Description
TEarchers feedback
Anne and I reviewed your submission for the Primary Study Review Assignment and we both agree you didn’t really answer all the questions satisfactorily. We want to give you another chance to revise your answers. I’m going to wait to grade your assignment for now because we hope that you can revise it, and then I will just grade the revised one instead.
You wrote something for each of the following points, but the responses were vague and did not answer the questions specifically concerning the paper you were reviewing:
(in particular, Main Effect Estimates & 95% CIs should include numbers).
rubric: Slide 1 – Study summary (14 points)
Study design
Study population, location & time period
Sample sizes
Primary exposure
Exposure window
Primary outcome
Main effect estimates & 95% CIs (and p-values, if available)
Slide 2 – Selection bias (4 points)
Potential for selection bias in recruitment strategy and direction of potential influence on results
Potential for selection bias in loss-to-follow-up and direction of potential influence on results
Slide 3 – Information bias (6 points)
Potential for non-differential or differential exposure misclassification & direction of influence on results
Potential for non-differential or differential outcome misclassification & direction of influence on results
Potential for recall bias & direction of influence on results
Slide 4 – Confounding (4 points)
How does the study design influence the potential for confounding?
Did the authors adequately control for confounding (i.e., through matching, covariate adjustment, and or stratification of analyses)?
Slide 5 – Generalizability/External validity (4 points)
How well does the study population match the target population? What information would you want to know to answer this?
In your opinion, to which population(s) can the study results be applied/generalized?
Citations and References (1 point)
All sources are referenced and cited correctly
Please include a complete reference for your study (and any other references) in your slide deck, and use your own words (i.e., do not cut and paste text from the paper). Refer to the Citation Style page as needed for guidance.
Rubric
Primary Study Review Rubric
Primary Study Review Rubric
Criteria Ratings Pts
This criterion is linked to a Learning OutcomeStudy summary: Study design
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Study population, location & time period
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Sample sizes
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Primary exposure
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Exposure window
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Primary outcome
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeStudy summary: Main effect estimates & 95% CIs (and p-values, if available)
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeSelection bias: Potential for selection bias in recruitment strategy & direction of potential influence on results
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeSelection bias: Potential for selection bias in loss-to-follow-up & direction of potential influence on results
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeInformation bias: Potential for non-differential or differential exposure misclassification & direction of influence on results
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeInformation bias: Potential for non-differential or differential outcome misclassification & direction of influence on results
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeInformation bias: Potential for recall bias & direction of influence on results
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeConfounding: How does the study design influence the potential for confounding?
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeConfounding: Did the authors adequately control for confounding (i.e., through matching, covariate adjustment, and or stratification of analyses)?
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeGeneralizability/External validity: How well does the study population match the target population? What information would you want to know to answer this?
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeGeneralizability/External validity: In your opinion, to which population(s) can the study results be applied/generalized?
2 pts
Full Marks
1 pts
Partial Marks
0 pts
No Marks
2 pts
This criterion is linked to a Learning OutcomeCitations and References: All sources are referenced and cited correctly.
1 pts
Full Marks
0.5 pts
Partial Marks
0 pts
No Marks
1 pts
Total Points: 33
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NIH Public Access
Author Manuscript
JAMA Psychiatry. Author manuscript; available in PMC 2014 May 13.
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Published in final edited form as:
JAMA Psychiatry. 2013 January ; 70(1): 71–77. doi:10.1001/jamapsychiatry.2013.266.
Traffic Related Air Pollution, Particulate Matter, and Autism
Dr. Heather E. Volk, PhD, MPH, Messer. Fred Lurmann, Messer. Bryan Penfold, Dr. Irva
Hertz-Picciotto, PhD, and Dr. Rob McConnell, MD
Department of Preventive Medicine, Pediatrics, Zilkha Neurogenetic Institute, Keck School of
Medicine, Children’s Hospital Los Angeles, University of Southern California (Dr. Volk),
Department of Public Health Sciences, University of California (Dr. Hertz-Picciotto), Sonoma
Technology, Inc., (Messers Lurmann and Penfold), Department of Preventive Medicine, Keck
School of Medicine, University of Southern California (Dr. McConnell)
Abstract
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Context—Autism is a heterogeneous disorder with genetic and environmental factors likely
contributing to its origins. Examination of hazardous pollutants has suggested the importance of
air toxics in autism etiology, yet little research has examined local level air pollution associations
using residence-specific exposure assignments.
Objective—To examine the relationship between traffic-related air pollution (TRP), air quality,
and autism.
Design, Setting and Population—This study includes data on 279 autism cases and 245
typically developing controls enrolled in the Childhood Autism Risks from Genetics and the
Environment (CHARGE) Study in California. The mother’s address from the birth certificate and
addresses reported from a residential history questionnaire were used to estimate exposure for
each trimester of pregnancy and first year of life. TRP was assigned to each location using a linesource air-quality dispersion model. Regional air pollutant measures were based on the
Environmental Protection Agency’s Air Quality System data. Logistic regression models
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Corresponding Author: Dr. Heather E. Volk, University of Southern California, 2001 N. Soto Street, MC9237, Los Angeles, CA
90089 ([email protected]).
Reprint Requests: Heather E. Volk, PhD, MPH, University of Southern California, 2001 N. Soto Street, MC 9237, Los Angeles, CA
90089 ([email protected])
A portion of this research was presented at the International Meeting for Autism Research on May 14, 2011 in San Diego, California
and at the meeting of the International Society for Environmental Epidemiology on September 16, 2011 in Barcelona, Spain.
The other authors declare no competing financial interests.
Author Contributions: Dr. Volk had full access to all the data in the study and takes responsibility for the integrity of the data and
the accuracy of the data analysis.
Study concept and design: Volk, McConnell
Acquisition of data: Lurmann, Penfold, Hertz-Picciotto
Analysis and interpretation of data: Volk, McConnell, Hertz-Picciotto, Lurmann
Drafting of the manuscript: Volk
Critical revision of the manuscript for important intellectual content: Volk, Hertz-Picciotto, McConnell, Lurmann, Penfold
Statistical analysis: Volk
Obtained funding: Hertz-Picciotto, McConnell, Volk
Study Supervision: Volk
Financial Disclosures: Fred Lurmann and Bryan Penfold are employed by Sonoma Technology Inc., Petaluma, CA. Rob McConnell
has received support from an air quality violations settlement agreement between the South Coast Air Quality Management District, a
California state regulatory agency, and BP. The other authors declare no competing financial interests.
Volk et al.
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compared estimated and measured pollutant levels for autism cases and typically developing
controls.
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Main Outcome Measures—Crude and multivariable-adjusted odds ratios (OR) for autism.
Results—Cases were more likely to live at residences in the highest quartile TRP exposure
during pregnancy (OR=1.98, 95%CI 1.20–3.31) and the first year of life (OR=3.10, 1.76–5.57)
compared to controls. Regional exposure measures of nitrogen dioxide (NO2) and particulate
matter less than 2.5 and 10 microns in diameter (PM2.5 and PM10) were also associated with
autism during gestation (NO2 OR=1.81/2SD, 95%CI 1.37–3.09; PM2.5 OR=2.08/2SD, 95%CI
1.93–2.25; PM10 OR=2.17/2SD, 95%CI 1.49–3.16) and the first year of life (NO2 OR=2.06,
95%CI 1.37–3.09; PM2.5 OR=2.12, 95%CI 1.45–3.10; PM10 OR=2.14, 95%CI 1.46–3.12).
Conclusions—Exposure to TRP, NO2, PM2.5, and PM10 during pregnancy and the first year of
life was associated with autism. Further epidemiological and toxicological examination of likely
biological pathways will help determine whether these associations are causal.
Introduction
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Autism spectrum disorders (ASDs) are a group of developmental disorders commonly
characterized by problems in communication, social interaction, and repetitive behaviors or
restricted interests.1 While the severity of impairment for the ASDs varies across the
spectrum (full syndrome autism being the most severe), the incidence rate of all ASDs is
now reported to be as high as 1 in 110 children.2 Emerging evidence suggests environment
plays a role in autism, yet at this stage, only limited information is available as to what
exposures are relevant, their mechanisms of action, stages of development in which they act,
and then how to develop effective preventive measures.
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Recently, air pollution has been examined as a potential risk factor for autism. Using the
Environmental Protection Agency’s (EPA) dispersion model-estimates of ambient
concentrations of Hazardous Air Pollutants (HAPs), Windham and colleagues identified an
increased autism risk based on exposure to diesel exhaust particles, metals (mercury,
cadmium and nickel) and chlorinated solvents in Northern California census tracts.3
Additional research using dispersion model-estimates of HAPs also reported associations
between autism and air toxics at the birth residence of children from North Carolina and
West Virginia.4 These epidemiologic findings on autism are supported by additional
research describing other physical and developmental effects of air pollution due to prenatal
and early life exposure. For example, high levels of air pollutants have been associated with
poor birth outcomes, immunologic changes, and decreased cognitive abilities.5,6
Recently, we reported an association between autism risk and early life residence within 309
meters of a freeway in the Childhood Autism Risks from Genetics and the Environment
(CHARGE) study.7 The near source traffic-related air pollutant (TRP) mixture has large
spatial variation, returning to near background daytime levels beyond this distance.8,9 Here
we report associations of autism with estimates of exposure to the mixture of TRP and with
regional measures of nitrogen dioxide (NO2), particulate matter < 2.5 μm aerodynamic
diameter (PM2.5), and particulate matter < 10 μm aerodynamic diameter (PM10) in the
CHARGE sample.
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Methods
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The CHARGE study is a population-based case-control study of preschool children. The
study design is described in detail elsewhere.10 Briefly, CHARGE subjects were between
the ages of 24 and 60 months at the time of recruitment, lived with at least one English- or
Spanish-speaking biologic parent, were born in California, and lived in one of the study
catchment areas. Recruitment was facilitated by the California Department of
Developmental Services (DDS), the Regional Centers with which they contract to
coordinate services for persons with developmental disabilities, and referrals from the
M.I.N.D. Institute clinic at the University of California, Davis (UCD) and from other
research studies. Population-based controls were recruited from the sampling frame of birth
files from the state of California, and were frequency matched by gender, age, and broad
geographic area to the autism cases.
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Each participating family was evaluated in person. Children with a previous diagnosis of
autism were evaluated using the Autism Diagnostic Observation Schedules (ADOS) and
parents were administered the Autism Diagnostic Interview-Revised (ADI-R).11,12 Children
with diagnosed developmental delay and general population controls were given the Social
Communication Questionnaire (SCQ) to screen for the presence of autistic features.13 If the
SCQ score was 15 or greater, the child was then given the ADOS and the parent the ADI-R.
In our study, autism cases were children with a diagnosis of full syndrome autism from both
the ADOS and the ADI-R. All children were also assessed using the Mullen Scales of Early
Learning (MSEL) and the Vineland Adaptive Behavior Scales (VABS) to collect
information on motor skills, language, socialization, and daily living skills.14,15 Controls
were children sampled from the general population set who received a score less than 15 on
the SCQ and who also showed no evidence of other types of delay (cognitive or adaptive).
Parents were interviewed to obtain demographic and medical information, and, among other
factors, residential histories. Race/ethnicity data were collected by self-report in categories
defined by the US Census (Table 1). The residential data captured addresses and
corresponding dates the mother and child lived at each location beginning 3 months before
conception and extending to the most recent place of residence. Further details about the
collection of clinical and exposure data have been previously reported.10
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To obtain model-based estimates of TRP exposure, we applied the CALINE4 line-source
air-quality dispersion model.16 The dispersion model was used to estimate average
concentrations for the specific locations and time periods (trimesters of gestation and first
year of life) for each subject. The principal model inputs are roadway geometry, link-based
traffic volumes, period-specific meteorological conditions (wind speed and direction,
atmospheric stability, and mixing heights), and vehicle emission rates. Detailed roadway
geometry data and annual average daily traffic counts were obtained from Tele Atlas/
Geographic Data Technology (GDT) in 2005. These data represent an integration of state-,
county-, and city-level traffic counts collected between 1995 and 2000. Because our period
of interest was 1997 to 2008, the counts were scaled to represent individual years based on
estimated growth in county average vehicle-miles-traveled (VMT) data.17 Traffic counts
were assigned to roadways based on location and street names. Traffic volumes on roadways
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without count data (mostly small roads) were estimated based on median volumes for
similar class roads in small geographic regions. Meteorological data from 56 local
monitoring stations were matched to the dates and locations of interest. Vehicle fleet
average emission factors were based on the California Air Resource Board’s EMFAC2007
(version 2.3) model. Annual average emission factors were calculated by year (1997–2008)
for travel on freeways (65 mph), state highways (50 mph), arterials (35 mph), and collectors
(30 mph). We used the CALINE4 model to estimate locally varying ambient concentrations
of nitrogen oxides (NOx) contributed by freeways, non-freeways, and all roads located
within 5 km of each child’s home. Previously, we have used the CALINE4 model to
estimate concentrations of other traffic-related pollutants, including elemental carbon and
carbon monoxide; and found that they were almost perfectly correlated (around 0.99) with
estimates for nitrogen oxides. Thus, our model-based concentrations should be viewed as an
indicator of the TRP mixture rather than any pollutant specifically.
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A second approach was to use the regional air quality data for the exposure assignments for
PM2.5, PM10, ozone (O3) and NO2. These were derived from the US EPA’s Air Quality
System (AQS) data (www.epa.gov/ttn/airs/airsaqs) supplemented by USC’s Children’s
Health Study (CHS) data for 1997–2009.18 CHS continuous PM data were used for a given
monitoring station when no Federal Reference/Equivalent Method data for PM were
available from AQS. The monthly air quality data from monitoring stations located within
50 km of each residence were made available for spatial interpolation of ambient
concentrations. The spatial interpolations were based on inverse distance-squared weighting
(IDW2) of data from up to four closest stations located within 50 km of each participant
residence; however, if one or more stations were located within 5 km of a residence then
only data from the stations within 5 km were used for the interpolation. Because special
studies have shown large offshore to onshore pollutant gradients along the southern
California coast, the interpolations were carried out with pseudo-stations, or theoretical
locations used for estimating pollution gradients from extant data when geography did not
permit observed data, located ~ 20–40 km offshore that had background concentrations
based on long-term measurements (1994–2003) at clean coastal locations (i.e., Lompoc,
CA).
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Periods and locations relevant to the modeled traffic exposure were identified based on dates
and addresses recorded on the birth certificate and from the residential history questionnaire.
The birth certificate addresses corresponded to the mother’s residence at the time of child’s
birth while the residential history captures both mother’s residences during pregnancy
(required for estimation of prenatal exposure) and child’s residences after birth through the
time of study enrollment. We determined the conception date for each child using
gestational age from ultrasound measurements or the date of last menstrual period, as
determined from prenatal records. We used these locations and dates to estimate exposure
for the first year of life, the entire pregnancy period, and each trimester of pregnancy. When
more than 1 address fell into a time interval, we created a weighted average to reflect the
exposure level of the participant across the time of interest taking into account changes in
residence. TRP was determined based on the required inputs reflecting change in each
address over the study period. For the regional pollutant measures, we assigned PM2.5,
PM10, and NO2 measurements based on average concentrations for the time period of
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interest. For O3, we calculated the averages for the period of interest based on the 1000–
1800 hours (reflecting the high 8 hour daytime) average. Based on these methods, we were
able to assign TRP estimates and regional pollutant measures for 524 mother-child pairs.
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Spearman correlations were calculated pairwise between TRP estimates and regional
pollution measures for pregnancy and the first year of life to assess independence of these
exposure metrics. We used logistic regression to examine the association between exposure
to traffic-related air pollution and autism risk. Models of autism risk as a function of TRP
exposure levels from all road types were fitted separately for each time period. Categories of
exposure were formed based on quartiles of the TRP distribution for all pregnancy as this
provided the most comprehensive data for each child. Levels of regional pollutants were
examined as continuous variables and effect estimates scaled to twice the standard deviation
of the distribution for the all pregnancy estimates. When levels of correlation permitted, we
examined both TRP and regional pollutants in a single model. Pertinent covariates were
included in each model to adjust for potential confounding due to socio-demographic and
lifestyle characteristics. We included children’s gender and ethnicity, maximum education
level of the parents, maternal age, and maternal smoking during pregnancy as described
previously.7 To examine if our findings were affected by living in an urban or rural area, we
included population density obtained from Environmental Systems Research Institute Inc.’s
2008 estimates of people per square meter (p/m2) using ArcGIS software (version 9.2). We
used the United States Census Bureau cut off of 2,500 p/m2 to categorize population density
into urban vs. rural areas and included this variable as a covariate in analysis of air pollution
effects from the first year of life since these residences were the most recent recorded.
We also fitted logistic additive models to evaluate the relationship between autism and TRP.
These models used the smoothing spline with three degrees of freedom for continuous TRP
and used the same adjustment variables as in the linear logistic models described above.
Statistical tests were conducted using an alpha level of 0.05 and 95% confidence limits were
used to measure precision. All analyses were conducted using the R package version 2.9.2
(www.r-project.org). Institutional review boards of the University of Southern California
and UCD approved the research.
Results
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Children in this study were predominantly male (84%) and most were non-Hispanic
Caucasian (50%) or Hispanic (30%). No differences were found between cases and controls
for any demographic, socioeconomic, or lifestyle variables we examined (eTable 1). Details
regarding the exposure distributions are presented in eFigures 1a and 1b. Spearman
correlations calculated for the first year of life and pregnancy time periods are presented in
Table 1. During pregnancy and the first year TRP was moderately correlated with PM2.5 and
PM10, highly correlated with NO2, but inversely correlated with O3. Among the regional
pollutant measures, PM2.5 and PM10 were nearly perfectly correlated and both were highly
correlated with NO2. Correlations with O3 were low and often negative, demonstrating an
inverse relationship. We also examined correlations of each pollutant across time periods
and high correlations were identified.
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Traffic Related Air Pollution Exposure
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Increased autism risk was associated with exposure to traffic related air pollution during the
first year of life. Children residing in homes with the highest levels of modeled TRP were
three times as likely to have autism compared to children with the lowest exposure (Table
2). Exposure in the middle quartile groups (2nd and 3rd) was not associated with an increased
risk of autism. In our analysis including population density, this association with the highest
quartile of exposure was still evident (OR=3.48, 95%CI 1.81–6.83) and living in an urban
area, compared to rural, was not associated with autism (OR=0.86, 95%CI 0.56–1.31).
When we examined TRP exposures during pregnancy, the highest quartile was also
associated with autism risk (OR=1.98, 95%CI 1.20–3.31) compared to the lowest quartile.
We further divided the pregnancy into three trimesters and modeled TRP based on these
intervals. During all three trimesters of pregnancy, we found associations with the highest
quartile of exposure (≥31.8 ppb), compared to the lowest quartile (≤9.7 ppb), and autism
(Table 2). Inclusion of demographic and socioeconomic variables in the models did not
greatly alter these associations (Table 2).
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Since our quartile-based categories indicated that there is a threshold upon which TRP
exposure is detrimental, we also examined the relationship of TRP exposure and autism
using smoothed models for the first year of life and all of pregnancy. An increasing
probability of autism was seen with increasing TRP estimates, with the odds reaching a
plateau when TRP estimates were above 25–30 ppb (Figure 1).
Regional Air Pollutant Exposure
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Higher levels of exposure to PM2.5, PM10, and NO2 based on the EPA’s regional air quality
monitoring program were associated with increased risk of autism (Table 3). Specifically for
an 8.7 unit increase (μg/m3) in PM2.5 (corresponding to twice the standard deviation of the
PM2.5 distribution) exposure during the first year of life, children were 2.12 times more
likely to have autism. Increases were also present for pregnancy and trimester-specific
estimates of PM2.5 with the smallest effects present in the first trimester. For PM10, a 14.6
unit increase (μg/m3) during the first year was associated with twice the risk of autism
(Table 3). Associations were present for pregnancy and each trimester with the first
trimester having the smallest magnitude. We did not find associations between levels of
regional O3 and autism. Regional NO2 exposure during the first year was associated with a
two-fold autism risk. Similar effects were identified for NO2 exposure during pregnancy.
While exposure during each of the three trimesters was associated with autism, effects of the
first trimester were the smallest. For all regional pollutant measures, adjustment for
demographic and socioeconomic variables did not alter the associations. As with TRP, when
we included population density in the models including exposure during the first year of life
associations with PM2.5, PM10, and NO2 did not change, nor did they change when living in
an urban area vs. a rural area was included (data not shown).
TRP, PM2.5, and PM10
Because pairwise correlations between TRP and PM2.5 and TRP and PM10 were moderate,
we included both in models to examine if local pollution estimates (TRP) and regional
pollution measures (PM2.5 and PM10) were independently associated with autism. In these
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analyses, we included the same set of covariates described above in the single pollutant
analysis. When examined in the same model, both the top quartile of TRP (OR=2.37, 95%CI
1.28–4.45) and PM2.5 (OR=1.58/2SD, 95%CI 1.03–2.42) exposure during the first year of
life remained associated with autism. Examining both TRP and PM10, we found that the top
quartile of TRP (OR=2.36, 95%CI 1.28–4.43) and PM10 (OR=1.61, 95%CI 1.06–2.47)
remained associated with autism. For all pregnancy, we found that both the top quartile of
TRP (OR=2.42, 95%CI 1.32–4.50) and PM2.5 (OR=1.60, 95%CI 1.07–2.40) were associated
with autism when examined in the same model. Similarly, both the top quartile of TRP
(OR=2.33, 95%CI 1.27–4.36) and PM10 (OR=1.68, 95%CI 1.11–2.53) remained associated
with autism when examined jointly.
Discussion
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This study found that local estimates of TRP and regional measures of PM2.5, PM10, and
NO2 at residences were higher in children with autism. The magnitude of these associations
appear to be most pronounced during late gestation and early life, though it was not possible
to adequately distinguish a period critical to exposure. Children with autism were three
times as likely to have been exposed during the first year of life to higher modeled trafficrelated air pollution as compared with typically developing controls. Similarly, exposure to
TRP during pregnancy was also associated with autism. Examination of TRP using an
additive logistic model demonstrated a potential threshold near 25–30ppb beyond which the
probability of autism did not increase. Exposure to high levels of regional PM2.5, PM10, and
NO2 were also associated with autism. When we examined PM2.5 or PM10 exposure jointly
with TRP, both regional and local pollutants remained associated with autism though the
magnitude of effects decreased.
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We previously reported an association between living near a freeway, based on the location
of the birth and third trimester address, and autism.7 That result relied on simple distance
metrics as a proxy for exposure to traffic related air pollution. The present study builds on
that result, demonstrating associations with both regional particulate and NO2 exposure and
to dispersion-modeled exposure to the near-roadway traffic mixture accounting for traffic
volume, fleet emission factors and wind speed and direction, in addition to traffic proximity.
The results provide more convincing evidence that exposure to local air pollution from
traffic may increase risk of autism. Demographic or socio-economic factors did not explain
these associations.
Toxicological and genetic research suggests possible biologically plausible pathways to
explain these results. Concentrations of many air pollutants, including diesel exhaust
particles (DEP) and other PM constituents, are increased near freeways and other major
roads, and DEP and the polycyclic aromatic hydrocarbons (PAHs) commonly present in
DEP affect brain function and activity in toxicological studies. 19–23 PAHs have been shown
to reduce expression of the MET receptor tyrosine kinase gene, which is important in early
life neurodevelopment and is markedly reduced in autistic brains.24,25 Other research
indicates that TRPs induce inflammation and oxidative stress after both short term and long
term exposures, processes which mediate effects of air pollution on respiratory and
cardiovascular disease and other neurological outcomes.26–29 Data examining biomarkers
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suggests that oxidative stress and inflammation may also be involved in the pathogenesis of
autism.30–33
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Emerging evidence suggests that systemic inflammation may also result in damage to
endothelial cells in the brain and compromise the blood-brain barrier.29 Systemic
inflammatory mediators may cross the blood-brain barrier, activating brain microglia, and
peripheral monocytes may migrate into the pool of microglia.34–36 In addition, ultrafine
particles (PM0.1) may penetrate cellular membranes.37,38 These particles translocate
indirectly through the lungs and from the systemic circulation or directly via the nasal
mucosa and the olfactory bulb into the brain.39,40 Toxicity may be mediated by physical
properties of PM, or by the diverse mixture of organic compounds, including PAHs, and
oxidant metals adsorbed to the surface.29 Neurodevelopmental effects of PAHs may be
mediated by aryl hydrocarbon hydroxylase induction in placenta, decreased exchange of
oxygen secondary to disruption of placental growth factor receptors, endocrine disruption,
activation of apoptotic pathways, inhibition of the brain antioxidant-scavenging system
resulting in oxidative stress, or epigenetic effects.21
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This study draws on a rich record of residential locations of typically developing children
and children with autism across California, allowing us to assign modeled pollutant
exposures for developmentally relevant time points. However, our results could also be
affected by unmeasured confounding factors associated with both autism and traffic related
air pollution exposure. While we did not find that including demographic or socio-economic
variables altered our estimates of effect, confounding by other factors could still occur.
These might include lifestyle, nutritional, or other residential exposures, if they were
associated with TRP or PM. We have also not explored indoor sources of pollution, such as
indoor NO or second-hand tobacco smoke, though prenatal smoking was examined and did
not influence the associations of ambient pollution with autism. Additionally, confounding
could have occurred if proximity to diagnosing physicians or treatment centers were also
associated with exposure. We included population density as an adjustment in an analysis
using estimates from the first year of life to examine the sensitivity of our results to urban or
rural locations, for which population density is a surrogate. We did not find that living in a
more densely populated area altered the association between autism risk and TRP or
regional pollutants. Despite our attempts to use a residential history to examine specific time
windows of vulnerability, incorporation of meteorology into our TRP models, and inclusion
of pollutants with seasonal variation, we are currently unable to disentangle effects
trimester-specific effects or during the first year of life because of the high correlation across
these time periods.
Exposure to TRP, PM, and NO2 were associated with increased autism risk. These effects
were observed from measures of air pollution with variation on both local and regional
levels suggesting the need for further study to understand both individual pollutant
contributions and the effects of pollutant mixtures on disease. Research on pollutant
exposure effects and their interaction with susceptibility factors may lead to identification of
biologic pathways activated in autism and improved prevention and therapeutic strategies.
While additional research and replication of these findings is needed, the public health
JAMA Psychiatry. Author manuscript; available in PMC 2014 May 13.
Volk et al.
Page 9
implications of these findings are large because air pollution exposure is common and may
have lasting neurological effects.
NIH-PA Author Manuscript
Supplementary Material
Refer to Web version on PubMed Central for supplementary material.
Acknowledgments
This work was supported by NIEHS grants ES019002, ES013578, ES007048, ES11269, ES015359, ES016535,
ES011627, EPA Star-R-823392, EPA Star-R-833292, and the MIND Institute matching funds and pilot grant
program. These funders did not in any way influence the design and conduct of the study; collection, management,
analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
Fred Lurmann and Bryan Penfold are employed by Sonoma Technology Inc., Petaluma, CA. Rob McConnell has
received support from an air quality violations settlement agreement between the South Coast Air Quality
Management District, a California state regulatory agency, and BP.
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