Socioeconomic Inequality in the Prevalence of Autism
Spectrum Disorder: Evidence from a U.S. Cross-Sectional
Maureen S. Durkin1,2,3*, Matthew J. Maenner1,3, F. John Meaney4, Susan E. Levy5, Carolyn DiGuiseppi6,
Joyce S. Nicholas7, Russell S. Kirby8, Jennifer A. Pinto-Martin9, Laura A. Schieve10
1Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America, 2Department
of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States of America, 3Waisman Center, University of Wisconsin-
Madison, Madison, Wisconsin, United States of America, 4Department of Pediatrics, University of Arizona Health Sciences Center, Tucson, Arizona, United States of
America, 5Department of Pediatrics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America, 6Department of Epidemiology, Colorado School of
Public Health, University of Colorado Denver, Aurora, Colorado, United States of America, 7Division of Biostatistics and Epidemiology, Departments of Neurosciences and
Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America, 8Department of Community and Family Health, University of South
Florida, Tampa, Florida, United States of America, 9School of Nursing and School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of
America, 10National Center on Birth Defects and Developmental Disabilities, Centers for Disease Control and Prevention, Atlanta, Georgia, United States of America
Background: This study was designed to evaluate the hypothesis that the prevalence of autism spectrum disorder (ASD)
among children in the United States is positively associated with socioeconomic status (SES).
Methods: A cross-sectional study was implemented with data from the Autism and Developmental Disabilities Monitoring
Network, a multiple source surveillance system that incorporates data from educational and health care sources to
determine the number of 8-year-old children with ASD among defined populations. For the years 2002 and 2004, there were
3,680 children with ASD among a population of 557 689 8-year-old children. Area-level census SES indicators were used to
compute ASD prevalence by SES tertiles of the population.
Results: Prevalence increased with increasing SES in a dose-response manner, with prevalence ratios relative to medium SES
of 0.70 (95% confidence interval [CI] 0.64, 0.76) for low SES, and of 1.25 (95% CI 1.16, 1.35) for high SES, (P,0.001).
Significant SES gradients were observed for children with and without a pre-existing ASD diagnosis, and in analyses
stratified by gender, race/ethnicity, and surveillance data source. The SES gradient was significantly stronger in children with
a pre-existing diagnosis than in those meeting criteria for ASD but with no previous record of an ASD diagnosis (p,0.001),
and was not present in children with co-occurring ASD and intellectual disability.
Conclusions: The stronger SES gradient in ASD prevalence in children with versus without a pre-existing ASD diagnosis
points to potential ascertainment or diagnostic bias and to the possibility of SES disparity in access to services for children
with autism. Further research is needed to confirm and understand the sources of this disparity so that policy implications
can be drawn. Consideration should also be given to the possibility that there may be causal mechanisms or confounding
factors associated with both high SES and vulnerability to ASD.
Citation: Durkin MS, Maenner MJ, Meaney FJ, Levy SE, DiGuiseppi C, et al. (2010) Socioeconomic Inequality in the Prevalence of Autism Spectrum Disorder:
Evidence from a U.S. Cross-Sectional Study. PLoS ONE 5(7): e11551. doi:10.1371/journal.pone.0011551
Editor: Landon Myer, University of Cape Town, South Africa
Received April 7, 2010; Accepted June 19, 2010; Published July 12, 2010
This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration which stipulates that, once placed in the public
domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose.
Funding: This work was funded by the Centers for Disease Control and Prevention (www.cdc.gov), Cooperative Agreements UR3/CCU523235 and UR3/
DD000078. Additional funding for graduate student support for data analysis was provided by the University of Wisconsin (www.wisc.edu). Scientists employed
by the funding agency participated in the study design and data collection and one of these scientists, Dr. Laura Schieve, participated in the analysis, decision to
publish, and preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com
Population indicators of socioeconomic status (SES), such as
household wealth or income and parental education and
occupation, are strongly correlated with the health and develop-
ment of children . For many chronic childhood disorders and
for developmental disabilities overall, the association with SES
often is found to be inverse, such that population prevalence
decreases with increasing levels of SES [2,3]. Documentation of
this pattern, as well as exceptions to it, might provide clues to
causal mechanisms underlying specific disorders or point to
disparities in access to services, including early access to services
that can stem the progression of mild conditions.
In the case of autism and autism spectrum disorder (ASD),
evidence for an association with SES has been mixed and more
often in the opposite direction of that for other childhood
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disorders. In the earliest clinical descriptions of children with
autism, Kanner noted a preponderance of ‘‘highly intelligent
parents’’ . A number of clinical [5–9] and population-based
[10–16] studies subsequently have reported positive associations
between autism or ASD and SES indicators such as parental
education, occupation, or income. In addition, ecological analyses
of school enrollment data have found significant inverse
associations between school district level proportions of children
receiving special education under the autism disability category
and SES indicators such as the proportion of students reported to
be economically disadvantaged  and county median house-
hold income . However, a nearly equivalent number of
studies, both clinical [19–23] and epidemiological [24–28], have
failed to find associations between SES and ASD, and one case-
control study found lower educational attainment of mothers of
children with autism compared to controls .
A compelling argument has been made that the positive
associations between SES and ASD prevalence that have been
observed likely are due either in part or entirely to ascertainment
bias [22–24,30,31]. For example, it has been suggested that ‘‘more
parents of high social class families [have] the necessary
information and financial resources to find their way to the
specialized facilities’’  and ‘‘a knowledgeable and determined
parent of an autistic child [is] more likely to obtain an informed
diagnosis’’ . To evaluate the role of biased ascertainment,
Wing  called for population-based studies large enough to
allow stratified analyses and evaluation of socioeconomic differ-
ences among subgroups.
In a previous analysis  of data from one site participating in
the Autism and Developmental Disabilities Monitoring (ADDM)
Network, we found a positive association between ASD prevalence
and SES, and concluded that there was a need for larger studies to
evaluate whether the SES gradient is found only among children
with a pre-existing ASD diagnosis — a finding which would
support the hypothesis that the SES gradient is a result of
ascertainment bias. Alternatively, evidence of a similar SES
gradient among children meeting diagnostic criteria for ASD who
had not previously been diagnosed or classified as having an ASD
would suggest that the ASD-SES association might not be entirely
due to ascertainment bias.
We designed the present study to examine—among a large,
diverse, population-based sample of 8-year-old children in the
United States in which ASD case status was determined regardless
of whether a child had a pre-existing ASD diagnosis—whether the
prevalence of ASD is associated with SES and, if so, whether the
association is consistent across subgroups defined by race/
ethnicity, gender, phenotypic characteristics, diagnosis, and data
Study Design and Data Sources
We implemented a population-based cross-sectional design in
which data from 12 participating ADDM Network sites were
analyzed . The ADDM Network, established by the Centers
for Disease Control and Prevention in 2000, is a population-
based surveillance program operating in selected geographic
locations in the United States. The surveillance program
incorporates abstracted data from records of multiple educational
and medical sources to determine the number of children who
appear to meet the ASD case definition, regardless of pre-existing
diagnosis. Clinicians determine whether the ASD case definition
is met by reviewing a compiled record of all relevant abstracted
Using the ADDM Network methodology, the network counted
a total of 3680 8-year-old children as having an ASD in 2002 and
2004 in all study sites with available case and SES information,
which were those located in Alabama, Arkansas, Arizona,
Colorado, Georgia, Maryland, Missouri, North Carolina, New
Jersey, Pennsylvania, South Carolina, and Wisconsin. ADDM
Network data from the states of Utah and West Virginia were
excluded because they did not include sufficient geographic
indicators to allow SES classification.
The population denominator comprised 557 689 8-year-old
boys and girls residing in the respective study areas in the two
study years according to the 2000 U.S. Census . We used the
2000 Census for both study years because it provided the most up-
to-date socioeconomic information at the block group level.
Compared with the 2000 Census, 2002 and 2004 intercensal
estimates of population counts (which do not include relevant SES
information at the block group level) were 3.9% lower. To
estimate racial and racial/ethnic distributions, we multiplied the
number of 8-year-olds within each census block group by the
proportion of 6- to 11-year-olds in the same block group that were
classified as non-Hispanic White, non-Hispanic Black or African
American, Hispanic, Asian, or other. We then summed the block
group frequencies of 8-year-old children in each racial/ethnic
group. Compared with 8-year-old children nationally (as detailed
in the 2000 U.S. Census), those in the study areas were more likely
to be non-Hispanic Black or African American (28.6% vs. 15.7%)
and less likely to be Hispanic (9.9% vs. 17.2%) (Table 1).
Autism spectrum disorder (ASD) refers to a group of neurodevel-
opmental disorders involving impairments in social interaction and
communication, as well as the presence of repetitive or stereotyped
behaviors. Specific disorders encompassed by ASD for which
diagnostic criteria are provided by the Diagnostic and Statistical
Manual Version IV-TR are autistic disorder, Asperger’s disorder,
and pervasive developmental disorder not otherwise specified .
Casestatus for the purpose of surveillance was determined based on a
comprehensive review of educational and clinical records. Children
were classified by experienced, trained clinician reviewers as having
an ASD if they either had a documented previous classification of an
ASD that was confirmed through review of diagnostic evaluation
records or had an evaluation record from an educational or medical
setting indicating behaviors consistent with Diagnostic and Statistical
Manual Version IV-TR criteria for an ASD . For children
without a documented ASD classification, but with an indication of
developmental delays or concerns consistent with a possible ASD
classification, data were abstracted and systematically reviewed for all
relevant ASD and developmental behaviors reported in the child’s
education or medical evaluations, or both, to determine whether
behaviors described by qualified professionals in and across these
evaluations were consistent with the Diagnostic and Statistical
Manual Version IV-TR criteria.
Of the 3680 children with ASD, 2436 (66.2%) had a pre-
existing ASD diagnosis. Of those with a pre-existing diagnosis,
1411 (58%) had a pre-existing diagnosis of autistic disorder, while
information on the remaining 42% was insufficient to determine
whether Diagnostic and Statistical Manual Version IV-TR criteria
were met for autistic disorder versus pervasive developmental
disorder not otherwise specified. Information from standardized
intelligence tests was available for 75% of the children with ASD.
Based on this information, children with an ASD were classified as
having intellectual disability (IQ,70) versus normal range
intelligence. Developmental regression was noted if the onset of
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ASD was characterized by loss of previously acquired skills in
communication, social interaction or behavior. Further details
regarding the ADDM Network methodology can be found in
previous publications [32,35].
SES Indicators and Computation of SES-Specific
To evaluate the association between SES and ASD, we
implemented the following procedure to compute the prevalence of
ASD in ‘‘Low SES,’’ ‘‘Medium SES,’’ and ‘‘High SES’’ tertiles of the
population. We used three different approaches, each based on a
different census indicator at the block group level, to identify
population SES tertiles based on: (1) the percentage of families with
children thathad incomesabove the federal povertylevel(abbreviated
here as ‘‘% above poverty’’); (2) the percentage of adults 25 years of
age or older who had a bachelor’s degree (abbreviated here as ‘‘%
bachelors’’); and (3) median household income (‘‘MHI’’). The purpose
of creating three sets of SES tertiles was to allow evaluation of
consistency of the findings across different indicators.
To create the population SES tertiles, we: (1) weighted each
census block group in the study areas by its number of 8-year-old
residents; (2) ranked the census block groups by their values on the
three census indicators (% above poverty, % bachelors, and MHI)
and computed percentiles for each indicator; and (3) classified the
block groups and thus the denominator of 8-year-olds into SES
tertiles based on their percentiles. The result was three sets of
population SES tertiles, one based on each indicator.
In the absence of current individual-level measures of SES in
the ADDM Network surveillance database, we attached area-
based SES measures to each child with ASD, using the approach
described by Krieger and colleagues , based on census block
group of residence of the child at the age of eight years. After
geocoding each case, we classified the case into high SES, medium
SES, or low SES categories based on the child’s census block
group values for the indicators % above poverty, % bachelors, and
MHI. We then computed the SES-specific prevalence of ASD per
1 000 by dividing the number of children with ASD in each SES
category by the general population in the same category.
To allow formal testing of a dose-response relationship between
SES and ASD risk, we computed prevalence ratios with medium
SES serving as the reference category, and Cochran-Armitage
trend tests. We used SAS version 9.1 for all statistical analyses. We
computed x2tests and 95% confidence intervals based on a
Poisson distribution and log-link function . To test for
differences in SES between ASD cases and the surveillance
population, we computed t-tests for the indicators % poverty and
% bachelors, and the two-sample median test for the indicator
To evaluate whether the associations between SES and ASD
varied by race/ethnicity, gender, phenotypic characteristics, pre-
existing diagnosis of an ASD, and ascertainment sources of
information, we performed stratified analyses and x2tests both of
the SES gradient within strata and of the difference in the SES
distribution of cases across strata, using the % above poverty
indicator for SES. We chose this indicator for the stratified analyses
after determining that the results were similar for all three SES
indicators, and because the % above poverty block group indicator
has been shown in previous studies to be correlated with a range of
other measures of SES among the general population .
In addition to use of the indicator ‘% above poverty’ in analyses
presented in Tables 2 and 3, we have provided information in
Table 1 about the ‘percentage of the population residing in poverty areas,’
where poverty areas are defined by the U.S. Census to include
census block groups in which more than 20% of families with
children have incomes below the poverty level .
Compared to all 8-year-old children in the study areas, those
with ASD were less likely to reside in census block groups classified
as poverty areas, and more likely to be male and live in block
groups with higher adult educational achievement and a higher
MHI (Tables 1 and 2). In addition, among both children with
ASD and those in the general study population, there were notable
differences in SES by race/ethnicity (Table 2).
The prevalence of ASD increased in a dose-response manner
with increasing SES, a pattern seen for all three SES indicators
used to define SES categories (Figure 1). When the results were
stratified by race/ethnicity, using the % above poverty to define
SES categories, significant SES gradients and dose-response
increases in ASD prevalence with increasing SES were seen for
all strata (Table 3).
Table 1. Demographic Characteristics of ASD Cases, Population of 8-Year-Old Children in the Surveillance Area and Overall United
States Population of 8-Year-Old Children.
Population of 8-Year-Old Children
Residing in the Surveillance Areaa
United States Population
of 8-Year-Old Childrena
Total3680 557 6894 179 230
Race/Ethnicity% Non-Hispanic White60.157.8 60.3**
% Non-Hispanic Black24.6*28.6 15.7**
% Hispanic7.7* 9.917.2**
% Asian 2.6 2.3 3.3**
% Other 1.7*2.53.5**
% Missing Race/Ethnicity3.2*00
aBased on 2000 Census data.
*p,0.05, comparing ASD cases to population of 8-year-old children residing in the surveillance area.
**p,0.05, comparing population of 8-year-old children residing in the surveillance area to United States population of 8-year-old children according to 2000 Census
Socioeconomic Status & Autism
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Table 4 presents additional stratified results showing a
significant trend toward increasing ASD prevalence with increas-
ing SES: (1) among both boys and girls; (2) regardless of whether
there was a pre-existing diagnosis of autistic disorder or an ASD;
(3) among children with ASD who did and did not have a history
of developmental regression; and (4) regardless of data source
(health records only, school records only, and both health and
school records). The SES gradient in prevalence, as indicated by
the prevalence ratios, was significantly weaker when restricted to
children with ASD without a pre-existing autism diagnosis than
when restricted to those with a pre-existing diagnosis (p,0.0001,
x2test comparing the SES distribution of cases with and without a
pre-existing diagnosis). In addition, when the children with ASD
were stratified by the presence or absence of co-occurring
cognitive impairment, there was no evidence of an SES gradient
in the prevalence of ASD with co-occurring cognitive impairment
and a relatively strong gradient in the prevalence of ASD without
cognitive impairment (Table 4).
This surveillance-based study showed increasing ASD preva-
lence associated with increasing SES in a dose-response manner,
with a stronger SES gradient in ASD prevalence in children with
versus without a pre-existing ASD diagnosis. The main results of
this study were consistent with the only study larger than this to
examine the association between ASD risk and an indicator of
SES. That study, published in 2002 by Croen and colleagues,
looked at more than 5000 children with autism receiving services
coordinated by the California Department of Developmental
Services and found a stepwise increase in autism risk with
increasing maternal education . Our results were somewhat
consistent, but also contrasted somewhat, with Bhasin and
Schendel’s case-control study based on surveillance data collected
in 1996 in Atlanta, Georgia. That study found a positive
association between SES and risk of ASD based on ascertainment
through health care providers, but not based on ascertainment
only from school records . Bhasin and Schendel suggested that
this difference by the type of information source might indicate
selection bias because in the U.S. access to school-based services is
universal whereas access to healthcare is not. In contrast to the
Bhasin and Schendel study, our study included a larger number of
children with autism identified only from school records (635 vs.
246), was restricted to 8-year-old children (an age at which
children with autism are more likely to have been identified,
whereas the age range of the Bhasin and Schendel study was 3
through 10 years), and covered the 2002 and 2004 study years
(versus 1996, a time when schools were just beginning to use the
autism category). Our finding of an SES gradient in autism
prevalence regardless of source of information (health vs. school)
was not consistent with the hypothesis that the frequency of
children with autism identified only through school sources is
constant across SES categories. This finding suggests that the
observed SES gradient in autism prevalence may not be due
entirely to ascertainment bias.
Epidemiologists long have suspected that associations between
autism and SES are a result of ascertainment bias, on the
assumption that as parental education and wealth increase, the
chance that a child with autism will receive an accurate diagnosis
also increases . A number of investigators and recent reviews
of the epidemiology of autism have concluded that any
association observed between autism risk and SES has been
due to such bias [26,27,30,31]. The present population-based
study of U.S. surveillance data provides some support for this
conclusion by showing a stronger SES gradient in prevalence
among children with ASD with than without a pre-existing ASD
diagnosis. In a previous analysis of ADDM Network data for
children identified by the surveillance system as meeting
diagnostic criteria for ASD, Mandell and colleagues found non-
Hispanic white and Asian children to be more likely than non-
Hispanic black and Hispanic children to have a pre-existing ASD
diagnosis . In addition to biased ascertainment resulting from
those with higher SES having greater access to diagnostic
services, it is possible that ‘‘diagnostic bias’’ on the part of
clinicians might contribute to ascertainment bias. In a study
designed to identify possible diagnostic bias, Cuccaro and
colleagues found evidence that clinicians might be more likely
to assign autism diagnoses to case vignettes of children with
developmental disabilities if the children’s backgrounds were
described as higher SES rather than lower SES . At the same
time, our observation of a significant, if weaker, SES gradient in
ASD prevalence when the results are restricted to cases without a
pre-existing diagnosis points to the possibility that factors other
than ascertainment bias might also contribute to the positive
association between ASD prevalence and SES.
A possible reason for the lack of consistency between our
findings and those of epidemiologic studies conducted in Denmark
 and Sweden , and which found no association between
Table 2. Socioeconomic Indicators for ASD Cases and the
Population of 8-Year-Old Children in the Surveillance Area.
Population of 8-Year-Old
Children Residing in the
Overall% Living in a
% of Adults with
MHI (US$) 50 114*42 898
% Living in a
% of Adults with
MHI (US$) 56 273* 51 890
% Living in a
% of Adults with
MHI (US$) 38 833* 31 339
Hispanic% Living in a
% of Adults with
MHI (US$) 40 910*36 075
Asian% Living in a
% of Adults with
MHI (US$) 59 892* 50 595
aBased on 2000 Census data.
bPoverty areas include census block groups in which more than 20% of families
with children have incomes below the poverty level .
*p,0.05, comparing ASD cases to population of 8-year-old children residing in
the surveillance area.
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autism risk and SES, might be that the Scandinavian countries
have less socioeconomic diversity and more equitable access to
services than the U.S. population. The lack of consistency also
could be due to the small number of cases and limited statistical
power in the Scandinavian studies, and differences in study
An important advantage of this study was that it was large
enough to allow stratified analyses of the association between
autism risk and SES among demographic and patient sub-
groups. It is notable that the SES gradient is observed in all four
racial/ethnic strata. Also notable is that, although the overall
ASD prevalence was higher among non-Hispanic White and
Asian children than among non-Hispanic Black or African-
America and Hispanic children, when the results were stratified
by SES, we saw that the racial/ethnic differences in prevalence
varied by SES (Table 3). The lower prevalence among non-
Hispanic Black or African-American and Hispanic children was
seen only in the low SES category, and the fact that more non-
Hispanic Black or African-American and Hispanic children live
in poverty contributed to the lower overall prevalence among
The only subgroup in which the SES gradient was not observed
was the subgroup with co-occurring autism and intellectual
disability (Table 4). The lack of an SES association among this
subgroup might have been due to counter-associations because
intellectual disabilities among children overall are inversely
associated with SES . It could also be an indication of
ascertainment bias if children with intellectual disabilities are more
Table 3. Prevalence (95% CIa) of ASD per 1,000 8-Year-Olds and Ratios of ASD Prevalence by SESb, Stratified by Race/Ethnicityc.
Non-Hispanic WhiteNon-Hispanic BlackHispanicAsian
Prevalence (95% CI) Overall6.9 (6.6, 7.3) 5.7 (5.3, 6.0)5.1 (4.5, 5.7) 7.6 (6.1, 9.1)
Low SES 5.7 (5.0, 6.4)4.1 (3.7, 4.6) 4.0 (3.2, 4.8) 3.9 (1.6, 6.3)
Medium SES 6.5 (6.0, 7.0)6.8 (6.0, 7.6)5.4 (4.3, 6.5)6.0 (3.7, 8.3)
High SES 7.7 (7.2, 8.1) 9.8 (8.4, 11.2)7.5 (5.9, 9.2)10.7 (7.9, 13.4)
trend test p-value
Prevalence Ratio (95% CI) Low SES0.88 (0.77, 1.02)0.61 (0.52, 0.70)0.74 (0.56, 0.97)0.66 (0.33, 1.34)
High SES1.18 (1.08, 1.30)1.44 (1.21, 1.71)1.40 (1.04, 1.89)1.80 (1.13, 2.85)
bSocioeconomic Status (SES) is indicated by the percentage of families with incomes above the federal poverty level who had children in the census block group of the
index child, divided into population SES tertiles.
cThe following differences in prevalence between ethnic group were statistically significant at p,0.05:
Overall: Non-Hispanic White versus Non-Hispanic Black; Non-Hispanic White versus Hispanic; Non-Hispanic Black versus Asian; and Hispanic versus Asian. In addition, the
overall prevalence of ASD differs at p,0.05 across race/ethnic groups.
Low SES: Non-Hispanic White versus Non-Hispanic Black; Non-Hispanic White versus Hispanic; and Non_Hispanic White versus Asian. In addition, within the low SES
stratum, the prevalence of ASD differs at p,0.05 across race/ethnic groups.
Medium SES: Non-Hispanic Black versus Hispanic.
High SES: Non-Hispanic White versus Non-Hispanic Black; Non-Hispanic White versus Asian; and Hispanic versus Asian. In addition, within the high SES stratum, the
prevalence of ASD differs at p,0.05 across race/ethnic groups.
Figure 1. Prevalence per 10001of ASD by three SES indicators based on census block group of residence.1Thin bars indicate 95%
confidence intervals. Within each SES indicator, both the trend test and x2tests were significant at p,0.0001.2MHI refers to median household
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likely than other children to be evaluated for developmental
disorders including autism.
An important limitation of this study was that the ADDM
Network surveillance system relies on information for children
who have access to diagnostic services for developmental
disabilities. We could not rule out the possibility that the quality
and quantity of evaluations and information available for case
ascertainment might have varied by SES. We looked for evidence
of this by examining the number of evaluations per child with ASD
recorded in the ADDM Network surveillance system, reasoning
that if the higher prevalence of ASD among children of higher
SES was due to increased access to diagnostic services, high SES
might be associated with a higher number of diagnostic
evaluations per child. However, we found no association between
the number of evaluations per child and SES. We also examined
the mean ages at diagnosis by SES and found that children of high
SES received an ASD diagnosis at an average age of 58.0 months,
1.1 month earlier than those of middle SES (p=0.2838) and 2.7
months earlier than those of low SES (p,0.0272). This modest
difference in age at identification may indicate that diagnostic bias
contributes to the SES gradient in ASD prevalence in some
studies, though not necessarily in the present study which relied on
surveillance at the age of eight years and included cases with and
without a pre-existing ASD diagnosis.
Another limitation of this study was the reliance on area-level
measures of SES that might not have served as accurate proxies for
the SES of individuals or specific families or households. Though
perhaps not ideal, these measures have been shown to be
reasonable proxies for individual-level SES and have the
advantage of serving as indicators of the social and economic
contexts in which children live but without introducing ecological
fallacy . Another limitation of the SES indicators used in this
study is that they were based on residential address at the age of
eight years rather than at the age of first diagnosis (for children
Table 4. Stratified Results: ASD and SESaPrevalence Ratios (95% CIb), Stratified by Gender, Pre-existing Diagnosis, Co-occurring
Intellectual Disability, Developmental Regression, and Data Source.
ASD Cases Prevalence Ratios (95% CI)
Living in a Poverty
Areac(%) Low SESMedium SESHigh SES p-value x2
Total 3680 (100) 16.80.70 (0.64, 0.76)Reference 1.25 (1.16,
GenderBoys2994 (81.4)16.30.67 (0.60, 0.74)Reference 1.23 (1.13,
Girls 686 (18.6) 19.00.82 (0.67, 1.01)Reference 1.32 (1.11,
Pre-existing ASD DiagnosisNone1244 (33.8) 19.10.78 (0.67, 0.90)Reference 1.09 (0.94,
ASD (all)2436 (66.2)15.70.65 (0.58, 0.73) Reference 1.35 (1.23,
Autistic Disorder 1411 (38.3)17.80.73 (0.63, 0.84)Reference 1.24 (1.10,
ASD Unspecified1025 (27.9)12.80.54 (0.45, 0.65)Reference 1.51 (1.31,
Present1179 (32.0) 22.80.86 (0.75, 1.00) Reference0.93 (0.81,
Absent1568 (42.6)14.00.52 (0.45, 0.61)Reference1.39 (1.25,
Unknown933 (25.4)17.60.76 (0.64, 0.92)Reference1.47 (0.27,
Developmental Regression Present 677 (18.4)16.40.67 (0.54, 0.82)Reference 1.22 (1.03,
Absent or unknown 3003 (81.6)16.90.70 (0.64, 0.77)Reference 1.26 (1.16,
Health & School1652 (60.9)15.90.65 (0.55, 0.76)Reference 1.30 (1.14,
Health Only 426 (15.7)15.10.88 (0.67, 1.14)Reference 1.21 (0.95,
School Only635 (23.4) 16.00.75 (0.64, 0.87)Reference 1.33 (1.17,
aSES indicator is % above poverty level based on United States Census 2000 block group data.
cPoverty areas include United States Census 2000 block groups in which more than 20% of families with children have incomes below the poverty level . Percent of
cases living in poverty is 20.4% in sites accessing only data from healthcare sources.
dIn addition to the x2test of the SES gradient within the stratum of children with no pre-existing ASD diagnosis, a separate x2test of the difference in the SES gradient
for children with and without a pre-existing ASD diagnosis also resulted in a p-value ,0.0001.
eRestricted to sites with access to school records (n=2713), including those in Arkansas, Arizona, Colorado, Georgia, Maryland, North Carolina, New Jersey, South
Socioeconomic Status & Autism
PLoS ONE | www.plosone.org6 July 2010 | Volume 5 | Issue 7 | e11551
with a pre-existing ASD diagnosis) or other time points, which
may have allowed evaluation of whether families of children with
ASD migrate to higher SES neighborhoods to improve their access
to services, as suggested by Palmer and colleagues . A further
limitation of our use of aggregate census data for denominator or
comparison group data in this study was that we were unable to
perform multivariable analyses to evaluate and control for
confounding effects of variables such as parental age and other
perinatal risk factors .
If the SES gradient found in this study is due only to
ascertainment bias, this would imply that there are significant
SES disparities in access to diagnostic and other services for
children with autism in communities across the United States. It
also would imply that the current estimate of ASD prevalence
might be substantially undercounted, with children of low and
medium SES being under-identified and underserved relative to
those with high SES.
The presence of an attenuated but still statistically significant SES
gradient when the analysis was restricted to children with no pre-
existing ASDdiagnosis suggeststheoverall SESgradient maynot be
entirely due to ascertainment bias and points to the possibility that
factors associated with socioeconomic advantage might be causally
associated with the risk for developing autism. The types of
exposures that might merit consideration in future research could
include a wide range of factors, from physical or social
environmental factors to which children living in more advantaged
environments might have higher exposures, to immunological
factors (such as that suggested by the ‘‘hygiene hypothesis’’ ) or
other biological factors (for example, those associated with parental
age). It is also possible that the SES association demonstrated in this
study was a result of confounding by unknown factors associated
with both high SES and susceptibility to ASD, or to effect
modification. Further research to identify such factors could lead
to advances in our understanding of the etiology and identification
of autism and to possible interventions.
The findings and conclusions in this report are those of the authors and do
not necessarily represent the official position of the Centers for Disease and
Control and Prevention.
Conceived and designed the experiments: MD MJM FJM SEL CD JSN
RSK JPM LAS. Performed the experiments: MD MJM SEL CD JSN RSK
JPM LAS. Analyzed the data: MD MJM FJM SEL CD JSN RSK JPM
LAS. Contributed reagents/materials/analysis tools: MJM FJM SEL CD
LAS. Wrote the paper: MD MJM FJM. Performed the literature review:
MD. Coordinated input from co-authors: MD. Interpreted the results: MD
MJM FJM SEL CD JSN RSK JPM LAS. Assisted with data collection:
MJM FJM SEL CD JSN RSK JPM LAS. Creation of analytic data files:
MJM. Drafting and editing the paper: MJM. Approved the final draft of
the paper: MJM FJM SEL CD JSN RSK JPM LAS. Contributed to the
design of the study: MJM FJM SEL CD JSN RSK JPM LAS. Revision and
editing of the paper: FJM SEL CD JSN RSK JPM LAS.
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