Autism: A Disease of the Rich

[Guest guest]

July 16, 2010, 2:00 pm

Autism: A Disease of the Rich?

By FREAKONOMICS

The higher rates of diagnosed autism among the wealthy has long been thought to

be a result of higher rates of diagnosis (or “diagnostic ascertainment bias”) –

i.e., wealthier families having better access to those who diagnose autism.

However, a new paper argues that the disease itself might actually be more

common at the higher end of the income spectrum. The paper relied on

“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

data.” Within all ethnic groups, wealthier parents were more likely to have

autistic children, and the pattern held for undiagnosed autistic children as

well. Neuroskeptic hypothesizes that paternal age may be partially responsible

for the disparity. (HT: Marginal Revolution)

http://freakonomics.blogs.nytimes.com/2010/07/16/autism-a-disease-of-the-rich/

[Guest guest]

The full study on this is here

http://www.plosone.org/article/info:doi/10.1371/journal.pone.0011551

Socioeconomic Inequality in the Prevalence of Autism Spectrum Disorder: Evidence

from a U.S. Cross-Sectional Study

Maureen S. Durkin1,2,3*, J. Maenner1,3, F. Meaney4, E. Levy5,

Carolyn DiGuiseppi6, Joyce S. 7, S. Kirby8, A.

Pinto-9, A. Schieve10

1 Department of Population Health Sciences, University of Wisconsin School of

Medicine and Public Health, Madison, Wisconsin, United States of America, 2

Department of Pediatrics, University of Wisconsin School of Medicine and Public

Health, Madison, Wisconsin, United States of America, 3 Waisman Center,

University of Wisconsin-Madison, Madison, Wisconsin, United States of America, 4

Department of Pediatrics, University of Arizona Health Sciences Center, Tucson,

Arizona, United States of America, 5 Department of Pediatrics, University of

Pennsylvania, Philadelphia, Pennsylvania, United States of America, 6 Department

of Epidemiology, Colorado School of Public Health, University of Colorado

Denver, Aurora, Colorado, United States of America, 7 Division of Biostatistics

and Epidemiology, Departments of Neurosciences and Medicine, Medical University

of South Carolina, ton, South Carolina, United States of America, 8

Department of Community and Family Health, University of South Florida, Tampa,

Florida, United States of America, 9 School of Nursing and School of Medicine,

University of Pennsylvania, Philadelphia, Pennsylvania, United States of

America, 10 National Center on Birth Defects and Developmental Disabilities,

Centers for Disease Control and Prevention, Atlanta, Georgia, United States of

America

Abstract Top

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), ative 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. 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: mdurkin@...

Introduction Top

Population indicators of socioeconomic status (SES), such as household wealth or

income and parental education and occupation, are strongly correlated with the

health and development of children [1]. 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 disorders. In the earliest clinical descriptions of

children with autism, Kanner noted a preponderance of " highly intelligent

parents " [4]. 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 [17] and county median household income [18]. 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 [29].

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 " [23] and " a knowledgeable and determined parent of an autistic child

[is] more likely to obtain an informed diagnosis " [24]. To evaluate the role of

biased ascertainment, Wing [24] called for population-based studies large enough

to allow stratified analyses and evaluation of socioeconomic differences among

subgroups.

In a previous analysis [16] 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 sources.

Methods Top

Study Design and Data Sources

We implemented a population-based cross-sectional design in which data from 12

participating ADDM Network sites were analyzed [32]. 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 data.

Study Sample

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, land, 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 [33]. 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).

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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.

doi:10.1371/journal.pone.0011551.t001

Case Definition

Autism spectrum disorder (ASD) refers to a group of neurodevelopmental 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 [34]. Case status 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 [34]. 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 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 Prevalence

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 that had

incomes above the federal poverty level (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 [36], 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.

Statistical Analysis

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 & #967;2 tests and 95% confidence intervals

based on a Poisson distribution and log-link function [37]. 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 MHI.

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 & #967;2 tests 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 [36].

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 [38].

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Table 2. Socioeconomic Indicators for ASD Cases and the Population of 8-Year-Old

Children in the Surveillance Area.

doi:10.1371/journal.pone.0011551.t002

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Table 3. Prevalence (95% CIa) of ASD per 1,000 8-Year-Olds and Ratios of ASD

Prevalence by SESb, Stratified by Race/Ethnicityc.

doi:10.1371/journal.pone.0011551.t003

Results Top

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).

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Figure 1. Prevalence per 10001 of 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 & #967;2 tests were significant at p<0.0001. 2MHI refers to

median household income.

doi:10.1371/journal.pone.0011551.g001

Table 4 presents additional stratified results showing a significant trend

toward increasing ASD prevalence with increasing 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,

& #967;2 test 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).

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Table 4. Stratified Results: ASD and SESa Prevalence Ratios (95% CIb),

Stratified by Gender, Pre-existing Diagnosis, Co-occurring Intellectual

Disability, Developmental Regression, and Data Source.

doi:10.1371/journal.pone.0011551.t004

Discussion Top

This surveillance-based study showed increasing ASD prevalence 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 [13]. 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

[14]. 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 [24]. 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 [39]. 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 [40]. 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 [26] and Sweden [27], and which found

no association between 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 designs.

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 subgroups. 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 these groups.

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 [3]. It could also be an indication of ascertainment bias if

children with intellectual disabilities are more 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 [36]. 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 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 [17]. 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 [41].

Conclusion

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 ASD diagnosis

suggests the overall SES gradient may not 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 " [42]) 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.

Acknowledgments Top

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.

Author Contributions Top

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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