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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Psychiatry</journal-id>
<journal-title>Frontiers in Psychiatry</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Psychiatry</abbrev-journal-title>
<issn pub-type="epub">1664-0640</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fpsyt.2025.1619085</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Psychiatry</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>Burden of autism spectrum disorder in Japan from 1992 to 2021 and its prediction until 2050: results from the GBD study</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Liu</surname>
<given-names>Jiabo</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/3039088/overview"/>
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<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Xie</surname>
<given-names>Xiaoyan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<uri xlink:href="https://loop.frontiersin.org/people/2025289/overview"/>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Li</surname>
<given-names>Yunxi</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
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</contrib>
<contrib contrib-type="author">
<name>
<surname>Pu</surname>
<given-names>Yiqi</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<role content-type="https://credit.niso.org/contributor-roles/writing-review-editing/"/>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Li</surname>
<given-names>Yan</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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</contrib>
<contrib contrib-type="author" corresp="yes">
<name>
<surname>Yang</surname>
<given-names>Lingling</given-names>
</name>
<xref ref-type="aff" rid="aff2">
<sup>2</sup>
</xref>
<xref ref-type="author-notes" rid="fn001">
<sup>*</sup>
</xref>
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<aff id="aff1">
<sup>1</sup>
<institution>Second Clinical Medical College, Guangzhou University of Traditional Chinese Medicine</institution>, <addr-line>Guangzhou, Guangdong</addr-line>,&#xa0;<country>China</country>
</aff>
<aff id="aff2">
<sup>2</sup>
<institution>Department of Psychology and Sleep Medicine, Guangdong Provincial Hospital of Traditional Chinese Medicine</institution>, <addr-line>Guangzhou, Guangdong</addr-line>,&#xa0;<country>China</country>
</aff>
<author-notes>
<fn fn-type="edited-by">
<p>Edited by: Guiomar Gon&#xe7;alves Oliveira, University of Coimbra, Portugal</p>
</fn>
<fn fn-type="edited-by">
<p>Reviewed by: Ay&#x15f;e K&#x131;l&#x131;n&#xe7;aslan, Independent Researcher, Istanbul, T&#xfc;rkiye</p>
<p>Catarina Prior, Centro Hospitalar Universit&#xe1;rio do Porto, Portugal</p>
</fn>
<fn fn-type="corresp" id="fn001">
<p>*Correspondence: Jiabo Liu, <email xlink:href="mailto:liujiabo99182426@163.com">liujiabo99182426@163.com</email>; Yan Li, <email xlink:href="mailto:janeliyan2005@gzucm.edu.cn">janeliyan2005@gzucm.edu.cn</email>; Lingling Yang, <email xlink:href="mailto:linglingyang@gzucm.edu.cn">linglingyang@gzucm.edu.cn</email>
</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>08</day>
<month>07</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="collection">
<year>2025</year>
</pub-date>
<volume>16</volume>
<elocation-id>1619085</elocation-id>
<history>
<date date-type="received">
<day>30</day>
<month>04</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>19</day>
<month>06</month>
<year>2025</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2025 Liu, Xie, Li, Pu, Li and Yang</copyright-statement>
<copyright-year>2025</copyright-year>
<copyright-holder>Liu, Xie, Li, Pu, Li and Yang</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<p>Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by persistent deficits in social communication and interaction, alongside restricted, repetitive patterns of behavior. This study aimed to analyze the temporal trends and project the future burden of ASD in Japan. Using Global Burden of Disease (GBD) 2021 data, we analyzed prevalence, disability-adjusted life years (DALYs), and age-standardized rates (ASR) (1992-2021) through age-period-cohort modeling, joinpoint regression, and autoregressive integrated moving average (ARIMA) forecasting. Age-standardized prevalence rate (ASPR) increased significantly (Average Annual Percentage Change [AAPC]=0.2744; 95%CI:0.2606-0.2882), with males disproportionately affected (male-to-female ratio 4:1). By 2050, crude prevalence is projected to decline 14.2%, while ASPR will rise 18.0%. Japan&#x2019;s ASD burden exceeds global averages, necessitating targeted interventions across the lifespan. These findings highlight the increasing burden of ASD in Japan and underscore the urgent need for enhanced healthcare planning and resource allocation.</p>
</abstract>
<kwd-group>
<kwd>ASD</kwd>
<kwd>GBD</kwd>
<kwd>prevalence</kwd>
<kwd>DALYs</kwd>
<kwd>APC/AAPC</kwd>
</kwd-group>
<contract-sponsor id="cn001">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content>
</contract-sponsor>
<counts>
<fig-count count="9"/>
<table-count count="0"/>
<equation-count count="0"/>
<ref-count count="32"/>
<page-count count="13"/>
<word-count count="4915"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-in-acceptance</meta-name>
<meta-value>Autism</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<p>Autism Spectrum Disorder (ASD), classified under the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) and the International Classification of Diseases, Eleventh Revision (ICD-11), constitutes a heterogeneous group of neurodevelopmental conditions characterized by persistent deficits in social communication and social interaction across multiple contexts, alongside restricted, repetitive patterns of behavior, interests, or activities (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>). The term &#x201c;spectrum&#x201d; reflects the substantial heterogeneity observed in symptom severity, functional impairment, and developmental trajectories. While core features typically emerge in early childhood, diagnosis can occur later, particularly in individuals with higher cognitive abilities or less pronounced symptoms (<xref ref-type="bibr" rid="B3">3</xref>).</p>
<p>The precise etiology of ASD remains incompletely elucidated; however, substantial evidence points to complex interactions between polygenic susceptibility and environmental influences (<xref ref-type="bibr" rid="B4">4</xref>). Globally, epidemiological studies report a significant increase in ASD prevalence estimates over recent decades. This observed rise is widely attributed to multiple factors: enhanced diagnostic sensitivity through refined criteria (DSM-5-TR, ICD-11) that better capture the spectrum&#x2019;s breadth (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B5">5</xref>), heightened clinical and public awareness facilitating earlier recognition (<xref ref-type="bibr" rid="B6">6</xref>), and improved accessibility to diagnostic services and standardized screening tools (<xref ref-type="bibr" rid="B7">7</xref>). Current global prevalence estimates are approximately 1-2%, with a consistently reported male-to-female ratio near 4:1 (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B8">8</xref>). Variations in prevalence across geographical regions and time periods may reflect differences in diagnostic practices, healthcare infrastructure, and cultural factors (<xref ref-type="bibr" rid="B5">5</xref>, <xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B9">9</xref>). Established risk factors include advanced parental age, familial aggregation suggestive of heritable components, prenatal complications, and preterm birth (<xref ref-type="bibr" rid="B10">10</xref>).</p>
<p>Within Japan, epidemiological trends mirror this global increase in ASD identification (<xref ref-type="bibr" rid="B11">11</xref>). Recent studies indicate prevalence rates in Japanese children and adults converging with estimates from Western nations (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B12">12</xref>). Specific socio-cultural and familial environmental contexts within Japan may influence the recognition and expression of ASD traits, warranting further investigation (<xref ref-type="bibr" rid="B13">13</xref>). The Japanese healthcare system has implemented various interventions, including early screening initiatives, Applied Behavior Analysis (ABA), Early Intensive Behavioral Intervention (EIBI), speech-language therapy, social skills training, and vocational support programs (<xref ref-type="bibr" rid="B14">14</xref>). Concurrently, policy measures provide financial assistance, professional counseling, and educational resources for affected individuals and families (<xref ref-type="bibr" rid="B15">15</xref>). Despite these advancements, challenges persist, including societal stigma impacting families (<xref ref-type="bibr" rid="B16">16</xref>), and the need for enhanced employment opportunities and social inclusion supports for adults with ASD (<xref ref-type="bibr" rid="B17">17</xref>). Japan contributes significantly to international ASD research, particularly in the domains of genetics and neuroimaging (<xref ref-type="bibr" rid="B18">18</xref>), with emerging investigations leveraging advanced methodologies such as deep learning and genomic analyses (<xref ref-type="bibr" rid="B19">19</xref>, <xref ref-type="bibr" rid="B20">20</xref>).</p>
<p>Quantifying the population health impact of ASD is essential for public health planning and resource allocation. The Global Burden of Disease (GBD) study provides a rigorous, comparative framework for assessing the burden of diseases and injuries, including ASD, using standardized metrics such as prevalence, incidence, years lived with disability (YLDs), and disability-adjusted life years (DALYs) across time and geography (<xref ref-type="bibr" rid="B21">21</xref>). While prior GBD iterations (e.g., GBD 2019) have documented the global burden of ASD (<xref ref-type="bibr" rid="B21">21</xref>), and recent analyses (e.g., GBD 2021 Autism Spectrum Collaborators) have specifically explored its global epidemiology (<xref ref-type="bibr" rid="B22">22</xref>), comprehensive analyses focusing on the longitudinal trends and future projections of ASD burden within Japan using the GBD framework remain limited.</p>
<p>Therefore, this study aims to: (1) Analyze the burden of ASD in Japan &#x2013; encompassing prevalence, incidence, YLDs, and DALYs &#x2013; from 1992 to 2021 utilizing data from the Global Burden of Disease Study; (2) Identify key factors associated with observed trends; and (3) Project the potential burden of ASD in Japan up to the year 2050. This analysis seeks to inform national health policies and service planning for individuals with ASD in Japan.</p>
</sec>
<sec id="s2" sec-type="materials|methods">
<label>2</label>
<title>Materials and methods</title>
<sec id="s2_1">
<label>2.1</label>
<title>Data source and study scope</title>
<p>This longitudinal analysis utilized data from the Global Burden of Disease Study 2021 (GBD 2021), coordinated by the Institute for Health Metrics and Evaluation (IHME). GBD 2021 provides comprehensive, standardized estimates of disease burden for 371 diseases and injuries across 204 countries and territories, including Japan and its 47 prefectures, from 1990 to 2021s (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Data were accessed via the IHME Global Health Data Exchange (GHDx) (<ext-link ext-link-type="uri" xlink:href="https://vizhub.healthdata.org/gbd-results/">https://vizhub.healthdata.org/gbd-results/</ext-link>). The study assessed the burden of ASD, defined according to GBD 2021 case definitions and mapped to ICD-10 codes (primarily F84) and DSM-5 criteria (<xref ref-type="bibr" rid="B1">1</xref>, <xref ref-type="bibr" rid="B2">2</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). We extracted data for Japan (overall and by prefecture), the High-Income Asia Pacific region, High Socio-demographic Index (SDI) locations globally, and the Global aggregate for the period 1992&#x2013;2021. Key outcome metrics extracted were: Prevalence: The total number of individuals living with ASD at a specific point in time (mid-year 2021 for point estimates; annual estimates for trends). Disability-Adjusted Life Years (DALYs): The sum of Years Lived with Disability (YLDs) and Years of Life Lost (YLLs) due to ASD. Given the low premature mortality associated with ASD, DALYs primarily reflect YLDs for this condition (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Age-Standardized Rates (ASRs): Rates adjusted to the GBD World Standard Population structure to enable comparison across populations with differing age distributions.</p>
</sec>
<sec id="s2_2">
<label>2.2</label>
<title>Key metrics and definitions</title>
<sec id="s2_2_1">
<label>2.2.1</label>
<title>Prevalence</title>
<p>Prevalence quantifies the proportion of a population living with ASD at a specific point in time. It is a core indicator of disease burden, reflecting the population-level impact of ASD and facilitating comparisons across groups, time, and geography (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>).</p>
</sec>
<sec id="s2_2_2">
<label>2.2.2</label>
<title>Disability-adjusted life years</title>
<p>DALYs represent the total health loss associated with ASD, combining: Years Lived with Disability (YLDs): Calculated as prevalence multiplied by a disability weight specific to ASD, reflecting the severity of health loss associated with the condition (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>). Years of Life Lost (YLLs): Calculated as the number of deaths due to ASD multiplied by the standard life expectancy at the age of death. Due to the very low mortality directly attributable to ASD, YLLs contribute minimally to ASD DALYs (<xref ref-type="bibr" rid="B18">18</xref>, <xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>).</p>
</sec>
<sec id="s2_2_3">
<label>2.2.3</label>
<title>Age-standardized rates</title>
<p>To account for confounding by population age structure and enable valid comparisons, we utilized: Age-Standardized Prevalence Rate (ASPR): Prevalence per 100,000 population standardized to the GBD World Standard Population. Age-Standardized DALY Rate (ASDAR): DALYs per 100,000 population standardized to the GBD World Standard Population.</p>
</sec>
<sec id="s2_2_4">
<label>2.2.4</label>
<title>Trend analysis metrics</title>
<p>Annual Percentage Change (APC): The estimated percentage change per year within a specific, homogeneous time segment identified by joinpoint regression. Average Annual Percentage Change (AAPC): A summary measure of the trend over the entire study period (1992&#x2013;2021), calculated as a geometrically weighted average of the APCs from the joinpoint model (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B21">21</xref>).</p>
</sec>
<sec id="s2_2_5">
<label>2.2.5</label>
<title>Socio-demographic index</title>
<p>SDI is a composite measure (scale: 0-1, where 0 represents the lowest theoretical level of development) of lag-distributed income per capita, average educational attainment in the population aged 15 and older, and the total fertility rate under age 25 (<xref ref-type="bibr" rid="B22">22</xref>). Locations are categorized into quintiles (e.g., High SDI represents the top 20% of locations globally based on SDI in a given year). Japan is classified within the High SDI quintile.</p>
</sec>
</sec>
<sec id="s2_3">
<label>2.3</label>
<title>Analytical methods</title>
<sec id="s2_3_1">
<label>2.3.1</label>
<title>Descriptive analysis</title>
<p>We summarized crude and age-standardized estimates (Prevalence, DALYs, ASPR, ASDAR) by year, sex, age group, geographic level (Japan prefectures, Japan national, High-Income Asia Pacific, High SDI, Global), and SDI. Absolute numbers and rates with 95% uncertainty intervals (UIs) were reported.</p>
</sec>
<sec id="s2_3_2">
<label>2.3.2</label>
<title>Temporal trend analysis (1992-2021)</title>
<p>Joinpoint Regression Analysis: We employed joinpoint regression (using the Joinpoint Regression Program, National Cancer Institute) to identify significant inflection points (joinpoints) in the temporal trends of ASPR and ASDAR (<xref ref-type="bibr" rid="B19">19</xref>&#x2013;<xref ref-type="bibr" rid="B21">21</xref>). The optimal number of joinpoints (0 to N max) was determined using permutation tests (&#x3b1;=0.05). For each resulting linear segment, the Annual Percentage Change (APC) and its 95% confidence interval (CI) were calculated. The Average Annual Percentage Change (AAPC) over the entire period (1992-2021) was computed as a summary measure. Statistical significance was set at p&lt;0.05. Pearson Correlation Analysis: The association between AAPC values for ASPR/ASDAR and baseline (1992) ASR levels or SDI values across Japanese prefectures was assessed using Pearson correlation coefficients (r) and linear regression.</p>
</sec>
<sec id="s2_3_3">
<label>2.3.3</label>
<title>Decomposition analysis</title>
<p>To quantify the relative contributions of key drivers to changes in the absolute number of prevalent ASD cases and DALYs in Japan between 1992 and 2021, we performed a factor decomposition analysis (<xref ref-type="bibr" rid="B23">23</xref>). Changes were decomposed into contributions from. Population Growth: Change due solely to the increase in total population size. Population Aging: Change due to shifts in the age distribution of the population. Epidemiological Change: Change due to variations in age-specific prevalence or DALY rates (reflecting changes in risk, diagnosis, or disability weighting).</p>
</sec>
<sec id="s2_3_4">
<label>2.3.4</label>
<title>Burden projection (2022-2050)</title>
<p>Future trends in ASPR and ASDAR for Japan were projected to 2050 using the Autoregressive Integrated Moving Average (ARIMA) model. Model selection (p, d, q parameters) was based on:</p>
<p>Examination of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots. Minimization of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). Assessment of model residuals for white noise using the Ljung-Box test. The best-fitting ARIMA model for each outcome (ASPR, ASDAR) was identified using the auto.arima() function within the forecast package in R (<xref ref-type="bibr" rid="B24">24</xref>, <xref ref-type="bibr" rid="B25">25</xref>). Model fit was validated by comparing predicted values for the final years of the observed data (e.g., 2015-2021) against actual GBD estimates. Projections are presented with 95% prediction intervals. The Mean Absolute Percentage Error (MAPE) was calculated for the validation period to assess forecast accuracy.</p>
</sec>
<sec id="s2_3_5">
<label>2.3.5</label>
<title>Software</title>
<p>All statistical analyses, excluding joinpoint regression, were performed using R software (Version 4.3.1). Key packages included gbd (for data access/processing), forecast, ggplot2, and demography (for decomposition).</p>
</sec>
</sec>
</sec>
<sec id="s3" sec-type="results">
<label>3</label>
<title>Result</title>
<sec id="s3_1">
<label>3.1</label>
<title>Comparison of the different regional of ASD by prevalence and DALYs</title>
<p>The bar chart compares the burden of ASD in Japan&#x2019;s 47 administrative regions with those in the global, high-SDI and high-income Asia-Pacific regions, showing the results at all ages and after age standardization. We found that prevalence and DALYs were significantly higher than global levels and high SDI regions in almost all administrative regions in Japan, while the high-income Asia-Pacific region was similar to the national burden of disease in Japan. Aomori, Fukuoka, Nagano, and Tokyo have a more prominent disease burden (<xref ref-type="fig" rid="f1">
<bold>Figure&#xa0;1</bold>
</xref>). Further deconstructing the data according to gender, we found that the prevalence rate and
DALYs of males were higher than those of females by shown of heatmap (<xref ref-type="supplementary-material" rid="SF1">
<bold>Supplementary Figure S1</bold>
</xref>). Based on the data changes from 1992 to 2021, the prevalence and DALYs trends with time are demonstrated by using area charts and graphs. It can be found that prevalence and DALYs fluctuate over time, but the overall trend is increasing, and the disease group is becoming younger, and the disease burden is increasing in the younger age group (<xref ref-type="fig" rid="f2">
<bold>Figure&#xa0;2</bold>
</xref>). We divided Japanese ASD patients into 47 administrative regions and used a map to show the trend of ASD prevalence and DALYs proportion per capita over time in different administrative regions (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3A</bold>
</xref>). A pyramid chart showed the comparison of ASD prevalence and DALYs in different age groups in different administrative regions, which revealed that the peak age group for ASD was 45&#x2013;49 years old, and a secondary peak age group was 70&#x2013;74 years old (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3B, C</bold>
</xref>). We ranked the disease burden of the 47 administrative regions according to the time-varying trend and performed a paired comparison, which showed that the top four administrative regions, Nagano, Aomori, Fukuoka, and Tokyo, maintained their positions, while the rankings of the remaining 43 administrative regions changed over time (<xref ref-type="fig" rid="f3">
<bold>Figure&#xa0;3D</bold>
</xref>). Finally, we used a dual-axis analysis to show the trend relationship between prevalence and ASR, DALYs and ASR over time, which suggested that both prevalence and DALYs were associated with the time trend of ASR (<xref ref-type="fig" rid="f3">
<bold>Figures&#xa0;3E, F</bold>
</xref>).</p>
<fig id="f1" position="float">
<label>Figure&#xa0;1</label>
<caption>
<p>Comparison of age&#x2212;standardized and all&#x2212;ages rates for Prevalence and DALYs across regions and sexes.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g001.tif">
<alt-text content-type="machine-generated">Bar charts comparing age-standardized and all-ages rates for prevalence and disability-adjusted life years (DALYs) across various regions and sexes. Green bars represent prevalence rates, while orange bars indicate DALYs per 100,000 population. The charts are divided into three rows for &#x201c;Both,&#x201d; &#x201c;Male,&#x201d; and &#x201c;Female&#x201d; categories. Locations are listed horizontally along the x-axis, including global and regional names.</alt-text>
</graphic>
</fig>
<fig id="f2" position="float">
<label>Figure&#xa0;2</label>
<caption>
<p>
<bold>(A, B)</bold> ASR of prevalence and DALYs across regions from 1990 to 2021. <bold>(C, D)</bold> Prevalence and DALYs rate by age group from 1990 to 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g002.tif">
<alt-text content-type="machine-generated">Heatmap graphs and tables showing age-specific prevalence and DALY rates across regions from 1990 to 2021. Figures A and B illustrate prevalence and DALY rates, respectively, with color gradients indicating different levels. Figures C and D are tables presenting prevalence and DALY rates by age group, using color coding for visual emphasis. The data ranges are shown for global, high SDI, high-income Asia Pacific, and Japan regions.</alt-text>
</graphic>
</fig>
<fig id="f3" position="float">
<label>Figure&#xa0;3</label>
<caption>
<p>
<bold>(A)</bold> Trends in numbers of key health indicators across 47 Japanese prefectures from 1990 to 2021. <bold>(B, C)</bold> Prevalence and DALYs numbers by age group across 47 Japanese prefectures in 2021. <bold>(D)</bold> Ranking of 47 Japanese prefectures by age&#x2212;standardized prevalence and DALYs rates from 1990 to 2021. <bold>(E, F)</bold> Prevalence/DALYs and ASPR/ASDAR from 1990 to 2021.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g003.tif">
<alt-text content-type="machine-generated">Maps showing the Average Annual Percent Change (AAPC) in age-standardized rates by prefecture in Japan. Panel A illustrates prevalence rates, while panel B shows DALY rates. Prefectures are color-coded based on AAPC, with a legend indicating colors corresponding to different AAPC ranges. Each map includes numbers representing specific prefectures, detailed in an accompanying legend.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Regional, gender, and age differences effect on trends in the burden of ASD</title>
<p>Joinpoint analysis revealed the change trend of disease burden of ASD under different variables (region, gender, age) from 1992 to 2021, and APC and AAPC were calculated for comparison.</p>
<p>The results show that the highest ASPR APC period was 2010-2015, APC=0.89(95%CI:0.85, 0.94), and the lowest ASPR APC period was 2015-2019, APC=-0.47(95%CI: -0.54, -0.4). The highest ASDAR APC period was 2010-2015, APC=0.89(95%CI: 0.85-0.92), and the lowest ASDAR APC period was 2015-2019, APC=-0.45(95%CI: -0.5, -0.4). The national AAPC values of ASPR and ASDAR in Japan from 1992 to 2021 were 0.2744 (95%CI: 0.2606, 0.2882) and 0.2782 (95%CI: 0.2673, 0.2892), respectively. The results were 0.2342 (95%CI: 0.2215, 0.2469) and 0.2362 (95%CI: 0.2248, 0.2475) for males and 0.3177 (95%CI: 0.2978, 0.3375) and 0.3117 (95%CI: 0.2342, 0.2469) and 0.2362 (95%CI: 0.2475) for females. 0.2955, 0.328. The APC and AAPC values of different time stages, regions and genders can be referred to <xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Tables S1, S2</bold>
</xref>. ASPR and ASDAR have increased year by year globally in the past 30 years, and the trend in Japan is close to the same as that in the Asia-Pacific high-income region (AAPC 0.27 vs 0.26), which is much higher than that in the global region and the region with high SDI (AAPC 0.27&amp;0.26 vs 0.06&amp;0.06). This result illustrates the high level of disease burden of ASD in Japan and its region (<xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4A, B</bold>
</xref>). After gender differentiation, it was found that the trend of disease burden indicators of different genders also increased year by year, but the ASPR and ASDAR of males were higher than those of females, indicating that males have always occupied a dominant position in the increasing ASD population in Japan (<xref ref-type="fig" rid="f4">
<bold>Figures&#xa0;4C, D</bold>
</xref>). After age differentiation, we found that on the basis of the increase of the overall disease burden index over time, ASPR and ASDAR also increased with the increase of age (<xref ref-type="fig" rid="f5">
<bold>Figure&#xa0;5</bold>
</xref>).</p>
<fig id="f4" position="float">
<label>Figure&#xa0;4</label>
<caption>
<p>
<bold>(A, B)</bold> Trends in ASPR and ASDAR across different regions with APC and AAPC. <bold>(C, D)</bold> Trends in ASPR and ASDAR rate by sex in Japan with APC and AAPC.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g004.tif">
<alt-text content-type="machine-generated">Chart A shows projected prevalence and age-standardized rates of cases from 1990 to 2060, separated by gender. Chart B displays projected DALY and age-standardized rates over the same period, also segmented by gender. Both charts include observed and predicted data, with a black vertical line indicating the transition from observed to predicted data. Bars are categorized into male, female, and both, with a visible decline in projected data beyond 2020.</alt-text>
</graphic>
</fig>
<fig id="f5" position="float">
<label>Figure&#xa0;5</label>
<caption>
<p>Trends in prevalence and DALY rate across age groups in Japan from 1990&#x2013;2020 with APC and AAPC.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g005.tif">
<alt-text content-type="machine-generated">Bar charts titled &#x201c;Decomposition of Prevalence&#x201d; (A) and &#x201c;Decomposition of DALY&#x201d; (B) show data by location and gender: Global, High SDI, High-income Asia Pacific, and Japan. Variables include aging, population, and epidemiological change. Brown bars dominate, indicating primary contribution from one variable across the groups. Each section is subdivided for both genders and combined.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>Age-period-cohort analysis reveals the disease burden difference in each administrative district throughout Japan from different age, period, and cohort</title>
<p>APC analysis decomposed the time dimension into age effect, period effect and cohort effect in order to unpack the independent influence of age, period and cohort. We calculated and plotted the age-period, time-cohort, age-cohort correlation curves with prevalence and DALYs in Japanese ASD patients by sex to determine the impact of different factors on disease burden. On this basis, APC analysis is conducted separately for the whole country and various administrative regions of Japan, and calibration is conducted on different age levels of prevalence and DALYs to provide a targeted analysis and point out the difference of ASD burden among different regions in Japan. From the comparison between stages we found that population prevalence with DALYs increased over time, both overall and by gender. Based on the difference contrasts of Age-cohort effect, we found that over time to 2021, the overall prevalence in Japan exceeded 1500 per 100000 population and DALYs also approached 350 per 100000 population. Among these, the male prevalence is nearly 2500 per 100000 population and DALYs also exceeds 400 per 100000 population, which far exceeds the overall ASD disease burden level in Japan, while the female prevalence is 1000 per 100000 population and DALYs does not exceed 200 per 100000 population (<xref ref-type="fig" rid="f6">
<bold>Figures&#xa0;6A, B</bold>
</xref>). However, based on the differential comparison of age-period effect, we found that after structured age, the prevalence and DALYs showed a negative correlation with age, and gradually decreased in both men and women, but the prevalence and DALYs were still much greater than that in women, for example male 2169.72(95%CI:1824.88, 2557.74) vs female 925.52(95%CI:776.29, 1099.33) per 100000 population/male 400.93(95%CI:279.32, 561.28) vs female 169.66(95%CI: 117.37, 234.72) per 100000 population of 50&#x2013;54 years old people prevalence/DALYs in 2017&#x2013;2021 and (<xref ref-type="fig" rid="f6">
<bold>Figures&#xa0;6C, D</bold>
</xref>). The differential comparison of the effects of period-cohort is consistent with the results of the former two (<xref ref-type="fig" rid="f6">
<bold>Figures&#xa0;6E, F</bold>
</xref>). In conclusion, we found that in the APC analysis, the overall disease burden of ASD in Japan increased with the period, showing a trend of young age, and the disease burden of men was much greater than that of women.</p>
<fig id="f6" position="float">
<label>Figure&#xa0;6</label>
<caption>
<p>
<bold>(A, B)</bold> Age&#x2212;cohort effects on prevalence and DALYs metrics. <bold>(C, D)</bold> Age&#x2212;period effects on prevalence and DALYs metrics. <bold>(E, F)</bold> Period-cohort effects on prevalence and DALYs metrics.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g006.tif">
<alt-text content-type="machine-generated">Six line graphs illustrate age-cohort, age-period, and period-cohort effects on prevalence and DALYs. Panels A and B display age-cohort effects, with prevalence and DALYs increasing over time for both genders. Panels C and D show age-period effects, with a decline in both metrics across age groups. Panels E and F reveal period-cohort effects, with upward trends over time. Each panel separates data into both genders, male, and female, with color-coded age or period groups. The x-axes represent birth cohorts or age, and the y-axes show rate per 100,000 population.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Comparison of disease burden in 47 Japanese regions based on AAPC and ASR</title>
<p>We first heat map the Japanese map against the AAPC values of the different administrative regions of Japan, applied the color difference to represent the level of AAPC, and ranked the 47 administrative regions according to the level of AAPC. The results suggested that the top three administrative regions with the highest AAPC were Aichi, Akita and Aomori, indicating that the disease burden of ASD was most significant and should be paid attention to (<xref ref-type="fig" rid="f7">
<bold>Figure&#xa0;7</bold>
</xref>). For a detailed description of the disease burden in these 47 regions, we drew calibration
curves by adjusting for the effects of time, period, and cohort. Based on the disease
characteristics of ASD, specific age groups (0&#x2013;4 years) and specific periods (1988-1992) were calibrated and displayed with change curves (<xref ref-type="supplementary-material" rid="SF2">
<bold>Supplementary Figures S2&#x2013;S11</bold>
</xref>).</p>
<fig id="f7" position="float">
<label>Figure&#xa0;7</label>
<caption>
<p>
<bold>(A)</bold> AAPC in age&#x2212;standardized prevalence rates by prefecture. <bold>(B)</bold> AAPC in age&#x2212;standardized DALY rates by prefecture.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g007.tif">
<alt-text content-type="machine-generated">Map charts display Japan's prefectures with color gradients indicating the AAPC in age-standardized rates. Chart A shows prevalence rates, while Chart B displays DALY rates. A legend provides AAPC values, with numbers identifying prefectures.</alt-text>
</graphic>
</fig>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>Decomposition analysis</title>
<p>We conducted a decomposition analysis to quantify contributions of three drivers to ASD burden changes between 1992 and 2021: Population growth (demographic expansion), Population aging (changing age structure), Epidemiological changes (disorder-specific risk factor prevalence) (<xref ref-type="fig" rid="f8">
<bold>Figure&#xa0;8</bold>
</xref>).</p>
<fig id="f8" position="float">
<label>Figure&#xa0;8</label>
<caption>
<p>
<bold>(A)</bold> Decomposition of prevalence into aging, population and epidemiological change by different regions. <bold>(B)</bold> Decomposition of DALYs into aging, population and epidemiological change by different regions.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g008.tif">
<alt-text content-type="machine-generated">Bar graphs illustrating the decomposition of prevalence (A) and disability-adjusted life years (DALY) (B) by location and gender. Each graph is divided into sections for both sexes, females, and males, with data for global, High SDI, High-income Asia Pacific, and Japan. Variables include aging, population, and epidemiological change, marked in different colors.</alt-text>
</graphic>
</fig>
<p>Globally, population growth was the dominant driver of increased ASD burden, accounting for 100.92% of the net prevalence change (+20,077,209 cases) and 102.26% of DALYs increase (+3,758,723 DALYs). This pattern held in high-SDI regions where population growth explained 104.32% of prevalence growth (+2,174,457 cases) and 110.90% of DALYs increase (+403,588 DALYs). Counteracting effects were observed globally: population aging reduced prevalence by 5.85% (-1,163,326 cases) and DALYs by 7.66% (-281,711 DALYs), while epidemiological changes increased prevalence by 4.92% (+979,662 cases) and DALYs by 5.40% (+198,639 DALYs). Similar trends occurred in high-SDI regions where aging reduced prevalence by 17.72% and epidemiological changes increased it by 13.40%. Japan demonstrated a divergent pattern. Population growth contributed moderately to prevalence (+26,831 cases, 55.02%) and DALYs (+4,986 DALYs, 112.50%). Crucially, population aging exerted substantial protective effects, reducing expected prevalence by 310.13% (-151,238 cases) and DALYs by 741.51% (-32,862 DALYs). Conversely, epidemiological changes drove the largest burden increases, accounting for 355.11% of net prevalence change (+173,173 cases) and 729.01% of DALYs change (+32,308 DALYs).</p>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>Prediction analysis</title>
<p>Moreover, the prediction results of the disease burden of ASD in Japan from 2022 to 2050 indicate that the ARIMA model predicted that disease burden prevalence and DALYs decrease annually, while their ASR increases continuously. By 2050, The prevalence prediction value of Japanese ASD in the ARIMA model will be calculated from 1797373.45551684 (1778254.63676399, 1816492.2742697) reduced to 1541547.43833842 (452394.414619911, 2630700.46205692), However, the predicted value of ASPR will be changed from 1458.68139124526 (1443.34004784751, 1474.02273464301) increased to 1720.90869986909 (482.783942768444, 2959.03345696973) (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9A</bold>
</xref>). Also, for the DALYs, In the prediction model from 336217.5175970309 in 1990 (328564.755586865, 343870.279607753) reduced to 281936.436409579 (63479.18170497, 500393.691114189), At the same time ASDAR predicted from 274.91919684919635 (268.74443821172, 281.084931627551) upgraded to 324.228788001257 (67.6272352112524, 580.830340791262) (<xref ref-type="fig" rid="f9">
<bold>Figure&#xa0;9B</bold>
</xref>).</p>
<fig id="f9" position="float">
<label>Figure&#xa0;9</label>
<caption>
<p>
<bold>(A)</bold> Projected prevalence and ASPR. <bold>(B)</bold> Projected DALYs and ASDAR.</p>
</caption>
<graphic mimetype="image" mime-subtype="tiff" xlink:href="fpsyt-16-1619085-g009.tif">
<alt-text content-type="machine-generated">Chart A shows projected prevalence and age-standardized rates of a condition from 1990 to 2050, divided by gender. Chart B displays projected Disability-Adjusted Life Years (DALY) and age-standardized rates for the same period. Both charts indicate observed and predicted trends with a black dashed line marking the year 2020. Cases and rates are segmented for both sexes, males, and females, with color-coded bars and lines.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>This study comprehensively analyzes the burden of ASD in Japan from 1990 to 2021 and projects trends until 2050 using data from the Global Burden of Disease (GBD) study. Our findings confirm a significant and sustained increase in the prevalence and overall disease burden of ASD in Japan over the past three decades, a trend consistent with reports globally and within other high Socio-demographic Index (SDI) regions (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B22">22</xref>).</p>
<sec id="s4_1">
<label>4.1</label>
<title>Rising prevalence and diagnostic trends</title>
<p>Multiple epidemiological studies within Japan align with our observed increase in ASD prevalence. Research in Yokohama reported a cumulative incidence of 16.2 per 10,000 and a prevalence of 21.1 per 10,000 for children born in 1988 (<xref ref-type="bibr" rid="B3">3</xref>). Similarly, studies in Nagoya and Fukushima-ken documented prevalence rates significantly higher than historical estimates (0.13% and 4.96 per 10,000, respectively), with notable urban-rural gradients (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B26">26</xref>). This upward trajectory is largely attributable to enhanced detection methods, broader diagnostic practices aligned with evolving criteria (DSM-5-TR, ICD-11), and increased societal and professional awareness. These factors have improved identification, particularly of individuals with milder phenotypes or without co-occurring intellectual disability, who were previously underdiagnosed (<xref ref-type="bibr" rid="B27">27</xref>). Furthermore, reduced stigma surrounding neurodevelopmental conditions encourages more families to seek assessment (<xref ref-type="bibr" rid="B28">28</xref>).</p>
</sec>
<sec id="s4_2">
<label>4.2</label>
<title>Disease burden and societal implications</title>
<p>The escalating prevalence directly translates to a growing societal burden, demanding substantial expansion of specialized educational, healthcare, and support services (<xref ref-type="bibr" rid="B29">29</xref>). Our analysis of Age-Standardized Rates (ASR) and Average Annual Percentage Change (AAPC) underscores the severity of the current situation in Japan relative to global, high-SDI, high-income, and other Japanese administrative region averages. The high prevalence of&#xa0;co-existing neurodevelopmental and psychiatric conditions (e.g., reported co-occurrence rates around 3.22% for other NDDs alongside ASD (<xref ref-type="bibr" rid="B28">28</xref>)) further amplifies the complexity and resource intensity of required support, necessitating comprehensive, multi-disciplinary care strategies (<xref ref-type="bibr" rid="B30">30</xref>). Critically, our projections indicate this burden will continue to rise steadily through 2050, demanding urgent long-term national planning for service provision and workforce training.</p>
</sec>
<sec id="s4_3">
<label>4.3</label>
<title>Gender disparities and diagnostic patterns</title>
<p>A pronounced male predominance in ASD prevalence was consistently observed in our data, mirroring global patterns (approximately 3:1 male-to-female ratio) (<xref ref-type="bibr" rid="B31">31</xref>). While biological factors likely contribute significantly to this disparity, evolving diagnostic practices and awareness may also influence detection rates differentially by gender. Females, particularly those without intellectual impairment, may present differently or employ more effective masking strategies, potentially leading to under-identification (<xref ref-type="bibr" rid="B7">7</xref>, <xref ref-type="bibr" rid="B10">10</xref>). Our data also reflects a declining age of diagnosis over the study period, indicating improved early detection efforts (<xref ref-type="bibr" rid="B28">28</xref>).</p>
</sec>
<sec id="s4_4">
<label>4.4</label>
<title>Regional variations and potential influences</title>
<p>Consistent with earlier reports (<xref ref-type="bibr" rid="B27">27</xref>), our analysis identified regional heterogeneity within Japan, with higher ASD burden indices often observed in more densely populated or complex urban environments. This pattern may reflect disparities in access to diagnostic services, specialist availability, or heightened parental awareness in urban centers (<xref ref-type="bibr" rid="B32">32</xref>), rather than solely indicating environmental etiology. While the search for etiological factors continues, encompassing genetic predispositions and potential environmental influences (e.g., prenatal/perinatal factors implicated in some studies (<xref ref-type="bibr" rid="B13">13</xref>, <xref ref-type="bibr" rid="B32">32</xref>)), it is crucial to note that large-scale epidemiological evidence refutes a causal link between vaccines and ASD incidence. The withdrawal of the MMR vaccine in Japan did not alter the upward trajectory of ASD diagnoses (<xref ref-type="bibr" rid="B26">26</xref>).</p>
</sec>
<sec id="s4_5">
<label>4.5</label>
<title>Projections and global context</title>
<p>Our projection models, extending the robust GBD methodology (<xref ref-type="bibr" rid="B21">21</xref>), indicate a persistent rise in ASD prevalence and associated disability burden in Japan through 2050. This trend aligns with projections for other high-income nations, reflecting the ongoing impact of diagnostic broadening, sustained awareness efforts, and the aging of existing prevalent cases (<xref ref-type="bibr" rid="B21">21</xref>, <xref ref-type="bibr" rid="B25">25</xref>). While the GBD 2021 Autism Collaborators highlight the global increase in ASD burden (<xref ref-type="bibr" rid="B22">22</xref>), our Japan-specific analysis reveals a burden profile exceeding the averages for high-SDI and high-income regions. This emphasizes the acute and growing challenge ASD presents within the Japanese healthcare and social support systems.</p>
</sec>
</sec>
<sec id="s5" sec-type="conclusions">
<label>5</label>
<title>Conclusion</title>
<p>The rising burden of ASD in Japan, confirmed by our analysis of GBD data and projected to increase through 2050, results from a complex interplay of improved ascertainment, evolving diagnostic criteria, heightened awareness, and true increases in prevalence. The significant male predominance and regional variations require further investigation into biological mechanisms and equitable access to services. The projected increase necessitates immediate and sustained investment in early identification programs, evidence-based interventions, lifespan support services, and targeted resource allocation, particularly in regions showing higher burden. Future research must prioritize understanding the drivers of the increase, refining early detection across genders, evaluating the long-term effectiveness of support services, and developing strategies to mitigate the projected burden on individuals, families, and society.</p>
</sec>
</body>
<back>
<sec id="s6" sec-type="data-availability">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref ref-type="supplementary-material" rid="SM1">
<bold>Supplementary Material</bold>
</xref>. Further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec id="s7" sec-type="ethics-statement">
<title>Ethics statement</title>
<p>Ethical approval was not required for the study involving humans in accordance with the local legislation and institutional requirements. Written informed consent to participate in this study was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and the institutional requirements.</p>
</sec>
<sec id="s8" sec-type="author-contributions">
<title>Author contributions</title>
<p>JL: Writing &#x2013; review &amp; editing, Data curation, Methodology, Writing &#x2013; original draft. XX: Conceptualization, Writing &#x2013; review &amp; editing, Data curation. YXL: Formal analysis, Methodology, Writing &#x2013; review &amp; editing. YP: Writing &#x2013; review &amp; editing, Conceptualization, Data curation. YL: Supervision, Funding acquisition, Resources, Writing &#x2013; review &amp; editing, Validation, Visualization. LY: Writing &#x2013; review &amp; editing, Funding acquisition, Validation, Supervision, Visualization, Project administration.</p>
</sec>
<sec id="s9" sec-type="funding-information">
<title>Funding</title>
<p>The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the Department of Psychology and Sleep Medicine at the Guangdong Provincial Hospital of Traditional Chinese Medicine. Funding was provided by the National Natural Science Foundation of China (Grant No. 82305167), the Guangdong Natural Science Foundation (Grant No. 2023A1515220036), and the Municipality-University Joint Funding Scheme organized by the Guangzhou Municipal Science and Technology Bureau (Grant Nos. 2023A03J0740 and 2023A03J0228).</p>
</sec>
<sec id="s10" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="s11" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declare that no Generative AI was used in the creation of this manuscript.</p>
</sec>
<sec id="s12" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec id="s13" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1619085/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fpsyt.2025.1619085/full#supplementary-material</ext-link>.</p>
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<supplementary-material xlink:href="Image2.jpeg" id="SF2" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image3.jpeg" id="SF3" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image4.jpeg" id="SF4" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image5.jpeg" id="SF5" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image6.jpeg" id="SF6" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image7.jpeg" id="SF7" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image8.jpeg" id="SF8" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image9.jpeg" id="SF9" mimetype="image/jpeg"/>
<supplementary-material xlink:href="Image10.jpeg" id="SF10" mimetype="image/jpeg"/>
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<supplementary-material xlink:href="DataSheet3.zip" id="SM3" mimetype="application/zip"/>
</sec>
<fn-group>
<title>Abbreviations</title>
<fn fn-type="abbr" id="abbrev1">
<p>ASD, Autism Spectrum Disorders; ASR, Age standardization rate; ASPR, Age standardization prevalence rate; ASDAR, Age standardization prevalence rate; APC, Annual Percentage Change; ARIMA, Autoregressive Composite Moving Average; AAPC, Average Annual Percentage Change; SDI, Socio-demographic Index; DALYs, disability-adjusted life-years.</p>
</fn>
</fn-group>
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