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Autism Epidemiology in Hong Kong Children and Youths Aged 6-17: Implications on Autism Screening and Sex Differences in the Community

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Title: Autism Epidemiology in Hong Kong Children and Youths Aged 6-17: Implications on Autism Screening and Sex Differences in the Community
Language: English
Authors: Oscar W. H. Wong (ORCID 0000-0002-0499-6082), Sandra S. M. Chan, Steven W. H. Chau, Winnie C. W. Chu, Carol S. W. Ho, Stephy W. S. Ho, Se Fong Hung, Samara Hussain, Kelly Y. C. Lai (ORCID 0000-0001-8623-6089), Angela M. W. Lam, Holly H. L. Lo, Karen K. Y. Ma (ORCID 0000-0002-9972-0256), Suk Ling Ma (ORCID 0000-0001-6175-5691), Flora Y. M. Mo, Pak Chung Sham, Caroline K. S. Shea, Suzanne H. W. So, Kelvin K. F. Tsoi, Patrick W. L. Leung (ORCID 0000-0002-0415-0124)
Source: Autism: The International Journal of Research and Practice. 2025 29(11):2872-2884.
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: 13
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Foreign Countries, Autism Spectrum Disorders, Epidemiology, Children, Adolescents, Gender Differences, Screening Tests, Psychometrics, Predictor Variables, Incidence, Disability Identification
Geographic Terms: Hong Kong
Assessment and Survey Identifiers: Autism Spectrum Quotient, Diagnostic Interview Schedule for Children
DOI: 10.1177/13623613251360269
ISSN: 1362-3613
1461-7005
Abstract: Epidemiological studies on autism lack representation from Asia. We estimated the prevalence of autism among children and youths in Hong Kong using a two-stage approach. In addition, we evaluated the psychometric properties of the screening instrument and explored sex differences within an epidemiological context. A random school-based sample of 5,865 children and youths were screened with the Autism Spectrum Quotient-10 (AQ-10). Then, a subsample of 317 participants underwent the Autism Diagnostic Interview-Revised assessment. Prevalence was estimated by applying positive and negative predictive values (PPV/NPV) of AQ-10 derived from the subsample to the entire cohort. None of the screened negative participants had autism, resulting in an NPV of 100%. Discrepant PPVs were noted for males (20.4%) and females (5.20%). The estimated prevalence was 2.57% using sex-specific PPVs. Explorative analysis on AQ-10 Positive participants without the diagnosis (i.e. 'false positives') showed significantly elevated autistic symptoms. The prevalence of autism in Hong Kong is comparable to the recent estimates in Western countries, which poses a significant public health challenge. Despite the high false-positive rates, AQ-10 remains valuable for excluding autism and identifying those with autistic symptoms. Furthermore, community-based studies are crucial to address sex differences in autism expression.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1487091
Database: ERIC
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  Value: <anid>AN0188761500;f9d01nov.25;2025Oct23.02:29;v2.2.500</anid> <title id="AN0188761500-1">Autism epidemiology in Hong Kong children and youths aged 6–17: Implications on autism screening and sex differences in the community </title> <p>Epidemiological studies on autism lack representation from Asia. We estimated the prevalence of autism among children and youths in Hong Kong using a two-stage approach. In addition, we evaluated the psychometric properties of the screening instrument and explored sex differences within an epidemiological context. A random school-based sample of 5,865 children and youths were screened with the Autism Spectrum Quotient-10 (AQ-10). Then, a subsample of 317 participants underwent the Autism Diagnostic Interview-Revised assessment. Prevalence was estimated by applying positive and negative predictive values (PPV/NPV) of AQ-10 derived from the subsample to the entire cohort. None of the screened negative participants had autism, resulting in an NPV of 100%. Discrepant PPVs were noted for males (20.4%) and females (5.20%). The estimated prevalence was 2.57% using sex-specific PPVs. Explorative analysis on AQ-10 Positive participants without the diagnosis (i.e. 'false positives') showed significantly elevated autistic symptoms. The prevalence of autism in Hong Kong is comparable to the recent estimates in Western countries, which poses a significant public health challenge. Despite the high false-positive rates, AQ-10 remains valuable for excluding autism and identifying those with autistic symptoms. Furthermore, community-based studies are crucial to address sex differences in autism expression. Although studies have found that autism is becoming more common, little is known whether this is true in Asian countries. This study looked into how many children and teenagers in Hong Kong might have autism. We first screened 5,865 school-aged children and youths with the Autism Spectrum Quotient-10 (AQ-10). Then, we conducted in-depth interviews with 317 of them to assess for autism. We found that around 2.57% of children and youths aged 6–17 years in Hong Kong might have autism. This number is similar to that of the Western countries. Furthermore, we also discovered that boys who were screened positive on the AQ-10 were more likely to have autism than girls. This could be because autism is more common in boys, and the AQ-10 might be better at spotting autism in boys than in girls. In the future, studies will need to find an optimal way of detecting autism in the community, considering how autism may present differently in boys and girls.</p> <p>Keywords: Asian; autism; epidemiology; prevalence; screening; sex differences</p> <hd id="AN0188761500-2">Introduction</hd> <p>Historically, autism was once considered a rare condition predominantly affecting males and accompanied by intellectual disability. However, with the reconceptualization of the condition as a spectrum and increased awareness, the condition is currently not as rare as previously considered. Indeed, a concurrent rising trend in the prevalence of autism reported worldwide has been speculated to reflect an actual increase ([<reflink idref="bib32" id="ref1">32</reflink>]). Epidemiological studies serve as a first step in delineating factors contributing to the prevalence of autism.</p> <p>Since the revision of the <emph>DSM</emph>-5 diagnostic framework in 2013, the median prevalence of autism reported by studies across the globe was 1%, and the prevalence reported varies in the range of 0.01%–4.36% ([<reflink idref="bib32" id="ref2">32</reflink>]). However, existing epidemiological studies on autism were largely based on Caucasian populations, with an underrepresentation of those from Africa and Asia. To comprehensively understand the global variations in autism prevalence, regional studies across diverse populations based on standardized case-identification approaches and contemporary diagnostic criteria are essential ([<reflink idref="bib14" id="ref3">14</reflink>]). A recent study conducted on 10,000 children aged 6–10 years in three metropolitan areas in China revealed a prevalence rate of around 1%, which was at the lower end of the global range ([<reflink idref="bib28" id="ref4">28</reflink>]). Hong Kong Special Administrative Region (Hong Kong SAR) of China is a unique community among all Chinese metropolitan regions, where a Westernized socioeconomic infrastructure is docked within a culturally-ethnically Chinese-predominant society. Before 2008, the prevalence of autism in Hong Kong was estimated to be 0.161% for youth under 15 years of age, using the <emph>DSM</emph>-4 diagnostic criteria and a clinic-registry approach ([<reflink idref="bib30" id="ref5">30</reflink>]).</p> <p>Other than the regional differences, methods of case identification can also contribute to variations in prevalence estimates. For instance, registry-based approaches that use single-stage case identification could underestimate the true prevalence owing to the presence of undiagnosed cases in the community. On the other hand, community-based studies might capture the 'unregistered' cases. Recognition of cases outside the registry is especially relevant to the contemporary conceptualization of autism, which includes individuals with optimal levels of functioning and do not require active support from the medical or educational systems ([<reflink idref="bib12" id="ref6">12</reflink>]).</p> <p>Given the complexity of autism diagnosis, large-scale community-based epidemiological studies, including the present study, often adopt a multi-stage design with screening followed by confirmation of diagnosis. The accuracy of the screening and diagnostic instruments is therefore of paramount importance. While studies often adopted screening instruments with established sensitivity and specificity, their positive and negative predictive values (PPV and NPV) were seldom examined when applied at the general population level. Psychometric studies often utilize a case-control design with a balanced proportion of cases and non-cases. The PPV and NPV yielded from such studies would not be applicable to the general population, as these values are heavily influenced by case prevalence, and the prevalence of most disorders is much lower in the general population. Furthermore, the differences in the instrument's psychometric properties between males and females warrant greater scrutiny. As differences in phenotypic presentation between autistic males and females have been described ([<reflink idref="bib13" id="ref7">13</reflink>]), the properties of the instruments theoretically would be expected to behave differently between sexes. Although cumulative data thus far have failed to support sex-specific scoring and algorithms for commonly used instruments, the existing studies were limited by ascertainment bias. This bias stemmed from diagnoses being established by the same diagnostic tools under examination, leading to a circularity of argument in the absence of alternative diagnostic benchmarks such as multi-source validated measures ([<reflink idref="bib15" id="ref8">15</reflink>]). Therefore, a community-based study, such as the present study, provides a rare opportunity to examine the psychometric properties of instruments in real-life situations where the whole spectrum of neurodiversity and its intersection with sex are present. For example, a recent study showed that there were sex differences in the psychometric properties of the Modified Checklist for Autism in Toddlers (M-CHAT-R/F) when applied to a community sample of toddlers ([<reflink idref="bib10" id="ref9">10</reflink>]).</p> <p>The present study reports a systematic investigation of the prevalence of autism in Hong Kong among children and adolescents, using the <emph>DSM</emph>-5 diagnostic criteria and a two-stage approach on a random epidemiological sample of the entire territory. Furthermore, the psychometric properties and sex differences of our screening instrument, namely the Autism Spectrum Quotient-10 (AQ-10), were examined at a community level.</p> <hd id="AN0188761500-3">Methods</hd> <p>The present study was part of a territory-wide epidemiological study of psychiatric disorders among children and adolescents aged 6–17 years in Hong Kong ([<reflink idref="bib4" id="ref10">4</reflink>]). The study recruited a random school-based sample for systematic evaluation of the presence of more than 30 <emph>DSM</emph>-5-defined psychiatric disorders with the Diagnostic Interview Schedule for Children Version 5 (DISC-5) ([<reflink idref="bib27" id="ref11">27</reflink>]). As autism was not included in the DISC-5, a two-stage approach was applied to the epidemiological sample for prevalence estimation.</p> <hd id="AN0188761500-4">School-based random sampling</hd> <p>The Hong Kong SAR Government provides 12 years of free education for children and adolescents from age 6 to 18 years, and up to 96.6% of our targeted population (i.e. all children and adolescents aged 6–17 years in Hong Kong) are attending schools. Furthermore, clinical psychologists at the Child Assessment Service in Hong Kong, a governmental service that provides territory-wide assessment services for all children, would conduct intelligence quotient (IQ) tests for those suspected of intellectual disability. Only children with a diagnosis of intellectual disability would be eligible for enrolment into special education schools. Given the context of the educational system in Hong Kong, a territory-wide school-based sampling provides the best available portal to systematically enrol a representative community sample. To encompass all main regions and types of schools in Hong Kong, a stratified random sampling approach was adopted. For the first stratum, a complete list of 1,291 schools was obtained from the Education Bureau of the Hong Kong SAR Government, with schools classified by levels (primary or secondary), funding types (private or subsidized), geographical districts (the New Territories East and West, Kowloon or Hong Kong Island) and curriculum (special education schools for intellectual disability or mainstream schools for normal intelligence). A random number was generated for each school under each category, and school administrators under each category were approached by the order of the random number list. With consent at the school level, the next stratum of sampling of students was performed by generating a random number list of anonymised students for every class in each grade corresponding to the actual headcounts of registered students. Parents or a main caregiver was then approached to enrol their children to participate in the study according to the order of appearance on the list. Those who were unapproachable or declined were replaced by the next student on the list. A sampling ratio of one in seven in each class was adopted to reach the target sample size of 6,000. This sample size was determined for the whole psychiatric epidemiological study with the software Epi Info by the US Centers for Disease Control and Prevention (CDC). It showed that a sample size of 6,000 could give a margin of error for disorders prevailing at 5% with a 95% confidence interval (CI) ± 0.55% (i.e. an 11% margin of error in either direction), given an estimated population size of 680,000 for children and adolescents in the age range 6–17 years in Hong Kong. While only 1.3% of our targeted population was studying in special schools ([<reflink idref="bib3" id="ref12">3</reflink>]), the special school group was oversampled to >300 to provide an adequate sample size for estimation of psychiatric disorders in DISC-5. The parents or caregivers of all participating students provided written informed consent, and the study was conducted in accordance with the Declaration of Helsinki.</p> <hd id="AN0188761500-5">Screening and identifying autism in two stages</hd> <p>In the first stage of the epidemiological study, the parent or caregiver of all participants completed the Autism Spectrum Quotient-10 Child Version (AQ-10-Child, for children up to 11 years old) or Adolescent Version (AQ-10-Adol, for adolescents between aged 12–17 years old) alongside the DISC-5 assessment and questionnaires on socioeconomic status, family background and the child's medical history and school adjustment. The AQ-10, a screening instrument for autism spectrum conditions, comprises 10 items rated on a four-point Likert-type scale and scored binarily ([<reflink idref="bib1" id="ref13">1</reflink>]). The brevity of the AQ-10, compared to other available screening instruments for autism, was critical for the feasibility of the epidemiological study given the extensive research procedures for parents or caregivers. Both AQ-10-Child and AQ-10-Adol were validated in Hong Kong, with established local cutoff scores of five (sensitivity = 0.82 and specificity = 0.87) and six (sensitivity = 0.80 and specificity = 0.87), respectively. Compared to the original version in the United Kingdom, a lower AQ-10-Child cutoff score of five was adopted, as the local validation study showed that the original score of six reduced sensitivity to 0.65 ([<reflink idref="bib21" id="ref14">21</reflink>]).</p> <p>The second stage was conducted simultaneously with the first stage to meet the study's timeline. Participants who completed the AQ-10 and DISC-5 were stratified by the AQ-10 result (AQ-10 Positive and AQ-10 Negative) and school level (primary and secondary). Consecutive participants from each group were invited for the second-stage confirmation of autism with the Autism Diagnostic Interview-Revised (ADI-R) ([<reflink idref="bib23" id="ref15">23</reflink>]) until the target sample size was reached, ensuring balanced representation across groups. The ADI-R was administered by psychiatrists with experience in child and adolescent mental health or by research staff majoring in psychology and with experience in conducting diagnostic assessments of psychiatric conditions for children and adolescents in research settings. All ADI-R interviewers were blinded to the AQ-10 score. A clinical psychologist qualified as an ADI-R trainer, and the co-author (PWLL) provided the training of the ADI-R for all research interviewers. The inter-rater reliability at the item level of the ADI-R was ascertained with Cohen's Kappa > 0.7 for all research interviewers. Regular research meetings were conducted by the first author and co-authors (OWHW, KYCL and PWLL, who were clinicians in child and adolescent mental health with more than 10 years of experience in assessing and treating autism) to ensure accurate scoring of each item in the ADI-R through reviewing the written responses verbatim and discussion with the interviewers. We adopted a scoring algorithm of the Chinese version of the ADI-R that aligns with the latest <emph>DSM</emph>-5 criteria, with its psychometric properties established in Hong Kong. In brief, the items in the ADI-R were mapped to the subdomains of the <emph>DSM</emph>-5 criteria: socio-emotional reciprocity (A1), nonverbal communicative behaviours (A2), social relationships (A3), motor movements (B1), adherence to routines/resistance to change (B2), highly restricted, fixated interests (B3) or hyper- or hypo-reactivity to sensory input (B4). A research diagnosis of autism is made when all three of the A subdomains and at least two of the four B subdomains score at or above the cutoff scores criteria ([<reflink idref="bib20" id="ref16">20</reflink>]). To determine the sample size needed for the second diagnostic stage by ADI-R, a Cochran equation that took into account the sensitivity and specificity of the AQ-10 and the sampling ratio was used ([<reflink idref="bib6" id="ref17">6</reflink>]), which yielded a minimum requirement of 300 participants. Each participating family received an honorarium of Hong Kong Dollars 300 upon completion of the research procedures. There was no community member involved in the design and conduct of the present study.</p> <hd id="AN0188761500-6">Sociodemographic characteristics of the study population</hd> <p>To assess the sociodemographic characteristics of the study population, parents or caregivers were asked about the highest level of education attained and the monthly household income. Education levels were categorized as primary or less, secondary, post-secondary or tertiary. Household income was classified into three groups: low (first to second decile), middle (third to eighth decile) and high (ninth to tenth decile) based on the 2021 Population Census of the Hong Kong SAR ([<reflink idref="bib3" id="ref18">3</reflink>]).</p> <hd id="AN0188761500-7">Prevalence estimation and statistical analysis</hd> <p>Based on the second stage's ADI-R results, the sex-specific PPV and NPV of the AQ-10 were computed and compared given the known phenotypic diversity in autism between males and females as mentioned earlier. They were then applied to the number of AQ-10 Positive and AQ-10 Negative males and females in the entire cohort to estimate the overall prevalence. Referencing the work of [<reflink idref="bib2" id="ref19">2</reflink>], we calculated the 95% CI of the prevalence estimations using binomial simulations with 10,000 iterations, where <emph>n</emph> = sample population and p = proportion of AQ-10 Positive/Negative. Between-group characteristics were compared with <emph>t</emph>-tests or the Mann–Whitney U test for continuous data, and chi-square tests for categorical data. All statistical analyses were performed with Jamovi version 2.3.28 and R version 4.3.2.</p> <hd id="AN0188761500-8">Results</hd> <p></p> <hd id="AN0188761500-9">Study population characteristics</hd> <p>Of all the schools approached, 72.8% (<emph>N</emph> = 126) consented to participate in the study. Reasons for rejection at the school level included administrative difficulties and insufficient manpower to support the study. Then, the average response rate at the participant level was 71.7%. Among those who did not respond, around 87.1% of the parents or caregivers declined due to study procedures being incompatible with their schedules or lack of interest after receiving an explanation of the study details, while approximately 12.9% could not be reached by the research team. The study population included in this report comprised 5,865 participants with AQ-10 scores, consisting of 5,552 children and adolescents with normal IQ from mainstream schools and 313 children and adolescents with intellectual disabilities from special education schools aged 6–17 years. The majority (86.2%) of the study population was of Chinese ethnicity, 12.1% was non-Chinese Asians, 0.3% was African, 0.2% was White and the rest (1.2%) was of mixed ethnicities. These participants completed the DISC-5 interview and provided valid responses for the AQ-10 screening. There were more males than females (3,410 vs 2,455), and significantly more males (24.6%) were screened positive on the AQ-10 than females (13.2%) (χ² = 116, p ⩽ 0.001). Otherwise, age and other sociodemographic characteristics were comparable between the AQ-10 Positive and AQ-10 Negative groups. The detailed breakdown of the study population, categorized by school level and type according to their AQ-10 results, is presented in Table 1.</p> <p>Table 1. Demographics of participants and the comparisons between AQ-10 Positive and AQ-10 Negative groups.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="2">All participants</th><th align="left" colspan="2">AQ-10 Positive</th><th align="left" colspan="2">AQ-10 Negative</th><th align="left" colspan="3">Statistics</th></tr><tr><th /><th align="left">Mean/n</th><th align="left">SD/%</th><th align="left">Mean/n</th><th align="left">SD/%</th><th align="left">Mean/n</th><th align="left">SD/%</th><th align="left">U/χ²</th><th align="left">df</th><th align="left">p</th></tr></thead><tbody><tr><td>n</td><td>5865</td><td /><td>1164</td><td>19.85</td><td>4701</td><td>80.15</td><td colspan="3">-</td></tr><tr><td>Age</td><td>10.9</td><td>3.15</td><td>10.85</td><td>3.17</td><td>10.9</td><td>3.15</td><td>2.70E + 06</td><td /><td>0.544</td></tr><tr><td colspan="10">Primary</td></tr><tr><td> Mainstream</td><td>3303</td><td>56.31</td><td>616</td><td>18.65</td><td>2687</td><td>81.35</td><td colspan="3" rowspan="2">-</td></tr><tr><td> Special school</td><td>178</td><td>3.03</td><td>117</td><td>65.73</td><td>61</td><td>34.27</td></tr><tr><td colspan="10">Secondary</td></tr><tr><td> Mainstream</td><td>2249</td><td>38.35</td><td>348</td><td>15.47</td><td>1901</td><td>84.53</td><td colspan="3" rowspan="2">-</td></tr><tr><td> Special school</td><td>135</td><td>2.30</td><td>83</td><td>61.48</td><td>52</td><td>38.52</td></tr><tr><td colspan="10">Sex</td></tr><tr><td> Male</td><td>3410</td><td>58.14</td><td>839</td><td>72.08</td><td>2571</td><td>54.69</td><td rowspan="2">116</td><td rowspan="2">1</td><td rowspan="2"><0.001<xref ref-type="table-fn" rid="tfn2">***</xref></td></tr><tr><td> Female</td><td>2455</td><td>41.86</td><td>325</td><td>27.92</td><td>2130</td><td>45.31</td></tr><tr><td> AQ-10 score</td><td>3.11</td><td>2.18</td><td>6.49</td><td>1.27</td><td>2.28</td><td>1.42</td><td>43,727</td><td /><td><0.001<xref ref-type="table-fn" rid="tfn2">***</xref></td></tr><tr><td colspan="10">Household income</td></tr><tr><td> Low</td><td>498</td><td>8.53</td><td>99</td><td>8.54</td><td>399</td><td>8.53</td><td rowspan="3">0.286</td><td rowspan="3">2</td><td rowspan="3">0.867</td></tr><tr><td> Middle</td><td>3973</td><td>68.07</td><td>782</td><td>67.47</td><td>3191</td><td>68.21</td></tr><tr><td> High</td><td>1366</td><td>23.40</td><td>278</td><td>23.99</td><td>1088</td><td>23.26</td></tr><tr><td colspan="10">Highest parental education attained</td></tr><tr><td> Primary or less</td><td>107</td><td>1.83</td><td>15</td><td>1.30</td><td>92</td><td>1.97</td><td rowspan="4">5.02</td><td rowspan="4">3</td><td rowspan="4">0.171</td></tr><tr><td> Secondary</td><td>2723</td><td>46.67</td><td>545</td><td>47.06</td><td>2178</td><td>46.57</td></tr><tr><td> Post-secondary</td><td>631</td><td>10.81</td><td>111</td><td>9.59</td><td>520</td><td>11.12</td></tr><tr><td> Tertiary</td><td>2374</td><td>40.69</td><td>487</td><td>42.06</td><td>1887</td><td>40.35</td></tr></tbody></table> </ephtml> </p> <p>1 AQ-10: Autism Spectrum Quotient-10.</p> <p>2 <emph>p</emph> < 0.001.</p> <hd id="AN0188761500-10">Second-stage ADI-R for confirmation of DSM -5 autism</hd> <p>A total of 728 participants from both AQ-10 Positive and AQ-10 Negative groups from primary and secondary schools were approached for the ADI-R, of which 154 (21.2%) could not be reached, 257 (35.3%) rejected the invitation and 317 (43.5%) agreed and completed the diagnostic interview. Age, sex and AQ-10 scores were comparable between those who participated and those who rejected the ADI-R assessment. However, families that participated in the ADI-R had a lower household income than those that did not participate in the ADI-R (8.54% vs 5.12%, χ² = 9.56, p = 0.008) (Table 2). The sociodemographic characteristics of these 317 participants also resembled those of the entire study population (Table S1 in Supplemental Materials). Among these 317 participants, 159 were from primary schools, while 158 were from secondary schools. There were 140 AQ-10 Positive participants (102 males and 38 females) and 177 AQ-10 Negative participants (91 males and 86 females). The sex distribution within the AQ-10 Positive and AQ-10 Negative groups in the subsample resembled that of the entire study population. Finally, 23 participants (21 males and 2 females) were diagnosed with autism by ADI-R in the AQ-10 Positive group, while none in the AQ-10 Negative group received the diagnosis (Figure 1).</p> <p>Table 2. Comparisons between participants not participating and completing the ADI-R.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" colspan="2">Not participating ADI-R</th><th align="left" colspan="2">Completed ADI-R</th><th /><th /><th /></tr><tr><th /><th align="left">Mean/n</th><th align="left">SD/%</th><th align="left">Mean/n</th><th align="left">SD/%</th><th align="left">U/χ²</th><th align="left">df</th><th align="left">p</th></tr></thead><tbody><tr><td>n</td><td>411</td><td>56.46</td><td>317</td><td>43.54</td><td /><td /><td /></tr><tr><td>Age</td><td>11.47</td><td>3.11</td><td>11.26</td><td>3.12</td><td>62,606</td><td /><td>0.365</td></tr><tr><td colspan="8">Sex</td></tr><tr><td> Male</td><td>248</td><td>60.34</td><td>194</td><td>61.20</td><td rowspan="2">0.0552</td><td rowspan="2">1</td><td rowspan="2">0.814</td></tr><tr><td> Female</td><td>163</td><td>39.66</td><td>123</td><td>38.80</td></tr><tr><td colspan="8">AQ-10</td></tr><tr><td> Score</td><td>4.18</td><td>2.54</td><td>4.04</td><td>2.548</td><td>62,889</td><td>0.42</td><td /></tr><tr><td> Positive</td><td>190</td><td>46.23</td><td>141</td><td>44.48</td><td rowspan="2">0.221</td><td rowspan="2">1</td><td rowspan="2">0.638</td></tr><tr><td> Negative</td><td>221</td><td>53.77</td><td>176</td><td>55.52</td></tr><tr><td colspan="8">School level</td></tr><tr><td> Primary</td><td>214</td><td>52.07</td><td>159</td><td>50.16</td><td rowspan="2">0.261</td><td rowspan="2">1</td><td rowspan="2">0.609</td></tr><tr><td> Secondary</td><td>197</td><td>47.93</td><td>158</td><td>49.84</td></tr><tr><td colspan="8">Household income</td></tr><tr><td> Low</td><td>21</td><td>5.12</td><td>27</td><td>8.54</td><td rowspan="3">9.56</td><td rowspan="3">2</td><td rowspan="3">0.008<xref ref-type="table-fn" rid="tfn4">**</xref></td></tr><tr><td> Middle</td><td>262</td><td>63.90</td><td>220</td><td>69.62</td></tr><tr><td> High</td><td>127</td><td>30.98</td><td>69</td><td>21.84</td></tr><tr><td colspan="8">Highest parental education</td></tr><tr><td> Primary or less</td><td>3</td><td>0.73</td><td>1</td><td>0.32</td><td rowspan="4">0.984</td><td rowspan="4">3</td><td rowspan="4">0.805</td></tr><tr><td> Secondary</td><td>193</td><td>47.07</td><td>157</td><td>49.68</td></tr><tr><td> Post-secondary</td><td>44</td><td>10.73</td><td>32</td><td>10.13</td></tr><tr><td> Tertiary</td><td>170</td><td>41.46</td><td>126</td><td>39.87</td></tr></tbody></table> </ephtml> </p> <ulist> <item>3 ADI-R: Autism Diagnostic Interview-Revised; AQ-10: Autism Spectrum Quotient-10.</item> <item>4 p < 0.01.</item> </ulist> <p>Graph: Figure 1. Overview of the two-stage study design and the results at each stage.</p> <hd id="AN0188761500-11">Effect of non-participation on prevalence estimation</hd> <p>Given the differences in household income among families that did and did not participate in the ADI-R assessment, we hypothesized that a latent motivational factor existed to influence the family's willingness to participate and examined whether this factor might bias the probability of identifying autism. We referenced the work by [<reflink idref="bib17" id="ref20">17</reflink>] to model this latent variable, where the participant's sex, AQ-10 scores and family income class were used to predict participation versus non-participation in the ADI-R assessment in a logistic regression. The regression showed that only family income (high vs low) significantly predicted lower odds of participating in the ADI-R assessment, with an odds ratio (OR) of 0.41 (95% CI: 0.22–0.78, p = 0.007), while sex and AQ-10 score were not significant predictors (p = 0.564 and 0.294). From the regression, a probability score of participating in the ADI-R assessment was generated to serve as a surrogate of the motivational factor for each participant ever invited. Then, within the subsample of the second stage's ADI-R assessment, another logistic regression was used to predict autism versus non-autism with the probability score, the participant's age, sex, the highest parental education level and the AQ-10 score as predictors. In this logistic regression, only the AQ-10 score predicted autism with an OR of 1.95 (95% CI: 1.47–2.59, p < 0.001), suggesting that the willingness to participate and other sociodemographic characteristics were not associated with the probability of identifying autism (Table S2 in Supplemental Materials).</p> <hd id="AN0188761500-12">Psychometric properties of AQ-10 in a community sample and sex differences</hd> <p>As none of the AQ-10 Negative participants received a diagnosis of autism in the second-stage ADI-R assessment, using the validated cutoffs of the AQ-10 resulted in an NPV of 100% for both males and females. The PPV of the AQ-10 for both males and females together was 16.31%. When stratified by sex, the PPV of the AQ-10 was significantly lower (χ² = 4.65, p = 0.03) in females (5.2%) than in males (20.4%) (Table 3). Further stratification by school level did not yield a significant difference between the PPVs in primary schools and secondary schools for males (χ² = 0.969, p = 0.33) and females (χ² = 0, p = 1). With the marked difference in PPV by sex, we accordingly estimated the prevalence of autism using sex-specific PPVs.</p> <p>Table 3. Results of the diagnostic confirmation of autism by the ADI-R.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="left">Autism</th><th align="left">Non-autistic</th><th align="left">PPV</th><th align="left">NPV</th><th align="left">FPR</th><th align="left">FNR</th></tr></thead><tbody><tr><td rowspan="2">Overall (male & female)</td><td>AQ-10 Positive</td><td>23</td><td>118</td><td rowspan="2">16.31</td><td rowspan="2">100</td><td rowspan="2">83.70</td><td rowspan="2">0</td></tr><tr><td>AQ-10 Negative</td><td>0</td><td>176</td></tr><tr><td rowspan="2">Male only</td><td>AQ-10 Positive</td><td>21</td><td>82</td><td rowspan="2">20.39</td><td rowspan="2">100</td><td rowspan="2">79.61</td><td rowspan="2">0</td></tr><tr><td>AQ-10 Negative</td><td>0</td><td>91</td></tr><tr><td rowspan="2">Female only</td><td>AQ-10 Positive</td><td>2</td><td>36</td><td rowspan="2">5.26</td><td rowspan="2">100</td><td rowspan="2">94.74</td><td rowspan="2">0</td></tr><tr><td>AQ-10 Negative</td><td>0</td><td>85</td></tr></tbody></table> </ephtml> </p> <p>5 ADI-R: Autism Diagnostic Interview-Revised; AQ-10: Autism Spectrum Quotient-10; PPV: positive predictive value; NPV: negative predictive value; FPR: false-positive rate; FNR: false negative rate.</p> <hd id="AN0188761500-13">Estimation of autism prevalence</hd> <p>To address the unbalanced sex distribution and enhance representativeness, we reconstituted the study population based on sex, school level, intelligence and household income according to the census data in Hong Kong before estimating the prevalence ([<reflink idref="bib3" id="ref21">3</reflink>]). This was done by (<reflink idref="bib1" id="ref22">1</reflink>) dividing the study population into 24 strata (2 sexes × 2 school levels × 2 intelligence groups × 3 household income levels), (<reflink idref="bib2" id="ref23">2</reflink>) computing the rate of AQ-10 Positive and AQ-10 Negative in each stratum (Table S3 in Supplemental Materials), (<reflink idref="bib3" id="ref24">3</reflink>) based on the sample size of 5,865 and the Census data, the distribution of the population in each stratum was projected (Table S4 in Supplemental Materials) and (<reflink idref="bib4" id="ref25">4</reflink>) using the projected number of the population in each stratum multiplied by the corresponding AQ-10 Positive/Negative rate to calculate the number of AQ-10 Positive and AQ-10 Negative in each stratum. In the reconstituted sample, there were 3,020 males and 2,845 females. Proportionally, there were 651 AQ-10 Positive males and 343 AQ-10 Positive females (Table S5 in Supplemental Materials). Applying the sex-specific PPVs, the overall prevalence of autism was 2.57% (95% CI: 2.23%–2.91%), with a male-to-female (M:F) ratio of 7.39:1. We also applied the original <emph>DSM</emph>-4 algorithm of the ADI-R to determine whether the prevalence would be significantly different. While there was no change in the classification of autism in females, there were two less males being classified as having autism, resulting in a decrease of the PPV of AQ-10 to 18.4%. Replicating the aforementioned procedures yielded a prevalence of 2.39% (95% CI: 2.08%–2.71%) using the <emph>DSM</emph>-4 algorithm.</p> <hd id="AN0188761500-14">Explorative analysis of autistic symptoms in AQ-10 Positive males and females without researc...</hd> <p>Given the low prevalence of autism in the population, the false positive rates (FPRs) of the AQ-10 in our ADI-R subsample for males and females were 79.6% and 94.7%, respectively (Table 3), indicating a large number of false positives by the AQ-10, that is, those who were AQ-10 Positive but did not meet the criteria of the ADI-R <emph>DSM</emph>-5 algorithm (<emph>n</emph> = 118, with 82 males and 36 females). Since autism is conceptualized as a continuum from individuals who are functionally impaired because of autistic symptoms to those with an absence of the symptoms ([<reflink idref="bib8" id="ref26">8</reflink>]), it is interesting to explore the symptoms of autism in the AQ-10 False-positive participants in contrast to the AQ-10 Negative participants (<emph>n</emph> = 176, with 91 males and 85 females). Using multivariate analysis of covariance (MANCOVA), log-transformed diagnostic algorithm subdomain scores of the ADI-R (ever or abnormal at age 4–5 years, with A1–3 reflecting the deficits in the social communication and interaction domain and B1–4 representing the restricted and repetitive behaviour domain) were compared between the two groups with age as a covariate. An interaction term of 'Group * Sex' was also added to the model to explore the differential symptoms between males and females. Compared to the AQ-10 Negative group, the AQ-10 False-positive group had significantly higher ratings on all seven symptoms, suggesting that symptoms of autism were present in the group despite not reaching the research diagnosis. Notably, while female sex was associated with lower ratings on the symptoms of A2 (deficits in nonverbal communicative behaviours), A3 (deficits in developing, maintaining and understanding relationships) and B1 (stereotyped/repetitive speech, motor, use of objects), significant 'Group * Sex' interactions were observed in A2 and B1 (Table 4 and Figure 2). Post hoc examination revealed that the AQ-10 False-positive females scored comparably to the AQ-10 Negative females on these two symptom domains (A2: <emph>t</emph> = 1.83, df = 289, p<subs>Tukey</subs> = 0.260; B1: <emph>t</emph> = 0.789, df = 289, p<subs>Tukey</subs> = 0.859).</p> <p>Table 4. Summary of MANCOVA on the ADI-R subdomain scores between the AQ-10 False-positive and AQ-10 Negative groups and their interaction with sex.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th align="left" colspan="7">Multivariate tests</th></tr><tr><th /><th /><th align="left">Pillai's trace</th><th align="left">F</th><th align="left">df1</th><th align="left">df2</th><th align="left">p</th></tr></thead><tbody><tr><td colspan="2">Group</td><td>0.06</td><td>16.99</td><td>7</td><td>283</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td colspan="2">Sex</td><td>0.05</td><td>2.34</td><td>7</td><td>283</td><td>0.025<xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td colspan="2">Group <xref ref-type="table-fn" rid="tfn7">*</xref> Sex</td><td>0.03</td><td>1.32</td><td>7</td><td>283</td><td>0.241</td></tr><tr><th align="left" colspan="7">Univariate tests</th></tr><tr><th /><th align="left">Dependent variable</th><th align="left">SS</th><th align="left">df</th><th align="left">MS</th><th align="left">F</th><th align="left">p</th></tr><tr><td rowspan="7">Group</td><td>A1</td><td>27.22</td><td>1</td><td>27.224</td><td>44.1953</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>A2</td><td>2.35</td><td>1</td><td>2.346</td><td>33.0856</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>A3</td><td>7.19</td><td>1</td><td>7.194</td><td>77.2126</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>B1</td><td>0.87</td><td>1</td><td>0.865</td><td>17.9771</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>B2</td><td>0.54</td><td>1</td><td>0.540</td><td>20.5089</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>B3</td><td>0.49</td><td>1</td><td>0.487</td><td>17.1578</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>B4</td><td>0.42</td><td>1E + 00</td><td>0.422</td><td>18.0543</td><td>< 0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td rowspan="7">Sex</td><td>A1</td><td>2.13</td><td>1</td><td>2.132</td><td>3.46158</td><td>0.064</td></tr><tr><td>A2</td><td>0.46</td><td>1</td><td>0.461</td><td>6.50186</td><td>0.011<xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td>A3</td><td>1.09</td><td>1</td><td>1.087</td><td>11.6706</td><td><0.001<xref ref-type="table-fn" rid="tfn7">***</xref></td></tr><tr><td>B1</td><td>0.29</td><td>1</td><td>0.289</td><td>6.01213</td><td>0.015<xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td>B2</td><td>0.03</td><td>1</td><td>0.027</td><td>1.03245</td><td>0.31</td></tr><tr><td>B3</td><td>0.04</td><td>1</td><td>0.039</td><td>1.37189</td><td>0.242</td></tr><tr><td>B4</td><td>0.04</td><td>1E + 00</td><td>0.042</td><td>1.79788</td><td>0.181</td></tr><tr><td rowspan="7">Group <xref ref-type="table-fn" rid="tfn7">*</xref> Sex</td><td>A1</td><td>0.33</td><td>1</td><td>0.33373</td><td>0.54</td><td>0.462</td></tr><tr><td>A2</td><td>0.31</td><td>1</td><td>0.30854</td><td>4.35</td><td>0.038<xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td>A3</td><td>0.05</td><td>1</td><td>0.0536</td><td>0.58</td><td>0.449</td></tr><tr><td>B1</td><td>0.21</td><td>1</td><td>0.21326</td><td>4.43</td><td>0.036<xref ref-type="table-fn" rid="tfn7">*</xref></td></tr><tr><td>B2</td><td>0.03</td><td>1</td><td>0.02649</td><td>1.01</td><td>0.317</td></tr><tr><td>B3</td><td>0.01</td><td>1</td><td>0.00529</td><td>0.19</td><td>0.666</td></tr><tr><td>B4</td><td>0.00</td><td>1</td><td>1E-04</td><td>0.00</td><td>0.948</td></tr><tr><td rowspan="7">Residuals</td><td>A1</td><td>178.02</td><td>289</td><td>0.61599</td><td /><td /></tr><tr><td>A2</td><td>20.49</td><td>289</td><td>0.0709</td><td /><td /></tr><tr><td>A3</td><td>26.93</td><td>289</td><td>0.09317</td><td /><td /></tr><tr><td>B1</td><td>13.91</td><td>289</td><td>0.04812</td><td /><td /></tr><tr><td>B2</td><td>7.61</td><td>289</td><td>0.02634</td><td /><td /></tr><tr><td>B3</td><td>8.20</td><td>289</td><td>0.02836</td><td /><td /></tr><tr><td>B4</td><td>6.75</td><td>289</td><td>0.02335</td><td /><td /></tr></tbody></table> </ephtml> </p> <ulist> <item>6 Results were controlled for age. ADI-R: Autism Diagnostic Interview-Revised; AQ-10: Autism Spectrum Quotient-10; A1: socio-emotional reciprocity; A2: nonverbal communicative behaviours; A3: social relationships; B1: motor movements; B2: adherence to routines/resistance to change; B3: highly restricted, fixated interests; B4: hyper- or hypo-reactivity to sensory input.</item> <item>7 p < 0.05; ***p < 0.001.</item> </ulist> <p>Graph: Figure 2. (A–C) Univariate analysis of the Group * Sex interaction effects on scores of subdomains A2, A3 and B1 of the ADI-R.</p> <hd id="AN0188761500-15">Discussion</hd> <p></p> <hd id="AN0188761500-16">Prevalence of autism among children and youth in Hong Kong</hd> <p>This is the first epidemiological study that applied <emph>DSM</emph>-5 diagnostic criteria to investigate the prevalence of autism in Hong Kong, a metropolitan city with unique internationality among the major cities in China and Asia. The study population was based on a representative random sample, covering a broad range of school-aged children and youth across varying socioeconomic statuses and intelligence levels. Its strengths include case identification at the population level, use of locally validated autism measures, a reasonably large sample size, an adequate number of screened children recruited for case determination, careful examination of potential bias in non-participation at the diagnostic stage and the use of sex-specific PPVs in prevalence estimation.</p> <p>The prevalence of autism in Hong Kong, estimated at 2.57% in the present study, aligns with contemporary estimates from other Asian countries, such as 2.64% in South Korea ([<reflink idref="bib17" id="ref27">17</reflink>]) and 3.22% in Japan ([<reflink idref="bib26" id="ref28">26</reflink>]). This figure reflects a marked increase from Hong Kong's earlier estimate of 0.16% in 2008 ([<reflink idref="bib30" id="ref29">30</reflink>]), consistent with trends observed in a recent meta-analysis, which reported significantly lower prevalence estimates in earlier years before 2008 than those of recent studies in China ([<reflink idref="bib29" id="ref30">29</reflink>]). Similarly, the U.S. national surveillance by the CDC reported a steady rise from 0.67% in 2000 to 2.76% in 2020 ([<reflink idref="bib24" id="ref31">24</reflink>]). Several factors may contribute to this global apparent increase in the prevalence of autism. The conceptualization of autism in terms of a spectrum and changes in the diagnostic criteria over the past two decades is a primary consideration ([<reflink idref="bib25" id="ref32">25</reflink>]). However, methodological aspects of the present study, including assessment tools, study design (two-stage approach), setting (community-based) and scope (encompassing the entire territory and wider age range) of our study and possible higher recognition of autism features among contemporary parents, may have underpinned the apparent rise in prevalence in Hong Kong. Given the substantial methodological disparities across existing studies, it is difficult to disentangle the extent to which the differences in prevalence estimates are due to methodological variations, including case definition, or an actual increase. Yet, one large-scale population study found that changes in case definition from <emph>DSM</emph>-4 pervasive developmental disorder to <emph>DSM</emph>-5 autism spectrum disorder did not bring any substantive changes in autism prevalence estimates in the same group of community children ([<reflink idref="bib16" id="ref33">16</reflink>]). In other words, changes in case definition in recent decades may not be as influential in determining prevalence as suspected. The present study also found an insignificant change in the prevalence estimate from using the earlier <emph>DSM</emph>-4 scoring algorithm (2.39%) to that of the recent <emph>DSM</emph>-5 (2.57%). Hence, the possibility of an actual increase in autism prevalence should not be readily dismissed, where standardized methodologies of case identification across studies and time are needed. Simultaneously, the escalating global autism prevalence from recent surveys should raise significant concerns from a public health standpoint given the resulting surge in demand for resources for early detection and treatment. Research has shown that early intervention for children with autism can offer considerable benefits to them and save a significant amount of costs needed for subsequent educational accommodation. For example, early intervention was estimated to save USD 208,500 per child across 18 years of education in Texas ([<reflink idref="bib5" id="ref34">5</reflink>]), while USD 250,000 per child would be saved in annual special education costs with early intervention for children with autism in North Carolina ([<reflink idref="bib7" id="ref35">7</reflink>]).</p> <p>When adopting the locally derived lower cutoff score of five for the AQ-10-Child following the local validation study ([<reflink idref="bib21" id="ref36">21</reflink>]), the screening instrument AQ-10 demonstrated a 100% NPV in both males and females in this population-based study. This is relevant to the precision of our prevalence estimation given that binarizing the AQ-10 into positive and negative for projection in the whole study population would fail to consider the dimensional positive correlation between the AQ-10 score and the probability of an autism diagnosis, as shown in our logistic regression (Table S2 in Supplemental Materials). Of note, 4 out of 17 males with autism diagnosed by the ADI-R had an AQ-10-Child score of five. Hence, adopting the original higher AQ-10 cutoff of six derived in the UK for our ADI-R participant selection could potentially result in an NPV of less than 100%, which may inflate the prevalence estimation owing to the high proportion of AQ-10 Negative participants in the population.</p> <hd id="AN0188761500-17">Utility of the AQ-10 as a screening instrument for autism in the community</hd> <p>The AQ-10 was developed as a brief screening tool to identify 'red-flag' symptoms that prompt comprehensive diagnostic assessment for autism ([<reflink idref="bib1" id="ref37">1</reflink>]). Thus, the 100% NPV found in the present study supports the instrument's effectiveness in excluding autism when utilized as a universal screening measure in the community. Importantly, this property remained consistent across sexes, affirming that the established cutoff can effectively distinguish individuals unlikely to have autism for both males and females.</p> <p>In the original study of the AQ-10, the PPV was 0.86 for the AQ-10-Adol and 0.94 for the AQ-10-Child among 1,000 autistic and 3,000 non-autistic individuals. As PPV is dependent on both the prevalence of the condition in a particular sample and the psychometric properties of the instrument, it is not surprising that the PPVs of the AQ-10 were only low to modest in our current study, as autism is relatively rare in the community setting (i.e. 2.57% compared with the original study's 25% in its case-control sample; [<reflink idref="bib1" id="ref38">1</reflink>]). Compared with the Social Communication Questionnaire (SCQ), another commonly used screening instrument for autism, a low PPV of 8.6% was also noted when the SCQ is applied in a population-based prevalence study with a similar two-stage design as the present study. This low PPV was considered acceptable for a relatively rare condition like autism in the general population ([<reflink idref="bib2" id="ref39">2</reflink>]). Furthermore, with autism being less prevalent among females, the PPV was even lower at only 5.26%. A lower female PPV was similarly reported in a recent study that applied the M-CHAT in a community sample of toddlers ([<reflink idref="bib10" id="ref40">10</reflink>]). On the surface, this could question the efficiency of using the AQ-10 to identify youth with autism, which would result in a significant number of false positives. However, our explorative analysis showed that the participants classified as 'false positive', despite not reaching the research diagnosis criteria, still exhibited elevated levels of autistic symptoms. Individuals with broad autism phenotypes or autistic traits are not without clinical needs and should not be overlooked ([<reflink idref="bib18" id="ref41">18</reflink>]; [<reflink idref="bib31" id="ref42">31</reflink>]). Hence, the AQ-10 is still useful in identifying individuals with elevated autistic symptoms, particularly in scenarios where other related clinical conditions (e.g. attention-deficit/hyperactivity disorder, anxiety and depression) are present. This can be crucial for formulating a comprehensive management approach that considers the role of autistic symptoms within the broader clinical context.</p> <hd id="AN0188761500-18">Sex-specific symptom expression may moderate the detection of autism in the community</hd> <p>While the inherent sex bias of autism could account for the lower PPVs observed in females, the projected M:F ratio in the present study at 7.47:1 exceeded ratios reported in other contemporary studies, including a meta-analysis that reported a pooled ratio of 4.20:1 ([<reflink idref="bib22" id="ref43">22</reflink>]). Although the reason behind this higher ratio in our study is unclear, we considered possibilities from the study's methodological approaches and the sexual dimorphism of autistic symptom presentation. Historically, the majority of the assessment and diagnostic instruments for autism were developed based on the male prototype ([<reflink idref="bib19" id="ref44">19</reflink>]), yet recent studies have identified quantitative and qualitative differences in autistic symptom expression between males and females, such as restricted and repetitive behaviours ([<reflink idref="bib13" id="ref45">13</reflink>]) and impairment of eye gaze to social stimuli ([<reflink idref="bib11" id="ref46">11</reflink>]). In our explorative analysis, the symptom load in nonverbal communication and stereotyped/repetitive behaviours of the AQ-10 False-positive females was significantly less than that of the AQ-10 False-positive males and yet comparable to that of the AQ-10 Negative females. This could suggest that the ADI-R may not be sensitive enough to detect these symptoms in females, or that the manifestation of these symptoms in the broad autism phenotype of females could be qualitatively different from that of males. A recent study showed that the sensitivity of the standardized diagnostic instrument, Autism Diagnostic Observation Schedule (ADOS), alone in detecting known diagnoses of autism is lower in females than in males ([<reflink idref="bib9" id="ref47">9</reflink>]). Collectively, this prompts a need for optimisation of diagnostic practices for females within research settings. Such investigations should involve a representative community cohort spanning the autism spectrum to mitigate ascertainment biases that limit current studies ([<reflink idref="bib15" id="ref48">15</reflink>]).</p> <hd id="AN0188761500-19">Limitations</hd> <p>This study has several limitations. First, the female population was relatively underrepresented in the second-stage ADI-R assessment, which may have affected the precision of PPV estimation in females. Second, the confirmation of an autism diagnosis relied solely on the ADI-R, without additional clinical evaluation and observational assessments, such as the ADOS. This may have reduced the accuracy of case detection and confirmation based on clinical best-estimate. Third, although the AQ-10 is the only validated screening instrument available in Hong Kong, its local validation study did not include autistic individuals with intellectual disabilities. Thus, there is less confidence in the accuracy of prevalence estimation within this subgroup. Finally, although we statistically examined the effect of non-participation on the probability of an autism diagnosis, the response rate for the ADI-R was only moderate. The lower participation of high-income families may be attributed to their heavier work schedules and the monetary incentives offered being relatively less appealing to them. The lower representation of high-income families in the second-stage ADI-R may still have unknown effects on our prevalence estimates.</p> <hd id="AN0188761500-20">Conclusion</hd> <p>This is the first territory-wide epidemiological study on the prevalence of autism in Hong Kong using the <emph>DSM</emph>-5 diagnostic framework and a two-stage diagnostic approach in a representative population sample. The prevalence of autism was estimated to be 2.57%, which was in line with the recent global trend of a rise in autism prevalence. This poses a significant public health challenge, demanding increased resources for early detection and treatment. The AQ-10 has proven to be useful in excluding autism and identifying autistic symptoms in the community using the locally validated age-specific cutoffs for both males and females. However, it remains obscure yet intriguing how plausible sexual dimorphism might impact the diagnosis and detection rate of autism in epidemiological studies. Future studies should re-examine the psychometric properties of diagnostic instruments for autism when applied in a community setting.</p> <hd id="AN0188761500-21">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613251360269 for Autism epidemiology in Hong Kong children and youths aged 6–17: Implications on autism screening and sex differences in the community by Oscar WH Wong, Sandra SM Chan, Steven WH Chau, Winnie CW Chu, Carol SW Ho, Stephy WS Ho, Se Fong Hung, Samara Hussain, Kelly YC Lai, Angela MW Lam, Holly HL Lo, Karen KY Ma, Suk Ling Ma, Flora YM Mo, Pak Chung Sham, Caroline KS Shea, Suzanne HW So, Kelvin KF Tsoi and Patrick WL Leung in Autism</p> <p>The authors would like to thank the team of Work Heart research assistants, along with Dr. Brian Or, Dr. Jason Wong and Dr. Iris Ma for their efforts in the recruitment of participants and data collection for this study.</p> <ref id="AN0188761500-22"> <title> References </title> <blist> <bibl id="bib1" idref="ref13" type="bt">1</bibl> <bibtext> Allison C., Auyeung B., Baron-Cohen S. 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Autism Research, 15(5), 778–790. https://doi.org/10.1002/AUR.2696</bibtext> </blist> </ref> <ref id="AN0188761500-23"> <title> Footnotes </title> <blist> <bibtext> Oscar WH Wong</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-0499-6082 Kelly YC Lai</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-8623-6089 Karen KY Ma</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0002-9972-0256 Suk Ling Ma</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-6175-5691 Patrick WL Leung</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0002-0415-0124</bibtext> </blist> <blist> <bibtext> The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The authors assert that all procedures contributing to this work comply with the ethical standards of the Joint CUHK-NTEC CREC (Ref: 2018.497).</bibtext> </blist> <blist> <bibtext> Written informed consent was obtained from the parents or legal guardian of the participants.</bibtext> </blist> <blist> <bibtext> Oscar WH Wong: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Writing – original draft; Writing – review & editing.Sandra SM Chan: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision; Writing – original draft; Writing – review & editing.Steven WH Chau: Investigation; Methodology; Writing – review & editing.Winnie CW Chu: Funding acquisition; Project administration; Writing – review & editing.Carol SW Ho: Investigation; Project administration; Writing – review & editing.Stephy WS Ho: Data curation; Methodology; Project administration; Writing – review & editing.Se Fong Hung: Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Project administration; Supervision; Writing – review & editing.Samara HUSSAIN: Data curation; Formal analysis; Investigation; Methodology; Project administration; Writing – review & editing.Kelly YC Lai: Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Project administration; Writing – review & editing.Angela MW Lam: Data curation; Formal analysis; Investigation; Writing – review & editing.Holly HL Lo: Data curation; Investigation; Project administration; Writing – review & editing.Karen KY Ma: Data curation; Formal analysis; Investigation; Writing – review & editing.Suk Ling Ma: Conceptualization; Methodology; Writing – review & editing.Flora YM Mo: Conceptualization; Methodology; Writing – review & editing.Pak Chung Sham: Conceptualization; Funding acquisition; Methodology; Supervision; Writing – review & editing.Caroline KS Shea: Methodology; Writing – review & editing.Suzanne HW So: Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Writing – review & editing.Kelvin KF Tsoi: Formal analysis; Methodology; Writing – review & editing.Patrick WL Leung: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Supervision; Writing – original draft; Writing – review & editing.</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 research was supported by the Phase I of the Commissioned Study on Mental Health Survey: Hong Kong Child and Adolescent Psychiatric Epidemiologic Survey: Age 6 to 17, Health and Medical Research Fund Commissioned Study, Food Health and Welfare Bureau (Ref: MHS-P1(Part 1)-CUHK).</bibtext> </blist> <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> OWHW has access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Oscar WH Wong; Sandra SM Chan; Steven WH Chau; Winnie CW Chu; Carol SW Ho; Stephy WS Ho; Se Fong Hung; Samara Hussain; Kelly YC Lai; Angela MW Lam; Holly HL Lo; Karen KY Ma; Suk Ling Ma; Flora YM Mo; Pak Chung Sham; Caroline KS Shea; Suzanne HW So; Kelvin KF Tsoi and Patrick WL Leung</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib32" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib14" firstref="ref3"></nolink> <nolink nlid="nl3" bibid="bib28" firstref="ref4"></nolink> <nolink nlid="nl4" bibid="bib30" firstref="ref5"></nolink> <nolink nlid="nl5" bibid="bib12" firstref="ref6"></nolink> <nolink nlid="nl6" bibid="bib13" firstref="ref7"></nolink> <nolink nlid="nl7" bibid="bib15" firstref="ref8"></nolink> <nolink nlid="nl8" bibid="bib10" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib27" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib21" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib20" firstref="ref16"></nolink> <nolink nlid="nl13" bibid="bib17" firstref="ref20"></nolink> <nolink nlid="nl14" bibid="bib26" firstref="ref28"></nolink> <nolink nlid="nl15" bibid="bib29" firstref="ref30"></nolink> <nolink nlid="nl16" bibid="bib24" firstref="ref31"></nolink> <nolink nlid="nl17" bibid="bib25" firstref="ref32"></nolink> <nolink nlid="nl18" bibid="bib16" firstref="ref33"></nolink> <nolink nlid="nl19" bibid="bib18" firstref="ref41"></nolink> <nolink nlid="nl20" bibid="bib31" firstref="ref42"></nolink> <nolink nlid="nl21" bibid="bib22" firstref="ref43"></nolink> <nolink nlid="nl22" bibid="bib19" firstref="ref44"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref46"></nolink>
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  Data: Autism Epidemiology in Hong Kong Children and Youths Aged 6-17: Implications on Autism Screening and Sex Differences in the Community
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  Data: <searchLink fieldCode="AR" term="%22Oscar+W%2E+H%2E+Wong%22">Oscar W. H. Wong</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0499-6082">0000-0002-0499-6082</externalLink>)<br /><searchLink fieldCode="AR" term="%22Sandra+S%2E+M%2E+Chan%22">Sandra S. M. Chan</searchLink><br /><searchLink fieldCode="AR" term="%22Steven+W%2E+H%2E+Chau%22">Steven W. H. Chau</searchLink><br /><searchLink fieldCode="AR" term="%22Winnie+C%2E+W%2E+Chu%22">Winnie C. W. Chu</searchLink><br /><searchLink fieldCode="AR" term="%22Carol+S%2E+W%2E+Ho%22">Carol S. W. Ho</searchLink><br /><searchLink fieldCode="AR" term="%22Stephy+W%2E+S%2E+Ho%22">Stephy W. S. Ho</searchLink><br /><searchLink fieldCode="AR" term="%22Se+Fong+Hung%22">Se Fong Hung</searchLink><br /><searchLink fieldCode="AR" term="%22Samara+Hussain%22">Samara Hussain</searchLink><br /><searchLink fieldCode="AR" term="%22Kelly+Y%2E+C%2E+Lai%22">Kelly Y. C. Lai</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8623-6089">0000-0001-8623-6089</externalLink>)<br /><searchLink fieldCode="AR" term="%22Angela+M%2E+W%2E+Lam%22">Angela M. W. Lam</searchLink><br /><searchLink fieldCode="AR" term="%22Holly+H%2E+L%2E+Lo%22">Holly H. L. Lo</searchLink><br /><searchLink fieldCode="AR" term="%22Karen+K%2E+Y%2E+Ma%22">Karen K. Y. Ma</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-9972-0256">0000-0002-9972-0256</externalLink>)<br /><searchLink fieldCode="AR" term="%22Suk+Ling+Ma%22">Suk Ling Ma</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6175-5691">0000-0001-6175-5691</externalLink>)<br /><searchLink fieldCode="AR" term="%22Flora+Y%2E+M%2E+Mo%22">Flora Y. M. Mo</searchLink><br /><searchLink fieldCode="AR" term="%22Pak+Chung+Sham%22">Pak Chung Sham</searchLink><br /><searchLink fieldCode="AR" term="%22Caroline+K%2E+S%2E+Shea%22">Caroline K. S. Shea</searchLink><br /><searchLink fieldCode="AR" term="%22Suzanne+H%2E+W%2E+So%22">Suzanne H. W. So</searchLink><br /><searchLink fieldCode="AR" term="%22Kelvin+K%2E+F%2E+Tsoi%22">Kelvin K. F. Tsoi</searchLink><br /><searchLink fieldCode="AR" term="%22Patrick+W%2E+L%2E+Leung%22">Patrick W. L. Leung</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0415-0124">0000-0002-0415-0124</externalLink>)
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Autism%3A+The+International+Journal+of+Research+and+Practice%22"><i>Autism: The International Journal of Research and Practice</i></searchLink>. 2025 29(11):2872-2884.
– 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: 13
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink><br /><searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Epidemiology%22">Epidemiology</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink><br /><searchLink fieldCode="DE" term="%22Adolescents%22">Adolescents</searchLink><br /><searchLink fieldCode="DE" term="%22Gender+Differences%22">Gender Differences</searchLink><br /><searchLink fieldCode="DE" term="%22Screening+Tests%22">Screening Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Psychometrics%22">Psychometrics</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Incidence%22">Incidence</searchLink><br /><searchLink fieldCode="DE" term="%22Disability+Identification%22">Disability Identification</searchLink>
– Name: Subject
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Hong+Kong%22">Hong Kong</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Autism+Spectrum+Quotient%22">Autism Spectrum Quotient</searchLink><br /><searchLink fieldCode="SU" term="%22Diagnostic+Interview+Schedule+for+Children%22">Diagnostic Interview Schedule for Children</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613251360269
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Epidemiological studies on autism lack representation from Asia. We estimated the prevalence of autism among children and youths in Hong Kong using a two-stage approach. In addition, we evaluated the psychometric properties of the screening instrument and explored sex differences within an epidemiological context. A random school-based sample of 5,865 children and youths were screened with the Autism Spectrum Quotient-10 (AQ-10). Then, a subsample of 317 participants underwent the Autism Diagnostic Interview-Revised assessment. Prevalence was estimated by applying positive and negative predictive values (PPV/NPV) of AQ-10 derived from the subsample to the entire cohort. None of the screened negative participants had autism, resulting in an NPV of 100%. Discrepant PPVs were noted for males (20.4%) and females (5.20%). The estimated prevalence was 2.57% using sex-specific PPVs. Explorative analysis on AQ-10 Positive participants without the diagnosis (i.e. 'false positives') showed significantly elevated autistic symptoms. The prevalence of autism in Hong Kong is comparable to the recent estimates in Western countries, which poses a significant public health challenge. Despite the high false-positive rates, AQ-10 remains valuable for excluding autism and identifying those with autistic symptoms. Furthermore, community-based studies are crucial to address sex differences in autism expression.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1487091
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1487091
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      – Type: doi
        Value: 10.1177/13623613251360269
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 2872
    Subjects:
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Epidemiology
        Type: general
      – SubjectFull: Children
        Type: general
      – SubjectFull: Adolescents
        Type: general
      – SubjectFull: Gender Differences
        Type: general
      – SubjectFull: Screening Tests
        Type: general
      – SubjectFull: Psychometrics
        Type: general
      – SubjectFull: Predictor Variables
        Type: general
      – SubjectFull: Incidence
        Type: general
      – SubjectFull: Disability Identification
        Type: general
      – SubjectFull: Hong Kong
        Type: general
      – SubjectFull: Autism Spectrum Quotient
        Type: general
      – SubjectFull: Diagnostic Interview Schedule for Children
        Type: general
    Titles:
      – TitleFull: Autism Epidemiology in Hong Kong Children and Youths Aged 6-17: Implications on Autism Screening and Sex Differences in the Community
        Type: main
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            – D: 01
              M: 11
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
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            – TitleFull: Autism: The International Journal of Research and Practice
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