United States
               Environmental Protection
               Agency
             Office of
             Prevention, Pesticides,
             and Toxic Substances
    EPA 747-R-94-003
    September, 1995
SEPA
SEASONAL RHYTHMS OF
BLOOD-LEAD LEVELS:
BOSTON, 1979-1983
               FINAL REPORT
               	      79
               	      80
               	      81
             Dec 79  Jun 80   Dec 80
               Jnn 81
Dec 81   Jun §2  Dec §2

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                                              September  1995

                                            EPA  747-R-94-003
                     FINAL REPORT
        SEASONAL RHYTHMS OF BLOOD-LEAD LEVELS:

                   BOSTON,  1979-1983
              Technical Programs Branch
             Chemical Management Division
      Office  of  Pollution  Prevention  and Toxics
Office of Prevention, Pesticides, and Toxic Substances
         U.S.  Environmental Protection Agency
               Washington, D.C.   20460

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                           DISCLAIMER
     The material in this document has been subject to Agency
technical and policy review and approved for publication as an
EPA report.  Mention of trade names,  products,  or services does
not convey, and should not be interpreted as conveying, official
EPA approval, endorsement, or recommendation.

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                    CONTRIBUTING ORGANIZATIONS
     This study was funded and managed by the U.S. Environmental
Protection Agency.  The study was conducted by Battelle Memorial
Institute and Midwest Research Institute under contract to the
Environmental Protection Agency.  Each organization's
responsibilities are listed below.
              Battelle Memorial  Institute  (Battelle)

     Battelle was responsible for the development of the analysis
approach, for conducting the statistical analysis of the data,
and for writing the final report.
                Midwest  Research  Institute  (MRI)

     Midwest Research Institute was responsible for the
completion of the final report.
            U.S.  Environmental  Protection Agency (EPA)

     The Environmental Protection Agency was responsible for
managing the study, for reviewing the final report, and for
arranging the peer review of the final report.  The EPA Work
Assignment Manager was John Schwemberger.   The EPA Project
Officers were Jill Hacker and Phil Robinson.

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                        ACKNOWLEDGEMENTS

     The study team would like to thank Dr. Mike Rabinowitz for
his gracious provision of the data for this analysis,  and for his
helpful comments based on his review of the draft report.

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                         Executive Summary
          Several researchers have observed increased incidence
of lead poisoning during summer months.  Reasons for seasonal
rhythms in blood-lead levels, if such a phenomenon is real,  are
not immediately apparent.  Altered human physiology and higher
levels of lead exposure during the summer months have both been
postulated as reasons for the temporal variations.
          This study was undertaken to examine temporal variation
in blood- and environmental-lead levels in data observed for a
sample of 249 children in Boston between 1979 and 1983 at the
Brigham and Women's Hospital.  The two primary objectives of this
study were to:
               Determine the extent to which blood-lead levels
               recorded in the study conducted at the Brigham and
               Women's Hospital exhibit seasonal variation.
               Determine if any existing seasonal trends in
               blood-lead levels are correlated with seasonal
               trends in environmental levels.
          For each child in the study,  blood-lead and
environmental-lead measurements were collected longitudinally
over a period of two years.  Levels of lead in air,  dust,  water,
and soil were included in the environmental data.  Nominally,
between two and five measurements were taken for each response
(blood or environmental lead)  in six month increments.
          For the investigation of seasonal trends in the blood
and environmental measures, each response was analyzed
separately.  For statistical reasons, responses were log
transformed before analysis.  In addition to seasonal variations,
the child's date of birth,  and age were considered for possible
effects.  Because significant correlations were observed between
the repeated measures taken on individual children,  these

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correlations were estimated and incorporated into the model
estimates.
          In determining whether seasonal components of variation
existed for each response, the first step was to model monthly
averages and determine whether they exhibited systematic monthly
variation.   Although this approach reflected a significant source
of variation,  the interpretation is cumbersome.  Therefore,
because many of the media sampled exhibited higher levels in the
summer and lower levels in the winter,  a sinusoidal  (Fourier)
model was investigated for the seasonal component with parameters
to represent the magnitude as well as the phase, or month of the
peak level.  This approach was sufficient for modeling lead
levels in the environmental media.  However, for blood, where the
maximum and the minimum did not occur six months apart, a
slightly more complicated Fourier model was required.
          Blood-lead levels were found to have highly significant
seasonal variations (p<0.0001), with the maximum modeled to occur
in late June,  and the minimum in March.  The estimated maximum-
to-minimum ratio was 2.5.  Without adjusting for other effects,
observed geometric mean blood-lead levels by month of year ranged
from 2.1 ug/dl in February to 7.5 ug/dl in July.  Age of child
was also found to be a significant factor; the square root of age
was found to be more linearly related to blood-lead levels than
was age itself.  Consistent with other studies, blood-lead levels
in children were found to increase with age.
          Air-, floor dust-,  furniture dust-, and window sill
dust-lead levels all exhibited highly significant seasonal
variation.   The estimated maximum-to-minimum ratios were 2.3 for
air lead, 1.5 for floor dust lead, 1.4 for furniture dust lead,
and 1.6 for window sill dust lead.  Modeled lead levels for air,
floor dust, and furniture dust all had peaks in July.  Oddly,
peak window sill dust-lead levels were modeled to occur in
November.  Each of these responses were also significantly
                                ii

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related to the date of measurement, with a decrease observed over
time.  This is not unexpected due to the concurrent reduction in
the use of leaded gasoline.
          The extent to which levels of lead in blood were
correlated with levels of lead in the environment was also
evaluated.  As stated above, the seasonal component of variation
in blood-lead levels was highly statistically significant.
However,  after adjusting for the linear effects of environmental
measures,  the (residual) blood-lead levels did not exhibit even
marginally significant seasonal variation (at the 10 percent
level).
          These results do not necessarily imply a causal
relationship between seasonal variation in environmental-lead
levels and seasonal rhythms in blood-lead levels.  The fact that
there were arguably parallel rhythms in blood- and, say,  floor-
dust lead levels,  doesn't imply that the blood-lead levels are
influenced by the floor-dust lead levels.  In particular, if the
floor dust-lead levels were to be multiplied by two, while
retaining the same blood lead levels, and models were refit, the
same statistical significance levels would be reported by this
analysis approach.  Thus, it would be important to develop a
physiological model relating levels of lead in the environment to
those in blood,  before proclaiming a causal relationship.
          Nonetheless in this data, which was collected in the
early 1980's from a specific set of children in Boston, there was
abundant evidence supporting the existence and parallelism of the
seasonal variations among blood-, air-, floor dust-, and
furniture dust-lead levels.  The three environmental-lead
measures peak in July which is very near the blood-lead peak
month of June.  In addition, the maximum-to-minimum ratios in the
environmental-lead measures, ranging from 1.4 to 2.3, are of the
same order of magnitude as the blood-lead ratio of 2.5.  Thus,

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based on the results of this study,  it is quite plausible that
seasonal variations in environmental-lead levels contribute to
the blood-lead rhythms.
                                IV

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                        TABLE OF CONTENTS


                                                              Page

1.0   INTRODUCTION  	    1

2 . 0   DATA	    1

3.0   STATISTICAL MODELING APPROACH    	    3

      3.1  FORM OF SEASONAL VARIATION    	    4
      3.2  CORRELATIONAL  DEPENDENCE AMONG
           REPEATED MEASURES  	    6
      3.3  BLOOD LEAD   	    9
      3.4  ENVIRONMENTAL  LEAD   	10
      3.5  BLOOD-LEAD LEVELS ADJUSTED  FOR
           ENVIRONMENTAL  LEAD LEVELS	11

4.0   RESULTS   	12

      4.1  DESCRIPTIVE  STATISTICS   	   13
      4.2  OUTLIER ANALYSIS   	   13
      4.3  MODELING RESULTS FOR BLOOD  LEAD	16
      4.4  MODELING RESULTS FOR ENVIRONMENTAL  LEAD	20
      4.5  MODELING RESULTS FOR BLOOD  LEAD  AFTER
           ADJUSTING FOR  ENVIRONMENTAL FACTORS  	   22

5.0   DISCUSSION	26

6.0   CONCLUSIONS AND RECOMMENDATIONS    	   29

REFERENCES	31


                          LIST OF  TABLES

Table 1.   Number of Observations Available for
           Each Response  Across Time	    2

Table 2.   Estimated Covariance Matrix for  (Log)
           Blood Lead on  an Individual Child	    8

Table 3.   Estimated Correlation Matrix for (Log)
           Blood Lead on  and Individual Child    	    8
Table 4 .    Geometric Means  and  Log  Standard Deviations
           of Various Measures  by Month    	   14
                                v

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Table 5.    Multivariate Outliers	16
                               VI

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                  TABLE OF CONTENTS (Continued)

                   LIST OF TABLES  (Continued)

Table 6.    Mean Blood-Lead Concentration  (ug/dl) by age
            (Controlling for Rate of Birth and Month of
           Measurement)   	   17

Table 7.    Results of Fitting Mixed ANOVA Model with Cyclic
           Seasonal Components to Blood Measures  	   18

Table 8.    Results of Fitting Mixed ANOVA Model with Cyclic
           Seasonal Components to Environmental Measures  .  .   21

Table 9.    Results of Fitting Mixed ANOVA Model with Cyclic
           Seasonal Components to Blood Measures Adjusting
           for Environments Lead Measures   	26



                         LIST OF FIGURES

Figure 1.   Unstructured covariance matrix   	    6

Figure 2.   Geometric average blood-lead levels with 95%
           confidence bounds by month and year	15

Figure 3.   Modeled seasonal variation and residual blood-lead
           levels after controlling for age and date of birth
           effects.  (Bars represent 95% confidential bounds
           of blood-lead residuals.)  	   19

Figure 4.   Modeled blood-lead levels over time,  showing effects
           of date, child's age, and seasonal variation   .  .   19

Figure 5.   Modeled air-lead levels  	   23

Figure 6.   Modeled floor dust-lead levels   	   23

Figure 7.   Modeled furniture dust-lead levels   	   24

Figure 8.   Modeled window sill dust-lead levels   	   24

Figure 9.   Estimated seasonal component of blood and
           environmental lead levels, overlaid  	   25

Figure 10.  Blood-lead levels for five selected
           children born in January   	28
                               VII

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Figure 11.  Blood-lead levels for five selected
           children born in July	28
                              VI11

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1.0  INTRODUCTION
           Several researchers have observed elevated levels of
lead contamination and/or increased incidence of lead poisoning
during summer months.  Reasons for seasonal rhythms in blood-lead
levels, if such a phenomenon is real,  are not immediately
apparent.  The temporal variation may result from either altered
human physiology1'2 or higher levels of lead exposure during the
summer months.  Determining the source of the temporal variation
in blood-lead levels may enhance our understanding of the
relationship between environmental-lead and its impact on body
burden.
           There were two primary  objectives of this study:
               Determine the extent to which blood-lead levels
               recorded in the study conducted at the Brigham and
               Women's Hospital exhibit seasonal variation.
               Determine if any existing seasonal trends in
               blood-lead levels are correlated with seasonal
               trends in environmental levels.
This report examines temporal variation in blood- and
environmental-lead levels in data observed on 250 children
sampled in Boston between 1979 and 1983.

2.0  DATA
           Umbilical cord blood samples were collected for
11,837 births at the Brigham and Women's Hospital (formerly the
Boston Hospital for Women)  from April 1979 to April 1981.  Of
these, 250 children were selected for an ongoing follow-up study
involving environmental and psychological measurements.    (One
additional child had blood lead measured only at six months.
This data was used to estimate the average lead level at six
months, but does not permit assessment of seasonal variation for
this child.)   The selection criteria for the follow-up study
included cord blood levels in the highest, lowest, and middle

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deciles of the distribution of cord blood-lead levels, residence
within 12 miles from the hospital, and likely to be available for
two years of sampling.  The resulting cohort differs from those
studied in other research on lead exposure in that family incomes
are relatively high, mothers were likely to be older, White,
college educated,  and working outside of the home.  This analysis
was based on the environmental and blood-lead data collected on
each of these 250 children up to 24 months of age.
           For each child in the  study, blood-lead and
environmental-lead measurements were collected at various times.
The number of measurements made varied from child to child.
Table 1 displays the number of observations available for
analysis for blood lead and environmental lead at each age level.

         TABLE 1.   NUMBER OF OBSERVATIONS AVAILABLE FOR
                   EACH RESPONSE ACROSS TIME


Blood
Air
Floor Dust
Furniture Dust
Window Sill
Dust
Water
Soil

0 1
249

247
247
240
245


Age of
6
220
217
228
231
231
230


Child in Months
12 18
208 213
193
205
204
203
17
152


24
202
125
191
190
189
17
148

           Typically floor dust was collected in the living
room, and furniture dust was collected from the kitchen table.
Kitchen sink water was collected after a 4-liter flush.  A 1. Gi-
ft2  template  was  used  to  collect  floor and  furniture  dust  wipes,
and a 0.5-ft2 template  was  used to  collect  window  sill  dust
wipes.  Data was not available on deviations from these
                                2

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protocols, so these measures were analyzed simply as ug.
However, the following units can be used to interpret the
results:
               Blood  (ug/dl)
               Air  (ug/m3)
               Floor Dust  (ug/ft2)
               Furniture Dust  (ug/ft2)
               Window Sill Dust  (ug/0.5 ft2
           As shown in Table  1, collection of water and  soil
samples were mostly limited to specific sampling campaigns.  For
those children with replicate measurements, there was a
substantial amount of variation among the water- and soil-lead
concentrations made during different months for the same child.
However, statistical hypothesis tests concluded that there were
no systematic variations in the water- and soil-lead
concentrations,  and therefore, it was reasoned that water- and
soil-lead concentrations remain relatively constant throughout a
span of two years.  Thus, replicate measurements of water- and
soil-lead concentrations were averaged and used as a baseline
explanatory measure for each child.
           Dust-lead measurements  should be interpreted  as
approximate loadings in ug/ft2.   However,  exact  dimensions  of the
areas sampled were not available in the data set analyzed.
Therefore,  in some cases they are only referred to as lead
"amounts".   Also,  information was not available regarding the
proximity of these samples to children's activity areas.

3.0  STATISTICAL MODELING APPROACH
           The approach  to the statistical analyses is described
in this section.   The five media investigated for seasonal trends
in lead were blood, air,  floor dust, furniture dust, and window
soil dust.   Since there was a significant seasonal component in

                                3

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each response, it was also investigated whether a seasonal
component remained present in the blood-lead levels after
adjusting for the effects of lead in the environmental media.
           For the investigation of seasonal trends in each  of
the blood and environmental measures,  each response was analyzed
separately.  When evaluating whether there was a significant
seasonal component in blood-lead levels, after controlling for
differences in environmental lead levels, the data were
restricted to those sampling campaigns in which measures of lead
in blood, floor dust, furniture dust,  window sill dust, and air
were obtained (see Table 1).  This restricted the data to those
collected at 6,  18, and 24 months.  Excluding observations with
incomplete data reduced the number of observations to 461 on 193
children.  The full data set contained 843 observations made on
250 children.
           Each blood and  environmental  lead measure was log
transformed before analysis.  There were two main reasons for
this.  First, these responses varied over one to four orders of
magnitude.  Second, after the log transformation all of the
responses were better modeled by a normal distribution, which is
an underlying assumption of the statistical analyses.
           Based on previous studies3'4,  factors suspected to
influence children's blood-lead levels include the child's date
of birth, age, and the time of year at which the sample is taken,
These factors were included in the models fitted to the blood
lead.  For environmental lead,  the dates of the measurement were
included instead of the child's date of birth and age to adjust
for overall trends.

3.1  FORM OF SEASONAL VARIATION
           The purpose of  this analysis  is to investigate the
presence of a systematic seasonal component of variation in

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blood- and environmental-lead levels.  It was not known at the
outset whether such a cyclic component existed, let alone its
functional form.  However, it was assumed that a complete cycle
for a seasonal variation, if present, would have a period of
twelve months.
           The  information in the data on date of sampling is
limited to month and year of sampling.  Therefore, the simplest
and most general approach is to consider month as a class
variable with twelve possible levels.  This approach allows each
month of the year to have its own mean after adjusting for other
factors in the model:

                       w  = v • -I- •    -4- •                       I ~\ \
                       Yi  xi  ^  m(i)  ^   i>                     \ -1 I

where x±  denotes the row vector  of covariates  for each of the
other factors in the model,  •  denotes the column vector of
parameters for the covariates, m(i)  denotes the month number for
the ith observation, % denotes  the  deviation  from the mean for
              / 12       \
the mth month   *=l •  = 0 and •,  denotes  random error.   A
              \m        /
limitation to this approach is the interpretation of the 12
values of %;  one is left with the burden of understanding 12
different monthly averages.   A second limitation is in the
estimation of the variance of these monthly parameters.  If a
simpler model with fewer parameters underlies this cyclic
variation, then estimates of its parameters would be more
precise.
           Since  for many of  the media sampled,  lead  levels  were
highest during the summer and lowest during the winter, a
sinusoidal (Fourier) form of the model was investigated:
              y± = x±« + 'cos  ( (m(i) -• ) *2«

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where •  denotes the amplitude and •  denotes the phase  (in months)
of the sinusoidal trend.  The phase represents the time in months
at which the maximum value of the sinusoidal trend, the
amplitude, occurs.  Freedom to vary the phase is necessary
because it is not known a priori when the maximum should occur.
A model with a single sinusoidal term implicitly assumes that
minimum levels occur six months after maximum levels.  Because
this phenomenon was not observed in all media, additional Fourier
terms were included in the models allowing for peaks and valleys
to be less or more than six months apart.  The specific forms of
these models are explained in Sections 3.3 and 3.4.
           Models employing the Fourier parameterization for the
seasonality effect were only fitted to the data if the month-to-
month variation was determined to be statistically significant
based on a model utilizing the twelve levels of *±.   Otherwise,
it was reasoned, other modeled factors satisfactorily explain the
variation observed in lead levels.

3.2  CORRELATIONAL DEPENDENCE AMONG REPEATED MEASURES
           It is  important that the model chosen to  fit to the
data takes into account possible systematic correlations among
repeated observations on the same child.  This section describes
the approach for modeling for correlational dependence.
           It is  sensible to assume that measurements made on
the same child are correlated.   However, the structure of this
correlation is not known.  For instance, measurements taken
farther apart in time (on a given child) may be less correlated
than those taken closer together in time.
           The matrix presented in Figure 1 illustrates the
structure of the covariance assumed among repeated blood measures
taken on each child, excluding the cord blood measure.

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1=1:
1=2:
1=3:
1=4:
6 months
12 months
18 months
24 months
j=l j=2
6 months 12 months
* 11 ' 12
*21 *22
* 31 ' 32
'41 *42
j=3
18 months
-13
'23
*33
*43
j=4
24 months
-14
'24
*34
*44





            Figure  1.   Unstructured covariance matrix.

            For example, • 23  represents  the  covariance between
measures taken at 12 months and 18 months on the same child,
after controlling for covariates.  By definition • ±j  = • j±  for
i,j = 1,2,3,4.  The diagonal terms, • n, • 22,  • 33,  • 44 represent the
variances for measures taken at each of the four increasing  ages,
respectively, after correcting for other factors in  the model.

            Without making any assumptions  about  the  structure  of
the covariance matrix, Figure 1 represents the most  general  form
possible.  This is referred to as unstructured.  No  relationships
among different covariances are assumed in the unstructured  form.
            Below are  two more structures which were  considered:
autorearessive and random child effect structures.
                      Autoregressive.   The variance of blood-Pb
                      concentration is assumed to be the same for
                      children of all  ages.  The correlation
                      between repeated measurements on the same
                      child is assumed to decrease with time
                      between measurements.

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                            Random Child Effect.  Variances are
                            assumed equal for all measures.  The
                            correlations between repeated
                            measures on the same child are
                            assumed to be equal regardless of
                            time between measurements.
                            Observations on different children
                            are assumed to be uncorrelated.
           The tradeoff among these covariance structures is
that although a more general model requires fewer assumptions
about correlations, it requires the estimation of more
parameters.  To determine an appropriate covariance structure,
the Akaike Information Criterion3  (AIC)  was used.   The AIC
provides a means of comparing models with the same fixed effects
but different covariance structures.  It is defined as

                         AIC = 2p(« )-2q,
where p(»)  is the calculated log likelihood and q is the number
of parameters.  This function adjusts the log-likelihood for the
number of parameters in the model,  including the terms for both
fixed effects and covariance.  The model having the largest AIC
is considered to provide the best fit to the data.  Due to the
high AIC and the fact that variability was observed to decrease
with the age of the child, the unstructured covariance was chosen
as most appropriate.  Table 2 displays the estimated unstructured
covariance matrix for the model fit to the blood-lead data.

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         TABLE 2.  ESTIMATED COVARIANCE MATRIX FOR  (LOG)
                   BLOOD LEAD ON AN INDIVIDUAL CHILD
Age
(months)
6
12
18
24
6
3.92
0.17
0.21
0.75
12
0.17
2.85
0.49
0.47
18
0.21
0.49
1.93
0.86
24
0.75
0.47
0.86
1.93
For illustrative purposes, Table 3 displays the correlation
matrix associated with the covariance matrix in Table 2.
         TABLE  3.   ESTIMATED CORRELATION MATRIX FOR (LOG)
                   BLOOD LEAD ON AN INDIVIDUAL CHILD
Age
(months)
6
12
18
24
6
1.00
0.05
0.08
0.27
12
0.05
1.00
0.21
0.20
18
0.08
0.21
1.00
0.45
24
0.27
0.20
0.45
1.00
           For consistency, no  assumptions were made  about  the
correlation structures of repeated environmental measures at a
child's home.  Therefore, unstructured covariance matrices were
assumed for each media.
           The following  sections describe the specific  models
fit to each of the responses.
3.3  BLOOD LEAD

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           Models of  the  following  form were  fit to blood-lead
concentrations:

              LBla = -0 +  -! DOB± + -2a1/2  +  Sm(la)  +  • la,            (2)
where
          i    =    child index,
          a    =    age of child in months,
          LBla  =    the logarithm of the  measured concentration
                    of Pb in the blood  (ug/dl) for the  ith  child
                    at age a,
          •0    =    intercept,
          DOB± =    date of birth of ith  child,
          •!    =    linear effect of date of birth,
          •2    =    linear effect of age,
         Sm(la)  =    seasonal effect for month  in which  child  i
                    was age a, and
          • ±j   =    random error  (*ij, • yz correlated only if i=y;
                    i.e., measures are  from same child).

          The model includes a date of birth effect  to  trace
changes between different "birth cohorts" of children,  and  an age
effect to reflect changes as a child grows regardless of  his/her
year of birth.  To test whether there was confounding between the
age and date of birth effects, the model  was also fit without the
date of birth effect.  The estimates and  significance levels
obtained for the age effect in both cases were very  similar.
Therefore,  it was concluded that the two  factors were not
confounded and both factors were included in our final  model.
          The seasonal effect is described by  the following
Fourier model,
                                10

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      Sm(la) = •1cos( (m(ia) —1)*2«/12)  + • 2cos ( (m(ia) — 2) *2
where
                    amplitude of annual cyclic variation,
                    phase of annual cyclic variation  (time  when
                    peak occurs in months),
                    amplitude of biannual cyclic variation,  and
                    phase of biannual cyclic variation  (time when
                    first peak occurs).
          The two-phase cyclic model was  selected  as  an  objective
compromise between a simple sinusoidal component with unknown
phase, and a model including a different  term  for  each month of
the year.  Certainly there was no reason  to  assume, a priori,
that the shape of the seasonal component  would fit a  sine  wave
perfectly.  A two-phase model was chosen  by  repeatedly adding
Fourier terms with unspecified phase and  period (12/k) months,
k=l,2,3... until the relative reduction of error was
insignificant.  For blood lead this process  was halted after
adding the biannual cyclic term, which cycles  twice per  year, to
the simple annual cyclic term.

3.4  ENVIRONMENTAL LEAD
          Models of the following form were  fit to levels  of lead
in air, floor dust, furniture dust, and window sill dust:
         (LAla,  LFLla,  LFUla, LWSla) = «0 + • ^ia + S
                                                m(ia)    ia I
where the factors not defined above for the blood-lead model  are
                                11

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          LAla  =    the  logarithm  of  the  measured indoor air-lead
                    concentration  (ug/m3)  in the  ith  child's  home
                    at age  a,

          LFLla     =    the  logarithm of the  measured indoor
                         floor  dust-lead  loading (ug) in the ith
                         child's home at  age  a,

          LFUla     =    the  logarithm of the  measured indoor
                         furniture dust-lead  loading (ug)  in the
                         ith  child's  home at  age a,

         LWSla  =    the  logarithm  of  the  measured window sill
                    lead loading  (ug)  in  the  ith child's home at
                    age  a,

          tla   =    date when ith  child was a  months old,  and

          •x   =    linear  effect  of  date.


          The  seasonal effect is described by  the following

Fourier model,  for all four environmental media:



         Sm(la)  =    •1cos( (m(ia) —  1)*2«/12) .


3.5   BLOOD -LEAD  LEVELS  ADJUSTED  FOR
      ENVIRONMENTAL LEAD LEVELS

          A model was fit to  the combined blood- and

environmental-lead data  to  determine  whether  there were seasonal
variations  in  blood-lead levels above and beyond those explained

by changes  (perhaps seasonal) in levels of lead in surrounding
environmental  media.  The equation for this model is as follows:
la
      LBla = *0 + *! DOB± +  -2a + %(la)  +  • iLAla + • 2LFLl

           + -3LFUla + -4LWSla +  •sLSi + -gLWAi + • la,              (4
where
                                12

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          LS-L   =    the logarithm of the measured concentration
                    (ppb)  of lead in soil outside the home of the
                    ith child,  and
          LWA±  =    the logarithm of the measured concentration
                    (ppb)  of lead in water at the home of the ith
                    child.
Notice that the most general seasonal component, %,  is  used
here, as was the approach for fitting models to each medium
separately.  (See equation  (1).)  As mentioned in Section 3.1,
the convention was to first fit models with the most general
seasonal formulation, but to investigate simplification only
after determining that the variation was significant after
controlling for other factors.
          It is important to note that several of the predictor
variables in this model are subject to error  (e.g., each of the
environmental lead measures).   These errors can potentially bias
estimates of these factors downward  (in magnitude).  The data did
not permit an assessment of the magnitude of these errors, and
therefore, it was not possible to adjust the estimates for the
measurement error.  Thus, it is reasonable to assume that our
estimates of these effects are conservative.

4 . 0   RESULTS
          This section presents the statistical analysis results.
Section 4.1 provides descriptive statistics.  Section 4.2
describes the outlier analysis.  This is followed by the model-
fitting results.  Section 4.3 presents the results of fitting the
seasonal model to blood-lead levels.  Corresponding model-fitting
results for the four environmental-lead responses are discussed
in Section 4.4.  Finally, in Section 4.5 we discuss results of
fitting blood-lead levels to the seasonal model after controlling
for environmental-lead levels.
                                13

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4 .1   DESCRIPTIVE STATISTICS
          For each of the responses measured there was evidence
of periodicity.  Table 4 displays estimates of the geometric mean
levels (on original scale)  of lead in blood, air, floor dust,
furniture dust, and window sill dust by month.  The number of
blood samples collected is also listed by month.  Approximate log
standard errors of these estimates are provided for each media,
along with the observed log standard deviations.  The averages
are often highest in the summer months (June, July, August) and
lowest in the winter months (February, March).  Four of the
measures, blood, furniture dust, air, and window sill dust appear
to have a relative minimum in September.   For reasons discussed
below, cord blood measures were excluded from the calculations
for blood lead.
          Figure 2 displays observed geometric average blood-lead
levels with 95 percent confidence bounds for each month of the
study.  These averages do not control for any covariates  (such as
age of child or date of birth).  This figure reveals a slight
cyclic variation, but does not show any sign of general change
over time.  It is not possible to distinguish between within-
child effects  (such as age) and between-child effects  (such as
date of birth) from this plot.  The statistical modeling results
presented next, allow this separation.  It is shown that the
within- and between-child effects actually counteract each other
in this figure.

4.2  OUTLIER ANALYSIS
          Multivariate outlier analyses were performed to
identify unusual data points.   A Hotelling T2 test  was  applied to
identify potential outliers based on the distance between an
observation and the average of the remaining observations
relative to the covariance matrix of the remaining observations.
                                14

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The test was applied to the subset of the sampling campaigns in
which each of the five measures:  blood, air, floor dust,
                                15

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      TABLE  4.   GEOMETRIC MEANS  AND LOG STANDARD  DEVIATIONS
                 OF VARIOUS MEASURES BY MONTH
Month
January
February
March
April
May
June
July
August
September
October
November
December
Approximate
Log Standard
Error*
Log Standard
Deviation"
Number of
Blood
Measures
72
54
100
74
68
63
77
51
96
73
53
62


Blood
(ug/dl)
3.10
2.13
2.87
3.87
4.64
5.13
7.52
4.05
2.49
3.06
4.51
3.61
0.20
1.65
Air
(ug/m3)
0.07
0.06
0.05
0.06
0.10
0.11
0.10
0.11
0.09
0.10
0.07
0.05
0.15
0.97
Floor
Dust
(ug)
3.06
2.58
2.39
3.54
4.43
3.65
4.40
5.58
3.95
3.52
4.98
3.32
0.14
1.16
Furniture
Dust
(ug)
2.46
2.12
1.85
2.58
3.27
2.68
2.58
3.92
2.63
3.53
3.36
2.90
0.13
1.08
Window
Sill
Dust
(ug)
10.54
8.03
8.54
10.49
12.25
8.94
13.87
16.78
11.64
19.39
17.32
12.44
0.18
1.54
   The actual standard error of the mean log-transformed values varied due to
   sample size differences across months.  These numbers represent the average
   value of these log-standard errors.
   This represents the estimated within-month standard deviation of the log-
   transformed responses.
furniture  dust,  and window  sill  dust were obtained.   This test
identified two observations  as outliers at the  10  percent level;
Table 5 displays the observations.   The last column  of the table
provides the  observed significance  level of the Hotelling T2  test
for the observations.  A Bonferroni-type critical  value is used
to compensate for the numerous simultaneous tests  performed and
to maintain a 10% overall significance.  The second  to last
column is  the appropriate threshold, based on the  Bonferroni
adjustment, to compare the  observed significance level with.   The
                                 16

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6th month floor dust measure on child 804391 was 600 ug, which
was the largest value of floor dust lead when all 5 responses
                                17

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      100.00
       10.00
        1.00
        0.10
        0.01
         Dec 79  Jun 80  Dec 80  Jun 81  Dec 81  Jun 82  Dec 82  Jun 83
                               Sampling Date
     Figure 2. Geometric average  blood-lead levels with 95%
               confidence bounds  by month and year.
were measured.  The  18th month  blood-lead measure on child 805874
was registered as 0  ug/dl  (below  the  detection limit),  but the
furniture and window sill  dust-lead measures were highest among
all observations with a blood measure of 0 ug/dl.  Models were
fit with and without these two  data points to evaluate their
effect on the conclusions.   Since models fitted to all data
yielded the same conclusions as models fitted to the data with
outliers removed, results  presented herein are based on analyses
including all of the data.
                                18

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                 TABLE 5.  MULTIVARIATE OUTLIERS
ID
804391
805874
Age
6
18
Lead Levels
Blood
(ug/dl)
4
0
Air
(ug/m3)
0.19
0.10
Floor
Dust
(ug)
600
1
Furniture
Dust
(ug)
210
12
Window
Sill Dust
(ug)
600
160
Bonferroni
10% Significance
Cutoff
1
1
95x10-4
95x10-4
Observed
Significance
Level
3.29x10'5
1.35X10'4
          Lower order principal components were also used to
visually inspect the data for outliers.  The lower order
principal components, by construction, are linear combinations of
the factors with the smallest variance.  Therefore,  outstanding
realizations of these principal components are often examined as
potential outliers.  There were no unusual observations noted in
a plot of the fourth and fifth of five principal components.  The
two outliers mentioned above were typical data points as measured
by these principal components.

4.3       MODELING RESULTS FOR BLOOD LEAD
          Table 6 displays estimates of mean blood-lead
concentration by age, adjusting for date of birth and month of
measurement.  (Least-squares means are presented which represent
the modeled mean for each age, holding date of birth at the
average observed level,  and averaging across the 12  months in
which measurements were collected.)   The average blood-lead level
at 6 months was significantly less than those observed at the
other ages, but cord blood-lead levels were actually higher than
those observed at 6, 12, 18, and 24 months (though not
significantly).   Since cord blood lead may be more associated
with the mothers' blood-lead level,  and does not appear to be
consistent with measurements taken at different time points, cord
blood-lead levels were excluded from the subsequent  analyses.
                                19

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          The results displayed in Table 6 also suggest that the
increase in blood lead has a nonlinear relationship with age.
Since at best there are only four time points on each child,
only one-parameter models were considered to fit this curvature.
A model containing a term for the square root of age appeared to
provide an adequate fit.
      TABLE  6.   MEAN BLOOD-LEAD  CONCENTRATION (jig/dl)  BY AGE
                (CONTROLLING FOR RATE  OF  BIRTH AND  MONTH OF
                MEASUREMENT)
Age
(months)
0
6
12
18
24
Mean
5.2
2.4
4.0
4.4
4.3
Lower Bound
4.5
1.8
3.2
3.6
3.5
Upper Bound
5.9
3.1
5.1
5.3
5.1
          Table 7 displays the results of fitting the model to
blood-lead levels.  Age of child was found to be significant,
date of birth was not; the magnitude of these effects is
discussed below.  As described earlier, both annual and biannual
cyclic components of variation were used in the model to describe
seasonal variation.  Both components were sinusoidal.  Phase is
estimated in months.  For interpretation, we assumed a phase of
1.0 corresponds to January 15, phase 2.0 corresponds to February
15, etc.  Added together, the peak of the systematic seasonal
component occurred in late June; lowest levels occurred in early
March.  The magnitude of this seasonal difference was 0.93 on a
log scale.  This corresponds to a multiplicative increase of 2.54
in blood lead during late June over levels in early March on the
same child.  This number is calculated as the range of the
seasonal component over a 12-month period.
                                20

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   TABLE 7.  RESULTS OF  FITTING MIXED ANOVA MODEL WITH CYCLIC
             SEASONAL COMPONENTS TO BLOOD MEASURES
Response
Blood
(843 obs. )
Factor
Intercept
Date of Birth (in
months)
Age (in months,
square root)
Cyclic Annual
Component (Amplitude)
Cyclic Annual
Component (Phase)
Cyclic Bi-Annual
Component (Amplitude)
Cyclic Bi-Annual
Component (Phase)
•
o
•
1
•
•
1
•l
•
2
•
Estimate
0.792
-0.010
0.194
0.319
6.659
(Jul. 5)
0.264
5.999
(Jun 15, Dec. 15)
Std. Error
0.327
0.013
0.055
0.059
0.179
(5 days)
0.094
0.381
(11 days)
Significance

0.4260
0.0005
<0.00011
0.01982
1  Statistical significance of overall seasonal component.
2  Significance of bi-annual cyclic component  after controlling for annual
  cyclic component.
          Figure  3 displays  the  multiplicative factor
corresponding to  the modeled seasonal component of variance for
each month of the year  connected by a solid line.  The bars
overlaid on this  plot display the mean and confidence bounds for
the residuals of  observed  blood-lead levels after controlling for
age and date of birth.   There is clearly a seasonal component
present in the residuals which parallels the estimated seasonal
component.  To facilitate  quantification of the cyclic component,
the values in this plot were scaled to force the minimum
multiplier to be  1.00.   Reference lines were placed at 1.00 and
2.54, where the minimum and  maximum occurred.  The minimum
appears in March, the maximum appears in June with a value close
to that modeled for July.  The values on this plot do not
represent predicted lead levels,  but rather the ratio of average
lead levels for different  months of the year to the level for the
month with the lowest average levels, i.e. March  (after
controlling for age and date of  birth).
                                21

-------
          Figure 4 illustrates the relative impact of each of the
factors in the complete model fitted for blood lead.   The figure
covers a span of about three years from December 1979 through
                               22

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               10.0
               1.0
               0.1
                  Jan  Fab Mar Apr  May  Jpn  Jul  Aug

                                Sampling Month
                                               Oct  NOT Dec
Figure 3.    Modeled seasonal  variation and residual blood-lead
             levels after controlling for age  and date of birth
             effects.(Bars represent 95% confidential bounds for
             blood-lead residuals.)
               10
             i
             I
             fi
                 	    Deo 79
                 	    Dec 80
                 	    Dec 81

                                  JunM
                                  Date

Figure 4.    Modeled blood-lead levels over time,showing effects
             of date, child's  age,  and seasonal  variation.
                                 23

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December 1982.  There are three curves displayed in the plot.
Each curve represents predicted blood-lead levels for a child
born at a different time.  The solid curve which begins earliest
and represents the modeled blood-lead levels for a child born in
December 1979.  The most striking feature of this plot is the
extensive cyclic variation about a generally increasing trend.
This increase reflects the significant age effect.  However, the
cyclic variation outweighs the effect of age.  Notice that for
children born in December, although the estimated minimum
seasonal variation occurs in March (Figure 3),  when the age
effect is added the relative minimum lead levels occur in
February.
          Because data was collected for only two years on each
child, the solid curve terminates after two years.  The second
and third lines represent modeled blood-lead concentrations for
children born one and two years later (in December 1980 and
December 1981).  There was a slight (and statistically
insignificant) decrease in blood-lead levels with date of birth.
This is reflected by the slightly lower starting points for
children born later.

4.4  MODELING RESULTS FOR ENVIRONMENTAL LEAD
          The final models fitted to the environmental media were
described in Section 3.4.  These models included a linear effect
for date and a cyclic seasonal effect.  An unstructured error
variance matrix was assumed for the repeated measures at each
child's home.  Both the date effect and the seasonal effect were
statistically significant for each of the four environmental
media investigated.
          Table 8 displays the estimated parameters for the fixed
effects along with the standard errors and significance levels.
Since the fitted model included only a single Fourier component
for each media the phase listed equals the time, in months, of
                                24

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the predicted maximum seasonal variation.   For each medium,  the
significance of both parameters of the cyclic component is tested
                               25

-------
   TABLE 8.  RESULTS OF FITTING MIXED ANOVA MODEL WITH CYCLIC
             SEASONAL COMPONENTS TO ENVIRONMENTAL MEASURES
Response
Air-Lead
(535
Obs. )
Floor
Dust-Lead
(871
Obs. )
Furniture
Dust-Lead
(872
Obs. )
Window
Sill
Dust-Lead
(863
Obs. )
Factor
Intercept
Date
Cyclic Annual
Component (Amplitude)
Cyclic Annual
Component (Phase)
Intercept
Date
Cyclic Annual
Component (Amplitude)
Cyclic Annual
Component (Phase)
Intercept
Date
Cyclic Annual
Component (Amplitude)
Cyclic Annual
Component (Phase)
Intercept
Date
Cyclic Annual
Component (Amplitude)
Cyclic Annual
Component (Phase)
Estimate
1.751
-0.025
0.408
6.815
(Jul. 9)
1.503
-0.008
0.203
6.896
(Jul. 12)
1.359
-0.012
0.184
7.349
(Jul. 25)
2.823
-0.012
0.240
10.588
(Nov. 3)
Std.
Error
0.153
0.005
0.059
0.141
(4 days)
0.123
0.004
0.040
0.198
( 6 days )
0.115
0.003
0.038
0.217
(7 days)
0.156
0.005
0.053
0.237
(7 days)
Significance

<0.0001
<0.0001

0.0391
<0.0001

0.0002
<0.0001

0.0107
<0.0001
simultaneously and was highly significant in all cases.  Notice
how close together in time the maxima are predicted for air lead,
floor dust lead,  and furniture dust lead.  Each of these peaks
are predicted to occur in July.
                                26

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          Figures 5 through 8 display modeled lead levels as
solid lines for air, floor dust, furniture dust, and window sill
dust, respectively.  Overlaid on these plots are the observed
geometric means by month with confidence bounds.  Considering the
numbers of observations represented, the models appear to fit
well for so few parameters.  Notice the slight decreasing trend
in each response over time.  The most drastic decrease was
observed for air-lead levels.  One obvious reason for this would
be the coincident reduction in use of leaded gasoline in
automobiles.
          Figure 9 displays the modeled seasonal components of
variation for the five responses overlaid for comparison.  Each
curve has been adjusted for trend effects.  This figure allows
direct comparison of the phase and magnitude of the estimated
seasonal components between the four modeled media.  The
fluctuations observed in blood were larger than those observed in
the environmental measures, but were similar in phase to
fluctuations of lead in floor dust, furniture dust, and air.
Window sill dust lead was predicted to reach its peak 4 to 5
months after blood lead.

4.5  MODELING RESULTS FOR BLOOD LEAD AFTER
     ADJUSTING FOR ENVIRONMENTAL FACTORS
          The extent to which levels of lead in blood were
correlated with levels of lead in the environment was also
evaluated.  Each of the measured environmental media were
included in a model, along with date of birth and age effects,
and a class month effect to evaluate seasonal rhythms in blood-
lead levels.   This model is described in detail in Section 3.5.
          Whereas the seasonal component of variation in blood
lead was significant before adjusting for the linear effects of
the environmental measures (with class month effect, p=<.0001),
it was not significant after adjusting for these effects
(p=.1148).  The significant predictive factors in this model were

                                27

-------
floor dust lead,  and age of the child.   The effect of soil lead
on blood lead was marginally significant.   Recall that only
                               28

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     100

*
      0.10
      0.01
        Dec 79 Jun 80 Dec 80 Jun 81 Dec 81 Jim 82 Dec 82 Jun 83
                           Sampling Dale


    Figure  5.   Modeled  air-lead levels.
    100
  fi
                        Jim M,                    Jun 83
                         Sampling Date
   Figure  6.  Modeled floor dust-lead  levels.
                          29

-------

     mo
  £
      0.1
                                             Jum 88
                        Sampling


 Figure  7.   Modeled furniture dust-lead levels
  £
     10
\
                                             Jum 83
                        Sampling Date


Figure  8.   Modeled window sill dust-lead levels
                         30

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           100.0
           mo
            10
            0.1
                     *-*-*
                  •^—^—^—*—*—*"
               Jan Bsb            Jun  Jul     Sap  CM     Dec
                                 Monti,
    Figure  9.  Estimated seasonal component of blood and
              environmental lead levels, overlaid.
average levels of lead in soil and water  were  available  for each
child.
          Since the remaining environmental  media were not
observed as significant,  a model  was  fit  with  age and date of
birth and the significant environmental  factors.  Under  the
smaller model, there remained a significant  month effect.  Upon
further investigation,  it was found that  adjusting  for air-lead
levels in addition to floor dust  and  soil lead levels reduces
the monthly component of  variation from  very significant  to
marginally significant.   Table 9  displays the  results of  the
final model fit.   Thus, much,  but not all, of  the seasonal
variation in these blood-lead levels  can  be  attributed to
variations in floor dust  lead,  soil lead,  and  air lead.
                               31

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          One must realize that the fact that certain
environmental factors were observed to be statistically
significant in these models does not necessarily mean that there
is a causal relationship.  For example, just because floor dust-
lead levels were observed as a highly significant effect in
predicting blood-lead levels,  one cannot conclude that seasonal
increases in floor dust-lead levels cause corresponding increases
in blood lead.
   TABLE 9.  RESULTS OF FITTING MIXED ANOVA MODEL WITH CYCLIC
             SEASONAL COMPONENTS TO BLOOD MEASURES ADJUSTING
             FOR ENVIRONMENTS LEAD MEASURES
Response
Blood-
lead
(463
Obs. )
Factor
Intercept
Date of Birth
Age
Floor Dust Lead
Soil Lead
Air Lead
•
•
i
•
2
•
2
•
•
1
Estimate
-0.071
0.013
0.046
0.320
0.115
0.049
Std. Error
0.539
0.015
0.010
0.063
0.063
0.060
Month (12 level class variable)
Significance

0.3779
<0.0001
<0.0001
0.0677
0.4099
0.1047
5.0  DISCUSSION
          The magnitude of the seasonal variation observed in
these data is substantial.  The reader is reminded that for this
study, blood- and environmental-lead levels were measured on
these children every six months at best.  To detect a cyclic
component, one must observe levels which are systematically
higher at one time during the year than at another.  Since base
blood-lead levels vary substantially across children, it is best
to examine repeated measures on the same child.  However,  if the
cyclic component were simply sinusoidal, the greatest within
                                32

-------
child contrasts would be observed (in this study)  for children
born in the month with the highest or lowest seasonal component
of variation.  For example, if the seasonal variation was such
that the lowest value occurred in January, and the highest value
occurred in July, then children born in January or July would
have the best chance of exhibiting major seasonal deviations if
they were sampled at 0, 6, 12, 18, and 24 months of age.
Figures 10 and 11 illustrate this phenomenon for children born in
January and July.  Figure 10 displays the observed blood-lead
levels for five selected children born in January.  Figure 11
displays blood-lead levels for five other children born in July.
These figures illustrate the type of variation in blood-lead
levels experienced by the children studied.  Because measures
were only taken six months apart, January and July were chosen
because they portray the greatest seasonal contrasts.
          However, if a child was born in April or October the
actual differences in the blood-lead levels at the times
measured, due to seasonal variation, would be near zero.  Plots
of these levels would be more flat.   These children would provide
little added value in estimating the magnitude of seasonal
variation.  Moreover, since measures are taken six months apart
it is difficult to estimate the parameters of higher-order cyclic
components.  Thus, if feasible, monthly or quarterly measures
would provide much better information about seasonal variation
from children not born in months where the maximum or minimum
occurs.
          The reader is also reminded that the use of leaded
gasoline was being phased out during the time this study was
conducted.  It is possible that leaded gasoline was the source of
much of the lead in the environment - particularly in the air.
Since more travelling is done in the summer than in the winter,
changes in emissions from leaded gasoline may have been a
significant contributor to the observed seasonal variations in
environmental lead levels.  Today, the use of leaded gasoline has

                                33

-------
been virtually eliminated.  Therefore,  seasonal variations may be
less pronounced today.
                                34

-------
            JOO.O
         ^ 50.0
         S
             10.0
             5.0

             10
             0.5
              0.1
                          §         12         18
                                at sampling thrift
24
Figure  10.  Blood-lead  levels for five selected children
             born in January.
                                35

-------
            100.00
            50.00
           i
             moo
             5.00

              100
         3  0.50
             0.05

              0.01
                           6         12        18
                                 at gflnnpling tima
24
Figure 11.   Blood-lead levels for  five selected children
              born in  July.
                                 36

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6.0  CONCLUSIONS AND RECOMMENDATIONS
          This report summarized an analysis of seasonal rhythms
in blood-lead levels for a longitudinal study of blood- and
environmental-lead levels on 250 children sampled in Boston
between 1979 and 1983.   The following conclusions were arrived
at:
             There was  evidence of  significant  seasonal variation
             in both blood-  and environmental-lead  levels observed
             in this study.
             Lead levels  in  blood,  air,  floor dust, and furniture
             dust were  typically highest  in the  summer and lowest
             in the late  winter.
             Much of the  modeled cyclic  seasonal variability in
             blood-lead levels was  explained by  adjusting for the
             effects of environmental  lead, specifically in floor
             dust and air.
          The magnitude of the seasonal component of variation in
blood lead was estimated to be 0.93 on a log scale.   This
corresponds to a multiplicative increase of 2.54 in blood-lead
during late June over levels in early March for the same child.
Thus for a child with blood-lead concentration measured in March
of 2.9 ug/dl, the predicted level in June would be about
7.4 ug/dl.
          If such a seasonal component of variation is confirmed
to exist in blood-lead levels today, it could have a major impact
on the development of health-based standards and the setting of
warning levels.  Specifically, it would suggest the need to take
into account the month of the year in determining whether a child
is at risk.  The results of this study suggest that a lower
blood-lead threshold should be used in February than in July,
because a child with a marginal blood-lead level in the winter is
anticipated to have a much higher level in the summer.
                                37

-------
          Although the results of this study show evidence of a
large cyclic component of variation in blood and environmental
lead levels, the reader must recognize that the children observed
all lived in Boston in the early 1980s.  The results suggest that
seasonal variations can be very large in magnitude.   However,
before attempting to adjust health-based standards or blood-lead
levels considered to be a health risk, more current
investigations covering a broader population base over more
varied geographic and socio-economic conditions should be
performed.  Also, to better understand the nature of seasonal
blood-lead variation,  more frequent measures should  be taken on
each child.  Specifically, it would be of greater value in the
assessment of seasonal rhythms in blood lead to sample fewer
children at more time points than to sample more children at
fewer time points.

-------
REFERENCES
     Hunter, J.M. (1978), "The Summer Disease, An Integrative
     Model of the Seasonality Aspects of Childhood Lead
     Poisoning."  Soc. Sci. Med. 11(14-16): 691-703.

     Barton, J.C., Huster, W.J.  (1987), "Seasonal Changes in Lead
     Absorption in Laboratory Rats."  Environmental Health
     Perspectives. 73:209-214.

     Akaike, H.  (1974), "A New Look at the Statistical Model
     Identification", IEEE Transaction on Automatic Control, AC-
     19, 716-723.

     Rabinowitz, M.  and Needleman,  H.  (1982), "Temporal Trends
     in Lead Concentration of Umbilical Cord Blood".  Science,
     Vol. 216.

     Mahaffey, K.R.,  Annest, J.L.,  Barbano, H.E., and Murphy,
     R.S. (1976-1978), "Preliminary Analysis of Blood-Lead
     Concentrations for Children and Adults".  NHANES II, In:
     Hemphill DD, ed. Trace Substances in Environmental Health -
     XIII.

     Rabinowitz, M.,  Leviton, A., and Bellinger, D.  (1985), "Home
     Refinishing, Lead Paint, and Infant Blood Lead Levels".
     American Journal of Public Health, Vol. 75, No. 4.
                                39

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50272-101
                     REPORT DOCUMENTATION
                                PAGE
         1. REPORT NO.
           EPA 747-R-94-003
3. Recipient's Accession No.
 4. Title and Subtitle

   Seasonal Rhythms of Blood-Lead Levels: Boston, 1979-1983
                                                   5. Report Date
                                                     September 1995
 7. Author(s)   John Kinateder, Ron Menton, Priti Kumar
                                                   8. Performing Organization Rept. No.
 9. Performing Organization Name and Address

           Battelle Memorial Institute
           505 King Avenue
           Columbus, Ohio 43201-2693
                                                   10. Project/Task/Work Unit No.
                                                               G301104-05
Midwest Research Institute
425 Volker Road
Kansas City, MO 64110
11. Contract(C) or Grant(G) No.
(C) 68-D2-0139, 68-DO-0137
(G)	
 12. Sponsoring Organization Name and Address

           U.S. Environmental Protection Agency
           Office of Pollution Prevention and Toxics
           401M Street, S.W.
           Washington, D.C. 20460
                                                   13. Type of Report & Period Covered
                                                                   Final
                                                   14.
 15. Supplementary Notes
           Bruce Buxton was the Program Manager for Battelle Memorial Institute.
           Paul Constant was the Program Manager for Midwest Research Institute.
 16. Abstract (Limit 200 words)

    It has been conjectured that both blood-lead and environmental-lead levels are increased during summer months.  Several researchers have observed elevated levels of
 lead contamination and/or increased incidence of lead poisoning during these months. Reasons for seasonal rhythms in blood-lead levels,
  if such a phenomenon is real, are not immediately apparent. The temporal variation may result from either altered human physiology or higher levels of lead exposure
 during the summer months. Determining the source of the temporal variation in blood-lead levels may enhance our understanding of the relationship between
 environmental-lead and its impact on body burden.
    There were two primary objectives of this study:

        Determine the extent to which blood-lead levels recorded in the study conducted at the Brigham and Women's Hospital exhibit seasonal variation.

        Determine if any existing seasonal trends in blood-lead levels are correlated with seasonal trends in environmental levels.

    This report examines temporal variation in blood- and environmental-lead levels in data observed on 249 children sampled in Boston at the Brigham and Women's
 Hospital between  1979 and 1983.
 17. Document Analysis
             a. Descriptors
               Lead, lead poisoning, contamination, statistical analysis, seasonal variations, Brigham and Women's Hospital, lead levels, blood lead levels,
                                                                                                                                                al
                                                                                                                                                rhythm
                                                                                                                                                sof
                                                                                                                                                blood
                                                                                                                                                lead
                                                                                                                                                levels,
                                                                                                                                                season
                                                                                                                                                al
                                                                                                                                                variati
                                                                                                                                                on in
                                                                                                                                                enviro
                                                                                                                                                nment
                                                                                                                                                al lead
                                                                                                                                                levels.
              b. Identifiers/Open-Ended Terms
               Lead, lead poisoning, trend analysis, seasonal variation, Fourier analysis.

              c. COSATI Field/Group
 18. Availability Statement

              Release Unlimited
         19. Security Class (This Report)
        	Unclassified	
21. No. of Pages
     31	

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                                                                     20. Security Class (This Page)
                                                                                 Unclassified
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