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Mini-Monograph
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Air Pollution Exposure Assessment for Epidemiologic Studies of Pregnant Women and Children: Lessons Learned from the Centers for Children’s Environmental Health and Disease Prevention Research Frank Gilliland,1 Ed Avol,1 Patrick Kinney,2 Michael
Jerrett,1 Timothy Dvonch,3 Frederick Lurmann,4 Timothy
Buckley,5 Patrick Breysse,5 Gerald Keeler,3 Tracy
de Villiers,1 and Rob McConnell1 1Department of Preventive Medicine, Keck School of Medicine, University
of Southern California, Los Angeles, California, USA; 2Mailman School
of Public Health, Columbia University, New York, New York, USA; 3School
of Public Health, University of Michigan, Ann Arbor, Michigan, USA; 4Sonoma
Technology, Inc., Petaluma, California, USA; 5Bloomberg School of
Public Health, Johns Hopkins University, Baltimore, Maryland, USA Abstract The National Children’s Study is considering a wide spectrum of airborne pollutants that are hypothesized to potentially influence pregnancy outcomes, neurodevelopment, asthma, atopy, immune development, obesity, and pubertal development. In this article we summarize six applicable exposure assessment lessons learned from the Centers for Children’s Environmental Health and Disease Prevention Research that may enhance the National Children’s Study: a) Selecting individual study subjects with a wide range of pollution exposure profiles maximizes spatial-scale exposure contrasts for key pollutants of study interest. b) In studies with large sample sizes, long duration, and diverse outcomes and exposures, exposure assessment efforts should rely on modeling to provide estimates for the entire cohort, supported by subject-derived questionnaire data. c) Assessment of some exposures of interest requires individual measurements of exposures using snapshots of personal and microenvironmental exposures over short periods and/or in selected microenvironments. d) Understanding issues of spatial-temporal correlations of air pollutants, the surrogacy of specific pollutants for components of the complex mixture, and the exposure misclassification inherent in exposure estimates is critical in analysis and interpretation. e) “Usual” temporal, spatial, and physical patterns of activity can be used as modifiers of the exposure/outcome relationships. f) Biomarkers of exposure are useful for evaluation of specific exposures that have multiple routes of exposure. If these lessons are applied, the National Children’s Study offers a unique opportunity to assess the adverse effects of air pollution on interrelated health outcomes during the critical early life period. Key words: air pollution, airborne, ambient, Centers for Children’s Environmental Health and Disease Prevention Research, Children’s Centers, cohort study, direct measurement, exposure assessment, modeling, National Children’s Study, personal measurement. Environ Health Perspect 113:1447-1454 (2005) . doi:10.1289/ehp.7673 available via http://dx.doi.org/ [Online 24 June 2005] This article is part of the mini-monograph “Lessons Learned from the National Institute of Environmental Health Sciences/U.S. Environmental Protection Agency Centers for Children’s Environmental Health and Disease Prevention Research for the National Children’s Study.” Address correspondence to F. Gilliland, Department of Preventive Medicine, USC Keck School of Medicine, 1540 Alcazar St., CHP 236, Los Angeles, CA 90033 USA. Telephone: (323) 442-1309. Fax: (323) 442-3272. E-mail: gillilan@usc.edu This work was supported by the National Institute of Environmental Health Sciences (ES009581, ES007048, ES009589, ES009600, ES009142, ES009089, ES003819, ES009606, ES10688) , the U.S. Environmental Protection Agency (R826708, R827027, R826724, and R826710) , the National Heart, Lung and Blood Institute (HL61768) , the Hastings Foundation, the Canadian Institutes of Health Research, and the National Children’s Study. The authors declare they have no competing financial interests. Received 12 October 2004 ; accepted 24 March 2005. |
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A major study design challenge for the National
Children’s Study will be to maximize and characterize
exposure contrasts in its cohort of 100,000 pregnant
women residing in multiple locations across the United
States, thereby enhancing the power to estimate exposure-response
relationships from childhood into adulthood. Multiple
outcomes are of interest, including pregnancy outcomes,
neurodevelopment, asthma, obesity, and pubertal development.
Exposures to a wide spectrum of environmental pollutants
are being considered for investigation in the study,
including air pollutants of indoor and outdoor origin
(National Children’s Study 2004).
Given the pollutants and health endpoints currently
under consideration, exposure assessment for the variable
periods during pregnancy, infancy, and childhood will
be needed. For asthma-related outcomes, daily, monthly,
yearly, and multiyear exposure metrics with varying
time integration periods may be required. For pregnancy
outcomes, monthly estimates as well as estimates for
critical periods may be needed. For neurodevelopment,
monthly, yearly, and multiyear metrics may be most
relevant. For these and other outcomes, time-integrated
average levels may capture the effects of chronic exposure
during specific periods, but more discrete and intense
sampling frequency or duration may be needed to better
assess specific exposure-response relationships.
The purpose of this article is to summarize exposure
assessment lessons learned in the Centers for Children’s
Environmental Health and Disease Prevention Research
(hereafter Children’s Centers) for air pollutants
and health outcomes of National Children’s Study
interest. Exposures to allergens and bioaerosols are
considered elsewhere in this mini-monograph. Many of
the Children’s Centers have active research programs
involving the assessment of air pollution in epidemiologic
studies (Table 1). On the basis of experience of investigators
from these centers, we provide recommendations for
air pollution exposure assessment consideration in
the study design, population selection, exposure data
collection, analysis, and interpretation of findings
of the National Children’s Study.
Lessons Learned in Air Pollution Exposure Assessment
An essential design element of environmental epidemiologic
studies is the a priori consideration of exposure
assessment to ensure that the study exposure range
will maximize the ability to evaluate key exposure-response
relationships (Navidi et al. 1994, 1999). Study population
selection and exposure assessment design are linked.
Successful selections require consideration of the
developmental time frames of interest and the biologic
outcome mechanisms, in addition to understanding the
spatial characteristics of airborne indoor and ambient
exposures. One potentially successful design strategy
is to maximize the number of contrasting pollution
profiles among study subjects by using a quasi-factorial
approach to select populations distributed over geographic
regions with different pollution profiles (and/or including
homes with different indoor sources and proximity to
specific sources) (Gauderman et al. 2000).
The National Children’s Study proposes to investigate
the relationships between patterns and histories of
exposure during critical periods and the development
of disease in later life. This creates an inherent
tension because exposure assessment in large cohort
studies requires a compromise between the optimal information
obtained from individual measurements and feasibility
constraints related to sampling methods, respondent
burden, and cost. Feasibility considerations likely
dictate that direct measurements will be limited to
subsets of subjects monitored for short time periods
(“snapshots”) in selected microenvironments,
whereas exposure metrics used in chronic effects analyses
for the entire cohort will be time-integrated over
extended periods (days to months). The proposed size
and duration of the National Children’s Study
will require the use of modeling to estimate time-integrated
exposures for the entire cohort even when direct measurements
using snapshots of exposure are available for subsets
of the cohort.
Several modeling frameworks are applicable to the
National Children’s Study. Basic approaches rely
on using questionnaire responses as a surrogate for
exposure and on assigning exposures based on air pollutants
measured at a central monitor. The latter approach
has been successfully employed to detect significant
health effects (Dockery et al. 1993; Gauderman et al.
2002; Pope et al. 2002; Ritz et al. 2000; Samet et
al. 2000). More refined approaches allow for estimation
within communities using dispersion models and information
on transport, land use, and meteorology (Brauer et
al. 2002; English et al. 1999; Finkelstein et al. 2003;
Hoek et al. 2002; Nafstad et al. 2004). Considerations
for modeled exposures include the availability of high-quality
input data on the appropriate geographic scale and
the need for validation and calibration studies to
enable exposure uncertainty assignments. There are
important limitations of modeling air pollution exposures
(Sarnat et al. 2001). Studies indicate that for some
pollutants, such as particulate matter (PM) and volatile
organic compounds, indoor sources can predominate (Sax
et al. 2004; Tonne et al. 2004; Wallace et al. 2004).
Any strategy that relies on ambient modeling should
also attempt to assess indoor exposures in subsamples
of homes and thorough questionnaire or inspection data
that examine important potential sources such as smoking
habits or the presence of an attached garage. This
is especially needed for air pollutants for which indoor
sources are often the most significant contributors
(Payne-Sturges et al. 2004). Understanding and assessing
the role of exposure measurement error in health effects
assessment are central issues for the design and implementation
of health effect cohort studies (Jerrett and Finkelstein
2005).
Finally, interpretation of National Children’s
Study findings will require information about specific
pollutant surrogates because of the complex mixture
of covarying pollutants in respirable air (Manchester-Neesvig
et al. 2003). Pollutants covary because they are emitted
from common sources or are produced by common atmospheric
chemistry and meteorologic processes. Identification
of source contributions within specific geographic
regions may enhance interpretability of single pollutant
associations with health outcomes (Laden et al. 2000;
Samet et al. 2000).
Table
1
![Table 1](tab1sm.gif) |
Table 2
![Table 2](tab2sm.gif) |
In the following sections, we provide recommendations
and issues that may need to be considered in implementing
them. These are supported by some specific examples
from the Children’s Centers listed in Table
1.
Specific Recommendations
National Children’s Study subject selection. Study
populations should be selected to maximize spatial
exposure contrasts for the pollutants of interest.
Because multiple pollutants are of interest for the
National Children’s Study, priorities must be
established to allow identification of individuals
with a wide range of exposure profiles for those key
pollutants of study interest.
Issues to consider include spatial scale variations
of pollutants, in order to select a study population
that maximizes exposure contrasts (Table 2). Table
2 identifies the spatial scales of variability for
ambient pollutants to consider in the study design
for the National Children’s Study. The scales
are categorized as regional (100-1,000 km), urban (4-50
km), neighborhood (50 m to 4 km), and household (≤ 50
m, including outdoor and indoor microenvironments).
For some exposures, contrast in exposure can be achieved
by considering indoor sources and behavior (e.g., smoking
vs. nonsmoking homes), if indoor-source pollutant health
effects are of interest. For PM, the spatial scale
variability of importance depends on the constituents
of interest. For example, elemental carbon (EC) from
ambient primary combustion processes varies on urban
and neighborhood scales. Indoor sources from combustion
also contribute to personal EC exposure (LaRosa et
al. 2002). In contrast, particulate sulfates typically
vary on a regional scale. To maximize exposure gradients
to EC, subjects would need to be selected on a neighborhood
scale, such as based on distance to busy roadways.
Sulfates’ regional nature would be better reflected
in a subject selection scheme involving different regions
of the United States.
To select subjects based on exposure contrasts for
ambient pollutants (e.g., ozone, sulfate), exposure
data on geographic variation in levels and spatial
gradients over time are needed. For criteria pollutants,
existing data are available from a national network
of monitoring stations. Data for many other pollutants
of biologic interest may be sparse or nonexistent (e.g.,
EC and air toxics). In addition, for other pollutants
with both indoor and outdoor sources (e.g., PM mass,
nitrogen oxides, volatile organic compounds), much
of the variability in exposure is driven by indoor
source activity and/or very proximate local sources
(e.g., traffic). For these pollutants, levels may need
to be measured or modeled with the appropriate spatial
and temporal resolution in pilot studies to ascertain
the appropriate spatial, temporal, and behavioral determinants.
In addition to variable pollutant source strengths,
subject-specific temporal-spatial-physical patterns
of activity may meaningfully affect both within and
between-group exposure assignments. Capturing this
variability in applicably useful ways for large study
population studies is challenging and often a multifaceted
approach using self-administered questionnaires, walk-through
surveys, instrument deployments, and sentinel monitoring.
Because several pollutants of interest for the National
Children’s Study are regional in nature, subject
selection from areas with contrasting pollution profiles
is likely to be most informative. The national scope
of the National Children’s Study provides the
opportunity to maximize the number of study profiles.
For example, the constituents of PM < 2.5 µm
in diameter (PM2.5) within a region are
highly correlated, but between regions the correlations
may be lower. PM2.5 sulfate is higher in
the eastern United States and lower in the western
United States, whereas PM2.5 nitrate is
lower in the eastern United States and higher in the
western United States. Therefore, the comparable effect
of these PM2.5 constituents may be separable
by study design. Replication of pollution profiles
in different regions is also important to allow for
effects of geographic variables such as weather and
other confounding variables to be controlled in the
analyses (Jerrett et al. 2003a, 2003b; Krewski et al.
2000; Peters 1997; Peters et al. 1999a). Exposures
within homes with common sources are also highly correlated
and may be separated by design.
An example of the integration of these approaches
is the Southern California Children’s Health
Study (CHS), a study performed by investigators in
the University of Southern California (USC)/University
of California at Los Angeles Children’s Environmental
Health Center. The USC CHS is a multiyear cohort study
of several thousand southern California school children
(Berhane et al. 2004; Kunzli et al. 2003; Peters 1997).
The primary USC CHS research question is whether ambient
air pollution causes chronic adverse respiratory health
effects during childhood and adolescent growth and
development. Almost 12,000 children from schools in
13 southern California communities have been recruited
into five cohorts since the study began in 1993.
Communities were selected to maximize differences
in outdoor air pollutant concentrations. To distinguish
the effects of different pollutants, communities were
selected to minimize the spatial correlations between
three priority study pollutants [O3, nitrogen
dioxide, and PM < 10 µm in diameter (PM10)].
However, the full quasi-factorial design could not
be fulfilled because all the potential pollution profiles
do not occur in nature. Specific community selections
were based on historical air pollution levels for several
years before study inception, exposure patterns, and
census demographic data. Because of differences in
the number of locations at which pollutants were measured
and the frequency and type of measurements made, data
available for selecting communities were more reliable
for O3 than for PM10, and more
reliable for PM10 than for NO2.
Demographically heterogeneous communities were selected
because they would be more likely to exhibit overlapping
distributions of confounding risk factors and would
allow adjustments for confounding in the analysis.
Replication of exposure profiles was employed to improve
the chance of including demographically comparable
communities and to allow estimation of residual variance
within pollution profiles. Additional details have
been described previously (Berhane et al. 2004; Peters
et al. 1999a, 1999b). This design resulted in contrasting
exposure profiles for O3 and a package of
correlated pollutants (PM10, PM2.5,
and NO2) primarily of mobile source origin.
This approach can be extended to other pollutants,
such as ultrafine particles whose concentrations may
also vary on a localized scale of ≤ 50
m. Selecting subjects within communities based on the
distance between the home and the nearest busy roadway
or other traffic density metric may maximize the exposure
contrasts of ultrafines within the profiles of other
pollutants such as O3.
Other potential valuable exposure sampling designs
might consider “matrix” sampling approaches,
which would draw on subsets of subjects for specific
substudies or specialty projects. In the larger perspective
however, maximizing differences in community exposure
profiles can provide a rich population base from which
to develop and inform multiple studies seeking to optimize
the National Children’s Study effort.
Exposure metrics. Because of the
large size, long duration, and diversity of outcomes
and exposures of interest in the proposed National
Children’s Study, the exposure assessment effort
should rely on modeling to provide estimates for the
entire cohort, supported by subject-derived questionnaire
data. Necessary survey information on temporal-spatial-
physical patterns of activity and household characteristics can be collected
for the entire cohort, and targeted exposure substudies can be performed in
selected subsamples of study subjects.
Issues to consider include modeling for large-scale
investigations over long periods (e.g., the National
Children’s Study), which is currently the only
feasible approach for assigning exposure estimates
for the entire cohort. This is especially true for
ambient air pollutants that display significant spatial
variation on urban, neighborhood, or household spatial
scales.
A variety of exposure assessment modeling approaches
are available, including proximity-based, geostatistical,
land-use regression (LUR), dispersion, integrated meteorologic
emission, and hybrid approaches involving personal
sampling in combination with one or more of the above
methods (Jerrett et al. 2004). Each model varies by
data input requirements, software/hardware, technical
expertise, and resulting accuracy and extrapolation
potential.
Modeled estimates can be refined using targeted substudies
designed to measure levels at geographic locations
over time on the scale of spatial and temporal variation
of the pollutants under study. The time resolution
of the exposure estimate needs to be appropriately
matched to outcomes to capture effects of frequency,
magnitude, and duration of peak or episodic exposure
events that may have effects during windows of vulnerability.
Long-term average exposures, including average peak
levels or hours above threshold levels, are likely
more important for relationships with chronic disease,
but this assumption needs to be evaluated for specific
agents and outcomes of focus in the National Children’s
Study.
Data availability and quality for model input are
critically important. Central-site monitoring data
can be used to assign exposure for outdoor environments,
but the utility of this assignment will depend on the
relative variability of the pollutant across the sampling
area of interest (intra- vs. intercommunity variability
issues). Estimates of indoor concentrations require
individual information on home operating conditions,
home source profiles and activity, factors influencing
the penetration of outdoor pollutants and/or the dilution
of pollutants of indoor origin (LaRosa et al. 2002;
Navidi et al. 1999). Information about temporal, spatial,
and physical activity patterns are also important but
are likely to have insufficient time resolution over
the period of study interest. Broader categories of “usual” patterns
of activity, household operation, and susceptibility
factors can be considered as modifying factors for
the exposure-response relationship using available
central-site monitoring data (Gauderman et al. 2000;
Janssen et al. 2002).
An existing national system of central site monitors
collects continuous data on criteria air pollutants
and more limited data on hazardous air pollutants [U.S.
Environmental Protection Agency (EPA) 2004]. It is
possible to add additional instruments to monitoring
sites to measure additional pollutants or speciate
PM at reasonable cost. However, the use of central-site
monitoring data for epidemiology studies requires a
quality assurance activity beyond that which is used
for regulatory activities as well as methods to address
missing data issues. The Health Effects Institute recently
funded a study to compile existing estimates of air
toxics into a coherent national database. When available,
these data may contribute to the National Children’s
Study, and selection of the sampling sites for the
National Children’s Study should take into account
the location of existing and upcoming monitoring data.
No similar monitoring network exists to assess exposure
from indoor sources, which may need to rely on questionnaire
information and substudies across regions.
Modeling of pollutants with large intracommunity
variation requires additional community measurements.
Substudies can be designed to exploit obtainable information
for modeling study subject exposures (Jerrett et al.
2005). These additional microenvironmental measurements
can be used for fitting models to better estimate exposure,
for model validation, and for assessment of errors
in exposure assignments. Calibration studies using
repeated personal monitoring may be designed and conducted
to validate the exposure estimates and correct for
exposure error in the analysis (Berhane et al. 2004;
Fraser and Stram 2001; Mallick et al. 2002; Stram et
al. 1995).
An illustration of these approaches may be seen in
the USC CHS. The USC CHS framework employed a hierarchical
approach for estimating exposure, ranging from the
coarsest spatial estimates based on community pollutant
levels measured at a single central monitor per community,
to the finest spatial-scale estimates based on integrated
models for individual exposure assessment. The framework
involved the following pollutant measurement and modeling
levels: a) continuous monitoring of O3,
NO2, and PM10, and of PM2.5 mass
and composition on a time-integrated 14-day basis,
at a central monitoring station in each community; b)
measurement of selected pollutants at multiple locations
within each community; and c) adjustment of
the central site monitor to the levels around children’s
homes and schools based on a limited number of field
measurements. This framework is augmented by a)
modeling of vehicle emissions using geostatistical
methods and spatial dispersion models, b) estimating
outdoor pollutant concentrations at schools and homes
for the entire study population using spatial statistical
models in a hybrid microenvironmental approach, and c)
modeling individual exposure estimates for the entire
study population using unified modeling methods that
integrated information with different spatial and temporal
resolutions. These unified methods include community
monitored pollutant levels, studies of indoor and outdoor
levels in homes and schools; step counters; questionnaire-based
data on time-activity patterns including commuting
patterns, traffic patterns, and housing characteristics;
and appropriate accounting of uncertainty in the exposure
estimates.
The USC CHS developed a microenvironmental exposure
model that, in principle, can provide estimates of
exposures to pollutants of ambient origin in five microenvironments.
These include residential outdoors, residential indoors,
school outdoors, school indoors, and inside vehicles.
The exposure model uses individual-level time-activity
and housing survey data, residence and school-level
traffic model estimates, and community-level air quality
measurement data and regional transport factors to
estimate short-term and long-term individual exposures.
The model estimates show the largest amount of within-community
variations in individual exposures of any of the models;
however, validating these types of models is difficult
and resource intensive (Peters 1997).
Newer modeling strategies such as LUR models are
promising. LUR employs the pollutant of interest as
the dependent variable and proximate land use, traffic,
and physical environmental variables as independent
predictors. The methodology seeks to predict pollution
concentrations at a given site based on surrounding
land use and traffic characteristics. The incorporation
of land use variables into the interpolation algorithm
detects small-area variations in air pollution more
effectively than do standard methods of interpolation
(i.e., kriging) (Briggs et al. 1997, 2000; Lebret et
al. 2000). These methods are promising for the National
Children’s Study because they can be extrapolated,
based on land use coverage, without need for extensive
monitoring in each location. Most major urban centers
maintain land use information, and the U.S. Census
has much of the information needed on population density
and employment structures. The National Children’s
Study could support the monitoring needed to calibrate
LUR models that are regionally representative of broad
land use and emission patterns. Derived coefficients
could then be applied to other places within the region
without need for extensive monitoring.
Use of limited substudies for exposure refinement. Assessment
of some exposures of interest will require individual
measurements of exposures using snapshots of personal
and microenvironmental exposures over short periods
and/or in selected microenvironments.
Issues to consider include the large number of interrelated
factors that are important in designing exposure substudies.
These include the substudy’s purpose, the population
sample to include, whether personal or microenvironmental
samples should be collected, the respondent burden,
study feasibility, sample collection and analytic costs,
temporal variation of exposure, subject activity patterns,
household operation by residents, and uses in model
validation and calibration.
These elements are nicely illustrated in the Columbia
Pregnancy Cohort Study (PCS), a study performed by
the Columbia University Center for Children’s
Environmental Health, which has focused on the effects
of pre- and postnatal exposures to air pollution on
birth outcomes and neurodevelopmental and respiratory
health outcomes in childhood via through recruitment
and follow-up of pregnant women and their offspring
(Miller et al. 2001; Perera et al. 2003, 2004a; Tonne
et al. 2004; Whyatt et al. 2003). In the Columbia PCS,
direct air pollution exposure assessment begins in
the third trimester of pregnancy with collection of
a 48-hr personal sample of PM2.5 and vapors
for each pregnant woman. These samples are analyzed
for polycyclic aromatic hydrocarbon (PAH) and pesticide
concentrations (i.e., a “snapshot” measurement
representing “usual” exposure). In a validation
substudy, the investigators also collected sequential
2-week integrated indoor samples, analyzed for the
same variables as above, for the entire third trimester
(preferred over the personal snapshot as an exposure
surrogate of third-trimester exposures, but obviously
more intensive laborwise, costwise, and subjectwise).
A home dust sample was also collected during the third
trimester from subjects and analyzed for standard allergens
relevant to maternal exposures and possible prenatal
sensitization, based on evidence emerging from the
Columbia PCS (Miller et al. 2001).
Another time interval of study exposure interest
was the first 2 years of life, when infants/toddlers
spend substantial amounts of time in the home; this
may be a critical exposure window for development of
allergy and asthma. Columbia PCS homes were visited
when the child reached 1 year of age, and a dust sample
was collected for allergen analysis. Additional sampling
was performed in a subset of 25% of the homes, where
2-week samples of indoor and outdoor air PM2.5,
black carbon, and NO2 were collected.
These samples are being used to develop and test a
spatial LUR model that will then be used to estimate
exposures in the full cohort that are representative
of those occurring in early childhood.
As a part of its investigations of childhood asthma
in Baltimore, Maryland, the Johns Hopkins Center for
Asthma in the Urban Environment (JHU Center) has conducted
an intervention trial and a cohort study of asthma
morbidity (Breysse et al. 2005; Swartz et al. 2004).
The exposure assessment efforts for these studies include
indoor and outdoor air pollution as well as indoor
allergens in approximately 400 homes. The major focus
of these studies was indoor air where investigators
assessed 3-day average indoor PM10, PM2.5,
NO2, O3, and nicotine at 3-month
intervals (Breysse et al. 2005). In addition, 3-day
time resolved PM was assessed using a data-logging
nephalometer. Ambient PM air pollution was assessed
using a monitoring site centrally located to the study
area.
Results from these studies demonstrate the importance
of assessing indoor air. Children, particularly young
children, spend the great majority of their time in
the home. Others have noted (Wallace et al. 2004) that
indoor PM concentrations are generally higher than
outdoor levels, and cigarette smoking as well as other
household activities are responsible for this increase.
In some cases, the PM contribution from cigarette smoking
to indoor PM is greater than that penetrating from
outdoor air. The JHU Center results indicate, for example,
that a single cigarette contributes between 1 and 2 µg/m3 to
indoor PM. In addition, a strategy that uses repeat
measures allows larger time frame variability to be
assessed (e.g., seasonal).
Results from the Michigan Center for the Environment
and Children’s Health demonstrate the importance
of focusing on the home as an important microenvironment
for children’s exposure (Keeler et al. 2002;
Yip et al. 2004). An important lesson from these studies
is that home-based exposure assessments are feasible
for studies involving hundreds of children and need
to be considered in the National Children’s Study.
This conclusion is particularly true for newborn children
who spend essentially all of their time in the home.
The microenvironments of importance include the indoor
environment in a range of housing types, because there
is a growing recognition that housing quality is an
important predictor of indoor air pollution and can
affect outdoor pollution penetration rates as well
as being a general risk factor for poor health (Kingsley
2003).
As described above, the USC CHS experience suggests
that exposure assignment accuracy can be improved by
conducting substudies with a limited number of measurements
extended temporally and spatially. In evaluating the
minimal sampling needed to successfully predict long-term
exposures in study communities, USC CHS investigators
found that the intraclass correlation between estimated
annual average of pollutants, based on 2-week subset
measurements, and the true annual average was greater
than 0.9 for O3, NO2, and nitric
oxide in southern California, if two winter, two summer,
and one spring sample were obtained. Greater numbers
of samples did not appreciably improve the correlation.
These results indicate that accurate estimates of the
pollutant annual average levels can be obtained at
homes, schools, and other central site locations with
a limited number of samples. Local measurements can
then be combined with concurrent central site measurements
to estimate neighborhood and household scale concentrations
for the entire cohort. Although the optimum number
of samples may differ by region of the country or in
different neighborhoods within communities, depending
on the pollutants of interest and geographic and temporal
variation in the processes driving air pollution, this
general strategy may be of use in planning efficient
National Children’s Study substudies.
Analytic and interpretation issues. Understanding
issues of spatial/temporal correlations of air pollutants,
the surrogacy of specific pollutants for components
of the complex mixture, and the exposure misclassification
inherent in exposure estimates will be critical in
analyzing and interpreting National Children’s
Study findings.
Issues to consider include the fact that air pollutants
occur as complex mixtures of gases and particles, but
coexisting constituents may covary, based on their
common sources or photochemical pathways. The ambient
level of one pollutant may therefore be a surrogate
for other pollutants arising from the same source,
so interpretation of findings for individual pollutants
must account for this surrogacy (Manchester-Neesvig
et al. 2003; Sarnat et al. 2001). Identification of
pollutant sources therefore provides a potentially
important mechanism to evaluate source-specific health
effects and can ultimately lead to effective strategies
for reducing population exposure.
Substudies among subjects in differing geographic
locations may be useful for defining pollutant relationships.
For example, in assessing PM, chemical tracers have
been identified that can serve as “fingerprints” for
individual sources, or source types, of air pollution
(Laden et al. 2000; Manchester-Neesvig et al. 2003;
Sarnat et al. 2002). This type of information can be
used to apportion contributions to the measured PM
mass on a per sample basis, along with providing data
critical to the assessment and interpretation of health
effects associated with individual chemical components
of PM. Quantitative assessments of source contributions
for large data sets are often determined using a statistical
receptor modeling approach. This type of data analysis
is best suited to longitudinal study designs and can
be limiting because it may require collection of a
large number of samples to obtain robust results.
The recent successful development and deployment
of several types of continuous portable PM mass and
number monitors offer the potential for producing real-time
(< 5-min interval) data. The continuous data collection
format of these samplers allows a better understanding
of source emission patterns and exposures, especially
in urban environments, and can be used to enhance investigations
of short-term peak exposures. These highly time-resolved
exposure data can be coupled with personal time-activity
pattern data to quantitatively identify exposures from
specific emission sources. To date, real-time PM samplers
do not yet offer the ability to determine PM chemical
speciation. A combination of methodologic approaches
(employing chemical tracers and continuous PM number
and mass count information) may improve the ability
to identify specific sources and source types contributing
to the measured exposure to PM and other pollutants.
Exposure misclassification is a critical issue for
exposure assessment efforts, especially modeled exposures.
In most large cohort studies, it is not possible to
accurately measure the true personal exposure of individuals
over the time interval that is most relevant for the
outcomes of interest. Thus, virtually all exposure
assessments provide at best estimates of true exposures,
with some error. Errors may arise because of temporal
factors (e.g., the exposure metric captures only a
snapshot of the relevant time interval) or spatial
factors (e.g., the exposure metric is collected at
a location different from where the subject lives and
breathes). Additionally, inherent imprecision in the
specific method selected for study application may
also result in some measurement error. For the results
of the study to ultimately be interpretable, it is
important in designing the study for investigators
to analyze the nature of the exposure misclassification
errors that are likely to be present. Quantitative
estimates of exposure errors can be obtained by carrying
out calibration substudies where results from more
complete exposure metrics are compared with results
from the modeled metrics (Berhane et al. 2004; Fraser
and Stram 2001; Mallick et al. 2002; Sarnat et al.
2001; Stram et al. 1995). Bayesian statistical frameworks
may assist with assessing the impact of measurement
error on the exposure-response relationships (Berhane
et al. 2004).
Modifiers of exposure-outcome relationships. “Usual” temporal,
spatial, and physical patterns of activity can be used
as modifiers of the exposure-outcome relationships.
Highly time-resolved activity information over the
study period of interest may not be necessary, and
is not likely to be available, for all National Children’s
Study participants throughout the study. Personal exposure
estimates, based on time in microenvironments, are
likely to be associated with large uncertainties. “Usual” patterns
of activity, such as time usually spent outdoors, can
be collected by questionnaire and used as modifiers
of exposure-outcome relationships (Gauderman et al.
2002). Activity-level assignments may be important
in moving from exposure to delivered dose of an airborne
pollutant to the lung. For example, for asthma prevalence
and incidence, USC CHS investigators saw little association
with community levels of exposure. However, when physical
activity was considered, O3 was strongly
associated with asthma incidence (where variation entered
from increased ventilation rates associated with exercise
and likely increased dose to the lung). An important
challenge for the National Children’s Study is
assessing activity patterns among mothers, infants,
and young children.
For extremely large study populations for which individual
questionnaires may be impractical to administer and/or
collect, randomized sampling schemes or oversampling
in certain nested subsamples of possible increased
interest may be worth careful consideration.
Use of biomarkers. Biomarkers of exposure
offer utility for evaluation of specific exposures
that have multiple routes of exposure. For specific
airborne pollutants, exposure assessments may need
to consider multiple routes of human exposure. In addition
to inhalation, dermal absorption and oral ingestion
may be important pathways of exposure for pollutants
of interest with regard to young children, infants,
and pregnant or lactating mothers. The use of exposure
biomarkers is one potentially valuable approach in
this area (Weaver et al. 1998). Interpreting the relationship
between these markers and exposures, however, is a
complex function of the timing and routes of exposure,
and of the pollutant toxicokinetics. As discussed above,
temporal-spatial-physical patterns of activity will
almost surely affect this dynamic in important ways,
from modification of ventilation rates to facilitated
dermal absorption during periods of elevated, increased,
or extended activities. As exposure assessment tools,
biomarkers offer the potential advantage of integrating
the net effect of all of these factors in producing
a given internal dose for a given individual. Such
measurements may better represent true health-relevant
exposures for an individual than any external measure
of exposure can.
Biomarker measurements are substantially integrated
into the exposure and health assessment designs of
the Columbia PCS. From an exposure perspective, biomarkers
focus on DNA-bound PAHs (Perera et al. 2004a, 2004b),
pesticides in blood plasma and meconium (Perera et
al. 2003; Whyatt et al. 2001, 2003, 2004), and the
environmental tobacco smoke (ETS) metabolite cotinine
in urine (Perera et al. 2004b), beginning with maternal
and infant cord blood samples at birth, and continuing
with follow-up assessments in the child at 2 and 5
years of age. PAH-DNA adducts also can be viewed as
early measures of procarcinogenic health effects (Perera
et al. 2004b). Other effect-related biomarkers focus
on the time course of sensitization to environmental
allergens, including measurements of maternal, cord-blood,
and child IgE, and production of proinflammatory cytokines
or proliferation of mononuclear cells in response to
specific allergens (Miller et al. 2001).
The integration of newly developed pesticide biomarkers
within the epidemiologic design of the Columbia PCS
has made possible significant new advances in our understanding
of the health effects and patterns of exposures to
pesticides among urban women and children (Perera et
al. 2003; Whyatt et al. 2001, 2003, 2004). A wide range
of pesticides have been shown to be quantifiable in
the plasma of women and their newborns, with significant
correlations between maternal and cord blood levels
in many cases (Whyatt et al. 2003). For some but not
all pesticides, correlations also were demonstrated
between plasma levels at birth (either cord blood or
maternal) and air measurements collected during the
third trimester of pregnancy. Cord plasma, but not
air, levels of the insecticide chlorpyrifos and diazinon
were significantly associated with decreased birth
weight and length (Whyatt et al. 2004). Of particular
significance, levels of several pesticides in both
air and plasma showed significant declines across women
enrolled before and after the U.S. EPA insecticide
phase-out (Whyatt et al. 2003). Furthermore, associations
with adverse birth outcomes were significant only for
infants born before the phase-out (Whyatt et al. 2004).
These findings illustrate the utility of well-targeted
biomarker measurements, in conjunction with health
and external exposure measures, for birth cohort studies.
Cotinine and nicotine as markers for ETS, an important
source of PM exposure, has a long history of use in
biomonitoring. Hair nicotine has the potential to provide
estimates of ETS exposure over a 2-3 month period or
longer (Jaakkola and Jaakkola 1997), and other nicotine
metabolites (e.g. cotinine) may be useful indicators
of both exposure and bioavailability.
The National Children’s Study offers a unique
opportunity to understand the adverse effects of air
pollution on a broad range of interrelated outcomes
during the critical period of early life development
and growth. Six recommendations for air pollution exposure
assessment are proposed from lessons learned in the
Children’s Centers.
-
National
Children’s Study subject selection. Study
populations should be selected to maximize spatial-scale
exposure contrasts for the pollutants of interest.
Because multiple pollutants are of interest for
the National Children’s Study,
priorities must be established
to allow identification of
individuals with a wide range of
exposure profiles for those key
pollutants of
study interest.
-
Exposure
metrics. Because
of the large size, long duration,
and diversity of outcomes and
exposures
of interest in the proposed
National Children’s
Study, the exposure assessment
effort should rely on modeling
to provide estimates for
the entire
cohort, supported by subject-derived
questionnaire data. Necessary
survey information on temporal-spatial-physical
patterns of activity and
household characteristics
can be collected for the
entire
cohort, and targeted exposure
substudies can be performed
in a selected
subsample of study subjects.
-
Use
of limited substudies for exposure refinement. Assessment
of some exposures
of interest will
require individual
measurements of exposures
using snapshots
of personal and
microenvironmental exposures
over
short periods
and/or in selected microenvironments.
-
Analytic
and interpretation issues. Understanding
issues of spatial-temporal correlations of air
pollutants, the surrogacy of specific pollutants
for components of the complex mixture, and the
exposure misclassification inherent in exposure
estimates will be critical in analyzing and interpreting
findings from the National Children’s
Study.
- Modifiers
of exposure-outcome relationships. “Usual” temporal,
spatial, and physical
patterns of activity
can be used as
modifiers of the
exposure/outcome
relationships.
- Use
of biomarkers. Biomarkers
of exposure may
be required for
evaluation of
specific exposures
that have multiple routes of exposure.
We have learned that there are many challenges to
assessing air pollution exposures to children. To overcome
these challenges, the National Children’s Study
will need to commit extensive resources to exposure
assessment activities. With optimal subject selection,
exposure estimates can be modeled for the entire cohort,
supported by direct measurement of selected pollutants
in a subset of the study population. Biomonitoring
is likely to be a valuable adjunct to the exposure
assessment design, helping to trace the mechanistic
linkages between exposures and effects. Prioritization
of pollutants of study interest and developmental periods
of study focus would allow optimization of the study
design for the National Children’s Study to maximize
contrasting pollution profiles and enhance the ability
to assess exposure-response relationships. |
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Last Updated: April 25, 2006
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