Several studies have found a relationship
between particulate matter (PM) air pollution and infant
mortality in countries with relatively high levels of PM air
pollution as well as in countries with lower pollution levels,
such as Canada and the United States (Bobak and Leon 1999;
Ha
et al. 2003; Lipfert et al. 2000; Loomis et al. 1999; Ritz et
al. 2006; Woodruff et al. 1997, 2006) These studies suggest
that PM
air pollution is more strongly associated with postneonatal
mortality (deaths occurring after 28 days of life) than with
neonatal mortality (deaths occurring up to 28 days of life)
and
that the association with postneonatal mortality appears to be
specific to respiratory causes (Bobak and Leon 1999; Ha et
al.
2003). However, a number of questions remain about the infant
mortality and PM air pollution relationship and the role of
other air pollutants as either potential confounders of the
relationship or as independent predictors of infant mortality.
Until recently, most studies of air
pollution and postneonatal infant mortality have focused on
larger particles, either measured as total suspended particles
or particles with an aerodiameter of ≤ 10 µm
(PM10)
(Bobak and Leon 1999; Ha et al. 2003; Lipfert et al. 2000; Ritz
et al. 2006; Woodruff et al. 1997). Although monitoring of
smaller particles measuring ≤ 2.5 µm (PM2.5) has become more widespread, only one study in
California has evaluated PM2.5 in relation to respiratory related infant
mortality; results support a positive association (Woodruff et al.
2006). Although many studies in adults suggest that PM2.5 is
more strongly associated with respiratory and cardiovascular
morbidity and mortality than PM10, other studies have found larger particles to
be important for certain outcomes (Brunekreef and Forsberg
2005; Pope and Dockery 2006).
In addition, few studies have evaluated
the contribution of other pollutants to infant mortality,
either on their own or as confounders of the association
between particles and infant mortality. A study in the Czech
Republic, examining nitrogen dioxide and sulfur dioxide, did
not find either pollutant significantly associated with
postneonatal respiratory deaths after including the other study
pollutants in the model (Bobak and Leon 1999). In a study of
U.S. infants born in 1990, controlling for carbon monoxide
in
the regression models did not affect the observed relationship
between PM and infant mortality (Lipfert et al. 2000). Another
study in Southern California has found some suggestion of an
independent effect of CO on respiratory postneonatal infant
mortality and an association between NO2 and sudden
infant death syndrome (SIDS) (Ritz et al. 2006).
Finally, although relatively consistent
results have been found for PM and respiratory postneonatal
infant mortality, varying results have been found for the
association between PM and SIDS. PM10 had been found in earlier studies to be
associated with SIDS in the United States (Lipfert et al. 2000;
Woodruff et al. 1997). However, a study in Canada found that
short-term increases in NO2 and SO2, but not PM2.5,
were associated with SIDS between 1984 and 1999 (Dales et al.
2004). Similarly, a recent analysis of
births in California during 1999–2000 did not find a
relationship between PM2.5 and SIDS (Woodruff et al. 2006).
Given the uncertainty in findings for
different particle size (coarse vs. fine), the varied findings
for studies with multiple pollutants, and the variability in
the results for SIDS, further evaluation is warranted. In this
study, we addressed these issues and evaluated the role of
chronic exposure to gaseous air pollutants (CO, SO2, and
ozone) and different particle size (both PM2.5 and PM10)
in a more contemporary national data set. We used linked infant
birth–death records for infants born in the United States
between 1999 and 2002 to examine the relationships between
these air pollutants and postneonatal respiratory and SIDS
infant mortality.
Study population. We obtained linked
birth and infant death files from the National Center for Health
Statistics, which consist of
birth certificate data linked to death certificates for those
infants who die within the first year of life, for all births
during 1999–2002 (National Center for Health Statistics
2005). Births in counties with < 250,000 residents were not
eligible for our study population because these counties are
not identified on public-use files. We limited eligible births
for our study to singleton births with known birth order, known
maternal race, known maternal education, known marital status,
known maternal age, known birth weight, and a reported
gestational age of up to 44 weeks. Maternal race and Hispanic
origin were collapsed into a single categorical variable with
three levels: non-Hispanic African American (hereafter black),
Hispanic, and non-Hispanic white (hereafter white). Asian and
American Indian deaths were excluded from the analyses because
of small numbers of infant deaths. We did not include infants
who died in the neonatal period (before 28 days) as eligible
births because these deaths often happen before the infant
leaves the hospital and are often attributed to pregnancy
complications or other factors intrinsically related to the
infant. Finally, we excluded postneonatal deaths if the
residential county at birth did not match the residential
county at death. These criteria gave an eligible study
population of 7,991,974 births of 16,066,160 births during the
study period. The decrease was attributed primarily to the
exclusion of births in counties containing < 250,000
residents.
Of the postneonatal deaths in our study
population, about 50% of the infants died between 28 days and
3 months of age, about 30% died between 3 and 6 months, and
the
remaining 20% of infants died between 6 months and 1 year of
age; this distribution of age of death was similar to that
for
all postneonatal deaths in the United States in 2002 (data not
shown).
Exposure assessment. PM10, PM2.5, O3, CO, and SO2 monitoring
data for 1999–2002 were
obtained from the U.S. Environmental Protection Agency (2004).
Both PM10 and PM2.5 were typically measured continuously for 24 hr
once every 6 days. Gaseous pollutant measurements were made
daily. For O3, we used the 24-hr daily average; long-term
averages based on the 24-hr or maximum daily 8-hr average are
highly correlated (data not shown). To focus on pollution
monitors most likely to reflect population exposures, we
excluded monitors intended to capture extreme downwind/upwind
pollutant levels, background levels, maximum levels, specific
source impacts, and economic impacts.
Table 1.
|
Table 2.
|
Table 3.
|
Table 4.
|
Table 5.
|
Table 6.
|
To calculate chronic
exposures for our study population, we matched eligible births
with the pollutant
data by mother's county of residence. For each infant,
we calculated the average concentration of each pollutant over
the
first 2 months of life as a measure of chronic exposure.
Monthly averages were calculated only if there were at least
three available measures for PM and at least 15 available
measures for the gaseous pollutants over the course of the
month. Infants without air pollution measures for all
pollutants for both of the first 2 months of life were
excluded. After considering the use of other exposures, the 2-month
exposure window was considered appropriate because a) a large number of
postneonatal infant deaths occur within 2 months; b) we were able to
assign comparable exposures to the deaths and the surviving
infants; c) larger windows of exposure time would have reduced
the number of births in the study due to missing data, and a
shorter window may not have adequately represented exposure; d) a 2-month average
for the pollutants is highly correlated with the annual
average, suggesting that different metrics of chronic exposure
would not produce significantly different results (data not
shown); and e) previous analyses have found that using longer
windows with declining levels of particulate matter can bias
the results (Woodruff et al. 1997).
These exclusions led to a final study
population of 3,583,495 births, including 6,639 postneonatal
deaths occurring in 96 counties throughout the United States.
All of these 96 counties were classified by the Office of
Management and Budget as metropolitan counties, which are
defined as a) central counties with one or more urbanized areas,
and b) outlying
counties that are economically tied to the core counties as
measured by work commuting (U.S. Department of Agriculture
Economic Research Service 2003).
Infant outcomes.We obtained International Classification of Diseases, 10th
Revision (ICD-10)
(World Health Organization 1993) codes for the underlying cause
of death from
the death certificate information included in the linked birth
and death records. Respiratory mortality primarily included
underlying cause of death codes from Chapter 10, "Diseases of the Respiratory System" (J000-99),
plus deaths coded P27.1 [bronchopulmonary dysplasia (BPD)].
SIDS was defined as R95, and "Other ill-defined and
unspecified causes of mortality" (referred to in this
analysis as "ill-defined") were defined as R99. In
addition, we evaluated all other deaths (any death not
classified as respiratory, cardiovascular, SIDS, or
ill-defined) as a control category. Finally, we further
examined the SIDS and other ill-defined cause of death by
evaluating them together. We combined the category of SIDS and
ill-defined deaths based on a recent analysis by Malloy and
MacDorman (2005), which suggested that during our study period,
many SIDS deaths may have been classified as R99.
Analysis. Because
the independence assumption needed for ordinary logistic
regression may be violated by inclusion of county-level variables (both
pollution exposures and census-level covariates) that can lead
to within-county correlation among births, we used logistic
regression that incorporated generalized estimating
equations (GEE) to estimate the odds ratios (ORs) for all-cause
and cause-specific postneonatal mortality by exposure to air
pollution (SAS Institute Inc., Cary, NC) (Zeger and Liang
1986). An exchangeable correlation structure was assumed for
the GEE models, which is appropriate when there is no time
dependence among the births within county and any ordering of
the births within county is valid within the data (Hardin and
Hilbe 2003).
All air pollution
exposures were modeled using a continuous, linear form. We evaluated
the
appropriateness of a linear form from analysis based on
quartiles of exposure, and determined the linear form as a
reasonable assumption (data not shown). Several covariates were
included in the regression models to obtain adjusted estimates.
Maternal characteristics from the birth certificate were
maternal race/ethnicity (black, white, Hispanic), marital
status, age (< 20 years, 20–34 years, ≥ 35
years), education (< 12 years, 12 years, 13–15 years, and > 15
years), and primiparity (first born). Perinatal research has
shown that neighborhood-level socioeconomic status (SES)
variables in addition to individual-level covariates from the
birth certificate can influence perinatal outcomes (Pearl et
al.
2001). To control for potential additional confounding that may
not be captured by individual-level variables on the birth
certificate, we included county-level poverty and per capita
income levels from the U.S. Census in the model (U.S. Census
Bureau 2000). We included year and month of birth dummy
variables to account for time trend and seasonal effects.
Finally, we controlled for region of the country, to account
for potential confounding by population and PM composition
variation, by classifying infants into one of six U.S. regions
(Southern California, Northwest, Southeast, Southwest,
Northeast, and Midwest) based on the regions defined in the
National Morbidity, Mortality, and Air Pollution Study (Samet
et al. 2000).
We calculated adjusted and unadjusted ORs
for each pollutant in single-pollutant models for overall
postneonatal mortality and for each cause of death. We compared
these estimates to ORs for each pollutant calculated from
multipollutant models to assess potential confounding of
copollutants. However, we did not include both PM2.5 and
PM10 in the same model because PM2.5 is a
component of PM10. All ORs are reported after adjustment
for the maternal characteristics and spatial and temporal factors
described above. In general, adjusting for all the potential
confounders in the model slightly decreased the ORs (≤
1% decrease; unadjusted data not shown). ORs are also reported
for
an interquartile range (IQR) increase in the pollutant to help
standardize comparisons across pollutants.
Of the 6,639 infant deaths
in our data set, there were 576 deaths from respiratory causes,
1,379
deaths from SIDS, 755 deaths from ill-defined causes, and 3,622
deaths due to other causes. Our study cohort was
demographically similar to the eligible births in the United
States, though we had a slightly higher percentage of mothers
with < 12 years of education and a slightly higher
percentage of Hispanic and black mothers (with a complementary
decrease in white mothers) (Table 1).
The median pollutant concentrations for
survivors and deaths are given in Table 2. The exposure
measurements were not highly correlated with each other (Table
3), the strongest being a negative correlation between CO and
O3 (–0.46),
reducing the potential for colinearity in multipollutant
models.
We found a statistically significant
relationship between PM10 and respiratory-related
causes of death in the single-pollutant models (Table 4). For
a 10-µg/m3
increase in PM10, rather than the IQR increase shown
in Table 4, the odds of respiratory-related death increased 16%
[OR =
1.16;
95% confidence interval (CI), 1.06–1.27]. There were
elevated, but not statistically significant, relationships
between respiratory-related postneonatal mortality and both PM2.5 and
CO in single-pollutant models (Table 4); there were no
associations between respiratory deaths and either O3 or SO2. The
relationship between PM10 and respiratory-related
postneonatal mortality remained elevated and significant in the
multipollutant model (Table 5). No other relationships in the
multipollutant model were highly elevated or significant for
respiratory-related postneonatal mortality (Table 5).
For SIDS, only O3 was
associated with a significant increased risk in the
single-pollutant models (Table 4). For a 10-ppb increase in
average O3 levels in the first 2 months of life, the
odds of SIDS mortality increased 20% (OR = 1.20; 95% CI,
1.09–1.32). In the multipollutant model, the OR for SIDS
and O3 was not substantially lower than that found in the
single-pollutant model (Table 5). We found elevated ORs between
ill-defined cause of death and both PM2.5 (OR
= 1.15; 95% CI, 0.95–1.38) and PM10 (OR = 1.13;
95% CI, 0.99–1.30) (for a
10-µg/m3 increase).
In single-pollutant models, there were no
relationships between any of the pollutants and other causes
of death (the control category) (Table 4). However, there were
slightly elevated ORs for PM10, PM2.5, O3 and all causes of death, likely driven by the
observed cause-specific relationships between PM and
respiratory causes and O3 and SIDS (Table 4).
To assess the robustness of the reported
findings, we conducted further analyses of the PM10 and
respiratory postneonatal deaths and O3 and SIDS. The
relationship between O3 and SIDS could be influenced by residual
confounding by season, despite controlling for month of birth
in the models. SIDS deaths follow a temporal pattern, with the
lowest percent of annual SIDS deaths occurring in the spring
(19%) and the highest percent occurring in the fall (30%) and
winter (25%); O3 concentrations are highest in the summer.
Therefore, we examined the relationship between SIDS deaths and
O3 by season of birth (Table 6) and found that
the ORs were generally consistent among the seasons, with a slight
increase for those babies born in the summer.
We then evaluated the relationship between
PM10 and BPD (ICD-10 code P2.71). Previous studies
in California had identified a higher association between BPD
and
particulate matter, suggesting that infants with BPD may have
particular susceptibility to PM (Ritz et al. 2006; Woodruff et
al.
2006) because they are most often born premature and have
underlying pulmonary pathology. We found a similar but
nonsignificant OR for the 158 deaths coded as BPD (OR = 1.19;
95% CI 0.85–1.65; IQR, multipollutant model) compared
with all postneonatal respiratory deaths.
Next, we stratified by birth weight to
assess potential increased susceptibility for low-birth-weight
babies. For respiratory postneonatal deaths and PM10,
we found an OR for normal-birth-weight babies of 1.19 (95% CI,
1.05–1.36; IQR, 241 deaths) and for low-birth-weight
babies 1.12 (95% CI, 0.95–1.31; IQR, 335 deaths). We
found for O3 and SIDS an OR for low-birth-weight babies
of 1.33 (95% CI, 0.94–1.88; IQR, 245 deaths) and an OR
for normal-birth-weight babies of 1.19 (95% CI,
1.01–1.39; IQR, 1,134 deaths).
We also examined the subset of birth
records with complete information on maternal cigarette smoking
to assess potential confounding by maternal smoking. As we
have
reported elsewhere, controlling for maternal smoking in the
model had no effect on the parameter estimates for any of the
pollutants (Darrow et al. 2006). We also evaluated the effect
of removing the variable for region in the model for
postneonatal respiratory deaths, under the possibility that
region is not a confounding factor. This produced an increased
OR for PM10 of 1.30 (95% CI, 1.04–1.61) for a
10-µg/m3 increase. There were no significant
relationships for the other pollutants.
We examined the ORs for increasing
quartiles of exposures, and found that infants in the highest
quartile of exposure had elevated odds of respiratory
mortality, compared with infants in the lowest quartile of
exposure both for PM2.5, of 1.39 (95% CI, 1.04–1.85),
and for PM10,
1.31 (95% CI, 1.00–1.71), with weaker responses at the
lower exposure levels (data not shown for other quartiles). There
was
a monotonic increase in odds of SIDS for each quartile of O3 exposure
compared with the lowest quartile (highest quartile OR = 1.51;
95% CI, 1.17–1.96). No other pollutants had
elevated ORs in the highest to lowest quartile comparisons.
Finally, we examined only those deaths
that occurred within the first 90 days, which most closely
matched our exposure metric of the average over the first 2
months of life. We found for PM10 and respiratory-related
deaths an adjusted OR of 1.25 (95% CI, 1.06–1.47 for an
IQR, 249 deaths). For O3 and
SIDS the adjusted OR was 1.33 (95% CI, 1.13–1.57 for an
IQR, 731 deaths).
A recent review of air
pollution and children's health in Europe by the World Health
Organization concluded that "the evidence is sufficient
to infer a causal relationship between particulate air
pollution and respiratory deaths in the post-neonatal
period" (World Health Organization 2005). This study, in
conjunction with previous U.S. studies, suggests this statement
remains true at PM levels found in the United States.
Our study builds
on previous large infant cohort studies by adding additional
features to the analysis.
We have assessed both sizes of particulates and other air
pollutants in a national study. We examined the role of air
pollution in SIDS, both over the whole study period and by
season; this contemporary analysis is important in light of the
changing patterns of SIDS in the last 15 years. We have
controlled for additional variables—both individual
maternal characteristics and contextual SES
variables—although these factors had little effect on the
reported associations. The observed relationships between PM10 and
respiratory postneonatal mortality and O3 and
SIDS remain relatively robust to model alternatives.
Several analytic decisions could have
influenced our study population and results. We evaluated those
infants that could be linked to multiple pollutants, rather
than focusing on the larger group of infants living in counties
with just PM monitors, thus reducing our study sample. Studies
comparing populations covered by multiple monitors versus those
covered by just PM monitors have found that the relationship
between PM and general health and birth weight varies only
slightly depending on which population is evaluated (Parker and
Woodruff, in press; Parker et al., in press). However, the
smaller study size does limit our ability to fully evaluate
regional difference, such as from variation in populations or
PM composition, and to completely characterize the relationship
in other parts of the country. This should be explored in
future studies.
In addition, the maternal demographics of
our study population, though similar to those for all births,
does have a slightly higher proportion of women who are at
greater risk of adverse birth outcomes (more women are
unmarried, of lower education, and black), and are at higher
risk of living in an area of poor air quality (Woodruff et
al.
2003). Some residual confounding could be influencing our
observed results after we controlled for individual-level and
contextual-level SES factors, though accounting for these
factors did not significantly alter effect estimates.
A recent study of infant deaths in
Southern California found a relationship between CO
concentrations in the last 2 weeks of life and respiratory
postneonatal mortality (Ritz et al. 2006). However, in our
study, we did not observe a relationship between postneonatal
respiratory mortality and early-life exposure to CO. This
discrepancy may be explained partly by differences in exposure
metrics. We use a measure that is larger spatially (county
averages vs. the closest monitor in Ritz et al.) and temporally
different (we used a more chronic measure, average first 2
months of exposure, and Ritz et al. used a more acute measure,
average of the last weeks before death). CO may have a more
acute effect, or a more refined geographic scale may be needed
to better analyze the potential role of this pollutant.
We examined whether a particular
respiratory condition, BPD, might have increased susceptibility
to exposure to PM, as suggested by two recent analyses in
California (Ritz et al. 2006; Woodruff et al. 2006). The OR for
this outcome suggested an increased but nonsignificant risk
of
mortality for exposure to PM10 (data not shown).
However, the increased OR for BPD found in this study was not
as great as in the
studies in California. This could be attributed to regional
differences in coding for cause of death or regional variation
in PM composition.
There remains uncertainty as to which
particles are related to respiratory postneonatal infant
mortality. We found that PM10, but not PM2.5, was associated with respiratory infant
mortality, though we found a relationship for the highest
quartile of PM2.5 exposure. A similar source of pollution may be
contributing to both PM10 and PM2.5 in this study, such as combustion-related
sources, because the study area includes only urban counties.
Several studies suggest the coarse fraction of PM10 (PM
2.5–10 µm in diameter) may be responsible for some
of the observed associations between PM10 and
other health outcomes. Using time-series and case–crossover
analyses, Lin et. al. (2002) observed a relationship between
asthma hospitalization and coarse PM; no relationship was
observed with either PM10 or PM2.5. A recent review of the health effects of
coarse PM concluded that compared with fine PM, coarse
particles show as strong or stronger acute effects on asthma,
chronic obstructive pulmonary disease, and respiratory hospital
admissions (Brunekreef and Forsberg 2005). Furthermore,
although short-term mortality appears to be more strongly
related to fine PM, coarse PM has been associated with
cardiovascular morbidity, such as heart rate variability and
various types of cardiovascular disease hospital admissions
(Brunekreef and Forsberg 2005; Lipsett et al. 2006). A recent
study also reports an association between prenatal exposure to
coarse particles and decrease birth weight, with little effect
from fine particles (Parker and Woodruff, in press).
The relationship between air pollution and
SIDS continues to warrant further study. Given our findings of
a relationship between O3 and SIDS, we might also expect O3 to be
related to respiratory postneonatal mortality, because O3 is a
well-established respiratory irritant (Bell et al. 2004;
Hubbell et al. 2005). In addition, a recent study by Triche and
colleagues (2006) found ambient measures of O3 to be
associated with respiratory symptoms in infants, such as wheeze
and difficulty breathing. However, neither our study nor a
recent Southern California study found O3 associated
with respiratory postneonatal mortality (Ritz et al. 2006). In
addition, studies in Southern California and Canada did not
find O3 associated with SIDS (Dales et al. 2004; Ritz
et al. 2006). Differences in exposure metrics, study design,
and study populations could contribute to observed differences.
We used the O3 averaged over the county for the first
2 months of the infant's life. Ritz et al. (2006) used
a more acute exposure metric based on O3 from the
nearest monitor averaged over the last 2 months or 2 weeks of
the infant's life. Dales et al.
(2004) used a time-series approach to assess acute (daily)
exposures to O3. Our O3 exposure metric could be a proxy for some other
air pollutant, though we might expect similar findings for
other pollutants and SIDS if this were the case. It is also
possible that early life is a more susceptible time period for
O3 exposure.
Shifts in the diagnostic
coding from SIDS to primarily ICD-10 R99—"ill-defined and
unspecified causes of mortality"—as found by Malloy
and MacDorman (2005) may also influence observed results. We
found a stronger relationship between PM10 and SIDS +
ill-defined causes of death, but the association between O3 and
SIDS + ill-defined causes of death was lower than for SIDS
alone. Finally, the back-to-sleep campaign may also be playing
some role in differences between associations between PM and
SIDS in the 1990s versus little association with SIDS around
the year 2000.
As with most other air pollution
epidemiology studies, we rely on outdoor monitors as a proxy
for exposures. PM ambient monitors have been found to be a
reasonable proxy for personal exposures (Sarnat et al. 2001,
2006). In addition, measurements from ambient monitors for
gaseous pollutants have been found to be better surrogates for
personal particulate exposures than personal gaseous pollutant
exposure (Sarnat et al. 2001, 2006). These findings support
using ambient monitors to assess exposures from particulate
matter. They also suggest that findings of associations between
health outcomes and gaseous pollutants may be attributed
partially to ambient concentrations of specific PM components.
The exposures in this study
focus on a metric of chronic exposure after the infant's
birth. Exposures that occur prenatally may further enhance
susceptibility and increase risk to infant death. Studies
suggest that air pollution can increase the risk of adverse
perinatal outcomes, including growth restriction and preterm
delivery, both of which increase risk of infant mortality
(Glinianaia et al. 2004; Huynh et al. 2006; Parker and
Woodruff, in press; Salam et al. 2005). Future analysis to
evaluate the role of prenatal exposures is warranted to
evaluate any potential prenatal contribution
This study builds on previous large infant
cohort studies by adding additional features to the analysis.
We have controlled for additional variables, both individual
maternal characteristics and contextual SES variables, and
assessed multiple pollutants, using both linear and categorical
forms of exposure. The observed relationships between PM10 and
respiratory postneonatal mortality and O3 and
SIDS remain relatively robust to model alternatives.
This study provides further support for PM
air pollution as a risk factor for respiratory-related
postneonatal infant mortality and suggests that O3 may
play a role in SIDS.
