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Prevalence, Incidence, and Characteristics of Autism Spectrum Disorder among Children in Beijing, China

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Title: Prevalence, Incidence, and Characteristics of Autism Spectrum Disorder among Children in Beijing, China
Language: English
Authors: Yanan Zhao (ORCID 0000-0002-1532-3641), Feng Lu, Ruoxi Ding, Dawei Zhu, Rong Zhang, Siwei Sun, Ping He, Xiaoying Zheng
Source: Autism: The International Journal of Research and Practice. 2025 29(4):884-895.
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: 12
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Incidence, Autism Spectrum Disorders, Symptoms (Individual Disorders), Foreign Countries, Comorbidity, Preschool Children, Disability Identification, Screening Tests, Diagnostic Tests, Clinical Diagnosis, Developmental Delays, Intellectual Disability, Attention Deficit Hyperactivity Disorder, Speech Impairments, Epilepsy, Neurological Impairments
Geographic Terms: China (Beijing)
Assessment and Survey Identifiers: Autism Diagnostic Observation Schedule, Childhood Autism Rating Scale
DOI: 10.1177/13623613241290388
ISSN: 1362-3613
1461-7005
Abstract: The prevalence of autism spectrum disorder in the world has increased over the last decade, but the prevalence, incidence, and characteristics of autism spectrum disorder in China were not well understood. Using administrative data, we aimed to estimate the prevalence and incidence of autism spectrum disorder and describe the co-occurring conditions in preschoolers in Beijing, China. The study focused on 0- to 6-year-old children with registered residence in Beijing, using cohorts derived from the Beijing Municipal Health Big Data and Policy Research Center. We conducted a detailed analysis of autism spectrum disorder prevalence among the cohorts, comparing estimates across 2 to 3 years for the same birth cohort (4 years, 5 years). For the 6-year-old cohort, we obtained 1-year prevalence estimates in 2021. Annual incidence rate was also calculated. The prevalence in 6-year-old children in 2021 was 10.5 per 1000 (95% confidence interval = 9.7--10.9). The male-to-female prevalence ratio was 4.3. Between 40% and 43% of preschool children had at least one co-occurring condition. The incidence for children 6 years old and under was 0.11% in 2019 and increased to 0.18% in 2021. Both the prevalence and incidence rates in Beijing were comparable to those reported in developed countries.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1466090
Database: ERIC
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  Value: <anid>AN0184233753;f9d01apr.25;2025Apr08.02:17;v2.2.500</anid> <title id="AN0184233753-1">Prevalence, incidence, and characteristics of autism spectrum disorder among children in Beijing, China </title> <p>The prevalence of autism spectrum disorder in the world has increased over the last decade, but the prevalence, incidence, and characteristics of autism spectrum disorder in China were not well understood. Using administrative data, we aimed to estimate the prevalence and incidence of autism spectrum disorder and describe the co-occurring conditions in preschoolers in Beijing, China. The study focused on 0- to 6-year-old children with registered residence in Beijing, using cohorts derived from the Beijing Municipal Health Big Data and Policy Research Center. We conducted a detailed analysis of autism spectrum disorder prevalence among the cohorts, comparing estimates across 2 to 3 years for the same birth cohort (4 years, 5 years). For the 6-year-old cohort, we obtained 1-year prevalence estimates in 2021. Annual incidence rate was also calculated. The prevalence in 6-year-old children in 2021 was 10.5 per 1000 (95% confidence interval = 9.7–10.9). The male-to-female prevalence ratio was 4.3. Between 40% and 43% of preschool children had at least one co-occurring condition. The incidence for children 6 years old and under was 0.11% in 2019 and increased to 0.18% in 2021. Both the prevalence and incidence rates in Beijing were comparable to those reported in developed countries. It is the first study to explore the prevalence, incidence, and co-occurring conditions of autism spectrum disorder for the preschoolers in China. The prevalence and incidence of autism spectrum disorder has increased in recent decades. Autism spectrum disorder has become an important public concern worldwide. In this study, all hospital confirmed cases had an associated diagnosis (International Classification of Diseases, 10th revision (ICD-10) codes: F84.0, 84.5, F84.9). In total, 4457 children aged 4–6 years were identified as having autism spectrum disorder. In 2021, 1 in 95 children aged 6 years, 1 in 115 children aged 5 years, and 1 in 130 children aged 4 years were estimated to have autism spectrum disorder in Beijing. The incidence was 0.11% in 2019 and increased to 0.18% in 2021. There has been a great emphasis on the importance of early autism spectrum disorder diagnosis in large cities in China.</p> <p>Keywords: autism spectrum disorders; diagnosis; co-occurring conditions; pre-school children; prevalence</p> <hd id="AN0184233753-2">Introduction</hd> <p>Autism spectrum disorder (ASD) is a range of neurodevelopmental disorders that are characterized by impairments in social interaction and communication and restricted, repetitive behaviors. The prevalence of ASD has increased in recent decades. For example, in the United States, the Centers for Disease Control and Prevention's (CDC) Autism and Developmental Disabilities Monitoring (ADDM) Network reported that the overall ASD prevalence among 4-year-old children reached 2.15% in 2020, with a 60% increase from 2010 ([<reflink idref="bib11" id="ref1">11</reflink>]; [<reflink idref="bib47" id="ref2">47</reflink>]). Research from Japan demonstrated a similar pattern, with ASD prevalence in children increased from 0.21% in 1996 to 1.31% in 2019 ([<reflink idref="bib18" id="ref3">18</reflink>]; [<reflink idref="bib43" id="ref4">43</reflink>]). These increases are partly due to improved awareness and increased opportunities to access services, resulting in better case identification ([<reflink idref="bib63" id="ref5">63</reflink>]) and may also be due to a real increase in the prevalence of ASD symptoms. ASD has emerged as a significant global public health priority, commanding widespread attention from medical professionals, policymakers, and society at large.</p> <p>Over the past decade, epidemiological studies of ASD in East Asia have been relatively scarce (see Table S1 in Supplemental Materials for a literature review). In Japan, four regional studies reported ASD prevalence ranging from 1.31% to 3.1% ([<reflink idref="bib24" id="ref6">24</reflink>]; [<reflink idref="bib36" id="ref7">36</reflink>]; [<reflink idref="bib43" id="ref8">43</reflink>]; [<reflink idref="bib44" id="ref9">44</reflink>]), and one study reported an incidence rate of 1.73% ([<reflink idref="bib43" id="ref10">43</reflink>]). A regional study in South Korea indicated that the prevalence of ASD was 2.64% ([<reflink idref="bib23" id="ref11">23</reflink>]), while a national study reported an incidence rate of 0.3‰–0.5‰ ([<reflink idref="bib41" id="ref12">41</reflink>]). China has consistently reported a lower prevalence of ASD than other countries. At the same time, there has been a lack of research on ASD incidence rates. According to an early national study in China, the prevalence of ASD among children and adolescents aged 0–17 years was 0.24% ([<reflink idref="bib27" id="ref13">27</reflink>]). A recent large-scale survey in China revealed an ASD prevalence of 0.7% among children aged 6–12 years ([<reflink idref="bib65" id="ref14">65</reflink>]). While this number is higher than earlier research in China ([<reflink idref="bib55" id="ref15">55</reflink>]; [<reflink idref="bib58" id="ref16">58</reflink>]), it remains comparatively low. Different research methods and case determination standards employed may lead to differences in studies. Active surveillance, parental reports, administrative registries, service claims, and population survey data with screening approaches can be used to estimate prevalence and incidence rate ([<reflink idref="bib4" id="ref17">4</reflink>]; [<reflink idref="bib8" id="ref18">8</reflink>]), but each method has its own limitations. Even if there is controversy over the assumptions used to derive estimates, or if the uncertainty of these assumptions is not considered, it can lead to differences in prevalence estimates ([<reflink idref="bib23" id="ref19">23</reflink>]; [<reflink idref="bib38" id="ref20">38</reflink>]). The unique design features of studies could account almost entirely for between-studies variations in prevalence proportions, making time trends of ASD prevalence difficult to access from published estimates ([<reflink idref="bib14" id="ref21">14</reflink>]).</p> <p>Accurate estimates of prevalence and incidence rates are significant for guiding service planning and identifying geographical risk factors. However, in China, these figures remain unclear and methodological weaknesses in existing studies hinder meaningful comparisons with other countries. Four primary flaws are evident. First, despite some national studies, sampling bias occurred because samples were screened for disability and only individuals suspected of having mental disabilities (ASD is classified as "mental disabilities" in China for government subsidy allocation) were examined and diagnosed ([<reflink idref="bib27" id="ref22">27</reflink>]). Therefore, it was likely only the more severe cases of autistic children were detected, resulting in an underestimation of the overall prevalence of ASD. Second, most studies have sampled from urban populations ([<reflink idref="bib57" id="ref23">57</reflink>]) and often with small sample sizes ([<reflink idref="bib27" id="ref24">27</reflink>]; [<reflink idref="bib50" id="ref25">50</reflink>]; [<reflink idref="bib57" id="ref26">57</reflink>]). Third, there has been inadequate focus on preschool-aged children. While studying school-aged children provides comprehensive information, assessing ASD prevalence and incidence in preschoolers enables more timely evaluation of early detection efforts ([<reflink idref="bib10" id="ref27">10</reflink>], [<reflink idref="bib11" id="ref28">11</reflink>]). Fourth, previous research has indicated that children with ASD frequently have co-occurring neurological and psychiatric conditions ([<reflink idref="bib48" id="ref29">48</reflink>]). Even among 3- to 4-year-old children, there was also a high proportion with co-occurring conditions ([<reflink idref="bib19" id="ref30">19</reflink>]). This high prevalence of co-occurring conditions might postpone the identification of ASD until later in childhood ([<reflink idref="bib32" id="ref31">32</reflink>]). According to recent research in China, 68.8% of ASD children aged 6–12 years had at least one comorbid psychiatric disorder ([<reflink idref="bib65" id="ref32">65</reflink>]). However, research on co-occurring conditions in Chinese children, particularly those of pre-school age, is still limited.</p> <p>While statistics from a single city may not represent an entire country, data from a major metropolitan area can thoroughly depict the state of economically advantageous regions in a country with substantial regional diversity. The outpatient and inpatient data from Beijing (2016–2021) provide a comprehensive dataset of residents' medical behaviors. Our epidemiological study utilized this hospital administrative database to generate retrospective cohorts, describing ASD prevalence, incidence, and co-occurring conditions among preschool-aged children in Beijing. We have also considered the inflow and outflow of the cohorts in this city. Given China's uneven economic development and the varied impact of ASD awareness across provinces, regional research on ASD prevalence and incidence rates is crucial. The findings of this study could inform service planning and policy initiatives aimed at early identification of children with ASD.</p> <hd id="AN0184233753-3">Methods</hd> <p></p> <hd id="AN0184233753-4">Target population/ region demographics</hd> <p>This study was carried out in Beijing, the capital and one of its largest cities in China (area, 16,410 km<sups>2</sups>; 16 administrative districted with 165 sub district offices, 178 townships; household registered population, 14,008,000) ([<reflink idref="bib6" id="ref33">6</reflink>]1). The target population (<emph>N</emph> = 513,600) included all children born in Beijing between 2015 and 2017 (ages 4–6 years in December 2021). ASD case ascertainment was based on the hospital records.</p> <hd id="AN0184233753-5">Data sources</hd> <p>The source of the data for ASD case identification originates from hospital admission records from 1 January 2016 to 31 December 2021. Hospital records were extracted from Beijing inpatient and outpatient database, which was a citywide computerized hospital admission surveillance system established by Beijing Municipal Health Big Data and Policy Research Center (BMHBD) on a person-identifiable basis. The surveillance system covered all the hospitals with the exception of primary hospitals and community hospitals, including 109 tertiary hospitals and 94 secondary hospitals in Beijing. The service volumes of these hospitals account for more than 90% of the city. The other details of the data have been described elsewhere ([<reflink idref="bib64" id="ref34">64</reflink>]). It contained the information of the date of visit or admission, date of birth, gender, family postal code and contact information, and all diagnosis, which were collected through a standard interface system and online reporting via the Beijing Health Comprehensive Statistics Information Platform (BHCSIP). All diagnosis was coded according to the International Classification of Diseases, 10th revision (ICD-10) codes ([<reflink idref="bib59" id="ref35">59</reflink>]).</p> <p>The data source pertained to birth cohorts was drawn from the civil registry database. The population base was the live births in the relevant year (<emph>N</emph> = 513,600) recorded by the Beijing Municipal Bureau of Statistics ([<reflink idref="bib6" id="ref36">6</reflink>]). Age- and gender-specific information on the number of people alive and living in Beijing were available for each year. Population flux data, including both in-migration and out-migration of registered residents in Beijing, can be incorporated to refine the cohorts.</p> <hd id="AN0184233753-6">Case ascertainment</hd> <p>The diagnostic coding of BMHBD in Beijing follows the ICD-10. Individuals with ASD were identified if they had a primary, secondary, or provisional diagnosis codes of F84.0 (autistic disorder, AD), F84.5 (Asperger's syndrome, AS), and F84.9 (pervasive developmental disorder, unspecified, PDD-NOS). Neither the BMHBD registry coverage nor the diagnostic criteria have changed during the study period. Although ICD-11 was released in 2018 ([<reflink idref="bib60" id="ref37">60</reflink>]), it is currently not widely adopted in Chinese hospitals. The majority of ASD individuals are diagnosed in outpatients of public hospitals, while some new ASD cases may be diagnosed during hospitalization in inpatients section. Contacts with emergency rooms were neglected due to their poor authenticity.</p> <p>All diagnosed cases in Beijing were confirmed through multiple observations, scale measures, and consultations with developmental pediatrician or psychiatrist (Figure 1), which followed a rigorous and reliable process. Specifically, they followed the "China Guidelines for the Diagnosis, Treatment, and Rehabilitation of Childhood Autism-2010" ([<reflink idref="bib33" id="ref38">33</reflink>]), which recommends a diagnostic assessment should made by mental examination, physical examination, psychological assessment, and developmental interview according to the ICD-10 (see Figure S1A in Supplemental Material for details about the recommendations). We interviewed four clinicians in major hospitals for ASD and all doctors confirmed that the diagnosis methods adopted by their hospital conformed to the "Guidelines-2010." The most frequently used screening instruments were the Clancy Autism Behavior Scale (CABS) ([<reflink idref="bib12" id="ref39">12</reflink>]), the Autism Behavior Checklist (ABC) ([<reflink idref="bib25" id="ref40">25</reflink>]), and the Modified Checklist for Autism in Toddlers (M-CHAT) (for those aged 16–30 months) ([<reflink idref="bib42" id="ref41">42</reflink>]). The final diagnosis for each sample needs to be combined with the scores of Childhood Autism Rating scale (CARS) ([<reflink idref="bib46" id="ref42">46</reflink>]) as well as the judgment of pedestrian or psychiatrists. Some scales, such as Autism Diagnostic Observation Schedule, Second Edition (ADOS-2), necessitate specialized training; therefore, they were used less frequently when compared to CARS.</p> <p>Graph: Figure 1. The process of clinical diagnosis in hospitals of Beijing.</p> <p>In accordance with prior research ([<reflink idref="bib39" id="ref43">39</reflink>]; [<reflink idref="bib58" id="ref44">58</reflink>]), we extracted, processed, and loaded data for analysis. This process included removing duplicates and cases with unreliable diagnosis. The diagnosis qualifications and capabilities of hospitals were verified according to the departments and the qualifications of doctors. A total of 30 cases were excluded from the study because they derived from hospitals without access to reliable diagnostic information. We scrutinized cases of children diagnosed with ASD between ages 0 and 1, confirming whether these diagnoses were corroborated in subsequent years. Two samples lacking new diagnostic records in follow-up assessments were excluded from the study, as a single diagnosis before age 2 may not be considered reliable. A total of 2706 cases were excluded in this study (Figure 2).</p> <p>Graph: Figure 2. Flow chart.</p> <p>The identification of co-occurring conditions permitted the identification of several groups with different mental health or related problems. Children were classified as having a co-occurring condition if their records contained a relevant diagnosis code during the study period. Ten types of co-occurring conditions were extracted from the BMHBD registry, including: (<reflink idref="bib1" id="ref45">1</reflink>) developmental delay (DD, ICD-10 code R62.8), (<reflink idref="bib2" id="ref46">2</reflink>) attention-deficit hyperactivity disorder (ADHD, ICD-10 codes, F90.0, F90.1, F90.9), (<reflink idref="bib3" id="ref47">3</reflink>) intellectual disability (ID, ICD-10 codes F79.0-F79.1), (<reflink idref="bib4" id="ref48">4</reflink>) speech disorder(SD, ICD-10 code F80.9), (<reflink idref="bib5" id="ref49">5</reflink>) epilepsy(EP, ICD-10 code G40.9), (<reflink idref="bib6" id="ref50">6</reflink>) tic disorder (TD, ICD-10 codes F95.2, F95.5, F95.9), (<reflink idref="bib7" id="ref51">7</reflink>) eating disorders (ED, ICD-10 codes, F50.0, F50.2, F50.9, F98.2, R63.3), (<reflink idref="bib8" id="ref52">8</reflink>) childhood emotional disorder (CED, ICD-10 code F93.9), (<reflink idref="bib9" id="ref53">9</reflink>) anxiety disorder (AD, ICD-10 codes F41.0, F41.1, F41.2, F41.9), and (<reflink idref="bib10" id="ref54">10</reflink>) sleep-wake disorders(SL, ICD-10 codes G47.0, G47.4, G47.9). Most of the ASD-related comorbidities mentioned in previous studies were covered by these co-occurring conditions ([<reflink idref="bib26" id="ref55">26</reflink>]; [<reflink idref="bib49" id="ref56">49</reflink>]).</p> <p>In order to avoid the estimated errors associated with transient populations, all children in the cohorts were registered in Beijing (possessing <emph>Hukou</emph> of Beijing) (see eBackground in the Supplemental Material for introduction of <emph>Hukou</emph>).</p> <hd id="AN0184233753-7">Data quality evaluation</hd> <p>Great efforts were made to perform a case validation of the ASD cases. First, system logic verification and an expert-based examination of medical records were used by data provider to identify and manage incorrect data in a timely manner and diagnosis (see eMethods in the Supplemental Material for details). Second, we tracked the relevant diagnostic scales' scores of ASD cases using children's unique code in National Medical Insurance Account (NMIA) to search the complete paper or electronic diagnostic medical records in hospitals to check information from a variety of sources (as a case shown in Table S2 in Supplemental Material). Third, we double validated for diagnosis by comparing the ICD-10 code in the diagnosis records with treatment billing codes and the detailed word records for patients to exclude code errors.</p> <hd id="AN0184233753-8">Statistical analysis</hd> <p>All children who were diagnosed with ASD for the first time were considered incident cases. For prevalence, this study included three cohorts of 4-, 5-, and 6-year-old children. The cohort prevalence of ASD (per 1000 births) was calculated as the number of ASD cases detected until 4, 5, and 6 years of age in children born each year, divided by the adjusted number of total births in the corresponding year (e.g. the 6-year ASD prevalence in 2021 was calculated as the number of children diagnosed with ASD until 2021 among those born in 2015, divided by the population of 6 years children in 2021, that the number of total adjusted births in 2015 minus the deaths and outflows and adds the inflows for the same period).</p> <p>The calculation formula was as follows</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>P</mi><mi>r</mi><mi>e</mi><mi>v</mi><mi>a</mi><mi>l</mi><mi>e</mi><mi>n</mi><mi>c</mi><msub><mi>e</mi><mi>i</mi></msub><mo>=</mo><mfrac><mtable columnalign="left"><mtr><mtd><mi mathvariant="normal">Number</mi><mspace width="0.25em" /><mi mathvariant="normal">of</mi><mspace width="0.25em" /><mi mathvariant="normal">ASD</mi><mspace width="0.25em" /><mi mathvariant="normal">cases</mi><mspace width="0.25em" /></mtd></mtr><mtr><mtd><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mi mathvariant="normal">until</mi><mspace width="0.25em" /><mi mathvariant="normal">year</mi><mspace width="0.25em" /><mi>i</mi><mi mathvariant="normal">among</mi><mspace width="0.25em" /><mi mathvariant="normal">children</mi><mspace width="0.25em" /></mtd></mtr><mtr><mtd><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mi mathvariant="normal">born</mi><mspace width="0.25em" /><mi mathvariant="normal">in</mi><mspace width="0.25em" /><mi mathvariant="normal">year</mi><mspace width="0.25em" /><mi>j</mi></mtd></mtr></mtable><mtable columnalign="left"><mtr><mtd><mi mathvariant="normal">Number</mi><mspace width="0.25em" /><mi mathvariant="normal">of</mi><mspace width="0.25em" /><mi mathvariant="normal">total</mi><mspace width="0.25em" /><mi mathvariant="normal">birth</mi><mspace width="0.25em" /><mi mathvariant="normal">in</mi><mspace width="0.25em" /><mi mathvariant="normal">year</mi><mspace width="0.25em" /><mi>j</mi></mtd></mtr><mtr><mtd><mo>+</mo><mi mathvariant="normal">Adjusted</mi><mspace width="0.25em" /><mi mathvariant="normal">number</mi><mspace width="0.25em" /><mi mathvariant="normal">until</mi><mspace width="0.25em" /><mi mathvariant="normal">year</mi><mspace width="0.25em" /><mi>i</mi></mtd></mtr></mtable></mfrac><mo>×</mo><mn>1000</mn></mrow></math> </ephtml> </p> <p>Graph</p> <p> <emph>"i"=</emph> <emph>4,5,6 (age);2019,2020,2021 (year); "j"=</emph> <emph>2015;2016;2017.</emph> </p> <p>The children born in 2015 were diagnosed in 2016 at the earliest. The six years diagnosis records were used to construct three 4-year-old cohorts (in 2019, 2020, 2021), two 5-year-old cohorts (in 2020, 2021) and one 6-year-old cohort (in 2021).</p> <p>In constructing the dynamic cohort, we accounted for mortality and migration. For instance, when considering 4-year-old children in 2021, we adjusted the cohort by adding the number of children who immigrated to Beijing annually from 2017 to 2021, while subtracting those who emigrated or died during this period. We derived this information from the Beijing Municipal Bureau of Statistics, employing the following formula to adjust the birth cohort denominator</p> <p> <ephtml> <math display="block" xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mrow><mo>{</mo><mrow><mtable><mtr><mtd><mtable columnalign="left"><mtr><mtd><mi>A</mi><mi>d</mi><mi>j</mi><mi>u</mi><mi>s</mi><msub><mi>t</mi><mrow><mo stretchy="false">(</mo><mn>0</mn><mo>−</mo><mn>4</mn><mo stretchy="false">)</mo><mi>j</mi></mrow></msub><mo>=</mo><mrow><mo>{</mo><mrow><mi>N</mi><msub><mrow><mo stretchy="false">(</mo><mn>0</mn><mo>−</mo><mn>4</mn><mi>y</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><mi>j</mi></msub></mrow></mrow><mo>−</mo><mo stretchy="false">[</mo><mi>N</mi><msub><mrow><mo>(</mo><mrow><mn>0</mn><mo>−</mo><mn>4</mn><mi>y</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>s</mi></mrow><mo>)</mo></mrow><mrow><mi>j</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo></mtd></mtr><mtr><mtd><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mi>N</mi><msub><mrow><mo>(</mo><mrow><mi>n</mi><mi>e</mi><mi>w</mi><mi>b</mi><mi>o</mi><mi>r</mi><mi>n</mi></mrow><mo>)</mo></mrow><mi>j</mi></msub><mo>−</mo><mrow><mrow><mi>N</mi><msub><mrow><mrow><mo>(</mo><mrow><mi>n</mi><mi>e</mi><mi>w</mi><mi>b</mi><mi>o</mi><mi>r</mi><mi>n</mi></mrow><mo>)</mo></mrow></mrow><mrow><mi>j</mi><mo>−</mo><mn>5</mn></mrow></msub><mo stretchy="false">]</mo></mrow><mo>}</mo></mrow><mo>/</mo><mn>5</mn></mtd></mtr></mtable></mtd></mtr><mtr><mtd><mtable columnalign="left"><mtr><mtd><mi>A</mi><mi>d</mi><mi>j</mi><mi>u</mi><mi>s</mi><msub><mi>t</mi><mrow><mo stretchy="false">(</mo><mn>5</mn><mo>−</mo><mn>6</mn><mo stretchy="false">)</mo><mi>j</mi></mrow></msub><mo>=</mo><mrow><mo>{</mo><mrow><mi>N</mi><msub><mrow><mo stretchy="false">(</mo><mn>5</mn><mo>−</mo><mn>9</mn><mi>y</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>s</mi><mo stretchy="false">)</mo></mrow><mi>j</mi></msub></mrow></mrow><mo>−</mo><mo stretchy="false">[</mo><mi>N</mi><msub><mrow><mo>(</mo><mrow><mn>5</mn><mo>−</mo><mn>9</mn><mi>y</mi><mi>e</mi><mi>a</mi><mi>r</mi><mi>s</mi></mrow><mo>)</mo></mrow><mrow><mi>j</mi><mo>−</mo><mn>1</mn></mrow></msub><mo>+</mo></mtd></mtr><mtr><mtd><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mspace width="0.25em" /><mi>N</mi><msub><mrow><mo>(</mo><mrow><mi>n</mi><mi>e</mi><mi>w</mi><mi>b</mi><mi>o</mi><mi>r</mi><mi>n</mi></mrow><mo>)</mo></mrow><mi>j</mi></msub><mo>−</mo><mrow><mrow><mi>N</mi><msub><mrow><mrow><mo>(</mo><mrow><mi>n</mi><mi>e</mi><mi>w</mi><mi>b</mi><mi>o</mi><mi>r</mi><mi>n</mi></mrow><mo>)</mo></mrow></mrow><mrow><mi>j</mi><mo>−</mo><mn>5</mn></mrow></msub><mo stretchy="false">]</mo></mrow><mo>}</mo></mrow><mo>/</mo><mn>5</mn></mtd></mtr></mtable></mtd></mtr></mtable></mrow></mrow></mrow></math> </ephtml> </p> <p>Graph</p> <p> <emph>"j"=</emph> <emph>2019;2020;2021; N</emph> <emph>=</emph> <emph>number</emph> </p> <p>See Table S3 in Supplemental Material for detailed adjustment numbers we used in the calculation. For incidence estimates of ASD for each study year between 2019 and 2021, the denominator in this study was the total number of children in the target population at each year, and the numerator was the total number of children who were newly diagnosed with ASD each study year. Although 6 years of data available, the data from 2016 to 2018 cannot be regressed, making it impossible to determine whether the elderly group is a new case.</p> <p>Prevalence and incidence rates were calculated overall and by sex (divided the sample into male and female groups and calculated the rates for each). Pearson chi-square tests were used to calculate the <emph>p</emph> value. The Wilson score method was used to calculate 95% confidence intervals (CIs). All statistical analyses were performed in STATA 15 (Stata Corp, College Station, TX, USA).</p> <hd id="AN0184233753-9">Community involvement</hd> <p>The authors of this project consulted with several pediatric psychiatrists and also consulted with families with ASD children to obtain information on their medical choices.</p> <hd id="AN0184233753-10">Results</hd> <p></p> <hd id="AN0184233753-11">Cohort description and prevalence</hd> <p>In our cohorts, a total of 4457 children were included. The prevalence in 6-year-old children in 2021 was 10.5 per 1000 (1 in 95; 95% CI = 9.9–11.1). The prevalence and trends of ASD in children in the 4-year-old cohort and the 5-year-old cohort were as follows: the prevalence of ASD among children aged 4 years was 7.8 per 1000 (1 in 128; 95% CI = 7.3–8.3) in 2019, 6.8 per 1000 (1 in 147; 95% CI = 6.5–7.2) in 2020 and 7.7 per 1000 (1 in 130; 95% CI = 7.3–8.1) in 2021. The prevalence of ASD among children aged 5 years was 8.8 per 1000 (1 in 114; 95% CI = 8.3–9.3) in 2020 and 8.7 per 1000 (1 in 115; 95% CI = 8.3–9.1) in 2021 (Table 1).</p> <p>Table 1. Prevalencea [<reflink idref="bib2" id="ref57">2</reflink>] of autism spectrum disorder among children aged 4, 5, and 6 years.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th /><th align="left">2019</th><th align="left">2020</th><th align="left">2021</th></tr></thead><tbody><tr><td rowspan="3">4 years</td><td>N</td><td>925</td><td>1366</td><td>1425</td></tr><tr><td>Population<xref ref-type="table-fn" rid="tfn3">b</xref></td><td>118,683</td><td>201,683</td><td>185,030</td></tr><tr><td>Prevalence</td><td>7.8 (7.3-8.3)</td><td>6.8 (6.5-7.2)</td><td>7.7 (7.3-8.1)</td></tr><tr><td rowspan="3">5 years</td><td>N</td><td /><td>1043</td><td>1770</td></tr><tr><td>Population<xref ref-type="table-fn" rid="tfn3">b</xref></td><td /><td>119,080</td><td>203,168</td></tr><tr><td>Prevalence</td><td /><td>8.8 (8.3-9.3)</td><td>8.7 (8.3-9.1)</td></tr><tr><td rowspan="3">6 years</td><td>N</td><td /><td /><td>1262</td></tr><tr><td>Population<xref ref-type="table-fn" rid="tfn3">b</xref></td><td /><td /><td>120,565</td></tr><tr><td>Prevalence</td><td /><td /><td>10.5 (9.9-11.1)</td></tr></tbody></table> </ephtml> </p> <p>1 N: number; CI: confidence interval.</p> <ulist> <item>2 Prevalence: per 1000 children aged 4 or 5 or 6 years living in Beijing according to the 2015–2017 number of births in Beijing, which was adjusted by the number of residents entering and exiting the cohorts.</item> <item>3 Unit: 1 person.</item> </ulist> <p>The prevalence of ASD in different age cohorts was calculated by sex (Table 2). The prevalence in 6-year-old children in 2021 was 16.6 per 1000 (95% CI = 15.6–17.6) for males and 3.9 per 1000 (95% CI = 3.4–4.4) for females. The prevalence sex ratio (male/female) was 4.3. In the 5-year-old age cohort, the prevalence of ASD among male children was 13.8 per 1000 (95% CI = 12.9–14.8) in 2020 and 13.9 per 1000 (95% CI = 13.2–14.6) in 2021. In female children, the prevalence was 3.3 per 1000 (95% CI = 2.9–3.8) in 2020 and 3.1 per 1000 (95% CI = 2.8–3.5). The prevalence sex ratio was 4.2 in 2020 and 4.5 in 2021. In the 4-year-old age cohort, the prevalence of ASD among male children was 12.4 per 1000 (95% CI = 11.6–13.3) in 2019, 10.8 per 1000 (95% CI = 10.2–11.4) in 2020, and 12.3 per 1000 (95% CI = 11.6–13.0) in 2021. In female children, the prevalence was 2.9 per 1000 (95% CI = 2.5–3.4) in 2019, 2.5 per 1000 (95% CI = 2.2–2.8) in 2020, and 2.8 per 1000 (95% CI = 2.5–3.2) in 2021. The prevalence sex ratio was 4.3 in 2019, 4.2 in 2020, and 4.4 in 2021.</p> <p>Table 2. Prevalencea [<reflink idref="bib5" id="ref58">5</reflink>] of autism spectrum disorder among children aged 4, 5, and 6 years, by 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="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left" rowspan="2">Year</th><th align="left" colspan="2">Number of cases</th><th align="left" colspan="2">Population</th><th align="left" colspan="3">Prevalence</th></tr><tr><th /><th align="left">Male</th><th align="left">Female</th><th align="left">Male<xref ref-type="table-fn" rid="tfn6">b</xref></th><th align="left">Female</th><th align="left">Male (95% CI)</th><th align="left">Female (95% CI)</th><th align="left">Rate ratio<xref ref-type="table-fn" rid="tfn7">c</xref> (95% CI)</th></tr></thead><tbody><tr><td rowspan="3">4 years</td><td>2019</td><td>758</td><td>167</td><td>61,240</td><td>57,443</td><td>12.4 (11.6–13.3)</td><td>2.9 (2.5–3.4)</td><td>4.3 (3.5–5.2)</td></tr><tr><td>2020</td><td>1126</td><td>240</td><td>104,068</td><td>97,615</td><td>10.8 (10.2–11.4)</td><td>2.5 (2.2–2.8)</td><td>4.3 (3.7–5.1)</td></tr><tr><td>2021</td><td>1174</td><td>251</td><td>95,475</td><td>89,555</td><td>12.3 (11.6–13.0)</td><td>2.8 (2.5–3.2)</td><td>4.4 (3.7–5.1)</td></tr><tr><td rowspan="2">5 years</td><td>2020</td><td>851</td><td>192</td><td>61,445</td><td>57,635</td><td>13.8 (12.9–14.8)</td><td>3.3 (2.9–3.8)</td><td>4.2 (3.5–5.0)</td></tr><tr><td>2021</td><td>1461</td><td>309</td><td>104,835</td><td>98,333</td><td>13.9 (13.2–14.6)</td><td>3.1 (2.8–3.5)</td><td>4.5 (3.8–5.1)</td></tr><tr><td>6 years</td><td>2021</td><td>1033</td><td>229</td><td>62,212</td><td>58,353</td><td>16.6 (15.6–17.6)</td><td>3.9 (3.4–4.4)</td><td>4.3 (3.6–5.1)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>4 CI: confidence interval.</item> <item>5 Prevalence per 1000 children aged 4 or 5 or 6 years living in Beijing according to the 2015–2017 number of births in Beijing, which was adjusted by the number of residents entering and exiting the cohort.</item> <item>6 The sex ratio of children aged 0–4 years in that year is used to estimate the number of births by sex.</item> <item>7 Male-to-female prevalence ratio, all significantly higher prevalence among males compared with females (Pearson chi-square, p < 0.05).</item> </ulist> <hd id="AN0184233753-12">Incidence rate</hd> <p>Figure 3 showed the sex-specific annual incidence of ASD from 2019 to 2021. For children aged 6 and under, the incidence rate of ASD showed notable changes over the 3-year period. In 2019, the rate was 1.13‰, with a minor decrease to 1.10‰ in 2020. However, 2021 saw a significant increase, with the incidence rate rising to 1.81‰. Males had a higher incidence rate than females in 3 years. Compared with male, the female incidence rate has not experienced a decline in 2020 (see the Table S4 in Supplemental Material for details).</p> <p>Graph: Figure 3. Incidence rate for ASD in Beijing from 2019 to 2021.</p> <hd id="AN0184233753-13">Co-occurring conditions</hd> <p>The proportions of co-occurring conditions were 40.05% in the 6-year-old cohort, 42.43% in the 5-year-old cohort, and 41.24% in the 4-year-old cohort. Most of the children had only one co-occurring condition (Table 3). In the cohort of 6-year-old children in 2021, six co-occurring conditions accounted for more than 1%. Children with DD made up 22.19% of the total. A total of 6.74% of children with ASD were also diagnosed with ID, and 8.48% of children with ASD were diagnosed with ADHD. We also compared the 4-year-old cohort to the 5-year-old cohort in 2021 and found that as age increased, the proportion of co-occurring conditions of DD and SD decreased. ID was mostly diagnosed in the 5- to 6-year-old cohorts. ADHD, EP, and TD were more prevalent in 6-year-old children. A small number of co-occurring conditions (with less than 5 confirmed cases) such as ED were not included in this analysis, see the Table S5 in Supplemental Material for details.</p> <p>Table 3. Number and percentage of children with co-occurring conditionsa [<reflink idref="bib9" id="ref59">9</reflink>] among children aged 4, 5, and 6 years with autism spectrum disorder, by sex, in 2021.</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="3">6 years</th><th align="left" colspan="3">5 years</th><th align="left" colspan="3">4 years</th></tr><tr><th /><th align="left">Male (n/%)</th><th align="left">Female (n/%)</th><th align="left">Total (n/%)</th><th align="left">Male (n/%)</th><th align="left">Female (n/%)</th><th align="left">Total (n/%)</th><th align="left">Male (n/%)</th><th align="left">Female (n/%)</th><th align="left">Total (n/%)</th></tr></thead><tbody><tr><td colspan="10">Co-occurring conditions</td></tr><tr><td> DD</td><td>230 (22.27)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>50 (21.83)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>280 (22.19)</td><td>424 (29.02)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>90 (29.13)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>514 (29.04)</td><td>37 1(31.60)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>64 (25.50)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>435 (30.53)</td></tr><tr><td> ID</td><td>67 (6.49)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>18 (7.86)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>85 (6.74)</td><td>76 (5.20)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>22 (7.12)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>98 (5.54)</td><td>36 (3.07)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>5 (1.99)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>41 (2.88)</td></tr><tr><td> ADHD</td><td>95 (9.20)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>12 (5.24)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>107 (8.48)</td><td>66 (4.52)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>14 (4.53)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>80 (4.52)</td><td>31 (2.64)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>1 (0.40)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>32 (2.25)</td></tr><tr><td> SD</td><td>24 (2.32)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>6 (2.62)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>30 (2.38)</td><td>57 (3.90)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>13 (4.21)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>70 (3.95)</td><td>86 (7.33)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>16 (6.37)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>102 (7.16)</td></tr><tr><td> EP</td><td>7 (0.68)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>3 (1.31)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>10 (0.79)</td><td>1 (0.07)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>9 (2.91)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>10 (0.56)</td><td>1 (0.09)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>0 (0.00)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>1 (0.07)</td></tr><tr><td> TD</td><td>10 (0.97)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>2 (0.87)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>12 (0.95)</td><td>10 (0.68)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>1 (0.32)<xref ref-type="table-fn" rid="tfn10">c</xref></td><td>11 (0.62)</td><td>0 (0.00)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>0 (0.00)<xref ref-type="table-fn" rid="tfn10">b</xref></td><td>0 (0.00)</td></tr><tr><td colspan="10">No. of conditions</td></tr><tr><td> 0</td><td colspan="3">738 (59.95)</td><td colspan="3">1003 (57.58)</td><td colspan="3">822 (58.76)</td></tr><tr><td> 1</td><td colspan="3">459 (37.29)</td><td colspan="3">696 (39.95)</td><td colspan="3">536 (38.31)</td></tr><tr><td> 2</td><td colspan="3">33 (2.68)</td><td colspan="3">43 (2.47)</td><td colspan="3">41 (2.93)</td></tr><tr><td> 3</td><td colspan="3">1 (0.08)</td><td colspan="3">0 (0.00)</td><td colspan="3">0 (0.00)</td></tr></tbody></table> </ephtml> </p> <ulist> <item>8 DD: developmental delay; ID: intellectual disorder; ADHD: attention-deficit hyperactivity disorder; SD: speech disorder; EP: epilepsy; TD: Tic disorder; N/n: number.</item> <item>9 From ICD-10 code. When the disease was recorded in the medical record, it was considered having this kind of comorbid disease. The data outside the brackets are the number of people, and the data inside the brackets are the constituent ratio (%).</item> <item>10 If there are different letters, there is significant difference between them (Pearson chi-square, p < 0.05).</item> </ulist> <hd id="AN0184233753-14">Discussion</hd> <p>Administrative data on ASD prevalence and incidence primarily serves to predict and adjust social, educational, and health resource needs. Our study is the first in China to examine ASD prevalence and incidence among preschool-aged children using clinically referred cohorts. Previous research in other countries has validated the accuracy of ASD diagnosis codes in hospital records ([<reflink idref="bib7" id="ref60">7</reflink>]; [<reflink idref="bib9" id="ref61">9</reflink>]; [<reflink idref="bib35" id="ref62">35</reflink>]) and similar methods have been applied to mental disorders in China using ICD-10 classifications from hospital electronic health records ([<reflink idref="bib61" id="ref63">61</reflink>]). We established retrospective birth cohorts of children born between 2015 and 2017 in Beijing, assessing age-specific cohort prevalence, co-occurring conditions, and annual incidence rates. The results showed that the prevalence exceeded 1% among children aged 6 years and the incidence rates in 2021 increased significantly compared to 2019. The patterns of co-occurring condition differed across three age cohorts. Specifically, there were four key findings.</p> <p>First, this study confirms that the prevalence of ASD in 6-year-old children in Beijing is 1.05% in 2021. Previous studies in Chinese people found that the prevalence of ASD for preschool-aged children were far lower than the prevalence reported in epidemiological studies in developed countries ([<reflink idref="bib21" id="ref64">21</reflink>]; [<reflink idref="bib27" id="ref65">27</reflink>]). Although several regional school-based studies found that the prevalence of ASD exceeds 1% in China ([<reflink idref="bib50" id="ref66">50</reflink>], [<reflink idref="bib51" id="ref67">51</reflink>]; [<reflink idref="bib62" id="ref68">62</reflink>]), they either focused on school-age children ([<reflink idref="bib50" id="ref69">50</reflink>], [<reflink idref="bib51" id="ref70">51</reflink>]) or lacked diagnostic process ([<reflink idref="bib62" id="ref71">62</reflink>]). Incidence rate was a precise indicator of the occurrence of a condition. The incidence of ASD was 0.113% in 2019 and increased 60.3% to 0.181% in 2021 for preschool-aged children in this study. There was relatively less research on the incidence rate for preschoolers. A study found that the incidence rate among children from 2 to 5 years old increased from 0.07% to 0.25% from 2009 to 2017 in Catalonia region, Spain ([<reflink idref="bib39" id="ref72">39</reflink>]). Our research is the first study to confirm the existing high ASD incidence rate and high increase rate of incidence in preschool-aged children in China. However, our prevalence result remains lower than the findings from the most recent estimate in the United States ([<reflink idref="bib47" id="ref73">47</reflink>]). Possible reasons include variances in sociocultural factors and methodological approaches. In China, barriers to receiving a diagnosis may result from a lack of public awareness ([<reflink idref="bib20" id="ref74">20</reflink>]) as well as cultural influences ([<reflink idref="bib56" id="ref75">56</reflink>]). What's more, different research may have variations in ascertainment methods, which could directly lead to differences in reported rates ([<reflink idref="bib38" id="ref76">38</reflink>]). More epidemiological studies should be conducted in the future to provide empirical evidence.</p> <p>Second, consistent with the results reported in previous studies ([<reflink idref="bib30" id="ref77">30</reflink>]; [<reflink idref="bib65" id="ref78">65</reflink>]), the sex prevalence ratios (male/female) remained stable at 4.2–4.5. There was study found that the sex ratio (male/female) had a downward trend over time ([<reflink idref="bib22" id="ref79">22</reflink>]), but opposite findings that a robust characteristic for sex ratio remained ([<reflink idref="bib15" id="ref80">15</reflink>]). In this study, the sex ratio showed stable, even a slight increase and the incidence rate for boys rose faster than girls. This might resulted from the short monitoring period in our study. Similar to the findings of another study ([<reflink idref="bib5" id="ref81">5</reflink>]), there was no significant sex differences in the age at diagnosis in this study. Most children in our cohorts were diagnosed at or before the age of 4 (Table S6 in Supplemental Material). In Beijing, there has been a great emphasis on awareness of early ASD diagnosis in those years.</p> <p>Third, this study was the first among epidemiological studies in China to analyze trends and thus can potentially yielded useful information. We found discontinuity as the prevalence decreased by 13% in 2020 and increased by 13% in 2021 in the 4-year cohort. In 2020, the incidence rate experienced a decline and then a sharp increase in 2021. The changes are unlikely attributable to alterations in registration procedures or diagnostic criteria, as these remained consistent throughout the study period. Instead, concurrent events may explain the fluctuating rates. In 2020, the world was affected by coronavirus disease 2019 (COVID-19) epidemic. The relationship between the incidence of mental diseases and COVID-19 is complex ([<reflink idref="bib13" id="ref82">13</reflink>]; [<reflink idref="bib52" id="ref83">52</reflink>]). The decrease in incidence in 2020 likely resulted from lockdowns and reduced accessibility to diagnostic services. When they reopened, the waitlists were long and centers worked overtime to try to catch up on diagnostic assessments in 2021. However, further research is necessary to fully elucidate these incidence and prevalence fluctuations, and caution should be exercised when drawing causal inferences.</p> <p>Fourth, nearly half of children with ASD had at least one co-occurring condition, aligning with previous studies ([<reflink idref="bib29" id="ref84">29</reflink>]; [<reflink idref="bib48" id="ref85">48</reflink>]). DD, ID, and ADHD were the most common co-occurring conditions. There were three findings that need more attention. First, as children aged, DD and SD rates decreased while ID and ADHD rates increased. This could be due to life stage variations in these conditions ([<reflink idref="bib29" id="ref86">29</reflink>]). This also reflects diagnostic guideline differences. For example, DD is typically reserved for children under 5 ([<reflink idref="bib34" id="ref87">34</reflink>]). Similar to the United States ([<reflink idref="bib10" id="ref88">10</reflink>]), Chinese pediatricians often diagnose DD instead of ID, as DD implies potential future resolution. Second, girls with ASD showed higher odds of ID, consistent with previous research ([<reflink idref="bib2" id="ref89">2</reflink>]). An explanation is the under ascertainment of girls with high IQ in the ASD group ([<reflink idref="bib16" id="ref90">16</reflink>]). These girls often "fly under the radar" due to subtler presentations and greater ability to mask autistic traits. Conversely, girls with ID are more readily recognized as having ASD ([<reflink idref="bib28" id="ref91">28</reflink>]; [<reflink idref="bib45" id="ref92">45</reflink>]). Our findings suggest this diagnostic bias, common in international studies, also exists in China, potentially leading to missed ASD diagnoses among girls with high or average IQ. Third, unlike previous studies, we found a lower rate of multiple co-occurring conditions. This may be due to our focus on children aged 6 and under. Accurately diagnosing multiple co-occurring conditions in very young children is challenging ([<reflink idref="bib31" id="ref93">31</reflink>]). Diagnostic criteria may not fully apply for, and many comorbid symptoms emerge later. For example, the median age at earliest ASD diagnosis was 52 months ([<reflink idref="bib3" id="ref94">3</reflink>]), but that for ADHD diagnosis was 7 years ([<reflink idref="bib53" id="ref95">53</reflink>]). As the cohort continues to be followed up, information on co-occurring conditions will be updated.</p> <hd id="AN0184233753-15">Strengths and limitations</hd> <p>Our study has several strengths, including its large sample and the carefully defined inclusion criteria for both ASD and co-occurring conditions. This is the first to study on the co-occurring conditions for the preschoolers in China. Furthermore, it supplements the estimation of incidence rate to better reveal the changes over times. Identifying cases from registry can help to avoid potential biases and provide a more complete coverage of a large population. The cases are expected to be close to real cases ([<reflink idref="bib35" id="ref96">35</reflink>]; [<reflink idref="bib15" id="ref97">15</reflink>]) and more time-efficient ([<reflink idref="bib17" id="ref98">17</reflink>]). It is the first epidemiological study for ASD with hospital administrative data in East Asia.</p> <p>However, there are also limitations. First, this study may underestimate ASD prevalence and incidence due to its reliance on hospital service registrations. This approach excludes individuals who don't access these services, and those treated in private clinics, or outside Beijing. While the impact is probably minimal given Beijing's concentrated psychiatric resources ([<reflink idref="bib37" id="ref99">37</reflink>]; [<reflink idref="bib66" id="ref100">66</reflink>]), it is a common challenge in clinical sample-based studies ([<reflink idref="bib1" id="ref101">1</reflink>]; [<reflink idref="bib49" id="ref102">49</reflink>]). Population screening or population-based surveillance will be used to supplement validation in the future. Second, we cannot confirm that the exactly same method for ASD diagnosis assessment has been used in all cases. Some "gold standard" tools were still not being widely adopted or official recommended, even by 2022 (see Figure S1B in Supplemental Material for the recommends in policies). In addition to differences in diagnostic method between hospitals, characteristics of visiting populations and data quality may differ. Future research needs to consider these factors in explaining differences in diagnostic patterns. Large-scale community-based cohorts with unified diagnosis instruments can be useful in the future. Third, the co-occurring condition of DD in our study is more inclined to physiological growth delay. Caution should be taken when extrapolating our findings. The overlap and shared symptomatology across diagnostic categories highlight the need for additional research in transdiagnostic constructs. Fourth, this study represented only one city in China and the results cannot be generalized for the entire country. Literature suggests that access to medical resources can affect ASD prevalence ([<reflink idref="bib54" id="ref103">54</reflink>]). In low-resource settings characterized by poor economic development, families may be unable to access public health services due to inadequate numbers of qualified hospitals, resulting in low prevalence of ASD. Despite household registration migration restrictions, Beijing, as China's capital and a major medical center, may attract families seeking better healthcare, potentially influencing ASD prevalence. Future studies should also collect data on household registration migration to better understand this factor's impact. Fifth, this study relied on ICD codes from the BMHBD registry for co-occurring conditions. However, EHR may underestimate the prevalence of co-occurring conditions, particularly in outpatient settings, as certain conditions are not always accurately captured or coded. For example, conditions that do not lead to hospital visits or have broad or nonspecific billing codes may be under-identified ([<reflink idref="bib40" id="ref104">40</reflink>]). Future research could benefit from incorporating a more comprehensive review of records to obtain more accurate data. Sixth, children with ASD who are not registered in Beijing will not be eligible for this study. Understanding the state of all young children in a city would be equally beneficial. Seventh, while we have taken measures to assure accuracy, we acknowledge that ASD diagnosis in children under 3 years old can be hard owing to ongoing developmental changes. Some diagnoses may require reassessment as children develop. Obtaining longer-term data and tracking the diagnoses of children might be beneficial in validating early ASD diagnosis.</p> <hd id="AN0184233753-16">Conclusion</hd> <p>This study provides new evidence of the prevalence, incidence, and co-occurring conditions of preschool-aged children with ASD using administrative registry data. The findings show that the prevalence of ASD in Beijing is over 1%, and that the incidence has increased significantly in the last 3 years, which underscores the need for enhanced government attention and policy support. The government should strengthen resources for ASD screening and diagnosis, especially for girls and those with co-occurring conditions, strive for early detection and intervention, and maximize cost savings in future life cycles. Ongoing information gathering will help monitor any future changes. All policies must begin with the creation of an effective early screening for ASD, which calls for the collaboration of researchers, medical professionals, and decision-makers. In the future, more research should be dedicated to determining the causes of geographical variances.</p> <hd id="AN0184233753-17">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613241290388 for Prevalence, incidence, and characteristics of autism spectrum disorder among children in Beijing, China by Yanan Zhao, Feng Lu, Ruoxi Ding, Dawei Zhu, Rong Zhang, Siwei Sun, Ping He and Xiaoying Zheng in Autism</p> <ref id="AN0184233753-18"> <title> Footnotes </title> <blist> <bibl id="bib1" idref="ref45" type="bt">1</bibl> <bibtext> P.H. and X.Z. conceptualized and designed the study. Y.Z. carried out the initial analyses and drafted the initial manuscript. F.L. and R.Z. conducted literature search and revised the manuscript. R.D., D.Z., and S.S. critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref46" type="bt">2</bibl> <bibtext> The datasets used during the current study are available from the corresponding author on reasonable request.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref47" type="bt">3</bibl> <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> <bibl id="bib4" idref="ref17" type="bt">4</bibl> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: All phases of this study were supported by Major Project of the National Social Science Fund of China (21&ZD187).</bibtext> </blist> <blist> <bibl id="bib5" idref="ref49" type="bt">5</bibl> <bibtext> All procedures involving human subjects/patients were approved by the ethics committee of Peking University Institutional Review Board. 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  Data: Prevalence, Incidence, and Characteristics of Autism Spectrum Disorder among Children in Beijing, China
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  Data: <searchLink fieldCode="AR" term="%22Yanan+Zhao%22">Yanan Zhao</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-1532-3641">0000-0002-1532-3641</externalLink>)<br /><searchLink fieldCode="AR" term="%22Feng+Lu%22">Feng Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Ruoxi+Ding%22">Ruoxi Ding</searchLink><br /><searchLink fieldCode="AR" term="%22Dawei+Zhu%22">Dawei Zhu</searchLink><br /><searchLink fieldCode="AR" term="%22Rong+Zhang%22">Rong Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Siwei+Sun%22">Siwei Sun</searchLink><br /><searchLink fieldCode="AR" term="%22Ping+He%22">Ping He</searchLink><br /><searchLink fieldCode="AR" term="%22Xiaoying+Zheng%22">Xiaoying Zheng</searchLink>
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  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(4):884-895.
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  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
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  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Autism+Diagnostic+Observation+Schedule%22">Autism Diagnostic Observation Schedule</searchLink><br /><searchLink fieldCode="SU" term="%22Childhood+Autism+Rating+Scale%22">Childhood Autism Rating Scale</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613241290388
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The prevalence of autism spectrum disorder in the world has increased over the last decade, but the prevalence, incidence, and characteristics of autism spectrum disorder in China were not well understood. Using administrative data, we aimed to estimate the prevalence and incidence of autism spectrum disorder and describe the co-occurring conditions in preschoolers in Beijing, China. The study focused on 0- to 6-year-old children with registered residence in Beijing, using cohorts derived from the Beijing Municipal Health Big Data and Policy Research Center. We conducted a detailed analysis of autism spectrum disorder prevalence among the cohorts, comparing estimates across 2 to 3 years for the same birth cohort (4 years, 5 years). For the 6-year-old cohort, we obtained 1-year prevalence estimates in 2021. Annual incidence rate was also calculated. The prevalence in 6-year-old children in 2021 was 10.5 per 1000 (95% confidence interval = 9.7--10.9). The male-to-female prevalence ratio was 4.3. Between 40% and 43% of preschool children had at least one co-occurring condition. The incidence for children 6 years old and under was 0.11% in 2019 and increased to 0.18% in 2021. Both the prevalence and incidence rates in Beijing were comparable to those reported in developed countries.
– 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: EJ1466090
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1466090
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613241290388
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 12
        StartPage: 884
    Subjects:
      – SubjectFull: Incidence
        Type: general
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Symptoms (Individual Disorders)
        Type: general
      – SubjectFull: Foreign Countries
        Type: general
      – SubjectFull: Comorbidity
        Type: general
      – SubjectFull: Preschool Children
        Type: general
      – SubjectFull: Disability Identification
        Type: general
      – SubjectFull: Screening Tests
        Type: general
      – SubjectFull: Diagnostic Tests
        Type: general
      – SubjectFull: Clinical Diagnosis
        Type: general
      – SubjectFull: Developmental Delays
        Type: general
      – SubjectFull: Intellectual Disability
        Type: general
      – SubjectFull: Attention Deficit Hyperactivity Disorder
        Type: general
      – SubjectFull: Speech Impairments
        Type: general
      – SubjectFull: Epilepsy
        Type: general
      – SubjectFull: Neurological Impairments
        Type: general
      – SubjectFull: China (Beijing)
        Type: general
      – SubjectFull: Autism Diagnostic Observation Schedule
        Type: general
      – SubjectFull: Childhood Autism Rating Scale
        Type: general
    Titles:
      – TitleFull: Prevalence, Incidence, and Characteristics of Autism Spectrum Disorder among Children in Beijing, China
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yanan Zhao
      – PersonEntity:
          Name:
            NameFull: Feng Lu
      – PersonEntity:
          Name:
            NameFull: Ruoxi Ding
      – PersonEntity:
          Name:
            NameFull: Dawei Zhu
      – PersonEntity:
          Name:
            NameFull: Rong Zhang
      – PersonEntity:
          Name:
            NameFull: Siwei Sun
      – PersonEntity:
          Name:
            NameFull: Ping He
      – PersonEntity:
          Name:
            NameFull: Xiaoying Zheng
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
ResultId 1