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Examining the Prevalence Rates of Autism Diagnosis by Race/Ethnicity for Medicaid-Eligible Children Enrolled in NYC Universal Pre-Kindergarten Programs

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Title: Examining the Prevalence Rates of Autism Diagnosis by Race/Ethnicity for Medicaid-Eligible Children Enrolled in NYC Universal Pre-Kindergarten Programs
Language: English
Authors: Strassfeld, Natasha M. (ORCID 0000-0002-8522-2272), Cherng, Hua-Yu Sebastian (ORCID 0000-0003-2444-4354), Wang, Scarlett (ORCID 0000-0003-2687-0365), Glied, Sherry (ORCID 0000-0001-9432-1662)
Source: Journal of Research in Childhood Education. 2023 37(3):476-491.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 16
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Education Level: Early Childhood Education
Preschool Education
Descriptors: Autism Spectrum Disorders, Incidence, Preschool Education, Racial Differences, Ethnicity, Probability, Clinical Diagnosis, Learning Disabilities, Physical Disabilities, Disproportionate Representation, Whites, Minority Groups, Children, Access to Education, Health Insurance, Low Income Groups
Geographic Terms: New York (New York)
DOI: 10.1080/02568543.2023.2213281
ISSN: 0256-8543
2150-2641
Abstract: This study examines autism diagnosis prevalence within the New York City (NYC) Universal Pre-K for All (UPK) program expansion into racially, ethnically, and socioeconomically diverse NYC neighborhoods. Here, it is hypothesized that racial/ethnic differences in autism diagnoses may close as more children are referred for testing by UPK programs, which they have more thorough interactions with, instead of by public health clinics or other medical avenues. Using NYC Medicaid claim data from 2006 to 2016, descriptive analyses were conducted by estimating linear probability regression and generalized multiple logistic regression to examine whether the probabilities of being diagnosed with autism in comparison to two other disability types (as counterfactuals), learning disabilities (LD) and physical disabilities (PD), differ by race. Subsequently, a difference in difference (DID) strategy (with pre- and post-UPK expansion cohorts) was used to examine the effects of UPK on the probabilities of receiving disability diagnoses. Notably, Latinx and "Other" racially identified children have much higher odds than White children of being diagnosed with autism. By contrast, all non-White groups had much lower odds of being diagnosed with a LD. These findings offer important insight for future UPK and childhood program implementation.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1396128
Database: ERIC
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  Value: <anid>AN0165472494;40z01jul.23;2023Jul25.06:07;v2.2.500</anid> <title id="AN0165472494-1">Examining the Prevalence Rates of Autism Diagnosis by Race/Ethnicity for Medicaid-Eligible Children Enrolled in NYC Universal Pre-Kindergarten Programs </title> <p>This study examines autism diagnosis prevalence within the New York City (NYC) Universal Pre-K for All (UPK) program expansion into racially, ethnically, and socioeconomically diverse NYC neighborhoods. Here, it is hypothesized that racial/ethnic differences in autism diagnoses may close as more children are referred for testing by UPK programs, which they have more thorough interactions with, instead of by public health clinics or other medical avenues. Using NYC Medicaid claim data from 2006 to 2016, descriptive analyses were conducted by estimating linear probability regression and generalized multiple logistic regression to examine whether the probabilities of being diagnosed with autism in comparison to two other disability types (as counterfactuals), learning disabilities (LD) and physical disabilities (PD), differ by race. Subsequently, a difference in difference (DID) strategy (with pre- and post-UPK expansion cohorts) was used to examine the effects of UPK on the probabilities of receiving disability diagnoses. Notably, Latinx and "Other" racially identified children have much higher odds than White children of being diagnosed with autism. By contrast, all non-White groups had much lower odds of being diagnosed with a LD. These findings offer important insight for future UPK and childhood program implementation.</p> <p>Keywords: Children with disabilities; diverse settings; early childhood special education; policy</p> <p>Recent research on minority disproportionate representation (MDR) in special education, whereby a student's racial or ethnic group classification serves as a potential link to the likelihood of being over- or underrepresented (Bollmer et al., [<reflink idref="bib2" id="ref1">2</reflink>]), has historically examined MDR patterns within diagnosis, disability identification, and placement within school settings (Strassfeld, [<reflink idref="bib20" id="ref2">20</reflink>]; Strassfeld & Cherng, [<reflink idref="bib21" id="ref3">21</reflink>]). This research suggests that placement into certain disability categories outlined within the Individuals with Disabilities Education Act (IDEA) may be shaped by a variety of factors, including racial bias and an overall lack of access to services for students within a particular disability category (Strassfeld, [<reflink idref="bib20" id="ref4">20</reflink>]). Moreover, for some disabilities referred to as "soft" or "socially determined" (Travers et al., [<reflink idref="bib24" id="ref5">24</reflink>], p. 41), it has been assumed that it may be more likely that children with disabilities are disproportionately over- or under-identified when IDEA disability categories are more inclusive of conditions and are, consequently, subject to greater interpretation. Conversely, it has been argued that for "hard" disabilities (e.g., traumatic brain injury, autism spectrum disorder ["autism"]), there should be less evidence of MDR on either continuum end because these disability categories are "less influenced by social variables [and] therefore less subject to disproportionate representation" (Travers et al., [<reflink idref="bib24" id="ref6">24</reflink>], p. 41).</p> <p>Relatedly, within special education research, there has been an ongoing discussion regarding whether or not certain disability diagnoses serve as "high roads" or "low roads" to disability (Ong Dean, [<reflink idref="bib17" id="ref7">17</reflink>]). Within Ong Dean's ([<reflink idref="bib17" id="ref8">17</reflink>]) theorization of disability, diagnosis may, in instances, be "favored" by affluent, White families (the "high road" to disability), but over time the same disability diagnosis may have a negative stigma or association in affluent communities (the "low road" to disability; Ong Dean, [<reflink idref="bib17" id="ref9">17</reflink>]). By being on this "low road" to a disability diagnosis, students, particularly racialized minority and ethnic students, experience "structural effects on disability diagnosis that might be expected to render disadvantaged students increasingly vulnerable to disability diagnosis over time" (Ong Dean, [<reflink idref="bib17" id="ref10">17</reflink>], p. 94).</p> <p>While recent research has explored racial disparities for students with autism within IDEA identification, evaluation, and referrals in school settings (Travers & Krezmien, [<reflink idref="bib23" id="ref11">23</reflink>]; Sullivan & Suldo, [<reflink idref="bib22" id="ref12">22</reflink>]), less is known about racial disparities, if any, within medical diagnoses for children with autism. Autism is a category of neurodevelopmental conditions that can be diagnosed in children as young as 18 months, with recent prevalence estimates from the Centers for Disease Control and Prevention (CDC) indicating that approximately 1 in 54 U.S. children have autism along the spectrum of disorders (Maenner et al., [<reflink idref="bib11" id="ref13">11</reflink>]). Children with autism typically exhibit a variety of behaviors, including atypical social skills and communication deficits and repetition within actions (Zuckerman et al., [<reflink idref="bib29" id="ref14">29</reflink>]). Thus, understanding prevalence rates of medical diagnosis is particularly important as it provides insight into health outcomes and links, if any, between medical diagnosis for federal/state programs, such as Medicaid and other eligibility-based programs, and service disparity by particular conditions (National Academies of Science, Engineering, and Medicine, [<reflink idref="bib12" id="ref15">12</reflink>]).</p> <p>Relatedly, medical diagnoses for Medicaid-eligible children with disabilities who are enrolled in universal pre-K (UPK) programs offer a unique opportunity to examine the impacts of UPK enrollment and Medicaid service receipt in conjunction with each other. For instance, others have noted that "there is a reciprocal relationship between health and education over time that contributes to disparities in each" (Fiscella & Kitzman, [<reflink idref="bib5" id="ref16">5</reflink>], p. 1074). Further, as prevalence rates for autism and the associated spectrum of disorders continue to increase (Centers for Disease Control and Prevention [CDC], [<reflink idref="bib3" id="ref17">3</reflink>]; Zablotsky et al., [<reflink idref="bib28" id="ref18">28</reflink>]), diagnosis rates for clinical autism may converge and diverge in important ways from how IDEA-eligible students with disabilities are identified and referred within school settings.</p> <hd id="AN0165472494-2">Diagnostic procedures within education and clinical settings</hd> <p>For eligible U.S. children, a child may receive a clinical autism diagnosis within a clinical or medical setting and/or a classification of autism for special education eligibility under IDEA (Nowell et al., [<reflink idref="bib16" id="ref19">16</reflink>]). These diagnostic processes are distinct from each other in that IDEA eligibility does not require a Diagnostic and Statistical Manual of Mental Disorders (DSM-5) diagnosis as long as a child has a disability, which impairs educational attainment (Individuals with Disabilities Education Act [IDEA], [<reflink idref="bib9" id="ref20">9</reflink>]). Moreover, "[the receipt of a] DSM-5 diagnosis of [autism] in a medical or clinical setting does not necessarily result in a special education classification of autism because of the requirement that there is evidence of both a disability condition and educational need for an individual to receive special education services" (Nowell et al., [<reflink idref="bib16" id="ref21">16</reflink>], p. 301).</p> <hd id="AN0165472494-3">Disproportionality within ASD identification in school settings</hd> <p>Travers et al. ([<reflink idref="bib24" id="ref22">24</reflink>]) examined MDR within a longitudinal study of IDEA eligibility data by determining risk and logistical odds ratios among White students and students of color. The study found that the overall risk of autism did increase each year (Travers et al., [<reflink idref="bib24" id="ref23">24</reflink>]). However, the study found that for most of the years under review (1998–2006), White students were twice as likely to be identified with autism than Latinx and Indigenous students during the majority of the years within the study (Travers et al., [<reflink idref="bib24" id="ref24">24</reflink>]). In addition, the study found that while the odds ratios for Asian and Black students showed an increased likelihood of overrepresentation within the early years within the dataset, overrepresentation of students from these racial/ethnic categories actually declined within subsequent years (Travers et al., [<reflink idref="bib24" id="ref25">24</reflink>]). Moreover, Latinx and Alaskan Native students were "significantly underrepresented" within each dataset year analyzed by the authors (Travers et al., [<reflink idref="bib24" id="ref26">24</reflink>], p. 41).</p> <p>While these findings contributed to the relatively scant literature base regarding IDEA autism eligibility, these findings also fit into a broader research base that has signaled challenges within autism diagnosis and identification, namely because autism may be subject to <emph>diagnostic substitution</emph> (e.g., where children who might have been diagnosed with another disability in previous years are now diagnosed with autism) and diagnosis latency issues (Travers et al., [<reflink idref="bib24" id="ref27">24</reflink>]). The authors posited that both issues may lead to underrepresentation, and latency threats may mean that "children from racially and ethnically diverse families may be less likely to receive a timely clinical diagnosis of autism outside of the school setting and instead may rely on special education screening and assessment processes for autism identification" (Travers et al., [<reflink idref="bib24" id="ref28">24</reflink>], p. 46).</p> <p>Similarly, other studies examining IDEA data have found variability within autism identification for minority youth, in comparison to White youth. For instance, in a national study of 2008 IDEA data from the Data Accountability Center, Sullivan and Suldo ([<reflink idref="bib22" id="ref29">22</reflink>]) found variability in autism identification broadly across states, with autism prevalence being significantly higher for White than Black, Latinx, or American Indian/Alaskan Native students. Also, in comparison to White students, Latinx and American Indian/Alaskan Native students were less likely to receive an autism identification, while Asian/Pacific Islander students were often more likely to receive an autism identification than their White counterparts (Sullivan & Suldo, [<reflink idref="bib22" id="ref30">22</reflink>]).</p> <hd id="AN0165472494-4">Disproportionality within ASD diagnoses in clinical or community health settings</hd> <p>From a clinical or community health perspective, studies have found MDR within autism diagnoses of children. First, the CDC found that autism prevalence rates between ethnic groups differ significantly when White children are the comparison group (Baio et al., [<reflink idref="bib1" id="ref31">1</reflink>]). Yet, when racial/ethnic groups are disaggregated, evidence of MDR can be "somewhat inconsistent" (Nowell et al., [<reflink idref="bib16" id="ref32">16</reflink>], p. 301). For instance, using the nationally representative dataset, the National Survey of Children's Health, a study found lower prevalence rates of autism diagnoses in Latinx families (26 out of 10,000) in comparison to non-Latinx families (51 out of 10,000) (Liptak et al., [<reflink idref="bib10" id="ref33">10</reflink>]). However, this same study found no significant differences in autism diagnosis rates between Black and White families (Liptak et al., [<reflink idref="bib10" id="ref34">10</reflink>]).</p> <hd id="AN0165472494-5">UPK and disproportionality</hd> <p>Studies from both a clinical diagnostic or IDEA disability identification perspective show evidence that minority children are disproportionately less likely to be identified or diagnosed with autism than their White counterparts. Yet, as more states and cities adopt UPK policies, there is the policy-based assumption that a universal set of early childhood education services will make uniform the educational experiences of children. That is, from a policy perspective, UPK programs across the United States have been premised along the policy line that uniform, widely available early childhood services will provide greater and more equitable education and health access, early diagnosis and services/resources for all children (Reid et al., [<reflink idref="bib18" id="ref35">18</reflink>]; Schilder et al., [<reflink idref="bib19" id="ref36">19</reflink>]). However, from a research perspective, preliminary findings suggest that UPK programs exist on a spectrum of early childhood options that are "characterized by extensive variation" (Gormley et al., [<reflink idref="bib7" id="ref37">7</reflink>], p. 873). Moreover, as more cities and states introduce UPK programs for children, there is also a growing need to understand the impact of UPK expansion on health outcomes of students by disability category (i.e., autism diagnostic patterns, particularly by race or ethnicity), because low-income, Medicaid-eligible students may also gain greater access to health/checkup screenings, diagnostic assessments, or immunizations as a byproduct of being enrolled within a UPK setting (Hong et al., [<reflink idref="bib8" id="ref38">8</reflink>]).</p> <hd id="AN0165472494-6">NYC Universal Pre-K policy</hd> <p>In 2014, the <emph>Pre-K for All</emph> expansion began as a way to strengthen early care and education programs for New York City (NYC) children and families. Specifically, the <emph>Pre-K for All</emph> program provides prekindergarten programming and education services to eligible children who are 4 years of age on or before December 1 of the eligibility year or who will be eligible to start public school kindergarten during the following school year. Within every prekindergarten program within a school district, there is an outlined, prescribed curriculum consisting of evidence-based early literacy, emergent reading instruction, and assessment practices to monitor prekindergarten progress annually (Universal Prekindergarten New York State Education Department Education Regulations, Subpart 151, [<reflink idref="bib26" id="ref39">26</reflink>]). Programs must be either all day or half day and operate 5 days a week for a minimum of 180 days per year (Universal Prekindergarten New York State Education Department Education Regulations, Subpart 151, [<reflink idref="bib26" id="ref40">26</reflink>]). In the most recently reported school year (2018–2019), approximately 70,000 four-year-olds were enrolled (New York City Department of Education, [<reflink idref="bib14" id="ref41">14</reflink>]). To complement the <emph>Pre-K for All</emph> program, NYC launched <emph>3-K for All</emph>, a free, full-day early education public school program for every eligible 3-year-old child (New York City Department of Education, [<reflink idref="bib14" id="ref42">14</reflink>]).</p> <p>With implementation of UPK in the largest U.S. school system, there is the possibility to assess how a comprehensive, multi-pronged UPK program impacts diagnostic outcomes, with special attention paid to race and ethnicity. Accordingly, the research questions are: 1) What is the prevalence of autism diagnoses, by student race/ethnicity, within a UPK-enrolled student population in NYC?; and 2) How do these rates and racial/ethnic patterns change for children pre- and post-UPK expansion detail? It is hypothesized that UPK and Medicaid enrollment will positively influence autism diagnoses across all racial categories, thus providing an important benefit to the lives of a population of racialized minority and ethnic children. Moreover, autism serves as the disability category of primary focus, but it is contextualized and examined in relation to other disability categories that have been historically identified as prevalent disability categories.</p> <hd id="AN0165472494-7">Methods</hd> <p></p> <hd id="AN0165472494-8">Data</hd> <p>The data used in this study are NYC Medicaid claims from 2006 to 2016. The data include Medicaid member demographic information, eligibility, medical services, and diagnostic information. For our analyses examining potential differences in diagnoses, identified children were born between January 1, 2006 to December 31, 2012 and their Medicaid claims from birth to age 5 were used for the analysis. Children with Medicare coverage were eliminated from analysis. When children have both Medicaid and Medicare, their health utilizations are covered by both insurances (U.S. Department of Health & Human Services, [<reflink idref="bib25" id="ref43">25</reflink>]). Since only Medicaid data were available, the authors cannot view children's health utilization covered by Medicare, resulting in selection bias. Children who had less than 4.5 years of eligibility on Medicaid before the fifth birthday, or children without a valid New York City address were removed from the final sample. The final analytic sample included 293,954 children.</p> <p>The outcome variables come from the list of International Classification of Diseases (ICD) codes from the New York State Early Intervention (NYS EI) billing support website to form four disability categories – autism, learning disabilities (LD), other disabilities, and no disability category (New York State Early Intervention Fiscal Portal, [<reflink idref="bib15" id="ref44">15</reflink>]). ICD 9 299×x and ICD 10 F84×x were used to identify children with autism. Clinical Classifications of Software (CCS) categories 650 through 670 were used to further collapse LD ICD codes into one category. The remaining ICD codes from NYS EI billing support were all physical disability (PD) codes (e.g., disability related to physical impairments or mobility issues). Autism, LD, and PD were mutually exclusive categories, and these categories were selected due to the high prevalence rates within the population for these combined categories. Also, examining LD and PD in conjunction with and as counterfactuals to autism also allows for an additional check on diagnostic substitution. Moreover, research suggests that race and ethnicity influence autism and LD diagnosis, but much less so for PD (Van Naarden Braun et al., [<reflink idref="bib27" id="ref45">27</reflink>]). Thus, this study includes all three categories to contrast the effect of race across disability categories. Children's addresses were geocoded, then linked to corresponding school districts in the Primary Land Use Tax Lot Output (PLUTO™) data file from NYC Open Data (New York City Department of City Planning, [<reflink idref="bib13" id="ref46">13</reflink>]). The independent variables are sex, age, race/ethnicity, school district, and birth year.[<reflink idref="bib1" id="ref47">1</reflink>]</p> <hd id="AN0165472494-9">Statistical analysis</hd> <p>SAS and STATA were used to perform the statistical analyses of the study. Bivariate analysis, linear probability regression, and generalized multiple logistic regression were performed to examine whether the probability of being diagnosed with each of the three disability types would differ by race. Then, a difference in difference (DID) model examined the effect of UPK on the probabilities of getting disability diagnoses.[<reflink idref="bib2" id="ref48">2</reflink>]</p> <p>In the DID design, the authors selected a subsample from the cohort design, in order to compare children born around the cutoff of the UPK expansion, namely children born between January 2008 and December 2011, and divided them into two cohorts, "cohort oldest" and "cohort youngest." Each cohort had two arms and two periods, treatment (UPK arm) and control, a pre-expansion (2013–2014 academic year) period and a post-expansion (2014–2015 academic year) period. Disability categories were measured in both periods. Cohort oldest used children born in 2010 (treatment) and 2009 (control) to measure the post-expansion effect, and children born in 2009 (treatment) and 2008 (control) to measure the pre-expansion effect. Cohort youngest used children born in 2010 (treatment) and 2011 (control) to measure the post-expansion effect, and children born in 2009 (treatment) and 2010 (control) to measure the pre-expansion effect. The authors estimated the UPK effect for the total subsample and then by race. The authors estimated the UPK effects separately for cohort youngest and cohort oldest (Model DID a), then pulled effects together for the overall UPK effect (Model DID b). Since nine models on the subsample were performed, the Bonferroni adjustment is used,</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>α</mi><mrow><mi>n</mi><mi>e</mi><mi>w</mi></mrow></msub></mrow><mo>=</mo><mrow><mfrac><mrow><mn>0.05</mn></mrow><mn>9</mn></mfrac></mrow><mo>=</mo><mn>0.0056.</mn></math> </ephtml> </p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi mathvariant="normal">M</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">d</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">l</mi><mtext /><mi mathvariant="normal">D</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">D</mi><mtext /><mi mathvariant="normal">a</mi></mrow></mrow><mo>:</mo><mrow><msub><mi>y</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow><mo>=</mo><mi>α</mi><mo>+</mo><mrow><msub><mi>ρ</mi><mn>1</mn></msub></mrow><mrow><msub><mi>D</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow><mo>+</mo><mn>1</mn><mfenced open="{" close="}"><mrow><mi>t</mi><mo>=</mo><mn>2014</mn></mrow></mfenced><mfenced open="[" close="]"><mrow><mi>θ</mi><mrow><msub><mi>D</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow></mrow></mfenced><mo>+</mo><mrow><msub><mi>ε</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow></math> </ephtml> </p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mi mathvariant="normal">M</mi><mi mathvariant="normal">o</mi><mi mathvariant="normal">d</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">l</mi><mtext /><mi mathvariant="normal">D</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">D</mi><mtext /><mi mathvariant="normal">b</mi></mrow></mrow><mo>:</mo><mrow><mrow><mi /></mrow></mrow><mrow><msub><mi>y</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow><mo>=</mo><mi>α</mi><mo>+</mo><mrow><msub><mi>ρ</mi><mn>1</mn></msub></mrow><mrow><msub><mi>D</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow><mo>+</mo><mn>1</mn><mfenced open="{" close="}"><mrow><mi>t</mi><mo>=</mo><mn>2014</mn></mrow></mfenced><mfenced open="[" close="]"><mrow><mi>θ</mi><mrow><msub><mi>D</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow></mrow></mfenced><mo>+</mo><mrow><msub><mi>d</mi><mi>i</mi></msub></mrow><mo>+</mo><mrow><msub><mi>ε</mi><mrow><mi>i</mi><mi>t</mi></mrow></msub></mrow></math> </ephtml> </p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi mathvariant="normal">w</mi><mi mathvariant="normal">h</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">r</mi><mi mathvariant="normal">e</mi><mo>,</mo></mrow><mtext /><mi>t</mi><mo>=</mo><mi>a</mi><mi>c</mi><mi>a</mi><mi>d</mi><mi>e</mi><mi>m</mi><mi>i</mi><mi>c</mi><mtext /><mi>y</mi><mi>e</mi><mi>a</mi><mi>r</mi><mo>,</mo><mrow><msub><mi>y</mi><mi>t</mi></msub></mrow><mo>=</mo><mi>d</mi><mi>i</mi><mi>s</mi><mi>a</mi><mi>b</mi><mi>i</mi><mi>l</mi><mi>i</mi><mi>t</mi><mi>y</mi><mtext /><mi>i</mi><mi>n</mi><mi>d</mi><mi>i</mi><mi>c</mi><mi>a</mi><mi>t</mi><mi>o</mi><mi>r</mi><mo>,</mo><mrow><msub><mi>D</mi><mi>t</mi></msub></mrow><mfenced open="{" close=""><mrow><mtable rowspacing="4pt" columnspacing="1em"><mtr><mtd><mrow><mn>1</mn><mo>=</mo><mi>t</mi><mi>r</mi><mi>e</mi><mi>a</mi><mi>t</mi><mi>m</mi><mi>e</mi><mi>n</mi><mi>t</mi></mrow></mtd></mtr><mtr><mtd><mrow><mn>0</mn><mo>=</mo><mi>c</mi><mi>o</mi><mi>n</mi><mi>t</mi><mi>r</mi><mi>o</mi><mi>l</mi></mrow></mtd></mtr></mtable></mrow></mfenced></math> </ephtml> </p> <p>Model DID a is estimated using either the sample of cohort-oldest children or the sample of cohort-youngest children. Model DID b is estimated using both samples, including</p> <p>Graph</p> <p> <ephtml> <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><msub><mi>d</mi><mi>i</mi></msub></mrow></math> </ephtml> as a fixed effect indicating that the observation is from the sample of the cohort-oldest children.</p> <hd id="AN0165472494-10">Results</hd> <p></p> <hd id="AN0165472494-11">Demographics</hd> <p>Across the 293,954 children selected, 3.3% had autism, 5.0% had LD, and 3.1% had PD. The sample consisted of 48.7% female, 16.8% white, 11.4% Asian, 21.8% Black, 35.4% Latinx, and 14.3% Other or Unknown Race. See Table 1 for a breakdown of the demographic composition of children within the sample. On average, children were diagnosed with autism at 2.5 years of age, while LD and PD were diagnosed a little younger, at 1.9 and 1.7 years of age, respectively. Between 13.2% (2006) and 14.8% (2010) of the sample were selected each year, across all 7 years. Between 0.6% (District 26) and 8.3% (District 20) of the sample were selected from each school district.</p> <p>Table 1. Demographic information by disability categories.</p> <p> <ephtml> <table><thead><tr><td /><td>Total Sample</td><td>Disability Categories</td></tr><tr><td /><td /><td>No Disability</td><td>Autism</td><td>Learning Disability</td><td>Physical Disability</td></tr></thead><tbody><tr><td>Total</td><td>293,954</td><td>260,260</td><td>9,767</td><td>14,817</td><td>9,114</td></tr><tr><td>%</td><td>-</td><td>88.5%</td><td>3.3%</td><td>5.0%</td><td>3.1%</td></tr><tr><td>Age at Diagnosis</td><td>-</td><td>-</td><td>2.53</td><td>1.89</td><td>1.69</td></tr><tr><td>Female (%)</td><td>48.7%</td><td>50.2%</td><td>22.9%</td><td>40.9%</td><td>43.9%</td></tr><tr><td>Race (column %)</td><td /><td /><td /><td /><td /></tr><tr><td>Asian</td><td>11.4%</td><td>11.7%</td><td>10.0%</td><td>8.0%</td><td>9.4%</td></tr><tr><td>Black</td><td>21.8%</td><td>22.1%</td><td>21.2%</td><td>19.8%</td><td>17.3%</td></tr><tr><td>Latinx</td><td>35.4%</td><td>34.6%</td><td>47.9%</td><td>38.9%</td><td>38.1%</td></tr><tr><td>White</td><td>16.8%</td><td>16.7%</td><td>11.2%</td><td>19.9%</td><td>21.8%</td></tr><tr><td>Other</td><td>2.9%</td><td>2.9%</td><td>2.4%</td><td>2.9%</td><td>3.2%</td></tr><tr><td>Unknown</td><td>11.4%</td><td>11.6%</td><td>7.0%</td><td>10.3%</td><td>10.0%</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-12">Linear probability model</hd> <p>A set of linear probability models were used to estimate the probability of having a diagnosis from each disability category with race as the main independent variable, adjusting for gender, school district, and birth year in the model (Appendices A–D).</p> <p>The patterns of predicted probabilities varied drastically by disability category and race/ethnicity. Latinx children were most likely to have an autism diagnosis at 4.3%, while White, Asian, and Black children each had a probability around 3%. White children were most likely to be diagnosed with LD at 6.6%, followed by Latinx children at 5.4%. Black (4.4%) and Asian (3.2%) children were much less likely to be diagnosed with LD than White and Latinx children. The probability of getting a PD diagnosis was roughly even among all groups, with the highest probability at 3.5% for Latinx children and the lowest probability at 2.3% for Asian children (see Figure 1).</p> <p>PHOTO (COLOR): Figure 1. Predicted probability of disability by race.</p> <hd id="AN0165472494-13">Generalized multiple logistic regression</hd> <p>Building upon the results from the linear probability models, the authors ran a generalized logistic regression model with disability categories as the outcome to isolate the pair-wise odds ratio comparisons. The model used race as the main independent variable and controlled for gender, school district, and birth year.</p> <p>The results from the generalized logistic regression showed that the odds of Latinx children getting an autism diagnosis were between 1.43 (Black) and 2.07 (Other) times higher than the odds of all other children. On the contrary, the odds ratios of getting an autism diagnosis comparing Asian children to White children (OR = 0.982, <emph>p</emph> =.702) and comparing Black children to White children (OR = 1.083, <emph>p</emph> =.060) were both insignificant. The odds of White children getting a LD diagnosis ranged from 1.23 (Latinx) to 2.16 (Asian) times higher than other groups of children, while the odds of Asian children getting a LD diagnosis were between 27.3% (Black) and 53.7% (White) lower than all others. Although significant, all pairwise odds ratios of getting a PD diagnosis were under 1.6, ranging between 1.07 (Asian vs. Other) and 1.56 (White vs. Other). See Table 2 for pairwise odds ratio comparisons by disability categories.</p> <p>Table 2. Pairwise odds ratio comparison by disability categories.</p> <p> <ephtml> <table><thead><tr><td>Odds Ratio</td><td>Estimate</td><td>95% Confidence Limits</td></tr></thead><tbody><tr><td>Autism</td></tr><tr><td>Asian vs White</td><td>0.982</td><td>0.896</td><td>1.077</td></tr><tr><td>Black vs White</td><td>1.083</td><td>0.997</td><td>1.176</td></tr><tr><td>Latinx vs White</td><td>1.547</td><td>1.437</td><td>1.666</td></tr><tr><td>Other vs White</td><td>0.747</td><td>0.680</td><td>0.819</td></tr><tr><td>Learning Disability</td></tr><tr><td>Asian vs White</td><td>0.463</td><td>0.431</td><td>0.498</td></tr><tr><td>Black vs White</td><td>0.637</td><td>0.600</td><td>0.676</td></tr><tr><td>Latinx vs White</td><td>0.811</td><td>0.770</td><td>0.854</td></tr><tr><td>Other vs White</td><td>0.655</td><td>0.615</td><td>0.697</td></tr><tr><td>Physical Disability</td></tr><tr><td>Asian vs White</td><td>0.674</td><td>0.619</td><td>0.735</td></tr><tr><td>Black vs White</td><td>0.788</td><td>0.730</td><td>0.851</td></tr><tr><td>Latinx vs White</td><td>1.069</td><td>1.000</td><td>1.140</td></tr><tr><td>Other vs White</td><td>0.856</td><td>0.793</td><td>0.925</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-14">Universal Pre-Kindergarten (UPK)</hd> <p>The DID design was used to estimate the effect of UPK accounts for preexisting discontinuities of diagnostic rates and the heterogeneous development trends by age (e.g., sensory and mental health issues) (Hong et al., [<reflink idref="bib8" id="ref49">8</reflink>]). After the adjustment, the UPK effects were small and not statistically significant. See Table 3 for the impact of UPK on disability diagnoses.</p> <p>Table 3. The impact of universal pre-kindergarten on disability diagnoses.</p> <p> <ephtml> <table><thead><tr><td /><td>Cohort Oldest</td><td>Cohort Youngest</td></tr><tr><td /><td>Before Expansion</td><td>After Expansion</td><td>Difference</td><td>Before Expansion</td><td>After Expansion</td><td>Difference</td></tr></thead><tbody><tr><td>All</td></tr><tr><td>Autism</td><td>−0.002</td><td>−0.001</td><td>−0.001</td><td>−0.001</td><td>−0.000</td><td>0.001</td></tr><tr><td>Learning Disability</td><td>−0.017</td><td>−0.019</td><td>−0.002</td><td>−0.002</td><td>0.001</td><td>0.003</td></tr><tr><td>Physical Disability</td><td>−0.019</td><td>−0.018</td><td>0.001</td><td>0.007</td><td>0.010</td><td>0.003</td></tr><tr><td>White</td></tr><tr><td>Autism</td><td>0.001</td><td>0.002</td><td>−0.003</td><td>−0.000</td><td>−0.001</td><td>−0.000</td></tr><tr><td>Learning Disability</td><td>−0.012</td><td>−0.009</td><td>0.002</td><td>−0.005</td><td>−0.002</td><td>0.003</td></tr><tr><td>Physical Disability</td><td>−0.012</td><td>−0.005</td><td>0.007</td><td>−0.000</td><td>0.006</td><td>0.006</td></tr><tr><td>Black</td></tr><tr><td>Autism</td><td>−0.002</td><td>−0.005</td><td>−0.003</td><td>0.001</td><td>−0.000</td><td>−0.002</td></tr><tr><td>Learning Disability</td><td>−0.021</td><td>−0.026</td><td>−0.004</td><td>−0.002</td><td>−0.002</td><td>0.000</td></tr><tr><td>Physical Disability</td><td>−0.028</td><td>−0.023</td><td>−0.005</td><td>0.011</td><td>0.013</td><td>0.002</td></tr><tr><td>Asian</td></tr><tr><td>Autism</td><td>−0.003</td><td>−0.001</td><td>0.002</td><td>−0.003</td><td>−0.003</td><td>−0.000</td></tr><tr><td>Learning Disability</td><td>−0.008</td><td>−0.013</td><td>−0.005</td><td>−0.003</td><td>−0.003</td><td>−0.000</td></tr><tr><td>Physical Disability</td><td>−0.012</td><td>−0.009</td><td>0.003</td><td>0.004</td><td>0.005</td><td>0.001</td></tr><tr><td>Latinx</td></tr><tr><td>Autism</td><td>0.003</td><td>0.000</td><td>−0.003</td><td>−0.002</td><td>0.002</td><td>0.004</td></tr><tr><td>Learning Disability</td><td>−0.022</td><td>−0.024</td><td>−0.002</td><td>−0.002</td><td>0.003</td><td>0.005</td></tr><tr><td>Physical Disability</td><td>−0.022</td><td>−0.022</td><td>−0.000</td><td>0.010</td><td>0.013</td><td>0.003</td></tr><tr><td>Other</td></tr><tr><td>Autism</td><td>−0.009</td><td>0.009</td><td>0.018</td><td>−0.003</td><td>0.006</td><td>0.009</td></tr><tr><td>Learning Disability</td><td>−0.025</td><td>−0.006</td><td>0.019</td><td>−0.015</td><td>−0.004</td><td>0.012</td></tr><tr><td>Physical Disability</td><td>−0.029</td><td>0.008</td><td>0.022</td><td>−0.003</td><td>0.005</td><td>0.008</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-15">Discussion</hd> <p>In summary, the findings from this study highlight the prevalence of diagnosis disparities by race and ethnicity. Notably, children identified as Latinx and "Other" have much higher odds than White children of being diagnosed with autism, in comparison to Asian and Black children who are at similar odds of diagnoses. By contrast, all non-White racial or ethnic groups had much lower odds of being diagnosed with LD. Similarly, Asian, Black, and "Other" children are also less likely to be diagnosed with PD. Moreover, when the study's findings are contextualized within the broader research base, some of the study's findings are consistent with prior research in this area – namely, that Black and White youth are at similar odds of diagnosis (Fombonne & Zuckerman, [<reflink idref="bib6" id="ref50">6</reflink>]). However, regarding Latinx children and the higher odds of an autism diagnosis in this study, are there potential explanations that help to clarify why this particular study's findings are in contrast to others? There are emerging data and studies that may shed insight on this study's particular findings.</p> <hd id="AN0165472494-16">Higher rates of autism diagnosis in Latinx and "Other" racial/ethnic categories</hd> <p>Liptak et al. ([<reflink idref="bib10" id="ref51">10</reflink>]) found that the prevalence of autism was actually lower for Latinx than non-Latinx children (26/10,000 vs. 51/10,000, respectively). However, the authors also found that the overall research field had incongruous findings in this area (Liptak et al., [<reflink idref="bib10" id="ref52">10</reflink>]). One reason why this study may have found lower rates of autism diagnosis among Latinx youth may be due to the nature of the self-reported survey data where youth hailing from various countries or U.S. territories of origin (e.g., Cuba, Mexico, Puerto Rico) were grouped together when disaggregation by country or territory of origin may have provided greater insight (Liptak et al., [<reflink idref="bib10" id="ref53">10</reflink>]). Moreover, the authors posited that rates for Latinx children may be lower than other groups due to language and communication barriers, particularly in language-based screening and assessment, between health professionals and patients (Liptak et al., [<reflink idref="bib10" id="ref54">10</reflink>]). Current study findings suggest, however, service delivery in a large, metropolitan, and racially, ethnically, and linguistically diverse city where primary care physicians (PCPs) may include a larger number of PCPs fluent in Spanish with more access to screening and assessment in Spanish.</p> <p>This current study also provides insight about disability diagnosis rates and Asian children. As previously found, research on autism diagnosis rates has primarily examined African American and Latinx children in comparison to White children. However, there has been limited research examining autism diagnosis rates for Asian children. Not only did the study consist of a sizable Asian sample (11.4%), the pattern of predicted variabilities also provided greater insight about Asian children – namely, that Asian children were less likely to be diagnosed with LD. There have been historical assumptions that Asian children's outcomes more closely resemble those of White children than children in other racial/ethnic categories (Strassfeld & Cherng, [<reflink idref="bib21" id="ref55">21</reflink>]). However, this emerging research signals that, as with research regarding under- and over-representation of Asian children and youth, there may be aspects of diagnosis and assessment where Asian children's outcomes may be more aligned with other racialized minority and ethnic children (Cooc, [<reflink idref="bib4" id="ref56">4</reflink>]).</p> <hd id="AN0165472494-17">LD and physical diagnosis rates for white children</hd> <p>This study also found that White children were most likely to be diagnosed with LD instead of autism. These results signal important trends in regard to diagnosis of children by disability classification. Moreover, this current work does hearken back to the research done by Ong Dean ([<reflink idref="bib17" id="ref57">17</reflink>]), which found that, during the 1970s, an LD classification was seen as an uncommon diagnosis. Yet, as the diagnosis became more prevalent, the diagnosis "may have represented for some [privileged] parents a new and better way of explaining their children's academic difficulties" (Ong Dean, [<reflink idref="bib17" id="ref58">17</reflink>], p. 93). Then, by the 1980s and 1990s, as more racialized minority and ethnic students were under-identified as having a "mental retardation," the LD diagnosis became more prevalent for non-White children whereby "less privileged students were more likely to receive an LD diagnosis" (Ong Dean, [<reflink idref="bib17" id="ref59">17</reflink>], p. 93). Although this current single study is not sufficient to signal a trend, there is strong evidence here to suggest that assumptions about links between students from a particular racialized minority or ethnic group and a particular disability classification or diagnosis may need to be reconsidered. Future research should continue to explore whether rates of racial representation in diagnosis categories remain stable or show evidence of emerging trends, and research should continue to examine how rates differ across grade and level groupings (e.g., UPK, elementary, upper elementary).</p> <hd id="AN0165472494-18">UPK and the potential to reduce differences in diagnoses</hd> <p>Finally, this study used Medicaid diagnoses data for Medicaid-eligible children with disabilities enrolled in NYC's UPK program to estimate the effect of UPK eligibility on autism diagnosis patterns. A robust finding here is that after adjustment, the UPK effects were small and not statistically significant for all three diagnoses – autism, LD, and PD. That is, when it pertains to Medicaid diagnosis, the UPK program may not, in and of itself, offset diagnosis rates across racial or ethnic categories. There are several possible reasons why this might be the case.</p> <p>First, in a recent study examining program and classroom data from UPK programs in NYC across a variety of early childhood quality factors including direct provision of services to children and families, finances, pedagogical strength of programs, transition to kindergarten services, and parent and family engagement (Reid et al., [<reflink idref="bib18" id="ref60">18</reflink>]), results from the study suggest the existence of issues embedded within school-based and community-based UPK programs that signal "the presence of systemic obstacles to the provision of consistently high-quality UPK programs" (Reid et al., [<reflink idref="bib18" id="ref61">18</reflink>], p. 201). However, UPK studies not examining education-related outcomes for children (e.g., school readiness, increases in cognitive ability) have found that UPK programs may lead to benefits over the long-term as the child matures into childhood (Hong et al., [<reflink idref="bib8" id="ref62">8</reflink>]). That is, UPK programs may help service professionals in identifying and diagnosing health conditions earlier than what occurs traditionally (Hong et al., [<reflink idref="bib8" id="ref63">8</reflink>]). Yet, studies in this area also acknowledge that more information is needed here to better understand <emph>which</emph> health-related intersections within UPK programs are most optimal to improve with greater health- and disability-related access and service delivery (Hong et al., [<reflink idref="bib8" id="ref64">8</reflink>]). Within the present study's context, a variety of factors may have impacted rates of diagnosis for particular conditions including provider bias or proficiency of providers' language and communication skills with families whose primary language is not English. Future research is needed to explore factors such as these and the relation, if any, to diagnosis rates and/or childhood education preparedness.</p> <p>UPK policy is broad and far-reaching in that education outcomes are the direct target of this education policy, though areas such as health, medical, and life functioning are indirect policy targets (Hong et al., [<reflink idref="bib8" id="ref65">8</reflink>]). As more data emerge from the handful of UPK programs across the United States, including the UPK program in NYC, it will be critically important to consider implementation and outcome challenges, particularly in areas that might be viewed as secondary policy aims (e.g., access to medical diagnostics and evaluation or early intervention professionals) under the policy itself (Hong et al., [<reflink idref="bib8" id="ref66">8</reflink>]).</p> <hd id="AN0165472494-19">Limitations</hd> <p>There are limitations to this study. In some limited circumstances, children with disabilities are eligible for services under Medicare. However, the dataset here only includes children with Medicaid claim data, thus resulting in selection bias. In addition, the dataset includes unique NYC data, and the findings may or may not be generalizable to other UPK programs in other cities or states. Given that the dataset provides new insight regarding sweeping and comprehensive education policy program rollouts with particular focus on impacts on children with disabilities, the study's analysis is an important contribution to this emerging field of study. Finally, future research should continue to explore how race and ethnicity influence bias within diagnoses and identification.</p> <hd id="AN0165472494-20">Conclusion</hd> <p>In conclusion, this study sheds important insight on the emerging UPK landscape in a large, racially and ethnically diverse metropolitan city. The study's findings highlight racial and ethnic disparities not previously found in prior examination, which suggests areas in need of further inquiry as UPK expands across NYC and other U.S. cities. Moreover, as UPK policy aims to address the needs of young children across systems (e.g., medical, mental/behavioral health, education), future policy revisions should consider how to bridge service delivery across systems to improve overall outcomes for young children as they mature into childhood.</p> <hd id="AN0165472494-21">Acknowledgments</hd> <p>The authors thank NYU Health Evaluation and Analytics Lab and the New York State Department of Health for making the Medicaid claims data available and gratefully acknowledge the funding for this research from the Robert Wood Johnson Foundation's Policies for Action program.</p> <hd id="AN0165472494-22">Disclosure statement</hd> <p>No potential conflict of interest was reported by the author(s).</p> <hd id="AN0165472494-23">Disclaimer</hd> <p>The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the New York State Department of Health. As per NYS Department of Health guidelines, the analyses reported in this article are only examples of analyses that could be run with these data. They should not be utilized in real-world analytic products.</p> <hd id="AN0165472494-24">Appendix A. Model summary</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Model</td><td>Type</td><td>Index</td><td>Result</td></tr></thead><tbody><tr><td>1</td><td>Linear Probability</td><td>A</td><td>Autism</td></tr><tr><td>B</td><td>Learning Disability</td></tr><tr><td>C</td><td>Physical Disability</td></tr><tr><td>2</td><td>Generalized Logistic Regression</td><td>Disabilities</td></tr><tr><td>DID</td><td>Difference in Difference</td><td>A</td><td>Cohort Youngest and Cohort Oldest</td></tr><tr><td>B</td><td>Pooled</td></tr><tr><td>DRD</td><td>Regression Discontinuity</td><td>A</td><td>Cohort Youngest and Cohort Oldest</td></tr><tr><td>B</td><td>Pooled</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-25">Appendix B. The impact of universal pre-kindergarten on disability diagnoses using difference...</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Cohort Oldest</td><td>Cohort Youngest</td><td>Pooled</td></tr><tr><td>Before Expansion</td><td>After Expansion</td><td>Difference</td><td>Before Expansion</td><td>After Expansion</td><td>Difference</td></tr></thead><tbody><tr><td>All</td></tr><tr><td>Autism</td><td>0.002</td><td>−0.002</td><td>−0.004</td><td>0.006</td><td>0.009</td><td>0.006</td><td>0.002</td></tr><tr><td>Learning Disability</td><td>−0.012</td><td>−0.015</td><td>−0.003</td><td>0.000</td><td>−0.003</td><td>0.000</td><td>−0.005</td></tr><tr><td>Physical Disability</td><td>−0.001</td><td>−0.019</td><td>−0.018</td><td>0.004</td><td>0.000</td><td>−0.004</td><td>−0.007</td></tr><tr><td>White</td></tr><tr><td>Autism</td><td>0.000</td><td>−0.008</td><td>−0.008</td><td>−0.002</td><td>0.012</td><td>0.014</td><td>0.004</td></tr><tr><td>Learning Disability</td><td>−0.003</td><td>−0.002</td><td>0.001</td><td>0.006</td><td>−0.020</td><td>−0.026</td><td>−0.006</td></tr><tr><td>Physical Disability</td><td>0.005</td><td>−0.001</td><td>−0.005</td><td>−0.013</td><td>0.007</td><td>0.020</td><td>0.004</td></tr><tr><td>Black</td></tr><tr><td>Autism</td><td>−0.017</td><td>−0.002</td><td>−0.001</td><td>0.009</td><td>0.020</td><td>0.011</td><td>−0.001</td></tr><tr><td>Learning Disability</td><td>−0.016</td><td>−0.020</td><td>−0.004</td><td>−0.006</td><td>0.004</td><td>0.010</td><td>−0.008</td></tr><tr><td>Physical Disability</td><td>−0.023</td><td>−0.008</td><td>0.014</td><td>0.001</td><td>−0.011</td><td>−0.011</td><td>−0.005</td></tr><tr><td>Asian</td></tr><tr><td>Autism</td><td>0.003</td><td>0.004</td><td>0.001</td><td>0.012</td><td>−0.031</td><td>−0.043</td><td>−0.011</td></tr><tr><td>Learning Disability</td><td>−0.001</td><td>0.021</td><td>0.021</td><td>−0.024</td><td>−0.009</td><td>0.015</td><td>−0.002</td></tr><tr><td>Physical Disability</td><td>0.020</td><td>−0.024</td><td>−0.045</td><td>0.000</td><td>0.001</td><td>0.001</td><td>−0.008</td></tr><tr><td>Latinx</td></tr><tr><td>Autism</td><td>0.004</td><td>0.002</td><td>−0.002</td><td>0.006</td><td>0.020</td><td>0.014</td><td>0.007</td></tr><tr><td>Learning Disability</td><td>−0.016</td><td>−0.021</td><td>−0.002</td><td>0.007</td><td>0.004</td><td>−0.003</td><td>−0.008</td></tr><tr><td>Physical Disability</td><td>−0.002</td><td>−0.021</td><td>−0.020</td><td>0.013</td><td>−0.005</td><td>−0.018</td><td>−0.014</td></tr><tr><td>Other</td></tr><tr><td>Autism</td><td>0.005</td><td>−0.036</td><td>−0.036</td><td>0.020</td><td>0.024</td><td>0.003</td><td>0.006</td></tr><tr><td>Learning Disability</td><td>−0.031</td><td>−0.029</td><td>0.003</td><td>0.002</td><td>0.030</td><td>0.028</td><td>0.025</td></tr><tr><td>Physical Disability</td><td>−0.010</td><td>0.003</td><td>0.013</td><td>0.043</td><td>0.098</td><td>0.055</td><td>0.010</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-26">Appendix C. Full demographics and covariates</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Total Sample</td><td>Disability Categories</td></tr><tr><td>No Disability</td><td>Autism</td><td>Learning Disability</td><td>Physical Disability</td></tr></thead><tbody><tr><td>Total</td><td>293,954</td><td>260,260</td><td>9,767</td><td>14,817</td><td>9,114</td></tr><tr><td>%</td><td>-</td><td>88.5%</td><td>3.3%</td><td>5.0%</td><td>3.1%</td></tr><tr><td>Age at Diagnosis</td><td>-</td><td>-</td><td>2.53</td><td>1.89</td><td>1.69</td></tr><tr><td>Female (%)</td><td>48.7%</td><td>50.2%</td><td>22.9%</td><td>40.9%</td><td>43.9%</td></tr><tr><td>Race (column %)</td></tr><tr><td>Asian</td><td>11.4%</td><td>11.7%</td><td>10.0%</td><td>8.0%</td><td>9.4%</td></tr><tr><td>Black</td><td>21.8%</td><td>22.1%</td><td>21.2%</td><td>19.8%</td><td>17.3%</td></tr><tr><td>Latinx</td><td>35.4%</td><td>34.6%</td><td>47.9%</td><td>38.9%</td><td>38.1%</td></tr><tr><td>White</td><td>16.8%</td><td>16.7%</td><td>11.2%</td><td>19.9%</td><td>21.8%</td></tr><tr><td>Other</td><td>2.9%</td><td>2.9%</td><td>2.4%</td><td>2.9%</td><td>3.2%</td></tr><tr><td>Unknown</td><td>11.4%</td><td>11.6%</td><td>7.0%</td><td>10.3%</td><td>10.0%</td></tr><tr><td>Race (row %)</td></tr><tr><td>Asian</td><td>-</td><td>91.0%</td><td>2.9%</td><td>3.5%</td><td>2.5%</td></tr><tr><td>Black</td><td>-</td><td>89.7%</td><td>3.2%</td><td>4.5%</td><td>2.4%</td></tr><tr><td>Latinx</td><td>-</td><td>86.6%</td><td>4.4%</td><td>5.5%</td><td>3.3%</td></tr><tr><td>White</td><td>-</td><td>87.8%</td><td>2.2%</td><td>5.9%</td><td>4.0%</td></tr><tr><td>Other</td><td>-</td><td>88.8%</td><td>2.7%</td><td>4.9%</td><td>3.4%</td></tr><tr><td>Unknown</td><td>-</td><td>90.6%</td><td>2.0%</td><td>4.5%</td><td>2.7%</td></tr><tr><td>Birth Year (column %)</td></tr><tr><td>2006</td><td>13.2%</td><td>13.5%</td><td>7.9%</td><td>9.9%</td><td>15.3%</td></tr><tr><td>2007</td><td>13.9%</td><td>14.1%</td><td>9.6%</td><td>12.6%</td><td>15.1%</td></tr><tr><td>2008</td><td>14.3%</td><td>14.4%</td><td>12.6%</td><td>14.4%</td><td>14.0%</td></tr><tr><td>2009</td><td>14.7%</td><td>14.6%</td><td>17.2%</td><td>16.4%</td><td>13.0%</td></tr><tr><td>2010</td><td>14.8%</td><td>14.6%</td><td>17.0%</td><td>17.6%</td><td>13.5%</td></tr><tr><td>2011</td><td>14.6%</td><td>14.5%</td><td>16.7%</td><td>15.3%</td><td>13.5%</td></tr><tr><td>2012</td><td>14.2%</td><td>14.0%</td><td>18.7%</td><td>13.4%</td><td>15.3%</td></tr><tr><td>Birth Year (row %)</td></tr><tr><td>2006</td><td>-</td><td>90.6%</td><td>1.9%</td><td>3.7%</td><td>3.6%</td></tr><tr><td>2007</td><td>-</td><td>89.8%</td><td>2.2%</td><td>4.5%</td><td>3.3%</td></tr><tr><td>2008</td><td>-</td><td>88.9%</td><td>2.9%</td><td>5.0%</td><td>3.0%</td></tr><tr><td>2009</td><td>-</td><td>87.7%</td><td>3.8%</td><td>5.6%</td><td>2.7%</td></tr><tr><td>2010</td><td>-</td><td>87.3%</td><td>3.8%</td><td>6.0%</td><td>2.8%</td></tr><tr><td>2011</td><td>-</td><td>88.0%</td><td>3.8%</td><td>5.2%</td><td>2.8%</td></tr><tr><td>2012</td><td>-</td><td>87.5%</td><td>4.3%</td><td>4.7%</td><td>3.3%</td></tr><tr><td>NYC School District (column %)</td></tr><tr><td>1</td><td>0.8%</td><td>0.7%</td><td>1.2%</td><td>0.9%</td><td>0.7%</td></tr><tr><td>2</td><td>1.5%</td><td>1.5%</td><td>1.8%</td><td>1.3%</td><td>1.4%</td></tr><tr><td>3</td><td>0.8%</td><td>0.8%</td><td>0.9%</td><td>0.9%</td><td>0.8%</td></tr><tr><td>4</td><td>1.3%</td><td>1.3%</td><td>1.8%</td><td>1.9%</td><td>1.0%</td></tr><tr><td>5</td><td>1.6%</td><td>1.6%</td><td>2.0%</td><td>1.5%</td><td>1.6%</td></tr><tr><td>6</td><td>2.9%</td><td>2.9%</td><td>3.1%</td><td>2.9%</td><td>2.2%</td></tr><tr><td>7</td><td>2.3%</td><td>2.3%</td><td>3.3%</td><td>2.6%</td><td>1.3%</td></tr><tr><td>8</td><td>2.9%</td><td>2.8%</td><td>4.4%</td><td>3.2%</td><td>2.2%</td></tr><tr><td>9</td><td>5.3%</td><td>5.2%</td><td>7.1%</td><td>5.4%</td><td>4.3%</td></tr><tr><td>10</td><td>6.1%</td><td>6.1%</td><td>7.7%</td><td>6.1%</td><td>4.5%</td></tr><tr><td>11</td><td>3.7%</td><td>3.7%</td><td>4.8%</td><td>3.3%</td><td>2.7%</td></tr><tr><td>12</td><td>2.9%</td><td>2.9%</td><td>3.9%</td><td>3.1%</td><td>2.2%</td></tr><tr><td>13</td><td>1.3%</td><td>1.4%</td><td>1.1%</td><td>1.1%</td><td>1.5%</td></tr><tr><td>14</td><td>5.3%</td><td>5.5%</td><td>1.6%</td><td>2.7%</td><td>7.6%</td></tr><tr><td>15</td><td>3.5%</td><td>3.5%</td><td>2.6%</td><td>2.7%</td><td>4.2%</td></tr><tr><td>16</td><td>1.3%</td><td>1.3%</td><td>1.2%</td><td>1.2%</td><td>1.1%</td></tr><tr><td>17</td><td>3.4%</td><td>3.4%</td><td>2.5%</td><td>3.3%</td><td>3.4%</td></tr><tr><td>18</td><td>1.5%</td><td>1.6%</td><td>1.1%</td><td>1.2%</td><td>0.9%</td></tr><tr><td>19</td><td>3.4%</td><td>3.4%</td><td>3.2%</td><td>3.0%</td><td>2.6%</td></tr><tr><td>20</td><td>8.3%</td><td>8.4%</td><td>4.9%</td><td>6.7%</td><td>10.5%</td></tr><tr><td>21</td><td>3.8%</td><td>3.6%</td><td>3.5%</td><td>5.2%</td><td>5.7%</td></tr><tr><td>22</td><td>3.4%</td><td>3.4%</td><td>3.2%</td><td>4.3%</td><td>3.5%</td></tr><tr><td>23</td><td>1.6%</td><td>1.6%</td><td>1.5%</td><td>1.5%</td><td>0.9%</td></tr><tr><td>24</td><td>7.0%</td><td>6.9%</td><td>7.7%</td><td>7.4%</td><td>7.2%</td></tr><tr><td>25</td><td>2.7%</td><td>2.7%</td><td>2.9%</td><td>3.0%</td><td>2.5%</td></tr><tr><td>26</td><td>0.6%</td><td>0.6%</td><td>0.6%</td><td>0.9%</td><td>0.6%</td></tr><tr><td>27</td><td>4.3%</td><td>4.3%</td><td>4.1%</td><td>4.7%</td><td>3.7%</td></tr><tr><td>28</td><td>2.9%</td><td>2.8%</td><td>3.0%</td><td>4.9%</td><td>2.4%</td></tr><tr><td>29</td><td>2.5%</td><td>2.5%</td><td>2.3%</td><td>2.3%</td><td>1.9%</td></tr><tr><td>30</td><td>4.2%</td><td>4.2%</td><td>4.1%</td><td>4.3%</td><td>4.1%</td></tr><tr><td>31</td><td>3.4%</td><td>3.3%</td><td>3.6%</td><td>3.1%</td><td>6.2%</td></tr><tr><td>32</td><td>2.1%</td><td>2.1%</td><td>2.1%</td><td>2.1%</td><td>2.7%</td></tr><tr><td>NYC School District (row %)</td></tr><tr><td>1</td><td>-</td><td>86.5%</td><td>5.0%</td><td>5.6%</td><td>2.7%</td></tr><tr><td>2</td><td>-</td><td>88.4%</td><td>3.9%</td><td>4.5%</td><td>3.0%</td></tr><tr><td>3</td><td>-</td><td>87.0%</td><td>3.9%</td><td>5.7%</td><td>3.2%</td></tr><tr><td>4</td><td>-</td><td>86.3%</td><td>4.3%</td><td>7.0%</td><td>2.2%</td></tr><tr><td>5</td><td>-</td><td>87.8%</td><td>4.1%</td><td>4.8%</td><td>3.1%</td></tr><tr><td>6</td><td>-</td><td>89.0%</td><td>3.4%</td><td>5.0%</td><td>2.3%</td></tr><tr><td>7</td><td>-</td><td>87.7%</td><td>4.7%</td><td>5.7%</td><td>1.8%</td></tr><tr><td>8</td><td>-</td><td>87.0%</td><td>4.9%</td><td>5.6%</td><td>2.3%</td></tr><tr><td>9</td><td>-</td><td>87.7%</td><td>4.4%</td><td>5.1%</td><td>2.5%</td></tr><tr><td>10</td><td>-</td><td>88.5%</td><td>4.1%</td><td>4.9%</td><td>2.2%</td></tr><tr><td>11</td><td>-</td><td>88.9%</td><td>4.2%</td><td>4.4%</td><td>2.2%</td></tr><tr><td>12</td><td>-</td><td>87.8%</td><td>4.4%</td><td>5.3%</td><td>2.3%</td></tr><tr><td>13</td><td>-</td><td>89.7%</td><td>2.6%</td><td>4.0%</td><td>3.5%</td></tr><tr><td>14</td><td>-</td><td>92.0%</td><td>1.0%</td><td>2.5%</td><td>4.4%</td></tr><tr><td>15</td><td>-</td><td>89.7%</td><td>2.5%</td><td>3.9%</td><td>3.7%</td></tr><tr><td>16</td><td>-</td><td>89.1%</td><td>3.2%</td><td>4.7%</td><td>2.8%</td></tr><tr><td>17</td><td>-</td><td>89.5%</td><td>2.4%</td><td>4.9%</td><td>3.0%</td></tr><tr><td>18</td><td>-</td><td>91.6%</td><td>2.4%</td><td>4.1%</td><td>1.8%</td></tr><tr><td>19</td><td>-</td><td>89.9%</td><td>3.1%</td><td>4.4%</td><td>2.4%</td></tr><tr><td>20</td><td>-</td><td>90.0%</td><td>1.9%</td><td>4.0%</td><td>3.9%</td></tr><tr><td>21</td><td>-</td><td>85.3%</td><td>3.0%</td><td>6.8%</td><td>4.6%</td></tr><tr><td>22</td><td>-</td><td>87.3%</td><td>3.1%</td><td>6.3%</td><td>3.2%</td></tr><tr><td>23</td><td>-</td><td>90.0%</td><td>3.1%</td><td>4.9%</td><td>1.8%</td></tr><tr><td>24</td><td>-</td><td>87.7%</td><td>3.6%</td><td>5.3%</td><td>3.2%</td></tr><tr><td>25</td><td>-</td><td>88.1%</td><td>3.5%</td><td>5.4%</td><td>2.7%</td></tr><tr><td>26</td><td>-</td><td>86.6%</td><td>3.1%</td><td>7.0%</td><td>3.0%</td></tr><tr><td>27</td><td>-</td><td>88.7%</td><td>3.1%</td><td>5.4%</td><td>2.6%</td></tr><tr><td>28</td><td>-</td><td>85.3%</td><td>3.4%</td><td>8.5%</td><td>2.6%</td></tr><tr><td>29</td><td>-</td><td>90.0%</td><td>3.0%</td><td>4.5%</td><td>2.3%</td></tr><tr><td>30</td><td>-</td><td>88.4%</td><td>3.2%</td><td>5.1%</td><td>3.0%</td></tr><tr><td>31</td><td>-</td><td>86.3%</td><td>3.5%</td><td>4.5%</td><td>5.5%</td></tr><tr><td>32</td><td>-</td><td>87.6%</td><td>3.3%</td><td>5.0%</td><td>4.0%</td></tr></tbody></table> </ephtml> </p> <hd id="AN0165472494-27">Appendix D. Pairwise odds ratio comparison by disability categories (all pairs)</hd> <p></p> <p> <ephtml> <table><thead><tr><td>Odds Ratio</td><td>Estimate</td><td>95% Confidence Limits</td></tr></thead><tbody><tr><td>Autism</td></tr><tr><td>Asian vs Other</td><td>1.315</td><td>1.196</td><td>1.447</td></tr><tr><td>Asian vs White</td><td>0.982</td><td>0.896</td><td>1.077</td></tr><tr><td>Black vs Other</td><td>1.450</td><td>1.337</td><td>1.572</td></tr><tr><td>Black vs White</td><td>1.083</td><td>0.997</td><td>1.176</td></tr><tr><td>Black vs Asian</td><td>1.103</td><td>1.011</td><td>1.202</td></tr><tr><td>Latinx vs Other</td><td>2.072</td><td>1.928</td><td>2.227</td></tr><tr><td>Latinx vs White</td><td>1.547</td><td>1.437</td><td>1.666</td></tr><tr><td>Latinx vs Asian</td><td>1.575</td><td>1.458</td><td>1.701</td></tr><tr><td>Latinx vs Black</td><td>1.429</td><td>1.350</td><td>1.513</td></tr><tr><td>White vs Other</td><td>1.339</td><td>1.221</td><td>1.471</td></tr><tr><td>Learning Disability</td></tr><tr><td>Asian vs Black</td><td>0.727</td><td>0.675</td><td>0.784</td></tr><tr><td>Asian vs Latinx</td><td>0.571</td><td>0.533</td><td>0.612</td></tr><tr><td>Asian vs Other</td><td>0.707</td><td>0.655</td><td>0.764</td></tr><tr><td>Black vs Other</td><td>0.973</td><td>0.916</td><td>1.400</td></tr><tr><td>Latinx vs Other</td><td>1.239</td><td>1.175</td><td>1.306</td></tr><tr><td>Latinx vs Black</td><td>1.274</td><td>1.212</td><td>1.337</td></tr><tr><td>White vs Asian</td><td>2.160</td><td>2.008</td><td>2.320</td></tr><tr><td>White vs Black</td><td>1.570</td><td>1.479</td><td>1.667</td></tr><tr><td>White vs Latinx</td><td>1.233</td><td>1.171</td><td>1.299</td></tr><tr><td>White vs Other</td><td>1.527</td><td>1.435</td><td>1.626</td></tr><tr><td>Physical Disability</td></tr><tr><td>Asian vs Other</td><td>0.788</td><td>0.718</td><td>0.864</td></tr><tr><td>Black vs Other</td><td>0.920</td><td>0.851</td><td>0.996</td></tr><tr><td>Black vs Asian</td><td>1.168</td><td>1.065</td><td>1.282</td></tr><tr><td>Latinx vs Other</td><td>1.249</td><td>1.168</td><td>1.336</td></tr><tr><td>Latinx vs White</td><td>1.069</td><td>1.000</td><td>1.140</td></tr><tr><td>Latinx vs Asian</td><td>1.585</td><td>1.462</td><td>1.718</td></tr><tr><td>Latinx vs Black</td><td>1.357</td><td>1.271</td><td>1.449</td></tr><tr><td>White vs Asian</td><td>1.484</td><td>1.361</td><td>1.616</td></tr><tr><td>White vs Black</td><td>1.269</td><td>1.175</td><td>1.370</td></tr><tr><td>White vs Other</td><td>1.168</td><td>1.081</td><td>1.261</td></tr></tbody></table> </ephtml> </p> <ref id="AN0165472494-28"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref31" type="bt">1</bibl> <bibtext> In addition to the covariates listed above, the authors ran models with first three-digit zip codes instead of school districts. Further, the authors used Medicaid aid categories. None of the models changed the study results.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref1" type="bt">2</bibl> <bibtext> The authors also conducted a difference in regression discontinuity model. The authors used local linear regression with a triangular kernel with a bandwidth of 60 days to estimate the UPK effects separately for cohort youngest and cohort oldest (Model DRD a), then pooled the effects together for the overall UPK effect (Model DRD b). Model DRD a: <emph>y<subs>it</subs></emph> = <emph>α</emph> + <emph>ρ<subs>1</subs> D<subs>it</subs></emph> + <emph>ρ<subs>2</subs></emph>(<emph>DOB<subs>i</subs> – c<subs>t</subs></emph>) + <emph>ρ<subs>3</subs></emph>(<emph>DOB<subs>i</subs> – c<subs>t</subs></emph>) + 1{<emph>t</emph> = 2014}[<emph>β</emph> + <emph>θD<subs>it</subs></emph> + <emph>ρ<subs>5</subs></emph>(<emph>DOB<subs>i</subs> – c<subs>t</subs></emph>) + <emph>ρ<subs>6</subs> D<subs>it</subs></emph>(<emph>DOB<subs>i</subs> – C<subs>t</subs></emph>)] + <emph>ɛ<subs>it</subs></emph></bibtext> </blist> </ref> <ref id="AN0165472494-29"> <title> References </title> <blist> <bibtext> Baio, J., Wiggins, L., Christensen, D. L., Maenner, M. J., Daniels, J., Warren, Z., Kurzius-Spencer, M., Zahorodny, W., Rosenberg, C. R., White, T., Durkin, M. S., Imm, P., Nikolaou, L., Yeargin-Allsopp, M., Lee, L.-C., Harrington, R., Lopez, M., Fitzgerald, R. T., Hewitt, A., and Dowling, N. F. (2018). Prevalence of autism spectrum disorder among children aged 8 years—Autism and developmental disabilities monitoring network, 11 sites, United States, 2014. Morbidity & Mortality Weekly Report: Surveillance Summaries, 67 (6), 1 – 23. https://doi.org/10.15585/mmwr.ss6706a1</bibtext> </blist> <blist> <bibtext> Bollmer, J. B., Munk, T., & Bitterman, A. (2014). Methods for assessing racial/ethnic disproportionality in special education: A technical assistance guide (Revised). Westat.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref17" type="bt">3</bibl> <bibtext> Centers for Disease Control and Prevention. (2020). Autism prevalence rises in communities monitored by CDC. Author. https://<ulink href="http://www.cdc.gov/media/releases/2020/p0326-autism-prevalence-rises.html">www.cdc.gov/media/releases/2020/p0326-autism-prevalence-rises.html</ulink></bibtext> </blist> <blist> <bibl id="bib4" idref="ref56" type="bt">4</bibl> <bibtext> Cooc, N. (2019). 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The National Academies Press. https://doi.org/10.17226/21794</bibtext> </blist> <blist> <bibtext> New York City Department of City Planning. (2018). MapPLUTO. https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page</bibtext> </blist> <blist> <bibtext> New York City Department of Education. (2020). All about 3-K for and pre-K for all. <ulink href="http://teachnyc.net/pathways-to-teaching/early-childhood-education/all-about">http://teachnyc.net/pathways-to-teaching/early-childhood-education/all-about</ulink></bibtext> </blist> <blist> <bibtext> New York State Early Intervention Fiscal Portal. (2018). ICD-9/ICD-10 conversion tables. https://support.eibilling.com/KB/a281/icd-9-icd-10-conversion-tables.aspx?KBSearchID=58340</bibtext> </blist> <blist> <bibtext> Nowell, K. P., Brewton, C. M., Allain, E., & Mire, S. S. (2015). The influence of demographic factors on the identification of autism spectrum disorder: A review and call for research. 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Education federalism and minority disproportionate representation monitoring: Examining IDEA provisions, regulations, and judicial trends. Journal of Disability Policy Studies, 30 (3), 138 – 147. https://doi.org/10.1177/1044207319835185</bibtext> </blist> <blist> <bibtext> Strassfeld, N. M., & Cherng, H. S. (2022). Services for juveniles with emotional disturbances in secure-care settings: An exploratory analysis of racial disparities and recidivism. Behavioral Disorders, 47 (4), 257 – 269. https://doi.org/10.1177/01987429211046552</bibtext> </blist> <blist> <bibtext> Sullivan, A. L., & Suldo, S. (2013). School-based autism identification: Prevalence, racial disparities, and systemic correlation. School Psychology Review, 42 (3), 298 – 316. https://doi.org/10.1080/02796015.2013.12087475</bibtext> </blist> <blist> <bibtext> Travers, J., & Krezmien, M. (2018). Racial disparities in autism identification in the United States during 2014. 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  Group: Ti
  Data: Examining the Prevalence Rates of Autism Diagnosis by Race/Ethnicity for Medicaid-Eligible Children Enrolled in NYC Universal Pre-Kindergarten Programs
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  Label: Language
  Group: Lang
  Data: English
– Name: Author
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  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Strassfeld%2C+Natasha+M%2E%22">Strassfeld, Natasha M.</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-8522-2272">0000-0002-8522-2272</externalLink>)<br /><searchLink fieldCode="AR" term="%22Cherng%2C+Hua-Yu+Sebastian%22">Cherng, Hua-Yu Sebastian</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2444-4354">0000-0003-2444-4354</externalLink>)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Scarlett%22">Wang, Scarlett</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-2687-0365">0000-0003-2687-0365</externalLink>)<br /><searchLink fieldCode="AR" term="%22Glied%2C+Sherry%22">Glied, Sherry</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-9432-1662">0000-0001-9432-1662</externalLink>)
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Journal+of+Research+in+Childhood+Education%22"><i>Journal of Research in Childhood Education</i></searchLink>. 2023 37(3):476-491.
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  Data: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
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  Label: Peer Reviewed
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  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 16
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2023
– 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="%22Early+Childhood+Education%22">Early Childhood Education</searchLink><br /><searchLink fieldCode="EL" term="%22Preschool+Education%22">Preschool Education</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="%22Incidence%22">Incidence</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Education%22">Preschool Education</searchLink><br /><searchLink fieldCode="DE" term="%22Racial+Differences%22">Racial Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Ethnicity%22">Ethnicity</searchLink><br /><searchLink fieldCode="DE" term="%22Probability%22">Probability</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Diagnosis%22">Clinical Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Disabilities%22">Learning Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+Disabilities%22">Physical Disabilities</searchLink><br /><searchLink fieldCode="DE" term="%22Disproportionate+Representation%22">Disproportionate Representation</searchLink><br /><searchLink fieldCode="DE" term="%22Whites%22">Whites</searchLink><br /><searchLink fieldCode="DE" term="%22Minority+Groups%22">Minority Groups</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Education%22">Access to Education</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Insurance%22">Health Insurance</searchLink><br /><searchLink fieldCode="DE" term="%22Low+Income+Groups%22">Low Income Groups</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22New+York+%28New+York%29%22">New York (New York)</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1080/02568543.2023.2213281
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0256-8543<br />2150-2641
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This study examines autism diagnosis prevalence within the New York City (NYC) Universal Pre-K for All (UPK) program expansion into racially, ethnically, and socioeconomically diverse NYC neighborhoods. Here, it is hypothesized that racial/ethnic differences in autism diagnoses may close as more children are referred for testing by UPK programs, which they have more thorough interactions with, instead of by public health clinics or other medical avenues. Using NYC Medicaid claim data from 2006 to 2016, descriptive analyses were conducted by estimating linear probability regression and generalized multiple logistic regression to examine whether the probabilities of being diagnosed with autism in comparison to two other disability types (as counterfactuals), learning disabilities (LD) and physical disabilities (PD), differ by race. Subsequently, a difference in difference (DID) strategy (with pre- and post-UPK expansion cohorts) was used to examine the effects of UPK on the probabilities of receiving disability diagnoses. Notably, Latinx and "Other" racially identified children have much higher odds than White children of being diagnosed with autism. By contrast, all non-White groups had much lower odds of being diagnosed with a LD. These findings offer important insight for future UPK and childhood program implementation.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2023
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  Label: Accession Number
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  Data: EJ1396128
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1396128
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      – Type: doi
        Value: 10.1080/02568543.2023.2213281
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 476
    Subjects:
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Incidence
        Type: general
      – SubjectFull: Preschool Education
        Type: general
      – SubjectFull: Racial Differences
        Type: general
      – SubjectFull: Ethnicity
        Type: general
      – SubjectFull: Probability
        Type: general
      – SubjectFull: Clinical Diagnosis
        Type: general
      – SubjectFull: Learning Disabilities
        Type: general
      – SubjectFull: Physical Disabilities
        Type: general
      – SubjectFull: Disproportionate Representation
        Type: general
      – SubjectFull: Whites
        Type: general
      – SubjectFull: Minority Groups
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Access to Education
        Type: general
      – SubjectFull: Health Insurance
        Type: general
      – SubjectFull: Low Income Groups
        Type: general
      – SubjectFull: New York (New York)
        Type: general
    Titles:
      – TitleFull: Examining the Prevalence Rates of Autism Diagnosis by Race/Ethnicity for Medicaid-Eligible Children Enrolled in NYC Universal Pre-Kindergarten Programs
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            NameFull: Cherng, Hua-Yu Sebastian
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            NameFull: Wang, Scarlett
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            NameFull: Glied, Sherry
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      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
          Identifiers:
            – Type: issn-print
              Value: 0256-8543
            – Type: issn-electronic
              Value: 2150-2641
          Numbering:
            – Type: volume
              Value: 37
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: Journal of Research in Childhood Education
              Type: main
ResultId 1