNANT
601
4134
0.778
0.7454
0.0424
0.0236
7.0745f
12.3983f
2.9737f
3.857
2.0795
2.4173
1.7812
1.6995
1.1326
1.006
0.6068
0.4893
0.2611
0.1987
0.0457
0.0328
0.0061
0.0015
0.0003f
0.0000
O.OOOOf
O.OOOOf
Leafy Vegetables
PREGNANT
NOT-PREGNANT
576
3978
0.4900*
0.6133
0.0334
0.0252
4.5369f
8.7540f
2.8725f
4.209
1.637
2.0113
1.3271
1.5144
0.7105
0.8758
0.2255
0.3322
0.0949
0.0844
0.0276
0.0022
0.0011
0.0005
O.OOOOf
0.0001
O.OOOOf
O.OOOOf
Legume Vegetables
PREGNANT
NOT-PREGNANT
597
4100
0.4019
0.3451
0.0781
0.0178
7.2348f
12.6565f
5.0871 f
3.2299
1.4662
1.42
0.8981
0.9059
0.5025
0.3949
0.0995
0.0943
0.0057
0.0059
0.002
0.0015
0.0007
0.0006
0.0002f
0.0001
O.OOOOf
O.OOOOf
Lettuce
PREGNANT
NOT-PREGNANT
381
2492
0.3715*
0.4559
0.0266
0.0192
3.4258f
4.8880f
2.1842f
3.1643
1.1383f
1.4736
0.8173
1.0351
0.4731
0.5986
0.2197
0.2612
0.1133
0.1094
0.0535
0.0586
0.0500f
0.0432
0.0317f
0.0163
0.0004f
0.0002f
Onion
PREGNANT
NOT-PREGNANT
595
4175
0.1916
0.1604
0.0157
0.0055
2.0222f
2.7597f
0.8775f
0.9458
0.5947
0.523
0.4595
0.3903
0.262
0.2239
0.1215
0.0983
0.0434
0.0271
0.0134
0.005
0.0055
0.0012
0.0012f
0.0001
0.0001 f
O.OOOOf
Peaches
PREGNANT
NOT-PREGNANT
317
2034
0.1649
0.0965
0.0403
0.0108
2.4715f
7.3052f
1,3779f
1.6141
1.1774f
0.5932
0.6737
0.2266
0.0328
0.015
0.0018
0.0018
0.0000
0.0000
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Pear
PREGNANT
NOT-PREGNANT
56
318
0.5241
0.6024
0.1086
0.0693
3.2842f
3.3764f
1,9690f
3.3764f
1.7184f
2.3513f
1.3259f
1.6176
0.6036f
0.9109
0.368
0.2354
0.1464f
0.1061
0.1166f
0.0405
0.0597f
0.0282f
0.0020f
0.0020f
0.0020f
0.0015f
Peas
PREGNANT
NOT-PREGNANT
101
743
0.2915
0.2768
0.025
0.0207
1.5291f
9.8985f
0.8559f
1,5275f
0.7997f
0.8697
0.5298f
0.6855
0.4135f
0.3537
0.2659
0.1636
0.1028f
0.0703
0.0506f
0.0335
0.0338f
0.0187
0.0145f
0.0082f
0.0096f
0.0007f
Pome Fruit
PREGNANT
NOT-PREGNANT
265
1354
0.9136
0.9177
0.1084
0.0425
3.9823f
6.9416f
3.2779f
3.9486f
2.7473f
2.8361
2.0214f
2.2962
1.3295
1.3911
0.7496
0.6751
0.1310
0.0931
0.0055f
0.0001
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
-------
>
to
to
Commodity
Pregnancy3
N
Mean
SE
max
P99
p95
p90
p75
p50
p25
p10
p5
p1
min
Root and Tuber
Vegetables
PREGNANT
NOT-PREGNANT
612
4315
1.0331*
0.8416
0.0747
0.0192
6.0423f
7.3177f
4.2019f
3.6881
2.6676
2.3595
1.9222
1.8614
1.4077
1.1519
0.7915
0.6144
0.4405
0.2829
0.1804
0.1249
0.0893
0.0712
0.0267f
0.0136
0.0017f
O.OOOOf
Stalk and Stem
Vegetables
PREGNANT
NOT-PREGNANT
131
764
0.1541*
0.213
0.025
0.0146
1.2545f
4.1523f
0.8364f
1,2383f
0.5211f
0.7425
0.2939f
0.5431
0.1737
0.2417
0.0961
0.1082
0.0458
0.0391
0.0238f
0.0197
0.0108f
0.0132
0.0062f
0.0009f
0.0052f
0.0001 f
Stone Fruit
PREGNANT
NOT-PREGNANT
340
2137
0.2749
0.1712
0.0585
0.017
3.3603f
7.3052f
2.2333f
2.1733
1.3290f
0.9654
1.2172
0.5724
0.2702
0.0811
0.0082
0.0057
0.0001
0.0000
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Strawberry
PREGNANT
NOT-PREGNANT
261
1532
0.1761
0.2122
0.0353
0.0245
2.5521 f
5.8368f
2.1447f
2.2975f
0.8059f
1.085
0.6344f
0.7302
0.1343
0.2074
0.0078
0.0127
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Tomatoes
PREGNANT
NOT-PREGNANT
578
3830
0.7252
0.7226
0.0409
0.0221
4.9361 f
8.0503f
2.561 Of
3.7422
1.926
2.1705
1.6511
1.5201
1.0677
0.9641
0.5503
0.487
0.2484
0.2360
0.0786
0.0869
0.0068
0.0339
0.0039f
0.0063
0.0012f
0.0002f
Tropical Fruits
PREGNANT
NOT-PREGNANT
447
2700
0.7283*
0.4465
0.0817
0.0233
5.2499f
10.2408f
3.6671 f
2.7993
3.2607f
1.8544
2.1907
1.4115
1.0625
0.7564
0.4187
0.0417
0.0006
0.0000
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
White Potatoes
PREGNANT
NOT-PREGNANT
563
3868
0.6993*
0.5335
0.0697
0.0194
5.5088f
6.8670f
3.241 ©f
3.1915
2.0394f
1.9811
1.5605
1.4324
1.0837
0.7708
0.4179
0.2917
0.1491
0.0078
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Total Fish
PREGNANT
NOT-PREGNANT
153
1204
0.7126
0.6456
0.1172
0.0357
5.3152f
8.6423f
3.7920f
3.4532f
2.3960f
1.9331
1.5882f
1.4405
0.9964
0.858
0.413
0.4265
0.1826
0.1796
0.021 Of
0.0012
0.0005f
0.0003
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Fin Fish
PREGNANT
NOT-PREGNANT
108
882
0.5895
0.5877
0.1005
0.0328
3.4282f
8.4828f
3.4282f
2.8633f
1.8013f
1.7459
1.3664f
1.2552
0.9390f
0.7956
0.373
0.4013
0.1677f
0.1403
0.0007f
0.0005
0.0002f
0.0001
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Shell Fish
PREGNANT
NOT-PREGNANT
75
492
0.5098
0.4827
0.0909
0.0525
2.6357f
5.3172f
2.6357f
2.9026f
1.7920f
1.7853
1.4464f
1.0997
0.6000f
0.5776
0.4302
0.3142
0.1305f
0.0988
0.0113f
0.0110
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
O.OOOOf
O.OOOOf
Rice
PREGNANT
NOT-PREGNANT
555
3690
0.239
0.2318
0.0383
0.0143
2.4799f
5.8933f
1.6971 f
1.9847
0.9795f
0.9516
0.7084
0.6498
0.3377
0.3018
0.0547
0.0617
0.0002
0.0001
0.0000
0.0000
O.OOOOf
0.0000
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Total Grain
PREGNANT
NOT-PREGNANT
612
4318
2.1215*
1.901
0.0675
0.0353
7.7600f
9.7936f
4.8763f
5.6565
3.9381
3.9351
3.4405
3.3588
2.6515
2.4417
1.9386
1.6773
1.3781
1.1244
1.0295
0.7499
0.7923
0.5308
0.5704f
0.2034
0.2139f
0.0003f
-------
Commodity
Pregnancy3
N
Mean
SE
max
P99
p95
p90
p75
p50
p25
p10
p5
p1
min
Total Cereal
PREGNANT
NOT-PREGNANT
612
4320
3.0212*
2.8005
0.0807
0.0375
10.5650f
13.2925f
7.2569f
8.0296
5.7144
5.7685
4.7326
4.8472
3.7025
3.5823
2.8268
2.4992
2.0819
1.6946
1.5491
1.1404
1.2502
0.8797
0.8298f
0.4203
0.5073f
0.0040f
Total Meats
PREGNANT
NOT-PREGNANT
607
4259
1.5989
1.563
0.053
0.0321
5.9123f
12.2299f
4.5395f
4.9672
3.3366
3.5589
2.943
3.0123
2.1465
2.0343
1.4788
1.3157
0.9328
0.8184
0.5141
0.4329
0.3205
0.2532
0.0279f
0.0073
0.0040f
O.OOOOf
Beef
PREGNANT
NOT-PREGNANT
540
3744
0.707
0.6721
0.0448
0.0209
4.7364f
8.8360f
3.2504f
3.2354
2.0882f
2.1274
1.5265
1.5758
0.9658
0.905
0.5091
0.4755
0.2225
0.1502
0.0459
0.0128
0.0058f
0.0017
0.0005f
0.0001
O.OOOOf
O.OOOOf
Poultry
PREGNANT
NOT-PREGNANT
488
3414
0.8237
0.8688
0.056
0.0259
4.4038f
12.2299f
3.4986f
3.5647
2.0535f
2.4044
1.7574
1.8348
1.0499
1.198
0.6921
0.6778
0.3300
0.3186
0.1395
0.0964
0.0287f
0.0188
O.OOOOf
0.0000
O.OOOOf
O.OOOOf
Pork
PREGNANT
NOT-PREGNANT
529
3397
0.3335
0.3549
0.0245
0.0133
3.3536f
5.0484f
1,9637f
2.3334
1.1193f
1.1883
0.7814
0.8873
0.4928
0.4678
0.2015
0.1772
0.0599
0.0657
0.0126
0.0111
0.0023f
0.0025
O.OOOOf
0.0001
O.OOOOf
O.OOOOf
Total Dairy
PREGNANT
NOT-PREGNANT
612
4310
5.0444*
3.5385
0.2752
0.1241
52.6777f
52.0738f
22.5236f
17.1615
12.5184
10.5089
10.0453
7.9718
6.9105
4.9051
3.8797
2.4097
2.2297
1.0336
0.8209
0.4418
0.4249
0.2213
0.0900f
0.0305
0.0014f
0.0005f
a Limited to females aged 13 -49 in both PREGNANT and NON-PREGNANT categories
Notes: indicates mean of pregnant female is statistically significantly different from the nonpregnant female; alpha=0.05 level. Significant differences
were NOT evaluated for percentiles values.
'f' indicates estimates are less statistically reliable based on np < 8 * 'Design Effect' guidance published in the Joint Policy on Variance Estimation and
Statistical Reporting Standards on NHANES III and CSFII
-------
Table 2: Table of Consumption Per Capita (g/kg-day)
Commodity
Pregnancy3
N
Mean
SE
Percent
max
P99
p95
p90
p75
p50
p25
p10
P5
p1
min
Total Vegetables
PREGNANT
NOT-PREGNANT
612
4321
2.7409*
2.4304
0.1362
0.0633
100
99.8448
18.3032f
17.0561 f
9.5662f
8.7948
6.2623
5.9334
4.9957
4.6917
3.5249
3.249
2.3098
2.0329
1.4698
1.1555
0.9457
0.6226
0.4169
0.3618
0.2254f
0.0454
0.0144f
O.OOOOf
Total Fruits
PREGNANT
612
1.6553*
0.13
92.4767
11.0089f
8.1086f
5.0151
4.3416
2.6476
0.9664
0.0677
-
-
~t
~t
NOT-PREGNANT
4321
0.9821
0.0434
83.5701
16.6742f
6.1871
3.7519
2.8349
1.4133
0.3885
0.0041
-
-
-
Apple
PREGNANT
NOT-PREGNANT
612
4321
0.3272
0.2432
0.0527
0.017
38.2497
28.11
3.6472f
6.941 ©f
2.8438f
2.8369
2.0214
1.6103
1.25
1.0502
0.2119
-
-
-
-
Banana
PREGNANT
612
0.3351*
0.041
63.3702
3.2114f
2.2835f
1.7832
1.2265
0.5351
-
-
-
-
NOT-PREGNANT
4321
0.2018
0.0114
49.3507
6.7417f
2.2139
1.2661
0.846
0.0124
-
-
-
-
-
Beans
PREGNANT
NOT-PREGNANT
612
4321
0.2024
0.1759
0.021
0.0089
46.5518
45.6579
4.961 ©f
4.0313f
1,5977f
1.6446
0.7963
0.8526
0.7066
0.5962
0.2864
0.2132
-
-
-
-
Berries and Small Fruits
PREGNANT
NOT-PREGNANT
612
4321
0.2497
0.2155
0.0273
0.0122
75.4711
65.7116
4.6918f
10.1279f
2.6544f
2.6247
1.2459
1.128
0.7939
0.6634
0.2015
0.1664
0.0338
0.0090
-
-
-
Broccoli
PREGNANT
NOT-PREGNANT
612
4321
0.0843
0.0926
0.0152
0.0088
14.5665
17.0872
3.5508f
3.6304f
1.1927f
1.4667
0.7369
0.6719
0.2817
0.2857
-
-
-
-
-
-t
Bulb Vegetables
PREGNANT
NOT-PREGNANT
612
4321
0.1939
0.1619
0.0155
0.0054
97.544
97.3381
2.0595f
2.7933f
0.9020f
0.963
0.6164
0.5334
0.4936
0.4039
0.2632
0.2252
0.1266
0.0989
0.0422
0.0270
0.0104
0.0039
0.0036
0.0006
Cabbage
PREGNANT
NOT-PREGNANT
612
4321
0.0190*
0.0465
0.0038
0.0056
8.0475
12.0242
2.4063f
3.9186f
0.4209f
1.0585
0.0626
0.2591
0
0.0173
-
-
-
-
-
-t
Carrot
PREGNANT
NOT-PREGNANT
612
4321
0.0952
0.1139
0.0114
0.0073
46.0861
45.707
2.2291 f
4.2628f
1.0661 f
1.1775
0.4615
0.5867
0.3508
0.3937
0.0920
0.1050
-
-
-
-
-t
Citrus
PREGNANT
NOT-PREGNANT
612
4321
0.2868 *
0.103
0.0548
0.0125
23.7326
20.5039
5.4783f
13.4653f
3.2278f
2.1311
1.7983
0.8047
0.9417
0.0724
-
-
-
-
-
-t
Corn
PREGNANT
NOT-PREGNANT
612
4321
0.4212*
0.306
0.0291
0.011
99.6278
95.8244
4.2018f
4.5603f
1,9405f
1.9473
1.4241
1.1319
1.1129
0.8026
0.5826
0.4207
0.2783
0.1507
0.0543
0.0265
0.0063
0.0023
0.001
-t
-------
Commodity
Pregnancy3
N
Mean
SE
Percent
max
P99
p95
p90
p75
p50
p25
p10
P5
P1
min
Cucumber
PREGNANT
NOT-PREGNANT
612
4321
0.1365
0.0939
0.0463
0.0065
46.3533
42.9631
3.8437f
3.4979f
3.8437f
1.2605
0.6454
0.4996
0.2417
0.2779
0.0712
0.0715
-
-
-
-
Cucurbits
PREGNANT
612
0.4841 f*
0.0852
55.5591
14.6581 f
5.2542f
2.9325
1.6992
0.2526
0.0276
-
-
-
NOT-PREGNANT
4321
0.2739
0.0358
50.5659
14.1597f
3.5024
1.4284
0.6261
0.1436
-
-
-
-
-
Fruiting Vegetables
PREGNANT
NOT-PREGNANT
612
4321
0.7707
0.7152
0.0416
0.0227
99.0631
95.9516
7.0745f
12.3983f
2.9737f
3.828
2.0795
2.333
1.7622
1.6552
1.1282
0.965
0.6010
0.4575
0.2484
0.1588
0.0427
0.0053
0.0061
Leafy Vegetables
PREGNANT
NOT-PREGNANT
612
4321
0.4578*
0.5694
0.0315
0.0234
93.4327
92.8396
4.5369f
8.7540f
2.8725f
4.1523
1.5813
1.9707
1.1935
1.4699
0.6463
0.8226
0.2043
0.2839
0.0783
0.0578
0.0002
0.0002
-
Legume Vegetables
PREGNANT
NOT-PREGNANT
612
4321
0.3820
0.3285
0.0746
0.017
95.0575
95.1878
7.2348f
12.6565f
5.0871 f
3.1194
1.3855
1.3741
0.8601
0.8767
0.4818
0.3779
0.0710
0.0776
0.0045
0.0044
0.0008
0.0007
-
Lettuce
PREGNANT
NOT-PREGNANT
612
4321
0.2384
0.2736
0.0199
0.0127
64.1721
60.0095
3.4258f
4.8880f
2.1842f
2.1578
0.8857
1.1534
0.7674
0.8039
0.2978
0.3428
0.0938
0.0767
-
-
-
Onion
PREGNANT
NOT-PREGNANT
612
4321
0.1867
0.1547
0.0154
0.0052
97.4429
96.4887
2.0222f
2.7597f
0.8775f
0.9437
0.5906
0.5182
0.4548
0.3847
0.2542
0.2153
0.1182
0.0929
0.0392
0.0229
0.0076
0.0020
0.0025
0.0002
Peaches
PREGNANT
NOT-PREGNANT
612
4321
0.0900*
0.0429
0.0237
0.0051
54.6015
44.4244
2.4715f
7.3052f
1.3107f
1.0803
0.7565
0.1401
0.2456
0.0188
0.0028
0.0009
-
-
-
-
Pears
PREGNANT
NOT-PREGNANT
612
4321
0.0467
0.0419
0.0109
0.0061
8.9124
6.9638
3.2842f
3.3764f
1,3259f
1.382
0.2148
0.1123
-
-
-
-
-
-
Peas
PREGNANT
NOT-PREGNANT
612
4321
0.0392
0.0509
0.0061
0.0045
13.449
18.3979
1.5291 f
9.8985f
0.6656f
0.8108
0.3695
0.3362
0.1028
0.1474
-
-
-
-
-
Pome Fruit
PREGNANT
NOT-PREGNANT
612
4321
0.3739
0.2851
0.0551
0.0182
40.9285
31.0724
3.9823f
6.941 ©f
2.8438f
3.1532
2.0214
1.8571
1.3295
1.1835
0.5293
0.0205
-
-
-
-
-t
Root and Tuber Vegetables
PREGNANT
NOT-PREGNANT
612
4321
1.0331*
0.8398
0.0747
0.0193
100
99.7796
6.0423f
7.3177f
4.2019f
3.6881
2.6676
2.3595
1.9222
1.8547
1.4077
1.1455
0.7915
0.6135
0.4405
0.2825
0.1804
0.1238
0.0893
0.0683
0.0267f
0.0081
0.0017f
Stalk and Stem Vegetables
PREGNANT
612
0.0362
0.0072
23.4862
1,2545f
0.5211f
0.2262
0.1189
-
-
-
-
-
-t
-------
>
to
On
Commodity
Pregnancy3
N
Mean
SE
Percent
max
P99
p95
p90
p75
p50
p25
p10
P5
P1
min
NOT-PREGNANT
4321
0.0453
0.004
21.2878
4.1523f
0.7754
0.2641
0.1186
-
-
-
-
-
-
Stone Fruits
PREGNANT
NOT-PREGNANT
612
4321
0.1660*
0.0807
0.0371
0.0088
60.3981
47.1751
3.3603f
7.3052f
2.2333f
1.5078
1.2221
0.5324
0.6737
0.1266
0.0223
0.0032
-
-
-
-
Strawberry
PREGNANT
NOT-PREGNANT
612
4321
0.0866
0.0811
0.0161
0.0102
49.1941
38.2386
2.5521 f
5.8368f
1,7978f
1.3393
0.6344
0.5974
0.2448
0.1869
0.0068
-
-
-
-
Tomatoes
PREGNANT
NOT-PREGNANT
612
4321
0.6961
0.6322
0.038
0.0206
95.9938
87.4921
4.9361 f
8.0503f
2.5387f
3.5407
1.882
2.0145
1.6297
1.4488
0.9937
0.8865
0.4975
0.4136
0.2159
0.1306
0.0232
0.0039
Tropical Fruits
PREGNANT
NOT-PREGNANT
612
4321
0.5242*
0.2651
0.0707
0.015
71.9747
59.3817
5.2499f
10.2408f
3.4876f
2.4093
2.3463
1.5285
1.6738
1.0067
0.7996
0.1701
0.0018
-
-
-
White Potatoes
PREGNANT
NOT-PREGNANT
612
4321
0.6500*
0.4811
0.0687
0.019
92.9504
90.1768
5.5088f
6.8670f
3.2416f
3.0696
2.0394
1.9173
1.5305
1.3505
1.0251
0.6881
0.3950
0.2072
0.0578
0.0002
-
-
Total Fish
PREGNANT
NOT-PREGNANT
612
4321
0.1892
0.1855
0.0335
0.014
26.5478
28.7341
5.3152f
8.6423f
2.3960f
2.2373
1.2774
1.0997
0.5529
0.678
0.0005
0.0336
-
-
-
-
Finfish
PREGNANT
NOT-PREGNANT
612
4321
0.1234
0.1344
0.0238
0.0113
20.931
22.8696
3.4282f
8.4828f
1.8013f
1.8532
0.939
0.9024
0.373
0.4803
-
-
-
-
-
Shellfish
PREGNANT
NOT-PREGNANT
612
4321
0.0658
0.0511
0.0153
0.0066
12.9067
10.5888
2.6357f
5.3172f
1,4464f
1.0997
0.4763
0.339
0.0807
-
-
-
-
-
-t
Rice
PREGNANT
NOT-PREGNANT
612
4321
0.2153
0.2009
0.0371
0.0131
90.0888
86.6753
2.4799f
5.8933f
1.6971 f
1.8148
0.9795
0.8548
0.66
0.6023
0.2718
0.2592
0.0268
0.0095
-
-
-
-t
Total Grain
PREGNANT
NOT-PREGNANT
612
4321
2.1215*
1.898
0.0675
0.0355
100
99.842
7.7600f
9.7936f
4.8763f
5.6565
3.9381
3.9351
3.4405
3.3588
2.6515
2.4413
1.9386
1.6768
1.3781
1.1233
1.0295
0.7472
0.7923
0.5263
0.5704f
0.1959
0.2139f
Total Cereal
PREGNANT
NOT-PREGNANT
612
4321
3.0212*
2.7994
0.0807
0.0375
100
99.9604
10.5650f
13.2925f
7.2569f
8.0296
5.7144
5.7685
4.7326
4.8472
3.7025
3.5823
2.8268
2.498
2.0819
1.6930
1.5491
1.1404
1.2502
0.8723
0.8298f
0.4120
0.5073f
Total Meats
PREGNANT
NOT-PREGNANT
612
4321
1.5884
1.5318
0.0527
0.0306
99.3392
98.0062
5.9123f
12.2299f
4.5395f
4.9672
3.3366
3.5359
2.943
2.9814
2.1465
2.0121
1.4682
1.2954
0.8934
0.7944
0.5047
0.3580
0.2914
0.1438
0.0040f
-------
Commodity
Pregnancy3
N
Mean
SE
Percent
max
P99
p95
p90
p75
p50
p25
p10
P5
P1
min
Beef
PREGNANT
NOT-PREGNANT
612
4321
0.6359
0.5758
0.0455
0.0192
89.9432
85.676
4.7364f
8.8360f
3.2504f
3.13
1.7828
1.9776
1.4426
1.4571
0.9422
0.8329
0.4735
0.3664
0.1266
0.0320
-
-
-t
Poultry
PREGNANT
NOT-PREGNANT
612
4321
0.6661
0.6677
0.0529
0.022
80.8758
76.8475
4.4038f
12.2299f
3.4986f
3.4113
1.987
2.1714
1.5126
1.635
0.9774
0.9934
0.5249
0.4598
0.0680
0.0027
-
-
-t
-t
Pork
PREGNANT
NOT-PREGNANT
612
4321
0.2851
0.2764
0.0211
0.0108
85.4822
77.8779
3.3536f
5.0484f
1,6992f
2.2973
1.0094
1.0677
0.7513
0.7863
0.3804
0.3506
0.1709
0.107
0.0220
0.0013
-
-
-t
-t
Total Dairy
PREGNANT
NOT-PREGNANT
612
4321
5.0444*
3.5258
0.2752
0.1246
100
99.6417
52.6777f
52.0738f
22.5236f
17.1615
12.5184
10.4803
10.0453
7.9399
6.9105
4.9026
3.8797
2.407
2.2297
1.0216
0.8209
0.4305
0.4249
0.2122
0.0900f
0.0187
0.0014f
a Limited to females aged 13 -49 in both PREGNANT and NONPREGNANT categories
Notes:
indicates mean of pregnant female is statistically significantly different from the nonpregnant female; alpha=0.05 level. Significant differences were NOT
^ evaluated for percentiles values.
to '"i"' indicates estimates are less statistically reliable based on np < 8 * 'Design Effect' guidance published in the Joint Policy on Variance Estimation and
Statistical Reporting Standards on NHANES III and CSFII
indicates either no reported per capita consumption at this percentile, or per capita consumption is <0.0001 g/kg bw
-------
Appendix 4: SAS codes
libname NCEA "F:\NCEA Pregnant women";
PROC IMPORT OUT= Work.FCID_description
DATATABLE= "FCID_Code_Description"
DBMS=ACCESS REPLACE;
Database = "G:\WWEIA-FCID\Final Databases\WWEIA FCID 2003-08 (1-17-13).mdb";
SCANMEMO=YES;
USEDATE=NO;
SCANTIME=YES;
RUN;
proc contents data=WORK.FCID description ;
run;
PROC IMPORT OUT= WORK.demo_0308
DATATABLE= "WWEIA_Demo_0308"
DBMS=ACCESS REPLACE;
DATABASE= "G:\WWEIA-FCID\Final Databases\WWEIA FCID 2003-08 (1-17-
13).mdb" ;
SCANMEMO=YES;
USEDATE=NO;
SCANTIME=YES;
RUN;
proc contents data=WORK.demo 03 0 8 ;
run;
PROC IMPORT OUT= NCEA.commodity_Intake_0308
DATATABLE= "Commodity Intake 0308"
DBMS=ACCESS REPLACE;
DATABASE= "G:\WWEIA-FCID\Final Databases\WWEIA FCID 2003-08 (1-17-
13).mdb" ;
SCANMEMO=YES;
USEDATE=NO;
SCANTIME=YES;
RUN;
proc sort data=WORK.demo 0308;
by seqn;
run;
A-28
-------
libname
libname
libname
libname
libname
libname
rpl xport
dml xport
rp2 xport
dm2 xport
rp3 xport
dm3 xport
"F:\NCEA Pregnant women\RHQ c.xpt";
"F:\NCEA Pregnant women\demo c.xpt";
"F:\NCEA Pregnant women\RHQ d.xpt";
"F:\NCEA Pregnant women\demo d.xpt";
"F:\NCEA Pregnant women\RHQ e.xpt";
"F:\NCEA Pregnant women\demo e.xpt";
options mprint;
data NCEA.data 34;
merge
dml.demo c(keep=seqn riagendr ridageyr ridrethl RIDEXPRG
rpl.RHQ c(keep=seqn RHD143 RHQ200 );
by seqn;
run;
sdmvpsu sdmvstra)
data NCEA.data 56;
merge
dm2.demo d(keep=seqn riagendr ridageyr ridrethl RIDEXPRG
rp2.RHQ d(keep=seqn RHD143 RHQ200 );
by seqn;
run;
sdmvpsu sdmvstra)
data NCEA.data 78;
merge
dm3.demo e(keep=seqn riagendr ridageyr ridrethl RIDEXPRG sdmvpsu sdmvstra)
rp3.RHQ e(keep=seqn RHD143 RHQ200);
by seqn;
run;
data demographicl;
set NCEA.data_34 NCEA.data_56 NCEA.data_78;
varunit=sdmvpsu ;
varstrat=sdmvstra;
age=ridageyr;
sex=riagendr;
if RIDEXPRG=1 then preg f=l;
else if RIDEXPRG=2 then preg f=0;
run;
PROC SQL;
CREATE TABLE demographic2 AS
SELECT a.*, b.preg_f, b.RHQ200, b.RHD143, b.RIDEXPRG, b.age, b.sex
FROM demo 0308 as a inner join demographicl as b
ON a.seqn=b.seqn
r
A-29
-------
QUIT;
proc contents data= demographic2;
run;
Proc sql;
CREATE TABLE FCID_commodity2 AS
SELECT *
FROM NCEA.commodity Intake 0308 as a inner join FCID description as b
ON a.FCID_CODE=b.FCID_code~ ~
r
QUIT;
proc contents data=FCID commodity2
run;
data NCEA.FCID commodity2;
set FCID commodity2;
run;
data NCEA.demographic2;
set demographic2;
run;
tttttt SAS MACRO tttttt?
%MA.CRO consumption();
Proc sql;
CREATE TABLE FCID_group AS
SELECT unique FCID_desc
FROM FCID_coittmodity3
QUIT;
ods rtf file="&PATH\FCID_group.rtf";
title "FCID Group
proc print data= FCID_group;
run;
ods rtf close;
A-30
-------
proc sort data=FCID_commodity3;
by seqn daycode;
run;
Proc univariate data =FCID_commodity3 noprint;
var intake_bw;
by seqn daycode;
output out = total_food_day sum = total;
run;
Proc transpose data = total_food_day out = average_total;
var total;
by seqn;
ID daycode;
run;
*proc print data=average_total;
*run;
Data average_total (keep = seqn average);
set average_total;
if _1 = . then _1 = 0;
if _2 = . then _2 = 0;
average = (_l+_2)/2;
run;
*proc print data=average_total;
*run;
proc sort data=demographic2;
by seqn;
run;
*====> Merge individual with the demographic file <====*;
Data total_food;
merge work.demographic2 average_total;
by seqn;
run;
Data total_food;
set total_food;
if average = . then do eater = "none_eater"; con_amt = 0; output;end;
else do eater = "eater"; con_amt = average; output;end;
run;
A-31
-------
Data total_foodl;
set total_food;
* preg f variable is a recoding of ridexprg variable ;
*if RIDEXPRG=1 then preg f=l;
*else if RIDEXPRG=2 then preg f=0;
if preg_f=l and sex=2 and age > 12 and age < 50 then Preg
"p" .
else if preg_f=0 and sex=2 and age > 12 and age < 50 then
Preg = "N" ;
preg_female_eater = Preg ;
if average = . then preg_female_eater =
"non_eater";
run;
***** pregnant and non-pregnant female consumption per capita ********;
proc format;
value $ prgy
"P" = " Pregnant 13-49 "
"N" = " Not pregnant 13-49 " ;
proc contents data=total_food ;
run;
Proc surveymeans data = total_foodl;
cluster sdmvpsu;
strata sdmvstra / NOCOLLAPSE ;
weight WT6_2DAY;
Domain preg;
var con_amt;
*format preg $prgy.;
ods output domain = pregnant_female (drop =
domainlabel) ;
run;
proc print data=pregnant_female ;
run;
proc contents data= pregnant_female ;
run;
A-32
-------
proc sort data=total_foodl;
by preg;
run;
Proc univariate data = total_foodl ;
weight WT6_2DAY;
var con_amt;
output out = pregnant_femalel min=min
pl=pl p5=p5 plO=plO p25=p25 p50=p50 p75=p75 p90=p90 p95=p95 p99=p99 max=max ;
by preg;
run;
data pregnant_female2 ;
set pregnant_femalel ;
if _N_ = 1 then delete;
run;
data pregnant_female;
set pregnant_female;
if preg ="p" then order =1;
else order=2;
run;
data pregnant_female2;
set pregnant_female2;
if preg ="p" then order =1;
else order =2;
run;
proc sort data =pregnant_female2;
by order;
run;
proc sort data =pregnant_female;
by order;
run;
proc print data=pregnant_female;
run;
*=== Proportion of eater ===*;
Proc sort data = total_foodl;
by preg;
run;
Proc freg noprint data = total_foodl;
by preg;
weight WT6_2DAY;
A-33
-------
table preg_female_eater /out =
proportion_pregnancy (drop=count) ;
run;
proc print data=proportion_pregnancy
run;
Data proportion_pregnancy (drop = preg );
set proportion_pregnancy;
if N = 1 then delete;
run;
Data proportion_pregnancy;
set proportion_pregnancy;
if Preg_female_eater = "P" or
Preg_female_eater = "N" ;
run;
data proportion_pregnancy ;
set proportion_pregnancy ;
if preg_female_eater ="p" then order
else order=l;
run;
proc sort data=proportion_pregnancy
by order;
run;
proc print data=proportion_pregnancy ;
run;
data proportion_pregnancy ;
set proportion_pregnancy ;
keep order percent;
run;
proc print data=proportion_pregnancy ;
run;
**** combine proportion, percentile and mean *****,-
data pregnant_female_summary;
merge pregnant_female proportion_pregnancy pregnant_female2 ;
by order;
run;
proc print data=pregnant_female;
run;
proc print data=pregnant_female2;
run;
A-34
-------
data pregnant_female_summary;
set pregnant_female_summary;
drop varname order ;
*label percent='proportion_eater';
run;
proc contents data= pregnant_female_summary;
run;
proc print data=pregnant_female_summary;
run;
ods csv f i1e="& PATH\& NAME percapita .csv";
proc print data=pregnant_female_summary;
format LowerCLMean Mean Percent StdErr UpperCLMEAN max min pi p5 plO p25 p50 p75
p90 p95 p99 14.4 ;
format preg $prgy.;
run;
ods csv close;
proc print data=pregnant_female_summary ;
run;
ods rtf file="&PATH\&NAME regression_per_capita.rtf";
ods graphics on;
title " per capita ";
Proc surveyreg data = total_foodl ;
class preg;
cluster sdmvpsu;
strata sdmvstra/NOCOLLAPSE;
model con_amt=preg /vadjust=none;
lsmeans preg /diff cl plots=( meanplot(cl));
weight WT6_2DAY;
format preg $prgy.;
run;
ods graphics off;
ods rtf close;
***** pregnant and non-pregnant female consumption eater only ********;
A-35
-------
data total_food2;
set total_foodl;
if average ne .;
run;
Proc surveymeans data = total_food2 ;
cluster sdmvpsu;
strata sdmvstra ;
weight WT6_2DAY;
Domain preg;
var con_amt;
ods output domain = pregnant_female_eaters
(drop = domainlabel) ;
run;
data pregnant_female_eaters;
set pregnant_female_eaters;
if preg="P" then order =1;
else order=2;
run;
proc print data=pregnant_female_eaters
run;
proc sort data=total_food2;
by preg;
run;
Proc univariate data = total_food2 ;
weight WT6_2DAY;
var con_amt;
output out = pregnant_female_p min=min
pl=pl p5=p5 plO=plO p25=p25 p50=p50 p75=p75 p90=p90 p95=p95 p99=p99 max=max ;
by preg;
run;
data pregnant_female_p
set pregnant_female_p ;
if N =1 then delete;
run;
data pregnant_female_p;
set pregnant_female_p;
if preg= "P" then order =1;
else order=2;
run;
proc print data=pregnant_female_p ;
run;
A-36
-------
proc sort data=pregnant_female_p;
by order;
run;
proc sort data=pregnant_female_eaters;
by order;
run;
title " female eaters only " ;
proc print data=pregnant_female_eaters;
run;
data prg_female_eaters;
merge pregnant_female_eaters pregnant_female_p ;
by order;
run;
data prg_female_eatersl;
set prg_female_eaters;
drop varname order ;
run;
title " female eaters only ";
proc print data= prg_female_eatersl;
run;
ODS CSV FILE= "& PATH\& NAME eaters.CSV";
proc print data=prg_female_eatersl ;
format LowerCLMean Mean StdErr UpperCLMEAN max min pi p5 plO p25 p5 0 p75 p90 p95
p99 14.4 ;
format preg $prgy.;
run;
ods csv close;
ods rtf file= "&PATH\&NAME regression_female_eaters.rtf" ;
ods graphics on;
title " Eaters Only ";
Proc surveyreg data = total_food2 ;
class preg;
cluster sdmvpsu;
strata sdmvstra / NOCOLLAPSE;
model con_amt=preg /vadjust=none;
lsmeans preg/diff cl plots=( meanplot(cl));
weight WT6_2DAY;
format preg $prgy.;
run;
A-37
-------
ods graphics off;
ods rtf close;
%MEND;
**** end of SAS MACRO ;
libname NCEA "F:\NCEA Pregnant women";
data demographic2 ;
set NCEA.demographic2;
run;
data FCID_commodity2 ;
set NCEA.FCID_commodity2;
com_code=FCID_code;
run;
%LET NAME = Apple; * replace the value of the name variable to reflect the name of
the commodity group;
%LET PATH = F:\NCEA Pregnant women; * replace the path name where you want to store
the file;
*====> Apple <====*;
data FCID_commodity3;
set FCID_commodity2;
* replace the commodity codes below if consumption is reguired for different commodity
groups*;
if comcode in ( 1100009000 , 1100009001 , 1100007000 , 1100008000 , 1100008001
, 1100011000 , 1100011001)
run;
% consumption();
*====> Total Vegetables <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 1800002000 , 401005000 , 103015000 , 103015001 ,
9500016000
A-38
-------
103017000 ,
401018000 ,
9500019000 ,
902021000 ,
9500022000
1901029000 ,
1901029001 ,
1901028000
1901028001
602033000
603036000 ,
603038000 ,
602037000 ,
603039000 ,
603040000
603041000 ,
603042000 ,
601043000 ,
601043001 ,
101050000
101050001 ,
200051000 ,
9500054000 ,
501061000 ,
502063000
501062000 ,
501061001 ,
501064000 ,
501069000 ,
502070000
501072000 ,
501071000 ,
9500073000 ,
901075000 ,
402076000
101078000 ,
101078001 ,
901075000 ,
103082000
103082001 ,
501083000 ,
101084000 ,
402085000 ,
402085001
402087000 ,
902088000 ,
603099000 ,
603098000 ,
603098001
101100000 ,
200101000 ,
902102000 ,
302103000 ,
401104000
1902105000 ,
1902105001 ,
1901118000
1901118001
1902119000
1902119001 ,
401138000 ,
103139000 ,
200140000 ,
1901144000
1902143000 ,
402152000 ,
301165000 ,
301165001
103166000 ,
103167000 ,
103166001 ,
101168000 ,
9500177000
603182000 ,
603182001 ,
1901184000 ,
1901184001 ,
502194000
501196000 ,
302198000 ,
1901202000 ,
401204000 ,
401205000
1901220000 ,
1901220001 ,
802234000 ,
301237000 ,
301238000
301238001 ,
301237001 ,
302239000 ,
9500243000 ,
1901249000
1901249001 ,
401248000 ,
101250000 ,
101251000 ,
101251001
603256000 ,
603256001 ,
601257000 ,
603258000 ,
602259000
602255000 ,
602255001 ,
802270000
802271000 ,
802271001 ,
802270001 ,
1902274000 ,
1902274001
802272000 ,
802273000 ,
802272001 ,
9500275000 ,
103296000
103297000 ,
103297001 ,
103298000 ,
103298001 ,
103300000
103300001 ,
103299000 ,
103299001 ,
902308000 ,
401313000
101316000 ,
200317000 ,
101314000 ,
200315000 ,
502318000
402322000 ,
101327000 ,
101331000 ,
200332000 ,
1901334000
9500335000 ,
9500335001 ,
301338000 ,
603348000 ,
603348001
600347000 ,
1902354000 ,
1902354001 ,
902356000 ,
902356001
902357000 ,
902357001 ,
103366000 ,
103366001 ,
402367000
103371000 ,
801374000 ,
801375000 ,
801378000 ,
801378001
801376000 ,
801376001 ,
801377000 ,
801377001 ,
801380000
801375001 ,
103387000 ,
502389000 ,
101388000 ,
9500397000
9500398000 ,
901399000 ,
103407000
103406000) ;
run;
% consumption();
*====> Total Fruit <====*;
data FCID_commodity3;
set FCID_coittmodity2 ;
if com_code in (
1100008000 ,
1202012000 ,
9500023000
1301055000
9500060000
1201090001
9500111000
1307130000
1302137000
1302149000
1302174000
9500183000
1304195000
1002206000
9500211000
9500001000
1100008001 ,
1202013000 ,
9500024000
1302057000
9500074000
1001106000
9500111001
1307131000
9500141000
9500151000
1304175000
9500183001
1002197000
1301208000
9500212000
1100009000
1100011000 ,
1202012001 ,
9500024001
1302057001
9500089000
1001107000
9500113000
1307130001
802148000 ,
9500153000
9500178000
1302191000
1002199000
9500209000
9500214000
1100009001 ,
1100011001 ,
9500020000 ,
9500023001
1301058000
1201090000
9500112000
1100129000
1302136000
9500154000
1003180000
9500193000
1002201000
1100210000
9500215000
1100007000
A-39
-------
9500216000 ,
1001240000 ,
9500245001 ,
1202260000 ,
1100266000 ,
9500279000 ,
9500284000 ,
1203286000 ,
1003307000 ,
9500333000 ,
1307359000 ,
1001369000 ) ;
run;
9500215001
1001242000
9500252000
1202261000
1100267000
9500280000
1203285000
1203286001
1100310000
9500346000
1307359001
1303227000 ,
9500245000 ,
9500252001
1202261001 ,
1100266001 ,
9500279001 ,
1203287000 ,
1203285001 ,
1301320000 ,
9500351000
9500361000 ,
1202230000 ,
9500246000 ,
9500254000 ,
1202260001 ,
9500277000 ,
9500283000 ,
1203287001 ,
9500289000 ,
1301320001 ,
9500358000 ,
9500368000 ,
% consumption() ;
*====> Banana <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 9500023000 , 9500024000 , 9500024001 , 9500023001);
run;
% consumption();
*====> Beans <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 603035000 , 603030000 , 603032000 , 602031000 , 603034000
602033000 , 603036000 , 603038000 , 602037000 , 603039000
603040000 , 603041000 , 603042000 , 601043000 , 601043001);
run;
% consumption();
*====> Berries and small fruit <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in ( 1301055000 ,
1301320001
1302057000 ,
1302149000
1307131000
1303227000
1301058000 , 1301208000 , 1301320000 ,
1302057001 , 1302136000 , 1302137000 ,
1302174000 , 1302191000 , 1307130000 , 1307130001 ,
1304175000 , 9500177000 , 9500178000 , 1304195000 ,
1307359000 , 1307359001);
run;
% consumption();
A-40
-------
*====> Broccoli <====*;
data FCID_commodity3;
set FCID_coittmodity2 ;
if com_code in ( 501061000 , 501061001);
run;
% consumption();
*====> Bulb Vegetables <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if com_code in ( 301165000 ,
301237001 , 301238000 ,
301338000 , 302103000 ,
run;
% consumption();
*====> Cabbage <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if comcode in ( 501069000 , 501072000 , 501071000);
run;
% consumption();
*====> Carrots <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if com_code = 101078000;
run;
% consumption();
*====> Citrus <====*;
data FCID_cominodity3;
set FCID_coinmodity2 ;
if comcode in ( 1001106000 , 1001107000 , 1003180000 , 1002197000
1002199000 , 1002201000 , 1002206000 , 1001240000 ,
1001242000 , 1003307000 , 1001369000);
run;
% consumption();
*====> Corn <====*;
data FCID_commodity3;
set FCID_commodity2;
301165001 , 302198000 , 301237000
301238001 , 302239000 ,
302338500);
A-41
-------
if comcode in (1500122000 , 1500120000 , 1500120001 , 1500121000 ,
1500121001
1500123000 , 1500123001 , 1500126000 , 1500127000 ,
1500127001);
run;
% consumption();
*====> Cucumber <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code = 902135000;
run;
% consumption();
*====> Cucurbits <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 901075000 , 901187000 , 901399000 , 902021000
902088000 , 902102000 , 902135000 , 902308000 , 902309000
902356000 , 902356001 , 902357000 , 902357001);
run;
% consumption();
*====> Fruiting Vegetable <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in (802148000
802271001 ,
801375000 ,
801377001 ,
802234000
802272000 ,
801375001 ,
801378000 ,
802270000
802272001 ,
801376000 ,
801378001);
802270001
802273000 ,
801376001 ,
802271000
801374000
801377000
run;
is consumption ()
*====> Lettuce <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in ( 401204000 , 401205000);
run;
% consumption();
*====> Leafy Vegetables <====*;
A-42
-------
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 200051000 , 200101000 , 200140000 , 200315000 , 200317000
200332000 ,
401134000 ,
401248000 ,
402085000 ,
402322000 ,
501064000 ,
501196000 ,
502229000 ,
9500335001 ,
run;
% consumption();
*====> Legume Vegetables <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 600347000 , 603348000 , 603348001 , 600349000 , 600349001
601043000 ,
602037000 ,
603032000 ,
603039000 ,
603098001 ,
603256000 ,
run;
% consumption();
*====> Onion <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 301237000 , 301238000 , 301238001 , 301237001 ,
302239000);
run;
% consumption();
*====> Pea <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 603256000 , 603256001 , 601257000 , 603258000 , 602259000
602255000 , 602255001);
run;
% consumption();
*====> Peach <====*;
data FCID_commodity3;
A-43
401005000 ,
401138000 ,
401313000 ,
402085001 ,
402367000 ,
501069000 ,
502063000 ,
502318000 ,
9500398000);
401018000
401150000
401355000
402087000
501061000
501071000
502070000
502389000
401104000 ,
401204000 ,
401355001 ,
402152000
501061001 ,
501072000 ,
502117000 ,
9500054000 ,
401133000
401205000
402076000
501062000
501083000
502194000
9500335000
601043001 ,
602255000 ,
603034000 ,
603040000 ,
603099000 ,
603256001 ,
601257000 ,
602255001 ,
603035000 ,
603041000 ,
603182000 ,
603258000);
602031000 ,
602259000 ,
603036000 ,
603042000 ,
603182001 ,
602033000
603030000
603038000
603098000
603203000
-------
set FCID_commodity2;
if comcode in ( 1202260000 , 1202261000 , 1202261001 , 1202260001);
run;
% consumption();
*====> Pear <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 1100266000 , 1100267000 , 1100266001);
run;
% consumption();
**** Pome Fruit ****;
data FCID_commodity3;
set FCID_commodity2;
if com_code in ( 1100007000 ,
1100009000 , 1100009001
1100011000 ,
1100266000
1100266001 ,
run;
% consumption();
*====> Root and Tuber Vegetables <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in ( 103015000
200051000 ,
101078001 ,
103139000 ,
101190000 ,
103297000 ,
103300001 ,
101327000 ,
101388000 ,
run;
% consumption();
*====> Strawberries <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in ( 1307359000 , 1307359001);
run;
1100008000 ,
1100011001 ,
1100267000 ,
1100008001 ,
1100129000 ,
1100310000);
1100210000
103015001
101052000 ,
103082000 ,
103166000 ,
101250000 ,
103297001 ,
103299000 ,
103366000 ,
9500397000 ,
103017000
101052001 ,
103082001 ,
103167000 ,
101251000 ,
103298000 ,
103299001 ,
103366001 ,
103407000 ,
101050000
101067000 ,
101084000 ,
103166001 ,
101251001 ,
103298001 ,
101316000 ,
103371000 ,
103406000) ;
101050001
101078000
101100000
101168000
103296000
103300000
101314000
103387000
A-44
-------
% consumption();
*====> Stalk and Stem Vegetables <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 9500016000 , 9500019000 , 9500022000 , 2100228000 ,
9500243000);
run;
% consumption();
*====> Stone Fruit <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 1202012000 , 1202012001 , 1202013000 , 1201090000 ,
1201090001
1202230000 , 1202260000 , 1202260001 , 1202261000 , 1202261001
1203285000 , 1203285001 , 1203286000 , 1203286001 , 1203287000
1203287001);
run;
% consumption();
*====> Tropical Fruit <====*;
data FCID_commodity3;
set FCID_commodity2;
if com_code in
9500024000
9500111000
9500151000
9500193000
9500215000
9500246000
9500279001
9500333000
9500368000);
run;
( 9500001000 ,
9500024001
9500111001
9500153000
9500209000
9500215001
9500252000
9500280000
9500346000
9500022000 ,
9500060000
9500112000
9500154000
9500211000
9500216000
9500252001
9500283000
9500351000
9500023000 ,
9500074000
9500113000
9500183000
9500212000
9500245000
9500254000
9500284000
9500358000
9500023001 ,
9500089000
9500141000
9500183001
9500214000
9500245001
9500279000
9500289000
9500361000
is consumption () ;
*====> Tomatoes <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 801375000 , 801378000 , 801378001 , 801376000 , 801376001
801377000 , 801377001 , 801375001);
run;
% consumption();
A-45
-------
*====> White Potatoes <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 103296000 , 103297000 , 103297001 , 103298000 , 103298001
103300000 , 103300001 , 103299000 , 103299001);
run;
% consumption();
*====> Total Fish <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 8000157000 , 8000158000 , 8000159000 , 8000160000 ,
8000161000 , 8000162000);
run;
% consumption();
*====> Total ShellFish <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if comcode in ( 8000161000 , 8000162000);
run;
% consumption();
*====> Total Finfish <====*;
data FCID_commodity3;
set FCID_commodity2;
if comcode in ( 8000157000 , 8000158000 , 8000159000 , 8000160000);
run;
% consumption();
^ Q0 ~k ~k ~k ~k ~k 'k •
data FCID_commodity3;
set FCID_commodity2;
A-46
-------
if comcode in ( 1500326000 , 1500326001 , 1500324000 , 1500324001
1500325000 , 1500325001 , 1500323000 , 1500323001);
run;
% consumption();
*====> Total Grain <====*;
data FCID_commodity3;
set FCID_coittmodity2 ;
if com_code
1500026001
in ( 9500006000
1500025000
1500025001
1500026000
run;
1500027000
1500121000
1500126000
1500232000
9500311000
1500325000
1500329000
1500401001
1500405000)
1500065000
1500121001
1500127000
1500232001
1500323000
1500325001
1500344000
1500402000
1500066000
1500122000
1500127001
1500233000
1500323001
1500326000
1500381000
1500402001
1500120000
1500123000
1500226000
1500233001
1500324000
1500326001
1500381001
1500403000
1500120001
1500123001
1500231000
9500306000
1500324001
1500328000
1500401000
1500404000
b consumption ()
*====> Total Cereal Grains <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if com_code
1500027000
in ( 1500025000
1500025001
1500026000
1500026001
1500065000
1500121001
1500124001
1500231000
1500323000
1500325001
1500344000
1500401001
1500405000
1500066000
1500122000
1500126000
1500232000
1500323001
1500326000
1500345000
1500402000
9500006000
1500120000
1500123000
1500127000
1500232001
1500324000
1500326001
1500381000
1500402001
9500306000
1500120001
1500123001
1500127001
1500233000
1500324001
1500328000
1500381001
1500403000
9500311000)
1500121000
1500124000
1500226000
1500233001
1500325000
1500329000
1500401000
1500404000
run;
% consumption();
*====> Total Beef <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if comcode in ( 3100044000 , 3100044001 , 3100045000 , 3100046000 ,
3100046001 , 3100047000 , 3100047001 , 3100048000 , 3100049000
3100049001);
run;
A-47
-------
% consumption();
*====> Total Pork <====*;
data FCID_commodity3;
set FCID_coittmodity2 ;
if comcode in ( 3400290000 , 3400290001 , 3400291000 , 3400292000 ,
3400292001 , 3400293000 , 3400293001 , 3400294000 , 3400295000);
run;
% consumption();
*====> Total Poultry <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
if comcode in ( 4000093000 , 4000093001 , 4000094000
4000095001
4000096000 , 4000096001 , 4000097000 ,
5000382001 , 5000383000 , 5000383001 ,
5000385000 , 5000385001 , 5000386000 ,
6000302000 , 6000303000 , 6000304000 ,
run;
4000095000
4000097001 ,
5000384000 ,
5000386001 ,
6000305000);
5000382000
5000384001
6000301000
i consumption () ;
*====> Total Meats <====*;
data FCID_commodity3;
set FCID_coramodity2;
if com_code in
3100046001
( 3100044000
3100047000
3200169000
3300189000
3400292001
3500339000
3500342000
4000093001
4000096001
5000383000
5000385001
6000303000
3100044001
3100047001
3200170000
3400290000
3400293000
3500339001
3500343000
4000094000
4000097000
5000383001
5000386000
6000304000
3100045000
3100048000
3200171000
3400290001
3400293001
3500340000
3800221000
4000095000
4000097001
5000384000
5000386001
6000305000)
3100046000
3100049000
3200172000
3400291000
3400294000
3500341000
3900312000
4000095001
5000382000
5000384001
6000301000
3100049001
3200173000
3400292000
3400295000
3500341001
4000093000
4000096000
5000382001
5000385000
6000302000
run;
% consumption{);
*====> Total Dairy <====*;
data FCID_commodity3;
set FCID_coinmodity2 ;
A-48
-------
if comcode in ( 3600222000 , 3600222001 , 3600223000 , 3600223001 ,
3600224000 , 3600224001 , 3600225001);
run;
% consumption();
^Minimum sample size calculation for upper and lower percentiles for pregnant
and non-pregnant female* ;
* This sample size calculation does not depend on a particular commodity or
commodity groups*;
libname NCEA "F:\NCEA Pregnant women";
data demographic2 ;
set NCEA.demographic2;
run;
data FCID commodity2 ;
set NCEA.FCID commodity2;
com code=FCID code;
run;
data FCID commodity3;
set FCID commodity2;
run;
proc sort data=FCID commodity3;
by seqn daycode;
run;
Proc univariate data =FCID commodity3 noprint;
var intake bw;
by seqn daycode;
output out = total food day sum = total;
run;
Proc transpose data = total food day out = average total;
var total;
by seqn;
ID daycode;
run;
Data average total (keep = seqn average);
A-49
-------
set average total;
if 1 = . then 1=0;
if 2 = . then 2=0;
average = ( 1+ 2)/2;
run;
proc sort data=demographic2;
by seqn;
run;
*====> Merge individual with the demographic file <====*;
Data total food;
merge work.demographic2 average total;
by seqn;
run;
*==== Pregnant Female Varaince Inflation Factor and sample size calculation
= = = •
then Preg = "P";
then Preg = "N" ;
Data total foodl;
set total food;
if preg f=l and sex=2 and age > 12 and age < 50
else if preg f=0 and sex=2 and age > 12 and age < 50
run;
proc format;
value $ prgy
"P" = „ pregnant 13-49 "
"N" = " Not pregnant 13-49 "
run
data total foodl;
A-50
-------
set total foodl;
weightl=WT6_2DAY**2;
weight2=WT6 2Day;
run;
data total food prg;
set total foodl;
if preg= "P";
run;
data total food nprg;
set total foodl;
if preg =~"N";
run;
Proc surveymeans data =
total food prg ;
cluster sdmvpsu;
strata sdmvstra;
var weightl weight2;
run;
data VIF2;
input numerator denominator;
datalines;
138257603 6253.756527
r
run;
data VIF2;
set VIF2;
denominator=denominator**2;
run;
data VIF2;
set VIF2;
VIF=numerator/denominator;
sample size
sample size
sample size
sample size
sample size
sample size
sample size
sample size
sample
n_01=8*(VIF/0.01)
n_05=8*(VIF/0.05)
n_10=8*(VIF/0.10)
n_2 5=8*(VIF/0.25)
n_7 5=8*(VIF/0.25)
n_90=8*(VIF/0.10)
n_95=8*(VIF/0.05)
n_99=8*(VIF/0.01)
n_9 9_9 9= 8*(VIF/0.0001);
run;
ods rtf file="F:\NCEA Pregnant women\Modification\ sample size pregnant
women.rtf";
required for 1st percentile
required for 5th percentile *;
required for 10th percentile '
required for 25th percentile '
required for 75th percentile '
90th percentile '
95th percentile '
99th percentile '
required for
required for
required for
size required for 99.99th percentile
title 'pregnant women sample size ';
A-51
-------
proc print data=VIF2;
run;
ods rtf close ;
***** Not-pregnant female VIF and sample size calculation ************
Proc surveymeans data = total food nprg ;
cluster sdmvpsu;
strata sdmvstra;
var weightl weight2;
*ods output statistics=VIF2 (keep=mean
varname) ;
run;
data VIF3;
input numerator denominator;
datalines;
527057037 14616
r
run;
data VIF4;
set VIF3;
denominator=denominator**2;
run;
proc print data=VIF4;
run;
data VIF4;
set VIF4;
VIF=numerator/denominator;
n 01=8*(VIF/0.01);* sample size required for 1st percentile *;
n 05=8*(VIF/0.05);* sample size required for 5th percentile *;
n 10=8*(VIF/0.10); * sample size required for 10th percentile *;
n 25=8*(VIF/0.25);* sample size required for 25th percentile *;
n 75=8*(VIF/0.25); * sample size required for 75th percentile *;
n 90=8*(VIF/0.10);* sample size required for 90th percentile *;
n 95=8*(VIF/0.05);* sample size required for 95th percentile *;
n 99=8*(VIF/0.01); * sample size required for 99th percentile *;
n 99 99= 8*(VIF/0.0001); * sample size required for 99.99th percentile *;
run;
ods rtf file="F:\NCEA Pregnant women\Modification\ sample size not-pregnant
women.rtf";
title ' sample size non-pregnant';
proc print data= VIF4;
A-52
-------
run;
A-53
-------
Appendix 5 :
References:
Appendix B: ANALYTIC AND REPORTING GUIDELINES:
The Third National Health and Nutrition Examination Survey, NHANES III (1988-94); Source :
National Center for Health Statistics, Hyattsville, Maryland, USA.
Healthy People 2010 Stat Notes. 2002 Jul;(24):l-12. Healthy People 2010 criteria for data
suppression: Klein RJ, Proctor SE, Boudreault MA, Turczyn KM.. Source : National Center for
Health Statistics, Hyattsville, Maryland, USA.
Exposure Factor Handbook ; available at http://cfpub.epa. gov/ncea/risk/recordisplay.cfm?deid
=236252#tab-3. Source : United States Environmental Protection Agency
A-54
-------
&EPA
United States
Environmental Protection
Agency
National Center for Environmental Assessment
Office of Research and Development
Washington, DC 20460
Official Business
Penalty for Private Use
$300
------- |