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Using Adaptive Behavior Scores to Convey Level of Functioning in Children with Autism Spectrum Disorder: Evidence from the Study to Explore Early Development

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Title: Using Adaptive Behavior Scores to Convey Level of Functioning in Children with Autism Spectrum Disorder: Evidence from the Study to Explore Early Development
Language: English
Authors: Sarah M. Furnier (ORCID 0000-0002-1766-1417), Susan Ellis Weismer, Eric Rubenstein (ORCID 0000-0002-9146-4497), Ronald Gangnon, Steven Rosenberg, Cy Nadler, Lisa D. Wiggins, Maureen S. Durkin
Source: Autism: The International Journal of Research and Practice. 2024 28(5):1135-1149.
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: 15
Publication Date: 2024
Sponsoring Agency: Centers for Disease Control and Prevention (CDC) (DHHS/PHS)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
Contract Number: U10DD000180
U01DD000750
U10DD000181
U10DD000182
U10DD000183
U10DD000184
U10DD000498
U01000750
U01000748
U01000752
U01000746
U01000749
U10DD000901
U01DD001215
U54HD090256
Document Type: Journal Articles
Reports - Research
Descriptors: Autism Spectrum Disorders, Cognitive Ability, Adjustment (to Environment), Severity (of Disability), Preschool Children, Scores, Socialization, Psychomotor Skills, Child Development
Geographic Terms: California, Colorado, Georgia, Maryland, North Carolina, Pennsylvania
Assessment and Survey Identifiers: Mullen Scales of Early Learning, Autism Diagnostic Observation Schedule, Vineland Adaptive Behavior Scales
DOI: 10.1177/13623613231193194
ISSN: 1362-3613
1461-7005
Abstract: We examined relationships between measures of adaptive behavior, cognitive ability, and autism symptom severity in 1458 preschool-aged children with autism from the Study to Explore Early Development. While publications commonly describe autistic children as "low-" or "high-functioning" based on cognitive ability, relying solely on cognitive scores may obscure meaningful variation in functioning. We found significant heterogeneity in adaptive behavior scores of children with cognitive scores both above and below the threshold of two or more standard deviations below the population mean specified in the diagnostic criteria for intellectual disability (ID). Although cognitive and adaptive behavior scores were strongly associated in our sample, considerable variation in overall adaptive behavior and more than half in socialization and motor skills was unaccounted for by cognitive ability, autism symptom severity, and other covariates. Among children who could be designated "low-functioning" based on cognitive scores, 39.7% had composite adaptive behavior scores indicating no significant delays, while among those who might be designated "high-functioning," 9.0% had significant delays in overall adaptive behavior and 22.2% in socialization. These results suggest adaptive behavior scores capture variations in the autism phenotype not accounted for by other measures we considered.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1423424
Database: ERIC
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  Value: <anid>AN0177036770;f9d01may.24;2024May07.05:18;v2.2.500</anid> <title id="AN0177036770-1">Using adaptive behavior scores to convey level of functioning in children with autism spectrum disorder: Evidence from the Study to Explore Early Development </title> <p>We examined relationships between measures of adaptive behavior, cognitive ability, and autism symptom severity in 1458 preschool-aged children with autism from the Study to Explore Early Development. While publications commonly describe autistic children as "low-" or "high-functioning" based on cognitive ability, relying solely on cognitive scores may obscure meaningful variation in functioning. We found significant heterogeneity in adaptive behavior scores of children with cognitive scores both above and below the threshold of two or more standard deviations below the population mean specified in the diagnostic criteria for intellectual disability (ID). Although cognitive and adaptive behavior scores were strongly associated in our sample, considerable variation in overall adaptive behavior and more than half in socialization and motor skills was unaccounted for by cognitive ability, autism symptom severity, and other covariates. Among children who could be designated "low-functioning" based on cognitive scores, 39.7% had composite adaptive behavior scores indicating no significant delays, while among those who might be designated "high-functioning," 9.0% had significant delays in overall adaptive behavior and 22.2% in socialization. These results suggest adaptive behavior scores capture variations in the autism phenotype not accounted for by other measures we considered. Autistic people are often described as "low-" or "high-functioning" based on their scores on cognitive tests. These terms are common in publications and in everyday communication. However, recent research and feedback from the autistic community suggests that relying on cognitive ability alone to describe functioning may miss meaningful differences in the abilities of autistic children and adults and in the kinds of support they may need. Additional methods are needed to describe "functioning" in autistic children. We examined whether scores from a test measuring adaptive behaviors would provide information on the functional abilities of children with autism that is different from cognitive ability and autism symptom severity. Adaptive behaviors include age-appropriate skills that allow people to function in their everyday lives and social interactions. We found that a large amount of the variation in adaptive behavior scores was not explained by cognitive development, autism symptom severity, and behavioral and emotional problems. In addition, there was a wide range of adaptive ability levels in children with autism in our study, including in those with low, average, or high cognitive scores. Our results suggest that adaptive behavior scores could provide useful information about the strengths and support needs of autistic children above and beyond measures of cognitive ability and autism symptom severity. Adaptive behavior scores provide important information on the needs of autistic people.</p> <p>Keywords: adaptive behavior; adaptive functioning; autism spectrum disorders; cognitive development; epidemiology; intelligence quotient</p> <hd id="AN0177036770-2">Introduction</hd> <p>Autism research reports often describe study participants who meet diagnostic criteria for autism <emph>and</emph> have cognitive or intelligence quotient (IQ) scores in the average or above average range as "high-functioning" ([<reflink idref="bib15" id="ref1">15</reflink>]; [<reflink idref="bib32" id="ref2">32</reflink>]; [<reflink idref="bib56" id="ref3">56</reflink>]; [<reflink idref="bib61" id="ref4">61</reflink>]). It is not uncommon for research designs to incorporate IQ-based functional classifications in their inclusion or exclusion criteria (e.g. [<reflink idref="bib9" id="ref5">9</reflink>]; [<reflink idref="bib22" id="ref6">22</reflink>]; [<reflink idref="bib29" id="ref7">29</reflink>]; [<reflink idref="bib46" id="ref8">46</reflink>]; [<reflink idref="bib68" id="ref9">68</reflink>]), and the phrases "low-" and "high-functioning autism" have been used widely in everyday communications as well. This approach is problematic given the consistently reported gap between cognitive and adaptive functioning in autistic individuals ([<reflink idref="bib3" id="ref10">3</reflink>]; [<reflink idref="bib30" id="ref11">30</reflink>]; [<reflink idref="bib57" id="ref12">57</reflink>]). Thus, these commonly used classifications of functioning may mask important variability in the relative strengths and difficulties in multiple domains of functioning of autistic individuals and obscure significant needs for support among those with cognitive scores above a given cutoff ([<reflink idref="bib3" id="ref13">3</reflink>]; [<reflink idref="bib66" id="ref14">66</reflink>]).</p> <p>A similar dichotomy based on cognitive scores is often applied in epidemiologic studies of autism to define co-occurring intellectual disability (ID), with co-occurring ID typically defined to include individuals who both meet diagnostic criteria for autism and have cognitive scores at least two standard deviations (SDs) below the population mean. Many population-based prevalence estimates of co-occurring ID among autistic children are based solely on cognitive scores ([<reflink idref="bib19" id="ref15">19</reflink>]; [<reflink idref="bib37" id="ref16">37</reflink>]; [<reflink idref="bib60" id="ref17">60</reflink>]; [<reflink idref="bib65" id="ref18">65</reflink>]; [<reflink idref="bib69" id="ref19">69</reflink>]) despite clinical diagnostic criteria for ID explicitly requiring consideration of adaptive performance as well ([<reflink idref="bib58" id="ref20">58</reflink>]). This study examines classifications of functioning and the relationship between cognitive development and adaptive behavior (AB) in children with autism in a large, well-characterized, community-based sample of preschool-aged children and aims to replicate and build upon relevant findings from previous studies by examining variability in AB scores across the spectrum of cognitive abilities. In doing so, this study provides a critical examination of the use of the terms "low-" and "high-functioning" and "co-occurring ID" based solely on IQ in the autism literature.</p> <p>Marked variation in AB among individuals with autism has been well-documented across study samples and data sources, age ranges (preschool to adulthood), and even in individuals considered "high-functioning" based on IQ ([<reflink idref="bib3" id="ref21">3</reflink>]; [<reflink idref="bib6" id="ref22">6</reflink>]; [<reflink idref="bib16" id="ref23">16</reflink>]; [<reflink idref="bib18" id="ref24">18</reflink>]; [<reflink idref="bib21" id="ref25">21</reflink>]; [<reflink idref="bib28" id="ref26">28</reflink>]; [<reflink idref="bib30" id="ref27">30</reflink>]; [<reflink idref="bib40" id="ref28">40</reflink>]; [<reflink idref="bib45" id="ref29">45</reflink>]; [<reflink idref="bib57" id="ref30">57</reflink>]). Several studies looking at domain scores from the Vineland Adaptive Behavior Scales (VABS), one of the most commonly used instruments for measuring AB in autistic individuals, have found evidence of an "autism profile," a characteristic pattern in VABS domain scores with relative strengths in daily living skills, intermediate scores in communication, and greatest delays in socialization (Bӧlte & Poustka, 2002; [<reflink idref="bib10" id="ref31">10</reflink>]; [<reflink idref="bib47" id="ref32">47</reflink>]; [<reflink idref="bib61" id="ref33">61</reflink>]; [<reflink idref="bib67" id="ref34">67</reflink>]), although others have not found this same pattern in their samples ([<reflink idref="bib47" id="ref35">47</reflink>]; [<reflink idref="bib49" id="ref36">49</reflink>]; [<reflink idref="bib57" id="ref37">57</reflink>]; [<reflink idref="bib59" id="ref38">59</reflink>]; [<reflink idref="bib67" id="ref39">67</reflink>]).</p> <p>In general, AB scores are positively correlated with cognitive ability ([<reflink idref="bib3" id="ref40">3</reflink>]; [<reflink idref="bib26" id="ref41">26</reflink>]; [<reflink idref="bib28" id="ref42">28</reflink>]; [<reflink idref="bib44" id="ref43">44</reflink>]; [<reflink idref="bib47" id="ref44">47</reflink>]; [<reflink idref="bib49" id="ref45">49</reflink>]), and the literature suggests this relationship may be stronger in children with autism with lower cognitive scores ([<reflink idref="bib33" id="ref46">33</reflink>]; [<reflink idref="bib59" id="ref47">59</reflink>]; [<reflink idref="bib67" id="ref48">67</reflink>]) than in those with higher cognitive scores. Despite this significant association, children with autism commonly exhibit discrepancies between cognitive ability and AB scores ([<reflink idref="bib11" id="ref49">11</reflink>]; [<reflink idref="bib30" id="ref50">30</reflink>]; [<reflink idref="bib31" id="ref51">31</reflink>]; [<reflink idref="bib57" id="ref52">57</reflink>]; [<reflink idref="bib62" id="ref53">62</reflink>]), and these gaps have been shown to be larger in individuals with higher cognitive scores and to widen with increasing age ([<reflink idref="bib3" id="ref54">3</reflink>]; [<reflink idref="bib8" id="ref55">8</reflink>]; [<reflink idref="bib28" id="ref56">28</reflink>]; [<reflink idref="bib44" id="ref57">44</reflink>]). Large discrepancies in which cognitive scores surpass AB scores have been documented in autistic individuals and IQs above the ID threshold; conversely, in children with autism and cognitive scores two or more SDs below the mean, AB scores are often similar to or exceed IQ ([<reflink idref="bib3" id="ref58">3</reflink>]; [<reflink idref="bib8" id="ref59">8</reflink>]; Bӧlte & Poustka, 2002; [<reflink idref="bib40" id="ref60">40</reflink>]; [<reflink idref="bib44" id="ref61">44</reflink>]; [<reflink idref="bib67" id="ref62">67</reflink>]). One longitudinal study found that toddlers with autism with average or above IQs were much more likely than typically developing peers to have below average AB socialization and daily living skills, with IQ/AB discrepancies appearing early and growing with age ([<reflink idref="bib8" id="ref63">8</reflink>]). These findings suggest that children with autism may have greater AB strengths and/or delays than would be expected based on their IQ, and these discrepancies may merit consideration in research on functioning of autistic children.</p> <p>AB scores are also generally inversely associated, albeit weakly or non-significantly, with autism symptom severity as measured by Autism Diagnostic Observation Schedule (ADOS) Calibrated Severity Scores (CSSs) and the Autism Diagnostic Interview–Revised (ADI-R) ([<reflink idref="bib20" id="ref64">20</reflink>]; [<reflink idref="bib25" id="ref65">25</reflink>]; [<reflink idref="bib26" id="ref66">26</reflink>]; [<reflink idref="bib41" id="ref67">41</reflink>]; [<reflink idref="bib44" id="ref68">44</reflink>]; [<reflink idref="bib67" id="ref69">67</reflink>]). Previous investigators have theorized that IQ/AB discrepancy patterns could be a result of cognitive ability limiting attainment of AB skills in children with lower IQs, while autism symptom severity might attenuate AB skill development in children with higher IQs ([<reflink idref="bib33" id="ref70">33</reflink>]; [<reflink idref="bib41" id="ref71">41</reflink>]; [<reflink idref="bib67" id="ref72">67</reflink>]). However, while both cognitive ability and autism symptom severity are associated with adaptive functioning, neither appears to fully explain variation in adaptive functioning, suggesting that AB scores capture unique variation in functioning and may be useful for characterizing support needs and variations in the autism phenotype ([<reflink idref="bib25" id="ref73">25</reflink>]; [<reflink idref="bib26" id="ref74">26</reflink>]; [<reflink idref="bib47" id="ref75">47</reflink>]; [<reflink idref="bib49" id="ref76">49</reflink>]).</p> <p>Here, we describe AB scores of participants in a large, community-based, epidemiological study of diverse, rigorously characterized 2- to 5-year-old children with autism and examine the association between AB and cognitive development as well as sociodemographic characteristics, autism severity, and behavioral and emotional health. We hypothesize that:</p> <p></p> <ulist> <item> There will be significant AB delays in children with autism, even among those with cognitive scores above the ID threshold of two SDs below the mean, with the greatest limitations in socialization, followed by communication, and then daily living skills and motor skills (i.e. the "autism profile").</item> <p></p> <item> While cognitive scores and autism symptom severity will explain significant variation in AB scores, a large amount of variation in AB scores in children with autism will remain unexplained by these and other factors associated with AB scores, including sociodemographic characteristics and behavioral and emotional health.</item> <p></p> <item> Children with autism with cognitive scores above the threshold for ID will exhibit significant discrepancies between cognitive and AB scores, with AB scores significantly lower than cognitive scores, while among children with cognitive scores below the ID threshold, AB scores will be similar to or exceed cognitive scores.</item> </ulist> <hd id="AN0177036770-3">Methods</hd> <p></p> <hd id="AN0177036770-4">Study sample</hd> <p>The Study to Explore Early Development (SEED) identified children with autism aged 30–68 months by end of data collection through multiple sources, including health, education, early intervention, and other service program settings at study sites within areas of six states (California, Colorado, Georgia, Maryland, North Carolina, Pennsylvania) ([<reflink idref="bib64" id="ref77">64</reflink>]). Caregivers were asked questions about sociodemographic characteristics, medical history, services and treatments, and other information via telephone interview and mailed questionnaires; caregivers also filled out various standard instruments such as the Child Behavior Checklist (CBCL) ([<reflink idref="bib1" id="ref78">1</reflink>]). All participating families completed the Social Communication Questionnaire (SCQ) ([<reflink idref="bib51" id="ref79">51</reflink>]). Children who had an autism spectrum disorder (ASD) diagnosis upon enrollment or had ASD risk noted on the SCQ were evaluated with the Mullen Scales of Early Learning (MSEL) ([<reflink idref="bib43" id="ref80">43</reflink>]) and ADOS ([<reflink idref="bib34" id="ref81">34</reflink>], [<reflink idref="bib35" id="ref82">35</reflink>]), and mothers completed the Vineland Adaptive Behavior Scale–Second Edition (VABS-II) ([<reflink idref="bib55" id="ref83">55</reflink>]) and ADI-R ([<reflink idref="bib36" id="ref84">36</reflink>]; [<reflink idref="bib52" id="ref85">52</reflink>]). Children who did not meet these criteria were evaluated only with the MSEL; if ASD behaviors were witnessed during MSEL administration, the family was asked to also complete the ADOS, ADI-R, and VABS.</p> <p>Final classification was based on diagnostic criteria for ASD from results of a comprehensive developmental evaluation, including the ADOS and ADI-R, performed by clinicians with established research reliability. Clinical judgment was also considered for children with an early learning age equivalent <24 months who met SEED ASD criteria on the ADOS and ADI-R since the ADI-R is not appropriate for children in this mental age range ([<reflink idref="bib54" id="ref86">54</reflink>]; [<reflink idref="bib63" id="ref87">63</reflink>]). This population-based ascertainment protocol supported the identification and rigorous assessment of children with autism with and without a previous diagnosis from clinical and community-based sources. Written informed consent was obtained from all participating families. For this research, analysis was restricted to children in the SEED ASD group (<emph>n</emph> = 1480). Sociodemographic characteristics of the sample are provided in Table 1.</p> <p>Graph</p> <p>Table 1. Child, maternal, and sociodemographic characteristics, overall and stratified by cognitive ability, of children with autism spectrum disorder, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011).</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" rowspan="2">Demographic characteristic <italic>n</italic> (%)</th><th align="left" colspan="3">Early Learning Composite<xref ref-type="table-fn" rid="tfn2">a</xref></th></tr><tr><th align="left">Overall (<italic>n</italic> = 1480)</th><th align="left">>70 (<italic>n</italic> = 530)</th><th align="left">⩽70 (<italic>n</italic> = 930)</th></tr></thead><tbody><tr><td colspan="4">Child age at assessment (years)</td></tr><tr><td> Mean (standard deviation)</td><td>4.7 (0.7)</td><td>4.7 (0.7)</td><td>4.8 (0.7)</td></tr><tr><td> Range</td><td>2.4–5.8</td><td>2.4–5.8</td><td>2.6–5.8</td></tr><tr><td> Missing</td><td>3 (0.2)</td><td>0 (0.0)</td><td>0 (0.0)</td></tr><tr><td colspan="4">Child sex</td></tr><tr><td> Male</td><td>1208 (81.6)</td><td>437 (82.5)</td><td>752 (80.9)</td></tr><tr><td> Female</td><td>272 (18.4)</td><td>93 (17.6)</td><td>178 (19.1)</td></tr><tr><td colspan="4">Child race and ethnicity</td></tr><tr><td> Non-Hispanic White</td><td>647 (43.7)</td><td>308 (58.1)</td><td>329 (35.4)</td></tr><tr><td> Non-Hispanic Black</td><td>309 (20.9)</td><td>57 (10.8)</td><td>248 (26.7)</td></tr><tr><td> Hispanic</td><td>273 (18.5)</td><td>79 (14.9)</td><td>192 (20.7)</td></tr><tr><td> Non-Hispanic other and multiracial</td><td>249 (16.8)</td><td>86 (16.2)</td><td>159 (17.1)</td></tr><tr><td> Missing</td><td>2 (0.1)</td><td>0 (0.0)</td><td>2 (0.2)</td></tr><tr><td colspan="4">Child birth order</td></tr><tr><td> Firstborn</td><td>702 (47.4)</td><td>284 (53.6)</td><td>410 (44.1)</td></tr><tr><td> Second or later born</td><td>713 (48.2)</td><td>228 (43.0)</td><td>473 (50.9)</td></tr><tr><td> Missing</td><td>65 (4.4)</td><td>18 (3.4)</td><td>47 (5.1)</td></tr><tr><td colspan="4">Current income, relative to poverty threshold</td></tr><tr><td> ⩽100%</td><td>133 (9.0)</td><td>32 (6.0)</td><td>100 (10.8)</td></tr><tr><td> 101%–200%</td><td>277 (18.7)</td><td>63 (11.9)</td><td>207 (22.3)</td></tr><tr><td> 201%–299%</td><td>234 (15.8)</td><td>80 (15.1)</td><td>151 (16.2)</td></tr><tr><td> 300%–399%</td><td>220 (14.9)</td><td>100 (18.9)</td><td>119 (12.8)</td></tr><tr><td> ⩾400%</td><td>561 (37.9)</td><td>246 (46.4)</td><td>309 (33.2)</td></tr><tr><td> Missing</td><td>55 (3.7)</td><td>9 (1.7)</td><td>44 (4.7)</td></tr><tr><td colspan="4">Maternal education</td></tr><tr><td> Less than high school</td><td>63 (4.3)</td><td>10 (1.9)</td><td>53 (5.7)</td></tr><tr><td> High school graduate</td><td>152 (10.3)</td><td>33 (6.2)</td><td>115 (12.4)</td></tr><tr><td> Some college</td><td>467 (31.6)</td><td>152 (28.7)</td><td>306 (32.9)</td></tr><tr><td> College graduate</td><td>467 (31.6)</td><td>176 (33.2)</td><td>287 (30.9)</td></tr><tr><td> Advanced degree</td><td>317 (21.4)</td><td>155 (29.3)</td><td>160 (17.2)</td></tr><tr><td> Missing</td><td>14 (1.0)</td><td>4 (0.8)</td><td>9 (1.0)</td></tr><tr><td colspan="4">Maternal age at enrollment (years)</td></tr><tr><td> Mean (standard deviation)</td><td>35.6 (5.6)</td><td>36.1 (5.2)</td><td>35.3 (5.7)</td></tr><tr><td> Range</td><td>20-52</td><td>21-52</td><td>20-52</td></tr><tr><td colspan="4">Mother born in the United States</td></tr><tr><td> Yes</td><td>1132 (76.5)</td><td>422 (79.6)</td><td>694 (74.6)</td></tr><tr><td> No</td><td>336 (22.7)</td><td>105 (19.8)</td><td>228 (24.5)</td></tr><tr><td> Missing</td><td>12 (0.8)</td><td>3 (0.6)</td><td>8 (1.0)</td></tr><tr><td colspan="4">Primary language spoken at home</td></tr><tr><td> English</td><td>1296 (87.6)</td><td>492 (92.8)</td><td>787 (84.6)</td></tr><tr><td> Spanish</td><td>78 (5.3)</td><td>12 (2.3)</td><td>65 (7.0)</td></tr><tr><td> Other</td><td>93 (6.3)</td><td>23 (4.3)</td><td>69 (7.4)</td></tr><tr><td> Missing</td><td>13 (0.9)</td><td>3 (0.6)</td><td>9 (1.0)</td></tr><tr><td colspan="4">Study site</td></tr><tr><td> California</td><td>235 (15.9)</td><td>91 (17.2)</td><td>139 (15.0)</td></tr><tr><td> Colorado</td><td>275 (18.6)</td><td>122 (23.0)</td><td>149 (16.0)</td></tr><tr><td> Georgia</td><td>285 (19.3)</td><td>83 (15.7)</td><td>202 (21.7)</td></tr><tr><td> Maryland</td><td>254 (17.2)</td><td>75 (14.2)</td><td>179 (19.3)</td></tr><tr><td> North Carolina</td><td>226 (15.3)</td><td>84 (15.9)</td><td>137 (14.7)</td></tr><tr><td> Pennsylvania</td><td>205 (13.9)</td><td>75 (14.2)</td><td>124 (13.3)</td></tr></tbody></table> </ephtml> </p> <p>1 Percentages may not add up to 100 due to rounding.</p> <p>2 MSEL Early Learning Composite scores were available for 1460 (98.6%) children with autism spectrum disorder.</p> <hd id="AN0177036770-5">Measures</hd> <p></p> <hd id="AN0177036770-6">VABS-II: survey interview form</hd> <p>The VABS-II ([<reflink idref="bib55" id="ref88">55</reflink>]) is a semi-structured interview designed to measure a child's current behavior with an item coding system ranging from "never or rarely performed without help" to "consistently performed without help." The VABS-II was administered in person to the participating child's caregiver. VABS domains include communication, socialization, daily living skills, and in children less than 6 years of age, motor skills, and an overall Adaptive Behavior Composite score can be derived from domain scores. For the composite and all domain scores, standard scores with mean 100, standard deviation 15, and higher scores indicating higher adaptive functioning were used in analyses. VABS-II scores were available for 1458 (98.5%) children with ASD.</p> <hd id="AN0177036770-7">MSEL</hd> <p>The MSEL ([<reflink idref="bib43" id="ref89">43</reflink>]) measures early learning ability in young children up to 68 months of age. In SEED, the MSEL was administered at the in-person assessment, and four subscales on the MSEL—fine motor, visual reception, expressive language, and receptive language—were collected. Subscale <emph>T</emph>-scores have a mean of 50 and standard deviation of 10, and the Early Learning Composite (ELC) is an overall measure of cognitive functioning with a standard score mean of 100 and standard deviation of 15; higher scores on the MSEL indicate higher cognitive ability. This research focused on the MSEL ELC and, to improve precision in the lower range in the regression analysis, a composite raw score was calculated by summing the raw scores from the four subscales. To avoid underestimating early learning ability in children with language delays that are likely present in this preschool-aged sample but may not persist over time or with intervention, we also performed supplemental analyses using the visual reception subscale raw score as a proxy for non-verbal cognitive ability, which has been shown to be more stable in young children with autism than verbal IQ due to the rapid language development evident in verbal scores measured in longitudinal studies of children with autism ([<reflink idref="bib2" id="ref90">2</reflink>]; [<reflink idref="bib12" id="ref91">12</reflink>]; [<reflink idref="bib17" id="ref92">17</reflink>]; [<reflink idref="bib27" id="ref93">27</reflink>]). For analyses stratified by cognitive ability, an ELC threshold of 70 (i.e. two SD below the mean) was used to stratify the sample by whether they would be considered "high-functioning" or "low-functioning" in the literature. MSEL ELC scores were available for 1460 (98.6%) children with ASD. We also conducted a sensitivity analysis using developmental quotients (DQs) and stratifying by a DQ threshold of 70. An overall DQ was calculated by averaging the age equivalent scores from the four subscales, dividing by the child's age in months at administration of the MSEL, and multiplying by 100; a visual reception DQ was also calculated from the visual reception age equivalent. These results are available in the Supplemental Materials (Supplementary Tables S3 and S4).</p> <hd id="AN0177036770-8">ADOS</hd> <p>The ADOS (Second Edition; [<reflink idref="bib34" id="ref94">34</reflink>], [<reflink idref="bib35" id="ref95">35</reflink>]) is a semi-structured diagnostic child observation that captures social affect and stereotyped behavior and restricted interests in four alternative modules: 1—preverbal/single words, 2—phrase speech/non-fluent, 3—fluent speech (child/adolescent), and 4—fluent speech (adolescent/adult). In SEED, module 1, 2, or 3 was administered at the in-person assessment depending on the expressive language abilities of the child. For this research, ADOS CSSs were used to measure autism symptom severity, with higher CSSs indicating higher severity ([<reflink idref="bib23" id="ref96">23</reflink>]).</p> <hd id="AN0177036770-9">Childhood Behavior Checklist/1½–5</hd> <p>The Childhood Behavior Checklist/1½–5 (CBCL/1½–5; [<reflink idref="bib1" id="ref97">1</reflink>]) is a parent-administered questionnaire on which a child's caregiver is asked to score behavioral and emotional items on a scale of "not true, as far as you know," "somewhat or sometimes true," and "very true or often true" with higher scores indicating greater problems. The CBCL includes three broad scales: externalizing (which includes attention problems and aggressive behavior), internalizing (which includes anxiety/depression, emotional reactivity, somatic complaints, and withdrawn behaviors), and total problems. CBCL forms were mailed to participating families and filled out by caregivers. <emph>T</emph>-scores with mean 50 and standard deviation 10 were collected for the externalizing, internalizing, and total problems scales with a score of ⩾65 indicating borderline to clinically significant problems and higher scores indicating more problems.</p> <hd id="AN0177036770-10">Statistical analysis</hd> <p>For Hypothesis 1, we computed VABS-II summary statistics. Mean scores were compared between VABS-II domains using Friedman tests and post hoc Wilcoxon signed-rank sum tests, overall and within each ELC stratum. To estimate the amount of variance in VABS-II scores explained by cognitive ability and autism symptom severity (Hypothesis 2), hierarchical linear regression analysis was performed. In univariate analysis, child's sex, race, ethnicity, age at administration of the MSEL, and birth order; family's current income relative to the federal poverty level; primary language spoken in the home; maternal age at enrollment, education, and place of birth; and study site were associated with VABS-II scores and included as predictors in Model 1. In subsequent separate steps, different predictors were added to the regression model, including MSEL composite raw score (Model 2); to account for the potential of a curvilinear relationship in which cognitive and adaptive scores were similar in the lower MSEL score range while cognitive development outpaced adaptive scores in the higher MSEL score range, we also included the quadratic term. In Model 3, ADOS CSS was added, followed by the interaction between ADOS CSS and the two cognitive variables (Model 4) in order to explore the potential effect modification of cognitive development on the relationship between adaptive scores and autism severity. Finally, we added CBCL total <emph>T</emph>-score in Model 5 to estimate the additional variance in AB scores explained by overall behavioral and emotional problems ([<reflink idref="bib20" id="ref98">20</reflink>]; [<reflink idref="bib24" id="ref99">24</reflink>]; [<reflink idref="bib31" id="ref100">31</reflink>]). We did not examine externalizing and internalizing problems separately due to significant correlations between these two domains. Nested models were then compared using Wald tests incorporating robust standard errors to determine whether the additional variance explained by a new predictor was significant. Separate regressions were run for the VABS-II composite and each domain score. Regression analysis was limited to complete cases (<emph>n</emph> = 1278 (86.4% of ASD cases) for the composite regression; <emph>n</emph> = 1291 (87.2% of ASD cases) for domain regressions), and we also performed sensitivity analyses using multiple imputation to account for children missing data.</p> <p>Finally, we described cognitive ability/AB discrepancies among children with ASD with both MSEL ELC and VABS-II scores recorded (Hypothesis 3), overall and within three age cohorts (2–3 years, 4 years, and 5 years of age). We estimated the percent of children with autism with either, both, or neither ELC or VABS-II score indicating significant challenges and calculated Cohen's kappa coefficients to assess the degree of agreement between the two measures in categorization of functioning ([<reflink idref="bib42" id="ref101">42</reflink>]). In addition, after converting both ELC and VABS-II scores into SD units rounded to two decimal places, we calculated the magnitude of cognitive ability/AB discrepancies and determined the percent of children within each cognitive stratum with one and two SD discrepancies between the two measures.</p> <p>Regression analysis was conducted using R Version 4.2.2 ([<reflink idref="bib50" id="ref102">50</reflink>]); the R packages "lmtest" ([<reflink idref="bib71" id="ref103">71</reflink>]) and "sandwich" ([<reflink idref="bib70" id="ref104">70</reflink>]; [<reflink idref="bib72" id="ref105">72</reflink>]) were used for nested model comparisons. All other statistical analyses were conducted using SAS Version 9.4 ([<reflink idref="bib53" id="ref106">53</reflink>]). The design and implementation of the SEED study have benefited from community stakeholder input from autistic self-advocates, family members, and service providers, although there was no direct community involvement in the reported study.</p> <hd id="AN0177036770-11">Results</hd> <p></p> <hd id="AN0177036770-12">Hypothesis 1</hd> <p>On average, the SEED ASD group exhibited significant AB delays. As hypothesized, the socialization mean was the lowest and motor skills the highest (1.9 and 1.4 SDs below the mean, respectively). In children with ELC ⩽ 70, mean VABS-II composite and domain scores were all 1.7 SD or more below the mean. In children with ELC > 70, all mean domain scores were below the population mean of 100, with mean composite, daily living skills, and socialization greater than one SD below the mean.</p> <p>Contrary to our hypothesis, communication scores were higher than daily living skills on average (Table 2). Between-domain analysis revealed that all mean domain scores significantly differed from one another (<emph>p</emph> < 0.0001 for all comparisons except communication versus daily living skills in children with ELC ⩽ 70 (<emph>p</emph> = 0.03)), overall and within each ELC stratum. Overall and for children with ELC ⩽ 70, mean motor skills scores were the highest, followed by communication, daily living skills, and socialization, while in children with ELC > 70, mean communication scores were the highest, followed by motor skills, daily living skills, and finally socialization.</p> <p>Graph</p> <p>Table 2. Summary statistics for selected instruments, overall and stratified by cognitive ability, for children with autism spectrum disorder, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011).</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="." /></colgroup><thead><tr><th align="left" rowspan="3">Variable</th><th align="left" colspan="3" rowspan="2">Overall</th><th align="left" colspan="6">MSEL Early Learning Composite</th></tr><tr><th align="left" colspan="3">⩽70</th><th align="left" colspan="3">>70</th></tr><tr><th align="left">Mean</th><th align="left">Standard deviation</th><th align="left">Range</th><th align="left">Mean</th><th align="left">Standard deviation</th><th align="left">Range</th><th align="left">Mean</th><th align="left">Standard deviation</th><th align="left">Range</th></tr></thead><tbody><tr><td colspan="10">VABS-II</td></tr><tr><td> Composite</td><td>73.2</td><td>13.3</td><td>41–114</td><td>67.3</td><td>11.2</td><td>41–98</td><td>83.6</td><td>9.9</td><td>47–114</td></tr><tr><td> Communication</td><td>77.8</td><td>17.7</td><td>36–131</td><td>69.7</td><td>15.5</td><td>36–108</td><td>92.1</td><td>11.1</td><td>40–131</td></tr><tr><td> Daily living skills</td><td>74.7</td><td>15.1</td><td>34–121</td><td>69.0</td><td>14.0</td><td>34–109</td><td>84.8</td><td>11.3</td><td>48–121</td></tr><tr><td> Socialization</td><td>72.1</td><td>12.6</td><td>44–112</td><td>67.6</td><td>11.2</td><td>44–105</td><td>80.0</td><td>11.1</td><td>49–112</td></tr><tr><td> Motor skills</td><td>79.7</td><td>13.4</td><td>49–124</td><td>74.9</td><td>12.0</td><td>49–117</td><td>88.0</td><td>11.7</td><td>49–124</td></tr><tr><td colspan="10">MSEL</td></tr><tr><td> Early learning composite</td><td>66.6</td><td>19.6</td><td>49–132</td><td>53.6</td><td>6.3</td><td>49–70</td><td>89.3</td><td>13.3</td><td>71–132</td></tr><tr><td> Composite raw score</td><td>131.0</td><td>39.7</td><td>14–197</td><td>109.9</td><td>32.8</td><td>14–173</td><td>167.8</td><td>17.8</td><td>94–197</td></tr><tr><td> Visual reception <italic>T</italic>-score</td><td>34.7</td><td>15.5</td><td>20–80</td><td>25.5</td><td>8.8</td><td>20–58</td><td>51.0</td><td>10.5</td><td>20–80</td></tr><tr><td> Visual reception raw score</td><td>37.4</td><td>10.2</td><td>0–50</td><td>32.3</td><td>9.2</td><td>0–50</td><td>46.3</td><td>4.0</td><td>27–50</td></tr><tr><td> Fine motor <italic>T</italic>-score</td><td>29.6</td><td>11.7</td><td>20–80</td><td>23.0</td><td>6.1</td><td>20–52</td><td>41.1</td><td>10.3</td><td>20–80</td></tr><tr><td> Fine motor raw score</td><td>33.8</td><td>8.8</td><td>6–49</td><td>29.6</td><td>7.4</td><td>6–47</td><td>41.2</td><td>5.6</td><td>23–49</td></tr><tr><td> Receptive language <italic>T</italic>-score</td><td>30.2</td><td>12.9</td><td>20–73</td><td>22.0</td><td>4.4</td><td>20–51</td><td>44.5</td><td>10.2</td><td>20–73</td></tr><tr><td> Receptive language raw score</td><td>30.5</td><td>11.5</td><td>0–48</td><td>24.5</td><td>9.4</td><td>0–45</td><td>41.1</td><td>5.6</td><td>20–48</td></tr><tr><td> Expressive language <italic>T</italic>-score</td><td>29.1</td><td>11.4</td><td>20–74</td><td>22.5</td><td>5.1</td><td>20–49</td><td>40.8</td><td>9.8</td><td>20–74</td></tr><tr><td> Expressive language raw score</td><td>29.2</td><td>11.5</td><td>3–50</td><td>23.5</td><td>9.9</td><td>3–44</td><td>39.3</td><td>5.7</td><td>20–50</td></tr><tr><td colspan="10">ADOS</td></tr><tr><td> Calibrated severity score</td><td>7.3</td><td>1.6</td><td>4–10</td><td>7.6</td><td>1.6</td><td>4–10</td><td>6.7</td><td>1.6</td><td>4–10</td></tr><tr><td colspan="10">CBCL</td></tr><tr><td> Total <italic>T</italic>-score</td><td>62.9</td><td>11.1</td><td>32–98</td><td>63.4</td><td>10.5</td><td>36–98</td><td>62.0</td><td>12.1</td><td>32–94</td></tr><tr><td> Externalizing <italic>T</italic>-score</td><td>59.7</td><td>11.3</td><td>28–97</td><td>60.0</td><td>10.7</td><td>35–97</td><td>59.2</td><td>12.4</td><td>28–92</td></tr><tr><td> Internalizing <italic>T</italic>-score</td><td>62.6</td><td>9.6</td><td>29–97</td><td>62.6</td><td>9.2</td><td>33–97</td><td>62.4</td><td>10.4</td><td>29–97</td></tr></tbody></table> </ephtml> </p> <p>3 MSEL: Mullen Scales of Early Learning; VABS-II: Vineland Adaptive Behavior Scales–Second Edition; ADOS: Autism Diagnostic Observation Schedule; CBCL: Childhood Behavior Checklist.</p> <hd id="AN0177036770-13">Hypothesis 2</hd> <p>There was wide variation in VABS-II scores in the overall ASD group and within cognitive strata. VABS-II composite scores ranged from 41 to 114 (3.9 SDs below the mean to 0.9 above the mean) (Table 2). Overall and in children with ELC > 70, communication scores had the largest range of any domain, while in children with ELC ⩽ 70, daily living skills scores had the largest range; socialization had the smallest range of any domain overall and in both strata (Table 2).</p> <p>In regression analysis, sociodemographic factors explained between 6.0% (motor skills) to 17.2% (communication) of variance in VABS-II scores (Table 3). Addition of the MSEL composite raw score explained significant and substantial variance in VABS-II scores (∆<emph>R</emph><sups>2</sups> = 51.1% for the VABS-II composite score), with domain-specific ∆<emph>R</emph><sups>2</sups> values ranging from 58.0% in communication to 31.9% in motor skills scores (Model 2a in Table 3). Addition of the ADOS CSS (Model 3a) explained a significant but small amount of additional variance in the communication (∆<emph>R</emph><sups>2</sups> = 0.1%), daily living skills (∆<emph>R</emph><sups>2</sups> = 0.2%), and socialization (∆<emph>R</emph><sups>2</sups> = 0.4%) domains. Addition of the MSEL/ADOS CSS interaction only explained significant additional variance for motor skills (∆<emph>R</emph><sups>2</sups> = 0.5%) (Model 4a). Finally, addition of the CBCL total <emph>T</emph>-score explained significant additional variance for all VABS-II scores, explaining most in socialization (∆<emph>R</emph><sups>2</sups> = 4.9%) and least in motor skills (∆<emph>R</emph><sups>2</sups> = 1.0%) (Model 5a). Altogether, these predictors explained 67.2% of the variance in composite AB scores, with most explained variance in VABS-II communication scores (<emph>R</emph><sups>2</sups> = 76.4%), followed by daily living skills (<emph>R</emph><sups>2</sups> = 57.3%), socialization (<emph>R</emph><sups>2</sups> = 48.9%), and motor skills (<emph>R</emph><sups>2</sups> = 39.5%) (Table 3).</p> <p>Graph</p> <p>Table 3. Results of linear regression modeling Vineland Adaptive Behavior Scales–Second Edition scores in children with autism spectrum disorder, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011).</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="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="left" rowspan="3">Model</th><th align="left" rowspan="3">Variables added</th><th align="left" colspan="10">VABS-II score</th></tr><tr><th /><th /><th align="left" colspan="2">Composite</th><th align="left" colspan="2">Communication</th><th align="left" colspan="2">Daily living skills</th><th align="left" colspan="2">Socialization</th><th align="left" colspan="2">Motor skills</th></tr><tr><th /><th /><th align="left"><italic>R</italic><sup>2</sup> (%)</th><th align="left">Δ<italic>R</italic><sup>2</sup> (%)</th><th align="left"><italic>R</italic><sup>2</sup> (%)</th><th align="left">Δ<italic>R</italic><sup>2</sup> (%)</th><th align="left"><italic>R</italic><sup>2</sup> (%)</th><th align="left">Δ<italic>R</italic><sup>2</sup> (%)</th><th align="left"><italic>R</italic><sup>2</sup> (%)</th><th align="left">Δ<italic>R</italic><sup>2</sup> (%)</th><th align="left"><italic>R</italic><sup>2</sup> (%)</th><th align="left">Δ<italic>R</italic><sup>2</sup> (%)</th></tr></thead><tbody><tr><td colspan="2" rowspan="6">Overall</td><td /><td><italic>n</italic></td><td colspan="2">1278</td><td colspan="2">1291</td><td colspan="2">1291</td><td colspan="2">1291</td><td colspan="2">1291</td></tr><tr><td>1a</td><td>Demographics<xref ref-type="table-fn" rid="tfn5">a</xref></td><td>13.2</td><td>NA</td><td>17.2</td><td>NA</td><td>14.1</td><td>NA</td><td>10.5</td><td>NA</td><td>6.0</td><td>NA</td></tr><tr><td>2a</td><td>+MSEL composite raw score<xref ref-type="table-fn" rid="tfn6">b</xref></td><td>64.3</td><td>51.1<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>75.2</td><td>58.0<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>54.8</td><td>40.7<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>43.5</td><td>33.0<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>37.9</td><td>31.9<xref ref-type="table-fn" rid="tfn8">***</xref></td></tr><tr><td>3a</td><td>+ADOS calibrated severity score</td><td>64.4</td><td>0.0</td><td>75.3</td><td>0.1<xref ref-type="table-fn" rid="tfn8">*</xref></td><td>55.0</td><td>0.2<xref ref-type="table-fn" rid="tfn8">*</xref></td><td>43.9</td><td>0.4<xref ref-type="table-fn" rid="tfn8">**</xref></td><td>37.9</td><td>0.0</td></tr><tr><td>4a</td><td>+Interaction<xref ref-type="table-fn" rid="tfn7">c</xref></td><td>64.4</td><td>0.0</td><td>75.3</td><td>0.0</td><td>55.0</td><td>0.0</td><td>44.0</td><td>0.2</td><td>38.4</td><td>0.5<xref ref-type="table-fn" rid="tfn8">**</xref></td></tr><tr><td>5a</td><td>+CBCL total <italic>T</italic>-score</td><td>67.2</td><td>2.8<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>76.4</td><td>1.1<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>57.3</td><td>2.3<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>48.9</td><td>4.9<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>39.5</td><td>1.0<xref ref-type="table-fn" rid="tfn8">***</xref></td></tr><tr><td rowspan="12">MSEL Early Learning Composite</td><td rowspan="6">⩽70</td><td /><td><italic>n</italic></td><td colspan="2">794</td><td colspan="2">802</td><td colspan="2">802</td><td colspan="2">802</td><td colspan="2">802</td></tr><tr><td>1b</td><td>Demographics<xref ref-type="table-fn" rid="tfn5">a</xref></td><td>10.4</td><td>NA</td><td>11.0</td><td>NA</td><td>15.7</td><td>NA</td><td>9.2</td><td>NA</td><td>6.6</td><td>NA</td></tr><tr><td>2b</td><td>+MSEL composite raw score<xref ref-type="table-fn" rid="tfn6">b</xref></td><td>61.4</td><td>50.9<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>71.2</td><td>60.2<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>53.8</td><td>38.1<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>42.9</td><td>33.8<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>30.8</td><td>24.1<xref ref-type="table-fn" rid="tfn8">***</xref></td></tr><tr><td>3b</td><td>+ADOS calibrated severity score</td><td>61.4</td><td>0.0</td><td>71.4</td><td>0.1<xref ref-type="table-fn" rid="tfn8">*</xref></td><td>53.8</td><td>0.0</td><td>43.7</td><td>0.8<xref ref-type="table-fn" rid="tfn8">**</xref></td><td>31.2</td><td>0.4<xref ref-type="table-fn" rid="tfn8">*</xref></td></tr><tr><td>4b</td><td>+Interaction<xref ref-type="table-fn" rid="tfn7">c</xref></td><td>61.5</td><td>0.1</td><td>71.4</td><td>0.1</td><td>53.9</td><td>0.1</td><td>43.8</td><td>0.1</td><td>31.3</td><td>0.0</td></tr><tr><td>5b</td><td>+CBCL total <italic>T</italic>-score</td><td>63.6</td><td>2.2<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>72.1</td><td>0.7<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>56.1</td><td>2.1<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>47.7</td><td>3.9<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>31.9</td><td>0.6<xref ref-type="table-fn" rid="tfn8">*</xref></td></tr><tr><td rowspan="6">>70</td><td /><td><italic>n</italic></td><td colspan="2">484</td><td colspan="2">489</td><td colspan="2">489</td><td colspan="2">489</td><td colspan="2">489</td></tr><tr><td>1c</td><td>Demographics<xref ref-type="table-fn" rid="tfn5">a</xref></td><td>13.3</td><td>NA</td><td>20.0</td><td>NA</td><td>11.4</td><td>NA</td><td>11.2</td><td>NA</td><td>9.6</td><td>NA</td></tr><tr><td>2c</td><td>+MSEL composite raw score<xref ref-type="table-fn" rid="tfn6">b</xref></td><td>20.1</td><td>6.9<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>33.4</td><td>13.4<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>14.9</td><td>3.5<xref ref-type="table-fn" rid="tfn8">**</xref></td><td>12.3</td><td>1.2<xref ref-type="table-fn" rid="tfn8">*</xref></td><td>13.0</td><td>3.4<xref ref-type="table-fn" rid="tfn8">**</xref></td></tr><tr><td>3c</td><td>+ADOS calibrated severity score</td><td>20.4</td><td>0.3</td><td>33.8</td><td>0.4</td><td>15.7</td><td>0.9<xref ref-type="table-fn" rid="tfn8">*</xref></td><td>12.5</td><td>0.1</td><td>14.0</td><td>1.1<xref ref-type="table-fn" rid="tfn8">*</xref></td></tr><tr><td>4c</td><td>+Interaction<xref ref-type="table-fn" rid="tfn7">c</xref></td><td>20.6</td><td>0.1</td><td>33.8</td><td>0.0</td><td>15.9</td><td>0.2</td><td>13.0</td><td>0.6</td><td>14.4</td><td>0.4</td></tr><tr><td>5c</td><td>+CBCL total <italic>T</italic>-score</td><td>28.7</td><td>8.1<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>39.0</td><td>5.2<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>20.2</td><td>4.3<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>22.0</td><td>9.0<xref ref-type="table-fn" rid="tfn8">***</xref></td><td>16.9</td><td>2.5<xref ref-type="table-fn" rid="tfn8">**</xref></td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 VABS-II: Vineland Adaptive Behavior Scales–Second Edition; NA: not applicable; MSEL: Mullen Scales of Early Learning; ADOS: Autism Diagnostic Observation Schedule–Second Edition; CBCL: Childhood Behavior Checklist.</item> <item>5 Demographic variables: child's sex, child's race and ethnicity, child's age at administration of MSEL, child's birth order, household income, primary language spoken in the home, maternal age at enrollment (and the quadratic term), maternal education, mother's place of birth, and study site.</item> <item>6 Includes MSEL composite raw score and its quadratic term.</item> <item>7 Interaction between MSEL variables and ADOS calibrated severity score.</item> <item>8 <emph>p</emph> < 0.05; **<emph>p</emph> < 0.01; ***<emph>p</emph> < 0.0001.</item> </ulist> <p>In children with MSEL ELC ⩽ 70, the full model explained between 72.1% (communication) and 31.9% (motor skills) of AB score variance (Table 3). The MSEL composite raw score explained significant additional variance for every VABS-II score, ranging from 60.2% for communication to 24.1% for motor skills. The ADOS CSS explained significant additional variance for communication (∆<emph>R</emph><sups>2</sups> = 0.1%), socialization (∆<emph>R</emph><sups>2</sups> = 0.8%), and motor skills (∆<emph>R</emph><sups>2</sups> = 0.4%), while the additional variance explained by the MSEL/ADOS CSS interaction was not significant for any score. CBCL total <emph>T</emph>-score explained significant additional variance for the composite and every domain, ranging from 3.9% for socialization to 0.6% for motor skills.</p> <p>In comparison, in children with ELC > 70, these predictors explained less of the VABS-II score variance with <emph>R</emph><sups>2</sups> values ranging from 39.0% for communication to 16.9% for motor skills (Table 3). The MSEL composite raw score explained significant additional variance for every VABS-II score, ranging from 13.4% for communication to 1.2% for socialization. Addition of the ADOS CSS explained significant additional variance in daily living skills (∆<emph>R</emph><sups>2</sups> = 0.9%) and motor skills (∆<emph>R</emph><sups>2</sups> = 1.1%), while the MSEL/ADOS CSS interaction did not explain significant additional variance for any VABS-II score. CBCL total <emph>T</emph>-scores explained significant additional variance for every VABS-II scores, ranging from 9.0% for socialization to 2.5% for motor skills. For all scores except communication, the additional variance explained by CBCL scores was similar to or greater than that explained by the MSEL composite raw score, particularly in the socialization domain in which the CBCL total <emph>T</emph>-score explained 9.0% significant additional variance compared to only 1.2% significant additional variance explained by the MSEL composite raw score (Table 3).</p> <p>Analysis of multiply imputed data resulted in similar <emph>R</emph><sups>2</sups> and Δ<emph>R</emph><sups>2</sups> values. In addition, patterns were generally similar when the visual reception raw score was used as our measure of cognitive ability, except that, generally, the visual reception raw score explained a smaller amount of additional variance in VABS-II scores than the composite raw score (see Supplementary Table S2).</p> <hd id="AN0177036770-14">Hypothesis 3</hd> <p>Figure 1 shows the wide range of composite AB scores among children with ELC scores below and above the two SD below the mean cutoff (Figure 1). Cohen's kappa coefficients indicated weak (composite (0.45)) to minimal (communication (0.36), daily living skills (0.37), socialization (0.38), and motor skills (0.26)) agreement between MSEL ELC and VABS-II scores regarding whether or not both scores fell above or below the two SD cutoff for significant functional impairment or delay. In our sample, 25.3% of children had ELC ⩽ 70 but VABS-II composite scores indicating no significant adaptive delays, while 3.3% and 8.1% had ELC > 70 but VABS-II composite and socialization scores, respectively, indicating significant delay (Figure 1 and Supplementary Table S5). Over a third (39.7%) of children with ASD and ELC ⩽ 70 did not have a composite AB score indicating significant adaptive delay (Figure 1, Quadrant I). In addition, among children with ELC > 70, 9.0% and 22.2% had VABS-II composite and socialization scores, respectively, indicating significant adaptive delay (Figure 1, Quadrant IV; Supplementary Table S5). Similar results for other VABS-II domain scores are provided in Supplementary Table S5. Patterns were similar when the relationship between VABS-II scores and the MSEL visual reception <emph>T</emph>-score was examined, but a smaller percentage of children had cognitive scores indicating significant delay when measured by the visual reception <emph>T</emph>-score rather than ELC (49.5% vs 63.7%) (see Supplementary Figure S1).</p> <p>Graph: Figure 1. Discrepant (I, IV) and concordant (II, III) categorizations of functioning based on cognitive development as measured by the Mullen Scales of Early Learning Composite and/or adaptive behavior scores as measured by the Vineland Adaptive Behavior Scales–Second Edition Composite in children with autism spectrum disorder, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011). Dashed lines indicate two standard deviations below the mean on the VABS-II (horizontal line) and MSEL (vertical line), the cutoff typically used to indicate significant impairment or delay.VABS-II: Vineland Adaptive Behavior Scales–Second Edition; ABC: Adaptive Behavior Composite; MSEL: Mullen Scales of Early Learning; ELC: Early Learning Composite.</p> <p>Consistent with our third hypothesis, we found discrepancies between MSEL ELC and VABS-II scores. Most children with ELC > 70 had cognitive scores exceeding their VABS-II scores (62.1% for the composite). The largest discrepancies were seen in the socialization domain, with 36.2% of children having ELC > AB with at least a one SD discrepancy and 12.3% having a two SD or more discrepancy between the two scores (Figure 2). Discrepancies were also large for other domains (Figure 2). In children with ELC ⩽ 70, adaptive scores often exceeded ELC (91.9% for the composite) and the proportion with AB scores at least one SD greater than their ELC ranged from 73.8% for motor skills to 43.1% for socialization (Figure 3).</p> <p>Graph: Figure 2. Discrepancies of one or more SD between cognitive development as measured by the Mullen Scales of Early Learning (MSEL) Composite (ELC) and Vineland Adaptive Behavior Scales–Second Edition Composite (ABC), communication (COM), daily living skills (DLS), socialization (SOC), and motor skills (MOT) scores among children with autism spectrum disorder and MSEL ELC > 70, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011).VABS: Vineland Adaptive Behavior Scales–Second Edition; ELC: Early Learning Composite; SD: standard deviation; ABC: Adaptive Behavior Composite.</p> <p>Graph: Figure 3. Discrepancies of one or more SD between cognitive development as measured by the Mullen Scales of Early Learning (MSEL) Composite (ELC) and Vineland Adaptive Behavior Scales–Second Edition Composite (ABC), communication (COM), daily living skills (DLS), socialization (SOC), and motor skills (MOT) scores among children with autism spectrum disorder and MSEL ELC ⩽ 70, Study to Explore Early Development phases one (birth years 2003–2006) and two (birth years 2008–2011).VABS: Vineland Adaptive Behavior Scales–Second Edition; ELC: Early Learning Composite; SD: standard deviation; ABC: Adaptive Behavior Composite.</p> <p>When stratified by age, the proportion of children with a two SD or more discrepancy between their ELC and VABS-II scores decreased with increasing age (Figures 2 and 3), with this trend generally more pronounced in children with ELC > 70. There were substantial proportions of children with at least a one or more SD difference in all age cohorts.</p> <hd id="AN0177036770-15">Discussion</hd> <p>Our findings of wide variation in adaptive functioning of preschool children with autism may have important implications for identifying service needs and targets of interventions, tracking short- and long-term outcomes, and describing functioning in future epidemiologic and clinical studies of ASD. With respect to the VABS "autism profile," we confirmed that children with ASD have significant socialization delays and, in comparison to other domains, relatively fewer motor delays, although scores in this domain still indicated meaningful limitations. In contrast to other studies (e.g. Bӧlte & Poustka, 2002; [<reflink idref="bib47" id="ref107">47</reflink>]; [<reflink idref="bib67" id="ref108">67</reflink>]), we found that communication scores exceeded daily living skills scores on average in our sample. These findings suggest that, while evidence for a characteristic "autism profile" is mixed, AB domain scores may be useful for characterizing more precise profiles of strengths and support needs in research examining phenotypic variation in children with autism.</p> <p>Consistent with our hypothesis, neither cognitive development as measured by MSEL raw scores nor autism severity fully explained variation in AB scores in this sample, particularly in the socialization and motor skill domains and in children with cognitive scores above the ID threshold, suggesting that AB scores capture important variation in the ASD phenotype otherwise missed by these other measures. As in past research in children with ASD (e.g. [<reflink idref="bib33" id="ref109">33</reflink>]; [<reflink idref="bib44" id="ref110">44</reflink>]; [<reflink idref="bib67" id="ref111">67</reflink>]), we found a strong positive relationship between cognitive ability and AB scores, especially for the communication domain, although, in the overall ASD group, cognitive scores explained less than half of the variation in daily living skills, socialization, and motor skills and even less within each cognitive stratum, explaining less than 14% of variation in any VABS-II score in children with ELC > 70. Severity of autism symptoms explained little variance while CBCL scores explained somewhat more, particularly in the socialization domain and in children with ELC > 70, consistent with previous findings of a significant negative association between adaptive functioning and behavioral and emotional problems ([<reflink idref="bib20" id="ref112">20</reflink>]; [<reflink idref="bib24" id="ref113">24</reflink>]; [<reflink idref="bib31" id="ref114">31</reflink>]).</p> <p>Finally, as hypothesized, we found that children with autism with cognitive scores indicating no significant cognitive delay exhibited substantial cognitive/AB discrepancies; many of these children had cognitive scores substantially exceeding AB scores, although contrary to our hypothesis, we saw a higher proportion of substantial cognitive/AB discrepancies in children with cognitive scores two or more SDs below the mean. These discrepancy findings are consistent with past research of children across different age groups (e.g. [<reflink idref="bib3" id="ref115">3</reflink>]; [<reflink idref="bib44" id="ref116">44</reflink>]; [<reflink idref="bib59" id="ref117">59</reflink>]). Together these results suggest that using cognitive scores, autism symptom severity, and behavioral and emotional problems as a proxy for "functioning"—either together or separately—obscures heterogeneity in adaptive functioning among children with autism and that large gaps between cognitive development and AB are apparent in children with autism across the spectrum of cognitive development as early as preschool. These findings may be important when designing treatment plans and interpreting research findings.</p> <p>Our results did not support the theory that cognitive ability would be the limiting factor in attainment of AB skills in children with lower cognitive ability and autism severity for children with greater cognitive development ([<reflink idref="bib33" id="ref118">33</reflink>]; [<reflink idref="bib41" id="ref119">41</reflink>]; [<reflink idref="bib67" id="ref120">67</reflink>]), as an interaction term between cognitive score and autism symptom severity did not explain substantial additional variance in AB scores and ADOS CSSs did not explain substantial additional variance in either cognitive stratum. However, previous study samples often included older individuals, and longitudinal studies have found an association between greater autism symptom severity and low or declining trajectories of AB skill attainment as well as widening IQ/AB gaps with increasing age ([<reflink idref="bib8" id="ref121">8</reflink>]; [<reflink idref="bib20" id="ref122">20</reflink>]; [<reflink idref="bib24" id="ref123">24</reflink>]). Because our sample was preschool-aged, the relationship between autism symptom severity and adaptive functioning and the influence of symptom severity on the gap between cognitive development and AB scores could strengthen as they age and develop. While we actually saw fewer large cognitive ability/AB discrepancies in 4- and 5-year-old children relative to those less than 4 years of age, previous research has shown that issues related to testability may impact how young children with ASD perform on the MSEL ([<reflink idref="bib2" id="ref124">2</reflink>]; [<reflink idref="bib14" id="ref125">14</reflink>]), and therefore, the MSEL may have underestimated the cognitive abilities—especially related to language—of the children in our sample, particularly those under 4 years of age.</p> <p>Other factors that may contribute to variation in adaptive functioning in children with autism are behavioral or emotional problems and co-occurring physical and mental health conditions. Previous studies have found associations between the two ([<reflink idref="bib20" id="ref126">20</reflink>]; [<reflink idref="bib24" id="ref127">24</reflink>]; [<reflink idref="bib31" id="ref128">31</reflink>]), although others have not ([<reflink idref="bib59" id="ref129">59</reflink>]). Our results suggest greater behavioral and emotional problems are associated with lower adaptive functioning, particularly in the socialization domain. In our sample, CBCL scores explained greater additional variance in AB scores than did autism severity, and in children with ELC scores indicating no significant cognitive delays, CBCL scores explained similar or greater additional variance than even cognitive scores in every domain except communication.</p> <hd id="AN0177036770-16">Strengths and limitations</hd> <p>The large, population-based SEED sample includes children with ASD from diverse racial, ethnic, and socioeconomic backgrounds. Participants all underwent rigorous, research-reliable, professional assessments and had nearly complete data on instruments measuring cognitive development, adaptive functioning, autism symptom severity, and behavioral and emotional health. In addition, recruitment procedures allowed for identification of ASD cases from a variety of community sources that may otherwise be missed when participants are recruited primarily from clinical populations, as is the case for much of the previous literature on adaptive functioning in children with autism. The broad diagnostic net for case identification in SEED also made possible the inclusion of children without a previous ASD diagnosis, mitigating the risk of a sample biased toward children with access to healthcare or intervention services.</p> <p>One limitation of our study was the lack of a measure of social demands and support, particularly given our finding that sociodemographic characteristics explained variation in adaptive functioning ([<reflink idref="bib13" id="ref130">13</reflink>]; [<reflink idref="bib39" id="ref131">39</reflink>]). Future research could examine the association between AB and measures of social determinants of health, such as access to resources like early diagnosis and early intervention programs and family and neighborhood environment. Future research could also examine domain-specific associations between cognitive ability, autism symptom severity, and behavioral and emotional problems and AB scores.</p> <p>Another consideration is the restricted age range of children in our sample. It is important to note that ID is not typically diagnosed in preschool-aged children due to the instability of IQ scores in this age group. However, early childhood cognitive scores have been shown to be predictive of adult outcomes, including IQ ([<reflink idref="bib5" id="ref132">5</reflink>]; [<reflink idref="bib38" id="ref133">38</reflink>]; [<reflink idref="bib48" id="ref134">48</reflink>]), and this study provides evidence that the gap between cognitive and AB scores found in previous ASD research emerges early and may have important implications for research on ASD with co-occurring ID and interpretation and of past research that classified children with autism as "high-" or "low-functioning" based solely on cognitive scores. Finally, this analysis focused on the MSEL and VABS-II, and the findings may not generalize to all measures of cognitive and adaptive development.</p> <hd id="AN0177036770-17">Conclusion</hd> <p>Our finding of significant heterogeneity in adaptive functioning and substantial discrepancies between cognitive and AB scores in preschool-aged children with autism suggests that understanding the relationship between AB and cognitive ability could lead to better characterization of the heterogeneity and needs for support of young autistic children. Autistic children, particularly those without significant cognitive delays, may have support needs not reflected by their cognitive ability; likewise, children with cognitive delays may have adaptive strengths and abilities not evident based on their cognitive scores. In our sample, reliance on cognitive scores alone to identify level of functioning among young children with ASD would potentially designate 25.3% of children as "low-functioning" or having co-occurring ID despite having no significant delays in overall adaptive functioning and 3.3% of children as "high-functioning" despite having significant delays in overall adaptive functioning. Moreover, 8.1% of children could be classified as "high-functioning" despite having significant delays in VABS-II socialization scores. In terms of service needs, autistic children who have cognitive scores indicating no significant delays have a range of adaptive ability levels and many may benefit from AB interventions.</p> <p>These findings support concerns often raised by researchers and autistic advocates that categorical labels of functioning may fail to capture an individual's unique strengths, challenges, and needs for support ([<reflink idref="bib3" id="ref135">3</reflink>]; [<reflink idref="bib4" id="ref136">4</reflink>]; [<reflink idref="bib7" id="ref137">7</reflink>]). They also suggest that reliance on cognitive scores alone may not be sufficient for describing the epidemiology of co-occurring ID and autism. Cognitive development explained only approximately half of the variation in overall adaptive functioning in our full sample and even less in children without significant cognitive delays. This suggests that assignment of functioning labels based on this measure offers an incomplete and potentially inaccurate picture of the abilities and support needs of autistic children. Future epidemiologic studies and other research focused on children with autism with and without co-occurring cognitive delays could consider incorporating measures of adaptive functioning and examining domain-specific patterns of strengths and weaknesses to better characterize functional abilities.</p> <hd id="AN0177036770-18">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613231193194 for Using adaptive behavior scores to convey level of functioning in children with autism spectrum disorder: Evidence from the Study to Explore Early Development by Sarah M Furnier, Susan Ellis Weismer, Eric Rubenstein, Ronald Gangnon, Steven Rosenberg, Cy Nadler, Lisa D Wiggins and Maureen S Durkin in Autism</p> <p>The authors thank Olivia Pokoski for her assistance in data verification. The authors also thank the SEED Data Coordinating Center team at the Clinical and Translational Sciences Institute of Michigan State University for their support. This study was carried out as part of S.M.F.'s doctoral dissertation, and S.M.F. would like to thank committee members Mei Baker and Karen Cruickshanks for their input and guidance. Finally, the authors gratefully acknowledge SEED staff and the children and families who participated in SEED.</p> <ref id="AN0177036770-19"> <title> References </title> <blist> <bibl id="bib1" idref="ref78" type="bt">1</bibl> <bibtext> Achenbach T. M. (1992). Child Behavior Checklist. Achenbach System of Empirically Based Assessment.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref90" type="bt">2</bibl> <bibtext> Akshoomoff N. (2006). Use of the Mullen Scales of Early Learning for the assessment of young children with autism spectrum disorders. Child Neuropsychology, 12(4–5), 269–277. https://doi.org/10.1080/09297040500473714</bibtext> </blist> <blist> <bibl id="bib3" idref="ref10" type="bt">3</bibl> <bibtext> Alvares G. A., Bebbington K., Cleary D., Evans K., Glasson E. J., Maybery M. 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Journal of Statistical Software, 95(1), 1–36. https://doi.org/10.18637/jss.v095.i01</bibtext> </blist> </ref> <ref id="AN0177036770-20"> <title> Footnotes </title> <blist> <bibtext> The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by 13 cooperative agreements from the Centers for Disease Control and Prevention (CDC): Cooperative Agreement Number U10DD000180, Colorado Department of Public Health, U01DD000750, University of Colorado Denver; Cooperative Agreement Number U10DD000181, Kaiser Foundation Research Institute (CA); Cooperative Agreement Number U10DD000182, University of Pennsylvania; Cooperative Agreement Number U10DD000183, Johns Hopkins University; Cooperative Agreement Number U10DD000184, University of North Carolina at Chapel Hill; Cooperative Agreement Number U10DD000498, Michigan State University; Cooperative Agreement Number U01000750, University of Colorado Denver; Cooperative Agreement Number U01000748, Kaiser Foundation Research Institute (CA); Cooperative Agreement Number U01000752, University of Pennsylvania; Cooperative Agreement Number U01000746, Johns Hopkins University; Cooperative Agreement Number U01000749, University of North Carolina at Chapel Hill; Cooperative Agreement Number U10DD000901, Michigan State University; Cooperative Agreement Number U01DD001215, University of Wisconsin System, and the Waisman Center (Eunice Kennedy Shriver National Institute of Child Health and Human Development grant number U54 HD090256). S.M.F. also received funding from the Wisconsin Distinguished Graduate Fellowship awarded by the University of Wisconsin–Madison. These data were independently verified at the University of Wisconsin–Madison. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the Centers for Disease Control and Prevention.</bibtext> </blist> <blist> <bibtext> Sarah M Furnier</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-1766-1417 Eric Rubenstein</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-9146-4497</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Sarah M Furnier; Susan Ellis Weismer; Eric Rubenstein; Ronald Gangnon; Steven Rosenberg; Cy Nadler; Lisa D Wiggins and Maureen S Durkin</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib15" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib32" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib56" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib61" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib22" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib29" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib46" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib68" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib30" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib57" firstref="ref12"></nolink> <nolink nlid="nl11" bibid="bib66" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib19" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib37" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib60" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib65" firstref="ref18"></nolink> <nolink nlid="nl16" bibid="bib69" firstref="ref19"></nolink> <nolink nlid="nl17" bibid="bib58" firstref="ref20"></nolink> <nolink nlid="nl18" bibid="bib16" firstref="ref23"></nolink> <nolink nlid="nl19" bibid="bib18" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib21" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib28" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib40" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib45" firstref="ref29"></nolink> <nolink nlid="nl24" bibid="bib10" firstref="ref31"></nolink> <nolink nlid="nl25" bibid="bib47" firstref="ref32"></nolink> <nolink nlid="nl26" bibid="bib67" firstref="ref34"></nolink> <nolink nlid="nl27" bibid="bib49" firstref="ref36"></nolink> <nolink nlid="nl28" bibid="bib59" firstref="ref38"></nolink> <nolink nlid="nl29" bibid="bib26" firstref="ref41"></nolink> <nolink nlid="nl30" bibid="bib44" firstref="ref43"></nolink> <nolink nlid="nl31" bibid="bib33" firstref="ref46"></nolink> <nolink nlid="nl32" bibid="bib11" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib31" firstref="ref51"></nolink> <nolink nlid="nl34" bibid="bib62" firstref="ref53"></nolink> <nolink nlid="nl35" bibid="bib20" firstref="ref64"></nolink> <nolink nlid="nl36" bibid="bib25" firstref="ref65"></nolink> <nolink nlid="nl37" bibid="bib41" firstref="ref67"></nolink> <nolink nlid="nl38" bibid="bib64" firstref="ref77"></nolink> <nolink nlid="nl39" bibid="bib51" firstref="ref79"></nolink> <nolink nlid="nl40" bibid="bib43" firstref="ref80"></nolink> <nolink nlid="nl41" bibid="bib34" firstref="ref81"></nolink> <nolink nlid="nl42" bibid="bib35" firstref="ref82"></nolink> <nolink nlid="nl43" bibid="bib55" firstref="ref83"></nolink> <nolink nlid="nl44" bibid="bib36" firstref="ref84"></nolink> <nolink nlid="nl45" bibid="bib52" firstref="ref85"></nolink> <nolink nlid="nl46" bibid="bib54" firstref="ref86"></nolink> <nolink nlid="nl47" bibid="bib63" firstref="ref87"></nolink> <nolink nlid="nl48" bibid="bib12" firstref="ref91"></nolink> <nolink nlid="nl49" bibid="bib17" firstref="ref92"></nolink> <nolink nlid="nl50" bibid="bib27" firstref="ref93"></nolink> <nolink nlid="nl51" bibid="bib23" firstref="ref96"></nolink> <nolink nlid="nl52" bibid="bib24" firstref="ref99"></nolink> <nolink nlid="nl53" bibid="bib42" firstref="ref101"></nolink> <nolink nlid="nl54" bibid="bib50" firstref="ref102"></nolink> <nolink nlid="nl55" bibid="bib71" firstref="ref103"></nolink> <nolink nlid="nl56" bibid="bib70" firstref="ref104"></nolink> <nolink nlid="nl57" bibid="bib72" firstref="ref105"></nolink> <nolink nlid="nl58" bibid="bib53" firstref="ref106"></nolink> <nolink nlid="nl59" bibid="bib14" firstref="ref125"></nolink> <nolink nlid="nl60" bibid="bib13" firstref="ref130"></nolink> <nolink nlid="nl61" bibid="bib39" firstref="ref131"></nolink> <nolink nlid="nl62" bibid="bib38" firstref="ref133"></nolink> <nolink nlid="nl63" bibid="bib48" firstref="ref134"></nolink>
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  Data: <searchLink fieldCode="AR" term="%22Sarah+M%2E+Furnier%22">Sarah M. Furnier</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1766-1417">0000-0002-1766-1417</externalLink>)<br /><searchLink fieldCode="AR" term="%22Susan+Ellis+Weismer%22">Susan Ellis Weismer</searchLink><br /><searchLink fieldCode="AR" term="%22Eric+Rubenstein%22">Eric Rubenstein</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9146-4497">0000-0002-9146-4497</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ronald+Gangnon%22">Ronald Gangnon</searchLink><br /><searchLink fieldCode="AR" term="%22Steven+Rosenberg%22">Steven Rosenberg</searchLink><br /><searchLink fieldCode="AR" term="%22Cy+Nadler%22">Cy Nadler</searchLink><br /><searchLink fieldCode="AR" term="%22Lisa+D%2E+Wiggins%22">Lisa D. Wiggins</searchLink><br /><searchLink fieldCode="AR" term="%22Maureen+S%2E+Durkin%22">Maureen S. Durkin</searchLink>
– 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>. 2024 28(5):1135-1149.
– 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: 15
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: SourceSuprt
  Label: Sponsoring Agency
  Group: SrcSuprt
  Data: Centers for Disease Control and Prevention (CDC) (DHHS/PHS)<br />Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (DHHS/NIH)
– Name: NumberContract
  Label: Contract Number
  Group: NumCntrct
  Data: U10DD000180<br />U01DD000750<br />U10DD000181<br />U10DD000182<br />U10DD000183<br />U10DD000184<br />U10DD000498<br />U01000750<br />U01000748<br />U01000752<br />U01000746<br />U01000749<br />U10DD000901<br />U01DD001215<br />U54HD090256
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Adjustment+%28to+Environment%29%22">Adjustment (to Environment)</searchLink><br /><searchLink fieldCode="DE" term="%22Severity+%28of+Disability%29%22">Severity (of Disability)</searchLink><br /><searchLink fieldCode="DE" term="%22Preschool+Children%22">Preschool Children</searchLink><br /><searchLink fieldCode="DE" term="%22Scores%22">Scores</searchLink><br /><searchLink fieldCode="DE" term="%22Socialization%22">Socialization</searchLink><br /><searchLink fieldCode="DE" term="%22Psychomotor+Skills%22">Psychomotor Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Child+Development%22">Child Development</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22California%22">California</searchLink><br /><searchLink fieldCode="DE" term="%22Colorado%22">Colorado</searchLink><br /><searchLink fieldCode="DE" term="%22Georgia%22">Georgia</searchLink><br /><searchLink fieldCode="DE" term="%22Maryland%22">Maryland</searchLink><br /><searchLink fieldCode="DE" term="%22North+Carolina%22">North Carolina</searchLink><br /><searchLink fieldCode="DE" term="%22Pennsylvania%22">Pennsylvania</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Mullen+Scales+of+Early+Learning%22">Mullen Scales of Early Learning</searchLink><br /><searchLink fieldCode="SU" term="%22Autism+Diagnostic+Observation+Schedule%22">Autism Diagnostic Observation Schedule</searchLink><br /><searchLink fieldCode="SU" term="%22Vineland+Adaptive+Behavior+Scales%22">Vineland Adaptive Behavior Scales</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613231193194
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We examined relationships between measures of adaptive behavior, cognitive ability, and autism symptom severity in 1458 preschool-aged children with autism from the Study to Explore Early Development. While publications commonly describe autistic children as "low-" or "high-functioning" based on cognitive ability, relying solely on cognitive scores may obscure meaningful variation in functioning. We found significant heterogeneity in adaptive behavior scores of children with cognitive scores both above and below the threshold of two or more standard deviations below the population mean specified in the diagnostic criteria for intellectual disability (ID). Although cognitive and adaptive behavior scores were strongly associated in our sample, considerable variation in overall adaptive behavior and more than half in socialization and motor skills was unaccounted for by cognitive ability, autism symptom severity, and other covariates. Among children who could be designated "low-functioning" based on cognitive scores, 39.7% had composite adaptive behavior scores indicating no significant delays, while among those who might be designated "high-functioning," 9.0% had significant delays in overall adaptive behavior and 22.2% in socialization. These results suggest adaptive behavior scores capture variations in the autism phenotype not accounted for by other measures we considered.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1423424
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1423424
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  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613231193194
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 1135
    Subjects:
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Adjustment (to Environment)
        Type: general
      – SubjectFull: Severity (of Disability)
        Type: general
      – SubjectFull: Preschool Children
        Type: general
      – SubjectFull: Scores
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      – SubjectFull: Socialization
        Type: general
      – SubjectFull: Psychomotor Skills
        Type: general
      – SubjectFull: Child Development
        Type: general
      – SubjectFull: California
        Type: general
      – SubjectFull: Colorado
        Type: general
      – SubjectFull: Georgia
        Type: general
      – SubjectFull: Maryland
        Type: general
      – SubjectFull: North Carolina
        Type: general
      – SubjectFull: Pennsylvania
        Type: general
      – SubjectFull: Mullen Scales of Early Learning
        Type: general
      – SubjectFull: Autism Diagnostic Observation Schedule
        Type: general
      – SubjectFull: Vineland Adaptive Behavior Scales
        Type: general
    Titles:
      – TitleFull: Using Adaptive Behavior Scores to Convey Level of Functioning in Children with Autism Spectrum Disorder: Evidence from the Study to Explore Early Development
        Type: main
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            NameFull: Sarah M. Furnier
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            NameFull: Susan Ellis Weismer
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            NameFull: Cy Nadler
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            NameFull: Lisa D. Wiggins
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            NameFull: Maureen S. Durkin
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            – D: 01
              M: 05
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
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              Value: 28
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            – TitleFull: Autism: The International Journal of Research and Practice
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