xvEPA
             unnea states
             Environmental Protection
             Agency
             UTTICB or
             Toxic Substances
             Washington, DC 20460
  DOU/ u-/a-
November 1980
             Office of Toxic Substances
PCB  Residue Levels
in Human Adipose Tissue,

A Statistical Evaluation
by Racial Grouping

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                                         EPA 560/13-79-015
                                         November, 1980
 PCB Residue Levels in Human Adipose Tissue,
 A Statistical Evaluation by Racial Grouping
                     by
               Robert M. Lucas
            Mitchell D. Erickson
              Phil V. Piserchia
             Stephen R. Williams
         Research Triangle Institute
Research Triangle Park, North Carolina
27709
           Contract No. 68-01-5848

         Task Manager: .Cindy Stroup
   Contract Project Officer:  Joseph Carra
        Design and Development Branch
        Exposure Evaluation Division
  Office of Pesticides and Toxic Substances
           Washington, D.C.  20460

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                            DISCLAIMER NOTICE

This  report was  prepared under  contract  to  an  agency of  the  United
States Government.  Neither  the  United States Government nor any of its
employees,  contractors,  subcontractors, or  their employees, makes  any
warranty,  expressed  or   implied,  nor  assumes  any  legal liability  or
responsibility for  any third party's use or the  results  of  such use of
any information, apparatus, product or process disclosed in this report,
nor  represents  that  its  use  by  such third  party would not  infringe
privately-owned rights.

Publication  of  the  data  in this  document  does  not signify  that  the
contents necessarily reflect the joint or separate views and  policies of
each  sponsoring  agency.   Mention of trade names  or commercial  products
does not constitute endorsement or recommendation for use.

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                            ACKNOWLEDGEMENTS
The authors  greatly  appreciate the cooperation of Ms. Sandra Strassman-
Sundy of the U.S. Environmental Protection Agency, Washington, D.C., for
providing information  on  the sample design and data  summaries;  John D.
Tessari  and  Larry Griffin  of Colorado State  University,  Fort Collins,
Colorado, for supplying information on the chemical analysis techniques;
Dr. R.  G.  Lewis  and  Wayne  Sovocool and their colleagues  of the Health
Effects  Research  Laboratory,  U.S.  Environmental  Protection  Agency,
Research Triangle Park, N.C.;  and Dr.  Henry Enos of the  University of
Miami, Miami, Florida, for consultation on the chemical analysis.  Also,
the authors greatly appreciate the helpful suggestions of Dr. Brenda Cox
in writing this  report.   Many thanks are also given  to the secretarial
staff,  Ms.  Laurine   Johnson,  Ms.  Pat  Parker,  Ms.  Sarah  Stumley,
Ms. Joanne Quate and Ms. Martha Clegg.
                                  -(i)-

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                            TABLE OF CONTENTS
ACKNOWLEDGEMENTS 	        i

LIST OF FIGURES	       iv

LIST OF TABLES	        v

1.   INTRODUCTION  	        1

     1.1.  The National Human Monitoring Program 	        1
     1.2.  Study Methodology 	        2
2.   CONCLUSIONS
     2.1.  Issue of Racial Differences 	        5
     2.2.  Statistical Design	        6
     2.3.  Chemical Analysis 	        7
     2.4.  Recommendations	      •  7
           2.4.1.  Statistical Design	        7
           2.4.2.  Chemical Analysis 	        8

3.   STATISTICAL SURVEY DESIGN 	       11

     3.1.  Overview	       11
     3.2.  Significance of Racial Differences Assuming
           No Design Effect or Measurement Error 	       13
     3.3.  Comments on the Impact of Measurement Error 	       14
     3.4.  Impact of Sample Design	       18

           3.4.1.  Restriction of Cities 	       18
           3.4.2.  Sampling Cadavers and Surgical Patients ....       20
           3.4.3.  Remarks on the Bias of Estimating
                   Racial Differences  	       23

4.   EVALUATION OF CHEMICAL ANALYSIS AND DATA INTERPRETATION ...       24

     4.1.  Objective	       24
     4.2.  Discussion	       24
           4.2.1.  Qualitative Assessment	       24
           4.2.2.  Quantitative Assessment 	       30
           4.2.3.  Potential Interference	       32
           4.2.4.  Assessment of TLC Methodology 	       32
           4.2.5.  Correlation of Data Generated by
                   the Two Methods	       34
                                 -Cii)-

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                        TABLE OF CONTENTS (cont.)
                                                                      Page
5.   WEIGHTED ANALYSIS OF COMPUTER ACCESSIBLE DATA FILES	      35
     5.1.  Introduction and Assumptions 	      35
     5.2.  Sample Weights	      35
           5.2.1.  Calculatons of Weights 	      35
           5.2.2.  Sample Sizes used for Calculating Weights. .  .      37
     5.3.  Racial Comparisons by Weighted Analysis	      38
           5.3.1.  Overview 	      38
           5.3.2.  Statistical Method 	      39
     5.4.  Comments on Assumptions	      48

REFERENCES	     49

APPENDIX A:    Letter from M. Aaronson	     A-l
APPENDIX B:    Letter from John D. Tessari	     B-l
APPENDIX C:    Interlaboratory PCB Analysis Results  	     C-l
APPENDIX D:    Breakdown of Census Regions and Divisions 	     D-l
APPENDIX E:    Survey and Site Quotas for Fiscal Years 1972-76 .   .     E-l
APPENDIX F:    Guidelines and General Information About
               Collecting Adipose Tissue for the National
               Human Monitoring Program for Pesticides 	     F-l
APPENDIX G:    National Data Summaries for Fiscal Years
               1972 to 1976	     G-l
                                 -(iii)-

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                             LIST OF TABLES






Table                                                                 Page
3-1
3-2
3-3
3-4
3-5
5-1
5-2
5-3
Summary of Results for Testing the Null Hypothesis
that P -P ~ 0 for FY72-FY76 	
w n
Effect of Measurement Variance on the Significance
Level of Test Statistics 	
Effect of Measurement Variance on the Probability
of Detecting Real Differences 	
Probability of Detecting Real Differences for
Various Levels of Positive Bias 	
Probability of Detecting Real Differences for
Various Levels of Negative Bias 	
Model Coefficients (in Percent) 	
Tests for Differential Slopes — White Versus Nonwhite . .
Tests for Average Racial Grout) Differences 	
15
17
IP
?1
??
4?
45
45
                                  -(v)-

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                             LIST OF FIGURES
Figure                                                                Page

3-1       Sampling Frame and Selection Procedures for Middle
          Atlantic Census Division in FY73	     12

4-1       Chromatogram of a Tissue Specimen on 4% SE30/6%
          OV-210 Column 	     25

4-2       Chromatogram of a Tissue Specimen on 4% SE30/6%
          OV-210 Column 	     26

4-3       Chromatogram of a Tissue Specimen on 4% SE30/6%
          OV-210 Column 	     27

4-4       Chromatogram of Equivalent to 1.5 ppm Aroclor 1260
          Standard	     28

4-5       Electron Capture Gas Chromatograms of (A) Aroclor 1016
          Standard and (B) PCB-residue	     29

4-6       Computer Reconstructed Total Ion and Mass Chromatogram
          of A Composite Human Adipose Tissue Extract 	     31

4-7       Chromatograms of Aroclors and Pesticide Standards
          Illustrating Potential Interference 	     33

5-1       Racial Comparisons Over Time:   Percent > 3 ppm PCB.  ...     46

5-2       Racial Comparisons Over Time:   Percent Positive
          PCB Detections	     47
                                 -(iv)-

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1.        INTRODUCTION
1.1.      The National Human Monitoring Program
     The  National Human  Monitoring Program  (NHMP)  was  established  in
1967 to monitor  incidences  of pesticide residues in  the  general United
States population and to assess changes and trends in these levels.  The
program was initiated by the United States Public Health Service and was
transferred  in  1970  to the  newly  created United States Environmental
Protection Agency.
     The  sample  design  of the National Human Adipose Tissue Survey, one
of  the  major  ongoing  programs operated by  the NHMP,  involves several
stages  of selection.    The  conterminous 48 states were stratified into
several geographic regions.  Within each stratum, cities with populations
greater than  25,000 were  randomly selected for fiscal years 1972-76.  In
fiscal  year   1977,  the  first-stage  units   were  changed  to  Standard
Metropolitan  Statistical  Areas  (SMSA's).  Hospitals  were  then selected
within  each  sample city or SMSA.   Cooperating  pathologists and medical
examiners supplied adipose tissue specimens.
     Tissue  specimens  were analyzed by laboratories designated  by the
NHMP.   Chemical  analysis  was  accomplished by thin  layer chromatography
(TLC)  until  November  1974 when  a  gas chromatographic   technique was
adopted.   These   techniques  are  discussed  in  more  detail  in Sections
4.2.4 and 4.2.1,  respectively.
     Polychlorinated biphenyls  (PCB's)  possess  chemical characteristics
similar to those of organochlorine insecticides; hence,  they also may be
detected  by  the  same  chemical analysis procedures used to analyze human
adipose  tissue  for  pesticides.   Because  quantitating  PCB's  is  more
difficult  than quantitating many  other substances,   the  PCB concentra-
tions in parts per million (ppm) were reported as falling in one of four
categories:  not  detected,  trace  (detected but <1 ppm), 1-3 ppm, and >3
ppm.
     NHMP adipose tissue surveys indicate that measurable residue levels
of PCB's occur in a large percentage of the general population.  Prelimi-
nary data summaries  suggest  that the  higher levels  of these chemicals
are more prevalent in the nonwhite population.  The overall objective of
this report is to present the results of an evaluation of these apparent
racial differences.
                                 -1-

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1.2.      Study Methodology
     The  following activities were undertaken  to  evaluate estimates of
PCB  residue  levels  in  adipose  tissue  with regard to  the statistical
meaning of apparent differences between  racial groups:
     1.   Estimate  and  discuss  the  statistical  impact  of  the  survey
          design employed by the NHMP on the PCB residue level estimates.
     2.   Evaluate the level of "reading" error in the interpretation of
          the PCB  chromatograms.
     3.   Investigate the  implications  of duplicity and possible biases
          related  to the use of autopsy and surgical materials.
     4.   Assuming  various plausible levels  of design  and measurement
          error effects,  compute  the  statistical precision of PCB resi-
          due  level  estimates  and  the probability  that  real  residue
          level  differences  between  racial  groups  would  be detected.
     5.   Statistically analyze the computerized data files.
     It was  concluded from a preliminary examination of a sample of the
chromatograms that any "reading" errors resulting from manually integrat-
ing the chromatograms  would  probably be small compared to other sources
of measurement error, i.e., in the analytical techniques used to measure
the chemical residues.  The analytical techniques were investigated, and
lower  bounds  for  the  relative  estimation precision of  the PCB residue
concentrations  were  developed  on  the  basis  of previously  published
results and experience.  Different levels of precision in estimating the
PCB  residue  concentrations  will  also  result  in  different  levels  of
precision in estimating the proportion (or percentage) of the population
falling in  a given  classification  category  (i.e., percentage >3 ppm).
The possible effects of measurement precision on the statistical signifi-
cance  of  differences  in  the  proportions of  racial  groups  are discussed
in Section 3.3.
     In estimating the  impact  of  the statistical survey design employed
by the NHMP,  the  discussion  in  Section 3.4 focuses on  the highest PCB
residue  level classification  (>3 ppm).  That  is,  the  proportions  of
nonwhites with greater than 3 ppm PCB residue levels are compared to the
proportions of whites  having  greater  than that level.   In this  report,
these  proportions  are  denoted  by  P  and P  ,  respectively.   The  discus-
sion is limited to this category for two reasons:   (1) any health effect
                                 -2-

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of PCB's would be most evident in the highest concentration category and
(2) one  category  is  sufficient to illustrate the possible effect of the
statistical design and measurement error.
     Estimates  for  the proportions  of whites and  nonwhites  having PCB
residues greater than 3 ppm were calculated for the 1972-76 fiscal years
assuming no  design  effect or measurement  error.   Tables  illustrate how
various  levels  of sample  design effect and measurement error can affect
the statistical significances of these differences.  Fiscal 1977 data on
the computer accessible data files were excluded because the first-stage
sampling units were  changed from the previous  years.   This  precluded
relating first-stage units  in  1977  and  other  aspects  of the  sample
design with the previous years.
     In  the  discussion  of the survey  design  impact,  an example is used
to illustrate the possible effects of the purposive exclusion of subpopu-
lations  resulting from sampling only  within  cities  with  populations of
more than  25,000.   The emphasis is on the bias potential for estimating
the difference  P  -P  ,  where the bias  is defined as the expected differ-
                w  n'                                     r
ence  minus  the  true  difference.   A  similar discussion  addresses the
final-stage  sampling of cadavers and  surgical patients and the methods
used in  obtaining the tissue specimens.
     The NHMP  computer  accessible  data  files  were  analyzed using  a
statistical method especially  adapted for multistage survey data.  This
technique  allows  investigation  of  differences  between   racial  groups
after adjusting for  other factors such as  sex,  age,  geographic region,
and fiscal year.   The  total variance of  the estimates is approximated
including  both the  measurement  and  sampling  components.   The analysis
was based on the following two assumptions:   (1)  quantitation and sampl-
ing biases are similar  for each  racial  group and  (2)  sampling within
sample cities  produced  a  nearly simple random sample  within  each city.
                                 -3-

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2.        CONCLUSIONS
2.1.      Issue of Racial Differences
     The issue  of whether  or  not racial differences exist  in  the  dis-

tribution of PCB  residue levels was examined for  the  category  ">3  ppm"

and for percentage  detected (percentage of population with a trace, 1-3

ppm, or >3 ppm).
     A summary of the analysis of the data on the NHMP computer  accessi-
ble data files  labeled  "PCB's" is given below.  (The PCB label is put in
quotes because  of  the  concerns noted below about quantitating PCB using

only a single isomer).
For percent "PCB" >3 ppm:

     1.   Over  the  fiscal years  analyzed  (1972-76),  nonwhites  averaged
          7.15 percentage points higher than whites.

     2.   Over  the  fiscal years 1972-76, there was  an  increasing trend
          in the percentage of people with "PCB" >3 ppm in their adipose
          tissue.  The rate of increase is estimated to be 1.73  percent-
          age points per year.  The percentages of  individuals  with >3
          ppm ranged from 2.58 percent in 1972 to  7.34 percent, in 1976
          for whites and 7.55  percent in 1972  to  16.67 percent in 1976
          for nonwhites.

     3.   The trends over time (increase per year)  for whites  and  non-
          whites  are   1.19  and  2.28  percentage  points   per  year,
          respectively.

For percent positive detections:

     1.   Over  the  fiscal years 1972-76, there was  an  increasing trend
          in  the  percentage  of  people  with some detectable  "PCB"  in
          their adipose tissue.  The rate of increase is estimated to be
          2.99  percentage points per  year  and  means ranged from 84.58
          percent in 1972 to 96.54 percent in 1976.

     2.   The trends  over time  for  whites and  nonwhites are  2.80 and
          3.18 percentage points per year, respectively.

     These findings listed above may not be precise or accurate  measure-

ments  of  PCB residues  in the  overall  population because of potential

inadequacies  in the sampling and chemical analysis.  However,  based on

the  following  assumptions:   (1) quantitation  and sampling  biases are

similar  for each  racial  group  and  (2)  sampling within  sample cities

produced a nearly simple  random sample within each city, several signifi-

cant  differences were  detected.   The percentage  of  nonwhites  having
                                 -5-

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greater than 3 ppm "PCB" is significantly higher than whites (probability
of no  difference  <.001).   There was no  evidence  of a difference in the
percentage of  whites  and  nonwhites with detected "PCB".  The percentage
in both categories investigated demonstrated a significant increase over
time  (probability  of  no time trend <.01).  However,  the  apparent trend
may be due in part to the change in chemical analysis methodology during
the period  studied.   The  trends  appear  to  be the  same  for whites and
nonwhites.
     The  apparent differences  between  racial  groups could  neither  be
confirmed  nor denied  because of  the  following two  important  reserva-
tions:  (1)  there  are uncertainties about the accuracy and precision of
using a single isomer to quantitate aggregate PCB's (see Section 4), and
(2) the  data  appears  to  contain  some  inconsistencies  and show greater
fluctuations than normally expected in surveys of this size (see Section
5).  The  first reservation admits the possibility of bias in estimating
differences  between  racial   groups.   The  second  is  an   indication  of
possible measurement  or sampling bias.   Because  of these reservations,
the data  in  their present form  should not be  used to  make  epidemio-
logical inferences about aggregate PCB residues.
2.2.      Statistical Design
     The geographic stratification used in the NHMP sample design appears
to have only a slight effect on the precision with which the proportions
of each  racial group having  PCB  levels >3 ppm or  percent detected are
estimated.   This  might be expected because of population mobility and
the widespread use of  PCB's.   The clustering of  sample  individuals  by
cities  is  expected  to decrease  the precision  of estimates,  but  this
component of the variance was  not estimated  separately.   Further, the
effect  on  the  variance of  subsampling within  the clusters cannot  be
estimated with any  assured   degree  of  accuracy  because  this  stage  of
sampling was not  conducted within a probability framework.  The assump-
tion of simple random sampling within cities is required for any statis-
tical  analysis of these data.  The exclusion of some individuals  in rural
areas  and the use of both surgical patients and cadavers qualify statis-
tical  inferences to the general U.S. population.
                                 -6-

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2.3.     • Chemical Analysis
     The peak  in the  gas  chromatograms labeled  "PCB" has been  tenta-
tively  identified  as  a heptachlorobiphenyl isomer  (Appendix A).   It is
also  possible that  other  halogenated  organics  may  coelute,  yielding
                   j-
false high  values.    The  "PCB"  values were  obtained by  measuring  one
peak and correlating it with an Aroclor 1260 standard. This implies that
the  rest  of the  Aroclor 1260 pattern is  "buried"  under  the  pesticides
and is present in a constant ratio to the measured peak.  This procedure
does not follow  the recommended protocol (USEPA 1974 Section 5A[1]) and
may  not provide  conclusive  information  on  racial  differences  in PCB
residues.   Some  chemists   knowledgeable  in  the  area  of human  tissue
analysis doubt  the reliability  of the technique employed by  the NHMP.
     The TLC  data  (pre-November 1974) are estimated to have ±50 percent
precision  (Appendix B).   Current data are  insufficient  to  confirm or
refute  this estimate.   As  far  as  can  be ascertained,  some  potential
interferences  (halogenated  aromatics)  may  yield   false  high  values.
2.4.      Recommendations
2.4.1.    Statistical Design
     The  purposive exclusion  from the sampling  frame of hospitals in
cities  with populations of less than 25,000  limits the inferences that
can be  drawn to the general U.S. population.  This exclusion results in
a substantial fraction  of the total U. S. population having no chance of
being selected in the sample.  The additional selection of sample hospi-
tals  from  cities  of 2,500  to 25,000 persons would  essentially eliminate
this  inferential  limitation.  Special arrangements may have  to be made
for  hospitals  in  these  smaller cities  that do  not  have  the medical
facilities  to secure and store sample tissues.  The technique of select-
ing alternate first-stage sample sites also needs improvement.  Repeating
the  process by which  the original sites  were  selected would be satis-
factory  (see Section 5.1).
     The  limitations  resulting from  the use  of  judgment or convenience
sampling are well known (Cochran  1977; Kish 1965).  Hence, the method of
selecting  hospitals (or pathologists and medical  examiners)  should be
conducted  as nearly  as possible  within  a probability framework.   For
instance,  sampling frames  for  hospitals  could  be  constructed for  each
 "Personal  Communications,  G.  W. Sovocool,  U.S. Environmental Protection
 Agency,  Research  Triangle Park,  North  Carolina,  October  1979,  and H.
 Enos, University  of Miami, Miami,  Florida,  October  1979.
                                 -7-

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sample city.  The hospitals could then be randomly selected with a known
probability.  The  judgment sampling of  tissue  specimens  by cooperating
professionals  (pathologists  or medical  examiners) should  also  be modi-
fied.  Simple  protocols for  selecting patients  or  cadavers  and tissue
specimens  should  be  developed.   Incorporating  probability  sampling
methods or  explicit  procedures (where probability sampling is not feas-
ible)  would significantly improve  the  inferential  value  of  the survey
data.
     For  the  majority of  patients  and  cadavers, the  zip  code  of resi-
dence can be recorded and, hence, the approximate geographic location of
their  residences  can be  ascertained.   If the zip code of residence is
not available,  then  the  zip code of the hospital could be substituted.
     The anatomical site from which the tissue sample is taken should be
recorded.  This may allow one to investigate and account for the differ-
ence  in  residue concentrations within the body.  Variation of  residues
within the  body does  not  preclude residue  estimation, but it  necessi-
tates  the  use  of  a  well-defined methodology for selecting or  at least
recording anatomical sites.
     The above comments should be accompanied by an awareness  of special
problems that led to the purposive selection of certain hospitals and to
the exclusion of hospitals in cities with populations of 25,000  or less.
For  example,  long-term  care  facilities  and mental  hospitals  in which
surgery  and  postmortem  examinations  are  not  usually performed  were
excluded.  The  smaller  cities  were  excluded because many  of  the hospi-
tals within these cities do not have health care facilities with labora-"
tory or pathology  departments  adequate for tissue sampling and  storage.
Some  of  these  hospitals  routinely embalm  cadavers  before autopsy  and
thus contaminate the adipose tissue.
2.4.2.    Chemical Analysis
2.4.2.1   Evaluation of Current and Past Techniques
     Data for PCB's obtained by gas  chromatographic  analysis in  the NHMP
should not  be used as  a basis for epidemiological inferences  unless  one
of two validations is made as discussed below:
     (1)   The  data  collected  over  the  past several  years  may  prove
          useful if  the  peak  being quantitated  can  be confirmed as  a
          specific heptachlorobiphenyl isomer  and shown to be free from
          interference by all halogenated organics known to be in general

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          tissue  samples.   The  response  of this  isomer then  could  be
          measured and the  concentration  calculated.   This would result
          in reporting a concentration (or range) of a given isomer that
          is observed but would not assume the other isomers are present
          when not observed.

     (2)  The  second  corrective  measure would  involve  demonstrating
          (with  a  statistically valid sample) that the  PCB profiles  in
          the "unseen" part of the chromatogram are similar between the
          racial groupings  and,  therefore,  that the heptachlorobiphenyl
          peak  being measured  is,  in fact,  indicative  of  "total  PCB"
          concentrations in adipose tissue.

Even with these precautions, the data should be assumed to have an error
of at  least  ±50 percent (see Section 4.2.2) unless better precision can

be demonstrated.
     The TLC  data should be further validated  to check  for  potential

interference  from  such compounds  as  polychlorinated  naphthalenes,  ter-
phenyls, benzene,  and  other aromatics and polybrominated aromatics.  If

used in the  future,  a selected portion  (e.g.,  10 percent) of the TLC

determination should be confirmed  by an independent  technique such  as
GC/ECD or GC/MS.

2.4.2.2.  Alternate Techniques for Future Work

     The selection of  analytical methods  is a compromise between sensi-

tivity and selectivity.  GC/ECD is not only highly selective for haloge-

nated  compounds  but  also  is one of the most sensitive techniques avail-
                                                            -12
able  with detection limits  approaching  the  picogram  (10   g)  level.
Several techniques are listed below in approximately increasing order  of

complexity and information content.

     1.   The easiest modification of current procedures  would  be simple
          confirmation of  selected specimens  (e.g.,  10  percent)  by  an
          independent   technique  such   as   GC/HECD   GC/MS,   TLC,   or
          perchlorination.

     2.   The method recommended in the EPA Pesticide  Manual (USEPA 1974
          Section 9 C) should be followed.  The separation of PCB's from
          pesticides will  produce more reliable  and   accurate  data and
          will allow detection of early-eluting PCB peaks.

     3.   Modern  chromatographic techniques  should be  considered.   In
          particular, glass  capillary GC  will permit  resolution of many
          more  components  and  will  significantly  reduce  chances  of
          coelutions.  Modern ECD's can be temperature-programmed.  This
          increases  the  effective  resolution of  the  chromatography.
          Precise retention times (Hewlett-Packard claims ±0.002 minimum
          precision)  would  also  improve  identification  certainties.
                                 -9-

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     4.   Detection  of PCB's could be  significantly improved with mass
          spectrometric  techniques.   The "normal"  electron  impact  MS
          detection  limit  of about 1 ng  would  produce  problems for the
          detection  of PCB's  in  NHMP  tissue  samples.   This detection
          limit may  be improved 10-100 fold by using selected ion moni-
          toring  (SIM),  which  is  available  on all  quadrupole and many
          magnetic   sector  instruments.   However,   techniques  such  as
          negative  ion MS have  achieved  extremely  good sensitivities—
          better  than  ECD.   If sufficient tissue specimen is available,
          high  resolution  MS will  provide precise mass measurements  to
          greatly aid  in determining molecular formulae.

     5.   A  combination of  the  above  suggestions—cleanup  by silicic
          acid  chromatography  and analysis by glass capillary GC/MS—is
          probably  the most  powerful  routine  PCB  analytical  system.
          Despite its  high capital costs, capillary GC/MS is considered
          routine by many laboratories and is feasible for this program.

     The above suggestions all move toward the ability to report indivi-
dual  PCB  isomers  (i.e.,  "70  pg  2,2',3,3',4'4'5-heptachlorobiphenyl"
instead of  "1.5  ng  Aroclor 1260").  While this increases  the burden of

calculation for both the chemist and the  statistician,  the extrapolation
to the health effects of PCB's is much more scientifically sound,  parti-
cularly when it  has  been shown  that  toxicity  and storage  vary with

individual  isomers   (Matthews  and  Anderson  1976; Biocca  et  al.  1976;

McKinney  1976).   Steps 1  and  2,  above,  along with a  rigorous quality

assurance program,  should be  considered  a minimum  for future analyses
for PCB's in tissue.
                                 -10-

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3.        STATISTICAL SURVEY DESIGN
3.1.      Overview
     The statistical  design  used to collect data for the Adipose Tissue
Survey of the NHMP has several stages of sample selection.  The contermi-
nous 48  states  were  stratified into several geographic regions.  Sample
cities were  selected from  a list  of  eligible places  (cities  >25,000)
proportional to their population.  The hospitals were purposively select-
ed according to type and size.  Because sampling was conducted in select-
ed hospitals in the  sample cities, the sampling areas may be considered
to be  the  union of the service area of the sample hospitals within each
city.   The  specimens  of  adipose  tissue  from  cadavers  and  surgical
patients were obtained through the cooperation of pathologists and medi-
cal examiners.   The  cooperating professionals judgmentally selected the
specimens.  Guidelines  were  given to aid in  the  selection of cadavers,
surgical patients, and anatomical sites (see Appendix F).
     Certain sampling components differ between fiscal years 1970-72 and
1973-76.   Before fiscal year  1973,  the nation was  stratified  into the
four  census  regions   for purposes of sample  allocation.   The number of
sampling  sites   within  each  region was assigned  in proportion  to the
region's population.   Population sizes  used  to set  age,  sex,  and race
quotas and  for  selection of cities were based  on the 1960 census (Yobs
1971).  Beginning in fiscal year 1973, the sample design was modified to
reflect  the demographic  distribution of the 1970 census.  The number of
strata was  increased from  the four  census  regions  to  the nine census
divisions.  The  quotas  were  adjusted to reflect the demographic distri-
bution  in each  stratum (USEPA  1972b).   Appendices  D  and E  contain  a
listing  of  the  census regions, divisions,  and  states  and an example of
the  quotas  assigned   for fiscal years  1973-76.   Figure  3.1 illustrates
the various stages of sampling for the Middle  Atlantic  census  division
in fiscal  year  1973.   The  sampling was conducted in an analogous manner
for each stratum and in each fiscal year.
     Data summaries for fiscal years 1972-76 were provided by EPA (USEPA
1977b).  These  summaries presented  frequencies  and unweighted relative
frequencies for  four residue concentration categories for each of several
racial groups.    Included are tables for each stratum  (census division or
region)  and  a   national  summary for each fiscal year.   Appendix G con-
                                 -11-

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37,152,813
16,607,245
 unknown
   183
Target Population — Total population of Middle
Atlantic Census Division.
                                  Purposive exclusions of cities less than 25,000.
1st Stage Sampling Frame — Cities greater than 25,000.
                                  Purposive exclusion of some.hospitals, pathologist
                                  and medical examiners, selection of only
                                  cadavers and surgical patients.
Sampled Population — Includes a subset of only
surgical patients and persons who died in FY73.
                                  Subsampling of surgical patients and cadavers
                                  by cooperating professionals.
Sample.
     Figure 3.1   Sampling  frame and selection proceudres for Middle
                   Atlantic  Census Division in  FY73.
     Source:  Based on information  contained  in references (USBC 1972,
               USEPA 1972a  and USEPA 1977b).
                                -12-

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tains the national  summaries.   In a majority of  the  census regions and
for  each  national summary,  the  proportion of nonwhites  falling  in the
high concentration categories  is  greater than the proportion of whites.
However, no  statistical  tests  concerning the equality  or inequality of
the  proportion of whites  and nonwhites  having  concentration  of  PCB's
greater than 3 ppm were performed by EPA on these data.
     Selected  statistical  analyses  (USEPA  1977c)  using  Statistical
Packages for  the Social  Sciences (SPSS) were supplied by EPA.   These
analyses were  run  on data  aggregated  over  several  years  and without
weighting  the  data.   The results  supported the  apparent  PCB residue
level differences between racial groups.  However, because these analyses
assumed simple random  sampling and did not account for sources of error
such  as measurement  variance,  the  statistical  significance  of  these
findings is  questionable.   In  the next  section  the  impact  of various
levels  of  measurement variance,  sampling  and measurement bias on the
apparent         significance         levels         is         discussed.
3.2.      Significance of Racial Differences Assuming No Design Effect or
          Measurement Error
     The  hypothesis   of  no  difference  between  racial  groups for the
category >3 ppm  (P   - P  = 0)  is tested using the national data summary
                  n     w
in Appendix F for the fiscal years 1972-76.   Because of the large sample
sizes,  it is adequate  to use the normal distribution to approximate the
significance level of the test statistic

                                    P -P
                            _        n  w	
                          C    [VAR (P -P )]^
where
               n         =    Number of whites in the sample,
               n         =    Number of nonwhites in the sample,
               p         _    Number whites in the sample with >3 ppm,
                w                                n
                                                  w
               p         _    Number nonwhites in the sample with >3 ppm,
                w                                n
                                                  n
and
                                 -13-

-------
                -v  ~               P (1-P )      P (1-P )
               Var[P  - P ]  =     -2	2-  +   -S	S_   •
                    w    n            n             n
                                       w             n

(The above  variance  formula is calculated by conditioning on n  and n ,
hence no covariance term appears in the expression.)
     Under  the assumptions  of  simple  random  sampling,  no measurement
variance, and  no  measurement or sampling bias,  the  rightmost column of
Table 3-1  can  be  thought of as the probability of no difference between
the  racial  groups.   Under these assumptions, fiscal years 1973 and 1976
exhibit  what  might be a  "statistically  significant"  difference between
races  (the  proportion of nonwhites is "significantly"  larger than that
of  whites).   In  addition,   there  is  a  lesser  indication that  such a
difference might exist in fiscal years 1972, 1974, and 1975.
     In the following sections, tables illustrate the effects of various
levels of measurement error, bias, and the sample design on the signifi-
cance levels in Table 3-1 and, hence, on  the  validity of the claims of
statistically  significant differences.
3.3.      Comments on the Impact of Measurement Error
     It  is  difficult to  relate the relative standard  deviation  of the
measurement techniques  (see Appendix B)  to the  error  in  estimating the
proportion of  the sample falling in a particular concentration range.  A
rigorous derivation  requires  knowledge  of or assumptions on the distri-
bution of the  residue in the sampled population.  Intuitively, it seems
that  any uncertainty  in classifying  tissue specimens  correctly would
increase the estimation  variance.   This  is illustrated in the following
example.
     Consider  the  population consisting  of five elements  (e1 , e?, e«,
e,,  e  }.   Elements e ,  e«,  and e_  have  real value one and  elements e,
and e  have real value zero.  However, assume that e~  and e^ are classi-
fied correctly 50 percent of the time and incorrectly 50 percent of the
time.  Let P denote the proportion of the population with the value one.
Hence, in this  example,  P = 0.6.  The mean and variance of the estimator
   *\                                              /\.
of P for  a  sample of size  two  is  calculated for P considering the case
above using first  principles (Mood  et al. 1974) and when no classifica-
tion error  occurs (error-free  case)  using the formula for  the mean of
                                 -14-

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          Table 3-1.  Summary of Results for Testing the Null

                      Hypothesis that P  - P  = 0 for FY72-FY76
                                       w    u.
FY
72
73
74
75
76
n
w
1469
981
798
680
569
n
n
303
132
119
105
103
*w
.0530
.0428
.0438
.0956
.0738
A
Pn
.0825
.1667
.0924
.1619
.1942
/v >\
P -P
n w
.0295
.1238
.0486
.0663
.1204
y\
SE
.0169
.0331
.0275
.0377
.0405
Significance
t level*
c
1.74
3.74
1.77
1.76
2.97
.082
.000
.077
.078
.003
The information in this table is given in Appendix F, or calculated using the

following equations.
     P    _    Number whites in the sample with > 3 ppm
      W   ^
                                 n
                                  w


     P    _    Number nonwhites in the sample with > 3 ppm

                                 n
                                  n


     SE   =    [Var(Pn - PJ]^
               P  - P
                n    w
      c                      *•
               [Var(P  - P )]"*
               L    v n    w

   *

     Probability of a value occurring greater in absolute value than t ,

     calculated by the equation
                                           _i, X2

                                            *   dx   (Mood et al.  1974).
i-  [    -L

    J     V~2T
                                 -15-

-------
the hypergeometric probability distribution  (Mood et al. 1974).  In both
cases,  the estimator P  has  an expected value of 0.6.   Hence,  P may be
estimated  in  an unbiased manner even  in  the classification error case.
In the  error-free case the variance is  0.09 and for the classification
error  case the variance  is  0.1025.   Even  in the  situation  where the
measurement errors  tend  to  be compensating,  the variance  of the esti-
mated proportion is  increased,  in this  case by more  than 10 percent.
     With  the  summary data available  (USEPA  1972a  and  USEPA 1977c), it
is not  possible to  estimate the reduction in precision  of the estimated
difference  due to the  presence of measurement  errors.   Techniques are
available  to obtain an overall estimate of the variance  of the difference
estimator  (Hansen et  al. 1953).  This would involve obtaining the esti-
mates of  the  proportions for each sample site from the  NHMP data files.
These techniques  are  based on the assumption that the sample site esti-
mators  are unbiased  (as  stated earlier,  however,  this   requires assump-
tions about the  data).   An analysis  technique  discussed in  Chapter 5
does approximate  the overall variance of the proportions (or percentages).
     Table 3-2  demonstrates  the increase in  the  significance level for
various  levels of increase  in variance due  to  measurement error.   The
relative standard error  (y) is defined as
                        o    o 3>
                  —  /"CT  4- rt *\ */CT7
                  —  (.or.  T cr ) /or, ,
                                                     ."N    A
where SE  is  the standard  error  of the  estimator P  - P   assuming no
                                                     w    n         6
increase in variance  due to measurement error, and a  is the additional
                                               '      m
variance  due   to measurement  error.   Small  increases  do  not  greatly
affect  the significance  level; however,  when the  measurement variance
          2
equals  SE   (y = 2),  the  significance  level  is drastically increased in
all cases.  An increase of variance of this magnitude or greater due to
the presence  of  measurement  errors  is not  beyond the  realm of possi-
bility,   considering  the  suspected measurement  errors   in  the chemical
analysis.
     Table 3-2  can be  used to estimate the true confidence of the usual
                                                     A.          A.
expression  for a 95 percent   confidence interval  (0-1.96SE,  Q+1.96SE)
when  measurement errors  exist.   For  example,  the true confidence for
such an interval would be about 88 percent  for a 25 percent increase in
the standard   error  due  to  measurement error and  81 percent for  a 50
percent   increase in the standard  error.
                                 -16-

-------
          Table 3-2.  Effect of Measurement Variance on the Significance
                      Level of Test Statistics
Relative SE
*
Y
1.00
1.01
1.05
1.10
1.25
1.50
1.75
2.00
3.00
True significance
level
for a"" =0.05
.0500
.0523
.0648
.0748
.1169
.1913
.2627
.3271
.5135
True significance
level
for a = 0.01
.0100
.0108
.0142
.0192
.0393
.0859
.1410
.1977
.3905
True significance
level
for a = 0.001
.0010
.0011
.0017
.0028
.0085
.0283
.0601
.1000
.2728
,v (SE2 +a2)^ . -
                                                                2
assuming no increase in variance due to measurement error, and a  is
additional variance due to measurement error.
**
      a is the probability of making a Type I error, that is, concluding
there is a difference when in fact no difference exists.
The entries in this table are calculated using the equation
     P  =  1  -    I      ——   e "* x dx,        (Mood et al. 1974)
                          V 20
where ZQ equals 1.96, 2.57, and 3.29 for a equal 0.05, 0.01, and 0.001,
respectively.
                                 -17-

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     Table 3-3  summarizes  the  effect of  the increase  in the standard
error  due to  the  presence of  measurement error  on the probability of
detecting  real  differences between racial groups, if the probability of
making  a  Type I error  is  0.05  (the probability of concluding there is a
difference between  racial  groups when in fact none exist).  This is done
for  various  levels  of  the relative  standard error y  and the relative
difference 8/SE where 6 is the value  of the true difference between P
                                                                       W
and P  .   If the difference is as large as  the standard error (0/SE = 1),
then  the  probability of  detecting a difference  of  this size decreases
from 0.17  (for y = 1.00)   to 0.0628 (for y = 3.0).   If 0/SE = 2, the de-
crease  is more  dramatic, from 0.5160 to 0.1022.
3.4.      Impact of the Sample Design
3.4.1.    Restriction of Cities
     The  sample hospitals were  selected  from cities  with populations
greater  than  25,000.   The purposive  exclusion  of  smaller  cities may
introduce bias  in  estimating  proportions  of  interest.   It is  also pos-
sible  that the bias could result in the  appearance of differences be-
tween racial groups  when no such differences exist.
     Consider  the   following situation.  The  eligible  cities constitute
approximately  42 percent   (.42)  and the excluded  areas  58 percent  (.58)
of the  population.   Let P   and P    denote  the true proportions of the
                           sw      sn
white and nonwhites  in the sampled subpopulation, respectively.  Let P
and  P    denote  the  true  proportions  in  the  unsampled subpopulation.
Hence, the proportions must satisfy the relationships
                     P   =  0.42 P   + 0.58 P
                     w           sw         uw
                     P   =  0.42 P   + 0.58 P  .
                     n           sn         un

It is possible  for P  to  equal P   and  for P   and P   to be quite dif-
                     w      ^     n           sw      sn       ^
ferent; that,  is the  subpopulation sampled  is  actually different from
the  target  population.   The  resulting  bias in the  estimation may give
misleading results.   To demonstrate,  let P   =0.0428 and P   = 0.1667.
                                           sw               sn
(See Table 3-1 for  fiscal  year 1973.)  If P  = P  =0.10, then the above
                                           w    n.
equations  can be  solved   for  P    and  P    yielding 0.1414 and  0.0517
                                U. W       Ll.ll
respectively.   Hence by sampling only a subset of the target population,
bias may be introduced into estimates.
                                 -18-

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Table 3-3.  Effect of Measurement Variance on the Probability of
            Detecting Real Differences
e/sE**
Y*
1.00
1.05
1.10
1.25
1.50
1.75
2.00
3.00
*
Y
assu
addi
a*
.05
.05
.05
.05
.05
.05
.05
.05
(SE2
SE
ming no
tional
0.25
.0572
.0565
.0560
.0546
.0532
.0523
.0518
.0508
+a )"*
increase in
variance due
6/SE is the value of
the standard error.
' a is the
there is a
The
entries
probability
difference,
0.50
.0790
.0763
.0740
.0685
.0628
.0594
• .0572
.0532
1.00
.1700
.1584
.1488
.1259
.1022
.0882
.0790
.0628
1.50
.3231
.2980
.2795
.2244
.1700
.1374
.1165
.0790
2.00
.5160
.4781
.4437
.3596
.2659
.2079
.1700
.1022
SE is the standard error of the estimator
2
variance due to measurement error, and a
to measurement error.
the true
difference
of making a Type I
when in fact no such
3.00
.8508
.8152
.7785
.6700
.5160
.4031
.3231
.1700
A, /N.
P -P
n w
is
4.00
.9793
.9678
.9532
.8925
.7601
.6277
.5160
.2659

between P and P divided by
w n '
error, that is, concluding
difference exists.
in the table are calculated using the
equation


1.96 + 9/SE




P


= 1 -
-1.

Y
/
96 + 9/SE
Y

j'
/HT


2
"^ X dx


(Mood et al.


1974) .




                            -19-

-------
     The  standardized  bias of  the estimator of P -P   is  a function of
                                                  w  n
the bias, P   - P   , and given by
           o W    o li
                         Bias  _  -0.1234     , ,,
                          SE   ~   0.0331    "      '
Tables  3-4  and 3-5  allow  one to gauge the  impact  of  this standardized
bias on  the  probability of concluding there is a difference between the
proportions  for the racial  groups.   They indicate that  between 85 and
97 percent of  the  time  one would erroneously  conclude that the racial
groups were  different.   Admittedly,  this may be  an extreme case.  It is
recognized  that hospitals  do  indeed  serve  outlying   areas  beyond the
sample  city  boundaries.  However,  in  many  states, particularly in the
Rocky  Mountain  region, a  significant  portion  of the  population is, in
fact,  excluded  from the  sampled population because they  live  in rural
areas.
     Table 3-5 must be evaluated in proper perspective.  At first glance,
one may  erroneously conclude  that bias may even increase the likelihood
of  detecting real  differences.   In some  situations,  bias may  in  fact
accentuate differences.   In others, bias  may  conceal  differences.  In
either  case, bias  can be  misleading  to  the  investigator in both the
magnitude and direction of differences.
3.4.2.    Sampling Cadavers and Surgical Patients
     By  restricting the sample  to cadavers and  surgical  patients, one
limits the sampled  population to elements not actually belonging to the
target population (cadavers) and to individuals that constitute a unique
subset  of the   entire  population (surgical  patients).   Bias  similar to
that discussed  in  the  previous section may be involved  if the  propor-
tions  for each  race of the sampled population and unsampled populations
differ.  Any inferences made about the general population are limited by
the assumption  that the sampled and unsampled populations do not differ
substantially.
     Another possible  source  of bias in using surgical specimens is the
chance  for   repeated  observations on  the  same individual,  which  will
result in disproportionate sampling of persons who are  prone to surgery.
Precautions  should  be  taken to prevent this from happening, but adjust-
ments  could  be  made in analysis to take this fact into account if it is
recorded.
                                 -20-

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     Table 3-4 .   Probability of Detecting Real Differences for
                  Various Levels of Positive Bias
' **BIAS/SE
8/SE"
0.00
0.25
0.50
1.00
1.50
2.00
3.00
4.00
0.00
.0500
.0572
.0790
.1700
.3231
.5160
.8508
.9793
0.25
.0572
.0790
.1165
.2396
.4169
.6141
.9015
.9890
0.60
.0790
.1165
.1700
.3231
.5160
.7054
.9382
.9945
1.00
.1700
.2396
.3231
.5160
.7054
.8508
.9793
.9988
1.50
.3231
.4169
.5160
.7054
.8508
.9382
.9945
.9998
2.00
.5160
.6141
.7054
.8508
.9382
.9793
.9988
1.0000
3.00
.8508
.9015
.9382
.9793
.9945
.9988
1.0000
1.0000
4.00
.9793
.9890
.9945
.9988
.9998
1.0000
1.0000
1.0000
     Standardized true difference.


     Standardized bias in estimating the difference 6.


     This table can also be used for the probability of detecting real
     differences for 9 and bias both negative.



The entries in this table are calculated using the equation


          1.96 + 6/SE + BIAS/SE
                                               /2
                                   —-—  e"^ X dx  (Mood et al. 1974)


          -1.96 + 6/SE + BIAS/SE
                                 -21-

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     Table 3-5 .   Probability of Detecting Real Differences for
                  Various Levels of Negative Bias
e/sE*
0.00
0.25
0.50
1.00
1.50
2.00
3.00
4.00
**BIAS/SE
0.00
.0500
.0572
.0790
.1700
.3231
.5160
.8508
.9793
-0.25
.0572
.0500
.0572
.1165
.2396
.4169
.7852
.9633
-0.50
.0790
.0572
.0500
.0790
.1700
.3231
.7054
.9382
-1.00
.1700
.1165
.0790
.0500
.0790
.1700
.5160
.8508
-1.50
.3231
.2396
. 1700
.0790
.0500
.0790
.3231
.7054
-2.00
.5160
.4169
.3231
.1700
.0790
.0500
.1700
.5160
-3.00
.8508
.7852
.7054
.5160
.3231
.1700
.0500
.1700
-4.00
.9793
.9633
.9382
.8508
.7054
.5160
.1700
.0500
     Standardized true difference.

     Standardized bias in estimating the difference 9.

     This table can also be used for determining the probability of
     detecting real differences for 6 negative and bias positive.


The entries in this table are calculated using the equation


          1.96 + 9/SE + BIAS/SE



                                   /In
1 "    '                 ——  e"^ x dx  (Mood et-al.  1974)
          -1.96 + 6/SE + BIAS/SE
                                 -22-

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     When a  method other  than probability sampling  is used  to  select
individuals, sampling bias  in the resultant data becomes  a  real  possi-
bility.  The  purposive  elimination of  particular  subpopulations  should
be  avoided   or,   if  unavoidable,  the  resulting  limitations  should  be
clearly used  to  qualify  the  results.   Biases  such as  those  discussed
previously resulting from  the purposive exclusion of small cities might
also  arise   with  purposive  exclusion  of  certain  types  of  hospitals.
3.4.3.    Remarks on the Bias in Estimating Racial Differences
     There is no prior evidence that any of the possible sources of bias
discussed earlier affect  the  racial groups differently.  If this  is the
real situation, the biasing effects would tend to cancel out when differ-
ences  are  estimated.   Hence,  the  biases  of  the  estimated difference
between subpopulation proportions could be small.  Also, bias introduced
by the method of  chemical analysis may be similar for each racial  group.
If true, this type of bias would also tend to cancel out when looking at
differences  between groups.
                                 -23-

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4.        EVALUATION OF CHEMICAL ANALYSIS AND DATA INTERPRETATION
4.1.      Objective
     The objective  of  this section is to  comment  on the reliability of
the raw PCS data reported as part of the NHMP.
4.2.      Discussion
4.2.1.    Qualitative Assessment
     Chromatograms  of  the  original data were obtained through the cour-
tesy of Mr.  John D. Tessari, Supervising  Chemist  and Laboratory Direc-
tor,  Colorado Epidemiologic  Pesticide  Studies  Center,  Colorado State
University, Fort Collins, Colorado.  A copy of the cover letter discuss-
ing the measurement calculation method is included as Appendix B.
     Tissue  specimens  were extracted  according  to  the  modified Mills-
Olney-Gaither  procedure  (USEPA  1974 Section 5A[1])  and  analyzed by gas
chromatography with electron  capture  detection (GC/ECD) on two columns,
as discussed in Mr. Tessari's letter.   The pesticides and PCB's were not
separated  using  silicic acid chromatography  as  recommended (USEPA 1974
Section 9 C).  Without this separation, some of the pesticides interfere
with the GC/ECD  analysis  of some of the isomers of PCB's.  It should be
noted  that the primary  objective  of  this  program was  pesticide moni-
toring;  the PCB  analysis was  appended with  a  directive  that  minimal
additional  effort  be  expended.    Three  chromatograms are  presented  in
Figures 4-1,  4-2,  and  4-3.   Figure 4-1  represents  a  "high"  PCB level
(>3 ppm) sample, Figure 4-2 a "medium" level (1-3 ppm), and Figure 4-3 a
"low"  level  (<1 ppm).   Figure  4-4  shows the standard Aroclor 1260 chro-
matograms  obtained under  conditions  similar  to  those in  Figures  4-1,
4-2, and 4-3, on October 2, 1979, at Colorado State University.  Compari-
son of Figures 4-1, 4-2 and 4-3 with the standard (Figure 4-4) indicates
that a clear Aroclor 1260 pattern is not discernible  among the pesticide
peaks.  Other  PCB  peaks  (in addition to the  later-eluting  peak  used in
quantitation) seem to  be present and lend support to  the PCB identifica-
tion, but  there  is no  clear "fingerprint."  This  is  reasonable  because
the environmental fate, absorption and excretion dynamics, and metabolic
rate differences  of the  various  PCB isomers dictate  that a PCB  pattern
in tissue will probably not be consistent with a  given commercial mixture
as shown in Figure 4-5.
                                 -24-

-------
 I
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                                                                                                                9 CHROMA1CWKAM
                                                                                                                   IDENTIFICATION

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             Figure  4.1   Chromatogram  of a  tissue  specimen on  4% SE30/6% OV-210 column,  reported as
                                       ">3ppm  PCB"  (factor  calculated as  18. along bottom)
                                                    See Appendix A  for source

-------
         HFTII. •• '*••
         DENTif C.\IIOI»
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I
                     Figure 4.2  Chromatogram of a tissue  specimen on 4% of SE30/6%  OV-210 column,
                                      reported as "1-3 ppm  PCB"  (factor calculated along  bottom
                                                  as 2.75).   See Appendix A for source.

-------
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                           Figure  4.3    Chromatogram of a tissue specimen  on 4%  SE30/6% OV-210 column,
                                              reported  as "
-------
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           ;tlROMATOGRAM

           .IDENTIFICATION!  . i
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                         Figure  4.4  Chromatogram of equivalent to 1.5  ppm Aroclor  1260 standard
                                       Note comment on chromatogram.   See Appendix A for source

-------
                           2        4
                              TIME, minutes
Figure 4.5  Electron capture gas chromatograms  of  (A)  Aroclor 1016 standard
            and (B) PCB-residue extracted  from  brain  of rat  fed on diet con-
            taining Aroclor 1016 for one year.   Reproduced  from (Lewis 1977).
                                   -29-

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     GC/MS  confirmation of  a  composite  (Figure 4-6)  and overwhelming
evidence  from previous  studies  suggest  that  PCB's  are  in  nearly all
human specimens.  In a two-year intensive study, PCB's were confirmed in
                      •ff
322  tissues  by GC/MS.    In addition,  recent  evidence  supplied  by the
Office  of Pesticides  and  Toxic  Substances  of the  U.S.  Environmental
Protecton Agency  (OPTS-EPA)  (Appendix A)  confirms this peak as heptach-
lorobiphenyl.   Although  the  evidence  indicates  that  the  peak being
measured  is  heptachlorobiphenyl,  interferences  may be  present  in some
specimens.
4.2.2.    Quantitative Assessment
     The single late-eluting peak was quantitated as discussed in Appen-
dix  B.   A comparison  of five  integration  methods (USFDA)  showed that
peak  heights,  disc integration,  triangulation,  peak  height x width-at-
half  height,  and retention  time  x  peak  height were  not  significantly
different.  Thus, the  precision of integration  is  not a major issue in
this evaluation.
     Assessing the quantitative accuracy and precision of the PCB values
reported is problematic  (Appendix B).  Disagreement exists among scien-
tists  in  the field as  to  the accuracy of  the  technique employed.  Mr.
Tessari estimates  precision of about ±50  percent.   Furthermore, Larry
Griffin,  also  of Colorado  State  University, in notes  on  the standard
chromatograms (Figure 4-4),  illustrates the calculations of the factors
(see  Appendix  B).  His  calculations  show factors  of   1.00  and 0.84 for
injections  of the  equivalent  to 1.5  ppm  Aroclor 1260 on the  two  GC
columns.  This  represents  a  16 percent uncertainty  in  the GC analysis
alone.  As he  further  noted, these factors "would have been reported as
> 3 ppm, therefore we do need to revise our factors."
     Dr.  R.   G.  Lewis  and  several  of his coworkers  at   the  Research
Triangle  Park laboratory  of  the Environmental  Protection  Agency  (US
EPA-RTP)  were  consulted about  this problem.   They  indicated   that  a
precision of ±50 percent was the best to be expected  in cases where the
pattern matches an Aroclor and where four or more peaks are quantitated.
When presented with the  quantitative method used by  NHMP,  they  felt it
would be  a  reasonable  estimate to  consider the precision  at best ±100
percent.
 Personal Communications, H. Enos, University of Miami, Miami, Florida,
October 1979.
                                 -30-

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      M-S7.0
         5
         u
         i
                                100
       M-40U
                                   SCANNUMMM .
Figure 4.6  Computer Reconstructed Total  ION  and  Mass Chromatograms of a
            Composite Human Adipose Tissue  Extract.   Column:   45.7 m Scot
            Column coated with SE-30.  Programmed from 170-240°C at 2 C/
            min (N.C. - not chlorinated).   Reproduced from (Lewis 1977).
                              -31-

-------
     The chromatograms and procedures  were described over the telephone
to Dr. Henry  Enos,  an expert in this field, who also felt that a preci-
sion  of  ±100  percent would  be  a reasonable  estimate.   He  noted  that
although this procedure  may be  valid  for Aroclor  1260,  it  does  not
detect any  of the  lower  PCB's that do  not contain heptachlorobiphenyl
due to the "masking of those peaks by pesticides.
     A further indication of accuracy and precision is presented in the
interlaboratory PCB analysis  results  supplied by R.G. Lewis in Appendix
C.  Excluding  two  outliers,  thirteen laboratories obtained a mean of 81
±68 percent on 10  (Jg/L of Aroclor 1254  in water.   A more recent inter-
laboratory check with spiked fat reported 96 percent accuracy and ±33.8
percent precision.   However, if the data are recalculated including both
the excluded "outlier" and the missed identification as "zero", the mean
recovery  is   65  percent  (accuracy)  and  ±96 percent  precision.   These
results  are  also provided in Appendix C.  It should  be  noted that all
participating  laboratories  were  aware  this  specimen was  a  check and
therefore  should  have devoted  special  attention  to achieving  their
"best".
     Thus, estimates of ±50 percent precision are optimistic.  Given the
above  arguments  of  precision and that a  clear  Aroclor 1260 pattern was
not evident in the sample, it appears that PCB quantitation based solely
on the peak  labeled as "PCB" by the methods used is not reasonable and
even "semiquantitation" is questionable.
4.2.3.  Potential Interference
     Many other compounds could coelute with PCB peaks giving erroneous-
ly high  readings.   Conversely, the early-eluting PCB  isomers could and
do interfere  with  quantitation of the pesticides.   This  may be seen by
comparing retention times of some of the pesticides and the Aroclor 1260
standard in Figures 4.1  through 4.4.  A  more  graphic  representation is
shown  in Figure  4.7 (USEPA 1974 Section  9E).   These chromatograms  were
obtained under instrumental conditions similar to those shown in Figures
4.1 through 4.4.
                                                                      *
4.2.4.    Assessment of TIC Methodology
     Prior to  November 1974,  PCB values in NHMP were obtained by a thin
layer  chromatographic  (TLC)   technique  (USEPA 1977a and  Mulhern et al.
1971).  This  semiquantitative technique is reported to have a precision
                                 -32-

-------
                        Revised 11/1/72
                                                                                                             Section 9, E
 i
(-0
U)
 i
                               4%SE-30/6%OV-210
                       Chroma to^ra-iis of three AROCLORS on column of
                       1% SE-30 / 6% OV-210. Column temp. 200°C.,
                       carrier flow 60 ml/min., ^H detector, electrom.
                       attenuation on an E-2 10 x 16; dotted line a
                       mixture of chlorinated pesticides, identity and
                       injection concentration given below:
1.  Diazinon
2.  Heptechlor -
3.  Aldrin
li.  Hept.Epox. -
5.  p,p'-EDE  -
6.  Dieldrin
  1.5 ng  7.  o,p'-DUT  — 0.2lj ng
- 0.03   8.  p,p'-DDD  —  ,2U
         9.  P,P'-DDT  —  .30
        10.  Dilan     —  .75
                                        -  .09
                                        -  .0°  11.  Methoxychlor  .60
                                           .12
                                             AROCLOa 12 J 4
                                                             AROCLOR 1248
                                                             6 ng ini«ction
                                                        AROCIOR 1260
                                                        4i>9 i
                            Figure  4.7  Chromatograms of Aroclors and Pesticide standards
                                         illustrating potential  interferences.
                                                  Reproduced  from (USEPA 1974  Section 9 E).

-------
of ±50 percent (Appendix B).  This technique is advantageous in that all
tested PCB  isomers  or,  more correctly, Aroclor mixtures have similar R,.
values (i.e.,  they  elute to nearly the same position on the TLC plate),
so  the PCB  value  reported  is  an integration  of all  isomers  present.
This  technique,  however, lacks specificity  for  individual  PCB  isomers.
     The analytical  conditions  used  by the NHMP elute PCB's essentially
at the solvent front with Rf values  ranging from 0.91 to 0.94 (Mulhern
et  al.  1971).  The  chromatographic  resolution  is  poor and  other com-
pounds  such  as  polychlorinated   naphthalenes   (PCN's),  polybrominated
biphenyls  (PBB's),  polychlorinated  terphenyls  (PCT's)  and  nonpolar
pesticides may coelute.  The best resolution on TLC is obtained at an R-
of  about  0.2 to  0.8  (Stahl  1969).   As an example, DDE  elutes  with the
PCB's and must be removed by oxidation prior to TLC analysis (Mulhern et
                                                                       »u
al.  1971).    That  proper  quality  control  procedures  were  followed,
including GC/MS  confirmation of the  pooled extracts, adds confidence to
the reported values.
4.2.5.    Correlation of Data Generated by the Two Methods
     Since  the "PCB"  values reported  by  the  two  chemical  analytical
methods are used in parallel for statistical analysis, it is appropriate
to comment  on the comparability of the data.  The TLC method reportedly
detects all  PCB's,  while  the  GC method  uses one isomer  (of  209 total
possible) and extrapolates the rest.   Evidence that the methods  generate
comparable  data   is  discussed by  Mr. Tessari  in Appendix B.  However,
because no  quantitative  estimation   of  the  relative bias  of  the  two
methods was  available,  their relative equivalence is  unknown.   For the
purpose of  discussion  in this  report, the differences  in  the data were
assumed to be negligible.
 Personal  Communication,  J.  Tessari,  Colorado  State  University,  Fort
Collins, Colorado, October 1979.
                                 -34-

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5.        WEIGHTED ANALYSIS OF COMPUTER ACCESSIBLE DATA FILES
5.1.      Introduction and Assumptions
     This  chapter includes  a technical  discussion of  the  statistical
analysis of NHMP computer accessible data files.  The following analysis
employs a  regression  technique  especially adapted for multistage survey
data.   This  technique approximates the total variance  of  the estimates
including the measurement and sampling components and also allows adjust-
ments  for other  factors such as  sex, age,  census region,  and fiscal
year.  The analysis is based on the following two important assumptions:
(1) quantitation  and  sampling biases are similar for  each racial group
and  (2)  sampling within  sample  cities produced a  nearly  simple random
sample  within each  city.  The  calculation of  the sample  weights  was
based  on  assumption  (2) and  the  significance  levels  of  all  tests  of
hypotheses depend on the validity of both (1) and (2).
5.2.      Sample Weights
     Because  some  stages of  selection  involve  nonprobability sampling,
the  true  probability of  selection cannot be  calculated.   Even  if  one
assumes  that the  selection probabilities of  a specimen  within sample
cities  are  approximately equal, the sample design of  the NHMP adipose
tissue network does  not give equal  "probabilities" of  selection to  all
elements in  the  sample.   Because of this situation, a sample weight  was
calculated for  each observation that  reflects  its  approximate probabi-
lity of selection.   Including these weights in  the analysis  may reduce
bias in estimating  means or proportions.  In the  following paragraphs,
the procedures used  for computing  approximate weights for the NHMP data
are described.
5.2.1     Calculation of Weights
     For the  purpose of   calculating weights,   two stages of selection
are  considered.   In the  first  stage, cities were  selected  within each
stratum with  probability approximately proportional to population.   In
the second stage, samples of cadavers and surgical patients were selected
in a nonprobabilistic manner from a hospital(s)  or other facility located
in the sample cities.  Equal probabilities of selection were assumed  for
this stage.
     The sample  weights  were calculated as the inverse product of  the
probabilities of  selection  for  each stage.   This can be expressed alge-
braically as
                                -35-

-------
          whij  =  f'hlv1"   .    .
where W, ..  denotes  the weight of the j-th specimen in the i-th city and
h-th stratum;  P..  denotes the probability of selecting the i-th city in
the h-th  stratum  (for large cities selected with  probability  1 this is
more accurately  described as  the expected number of  times  the city is
selected  in  the  sample);  and P. .. denotes the probability of selecting
the j-th  specimen  given the i-th city in the h-th stratum was selected.
To calculate W, . ., it is necessary to calculate P.. and P.., ..
     The  cities  were  selected  independently within each  stratum.   For
each  stratum,  the  cumulative total population  of eligible cities  was
divided  by the number of cities  to  be  selected  in the  h-th  stratum.
This calculation  gives the sample selection interval.  A  random number
is  then selected between  "1" and the value of  the  selection  interval;
this  gives  the  random start.   The method  then  involves listing  the
cities  in a random  order,  calculating the  cumulative totals  and  then
selecting  cities by  matching the cumulative totals to  the random start
and integer  multiples of  the selection  interval  plus  the random start
(USEPA  1973).
     The  above can be expressed algebraically in  the  following manner.
For each stratum, the selection interval is

          :h  =  Vmh '

where I, ,  N, ,  and  m,  denote the  selection interval,  cumulative popula-
tion total, and number to be selected for the h-th stratum, respectively.
The cities  selected in the  sample are  those  for which  the  cumulative
totals  match  r,  + K x I,   for K = 0,...,m,-l,  where  r,  is the  random
               h        h                 h             h
start for the h-th stratum.
     This method assigns  a  probability of selection to each  city equal
to the  population  of the  city divided by the  selection interval.  This
can be expressed  algebraically as

          P,  .  =   N,  ./I,  ,
           hi      hi  h '

where N, .  denotes  the population  of  the  i-th city in  the h-th stratum
and P, .  and I,  are defined above.
     hi      h
                                -36-

-------
     Under  the assumption  made above  for  random selection  of samples
within  a  city, the calculation is  straightforward.   The probability of
selection is  given by dividing the number of specimens selected in each
city by the population of the city.  This can be expressed algebraically
as

          Pj|hi  =  nhi/Nhi

for j = l,...,n, .,  where  n, .  denotes the number of samples collected in
the i-th  city in the h-th  stratum  and  P. ., .  and N, .  are defined above.
                                          I
     The weight can be written as

          W, .  .  =   [N, ./I, x n,  ./N,  .J"1  ,
           hij      l hi  h    hi  hij    '

which simplifies to
          W,  . .  =  I,/n,  . '  .
           hij      h' hi

Hence,  the  approximate  sampling  weight for  the  i-th  city  in the h-th
stratum is the  same for all samples collected in the city and is calcu-
lated  by  dividing  the  selection interval  for  the h-th  stratum by the
number of specimens collected in the city.
     The  method  described  above  does yield  selection  probabilities
proportional  to  the population  of the  cities  when certain precautions
are  followed.  Adequate  methods  for  selecting  alternate (substitute)
cities  were   not  taken.    Hence,  the  procedures  followed in  the study
alter  the probability  of  selection   for  some  cities,  but  the  degree,
although probably minor, is undetermined.  For the purpose of calculating
approximate  weights,  these special situations were treated  the  same as
all others.
5.2.2.    Sample Sizes Used for Calculating Weights
     As noted earlier,  the  weighted   analysis  was  performed using only
fiscal years  1972-76.   Fiscal  year 1977 was  excluded for the following
two  reasons:   (1)  the initial data summaries  received  from EPA did not
include  1977 and  (2)  the sampling  frames  and primary  sampling units
(PSU,  the unit  selected in the first  stage of sampling) were changed in
                                -37-

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1977.   In  1977  the  PSU's  were  changed from  cities  with  populations
greater than 25,000 to Standard Metropolitan Statistical Areas (SMSA's).
     Not  all  data  for  fiscal years 1972-76 were  used  in the analysis.
Out  of  a  total of 8,372 observations, only 5,880 were used.   The exclu-
sion  of  2,492  observations  resulted  for  several reasons.   Of these,
2,380  observations  were volunteer contributions  to  the  survey  from
cities  not  even  in  the  sample.   These records were  not  members of the
survey  design,  and  hence,  their  probability of  selection  cannot  be
reasonably  approximated.   The other 112  observations were excluded for
one of several possible reasons; e.g., the percent extractable lipid may
have  been to  small  or  the  confidence codes on the  data  file indicated
uncertainties about data quality.  The precise rules were the following:
          If the  record  indicated that a technical error had been made
          for  a  particular  residue  amount,  that  residue   amount was
          considered missing.
          If  the  confidence  code for a  particular  residue  amount was
          blank, that residue amount was considered missing.
          If the  record indicated  that  there was less than 10 percent
          lipid extractable material, all residue amounts  were consider-
          ed missing for that record.
5.3.      Racial Comparisons by Weighted Analysis
5.3.1.    Overview
     This  section presents  results from  weighted  regression analyses
that  compare  whites with nonwhites over time  (fiscal  years 1972-1976)
adjusting for  age  (Age  1  <  14,  15 £ Age  2  £ 44, Age 3  >^  45), census
region (CR1 = North East, CR2 = North Central, CR3 = South, CR4 = West),
and  sex.   Because the  strata in fiscal year  1972 were census regions,
the census divisions within each census region were grouped together for
fiscal years 1973-76. Racial  comparisons were performed for  each of two
variables; they are

          Percent of individuals with greater than 3 ppm "PCB's"
            (percent >3 ppm)
          Percent of individuals with detected "PCB's"
            (percent positive detections  (trace,  1-3 ppm and  >3 ppm))
In summary,  the analysis showed:
                                -38-

-------
     For percent >3 ppm "PCB":

     (i)  Over  the  fiscal years 1972-76, there was  an increasing trend
          in  the  percentage of  people  with  greater  than 3 ppm  PCB in
          their adipose tissue.  The rate of increase is estimated to be
          1.73 percentage  points  per year,  with a standard error of the
          estimate of  0.54.   A test of the hypothesis  of no time trend
          was rejected  at the 1 percent significance (99 percent confi-
          dence)  level  by the  estimated trend and  its standard error.

   (ii)   Over  the  fiscal years  analyzed  (1972-76),  nonwhites averaged
          7.15 percentage points higher than whites.   The standard error
          of  the  estimate is  1.41 and the racial difference is signifi-
          cant  at  the 0.1  percent  (99.9  percent   confidence)  level.

  (iii)   The hypothesis  that there is a differential  trend for whites
          and nonwhites  is  not  supported  by the  data.   That  is,  the
          white-nonwhite  differential is  reasonably  constant over time.

     For percent positive detections:

  (i)     There  is  an  increasing  trend  (fiscal years  1972-76)  of an
          estimated  2.98   percentage  points  per  year  with a  standard
          error  of  0.82.   The estimated trend  and   its  standard error
          reject  the  hypothesis of  no  trend  at  the 0.1  percent (99.9
          percent confidence) level.

   (ii)   Over  time,  differences between whites  and nonwhites  are not
          significantly different from  zero  in either absolute level or
          trend.
5.3.2.    Statistical Method

     Each individual  for  which a sampling weight could be calculated in

fiscal years  1972-76  was  used in the analysis  (except for quality-code

related  exclusions).   The analyses  proceeded  by defining  an indicator

variable  for  each sample  member.  Specifically,  the  variables  Y.. . ,  Y-.

were created for the i-th individual such that:


               1 if the i-th sample member's adipose tissue had
                 >3 ppm PCB's and
     Y
               0 if <3 ppm PCB's,
               1 if any PCB's detected and
     Y    =
       1       0 if no PCB's detected.
                                -39-

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     For  each  of  these  dependent  (Y)  variables,  a  linear  model was
specified,  which  attempted  to  relate the  Y variables  to  the  sample
member's  age,  race,  sex, census region,  and  year of tissue collection.
Specifically, Y was approximated by:
Y =  M
 2
 I   A.X.
.  ,   11
                                        I  CR  £
                                        =    J  J
                                            .
                                                J V;
                                                               (i)
where
     X.  =
      i
          1 if an individual is in age group i (i = 1,2),
          0 otherwise.
               1 if an individual is white,
               0 otherwise.
     XM  =
          1 if an individual is a male,
          0 otherwise.
     i.  =
      J
          1 if an individual is in census region j (j = 1,2,3,4)
          0 otherwise.
     FY  =     Fiscal Year - 1970 (coding by subtracting 1970 from
               fiscal year).
     In  the  sample,   the  X.,...,  FY  are  known  and the  coefficients
        *                   L
(M,...,p.) must be  estimated  from the data and  represent  the "effects"
of an  individual  being a member of a particular age, race, sex combina-
tion living in a specific census region at a specific time.
     In general,  the model  is  simply a  generalization  of a covariance
model  (with FY  being  the covariate) that allows a function linear in FY
to be  fitted  simultaneously  for each combination  of  age,  race,  sex and
                                -40-

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census region.  As  an example, suppose two  individuals  are  in the same
age  group  (age group  1,  say),  both are males, and both live  in census
region 1 however, one is white and one is nonwhite.  Then, for the white
individual:

     Yw  =  M + A1 + Rw + SM + CRj + FY (PQ + PX + Pw + PM + P*) >   (2)

and for the nonwhite individual:

    Y^  =  M + Aa + 0 + SM + CRj + FY (PQ + Pj + 0 + Pj, + P*) .     (3)

Combining terms common to both individuals gives:
Y = M
*W "ill
Y = M
NW "ill
> Rw + C
. B1UFY
where MI   and  BI  1  are the sum of the terms common to both individuals
and  (111)  indexes  the age group, sex,  and  census  region of the indivi-
duals.  Hence,  the  model is simply a  formulation  of a covariance model
that allows  the estimation and testing of a differential FY slope (time
trend) by  different  subgroups  of the population.  Hence, by appropriate
manipulation of  the  slopes and intercepts of the  fitted lines,  differ-
ences between whites  and nonwhites  may be estimated and tested statis-
tically.
     The  parameters   (the  coefficients above)  were estimated  by  using
weighted  least  squares, where  an individual's  weight  was  the sampling
weight  discussed in Subsection 5.2.   The variance-covariance  matrix of
the  estimated coefficients was computed by the use of Taylorized devia-
tions (Shah et al. 1978).
     For  the  two  variables  analyzed (percent >3 ppm "PCB",  percent
positive detections)  the estimated  coefficients are given in Table 5.1.
     Following  the  notation developed  in (4)  and  (5)  above,  for every
combination of  age,  sex and census region  (denoted  as  i,j,k), an esti-
mated line can be produced for each race.   They are of the form:
                                -41-

-------
                        JU
               Table 5-1'  Model Coefficients (Percent)
Percent >3 ppm "PCS"
Intercept
Terms
M
Al
A2
Rw
SM

CR1

CR2

CR3
-4.
3.
2.
-2.
1.

3.

9.

7.
68
15
68
79
20

40

38

78
Slope
Terms
PO 4-
p - ~ 2. •
Po ""1 •
PW -1-
PM °-
*
P! i-
*
Pf)
~ £, ,
*

Percent
Positive
Intercept
Terms
39
96
89
09
04

29

03

32
M
Al
A2
RW
SM

CR1

CR,

CR3
79
-5
1
2
6

-5

-6

-5
.84
.65
.96
.47
.00 -

.57

.06

.32
Detections
Slope
Terms
PO 3
PI "0
P2 -o
PW -°
PM -1
.<.
PI x
A
P2 l
->-
Po 0


.36
.87
.36
.38
.27

.22

.27

.95
     *Based on data for fiscal years 1972-76 from NHMP computerized data
files, using methodology defined in section 5.2.
                                -42-

-------
For whites:
For nonwhites:

    A              /\      «%
     Nw? ^ J J j ^      i T If    T n k"     *

Now, there are 3 age groups, 2 sexes and 4 census regions; therefore,

averaging over the 24 distinct lines in (6) and (7) gives:


     Yw  =  M + ^ + (B + BW)FY   ,                                   (8)



    ¥„,  =  M + B FY  .                                              (9)
where

     "           324
                 Z   Z   Z
                i=l j=l k=l
     M  =   Z~   Z   Z   Z  M  (i, j, k)
                3   2   4
     B  =  27-  Z   ^   Z  B  (i, j, k)
               i=l  =l k=l
For the data used in the analyses,  (8) and  (9) are:


For percent >3 ppra PCB,


     YW  =  2.99 - 2.79 + (2.28 - 1.09)FY   ,

         =  0.20 + 1.19 FY   ,                                        (10)


    A
    Y^  =  2.29 + 2.28 FY   .                                        (11)


For percent Positive Detections,
                                -43-

-------
     YW  =  77.37 + 2.47 + (3.18 - 0.38)FY
         =  79.8 + 2.80 FY ,                                        (12)
    Y^  =  77.37 + 3.18 FY  .                                      (13)

     To  test  for slope  differences,  it  is  sufficient to  test &. = 0.
The tests are presented in Table 5-2.
     Now assuming the slopes are not different for the two racial groups
(a  hypothesis supported  by  the  tests  in Table  5-2),  the  difference
between  the two  levels  may be estimated by averaging the yearly differ-
ence in  the  lines over the years.  This  is  algebraically equivalent to
testing  the difference  in the lines at FY =  4  (1974).   Table 5-3 gives
the results.
     The tests given  in Table 5-2 indicate that for both percent >3 ppm
"PCB"  and percent  positive  detections,   no  differences exist  between
whites  and nonwhites  with  respect  to  their  time  trend   (FY  slope).
Therefore,  a  common  trend is  given  as   the average  of the  two  race-
specific slopes.   They are the following:
     For percent >3 ppm PCB,
     *.                                         f\
     PAVG  =  %(2.28 + 1.19)  =  1.73, with SE(BAVG)  =  0.54  ,

     For percent Positive Detections,
     ••s                                         ^
     PAVG  =  35(2.80 + 3.18)  =  2.99, with SE(6AVG)  =  0.82  .

Both  of  these  common  slopes  are  significantly  greater  than  zero
(p < .01).
     Figures 5-1  and 5-2 present the above estimates and analyses graphi-
cally.   In addition to the trend lines discussed above, certain directly
adjusted white and nonwhite means are plotted.  These are plotted merely
to present a  sense  of the data and are, roughly,  what the race-specific
lines are  predicting.   To compute these means, the  following procedure
was followed.   First, weighted  means  were computed for each combination
                              -44-

-------
     Table 5-2.'   Tests for Differential Slopes—White Versus Nonwhite
                             Percent >3 ppm "PCB"  Percent Positive
                             Detections
   SE(fJw)


Z = PW/SE(PW)

Significance
                              -1.09


                               2.96


                              - .36

                         Not Significant
     -.38


     1.22


    - .31

Not Significant
 Based on data for fiscal years 1972-76 from NHMP computer accessible
data files, using methodology defined in Section 5.2.
     Table 5-3.   Tests for Average Racial Group Differences
                             Percent >3 ppm "PCB"
                             Detections
                                                    Percent Positive
  Difference Between
         ^»
  Lines (A) :White-Nonwhite
                                   -7.15
          .95
     SE(A)
                                    1.41
         1.89
  Z = A/SE(A)

Significance
                                   -5.07

                                 p < .001
         0.50

     Not Significant
 Based on data for fiscal years 1972-76 from NHMP computer accessible
data files, using methodology defined in Section 5.2.
                               -45-

-------
                 Figure 5-1.    Racial Comparisons Over Time:  Percent > 3 ppm "PCB1

'% > 3 ppm "PCB" (Y)
   20% -
   15% -
   10% -
    5% -
                                                        I	Nonwhite  Trend  Line  (%)
                                                              Y =  2.99  + 2.28  FY
                                                          White Trend  Line  (%)
                                                           Y = 0.20 +  1.19  FY
       1972
1973
1974
1975
1976   Years
       LEGEND

       O =  Nonwhite estimated means  adjusted  for  Age,  Sex, and  Census  Region.

       • =  White  estimated neans  adjusted  for Age,  Sex, and  Census Region.

      FY =  Years  -  1970

       STATISTICAL  TESTS
1.
2.
Test
Slope Differences
White Versus Nonwhite
Average Distance Between
Estimate
-1.09
7.15
Standard Error
2.96
1.41
           Lines
            White  and Nonwhite
       3.   Trend  Summary
            Average  of White  and
            Nonwhite slopes
                 1.73
             0.54
          Significance

       Not Significant


       Highly Significant
           (p < .001)


       Highly Significant
           (P < .01)
       *  Based on data  for  fiscal  years  1972-76  from NHMP computer accessible data
          files using methodology defined in Section 5.2.
                                       -46-

-------
               Figure 5-2.   Racial Comparison Over Time:  Percent Positive "PCS"  Detections
    % Positive
Detections ."PCB"  (Y)
    100% J
     95%-
     90%-
     85%-
                               Average Distance Between
                                    Lines (< 1%)
              White Trend Line (%)
               Y- = 79.8 + 2.80 FY
                                                Overall Trend Line (%)
                                                 Y = 73.6 + 2.99 FY
                                   Nonwhite Trend Line (%)
                                     Y = 77.4 + 3.18 FY
                              (67.7%)
        1972
1973
1974
1975
1976   Years
         LEGEND

         a =» Nonwhite estimated means adjusted by Age, Sex, and Census Region.

         • =» White estimated means adjusted by Age, Sex, and Census Region.

        FY = Years - 1970
         STATISTICAL TESTS
                                         Estimate   Standard Error
         1.  Slope Differences
               White Versus Honwhite

         2.  Average Distance Between
             Lines
               White and Nonwhite
         3.  Trend Summary
               Average of White and
               Nonwhite Slopes
                - .38
                  .95
                 2.98
             1.22
             1.89
              .82
          Significance

       Not Significant


       Not Significant
       Highly Significant
           (P < • oi);
                Based on data for fiscal years 1972-76 from NHMP  computer  accessible data
                files using methodology defined in Section 5.2.
                                          -47-

-------
of age, race, sex, census region, and fiscal year.  There are potentially
240 (=3x2x2x4x5) such  means.  However,  due  to  the  absence in
the sample  of  young nonwhites in certain years in census region 4, only
234 means were  computed.   In essence, a five-dimensional table with one
entry per cell  was  produced  (the weighted means).  Next, for each race
and year,   the  simple  average  of  the  weighted averages  was computed.
These averages  are  rough estimates of the means by race and year. It is
not difficult  to  show that  the race-specific lines drawn in Figures 5-1
and 5-2 and these means estimate the same quantity if the assumed model
form  (1)  is correct.   Finally,  these means are plotted  along with the
estimated lines in the two figures.
5.4.      Comments on Assumptions
     Several  apparent  inconsistencies  in the  data  cast doubt  on the
assumptions.   The most  striking  is  the very  low value  of  percentage
detected for  nonwhites in  1973.   The 67.7  percent is more  than three
standard deviations  below the predicted mean value of 87.6 percent (see
Figure 5-2).   Thus   this  low value is  not  likely  to  be  due to random
variation in the  data.   The change in the chemical analysis  methods in
1974 may  contribute to  the  higher percentages  for later  fiscal years
than for early  fiscal years, and the trend over time may be in part due
to the change in chemical analyses.
                                -48-

-------
                               References








Biocca M, Moore JA, Gupta BN, McKinney JD.  1976.  Toxicology of selected




symmetrical  hexachlorobiphenyl  isomers:   I.  Biological  responses  in




chicks and mice.  In:  National Conference on Polychlorinated Biphenyls.




1975.  Chicago IL.  Ayer FA, ed: 67-72.  EPA-510/6-75-004.








Cochran WG,  1977.   Sampling Techniques.   New York, NY:  John Wiley and




     Sons.








Hansen MH, Hurwitz WN, Madow WG. 1953.  Sample Survey Methods and Theory,




Vol. 1 and 2.  New York, NY:  John Wiley and Sons.








Kish L.  1965.   Survey  Sampling.   New York,  NY:  John  Wiley and Sons.








Kutz FW, Yobs AR, Strassman SC.  1976.  Organochlorine pesticide residues




in  human  adipose tissue.   Society  of Pharmacological and  Environmental




Pathologists 4(1):17-19.








Lewis  RG.  1977.    Organic  trace  analysis—are  we  on  target?   Nevada




Section meetings, American Chemical Society.  Las Vega, NV.








Matthews HB, Anderson M.'1976.  PCB chlorination versus PCB distribution




and  excretion.   In:  National  Conference  on Polychlorinated Biphenyls.




1975.  Chicago, IL.  Ayer FA, ed: 50-56.   EPA-510/6-75-004.
                               -49-

-------
McKinney JD. 1976.  Toxicology of selected symmetrical hexachlorobiphenyl




isomers:   correlating  biological effects with  chemical  structure.   In:




National  Conference on  Polychlorinated  Biphenyls.  1975.   Chicago,  IL.




Ayer FA: 73-75.  EPA-510/6-75-004.








Mood  AM,   Grayhill  FA,  Boes  DC.  1974.   Introduction  to the  Theory of




Statistics.  New York, NY:  McGraw-Hill.








Mulhern BM, Cromartie E, Riechel WE, Belisle AM. 1971.   JAOAC 54: 548-550.








Shah, BU,  Holt MM,  Folsom RE. 1978.   Inference  about  regression models




from  sample  survey data.   In:  International  Association of  Sampling




Statisticians.  New Delhi.








Stahl E,  ed.   1969.   Thin Layer Chromatography,  a  Laboratory Handbook.




New York, NY:   Springer-Verlag, Inc.








USBC. 1972.   U.S.  Bureau  of the Census.   Census of  population:   1970




general population  characteristics,  final report PC(1)-B1, United States




summary.  Washington DC:  U.S. Government Printing Office.









USEPA.  1972a.   U.S.  Environmental Protection Agency.   Listing of cities




with  populations  greater  than 25,000  for  the  Middle  Atlantic Census




division,  based  on  1970  Census.   Washington DC:  Office of  Pesticides




and Toxic Substances, U.S. Environmental  Protection Agency.
                              -50-

-------
USEPA.   1972b.   U.S. Environmental  Protection Agency.  National  human




monitoring program--1972. Washington DC:  Office of Pesticides and Toxic




Substances, U.S. Environmental Protection Agency.








USEPA. 1973.  U.S. Environmental Protection Agency.  Listing of eligible




cities  with  cumulative  populations,   selection  intervals,   and  random




start  numbers.    Washington,  DC:  Office  of   Pesticides   and   Toxic




Substances, U.S. Environmental Protection Agency.








USEPA.   1974.   U.S.  Environmental   Protection  Agency.   Analysis  of




pesticide residue in human and environmental samples.  Research Triangle




Park, NC: U.S. Environmental Protection Agency.








USEPA. 1977a.  U.S. Environmental Protection Agency.  Analysis of pesti-




cide  residue  in human and  environmental  samples,  section  9D.  Research




Triangle Park, NC.  U.S.  Environmental Protection Agency.








USEPA.   1977b.    U.S.   Environmental   Protection  Agency.    Tables  of




frequency of levels of polychlorinated biphenyls in human adipose tissue




by geographic area  and  race,  wet weight basis for fiscal years 1972-76.




Washington,  DC:   Office  of   Pesticides  and  Toxic  Substances,  U.S.




Environmental Protection Agency.








USEPA. 1977c.  U.S. Environmental Protection Agency.  Computer printout:




computer  analysis  of PCB data by Statistical  Packages for  the  Social




Sciences  (SPSS).   Washington,  DC:    Office  of   Pesticides  and  Toxic




Substances, U.S. Environmental Protection Agency.
                               -51-

-------
USFDA.  U.S. Food and Drug Administration. , Pesticide Analytical Manual,




Vol.   1,   Section  300.63.    Washington,   DC:   U.S.  Food   and  Drug




Administration.








Yobs  AR.  1971.  The  national  human monitoring  program  for pesticides.




Pesticides Monitoring J.  5(l):44-46.   Reprinted by:  U.S.  Environmental




Protection Agency.
                              -52-

-------
                      APPENDIX A

               Letter from M.  Aaronson
(Reproduced with permission of S.  Strassman-Sundy)
                       A-l

-------
                                                      January 7, 1980



Mrs. Sandra Strassman
Project Officer
National Human Monitoring Program
Field Studies Branch
Survey and Analysis Division (PS-793)
U.S. E.P.A.
401 "M" Street S.W.
Washington, D.C. 20460

Dear Sandy:

Enclosed please find a copy of the following items:

    1) Reconstructed ion chromatogram (RIG) of Tissue Pool sample 79-A,6%

    2) The mass spectrum of scan #266

    3) The library search of scan #266

    4) A comparison of the mass spectrum of scan #266 to that of
       heptachlorobiphenyl
The RIC begins at pp-DDT (scan #51) and runs for twenty minutes.  Scan
#266 is the peak utilized by the laboratory for the semi-quantitative
estimation of PCB's in tissue extracts.  Further examination of the
peak represented by scan #266 tentatively identifies it as a heptachloro-
biphenyl.  The actual heptachlorobiphenyl standard would be required
to produce a more definitive identification.

If you have any further questions or if I can be of more assistance do
not hesitate to call.
                                            Sincerely
                                            Michael J. Aaronson
                             A-2

-------
                 BIC
                 10/10/79  10:50:00
                         TISSUE  pnm.
100.0-1
  BIC
                               DATA: MS65 H263
                               CALI: 10579 113
                             I. 600   LABEL:  »  0.  4.0  (HJAN:  A  0.  1.0  BASE: U 20.  3
                       100
                       3:20
209
6:40
 300
10:00
• 400
13:20
 5i
16

-------
                  riASS SPECTRUM                                              DATA: HS65 U266
                  10/IO/79  10:50:00 +   8:52                                 CALI: 10579 113
                  SAMPLE: TISSUE P001.
                   11263 TO  »269 SUUI1ED - H253  TO «258 -  H274 TO 11279 XI.CO
                                                                                                             BASE H/E: 30-i
                                                                                                             RIG:
                                                                                 ••>•*• "fit »
                                                                                 J J.* v'*.
100.On
50.0-
                                                                                                                                    3316.
                                                                                                                   254
        73..

 n/E         so
100.0-1
                       100
120
                                                HO
                                                                                                         T*7
                                                   1. -          I                   A f\*~w        S\ 4 ft          ^.-*J*
                                                   44         I           182      197        216           |
                             ^9   1|9 |27   .III,     15711          ,i[     ,.,11.203      |.|	I  ,1 I 247
                             i  I i i i "I i i > I rl'iTi I'l'l'l'i'i i i ri'i1 'I'l'i'i i i I'l'l'l'l i I'rl'J ri'i I'l'r'i'i'i r» I'l'i'i i i | i  ri'l'i'i i  i11
                                   180
                       200
220
240
260
50.0-
                 239
             2821
            r-r-|-r-<

            «0
                                315
                                                          359
                                                      TH"
20
:6'J
                                                                                    00
            T"
             40

-------
LIBRARY SEARCH
10/10/79 10:50:00 +  8:52
SAMPLE: TISSUE POOL /?
 * 263 TO * 269 SUMMED -
                    DATA:
                    CALI:
                     MS65  * 26S
                     10579 *  3
                            BASE IVE:   394
                            RIC:     29727.
               * 253 TO * 258 - * 274 TO * 279 XI.00
25409 SPECTRA IN LIBRARYN9'SEARCHED FOR MAXIMUM PURITY
   54 MATCHED AT LEAST 3 OF THE 16 LARGEST PEAKS IN THE UNKNOUN

RANK IN NAME
1 20174 1.1'-BIPHENYL.2,2'.3.4.5,5',6-HEPTACHLORO-
2  9039 1.3,5-TRIAZINE.2,4,6-TRIS(TRICHLOROMETHYL:i-
3 23034 BENZENE,1,1'-THIOBIS-.TETRACHLORODERIV.
4 17181 FERROCENE.l,l',2,2'-TETRflCHLORO-
5 16534 1.1'-6IFH£NYL,2.3',4,4',5-PENTACHLORO-
RfiSK  FORMULA
1 C12.H3.CL7
2 CS.H3.CL9
3 C12.HG.S.CL4
4 C1G.H5.CL4.FE
5 C12.H5.CL5

MASS   INTEN
  36
  39
  47
  49
  59
  51
  56
  63
  73       0
                              M.UT B.PK
                               392  394
                                     PURITY
                                        764
                                  FIT
                                  945
RFIT
 795
  f -^
  75
  77
  62
  84
  91
  93
  94
  97
  99
 1C3
 103
 112
 1! !
 117
 119
 125
 126
 127
 131
 132
 133
 134
 135
 135
 142
 143
 144
 145
 U'3
 147
 143
 157
 160
17
19
13
10
 5
 8
12
18

13
44
60
43
51
30
16
19
             2
           153

           231
            63
             14
              4

             23
             21
             35
             34
           210
           129
133
119
 47
      187

       80
 61
 98
84
37
66
                               429  398
                               322  324
                               322  324
                               324  326
                                        338
                                        309
                                        215
                                        185
                                  568    445
                                  935    327
                                  780    261
                                  743    199
                    4

                   75
                        78
                       543
                        95
                        41
                        49



231
215



196

552

343

275
265


15
19

7


171
54
55
49




43

                        73
                       274
     103

      67
                          A-5

-------
LIBRAIJY SEARCH
10/10/79  10:50:00 +  8:52
SAHPLE: TISSUE  POOL
 II 263 TO II 269 SUI1HED - » 253 TO it 258 -  «  274 TO (I 279 XI.00
DATA: ttS65 tl 266
CAL1: 10579 it   3
BASE ll/E: 3S4
RIG:    29727.
1000 -i
SAHPLE
•

C12.H3.CL;
1000-1
H UT 392
B PK 394
RANK 1
IH 20174
PUR 764 .


1000-
0
mnn -
1UUU
H/E


\^
	 , ,1,1,. . ill,. ...n . .,n. ., ii,(|. ,ii, Ji!/ ilh .mi.
j ' ' ' ' ' ' ' ' ' j' I'-IBIPUEMYL 2 2* 3 45 51' G-HEPTACHLORO- ' 	 '

II II
l| II . il| III . n, . Ill .|,
SAHPLE HINUS LIBRARY ' ' '
i i. . ... yi q.ll. i ,01,1 ,,|ji i i 1 	 lillh ll.ll . I.I.I. , . lll.l . ill, . |
• I i I • I i | i | i | i (— r— j'"-r | •!-'•>- !• > | • | i | < | • | -t 	 1 — •- j •• r-'T'-i 	 1 	 1 	 r" • 1 • | ' 1 ' 1
100 150 260 250 300



ill.
• • I i i i i

!
llh

1 	 	
• I • I • I • i - . • i • .
350

-------
           APPENDIX B

 Letter from John D. Tessari
(Reproduced with Permission)
           B-l

-------
                                                         Colorado State University
College of Veterinary Medicine                                  Fort Collins, Colorado
  and Biomedical Sciences                                     80523
Institute of Rural Environmental Health
Colorado Epidemiologic Pesticide Studies Center
Spruce Hall
                                                         September 21,  1979


Dr. Mltchel Erickson
P.O. Box  12194
Drafysus  Lab
Research  Triangle Institute
RTF-  North Carolina  27709
Dear Dr. Erickson:

     Enclosed  find  the gas chromatographic charts you requested
with our estimate of the PCS content of those samples.  We
randomly chose 38 of our most recently analyzed samples.  Also
find enclosed  a chart showing sample numbers and data.
     The calculation method utilizes the isolated la"rge PCS peak
(Arochlor  1260) eluting 8-12 cm beyond p,p'-DDT on 1.5% OV-17/
1.95% OV-210  (6.0-6.9 relative retention time to aldrin) and on
4% SE-30/6% OV-210  (4.6-5,0 relative retention time to aldrin).
A comparison is made between the peak height of 40 pg of aldrin
(5 ul of an 8  pg/ul aldrin standard) and the peak height of this
PCB peak in 1  mg of sample (5 ul of a 200 ug/ul dilution of
sample) according to the following relationships

     	peak height (mm) of 40 pg aldrin	
                                                      = FACTOR
     peak height  (mm)  of PCB peak in mg of sample

     The factor determines the amount of PCB's present, which  is
reported to  the EPA by code letter, according to the following:

         FACTOR             PCB's Present       Code Letter

     No peak present           0 PPM                V
         > 3.5                  <1 PPM                W
        3.5-2.6               1-3 PPM                Y
        < 2.6                  >3 PPM                Z

     This method  is based on the TLC method of Mulhorn et al,  (1)
which has a  precision  of - 50% when using Arochlor 1260 as the
reference standard.  A series of representative adipose samples
were analyzed  for PCB's by this method, and for all chlorinated
pesticides by  the more accurate standard adipose tissue method
(2).  A direct correlation was noted between the PCB result by
TLC and the  height of  the afore mentioned peak.  Finally, a
relationship was  established between the ratio of a specified
                                B-2

-------
                                -2-
amount of aldrin and the PCB peak height and concentration in
order to produce the factors.  Thus, our method essentially relates
the relative peak height of the PCB peak to aldrin to obtain a PCB
concentration.
     Due to the semi-quantitative nature of the method, certainly
no'better than the reference method which claims  - 50% precision,
discrepancies between the two GLC columns occassionally arise.
For example, 1-3 PPM may be noted on one column and <1 PPM on
another.  Judgement is applied to the individual situation to
determine which result to report.  A factor of 3.5 (just in the
1-3 PPM range) from one column and a factor of 4.0 (<1 PPM) would
probably be reported as <1 PPM.  A factor of 3.0  (1-3 PPM) and
a factor of 3.6 (just <1PPM) would probably be reported as 1-3
PPM.  If the discrepancies are extreme (<1 PPM on one column, >3
PPM on the other), we attempt to resolve the difference.  If no
errors can be found, an average is usually reported (1-3 PPM).
     If you have any questions please contact us.
                                         John D. Tessari
                                         Supervising Chemist
                                         Laboratory Director
 (1)  Analysis of Pesticide Residues in Human and Environmental
     Samples, U.S. EPA, R.T.P. N.C.,,. 1974, Sect 9,D.

 (2)  Ibid.,  Section 5,A,C1.).
                                B-3

-------
factor
CPM
                        tter
facf
  3-5
 Jo. o
  £7
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 3.2
 22-7
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 >3
           >3
          >3
                      ut/'
                                                   y
            IA/
                      (A/
                      (A/
                     W
                      \fj
             Y
            w/
            Y

            iV
            IA/
            uJ
            z

            T
            ~z.
            -z,
             •z.
                                / 2-
                                3-6
                                Y.
                              5-0


                              za.

                              5.7
                               /•*?

                               A?
                               3-7
                                         1-3
          >3
          >3
          7-3

          >3
                                                    Z.
                                                    Z.
                                                    (AJ
                                           Y
                                                   (A/
                                                   Y

                                                   ~v
Y
Z

•2.

Y
                                                     z.
                                                     u/
                                                    Y
                                                     /-J
                                          Y
                                                                         y
                                                                tA/
                                          IA/

                                          (A/
                                          Y
                                          Y
                                          Y
                                          ~z_
                                          Y
                                          Y
z
Y
-z.
                              •R-A

-------
                  APPENDIX C

   Interlaboratory PCS Analysis Results
(Reproduced with permission of R.  G.  Lewis)
                   C-l

-------
SUBJECT:
                                    :sTAL TOXICOLOGY DIVISION
                           HEALTH EFFECTS RESEARCH LAEOSATCKY
                    UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
  DATE:  August 17, 1977
Water Blind Sample No.
49
  FROM:  Chief,  equality Assurance Section,
         HSRL, ETD, ACS (MD-69) ,  RTP, NC  27711

    TO:  All Participating Laboratories

              On June 3 a water round robin sample  (No. 49) was mailed to 25
         laboratories who had previously signified they wished to participate
         in this interlaboratory excercise.  Nineteen laboratories mailed in
         their analytical data, and a brief summary report was mailed back  to
         all 25 laboratories on July 8 so' that the formulation would be  avail-
         able to all, coincidental with the receipt of the 6-m.cnth water SPRM
         in acetone which v:as mailed June 28.       ••              .

         GEKT^AL COMMENTS: •
              We think that most of you will agree that this was.not the easiest
         sarrcle possible nor was it intended to be..  We think, however, that
         it was probably no more difficult than many routine samples;  in fact,  it
         was probably somewhat less difficult than many monitoring samples of
         unknown composition because of the spike with intact parent compounds.
         Routine environmental samples are far more likely to contain-partially
         altered compounds.

              We were somewhat disappointed that five Labs failed to detect
         both DOT metabolites, and seven Labs failed on oxychiordar.s.  Five
         Labs overlooked HCB, and to their credit all except three Labs identi-
         fied the PC3 (Aroclor1254).   Those three who overlooked this compound
         shonld be deeply concerned because the chromatographic fingerprint
         should have been unmistakable on just about any GC ccluirn cr;£ chess
         to use.  Similarly, the HCB peak should have bean most glaringly       '
         apparent.  Two of the Labs who failed on Arcclcr 1254 thought they
         saw all sorts of other things--dieldrin, Kepcr.e, and what have you.

              G.C. column selection was undoubtedly one kay factor in  the failure
         to detect certain compounds in the presence cf the multitude  of A::oclor
         peaks.  Hut another, and we think more cogent factor, was the failure  to
         apply a high degree of reasoning in the interpretation of che available
         cliror.-.atogrciphic data.  By way of illustration,- p,p'-DDT ar.d a major
         peak of Aroclor 1254 nearly completely-superimpose "on the OV-17/OV-210
         column operated at 2C.O"C.  One laboratory had a peak i»: their sample   .-•>;
         extract which calculated an FJ
-------
                                    -2-

this Aroclor peak had a base width of 19 nzn, a most clear indication
that something else besides the Aroclor was elutir.g frcra the sample
extract at this retention site.  The prime suspect, of course, would •
be p,p'-DDT.

     We could document a dozen more similar illustrations, but we
believe the point sho'-uld be clear that in the process of interpretation,
a number of factors.must be weighed, to a much greater extent than
simply measuring retentions.

QUANTITATION;

     The most glaring irregularity in qxiantitation was experienced by
Lab Code 16 wherein the reported values were apparently in error by
an approximate factor of x 100.  This laboratory was requested to review
their reported data but the values reported back were still far off.

     The very best that could be done with HCB is about 85% recovery.
Therefore, no Lab should be expected to report a value in excess of
0.25 ppb.  It was found in our Lab that the 4% SE-30/6% OV-210 was
most suitable for quantitating the HCB as this was the only column
producing baseline separation from the solvent peak.

     Rejecting those outlier values designated by asterisks in Table 1,
the mean recovery values from all Labs, with respect'to formulation,
were as follows:

                      HCB	67%
                      Oxychlordane 	 83
                      p,p'-DDE —	•— 85
                      p,p'-DDT	86
                      Aroclor 1254	81

     Considering the relative difficulty of the sample, we think the
overall performance was not too bad.  In terms of interlaboratory
precision, relative standard deviation values in the range of the
calculated values were quite acceptable.  On less complex formulations
"Total Error" values in the under 50% range are generally considered .
satisfactory.  Cn this particular sample, however, the calculated
values could not be considered too far out of line.
                                                          •

     Our apologies to Lab Code 23 for overlooking their reported value
for p,p'-DDE.  This correction is reflected in the attached corrected
summary  (Table 1).

RELATIVE PSRFOr-yAIICE;  .

     Table 2 shows the relative overall performance of the 19 partici-
pating laboratories.   The method for this computation may be found in
our Quality Control Manual, Chapter 2.
                                     C-3

-------
                                    -3-                   .

     Tho3
-------
                  TABLE  1
                                                            INTF.RIJVGORATORY CUFCK bAMI'LK NO. 49,  KATEP - SUX.".r.aY OF  RESULTS
1J\{\
r:^fjf.
Forculatlon
8
16"
15
6 -.•
7
11
34
9
16
26
13
3 ' '
?3
1
24
25
5
12
10
>
Over. -ill Mean
Standard Deviation
Relative Std. Devlat., Z
Totbl Error, Z
PESTICIDES KITGUTED IN MICi
-------
             •7 -•         U'viTED STATES ENVIRONMENTAL PROTECTION AGENCY
   DATE:  January 29, 1980

SUBJECT:  Fat Blind No.  70, Check Sample Report

   FROM:  Chief, Quality Assurance Section (MD-69)  /?  J?
          ACB/ETD/HERL                             /L '"'
          Research Triangle Park, NC  27711

     TO:  All Laboratories Receiving SPRM No.  70


               On December 3, a fat blind sample was sent to the 22 laboratories
          which had previously signified they  wished to participate in the inter-
          laboratory exercise.  Eighteen laboratories returned reports of analy-
          tical results.   One Epidemiology Studies  laboratory did not participate.

          General Comments

               This check sample study was designed to measure proficiency of a
          laboratory in recognizing and quantitatively determining PCB contami-
          nation in an adipose sample containing common organochlorine pesticide
          residues.  The.pesticides and fortification levels for SPRM No.  70 were
          derived from national surveys of residues in human adipose tissue.   This
          exercise therefore represented a realistic analytical  problem that might
          be encountered in a residue laboratory.

               Specific comments concerning submitted sample report sheets and
          chromatograms may be found in individual  reports.

          Results and Discussion

               Table 1 presents a summary of the standard pesticide reference
          material (SPRM) formulation and analytical results.  Laboratories above
          the double line are Epidemiclogy/Human Monitoring  contract laboratories.

               This sample proved to be a difficult challenge for several  of the
          participants.   Performance scores of 190  points or above (out of 200
          possible) were obtained by only five laboratories.  Eight of the eighteen
          total respondents and four out of the eleven "epidemiology/human moni-
          toring" laboratories failed to recognize  the PCB residue pattern.  Analysts
          that did not recognize the PCB pattern often misidentified PCB peaks as
          pesticides.
                                       C-7

-------
     The percent total error (TE) figures in Table 1 demonstrate a
generally unacceptable level of performance for this analysis.  Five of
the nine TE figures were greater than 50% and therefore unacceptable
(for explanation of total error see EPA "Manual of Analytical. Quality
Control for Pesticides in Human and Environmental Media" section 2K).
Total error figures for only the EPA contract laboratories are as follows:
HCB, 53.1%; e-BHC, 16.4%; oxychlord^ns, 45.7%; t-nonachlor, 22.3%; Kept.
Epox., 43.5%; p.p'-DDE, 46.7%; p.p'-DDT, 30.1%; dieldrin, 42.7%; and PCB
1254, 120%.

     GC column pairs used for analysis and confirmation were generally
OV-17/OV-210 and SE-30/OV-210.  These are both good columns but gen-
erally do not make a good complementary pair for qualitative and quan-
titative confirmation.  A better suggestion for most residues would be
either of the two mixed columns (SE-30/OV-210 or OV-17/OV-210) paired
with the. 5% OV-210.  The apparent best selection for this sample No. 70
would have been OV-17/OV-210 paired with the OV-210.  Pairing of either-
mixed phase with OV-210 unfortunately entails GC runs at 200 and 180°C
and necessitates either two GC's or change of column temperature and re-
run of sample extracts and standards.

     Laboratories using silica columns on the 6% Florisil fraction to
effect a separation of PCB's and chlorinated pesticides appeared to
experience some difficulty with achieving proper fractionation.   PCB's
sometimes eluted in the pesticide fraction causing a PCB peak to be
misidentified as a pesticide, i.e., o,p'-DDT.

     At least two laboratory reports indicated some Florisil fractiona-
tion problem.   These problems should be resolved as soon as possible.
Florisil that is too retentive could result from (1) improper activation
temperature, (2) improper percent of ethyl ether in pet. ether,  and (3)
ethyl ether that does not contain the required 2% ethanol (read  the fine
print analytical information on the can or bottle).  Florisil that
appears insufficiently retentive or inactive might result from (1) or
(2) above, and (3) residual amounts of a polar solvent in the sample or
standard being placed on the column.  Likely candidates here could be
acetonitrile from the sample partition cleanup (if the water-out steps
where not performed properly) or incomplete removal of benzene (or other
solvent more polar than hexane) from a standard solution placed  on the
column.   Other sources of Florisil  problems are undoubtedly possible.

Relative Performance

     Table 2 shows the overall performance ranking of the eighteen
participating laboratories.   A possible score of 200 points is derived
from 100 points each for qualitative and quantitative analytical results.
                               Co
                             —O

-------
 General  Recommendations

      Residue  analysts  should  become  familiar with  PCS  elution  patterns
 from  their  common  GC columns.   Chromatograms could be  exhibited on a
 wall.or  filed in some  other convenient manner for  easy referral.;

      The 5f; OV-210 column  should  he  used  routinely for identity con-
 firmations.                      '    '

      Laboratories  should investigate the  various schemes  that  have been
 offered  in  the  literature  for separation  of PCB's  from other chlorinated
pesticide residues.  Silicic  acid column  chromatography of the 6% Florisil
 fraction to achieve this separation  is being used  by the  EPA human milk
 monitoring  program.
CC:  Dr. V.  R. Hunt
     Dr. F.  W. Kutz
     Ms. S.  C. Strassman-Sundy
     Ms. Madeline  Dean
     Dr. Hale  Vandermer
     Dr. C.  W. Miller
     Dr. Lee Leiserson
     Dr. John  Kliewer
     Dr. W.  F. Durham
                             C-9

-------
                TABLE 1
                                                            INTERLABORATORV CHECK SAMPLE NO. 70, FAT-SUMMARY Of RESULTS
LAI!
cow:
4
5
Formulation


7
8
10
11
1?
14
24
25
26
51
1
6
9
13
16
38
52
Overall Mean
Standard Deviation
Relative Std. Devlat., X
Total Error, %
Ave. % Recovery
(*0utl ier)
PESTICIDES REPORTED IN PPM
IICU
0.061
0.031!
N 0
0.035
0.038
0.041
0.032
0.042
0 032
0.049
0.042
0.022
0.04
0.06
0.04!i
0.042
0.024
0.045
0.043
0.014*
0.040
0.009
23.0
65.0
66.7
G-WIC
0.25.
0.278
R E P 0 R
0.269
0.22
0.244
0.23
0.278
0.234
0.180
0.258
0.237
0.25
_
„
0.264
0.242
0.212
0.223
0.119*
0.24
0.03
11.0
24.8
96.0
OXYCIILOR-
tlANF
0.10
0.114
T
0.079
0.10
0.094 .
0.075
0.117
0.057
0.074
0.105
0.105
0.09
0.16
_
0.182*
0.083
0.122
0.058
0.036
0.092
0.030
32.6
68. 0
92.0
/•IVIM.'i-
NflN/lf 111 OR
0.15
0.156

0.175
0.16
0.154
0.12
0.148
0.151
0.130
0.152
0.13
0.15
0.11
0.12
0.208*
0.143
0.165
0.116
0.115
0.14
0.02
14.0
32.4
93.3
1IEPT.
FPOXIDE
0.081
0.092

0.091
0.105
0.095
0.086
0.096
0.062
0.080
0.090
0.059.
0.10
0.03
0.064
0.086
0.094
0.073
0.053
0.015*
0.080
0.02
25.1
51.1
98.8
P.P'-DDE
3.50
3.39

3.289
3.31
3.212
3.3
2.148
2.097
2.40
3.964
2.96
3.10
3.36
3.40
2.577
2.061
2.98
2.992
2.166
2.93
0.56
19.0
48.1
83.7
P.P'-DDT'
0.60
0.675

0.705
0.59
0.581
0.68
0.741
0.481
0.470
0.559
0.588
0.60
0.80
0.58
0.819
0.575
1.01*
0.503
0.403
0.61
0.12
18.9
39.9
101.7
niFIDRIN
0.13
0.162

0.149
0.133
0.081
0.13
0.132
0.082
-
0.152
0.148
0.13
0.13
0.12
OJ02
0.029
0.124
0.118
0.019*
0.12
0.03
27.7
58.8
92.3
AROCLOR
1254
1.00
1.99*

+
1.40
-
-
-
-
0.505
1.328
0.943
0.96
-
1.00
.
_
-
1.00
0.519
0.96
0.32
33.8
69.1
96.0
NON- SPIKE



O.P'-ODT = 0.057

O.P'-DDT Aldrin
n nfi? n ni4

Aldrin
rt 01 H

O.P'-DDT
n.ns?
Vn4RDT VnHP


O.P'-DDT Aid. Lindarifi
n 14 0.02 0.05







n
 i

-------
                                    TABLE 2



             CHECK SAMPLE NO. 70, FAT-RELATIVE PERFORMANCE RANKING  ''
LAB
CODE
51*
8*''
26*
38,
4*
52
25*
11*
7*
16
14*
13
9
12*
24*
6
10*
1
COMPOUNDS
MISSED
0
0
0
0
0
0
0
1
0
1
1
1
1
1
1
2
1
2
FALSE
IDENTIFICATION
0
0
0
0
0
0
2
0
1
0
0
0
0
1
1
0
2
3
IDENTIFICATION
SCORE
100
100
100
100
100
100 .
77.78
88.89
88.89
88.89
88.89
88.89
88.89
77.78
77.78
77.78
66.67
44.44
QUANTITATION
SCORE •'
95.57
92.89
91.17
90.37
90.24
73.80
94.03
81.30
80.99
79.14
77.68
77.12
75.95
80.62
78.38
73.33
83.21
69.10
TOTAL
SCORE
195.57
192.89
191.17
190.37
190.24
173.80
171.81
170.19
169.88
168.03
166.57
166.01
164.84
158.40
156.16
151.11
149.88
113.54
*Epidemiology/Human Monitoring Contract Laboratory
                                  C-ll

-------
 Recalculation of Fat Check Sample No.  70.
 1)    All  quantisations   including  Lab  #4  which  was  termed  "outlier"
      n              9
      Mean        1.07
      SD            46
      RSD (%)        43

'Accuracy   Mean   107%
           Actual


 2)   All  reporting  laboratories  with  "zero" included for those  who  did
 not report Aroclor 1254
      n              18
      Mean         0.65
      SD           0.62
      RSD (%)         96
            Mean
           Actual
A          Mean     ,_0,
Accuracy  	    65%
                              C-12

-------
                    APPENDIX D

Breakdown of Census Regions and Divisions by State
                 (Provided by EPA)
                      D-l

-------
             Census Breakdowns of the United States
Region

North East
Division
New England
North Central
                         Middle Atlantic
East North Central
                         West North Central
South
South Atlantic
                         East South Central
                         West South Central
States

Connecticut
Maine
Massachusetts
New Hampshire
Rhode Island
Vermont

New Jersey
New York
Pennsylvania

Illinois
Indiana
Michigan
Ohio
Wisconsin

Iowa
Kansas
Minnesota
Missouri
Nebraska
North Dakota
South Dakota

Delaware
District of Columbj
.Florida
Georgia
Maryland
North Carolina
South Carolina
Virginia
West Virginia

Alabama
Kentucky
Mississippi
Tennessee

Arkansas
Louisiana
Oklahoma
Texas
                           D-2

-------
Census Breakdowns of the United States (Continued)
Region                   Division                 States

West                     Mountain                 Arizona
                                                  Colorado'
                                                  Idaho
                                                  Montana
                                                  Nevada
                                                  .New Mexico
                                                  Utah
                                                  Wyoming

                         Pacific        .          Alaska
                                                  California
                                  ;       .        Hawaii
                                                  Oregon
                                                  Washington
                            D-3

-------
                     APPENDIX E
Survey and Site Quotas for Fiscal Years 1972-1976
                  (Provided by EPA)
                      E-l

-------
        Census Divisions

National Human Monitoring Program
  Age, Race, Sex Distributions


New England (1)





Middle Atlantic (2)





East North Central (3)





West North Central (4)





South Atlantic (5)





Age Groups

0-14
15-44
45+
Total:


0-14
15-44
45+
Total:


0-14
15-44
45+
Total:


0-14
15-44
45+
Total:


0-14
15-44
45+
Total:

Sex
M

4
5
_4
13
# Negroes = 1

4
5
_4
13
# Negroes = 3

4
5
^4
13
- # Negroes = 3

4
5
_4
13
// Negroes = 1

4
5
_4
13
// Negroes = 6
F

4
5
_5
14


3
6
_5
14


4
6
_4
14


4
5
_5
14


4
6
_4
14

Total

8
10
_9
27


7
11
_9
27


8
11
_8
27


8
10
_9
27


8
11
_8
27

            E-2

-------
        Census Divisions

National Human Monitoring Program
  Age, Race, Sex Distributions

Age Groups
East S'outh Central (6)
0-14
15-44
45+
Total:

West South Central (7)
0-14
15-44
45+
Total:

Mountain (8)
0-14
15-44
45+
Total:

Pacific (9)
0-14
15-44
45+
Total:

Sex
M

4
5
_4
13
# Negroes = 5

4
6
_3
13
# Negroes = 4

4
6
_4
14
# Negroes = 1

4
6
_4
14
# Negroes = 2
F

4
6
_j4
14


4
6
_4
14


4
5
_4^
13


3
6
_4
13

Total

8
11
_8
27


8
12
_8
27


8
11
_8
27


7
12
_8
27

            E-3

-------
National Human Monitoring Program
  Collected by Census Division
                             Sex
    Age Groups
M
Total
New England (1) -
4 Collection Sites - 4%
s




Middle Atlantic (2) -
14 Collection Sites - 19%





East North Central (3) -
14 Collection Sites - 19%





West North Central (4) -
5 Collection Sites - 7%







0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



16
20
16
52
# Negroes


56
70
56
182
# Negroes


56
70
56
182
// Negroes


20
25
20
65
# Negroes


16
20
20
56
= 4


42
84
70
196
= 42


56
84
56
196
= 42


20
25
25
70
= 5


32
40
M
108



98
154
126
378



112
154
112
378



40
50
£5
135

            E-4

-------
National Human Monitoring Program
  Collected by Census Division

Sex

South Atlantic (5) -
11 Collection Sites - 15%

•*



East South Central (6) -
7 Collection Sites - 9%





West South Central (7) -
7 Collection Sites - 9%





Mountain (8) -
3 Collection Sites - 4%





Pacific (9) -
10 Collection Sites - 13%





Age Groups


0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



0-14
15-44
45+
Total:



0-14
15-44
45+
Total:

M


44
55
44
143
# Negroes


28
35
28
91
# Negroes


28
42
21
91
# Negroes


12
18
12
42
# Negroes


40
60
40
140
# Negroes
F


44
66
44
154
= 66


28
42
28
98
= 35


28
42
28
98
= 28


12
15
_12
39
= 3


30
60
40
130
= 20
Total


88
121
88
297



56
77
56
189



56
84
49
189



24
33
24 -
81



70
120
80
270

           E-5

-------
                       National Human Monitoring Program
                         Collected by Census Division
                                                 Sex
                           Age Groups
 M
       Total
Percent
Summary:
                             0-14
                            15-44
                             45+
                              Total:
300
395
293
988
        576
        833
        616
1037   2025
 28.4
 41.1
 30.4
 99.9
                                           # Negroes = 245

                                           Percent = 12%
Total Males    =  49%
Total Females  =  51%
Total Negroes  =  12%
                                   E-6

-------
                       APPENDIX F

 EPA Guidelines and General Information About Collecting
Adipose Tissue for the National Human Monitoring Program
                     for Pesticides
                          F-l

-------
 f
?  £* \                    •         •         .     •
       j    UNITED STATES ENVIRONMENTAL PROTECTION AGENCY
                             WASHINGTON. D.C. 20460
                       Guidelines and General Information
                         About Collecting Adipose Tissue
            For the National Human Monitoring Program for Pesticides

             The National Human Monitoring Program for Pesticides is responsible
        for determining, on a national basis, the incidences, levels and other
        evidences of exposure to pesticides in the general population of the
        United States.  At present, the program collects and analyzes adipose
        tissues for selected pesticides and their metabolites known to be stored
        in'the lipid portion of these tissues.  The results from the program are
        ysed in evaluating various factors and conditions pertaining to human
        health and effective pesticide regulation..
             The adipose tissue for this program is secured through the coop-  •
        eration of participating pathologists and medical examiners located
        throughout the continental United States.  The tissue is obtained from
        surgical specimens previously excised for pathological examination and
        from postmortem examinations.  The specimens are sent to the program
        office in Washington, D. C., from which they are subsequently forwarded
        to contract laboratories for chemical analysis.  Periodic reports of the
        laboratory results are sent to each  participating pathologist for the
        tissues which were submitted under his auspices.  Summaries comparing
        results with other regions of the country are also provided as they
        become available.'
                                  .F-2

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      In order  to develop valid  information on a national basis, col-
 lections must  be made  according  to an experimental design which dictates
 the  number of  samples  required  according to  the demographic distribution
 of the population  in the appropriate census  division.  You should have a
 copy of the annual quota of  samples expected to be collected from your
 location on a  fiscal year  basis.  All collections should be made
 according to this  age/sex/race  distribution.  You should be able to.
^                                                                   • '
 collect the number of  samples required  in each category.  Since our
 total sample is relatively small and the validity of  the results depends
 on a high response rate, your participation.is particularly important.
 If you feel that you will  be unable to  collect the number of samples
 required, please let us  know.
 Criteria for Selection of  Patients tp_ be_ Sampled
      Since the program objective is to  reflect pesticide incidences and
 levels in the  general  (man-on-the-street) population,  a few suggestions
 are  listed here for your guidance:
      •  The highest priority should be  given to satis-
        fying  the  number and demographic distribution
        of your annual quota.   This quota should be
        completed  as soon  after the start of the fiscal
        year as possible.
     '•  Patients having  known or suspected pesticide
        poisoning  should not be sampled.  If you are
        involved with  a  potential pesticide  poisoning,
        we would like  to know about it.  However,
        samples should not be taken for the  National
        Human  Monitoring Program for Pesticides.
                             F-3

-------
     t  Patients exhibiting cachexia or who have been
        institutionalized for long periods' should not
        be sampled for the national  program.
Legal Considerations                       •
  .   The National Human Monitoring Program for Pesticides i.s both
interested and deeply concerned about the legal ramification of this
human research project.  Since the program operates in about 40 states,-.
it is not feasible for us to handle the variety of local or state
interpretations from our location in Washington.  Therefore, as a
matter of policy, the legal requirements, i.e., informed consent,
confidentiality, are matters for your consideration and resolution.
Collections for this program must be made in  conformance with .the appli-
cable HEW guidelines on the protection of human subjects of biomedical
and behavioral research.  We will, however, be pleased to assist you in
any way possible.
     .We have completed several studies on these matters and do not
believe that they present major obstacles to your participation.  In
most documents authorizing postmortem examinations, there is a clause  '
granting the examining physician permission to remove tissues for
research purposes.  We consider this project to be included in that
category.  -In the case of specimens recovered  from your surgical practice,
the  use of a small amount of tissue from a previously excised specimen
certainly does not place the patient at risk in any way whatsoever.
     As you will notice in our discussion of data needed for each patient
sampled, we do have several mechanisms to assure confidentiality.   In
fact, the disclosure or release of certain data is protected by federal
statute.  The fees paid to you by our program  are solely intended to
remunerate you or your designee for professional services rendered.
                             F-4

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 Collection of Surgical Adipose Tissue
      Collect samples of adipose tissu'e from unfixed specimens which have
 been surgically excised for therapeutic reasons.   Take special care to
 keep samples from different patients separate, correctly and securely
 labeled, and avoid their contact with other chemicals, such as paraffin,
 disinfectants, preservatives, or plastics.
      At least five grams of good quality (subcutaneous, perirenal, or
^-mesenteric) adipose tissue should be collected; avoid fibrous or connective
 tissue, 'i.e., omentum.  Place the fat, without any fixatives or preservatives
 •into the provided chemically-cleaned container; legibly complete and
 attach the self-adhesive label in ball-point pen or pencil.  The bottle
 labels should be affixed before freezing.  Store the specimens up-right
 in a freezer at -4°F (-20°C) until shipment.
 Collection of Postmortem Adipose Tissue
      Adipose tissue samples must be obtained only from unembalmed
 cadavers.  The interval between death and the collection of tissue
 should be as short .as possible and must not exceed 24 hours, assuming
 refrigeration during the interval.  Samples of adipose tissue must weig"h
 at least five grams and should be placed in the supplied, chemically-
 clean container with a completed label affixed. .Specimens should be.
 stored at -4°F (-20°C) without any fixative or preservative until
 shipment.  Submit only good quality fat; do not submit omentum as it
 contains too much connective tissue for satisfactory analysis.
                             F-5

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     Adipose should be taken dry, and should not be rinsed before
placing in the provided containers.   Many water supplies contain materials
which would interfere with chemical  analysis.
     Instruments should be well-rinsed with distilled water and dried
before taking the adipose sample.
Completion o_f_ the Patient Summary Report
     A Patient Summary Report should.be completed for each patient from
whom a sample was taken.  Special attention should be given to the   .   .
completeness of the data.  All medical information submitted is protected
from disclosure or release by U.S.C'. 552, (b) (6); 45 CFR Part 5.  First
and last initials, in that order, should be used instead of the complete
name to insure that confidentiality is maintained.  The initials, along
with the data of birth, sex, and race, are used in this office to
compose.the AMA identification number.  The patient's identification
number and/or the pathology department's accession number are for your.
information in referring back to the individual patient when you receive
the results of the pesticide analysis.                      .
     Confirmed diagnosis should  be detailed  in the spaces provided.
Only the major ones should be supplied.
     Other information  required  should be completed as accurately as
possible.  The complete forms should be held and sent under the lid of
the insulated container when shipment  is made.
                            F-6

-------
 Packing  and  Shipping
      Tighten all  lids  on the specimen bottles  carefully.   This  is  •
 important since we are required  to  use special  aluminum foil  cap liners  .
 which make tightening  a little difficult.   Be  certain that a  completed
 bottle label is firmly attached  to  each specimen bottle.   Wrap  each
 bottle in gauze or paper to prevent breakage during  shipment  and to  keep
 the- label  on' the  container.  Place  the s'pecimen bottles in the  insulated
'mailer and fill it with dry ice.   If you have  difficulty obtaining dry
 'ice, please  call  us and we can arrange alternative methods of refriger-
 ation for you.                                                  •   .
      A franked addressed label is on the reverse side of the  address
 card.  This  card  is marked AIR MAIL - SPECIAL  DELIVERY.  (Do  not send
 Air Express, please).   There is  no  cost to the sender because of the
 franked  label.  All insulated mailers should have a  PERISHABLE-PACKED IN
 DRY ICE  label visible  from all sides on the outside..
      Specimens should  be mailed  on  a Monday or Tuesday of a week with ino
 federal  holidays.   This assures  that they will  arrive before  the end of
 the work week on  Friday.              .              .
      Patient Summary  Reports should be sent in the carton with.the
 specimens when possible.  They can  be folded and placed on the  top of
 the.polyfoam lids.
                             F-7

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     Only samples which meet our criteria and are handled according to

the guidelines can be accepted.  Mo substitute containers will  be

accepted.                                           •

For Further Information

     If you have any questions or comments, please contact us.   Telephone

(collect):  ,202/755-8060.
                             Sandra C. Strassman
                             Frederick W. Kutz, Ph.D.
                             National Human Monitoring Program
                              for Pesticides (WH-569)
                              F-8

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                    APPENDIX G

National Data Summaries for Fiscal Years 1972-1976
                 (Provided by EPA)
                      G-l

-------
o
                                                         FY 1972
                                                                                         \



     FREQUENCY AND  LEVELS  OF  POLYCHLORINATED  BIPHENYLS  IN  HUMAN ADIPOSE  TISSUE,  BY  GEOGRAPHIC AREA AND RACE: WET. WT.  BASIS

Stratification
NATIONAL
RACIAL GROUP
Caucasian
Negro
Mexican American
Puerto Rican
Oriental
Other
NATIONAL
RACIAL GROUP
Caucasian
Negro
Mexican American
Puerto Rican
Oriental
Other
American Indian

Sample size
1778

1469
303
	 *
—
3
3
4098

3370
708
—
—
4
7
9
Percent not
Detected
10.24

9.12
15.18


0.00
66.66
26.06

25.16
30.22


0.00
28.57
44.44
Percent less
than 1 ppm
22.05

23.48
15.18


0.00
33.33
15.45

18.52
10.45


25.00
14.28
0.00
Percent
1-3 ppm
61.87

62.08
61. 38


66.66
0.00
50.63

51.57
46.46


50.00
14.28
55.55
Percent greater
than 3 ppm
5.85

5.30
8.25


33.33
0.00
7.86

6.73
12.86


25.00
42.65
0.00
File Type
DESIGN

DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
MASTER

MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
      Indicates no samples in  this group.

-------
o
OJ
                                                         FY 1973
                                                                                         \
     FREQUENCY AND LEVELS OF POLYCHLORINATED BIPHENYLS IN HUMAN ADIPOSE TISSUE, BY GEOGRAPHIC AREA AND  RACE: WET. WT. BASIS
Stratification

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
  American Indian

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
  American Indian
                              Sample size

                                 1120
 918
 132
   2

   1
   3
   1

1279
                                 1102
                                  169
                                    2

                                    2
                                    3
                                    1
                  Percent not
                   Detected

                     21.43
                                                      20.18
                                                      31.06
                                                       0.00

                                                       0.00
                                                      33.33
                                                       0.00
                     22.86
                     36.09
                      0.00

                      0.00
                     33.33
                      0.00
Percent less     Percent     Percent  greater
 than 1 ppm      1-3 ppm       than 3 ppm    File Type
   41.61
31.16
5.80
DESIGN
45.25
14.39
100.00

0.00
33.33
0.00
30.27
37.87
0.00

0.00
33.33
100.00
4.28
16.66
0.00

100.00
0.00
0.00
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
                                                                                                                  MASTER
43.82
17.75
50.00

50.00
33.33
0.00
29.12
32.54
0.00

0.00
33.33
100.00
4.17
13.60
50.00

50.00
0.00
0.00
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER

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                                                         FY  1974
                                                                                         •\
     FREQUENCY  AND LEVELS OF POLYCHLORINATED  BIPHENYLS  IN HUMAN ADIPOSE TISSUE, BY GEOGRAPHIC AREA AND RACE: WET. WT. BASIS
o
Stratification

NATIONAL

RACIAL GROUP
  Caucasion
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
                              Sample size

                                  926
 798
 119
   1

   1
   7

1051
                                  891
                                  150
                                    1

                                    1
                                    8
                  Percent  not
                   Detected

                      9.29
 8.27
14.28
 0.00

 0.00
42.85

 9.04
                      8.30
                     12.00
                      0.00

                      0.00
                     37.50
               Percent less
                than 1 ppm

                  51.62
 54.38
 33.61
100.00

100.00
 25.57

 50.62
                  53.87
                  32.00
                 100.00
                 100.00
                  25.00
               Percent
               1-3 ppm

                34.02
32.95
42.85
 0.00

 0.00
14.28

35.49
                33.55
                48.00
                 0.00

                 0.00
                25.00
           Percent greater
             than 3 ppm     File Type
5.08
4.38
9.24
0.00

0.00
14.28
4.85
4.26
8.00
0.00

0.00
12.50
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER

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                                                         FY 1975
     FREQUENCY AND LEVELS OF POLYCHLORINATED BIPHENYLS IN HUMAN ADIPOSE  TISSUE, BY GEOGRAPHIC AREA AND RACE: WET. WT.  BASIS
o
Cn
Stratification

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
                              Sample size

                                  793
680
105
  4

  2
  2

910
                                  756
                                  135
                                   13

                                    2
                                    4
                 Percent not
                  Detected

                     5.80
 5.88
 4.76
25.00

 0.00
 0.00

 5.82
                     5.95
                     4.44
                    15.38

                     0.00
                     0.00
               Percent less
                than 1 ppm

                  56.49
 57.35
 52.38
  0.00

100.00
 50.00

 55.93
                  57.01
                  52.59
                  30.77
                 100.00
                  25.00
               Percent
               1-3 ppm

                27.24
27.21
26.67
75.00

 0.00
 0.00

27.58
                26.98
                29.63
                46.15

                 0.00
                25.00
           Percent  greater
             than 3 ppm     File Type
10.47
9.56
16.19
0.00

0.00
50.00
10.66
10.05
13.33
7.69

0.00
50.00
DESIGN -
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER

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                                                         FY 1976
                                                                                          \
     FREQUENCY AND LEVELS OF POLYCHLORINATED BIPHENYLS IN HUMAN ADIPOSE  TISSUE,  BY GEOGRAPHIC AREA AND RACE: WET. WT. BASIS
o
Stratification

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
  American Indian

NATIONAL

RACIAL GROUP
  Caucasian
  Negro
  Mexican American
  Puerto Rican
  Oriental
  Other
  American Indian
                              Sample size

                                  684
569
103
  1

  2
  9
  5

785
                                  645
                                  120
                                    1

                                    2
                                   12
                                    5
                 Percent not
                  Detected

                     2.03
 1.76
 1.94
 0.00

 0.00
22.22
 0.00

 1.78
                       55
                       67
                     0.00

                     0.00
                    16.67
                     0.00
               Percent less
                than 1 ppm

                  59.36
 61.16
 49.51
100.00

100.00
 44.44
 60.00

 60.00
                  61.86
                  50.00
                 100.00
                 100.00
                  50.00
                  60.00
               Percent
               1-3 ppm

                29.32
29.70
29.13
 0.00

 0.00
11.11
28.54

28.54
                28.84
                28.33
                 0.00
                 0.00
                16.67
                40.00
           Percent greater
             than 3 ppm     File Type
9.29
7.38
19.42
0.00

0.00
22.22
9.68
9.68
7.75
0.00
0.00

0.00
16.67
0.00
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
DESIGN
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER
MASTER

-------