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Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools

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Title: Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools
Language: English
Authors: Paul L. Morgan (ORCID 0000-0001-9347-6486), Eric Hengyu Hu (ORCID 0000-0002-2113-3940)
Source: Autism: The International Journal of Research and Practice. 2026 30(6):1488-1503.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 16
Publication Date: 2026
Sponsoring Agency: Institute of Education Sciences (ED)
Contract Number: R324A220271
Document Type: Journal Articles
Reports - Research
Education Level: Elementary Education
Grade 4
Intermediate Grades
Descriptors: Autism Spectrum Disorders, Elementary Schools, Elementary School Students, Student Characteristics, Disproportionate Representation, Grade 4, National Competency Tests, Race, Ethnicity, Reading Achievement, Sex, Family Income, Lunch Programs, Bilingual Students, Multilingualism, English Learners, Disability Identification
Assessment and Survey Identifiers: National Assessment of Educational Progress
DOI: 10.1177/13623613261434432
ISSN: 1362-3613
1461-7005
Abstract: Whether and to what extent sociodemographic disparities in school-based autism identification have been occurring in U.S. elementary schools is currently unclear. We investigated for disparities attributable to race, ethnicity, biological sex, family income, and language use by analyzing repeated cross-sectional data collected on very large samples of U.S. fourth graders participating in the National Assessment of Educational Progress from 2003 to 2022 (ns = 103,150-205,860). Multivariable logistic regression models accounting for potential confounds including student-level academic achievement and school-level resources repeatedly indicated that students of color, females, students from low-income families, and multilingual learners (MLs) are less likely to be identified with autism while attending U.S. elementary schools. These disparities have been largely stable over time, particularly for Black students, females, and MLs. Health and educational policies that ensure equal access to autism supports and services in U.S. elementary schools including by students from historically marginalized communities are warranted.
Abstractor: As Provided
IES Funded: Yes
Entry Date: 2026
Accession Number: EJ1506651
Database: ERIC
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  Value: <anid>AN0193858191;f9d01jun.26;2026May21.02:15;v2.2.500</anid> <title id="AN0193858191-1">Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools </title> <p>Whether and to what extent sociodemographic disparities in school-based autism identification have been occurring in U.S. elementary schools is currently unclear. We investigated for disparities attributable to race, ethnicity, biological sex, family income, and language use by analyzing repeated cross-sectional data collected on very large samples of U.S. fourth graders participating in the National Assessment of Educational Progress from 2003 to 2022 (n s = 103,150–205,860). Multivariable logistic regression models accounting for potential confounds including student-level academic achievement and school-level resources repeatedly indicated that students of color, females, students from low-income families, and multilingual learners (MLs) are less likely to be identified with autism while attending U.S. elementary schools. These disparities have been largely stable over time, particularly for Black students, females, and MLs. Health and educational policies that ensure equal access to autism supports and services in U.S. elementary schools including by students from historically marginalized communities are warranted.</p> <p>Plain Language Summary: Students of Color, Females, Students from Low-income Families, and Multilingual Learners Are Less Likely to be Identified with Autism While Attending U.S. Elementary Schools Whether students who are Black or Hispanic, females, from low-income families, or who are multilingual learners have been less likely to be identified with autism while attending U.S. elementary schools is currently unclear. Prior work reports conflicting findings and has often been unable to approximate contrasts between similarly situated students including those displaying the same levels of academic achievement and who are attending the same schools. Such contrasts of similarly situated students are necessary to better evaluate for the possibility of differential treatment due to biased or discriminatory practices. We used statistical methods to account for potential alternative explanatory factors (e.g. differences in family income, language use, or academic achievement) to better approximate contrasts between similarly situated students. Doing so provides stronger evidence of disparities in school-based autism identification attributable to race, ethnicity, biological sex, family income, and language use and not instead to alternative explanatory factors. To investigate how these disparities have changed across time, we analyzed very large cross-sectional samples of fourth-grade students from 2003 to 2022. These analyses repeatedly indicated that students who are White, boys, those from higher-income families, or students who are English-speaking are more likely to be identified with autism than students of color, females, those from low-income families, or students who are multilingual learners including among those who are displaying similar levels of academic achievement and who are attending the same schools. Although autism prevalence rates have increased for students from historically marginalized communities, students from these communities are still less likely to be identified with autism while attending U.S. elementary schools. Efforts are needed to ensure equal access to autism services and supports among students attending U.S. elementary schools.</p> <p>Keywords: autism; disparities; elementary school; multilingual learners; students of color</p> <p>Students of color, females, those from low-income families, and students from non-English-speaking households are reported to be less likely to be using treatments and services for autism including in U.S. health care settings ([<reflink idref="bib5" id="ref1">5</reflink>]; [<reflink idref="bib11" id="ref2">11</reflink>]; [<reflink idref="bib51" id="ref3">51</reflink>]; [<reflink idref="bib52" id="ref4">52</reflink>]). These sociodemographic disparities have been stable over time ([<reflink idref="bib26" id="ref5">26</reflink>]). Black, Hispanic, and Asian parents are more likely to report that their children have unmet needs for mental health conditions ([<reflink idref="bib7" id="ref6">7</reflink>]).</p> <p>Yet to what extent sociodemographic disparities are occurring in school-based autism identification and resulting service access is currently unclear ([<reflink idref="bib5" id="ref7">5</reflink>]; [<reflink idref="bib11" id="ref8">11</reflink>]; [<reflink idref="bib28" id="ref9">28</reflink>]; [<reflink idref="bib51" id="ref10">51</reflink>]; [<reflink idref="bib52" id="ref11">52</reflink>]; [<reflink idref="bib54" id="ref12">54</reflink>]). The currently available knowledge base on whether these sociodemographic disparities are occurring is "small" ([<reflink idref="bib52" id="ref13">52</reflink>], p. 42) and provides "insufficient evidence" to indicate whether students of color are less likely to be accessing services ([<reflink idref="bib11" id="ref14">11</reflink>], p. 1324). Whether disparities attributable to biological sex, family income, and language use are occurring is also unclear ([<reflink idref="bib2" id="ref15">2</reflink>]).</p> <hd id="AN0193858191-2">Conflicting Evidence of Sociodemographic Disparities in Autism Identification in U.S. Element...</hd> <p>The limited number of studies currently available report conflicting findings ([<reflink idref="bib54" id="ref16">54</reflink>]). For example, some studies analyzing nationally representative samples and adjusting for possible confounds find that Black and Hispanic students are more likely to access school-based services for autism ([<reflink idref="bib4" id="ref17">4</reflink>]; [<reflink idref="bib3" id="ref18">3</reflink>]). Narendorf and colleagues (2011) reported that the odds that Black student with autism used services at school were about four times greater than White students with autism in analyses of nationally representative longitudinal sample of students aged 13–17 receiving special education services in analyses adjusting for student- and family-level confounds including social skills. Yet other studies find that students of color, those from low-income families, and who are language minorities are less likely to access autism services prior to school entry in analyses adjusting for individual-level confounds ([<reflink idref="bib28" id="ref19">28</reflink>]; [<reflink idref="bib44" id="ref20">44</reflink>]). Analyses of two nationally representative cross-sectional samples of students find that students who are Black, Hispanic, female, from low-income families, and who are multilingual learners (MLs) are less likely to be identified with autism while attending U.S. elementary and middle schools in analyses adjusting for student- and school-level confounds ([<reflink idref="bib31" id="ref21">31</reflink>]).</p> <p>Methodological limitations in the currently available work may help to explain these conflicting findings. Most prior work examining sociodemographic disparities in autism identification and access to services has reported findings based on convenience or state-specific samples that may not be generalizable to the diverse U.S. school population ([<reflink idref="bib28" id="ref22">28</reflink>]; [<reflink idref="bib53" id="ref23">53</reflink>]; [<reflink idref="bib63" id="ref24">63</reflink>]). Prior work has also been unable to contrast similarly situated students including those displaying similar levels of academic achievement ([<reflink idref="bib2" id="ref25">2</reflink>]). Contrasting similarly situated students better establishes whether sociodemographic disparities in disability identification are occurring ([<reflink idref="bib22" id="ref26">22</reflink>]; [<reflink idref="bib37" id="ref27">37</reflink>]; [<reflink idref="bib58" id="ref28">58</reflink>]). The available studies also vary in methods, samples, and measures.</p> <p>It is currently unclear whether and to what extent sociodemographic disparities in autism identification in U.S. elementary schools have been stable across time. Such research is especially timely. Autism prevalence rates for Black, Hispanic, and Asian students now exceed those for White students ([<reflink idref="bib17" id="ref29">17</reflink>]; [<reflink idref="bib40" id="ref30">40</reflink>]; [<reflink idref="bib43" id="ref31">43</reflink>]). The increased prevalence rates for students from historically marginalized communities may be resulting in corresponding over-time changes in the size or direction of sociodemographic disparities in autism identification.</p> <hd id="AN0193858191-3">Mechanisms Possibly Resulting in Sociodemographic Disparities in Autism Identification in U.S...</hd> <p>Sociodemographic disparities in autism identification attributable to race, ethnicity, biological sex, family income, and language use may be occurring in U.S. elementary schools due to overlapping mechanisms operating across families, schools, communities, and society. Possible mechanisms include limited access to culturally sensitive health care providers and teachers, receipt of fewer screenings and referrals including due to long wait lists, financial barriers, and dismissive or indifferent providers, experiences of racism and stereotypes in health care and educational settings, stigma in historically marginalized communities, and limited English proficiency ([<reflink idref="bib1" id="ref32">1</reflink>]; [<reflink idref="bib10" id="ref33">10</reflink>]; [<reflink idref="bib15" id="ref34">15</reflink>]; [<reflink idref="bib19" id="ref35">19</reflink>]; [<reflink idref="bib24" id="ref36">24</reflink>]; [<reflink idref="bib31" id="ref37">31</reflink>]; [<reflink idref="bib46" id="ref38">46</reflink>]; [<reflink idref="bib51" id="ref39">51</reflink>]; [<reflink idref="bib60" id="ref40">60</reflink>]). Additional possible mechanisms include less access to evidence-based information about autism as a developmental disability and inaccessible due process materials reporting on disability evaluation, eligibility, and treatment procedures used in U.S. elementary schools ([<reflink idref="bib9" id="ref41">9</reflink>]; [<reflink idref="bib18" id="ref42">18</reflink>]; [<reflink idref="bib23" id="ref43">23</reflink>]; [<reflink idref="bib34" id="ref44">34</reflink>]; [<reflink idref="bib46" id="ref45">46</reflink>]; [<reflink idref="bib64" id="ref46">64</reflink>]). Alternatively, students and families from historically marginalized communities may be more likely to access autism services in U.S. elementary schools due to encountering relatively fewer barriers in educational than in health care settings as well as a higher likelihood of attending schools where most students also are from historically marginalized communities ([<reflink idref="bib4" id="ref47">4</reflink>]).</p> <p>Establishing whether and to what extent sociodemographic disparities in autism identification are occurring in U.S. elementary schools should help inform efforts to ensure equal health and educational opportunities for students including those from historically marginalized communities ([<reflink idref="bib6" id="ref48">6</reflink>]). Schools are the most common setting for accessing services by children and adolescents ([<reflink idref="bib13" id="ref49">13</reflink>]) including those with autism ([<reflink idref="bib29" id="ref50">29</reflink>]; [<reflink idref="bib55" id="ref51">55</reflink>]). School-based services academically benefit students with disabilities including those of color ([<reflink idref="bib41" id="ref52">41</reflink>]; Woods et al., 2025). Teachers display greater understanding of classroom behavioral difficulties including in social communication when informed that students have an autism diagnosis ([<reflink idref="bib35" id="ref53">35</reflink>]). Students with developmental disabilities who receive greater amounts of services display greater adaptive behaviors as they age ([<reflink idref="bib61" id="ref54">61</reflink>]). Yet Black and Hispanic adults with autism display relatively greater physical and mental health needs than White adults with autism, suggesting the possibility of earlier difficulties in accessing similar levels of disability-related services and supports ([<reflink idref="bib48" id="ref55">48</reflink>]).</p> <hd id="AN0193858191-4">Study's Purpose and Research Questions</hd> <p>We investigated for sociodemographic disparities in autism identification attributable to race, ethnicity, and the additional sociodemographic characteristics of biological sex, family income, and language through analyses of repeated cross-sectional data from nationally representative samples of U.S. fourth-grade students assessed across 2003 to 2022. To better investigate for sociodemographic disparities attributable to bias, discrimination, or other impediments operating across the U.S. health and educational systems ([<reflink idref="bib22" id="ref56">22</reflink>]; U.S. Department of Education, 2016), we statistically controlled for potential confounds including directly assessed academic achievement as an indicator of the clinical appropriateness of school-based autism identification. We also used school fixed effects to contrast the likelihood of autism identification among students attending the same U.S. elementary schools. We investigated the following research questions:</p> <p></p> <ulist> <item> To what extent are there racial and ethnic differences in the likelihood of autism identification in U.S. elementary schools? Have these racial and ethnic differences changed across 2003 to 2022? We hypothesized that racial and ethnic differences have been occurring over time in U.S. elementary schools ([<reflink idref="bib48" id="ref57">48</reflink>]).</item> <p></p> <item> To what extent are racial, ethnic, and additional sociodemographic disparities occurring in the likelihood of autism identification in U.S. elementary schools among similarly situated students? Have these disparities changed across 2003 to 2022? We hypothesized that racial, ethnic, and additional sociodemographic disparities in the likelihood of autism identification in U.S. elementary schools would be evident in adjusted analyses ([<reflink idref="bib28" id="ref58">28</reflink>]; [<reflink idref="bib31" id="ref59">31</reflink>]; [<reflink idref="bib52" id="ref60">52</reflink>]). We hypothesized that these disparities have been largely stable across time ([<reflink idref="bib26" id="ref61">26</reflink>]; [<reflink idref="bib31" id="ref62">31</reflink>]).</item> </ulist> <hd id="AN0193858191-5">Methods</hd> <p></p> <hd id="AN0193858191-6">Datasets</hd> <p>We analyzed student-level data from repeated cross-sectional samples of U.S. fourth graders participating in the National Assessment of Educational Progress (NAEP) reading assessments from 2003 to 2022 (https://nces.ed.gov/nationsreportcard/). Detailed information on the NAEP's sample design is available online (https://nces.ed.gov/nationsreportcard/tdw/sample%5fdesign/). On average, each assessment year included 30–60 students from about 100 grade-eligible schools per jurisdiction ([<reflink idref="bib38" id="ref63">38</reflink>]). Our final analytic samples ranged from 103,150 to 205,860 students across 2003–2022. We only included students with valid reading achievement scores, which represented over 95% of each year's sample. There were no missing data on the study's other variables. Students with disabilities and MLs were provided with testing accommodations including extended time, small-group administration, bilingual glossaries, and/or directions read aloud in Spanish (for a full description of the NAEP's testing accommodations, see https://nces.ed.gov/nationsreportcard/about/accom%5ftable.aspx). Exclusion rates for students with disabilities and/or MLs ranged from 5% to 6% in earlier years (2003–2009) and declined to about 2% beginning in 2015, reflecting increased inclusion policies (For additional detail, see: https://nces.ed.gov/nationsreportcard/about/inclusion.aspx#rates).</p> <hd id="AN0193858191-7">Measures</hd> <p></p> <hd id="AN0193858191-8">Autism Identification</hd> <p>School staff reported whether students were receiving disability services and supports, as indicated by an Individualized Education Program (IEP) or equivalent document (e.g. a 504 plan) on file. School staff also reported the primary disability condition listed on these documents. We used this information to indicate whether students had been identified with autism and were receiving school-based supports and services for this specific condition.</p> <hd id="AN0193858191-9">Race or Ethnicity</hd> <p>Student race and ethnicity information was primarily obtained from school administrative records. For a small proportion of students (<0.5%) whose information was unavailable from school records, self-reported data from the student questionnaire were used. For the very small number of remaining cases (<0.05%), NAEP used a hot-deck imputation procedure to assign race/ethnicity based on similar respondents. We used the following racial and ethnic categories to ensure consistent categorization across 2003 to 2022 and to account for small sample sizes: (a) White, non-Hispanic (the analytical reference group); (b) Black or African American, non-Hispanic; (c) Hispanic, any race; and (d) Other race or ethnicity, non-Hispanic.</p> <hd id="AN0193858191-10">Reading Achievement</hd> <p>Students completed well-designed measures of general reading achievement ([<reflink idref="bib38" id="ref64">38</reflink>]). The NAEP's reading assessments use item response theory (IRT) models to ensure accurate proficiency estimates and display high interrater reliability and strong validity ([<reflink idref="bib42" id="ref65">42</reflink>]). NAEP reports plausible values to limit measurement error and improve the accuracy of group-level estimates. The plausible values are multiple imputed estimates of a student's true reading ability ([<reflink idref="bib42" id="ref66">42</reflink>]). From 2003 to 2011, NAEP provided five plausible values per student. Beginning in 2013, NAEP provided 20 plausible values per student. To maintain consistency across years, we replicated each of the five plausible values three times to generate 20 plausible scores per student for the earlier years. As recommended by NAEP statisticians ([<reflink idref="bib42" id="ref67">42</reflink>]), we treated these plausible values as imputed data and used Rubin's multiple imputation procedures to estimate the coefficients.</p> <p>We used scores on the reading assessments as an indicator of academic achievement and the clinical appropriateness of receiving specialized services ([<reflink idref="bib22" id="ref68">22</reflink>]). Doing so better evaluated for evidence of differential treatment attributable to sociodemographic characteristics by approximating contrasts between similarly situated students (U.S. Department of Education, 2016). Students are only eligible for autism services in U.S. elementary, middle, and high schools if the condition is adversely affecting their educational performance ([<reflink idref="bib21" id="ref69">21</reflink>]; [<reflink idref="bib45" id="ref70">45</reflink>]). Reading achievement is strongly associated with whether students receive services for disabilities in U.S. schools including for autism ([<reflink idref="bib31" id="ref71">31</reflink>]). Students with autism are more likely to display lower levels of reading or mathematics achievement ([<reflink idref="bib12" id="ref72">12</reflink>]). Adjusting for reading achievement or some other indicator of clinical need is necessary in order to evaluate for sociodemographic disparities including in autism identification ([<reflink idref="bib22" id="ref73">22</reflink>]) and better evaluates for the possibility of differential treatment ([<reflink idref="bib37" id="ref74">37</reflink>]; U.S. Department of Education, 2016). However, doing so may also attenuate estimated disparities. This is because the explanatory factors of reading achievement are multivariable and may include structural disadvantages.</p> <hd id="AN0193858191-11">Additional Sociodemographic Characteristics</hd> <p>We also examined for disparities attributable to biological sex, family income as indicated by eligibility for free or reduced-price lunch, and ML status. These additional sociodemographic characteristics were obtained from school records. Students self-reported their biological sex, which was then verified using school records. NAEP classifies students as English Learners (ELs) if schools identify them as being in the process of acquiring English language skills and receiving English language development services (https://<ulink href="http://www.nationsreportcard.gov/focus%5fon%5fnaep/student%5fgroups/#/english-learners">www.nationsreportcard.gov/focus%5fon%5fnaep/student%5fgroups/#/english-learners</ulink>). Prior to the 2005 assessment, NAEP documentation used the term Limited English Proficient (LEP) for this group. In this study, we refer to these students as MLs to reflect a more inclusive, asset-oriented perspective.</p> <hd id="AN0193858191-12">Analyses</hd> <p>We estimated the likelihood of school-based autism identification using multivariable logistic regression models. We estimated four sequential models. Model 1 included only race or ethnicity. Model 2 added student-level reading achievement as statistical control for academic achievement. Contrasting students on materially relevant factors including academic achievement better evaluates for the possibility of biased or discriminatory practices ([<reflink idref="bib22" id="ref75">22</reflink>]; [<reflink idref="bib37" id="ref76">37</reflink>]; U.S. Department of Education, 2016). Model 3 added biological sex, family income, and ML status as additional sociodemographic characteristics. Doing so allowed us to evaluate for disparities attributable to these additional sociodemographic characteristics as well as to adjust for student- and family-level potential confounds of the racial and ethnic disparities (e.g. language use, family income). Model 4 used school fixed effects. Doing so allowed us to control for school-level potential confounds by contrasting only those students attending the same U.S. elementary schools.</p> <p>We used Stata 18.0, sampling weights, and cluster-robust standard errors when analyzing the datasets. We standardized the reading achievement scores within each assessment year to allow for interpretation in <emph>SD</emph> units. Online Supplemental Table S1 reports the number of students with autism by their race or ethnicity across the study's samples. Online Supplemental Table S2 reports a robustness check using mathematics achievement instead of reading achievement as the statistical control for student-level academic achievement. Online Supplemental Table S3 reports disparities estimates for specific subgroups of students of other race or ethnicity. Online Supplemental Table S4 reports on statistical interactions. These two supplemental tables use the 2019 and 2022 survey waves, which allowed for other race or ethnicity subgroup sample sizes that were sufficiently large for reliable estimation. We report the multivariable regression model estimates as unadjusted odds ratios (ORs) or adjusted odds ratios (aORs).</p> <hd id="AN0193858191-13">Results</hd> <p>Table 1 displays descriptive statistics for the cross-sectional samples of fourth-grade students assessed across 2003 to 2022. The NAEP samples were diverse across race and ethnicity, biological sex, family income, and language use. Table 2 displays results from the multivariable regression models.</p> <p>Table 1. Descriptive Statistics of the NAEP 4th-Grade Reading Assessments Across Years.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">2003</th><th align="left">2005</th><th align="left">2007</th><th align="left">2009</th><th align="left">2011</th><th align="left">2013</th><th align="left">2015</th><th align="left">2017</th><th align="left">2019</th><th align="left">2022</th></tr></thead><tbody><tr><td><italic>N</italic></td><td>175,600</td><td>156,800</td><td>184,350</td><td>173,470</td><td>205,860</td><td>184,830</td><td>134,790</td><td>143,270</td><td>144,920</td><td>103,150</td></tr><tr><td /><td colspan="10">Percent (%)</td></tr><tr><td>Autism</td><td>0.1</td><td>0.2</td><td>0.3</td><td>0.3</td><td>0.4</td><td>0.6</td><td>0.7</td><td>0.7</td><td>0.9</td><td>1.1</td></tr><tr><td>White (reference)</td><td>59.1</td><td>57.7</td><td>57.0</td><td>55.1</td><td>53.1</td><td>51.2</td><td>50.0</td><td>47.7</td><td>46.5</td><td>46.0</td></tr><tr><td>Black</td><td>17.3</td><td>16.8</td><td>16.5</td><td>16.3</td><td>15.8</td><td>15.4</td><td>14.9</td><td>15.2</td><td>15.2</td><td>14.8</td></tr><tr><td>Hispanic</td><td>17.7</td><td>18.8</td><td>19.5</td><td>21.0</td><td>22.6</td><td>24.4</td><td>25.7</td><td>26.9</td><td>27.5</td><td>27.6</td></tr><tr><td>Other race or ethnicity</td><td>5.9</td><td>6.7</td><td>7.0</td><td>7.7</td><td>8.5</td><td>9.2</td><td>9.4</td><td>10.2</td><td>10.8</td><td>11.7</td></tr><tr><td>Female</td><td>49.4</td><td>49.7</td><td>49.7</td><td>49.4</td><td>49.4</td><td>49.2</td><td>49.1</td><td>49.1</td><td>49.0</td><td>49.2</td></tr><tr><td>FRPL eligible</td><td>44.4</td><td>45.0</td><td>44.0</td><td>47.0</td><td>51.3</td><td>53.1</td><td>55.3</td><td>53.9</td><td>54.4</td><td>52.5</td></tr><tr><td>ML</td><td>8.3</td><td>8.6</td><td>8.9</td><td>9.0</td><td>10.4</td><td>10.1</td><td>11.0</td><td>11.4</td><td>12.5</td><td>13.9</td></tr><tr><td /><td colspan="10">Mean (<italic>SE</italic>)</td></tr><tr><td>Reading achievement</td><td>216.93</td><td>217.80</td><td>220.15</td><td>219.86</td><td>220.35</td><td>220.96</td><td>221.62</td><td>221.11</td><td>219.85</td><td>216.20</td></tr><tr><td /><td>(0.09)</td><td>(0.09)</td><td>(0.08)</td><td>(0.09)</td><td>(0.08)</td><td>(0.09)</td><td>(0.10)</td><td>(0.10)</td><td>(0.10)</td><td>(0.13)</td></tr></tbody></table> </ephtml> </p> <p>1 Source: U.S. Department of Education, National Center for Education Statistics (NCES), National Assessment of Educational Progress (NAEP), 2003–2022 Fourth Grade Reading Full Sample Restricted-Use Data File.</p> <p>2 ML = multilingual learner; FRPL = free or reduced-price lunch; <emph>SE</emph> = standard error. Sampling weights were applied. Following NCES procedures to protect participant confidentiality, all numbers were rounded to the nearest 10, and percentages were reported to one decimal place.</p> <p>Table 2. Odds Ratios of Autism Diagnosis, NAEP 4th-Grade Reading Assessments Across Years.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><tbody><tr><th /><th align="left" colspan="4">2003</th><th align="left" colspan="4">2005</th></tr><tr><th /><th align="left">M1</th><th align="left">M2</th><th align="left">M3</th><th align="left">M4</th><th align="left">M1</th><th align="left">M2</th><th align="left">M3</th><th align="left">M4</th></tr><tr><td>Black</td><td>0.37<xref ref-type="table-fn" rid="tfn5">**</xref>[0.18, 0.78]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.08, 0.35]</td><td>0.26<xref ref-type="table-fn" rid="tfn5">**</xref>[0.12, 0.59]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">*</xref>[0.16, 0.86]</td><td>0.22<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.42]</td><td>0.12<xref ref-type="table-fn" rid="tfn5">***</xref>[0.06, 0.24]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.09, 0.34]</td><td>0.16<xref ref-type="table-fn" rid="tfn5">***</xref>[0.06, 0.41]</td></tr><tr><td>Hispanic</td><td>0.78[0.37, 1.67]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">*</xref>[0.17, 0.80]</td><td>0.63[0.23, 1.73]</td><td>0.70[0.21, 2.35]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.09, 0.32]</td><td>0.10<xref ref-type="table-fn" rid="tfn5">***</xref>[0.05, 0.18]</td><td>0.21<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.39]</td><td>0.23<xref ref-type="table-fn" rid="tfn5">**</xref>[0.09, 0.60]</td></tr><tr><td>Other race or ethnicity</td><td>0.17<xref ref-type="table-fn" rid="tfn5">**</xref>[0.06, 0.49]</td><td>0.12<xref ref-type="table-fn" rid="tfn5">***</xref>[0.04, 0.35]</td><td>0.16<xref ref-type="table-fn" rid="tfn5">**</xref>[0.05, 0.48]</td><td>0.12<xref ref-type="table-fn" rid="tfn5">***</xref>[0.05, 0.32]</td><td>0.32<xref ref-type="table-fn" rid="tfn5">*</xref>[0.13, 0.78]</td><td>0.26<xref ref-type="table-fn" rid="tfn5">**</xref>[0.10, 0.65]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">*</xref>[0.15, 0.95]</td><td>0.22<xref ref-type="table-fn" rid="tfn5">***</xref>[0.10, 0.48]</td></tr><tr><td>Reading score</td><td /><td>0.38<xref ref-type="table-fn" rid="tfn5">***</xref>[0.31, 0.47]</td><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.29, 0.45]</td><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.48]</td><td /><td>0.48<xref ref-type="table-fn" rid="tfn5">***</xref>[0.41, 0.56]</td><td>0.45<xref ref-type="table-fn" rid="tfn5">***</xref>[0.39, 0.53]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">***</xref>[0.31, 0.45]</td></tr><tr><td>Female</td><td /><td /><td>0.21<xref ref-type="table-fn" rid="tfn5">***</xref>[0.11, 0.41]</td><td>0.22<xref ref-type="table-fn" rid="tfn5">***</xref>[0.10, 0.47]</td><td /><td /><td>0.19<xref ref-type="table-fn" rid="tfn5">***</xref>[0.13, 0.30]</td><td>0.18<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.28]</td></tr><tr><td>FRPL eligible</td><td /><td /><td>0.32<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.60]</td><td>0.46<xref ref-type="table-fn" rid="tfn5">*</xref>[0.25, 0.87]</td><td /><td /><td>0.41<xref ref-type="table-fn" rid="tfn5">***</xref>[0.28, 0.60]</td><td>0.50<xref ref-type="table-fn" rid="tfn5">**</xref>[0.31, 0.81]</td></tr><tr><td>ML</td><td /><td /><td>0.95[0.30, 2.99]</td><td>0.63[0.15, 2.53]</td><td /><td /><td>0.28<xref ref-type="table-fn" rid="tfn5">*</xref>[0.10, 0.85]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">*</xref>[0.04, 0.84]</td></tr><tr><td>School fixed effect</td><td /><td /><td /><td>Y</td><td /><td /><td /><td>Y</td></tr><tr><td><italic>N</italic></td><td>175,600</td><td>175,600</td><td>175,600</td><td>5,280</td><td>156,800</td><td>156,800</td><td>156,800</td><td>5,840</td></tr><tr><td /><td colspan="4">2007</td><td colspan="4">2009</td></tr><tr><td /><td>M1</td><td>M2</td><td>M3</td><td>M4</td><td>M1</td><td>M2</td><td>M3</td><td>M4</td></tr><tr><td>Black</td><td>0.40<xref ref-type="table-fn" rid="tfn5">***</xref>[0.24, 0.64]</td><td>0.22<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.35]</td><td>0.32<xref ref-type="table-fn" rid="tfn5">***</xref>[0.20, 0.52]</td><td>0.27<xref ref-type="table-fn" rid="tfn5">***</xref>[0.15, 0.47]</td><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.29, 0.63]</td><td>0.22<xref ref-type="table-fn" rid="tfn5">***</xref>[0.15, 0.33]</td><td>0.32<xref ref-type="table-fn" rid="tfn5">***</xref>[0.21, 0.49]</td><td>0.26<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.46]</td></tr><tr><td>Hispanic</td><td>0.48<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.72]</td><td>0.25<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.38]</td><td>0.59<xref ref-type="table-fn" rid="tfn5">*</xref>[0.36, 0.94]</td><td>0.85[0.50, 1.43]</td><td>0.30<xref ref-type="table-fn" rid="tfn5">***</xref>[0.18, 0.49]</td><td>0.14<xref ref-type="table-fn" rid="tfn5">***</xref>[0.08, 0.25]</td><td>0.30<xref ref-type="table-fn" rid="tfn5">***</xref>[0.18, 0.49]</td><td>0.34<xref ref-type="table-fn" rid="tfn5">**</xref>[0.17, 0.68]</td></tr><tr><td>Other race or ethnicity</td><td>0.83[0.51, 1.36]</td><td>0.68[0.41, 1.12]</td><td>0.98[0.59, 1.63]</td><td>0.81[0.39, 1.65]</td><td>0.43<xref ref-type="table-fn" rid="tfn5">**</xref>[0.22, 0.81]</td><td>0.33<xref ref-type="table-fn" rid="tfn5">**</xref>[0.17, 0.65]</td><td>0.46<xref ref-type="table-fn" rid="tfn5">*</xref>[0.23, 0.89]</td><td>0.44<xref ref-type="table-fn" rid="tfn5">**</xref>[0.24, 0.82]</td></tr><tr><td>Reading score</td><td /><td>0.46<xref ref-type="table-fn" rid="tfn5">***</xref>[0.40, 0.52]</td><td>0.42<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.48]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">***</xref>[0.31, 0.44]</td><td /><td>0.40<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.45]</td><td>0.38<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.43]</td><td>0.35<xref ref-type="table-fn" rid="tfn5">***</xref>[0.29, 0.41]</td></tr><tr><td>Female</td><td /><td /><td>0.15<xref ref-type="table-fn" rid="tfn5">***</xref>[0.09, 0.22]</td><td>0.14<xref ref-type="table-fn" rid="tfn5">***</xref>[0.09, 0.21]</td><td /><td /><td>0.16<xref ref-type="table-fn" rid="tfn5">***</xref>[0.11, 0.24]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.11, 0.25]</td></tr><tr><td>FRPL eligible</td><td /><td /><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.26, 0.50]</td><td>0.48<xref ref-type="table-fn" rid="tfn5">***</xref>[0.32, 0.71]</td><td /><td /><td>0.34<xref ref-type="table-fn" rid="tfn5">***</xref>[0.25, 0.46]</td><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.30, 0.62]</td></tr><tr><td>ML</td><td /><td /><td>0.30<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.61]</td><td>0.19<xref ref-type="table-fn" rid="tfn5">***</xref>[0.07, 0.51]</td><td /><td /><td>0.45[0.20, 1.02]</td><td>0.35<xref ref-type="table-fn" rid="tfn5">**</xref>[0.17, 0.75]</td></tr><tr><td>School fixed effect</td><td /><td /><td /><td>Y</td><td /><td /><td /><td>Y</td></tr><tr><td><italic>N</italic></td><td>184,350</td><td>184,350</td><td>184,350</td><td>12,490</td><td>173,470</td><td>173,470</td><td>173,470</td><td>12,450</td></tr><tr><td /><td colspan="4">2011</td><td colspan="4">2013</td></tr><tr><td /><td>M1</td><td>M2</td><td>M3</td><td>M4</td><td>M1</td><td>M2</td><td>M3</td><td>M4</td></tr><tr><td>Black</td><td>0.37<xref ref-type="table-fn" rid="tfn5">***</xref>[0.25, 0.55]</td><td>0.21<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.31]</td><td>0.28<xref ref-type="table-fn" rid="tfn5">***</xref>[0.19, 0.43]</td><td>0.27<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.50]</td><td>0.38<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.54]</td><td>0.21<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.29]</td><td>0.27<xref ref-type="table-fn" rid="tfn5">***</xref>[0.19, 0.39]</td><td>0.26<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.47]</td></tr><tr><td>Hispanic</td><td>0.38<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.55]</td><td>0.21<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.31]</td><td>0.47<xref ref-type="table-fn" rid="tfn5">***</xref>[0.31, 0.71]</td><td>0.28<xref ref-type="table-fn" rid="tfn5">***</xref>[0.16, 0.49]</td><td>0.32<xref ref-type="table-fn" rid="tfn5">***</xref>[0.23, 0.44]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.24]</td><td>0.35<xref ref-type="table-fn" rid="tfn5">***</xref>[0.24, 0.51]</td><td>0.32<xref ref-type="table-fn" rid="tfn5">***</xref>[0.21, 0.51]</td></tr><tr><td>Other race or ethnicity</td><td>0.57<xref ref-type="table-fn" rid="tfn5">*</xref>[0.37, 0.88]</td><td>0.46<xref ref-type="table-fn" rid="tfn5">***</xref>[0.30, 0.72]</td><td>0.62<xref ref-type="table-fn" rid="tfn5">*</xref>[0.40, 0.96]</td><td>0.61[0.36, 1.03]</td><td>0.60<xref ref-type="table-fn" rid="tfn5">**</xref>[0.42, 0.88]</td><td>0.48<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.71]</td><td>0.65<xref ref-type="table-fn" rid="tfn5">*</xref>[0.45, 0.96]</td><td>0.54<xref ref-type="table-fn" rid="tfn5">*</xref>[0.33, 0.89]</td></tr><tr><td>Reading score</td><td /><td>0.44<xref ref-type="table-fn" rid="tfn5">***</xref>[0.41, 0.48]</td><td>0.41<xref ref-type="table-fn" rid="tfn5">***</xref>[0.38, 0.45]</td><td>0.37<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.42]</td><td /><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.40, 0.47]</td><td>0.40<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.44]</td><td>0.35<xref ref-type="table-fn" rid="tfn5">***</xref>[0.32, 0.39]</td></tr><tr><td>Female</td><td /><td /><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.22]</td><td>0.16<xref ref-type="table-fn" rid="tfn5">***</xref>[0.12, 0.22]</td><td /><td /><td>0.18<xref ref-type="table-fn" rid="tfn5">***</xref>[0.14, 0.23]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.13, 0.22]</td></tr><tr><td>FRPL eligible</td><td /><td /><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.32, 0.56]</td><td>0.50<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.70]</td><td /><td /><td>0.45<xref ref-type="table-fn" rid="tfn5">***</xref>[0.35, 0.56]</td><td>0.56<xref ref-type="table-fn" rid="tfn5">***</xref>[0.44, 0.71]</td></tr><tr><td>ML</td><td /><td /><td>0.33<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.62]</td><td>0.29<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.49]</td><td /><td /><td>0.28<xref ref-type="table-fn" rid="tfn5">***</xref>[0.18, 0.43]</td><td>0.23<xref ref-type="table-fn" rid="tfn5">***</xref>[0.13, 0.40]</td></tr><tr><td>School fixed effect</td><td /><td /><td /><td>Y</td><td /><td /><td /><td>Y</td></tr><tr><td><italic>N</italic></td><td>205,860</td><td>205,860</td><td>205,860</td><td>23,040</td><td>184,830</td><td>184,830</td><td>184,830</td><td>26,190</td></tr><tr><td /><td colspan="4">2015</td><td colspan="4">2017</td></tr><tr><td /><td>M1</td><td>M2</td><td>M3</td><td>M4</td><td>M1</td><td>M2</td><td>M3</td><td>M4</td></tr><tr><td>Black</td><td>0.47<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.66]</td><td>0.25<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.34]</td><td>0.33<xref ref-type="table-fn" rid="tfn5">***</xref>[0.23, 0.47]</td><td>0.27<xref ref-type="table-fn" rid="tfn5">***</xref>[0.17, 0.44]</td><td>0.71<xref ref-type="table-fn" rid="tfn5">*</xref>[0.53, 0.96]</td><td>0.39<xref ref-type="table-fn" rid="tfn5">***</xref>[0.29, 0.53]</td><td>0.47<xref ref-type="table-fn" rid="tfn5">***</xref>[0.34, 0.65]</td><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.69]</td></tr><tr><td>Hispanic</td><td>0.89[0.66, 1.21]</td><td>0.46<xref ref-type="table-fn" rid="tfn5">***</xref>[0.34, 0.63]</td><td>0.92[0.65, 1.31]</td><td>0.73[0.48, 1.11]</td><td>0.82[0.62, 1.08]</td><td>0.46<xref ref-type="table-fn" rid="tfn5">***</xref>[0.34, 0.62]</td><td>0.75[0.53, 1.07]</td><td>0.54<xref ref-type="table-fn" rid="tfn5">**</xref>[0.34, 0.85]</td></tr><tr><td>Other race or ethnicity</td><td>0.61<xref ref-type="table-fn" rid="tfn5">**</xref>[0.43, 0.85]</td><td>0.51<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.73]</td><td>0.68<xref ref-type="table-fn" rid="tfn5">*</xref>[0.48, 0.96]</td><td>0.61<xref ref-type="table-fn" rid="tfn5">*</xref>[0.40, 0.94]</td><td>0.75[0.52, 1.06]</td><td>0.65<xref ref-type="table-fn" rid="tfn5">*</xref>[0.46, 0.91]</td><td>0.77[0.54, 1.09]</td><td>0.75[0.48, 1.15]</td></tr><tr><td>Reading score</td><td /><td>0.42<xref ref-type="table-fn" rid="tfn5">***</xref>[0.38, 0.46]</td><td>0.38<xref ref-type="table-fn" rid="tfn5">***</xref>[0.34, 0.42]</td><td>0.33<xref ref-type="table-fn" rid="tfn5">***</xref>[0.29, 0.37]</td><td /><td>0.42<xref ref-type="table-fn" rid="tfn5">***</xref>[0.38, 0.45]</td><td>0.39<xref ref-type="table-fn" rid="tfn5">***</xref>[0.35, 0.43]</td><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.32, 0.40]</td></tr><tr><td>Female</td><td /><td /><td>0.20<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.19<xref ref-type="table-fn" rid="tfn5">***</xref></td><td /><td /><td>0.19<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.18<xref ref-type="table-fn" rid="tfn5">***</xref></td></tr><tr><td /><td /><td /><td>[0.14, 0.27]</td><td>[0.14, 0.26]</td><td /><td /><td>[0.14, 0.26]</td><td>[0.13, 0.24]</td></tr><tr><td>FRPL eligible</td><td /><td /><td>0.41<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.48<xref ref-type="table-fn" rid="tfn5">***</xref></td><td /><td /><td>0.56<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.79</td></tr><tr><td /><td /><td /><td>[0.32, 0.53]</td><td>[0.36, 0.64]</td><td /><td /><td>[0.43, 0.72]</td><td>[0.59, 1.07]</td></tr><tr><td>ML</td><td /><td /><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.46<xref ref-type="table-fn" rid="tfn5">*</xref></td><td /><td /><td>0.45<xref ref-type="table-fn" rid="tfn5">***</xref></td><td>0.47<xref ref-type="table-fn" rid="tfn5">**</xref></td></tr><tr><td /><td /><td /><td>[0.21, 0.61]</td><td>[0.23, 0.91]</td><td /><td /><td>[0.28, 0.71]</td><td>[0.28, 0.79]</td></tr><tr><td>School fixed effect</td><td /><td /><td /><td>Y</td><td /><td /><td /><td>Y</td></tr><tr><td><italic>N</italic></td><td>134,790</td><td>134,790</td><td>134,790</td><td>17,600</td><td>143,270</td><td>143,270</td><td>143,270</td><td>19,450</td></tr><tr><td /><td colspan="4">2019</td><td colspan="4">2022</td></tr><tr><td /><td>M1</td><td>M2</td><td>M3</td><td>M4</td><td>M1</td><td>M2</td><td>M3</td><td>M4</td></tr><tr><td>Black</td><td>0.65<xref ref-type="table-fn" rid="tfn5">**</xref>[0.50, 0.85]</td><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.47]</td><td>0.45<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.60]</td><td>0.39<xref ref-type="table-fn" rid="tfn5">***</xref>[0.27, 0.57]</td><td>1.11[0.83, 1.48]</td><td>0.64<xref ref-type="table-fn" rid="tfn5">**</xref>[0.47, 0.86]</td><td>0.72<xref ref-type="table-fn" rid="tfn5">*</xref>[0.53, 0.98]</td><td>0.66[0.41, 1.06]</td></tr><tr><td>Hispanic</td><td>0.71<xref ref-type="table-fn" rid="tfn5">*</xref>[0.55, 0.92]</td><td>0.43<xref ref-type="table-fn" rid="tfn5">***</xref>[0.33, 0.57]</td><td>0.69<xref ref-type="table-fn" rid="tfn5">*</xref>[0.51, 0.93]</td><td>0.62<xref ref-type="table-fn" rid="tfn5">*</xref>[0.43, 0.89]</td><td>0.98[0.75, 1.28]</td><td>0.63<xref ref-type="table-fn" rid="tfn5">**</xref>[0.48, 0.83]</td><td>0.89[0.65, 1.23]</td><td>0.65<xref ref-type="table-fn" rid="tfn5">*</xref>[0.44, 0.95]</td></tr><tr><td>Other race or ethnicity</td><td>0.91[0.70, 1.19]</td><td>0.84[0.64, 1.10]</td><td>1.03[0.78, 1.35]</td><td>0.89[0.66, 1.20]</td><td>1.13[0.81, 1.57]</td><td>1.10[0.79, 1.54]</td><td>1.25[0.90, 1.73]</td><td>1.00[0.62, 1.59]</td></tr><tr><td>Reading score</td><td /><td>0.42<xref ref-type="table-fn" rid="tfn5">***</xref>[0.39, 0.46]</td><td>0.40<xref ref-type="table-fn" rid="tfn5">***</xref>[0.36, 0.44]</td><td>0.36<xref ref-type="table-fn" rid="tfn5">***</xref>[0.32, 0.40]</td><td /><td>0.46<xref ref-type="table-fn" rid="tfn5">***</xref>[0.42, 0.50]</td><td>0.44<xref ref-type="table-fn" rid="tfn5">***</xref>[0.40, 0.48]</td><td>0.39<xref ref-type="table-fn" rid="tfn5">***</xref>[0.35, 0.43]</td></tr><tr><td>Female</td><td /><td /><td>0.18<xref ref-type="table-fn" rid="tfn5">***</xref>[0.13, 0.24]</td><td>0.17<xref ref-type="table-fn" rid="tfn5">***</xref>[0.13, 0.23]</td><td /><td /><td>0.33<xref ref-type="table-fn" rid="tfn5">***</xref>[0.25, 0.43]</td><td>0.33<xref ref-type="table-fn" rid="tfn5">***</xref>[0.25, 0.44]</td></tr><tr><td>FRPL</td><td /><td /><td>0.50<xref ref-type="table-fn" rid="tfn5">***</xref>[0.39, 0.63]</td><td>0.63<xref ref-type="table-fn" rid="tfn5">**</xref>[0.48, 0.84]</td><td /><td /><td>0.67<xref ref-type="table-fn" rid="tfn5">***</xref>[0.53, 0.83]</td><td>0.75[0.55, 1.03]</td></tr><tr><td>ML</td><td /><td /><td>0.54<xref ref-type="table-fn" rid="tfn5">**</xref>[0.36, 0.79]</td><td>0.51<xref ref-type="table-fn" rid="tfn5">**</xref>[0.33, 0.78]</td><td /><td /><td>0.62<xref ref-type="table-fn" rid="tfn5">**</xref>[0.43, 0.89]</td><td>0.56<xref ref-type="table-fn" rid="tfn5">**</xref>[0.39, 0.81]</td></tr><tr><td>School fixed effect</td><td /><td /><td /><td>Y</td><td /><td /><td /><td>Y</td></tr><tr><td><italic>N</italic></td><td>144,920</td><td>144,920</td><td>144,920</td><td>22,840</td><td>103,150</td><td>103,150</td><td>103,150</td><td>18,060</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 Source: U.S. Department of Education, National Center for Education Statistics (NCES), National Assessment of Educational Progress (NAEP), 2003–2022 Fourth Grade Reading Full Sample Restricted-Use Data File.</item> <item>4 ML = multilingual learner; FRPL = free or reduced-price lunch. Odds ratios and 95% confidence intervals (in square brackets) reported. Sampling weights and cluster-robust standard error were applied.</item> <item>5 <emph>p</emph> <.05. **<emph>p</emph> <.01. ***<emph>p</emph> <.001.</item> </ulist> <p>Model 1 displays unadjusted estimates of racial and ethnic differences in the likelihood of school-based autism identification across 2003–2022. Black students were consistently less likely than White students to receive school-based services for autism (OR range = 0.22–0.71) from 2003 to 2019, but not in 2022 (OR = 1.11, <emph>p</emph> >.05). Hispanic students were significantly less likely to receive services in 2005, 2007, 2009, 2011, 2013, and 2019 (OR range = 0.17–0.71). Students of other race or ethnicity were less likely to receive services in 2003, 2005, 2009, 2011, 2013, and 2015 (OR range = 0.17–0.61) but not in 2007, 2017, 2019, or 2022 (OR range = 0.75–1.13, <emph>p</emph>s >.05).</p> <p>Model 2 adjusted for student-level reading achievement. Students displaying greater reading achievement were repeatedly less likely to receive services for autism (aOR range = 0.38–0.48). Black (aOR range = 0.12–0.64) and Hispanic (aOR range = 0.10–0.63) students were repeatedly less likely to receive autism services in analyses adjusting for student-level reading achievement. Students of other race or ethnicity were repeatedly less likely to receive autism services (aOR range = 0.12–0.65), except in 2007, 2019, and 2022 (aOR range = 0.68–1.10, <emph>p</emph>s >.05).</p> <p>Model 3 adjusted for the additional sociodemographic characteristics of biological sex, family income, and ML status. Females (aOR range = 0.15–0.33) as well as students from low-income families (aOR range = 0.32–0.67) were less likely to receive autism services across 2003–2022. MLs were repeatedly less likely to receive services (aOR range = 0.28–0.62), except in 2003 and 2009 (aORs of 0.95 and 0.45, <emph>p</emph>s >.05). Black students were repeatedly less likely to receive autism services across 2003–2022 in analyses adjusting for both student-level academic achievement and additional sociodemographic characteristics including family income (aOR range = 0.17–0.72). The disparities between Hispanic and White students were attenuated in 2003 (aOR from 0.37, <emph>p</emph> <.05, to 0.63, <emph>p</emph> >.05), 2015 (aOR from 0.46, <emph>p</emph> <.001, to 0.92, <emph>p</emph> >.05), 2017 (aOR from 0.46, <emph>p</emph> <.001 to 0.75, <emph>p</emph> >.05), and 2022 (aOR from 0.63, <emph>p</emph><emph><</emph>.01, to 0.89, <emph>p</emph> >.05) but were statistically significant in 2005, 2007, 2009, 2011, 2013, and 2019. Disparities for students of other race or ethnicity were fully explained in 2017 (aOR from 0.65, <emph>p</emph> <.05, to 0.77, <emph>p</emph> >.05), but still statistically significant in 2003, 2005, 2009, 2011, 2013, and 2015.</p> <p>Model 4 further adjusted these disparity estimates using school fixed effects. Black students were less likely than similarly situated White students to receive school-based services for autism in 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, and 2019 (aOR range = 0.16–0.43). Control for school contexts fully explained this disparity in 2022 (aOR from 0.72, <emph>p</emph> <.05, to 0.66, <emph>p</emph> >.05). The Hispanic-White disparities continued to be statistically significant in 2005, 2009, 2011, 2013, 2017, 2019, and 2022 (aOR range = 0.23–0.65). The estimates for students of other race or ethnicity were less consistent and not statistically significant in more recently collected data from 2017, 2019, and 2022 (aOR range = 0.75–1.00). Female students were less likely than similarly situated male students to be identified with autism while attending U.S. elementary schools across 2003–2022 in the fully adjusted models (aOR range = 0.14–0.33). The estimates for low-income students were fully explained in 2017 and 2022 but were otherwise statistically significant. MLs have been less likely to be identified with autism while attending U.S. elementary schools since 2005 (aOR range = 0.17–0.56). Figures 1 and 2 display the over-time trends in the unadjusted and fully adjusted Black-White and Hispanic-White disparities estimates using ORs, aORs, and 95% confidence intervals.</p> <p>Graph: Figure 1. Plotted Unadjusted and Adjusted Odds Ratios (ORs) for Black (vs White) Students Over Years.ORs obtained from Table 2's Model 1 and 4's results.</p> <p>Graph: Figure 2. Plotted Unadjusted and Adjusted Odds Ratios for Hispanic (vs White) Students Over Years.ORs obtained from Table 2's Model 1 and 4's results.</p> <p>We also conducted a robustness check that used the NAEP's mathematics achievement assessments as an alternative statistical control for student-level academic achievement. Results from this robustness check (see Supplemental Table S2) were consistent with our main findings using reading achievement as a statistical control. Across most survey waves, Black and Hispanic students, females, those eligible for free or reduced lunch, and MLs continued to have lower odds of being identified with autism.</p> <hd id="AN0193858191-14">Discussion</hd> <p>Our analyses of very large, nationally representative cross-sectional samples repeatedly indicated sociodemographic disparities in autism identification in U.S. elementary schools attributable to race, ethnicity, biological sex, family income, and language use. Sociodemographic disparities in autism identification have been occurring in U.S. elementary schools since at least 2003. We observed some over-time changes in these sociodemographic disparities including in more recently collected datasets. The disparities for Black students, females, and MLs have been especially stable.</p> <hd id="AN0193858191-15">Study's Contributions and Implications</hd> <p>Our findings advance the field's limited understanding of sociodemographic disparities in autism identification in U.S. elementary schools. Whether sociodemographic disparities are occurring in school-based autism identification and resulting service access has been unclear ([<reflink idref="bib5" id="ref77">5</reflink>]; [<reflink idref="bib11" id="ref78">11</reflink>]; [<reflink idref="bib28" id="ref79">28</reflink>]; [<reflink idref="bib51" id="ref80">51</reflink>]; [<reflink idref="bib52" id="ref81">52</reflink>]; [<reflink idref="bib54" id="ref82">54</reflink>]). The current knowledge base is small as well as insufficient to indicate whether students of color are less likely to be accessing services ([<reflink idref="bib11" id="ref83">11</reflink>]; [<reflink idref="bib52" id="ref84">52</reflink>]). Whether disparities attributable to biological sex, family income, and language use are occurring is also unclear ([<reflink idref="bib2" id="ref85">2</reflink>]). Prior work has mostly investigated disparities in health care ([<reflink idref="bib5" id="ref86">5</reflink>]; [<reflink idref="bib14" id="ref87">14</reflink>]; [<reflink idref="bib51" id="ref88">51</reflink>]; [<reflink idref="bib52" id="ref89">52</reflink>]), receipt of specific treatments or services ([<reflink idref="bib11" id="ref90">11</reflink>]; [<reflink idref="bib59" id="ref91">59</reflink>]), or for services provided before school entry ([<reflink idref="bib16" id="ref92">16</reflink>]; [<reflink idref="bib28" id="ref93">28</reflink>]; [<reflink idref="bib30" id="ref94">30</reflink>]; [<reflink idref="bib50" id="ref95">50</reflink>]). The small body of work available investigating for sociodemographic disparities in school-based autism identification and resulting service receipt has largely been unable to account for student- or school-level confounds ([<reflink idref="bib2" id="ref96">2</reflink>]; [<reflink idref="bib57" id="ref97">57</reflink>]; [<reflink idref="bib56" id="ref98">56</reflink>]), including academic achievement ([<reflink idref="bib25" id="ref99">25</reflink>]; [<reflink idref="bib36" id="ref100">36</reflink>]; [<reflink idref="bib59" id="ref101">59</reflink>]).</p> <p>Establishing whether and to what extent sociodemographic disparities in school-based autism identification are occurring is important for ensuring health and educational equity. Schools are the most common setting for receiving disability-related treatments, supports, and services including for autism ([<reflink idref="bib29" id="ref102">29</reflink>]). School-based services are reported to benefit students with disabilities including those of color ([<reflink idref="bib41" id="ref103">41</reflink>]; Woods et al., 2025). Our analyses strengthen the field's currently limited understanding of sociodemographic disparities in autism identification including their size, direction, and over-time stability.</p> <p>Our findings are consistent with reports of sociodemographic disparities in autism identification and service access prior to school entry based on analyses of smaller convenience ([<reflink idref="bib28" id="ref104">28</reflink>]; [<reflink idref="bib44" id="ref105">44</reflink>]; [<reflink idref="bib50" id="ref106">50</reflink>]) or nationally representative ([<reflink idref="bib16" id="ref107">16</reflink>]; [<reflink idref="bib30" id="ref108">30</reflink>]) samples that adjusted for individual-level confounds. Our findings are also consistent with prior work analyzing two nationally representative cross-sectional samples, which were collected in 2013, of school-aged students. This work reported that students of color, females, those from low-income families, and who are MLs attending U.S. elementary schools are less likely to be identified with autism in analyses adjusting for student- and school-level confounds ([<reflink idref="bib31" id="ref109">31</reflink>]). Our analyses extend findings from this prior work by examining sociodemographic disparities across a wider time period than previously examined and by including more recently collected data. As a result, our findings contribute new knowledge about the stability and on-going occurrence of these sociodemographic disparities for U.S. policymakers, researchers, and providers. Our analyses directly respond to calls for research that accounts for school context when investigating disparities in autism identification and service receipt ([<reflink idref="bib4" id="ref110">4</reflink>]). Our findings directly contribute to the currently small literature that has investigated for these disparities in school settings ([<reflink idref="bib52" id="ref111">52</reflink>]; [<reflink idref="bib54" id="ref112">54</reflink>]).</p> <p>Yet our findings conflict with some prior work examining racial and ethnic disparities in autism identification in U.S. schools including in analyses adjusting for student-level potential confounds. For example, [<reflink idref="bib3" id="ref113">3</reflink>] reported that Black students were about 8% more likely to be receiving school-based autism services than White students. This finding was based on a nationally representative cross-sectional parental survey collected in 2011 of children with special health care needs ages 6–17 years who had been diagnosed with autism. The analyses used statistical controls including for functional limitations. (We note that the reported <emph>p</emph>-value was 0.04, but the reported 95% confidence interval ranged from -0.08 to 16.9 and so included the null value.) The adjusted disparity estimate between Hispanic and White students was not statistically significant. Bilaver and colleagues (2021) similarly reported that Black and Asian students were, respectively, about 6% and 5% more likely to be receiving autism services in schools than White students. This finding was based on a very large sample of children of ages 3–17 years initially drawn from Medicaid claims data across 46 states and the District of Columbia. The analyses adjusted for biological sex, age, and comorbidities as well as home, community, and county confounds, but not student-level functional limitations. The adjusted disparity estimate between Hispanic and White students was not statistically significant.</p> <p>There are at least three explanations for why our findings conflict with some prior studies. One explanation is differences in statistical controls. The aforementioned prior studies were unable to control for directly assessed academic achievement as an indicator of functioning and so of clinical appropriateness of school-based disability identification and service access. As repeatedly observed in our study's analyses across 10 survey waves as well as reported in prior work, student-level academic achievement is a particularly strong and consistent explanatory factor of whether students are identified with disabilities including for autism while attending U.S. elementary, middle, and high schools ([<reflink idref="bib32" id="ref114">32</reflink>], [<reflink idref="bib31" id="ref115">31</reflink>]; [<reflink idref="bib33" id="ref116">33</reflink>]).</p> <p>A second explanation is differences in sampling. Unlike the aforementioned studies, our analyses examined one elementary school grade, and so examined students of approximately the same age. It is possible that sociodemographic disparities in school-based autism identification differ across grade levels. For example, sociodemographic disparities may be more likely to occur in earlier school grades, possibly due to initial delays in autism diagnoses and limited access to early intervention and early childhood special education services ([<reflink idref="bib16" id="ref117">16</reflink>]; [<reflink idref="bib28" id="ref118">28</reflink>]; [<reflink idref="bib30" id="ref119">30</reflink>]; [<reflink idref="bib43" id="ref120">43</reflink>]). Two of the prior studies reporting that Black students were more likely to be receiving autism services in U.S. schools analyzed data aggregated across a wide range of student ages ([<reflink idref="bib4" id="ref121">4</reflink>]; [<reflink idref="bib3" id="ref122">3</reflink>]). Additional work that examines disparities across a wider range of specific school grades (e.g. first, second, third, fourth, and fifth grade, as well as specific middle or high school grades) is warranted.</p> <p>A third explanation is differences in measurement of autism. Studies reporting relatively greater use of school-based services by students of color analyzed responses based on parent report following an autism classification ([<reflink idref="bib36" id="ref123">36</reflink>]), parent-reported service receipt and parent-reported autism diagnosis ([<reflink idref="bib3" id="ref124">3</reflink>]), or Medicaid claims data including as provided by local education agencies ([<reflink idref="bib4" id="ref125">4</reflink>]). In contrast, our study used reports by school staff of an IEP or 504 plan on file for the primary disability condition of autism as the indicator of school-based autism identification and resulting service use. Future studies are needed that investigate for these disparities using well-validated measures of autism identification in analyses adjusting for student-, family-, and school-level potential confounds including directly assessed academic and sociobehavioral functioning. Our findings do not provide empirical support for mechanisms hypothesized to be resulting in relatively greater use of school-based services by students of color including relatively easier access to services in educational than in health care settings or school contextual factors ([<reflink idref="bib4" id="ref126">4</reflink>]).</p> <p>Our findings have implications for U.S. health and educational policies. Recent studies find that sociodemographic differences in autism prevalence rates have changed across time in the U.S. Autism prevalence rates are now greater for students who are Black or Hispanic and those being raised by parents with low levels of resources than for White students or those being raised by parents with high levels of resources ([<reflink idref="bib40" id="ref127">40</reflink>]; [<reflink idref="bib43" id="ref128">43</reflink>]; [<reflink idref="bib47" id="ref129">47</reflink>]; [<reflink idref="bib49" id="ref130">49</reflink>]). Autism prevalence rates for Black children of ages 3–5 years began exceeding the rates of White children by birth year 2009 in most U.S. states ([<reflink idref="bib40" id="ref131">40</reflink>]). The prevalence rates for Hispanic children began exceeding those of White children between 2010 and 2013 ([<reflink idref="bib40" id="ref132">40</reflink>]). Possible explanations for these changing differences in autism prevalence rates include expanded access to health insurance, improved screening and referral practices in pediatric clinics, increased educational attainment by parents from historically marginalized communities, and White and wealthy families being more likely to access prenatal and postnatal care and intensive behavioral interventions that may help lessen autism's over-time severity ([<reflink idref="bib17" id="ref133">17</reflink>]; [<reflink idref="bib39" id="ref134">39</reflink>]).</p> <p>Yet whether these increased autism prevalence rates for students from historically marginalized communities are resulting in greater likelihoods of autism identification and service use in U.S. elementary schools has been unclear. Our analyses of over-time data suggest that this has largely not been the case, as evidenced by stable lower odds of autism identification in U.S. elementary schools attributable to race or ethnicity, biological sex, family income, and language use. Our findings suggest that the relatively greater autism prevalence rates for Black and Hispanic students reported in other work may not be resulting in correspondingly greater odds of autism identification and service use by Black and Hispanic students attending U.S. elementary schools. We fail to observe any evidence that students of color are relatively more likely to be identified with autism while attending U.S. elementary schools across any of the study's 10 survey waves including across both the unadjusted or adjusted estimates. Instead, our analyses suggest that students of color as well as other groups from historically marginalized communities continue to be relatively less likely to be identified with autism while attending U.S. elementary schools including when displaying similar levels of academic achievement and attending the same schools. Future quantitative and qualitative studies should investigate why this is occurring. Health and educational policies are warranted that ensure equal access to autism services and supports for students from historically marginalized communities attending U.S. elementary schools.</p> <hd id="AN0193858191-16">Study's Strengths and Limitations</hd> <p>Our study has both strengths and limitations that should be considered when interpreting the findings. Our analyses are based on very large samples. This helps to address the relatively low statistical power available in prior work ([<reflink idref="bib3" id="ref135">3</reflink>]; [<reflink idref="bib36" id="ref136">36</reflink>]). To our knowledge, over-time estimates of sociodemographic disparities in school-based autism identification have been previously unavailable. Our use of student-level academic achievement helped account for the clinical appropriateness of observed difference in autism identification. Our use of school fixed effects allowed us to control for unmeasured confounds including in school-level resources.</p> <p>However, we were unable to account for other potential confounds. This includes student-level behavioral functioning and social communication abilities. We also were unable to account for family preferences for school-based autism identification, which would better evaluate for potential sociodemographic disparities ([<reflink idref="bib22" id="ref137">22</reflink>]). Families of color may be especially likely to experience stigma and also be more likely to prefer autism supports through extended families, religious organizations, or local communities than through schools ([<reflink idref="bib46" id="ref138">46</reflink>]). Our use of school fixed effects does not allow us to model the varying aspects of school contexts (e.g. racial, ethnic, and economic composition) that may be most related to sociodemographic disparities in autism identification ([<reflink idref="bib20" id="ref139">20</reflink>]). Our results are not causal. Additional work is needed that investigates the underlying student, family, school, community, and societal mechanisms that may be resulting in the observed sociodemographic disparities.</p> <p>NAEP collects data from U.S. elementary schools only as students attend fourth grade. This prevents us from examining for disparities in autism identification in other elementary school grades. We also are unable to report on disparities in the timing of autism identification as students attend the elementary school grades as well as across middle and high school. Students from historically marginalized communities have been reported to experience significant over-time delays in autism diagnosis ([<reflink idref="bib8" id="ref140">8</reflink>]; [<reflink idref="bib27" id="ref141">27</reflink>]). We also are unable to examine disparities in specific types of services (e.g. speech or language therapy, behavioral modification, counseling), in service quality, or in the outcomes resulting from service access across sociodemographic populations.</p> <p>Our estimates for Black and Hispanic students in the earliest assessment years are based on small sample sizes and therefore should be interpreted cautiously. The complex, stratified sampling design of NAEP and the use of sampling weights likely helped stabilize the variance estimates. The 95% confidence intervals reported in Table 2 across the survey waves are not excessively wide, suggesting that the rare-event bias was unlikely to substantially affect the findings. NAEP only surveys primary disability classification. Secondary or comorbid disability conditions were not surveyed. Consequently, students whose autism was listed in IEPs or 504 plans as a secondary condition were not included as having autism in our analyses. If some student groups (e.g. Black or female students) were more likely to have autism listed as a secondary condition, this may introduce some measurement error and result in more conservative estimates of the autism identification likelihoods.</p> <hd id="AN0193858191-17">Conclusion</hd> <p>Our analyses of 10 survey waves collected from 2003 to 2022 of very large and nationally representative samples of fourth graders indicated that, among similarly situated students, those who are Black or Hispanic, female, from low-income families, or MLs have been less likely to be identified with autism while attending U.S. elementary schools. The disparities have been largely stable across time, particularly for Black students, females, and MLs. We repeatedly observed racial and ethnic disparities in school-based autism identification despite reports that the autism prevalence rates of Black and Hispanic students have exceeded those of White students. Sociodemographic disparities in autism identification in U.S. elementary schools are not explained by differences in student-level academic achievement, school-level resources, or other potential confounds. Collectively, the findings suggest a need for health and educational policies that ensure equal access to school-based autism services and supports including for students from historically marginalized communities.</p> <hd id="AN0193858191-18">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613261434432 for Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools by Paul L. Morgan and Eric Hengyu Hu in Autism</p> <ref id="AN0193858191-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref32" type="bt">1</bibl> <bibtext> Batz R., Yadav A. (2024). Parents' experiences navigating early intervention and early childhood special education services: A qualitative metasynthesis. 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Morgan: Conceptualization; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Writing—original draft; Writing—review & editing.Eric Hengyu Hu: Formal analysis; Investigation; Methodology; Writing—original draft.</bibtext> </blist> <blist> <bibtext> The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> Data used in this study were obtained from the restricted-use National Assessment of Educational Progress (NAEP) database. Student-level NAEP data are not publicly available due to federal confidentiality regulations. Researchers interested in accessing student-level NAEP data may submit a request to the National Center for Education Statistics by following the procedures described at https://nces.ed.gov/statprog/instruct.asp. The code (a Stata do-file) for the study's analyses is provided in the https://journals.sagepub.com/doi/suppl/10.1177/13623613261434432.</bibtext> </blist> <blist> <bibtext> The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The study's analyses were supported by the U.S. Department of Education's Institute of Education Sciences (grant number R324A220271) to the University at Albany, State University of New York. Opinions expressed are those of the authors and do not represent the views of the Institute of Education Sciences or the U.S. Department of Education.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> <blist> <bibtext> Paul L. Morgan</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-9347-6486 Eric Hengyu Hu</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-2113-3940</bibtext> </blist> </ref> <aug> <p>By Paul L. Morgan and Eric Hengyu Hu</p> <p>Reported by Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib11" firstref="ref2"></nolink> <nolink nlid="nl2" bibid="bib51" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib52" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib28" firstref="ref9"></nolink> <nolink nlid="nl6" bibid="bib54" firstref="ref12"></nolink> <nolink nlid="nl7" bibid="bib44" firstref="ref20"></nolink> <nolink nlid="nl8" bibid="bib31" firstref="ref21"></nolink> <nolink nlid="nl9" bibid="bib53" firstref="ref23"></nolink> <nolink nlid="nl10" bibid="bib63" firstref="ref24"></nolink> <nolink nlid="nl11" bibid="bib22" firstref="ref26"></nolink> <nolink nlid="nl12" bibid="bib37" firstref="ref27"></nolink> <nolink nlid="nl13" bibid="bib58" firstref="ref28"></nolink> <nolink nlid="nl14" bibid="bib17" firstref="ref29"></nolink> <nolink nlid="nl15" bibid="bib40" firstref="ref30"></nolink> <nolink nlid="nl16" bibid="bib43" firstref="ref31"></nolink> <nolink nlid="nl17" bibid="bib10" firstref="ref33"></nolink> <nolink nlid="nl18" bibid="bib15" firstref="ref34"></nolink> <nolink nlid="nl19" bibid="bib19" firstref="ref35"></nolink> <nolink nlid="nl20" bibid="bib24" firstref="ref36"></nolink> <nolink nlid="nl21" bibid="bib46" firstref="ref38"></nolink> <nolink nlid="nl22" bibid="bib60" firstref="ref40"></nolink> <nolink nlid="nl23" bibid="bib18" firstref="ref42"></nolink> <nolink nlid="nl24" bibid="bib23" firstref="ref43"></nolink> <nolink nlid="nl25" bibid="bib34" firstref="ref44"></nolink> <nolink nlid="nl26" bibid="bib64" firstref="ref46"></nolink> <nolink nlid="nl27" bibid="bib13" firstref="ref49"></nolink> <nolink nlid="nl28" bibid="bib29" firstref="ref50"></nolink> <nolink nlid="nl29" bibid="bib55" firstref="ref51"></nolink> <nolink nlid="nl30" bibid="bib41" firstref="ref52"></nolink> <nolink nlid="nl31" bibid="bib35" firstref="ref53"></nolink> <nolink nlid="nl32" bibid="bib61" firstref="ref54"></nolink> <nolink nlid="nl33" bibid="bib48" firstref="ref55"></nolink> <nolink nlid="nl34" bibid="bib38" firstref="ref63"></nolink> <nolink nlid="nl35" bibid="bib42" firstref="ref65"></nolink> <nolink nlid="nl36" bibid="bib21" firstref="ref69"></nolink> <nolink nlid="nl37" bibid="bib45" firstref="ref70"></nolink> <nolink nlid="nl38" bibid="bib12" firstref="ref72"></nolink> <nolink nlid="nl39" bibid="bib14" firstref="ref87"></nolink> <nolink nlid="nl40" bibid="bib59" firstref="ref91"></nolink> <nolink nlid="nl41" bibid="bib16" firstref="ref92"></nolink> <nolink nlid="nl42" bibid="bib30" firstref="ref94"></nolink> <nolink nlid="nl43" bibid="bib50" firstref="ref95"></nolink> <nolink nlid="nl44" bibid="bib57" firstref="ref97"></nolink> <nolink nlid="nl45" bibid="bib56" firstref="ref98"></nolink> <nolink nlid="nl46" bibid="bib25" firstref="ref99"></nolink> <nolink nlid="nl47" bibid="bib36" firstref="ref100"></nolink> <nolink nlid="nl48" bibid="bib32" firstref="ref114"></nolink> <nolink nlid="nl49" bibid="bib33" firstref="ref116"></nolink> <nolink nlid="nl50" bibid="bib47" firstref="ref129"></nolink> <nolink nlid="nl51" bibid="bib49" firstref="ref130"></nolink> <nolink nlid="nl52" bibid="bib39" firstref="ref134"></nolink> <nolink nlid="nl53" bibid="bib20" firstref="ref139"></nolink> <nolink nlid="nl54" bibid="bib27" firstref="ref141"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Paul+L%2E+Morgan%22">Paul L. Morgan</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9347-6486">0000-0001-9347-6486</externalLink>)<br /><searchLink fieldCode="AR" term="%22Eric+Hengyu+Hu%22">Eric Hengyu Hu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-2113-3940">0000-0002-2113-3940</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2026 30(6):1488-1503.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2026
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Institute of Education Sciences (ED)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: R324A220271
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Elementary+Education%22">Elementary Education</searchLink><br /><searchLink fieldCode="EL" term="%22Grade+4%22">Grade 4</searchLink><br /><searchLink fieldCode="EL" term="%22Intermediate+Grades%22">Intermediate Grades</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+Schools%22">Elementary Schools</searchLink><br /><searchLink fieldCode="DE" term="%22Elementary+School+Students%22">Elementary School Students</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Characteristics%22">Student Characteristics</searchLink><br /><searchLink fieldCode="DE" term="%22Disproportionate+Representation%22">Disproportionate Representation</searchLink><br /><searchLink fieldCode="DE" term="%22Grade+4%22">Grade 4</searchLink><br /><searchLink fieldCode="DE" term="%22National+Competency+Tests%22">National Competency Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Race%22">Race</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Reading+Achievement%22">Reading Achievement</searchLink><br /><searchLink fieldCode="DE" term="%22Sex%22">Sex</searchLink><br /><searchLink fieldCode="DE" term="%22Family+Income%22">Family Income</searchLink><br /><searchLink fieldCode="DE" term="%22Lunch+Programs%22">Lunch Programs</searchLink><br /><searchLink fieldCode="DE" term="%22Bilingual+Students%22">Bilingual Students</searchLink><br /><searchLink fieldCode="DE" term="%22Multilingualism%22">Multilingualism</searchLink><br /><searchLink fieldCode="DE" term="%22English+Learners%22">English Learners</searchLink><br /><searchLink fieldCode="DE" term="%22Disability+Identification%22">Disability Identification</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22National+Assessment+of+Educational+Progress%22">National Assessment of Educational Progress</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613261434432
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Whether and to what extent sociodemographic disparities in school-based autism identification have been occurring in U.S. elementary schools is currently unclear. We investigated for disparities attributable to race, ethnicity, biological sex, family income, and language use by analyzing repeated cross-sectional data collected on very large samples of U.S. fourth graders participating in the National Assessment of Educational Progress from 2003 to 2022 (ns = 103,150-205,860). Multivariable logistic regression models accounting for potential confounds including student-level academic achievement and school-level resources repeatedly indicated that students of color, females, students from low-income families, and multilingual learners (MLs) are less likely to be identified with autism while attending U.S. elementary schools. These disparities have been largely stable over time, particularly for Black students, females, and MLs. Health and educational policies that ensure equal access to autism supports and services in U.S. elementary schools including by students from historically marginalized communities are warranted.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: CodeSource
  Label: IES Funded
  Group: SrcInfo
  Data: Yes
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1506651
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1506651
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613261434432
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1488
    Subjects:
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Elementary Schools
        Type: general
      – SubjectFull: Elementary School Students
        Type: general
      – SubjectFull: Student Characteristics
        Type: general
      – SubjectFull: Disproportionate Representation
        Type: general
      – SubjectFull: Grade 4
        Type: general
      – SubjectFull: National Competency Tests
        Type: general
      – SubjectFull: Race
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      – SubjectFull: Ethnicity
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      – SubjectFull: Reading Achievement
        Type: general
      – SubjectFull: Sex
        Type: general
      – SubjectFull: Family Income
        Type: general
      – SubjectFull: Lunch Programs
        Type: general
      – SubjectFull: Bilingual Students
        Type: general
      – SubjectFull: Multilingualism
        Type: general
      – SubjectFull: English Learners
        Type: general
      – SubjectFull: Disability Identification
        Type: general
      – SubjectFull: National Assessment of Educational Progress
        Type: general
    Titles:
      – TitleFull: Over-Time Estimates of Sociodemographic Disparities in Autism Identification in U.S. Elementary Schools
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            NameFull: Paul L. Morgan
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            NameFull: Eric Hengyu Hu
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          Dates:
            – D: 01
              M: 06
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
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              Value: 30
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              Value: 6
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            – TitleFull: Autism: The International Journal of Research and Practice
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