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,
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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
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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
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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.
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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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
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
-------
20. Security Class (This Page)
Unclassified
22. Price
(SeeANSI-239.18)
See Instructions on Reverse
OPTIONAL FORM 272 (4-77)
(Formerly NTIS-35)
Department of Commerce
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