Graphical abstract

Introduction

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by difficulties in social relationships and repetitive or restricted behaviors [1]. The spectrum encompasses various disorders, including autistic disorder, Rett disorder, Asperger syndrome, and pervasive developmental disorder [2]. Its prevalence is increasing worldwide, presenting significant challenges for affected individuals and their families [3]. The World Health Organization estimates that autism affects 0.76% of children globally, with higher incidence rates in developed countries [4]. Autism can be diagnosed as early as 18 to 24 months, as its symptoms often become distinguishable from typical development and other developmental delays during this period. Changes in diagnostic criteria have contributed to an increase in reported cases, along with heightened public awareness and understanding of ASD [5]. Environmental assessments and genetic testing play critical roles in identifying risk factors and enhancing predictions of ASD [6,7]. Together, these elements are driving the increased prevalence of ASD in developed countries [7].

Over the past 50 years, ASD has transformed from a narrowly defined, rare childhood condition into a widely recognized, researched, and advocated lifelong disorder. It is now understood to be common and highly heterogeneous. While the core features—social communication deficits and repetitive or unusual sensory-motor behaviors—remain largely unchanged [8], autism is now viewed as a spectrum that ranges from mild to severe. Many individuals with ASD require lifelong support, although this is not the case for everyone. ASD remains one of the top neurodevelopmental disorders among children. The first case of ASD was reported in 1943, and since then, the number of cases has increased worldwide. According to epidemiological data, the prevalence of ASD in the United States of America (USA) is reported to be 18.5 per 1,000 children under 8 years of age. The majority of countries have reported an increase in the number of ASD cases over the past decades [9]. Although South Asia accounts for over 20% of the global population, the prevalence of ASD is underreported there [10].

The Centers for Disease Control and Prevention reports that 1 in 54 children in the USA was diagnosed with ASD from 2014 to 2016 [11]. In Italy, the prevalence among 7- to 9-year-olds is 1.15% [12]. During the 1960s and 1970s, ASD rates ranged from 0.5 to 0.7 cases per 10,000 people. More recently, the Autism and Developmental Disabilities Monitoring Network found prevalence rates of 67 to 230 cases per 10,000, reflecting a 243% increase in the USA [13]. The revised Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision (DSM-5-TR) (2022) criteria require a higher threshold of clinical symptoms compared to the DSM, 4th Edition, Text Revision (DSM-IV-TR) criteria, enhancing the accuracy of ASD diagnoses among children [14,15]. Variations in diagnostic criteria and parental awareness may influence these prevalence rates. Early detection, standardized diagnostic criteria, counseling, awareness of ASD, and a safe environment can improve the reporting of ASD [16].

The updated DSM-5, improves diagnostic accuracy, particularly for cases that were previously undiagnosed [17]. In the USA, the estimated cost of autism, at 268.3 billion US dollars (USD), exceeds that of stroke and hypertension. The annual costs vary from 1.4 to 2.4 million USD. If current trends persist, these costs are projected to increase to between 11.5 trillion and 15 trillion USD by 2029 [18].

Thus, early diagnosis and intervention are crucial to reduce the financial burdens of this condition. An updated estimate assists health professionals in formulating public health strategies. Accurately estimating the prevalence of autism is crucial for assessing the economic burden and allocating adequate resources and services for individuals with autism and their families. Additionally, determining prevalence helps identify vulnerable groups and associated geographical and environmental risk factors [19]. While previous meta-analyses have focused on ASD prevalence in the general population and reported cumulative results [13], our study specifically targets children and includes subgroup analyses based on sex and the income levels of countries. This review aims to provide a comprehensive pooled estimate of global ASD prevalence among children.

Materials and Methods

Eligibility Criteria

This systematic review and meta-analysis aimed to determine the pooled global prevalence of ASD in children. The review protocol was registered with PROSPERO (CRD42023445469), adhered to Cochrane collaboration guidelines [20], and was reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [21]. The review included studies on children aged 3 to 18 years that utilized standardized measurement tools and provided cut-off scores for ASD. It considered original studies published in the English language from January 1, 2008, to June 31, 2024, that reported on the prevalence of ASD in children. Publications in non-English languages were excluded due to their limited impact and small sample sizes. Additionally, the results from these studies could not be validated by official authorities. Qualitative studies, case reports, and studies lacking prevalence data were also excluded. The primary outcome analyzed in this systematic review and meta-analysis was the prevalence of ASD.

Information Sources

A systematic review and meta-analysis were conducted by searching PubMed, ClinicalKey, Scopus, Embase, CINAHL, and Web of Science for studies published from January 1, 2008, to June 31, 2024.

Search Strategy

The search included terms such as “autism spectrum disorder,” “prevalence,” “autism,” “autism spectrum disorders,” “child development disorder,” “deprived child,” “pervasive developmental disorders,” “children,” “infant,” “toddler,” “school age,” “adolescence,” “cross-sectional study,” “observational study,” and “cohort study.” Two authors (K.H. and A.I.) independently conducted searches on PubMed, ClinicalKey, Scopus, Embase, CINAHL, and Web of Science.

A comprehensive search strategy was developed using keywords and Medical Subject Heading (MeSH) terms related to population, intervention, comparator, and outcomes in Medline, and this strategy was adapted for use in other databases. The identified articles were imported into Rayyan software, where duplicates were eliminated using the “check for duplicates” tool [22]. The citations and references of relevant articles that met the inclusion criteria were manually searched to identify additional studies. All remaining original full-text articles were then screened according to the inclusion criteria.

Selection Process

A data extraction sheet was developed to gather information from the included studies. Two reviewers (K.H. and A.I.) independently extracted the data, which was subsequently reviewed by a third reviewer (A.S.). The extraction form captured several details, including the authors, publication year, country, study design, sample size, participant characteristics, tools used, and key findings as reported by the authors.

Data Collection Process

Two independent authors (K.H. and A.I.) conducted the data extraction procedure, while a third reviewer verified the accuracy of the retrieved data. The figures and tables that summarize and report the extracted data are available in Table S1.

Study Risk of Bias Assessment

The methodological quality and risk of bias in the articles were evaluated using a modified Newcastle-Ottawa scale (NOS). This scale ranges from 1 to 5, where 1 is the lowest and 5 is the highest possible score. A higher score indicates better study quality, which typically suggests a lower risk of bias. The scale is particularly suited for assessing the quality of non-experimental studies, including observational, cohort, and retrospective studies. The quality assessment encompasses 5 criteria: sample representativeness, sample size, response rate, measurement tools and cut-off scores, and statistical details. Each criterion contributes 1 point to the total score, with studies achieving a score above 3 considered to be at low risk.

Effect Measures

The pooled data from individual studies were manually entered and coded in Microsoft Excel (Microsoft Corp.) before being transferred to Stata software ver. 17 (Stata Corp LP). We assessed heterogeneity among the studies using the I² test, categorizing values as high (>75%), medium (50%–75%), and low (<50%) [23]. Due to the observed heterogeneity, a random effects model was employed. The effect size (ES) was calculated, along with 95% confidence intervals (CIs), using the Metaprop and Metan commands. We conducted subgroup analysis based on geographical differences in the prevalence of ASD.

Synthesis Methods

Various subgroup analyses were initially planned for the retrieved data. However, the data demonstrated insufficient information and subtle differences between children and adults. As a result, we were unable to conduct a meta-analysis among the adult population. Consequently, this systematic review and meta-analysis included descriptive studies, and the pooled data were presented in dichotomous form.

Results

Study Selection

The search yielded 26,849 studies from online databases and an additional 4 from printed sources. After eliminating 4,514 duplicates, 21,987 articles were excluded for failing to meet the review criteria. We assessed 348 full-text articles, of which 88 and 194 were further excluded due to various issues, including difference in primary outcome, other illness, missing sample sizes and measurement tools, unspecified cut-off scores, inclusion of participants with other illnesses, and differences in primary outcomes. Ultimately, 66 articles were included in the meta-analysis. A flow diagram of the study selection process is depicted in Figure 1.

Study Characteristics

This systematic review and meta-analysis encompassed a total of 21.31 million children screened for ASD. A global compilation of 66 studies reported on the prevalence of ASD among children. Of these, 25 studies were conducted in Europe [12,24–47], 22 in Asia [48–68], and 13 in America [69–81]. Additionally, 3 studies each were reported from Africa [68,82,83] and Australia [84–86]. Among these studies, 25 articles specifically reported the prevalence of ASD among boys (Table 1) [12,24,27–29,37,44,46,49–53,56–58,60,61,63,67,70,84,86–88].

Further, 47 studies were reported from high-income countries [12,24–47,56,62,65,66,69–80,84–87,89,90], 16 studies from middle-income countries [48–55,57–59,61,63,64,67,88], and 3 studies from low-income countries (Table 1) [68,82,83].

The sample sizes in the included trials ranged from 374 [59] to 6,900,000 [35], with participant ages varying from 1 to 18 years. The primary outcome of these studies was the prevalence of autism. Various instruments were employed to measure this prevalence, including the modified checklist for autism in toddlers (CHAT), social communication questionnaire (SCQ), Indian scale for assessment of autism (ISAA), DSM-IV, International Classification of Disease (ICD-9), autism diagnostic observation schedule (ADOS), DSM-5, strengths and difficulties questionnaire (SDQ), Qatar School survey (QSS), and autism diagnostic interview-revised (Table 2) [12,24–53,55–79,81–90].

Results of Individual Studies

Prevalence of ASD in different geographical regions

The use of more sophisticated diagnostic criteria may increase the prevalence rates. Many countries do not maintain statistical data on ASD; therefore, cases may only be recognized when a child visits the hospital for other health issues. Symptoms can range from mild to severe, and many children improve and lead normal lives. The meta-analysis found that 0.77% of children worldwide were diagnosed with ASD (ES, 0.77; 95% CI, 0.52–0.86; p=0.001; I²=97.5%). Among the continents, Australia had the highest prevalence of ASD in children (ES, 2.18; 95% CI, 0.08–4.28; p=0.001; I²=99.3%), followed by Africa (ES, 1.51; 95% CI, 0.28–2.74; p=0.001; I²=98.2%) [68,82,83], America (ES, 1.10; 95% CI, 0.79–1.41; p=0.00; I²=98.9%) [69–81], Europe (ES, 0.71; 95% CI, 0.54–0.88; p=0.001; I²=97.5%) [12,24–47], and Asia (ES, 0.28; 95% CI, 0.19–0.38; p=0.001; I²=99.7%) (Figure 2) [48–68].

Prevalence of ASD in boys

A meta-analysis was conducted of 25 studies that reported the prevalence of ASD in boys. Most primary studies indicated that the prevalence in boys was 2 to 3 times higher than that in girls. This meta-analysis found that 1.14% of children worldwide were diagnosed with ASD (ES, 1.14; 95% CI, 0.61–1.67; p=0.00; I²=99.9%) (Figure 3) [12,24,27–29,37,44,46,49–53,56–68,70,84,86–88].

The prevalence of ASD among children in low middle- and high-income countries

According to the World Bank, countries are classified as high-income, middle-income, or low-income. Based on 47 studies with relevant data reported that 0.86% of ASD cases were from high-income countries (ES, 0.86; 95% CI, 0.66–1.06; p=0.00; I²=99.9%) [12,24–47,56,62,65,66,69–80,84–87,89,90]. Among these, 2 studies reported notably higher prevalence rates: 3.6% in Sweden [44] and 4.2% in Australia [84] (Figure 4).

Similarly, 16 studies reported a prevalence of ASD in middle-income countries (ES, 0.30; 95% CI, 0.17–0.43; p=0.00; I²=99.4%) [48–55,57–59,61,63,64,67,88]. Three studies reported prevalence of ASD from low-income countries (ES, 1.5; 95% CI, 0.28–2.74; p=0.00; I²=99.4%) (Figure 5) [68,82,83].

Developed countries have focused more on health infrastructure and services. In contrast, low- and middle-income countries face challenges such as inadequate knowledge of diagnostic tools, poor healthcare infrastructure, and a lack of trained medical professionals. Additionally, these countries experience a scarcity of research studies. Cultural values and traditional practices also contribute to disparities in access to healthcare facilities [91].

Heterogeneity of included studies

The Galbraith plot is a graphical representation that illustrates study-specific ESs and their precisions, as well as the overall ES, and detects potential outliers. The plot features 2 horizontal lines: the green line, which represents the reference line indicating no effect, and the red line, which is the regression line. The slope of the red line reflects the overall ES and the standardized log risk ratio for each study. Circles below the green line indicate an increased risk. However, no studies were reported below the reference line (green line) in this analysis. The present meta-analysis revealed that most circles were found within the shaded region, except for 3 studies, suggesting that these studies appeared within the 95% CI. The Galbraith plot concluded that 3 out of the 66 studies fell outside the shaded region, indicating considerable heterogeneity after employing a random effect model among the ESs in the present meta-analysis. Heterogeneity and publication bias are reported in Figures 6 and 7.

Discussion

This updated systematic review and meta-analysis estimated the global prevalence of ASD over the past decade. The adoption of standardized diagnostic criteria and assessment tools has led to an increase in reported ASD cases worldwide. Additionally, improved screening and community surveillance have improved detection at the peripheral level [92,93]. Although previous studies have explored the diagnosis and occurrence of ASD [94], there is still a lack of evidence concerning its identification and management. A meta-analysis indicates that perinatal and postnatal factors could increase the risk of ASD, although the specific risk factors are inconsistent. Genetic factors play a significant role, as the presence of siblings with autism increases the incidence [94]. Environmental factors, including exposure to various drugs and chemicals, may also contribute to the risk of ASD [95–97].

The findings of this systematic review and meta-analysis provide significant insights into the global prevalence of ASD among children, with a substantial sample size exceeding 21 million. The meta-analysis incorporated data from 66 studies spanning various continents, revealing geographical disparities in ASD prevalence rates. Notably, Australia showed the highest prevalence rate, with an ES of 2.18, marking it as a critical area for public health focus [98]. In contrast, the lowest prevalence was recorded in Asia, with an ES of 0.34, which may reflect differences in diagnostic practices or reporting mechanisms [99].

The data also highlighted that the prevalence of ASD is notably higher in boys, with an ES of 1.14, suggesting a male-to-female ratio ranging from 2:1 to 3:1 across various studies [36,100]. This finding aligns with previous research indicating a disparity between the sexes in ASD diagnoses, warranting further investigation into the biological and environmental factors that may contribute to this difference.

The analysis further categorized studies by income level, revealing that high-income countries reported an ASD prevalence of 0.86% (ES, 0.86), with Sweden and Australia showing particularly elevated rates (3.6% and 4.2%, respectively). This suggests that higher-income countries may have better resources and screening practices, leading to more accurate diagnoses. In contrast, middle-income countries reported a lower prevalence of 0.30%, indicating potential underdiagnoses or differences in healthcare access and awareness [13,101].

The reported prevalence of anxiety in low-income countries is alarmingly high [102], highlighting concerns about the availability of mental health resources in these areas. This variation in prevalence across different income levels underscores the importance of designing interventions that are tailored to specific socioeconomic contexts.

The significant heterogeneity observed in the included studies, with I² values frequently exceeding 97%, underscores the difficulties in comparing findings across diverse populations and methodologies. This variability could be attributed to differences in diagnostic criteria, cultural perceptions of ASD, and the structures of health systems. Many countries do not have comprehensive statistical data on ASD and often only identify cases during healthcare visits for unrelated issues.

Culture, race, and ethnicity play a significant role in the neuropsychological development of children and affect early diagnosis and treatment. Healthcare professionals must consider the cultural backgrounds and customs of families, which in turn helps them understand perceptions of healthcare services. Due to social stigma, parents may hesitate to disclose their child’s symptoms. Therefore, understanding cultural perceptions is a crucial component in diagnosing ASD [103–105].

The prevalence of ASD has increasingly garnered global attention. However, assessing its occurrence poses challenges due to the absence of national data in many regions. Some nations have begun to officially recognize ASD as a disability, providing valuable data that aids in calculating and categorizing its prevalence across different ages, genders, and geographical locations. In contrast, many countries still fail to report local and national ASD data. Previous research has indicated that phenotypic characteristics, environmental factors, and gender differences contribute to a higher incidence of ASD in boys than in girls [106,107]. Nevertheless, further experimental studies are necessary to accurately determine the risk factors associated with different genders and geographical regions.

According to the Australian National Disability Insurance Agency, the annual cost of ASD is projected to increase by 100 billion USD by 2032. The prevalence of ASD in Australia is similar to that in Japan, where early diagnosis and timely interventions contribute to higher reported numbers. Additionally, several studies indicate that changes in diagnostic criteria and increased awareness of ASDs have resulted in more people receiving diagnoses. However, some clinicians have been confusing the diagnosis of ASD with attention deficit hyperactivity disorder and dyslexia. Moreover, government policies, particularly the National Disability Insurance Scheme, play a crucial role in encouraging more reporting of ASD cases in Australia. The current study revealed that the prevalence of ASD in Australia is higher among boys, but the reason for this disparity remains unclear [108–112].

Overall, the findings of this meta-analysis not only contribute to the existing literature on ASD prevalence but also underscore the need for increased global awareness, improved diagnostic practices, and targeted public health initiatives, especially in underrepresented regions and low-resource settings. As our global understanding of ASD continues to evolve, further research is crucial to navigate the complexities of diagnosis, support, and treatment for affected children and their families.

Conclusion

The recent increase in ASD prevalence is concerning, particularly in developing countries where accurate estimates are essential for devising effective public health strategies. Early diagnosis and intervention can significantly enhance outcomes for children with ASD. However, it remains uncertain whether this rise indicates true trends or merely reflects changes in diagnostic criteria. Future research should utilize consistent methodologies, as many existing studies are not accessible due to language barriers. Moreover, the absence of prevalence data in some countries highlights the urgent need for further research to improve ASD management globally.

HIGHLIGHTS

• This systematic review and meta-analysis investigated autism spectrum disorder (ASD), a chronic neurodevelopmental disorder that is increasing dramatically worldwide, posing significant challenges for healthcare workers, patients, and their families.

• Many countries, particularly low and middle-income nations, lack sufficient data on ASD. The global prevalence of ASD has been reported at 0.77%, with rates being higher among males at 1.14 per 100 children.

• High-income countries had an ASD prevalence of 0.86%. Notably, Sweden and Australia exhibited significantly higher rates, at 3.6% and 4.2%, respectively.

• Low and middle-income countries exhibited a lower prevalence of 0.30%, suggesting potential underdiagnoses or differences in healthcare access and awareness as possible explanations.

• The identification of mental disorders in children is often neglected and overlooked, potentially increasing the risk of mental health burdens and long-term psychiatric disorders in the future. Therefore, it is crucial to develop policies that address the psychological needs of both children and adults.

Supplementary Material

Article information

Ethics Approval

This is a review study registered with PROSPERO (CRD42023445469), and available online.

Conflicts of Interest

The authors have no conflicts of interest to declare.

Funding

None.

Availability of Data

The datasets are not publicly available but are available from the corresponding author upon reasonable request.

Authors’ Contributions

Conceptualization: KH, AI; Data curation: AS, LT, VRV; Formal analysis: PM, VK; Investigation: KH, AS, IC; Methodology: KH, KK, PM; Project administration: AS, LT; Resources: AI, KK; Software: PM, VK; Supervision: AS; Validation: PM, LT; Visualization: AS, KK; Writing–original draft: KH, AI; Writing–review & editing: all authors. All authors read and approved the final manuscript.

Figure 1.

Flow diagram of study selection. ^a)The PubMed, Clinical Key, Scopus, Embase, CINAHL, and Web of Science. ^b)Excluded after title and abstract screening.

Figure 2.

Global prevalence of autism spectrum disorder by continent. CI, confidence interval.

Figure 3.

Prevalence of autism spectrum disorder among boys. CI, confidence interval.

Figure 4.

Prevalence of autism spectrum disorder in high-income countries. CI, confidence interval.

Figure 5.

Prevalence of autism in middle- and low-income countries. CI, confidence interval.

Figure 6.

Heterogeneity of the included studies. CI, confidence interval.

Figure 7.

Publication bias. CI, confidence interval.

References

• 1. Hodges H, Fealko C, Soares N. Autism spectrum disorder: definition, epidemiology, causes, and clinical evaluation. Transl Pediatr 2020;9(Suppl 1). S55−65. ArticlePubMedPMC
• 2. Taylor MJ, Rosenqvist MA, Larsson H, et al. Etiology of autism spectrum disorders and autistic traits over time. JAMA Psychiatry 2020;77:936−43. ArticlePubMedPMC
• 3. Lord C, Elsabbagh M, Baird G, et al. Autism spectrum disorder. Lancet 2018;392:508−20. ArticlePubMedPMC
• 4. Zeidan J, Fombonne E, Scorah J, et al. Global prevalence of autism: a systematic review update. Autism Res 2022;15:778−90. ArticlePubMedPMCPDF
• 5. Rosen NE, Lord C, Volkmar FR. The diagnosis of autism: from Kanner to DSM-III to DSM-5 and beyond. J Autism Dev Disord 2021;51:4253−70. ArticlePubMedPMCPDF
• 6. Modabbernia A, Velthorst E, Reichenberg A. Environmental risk factors for autism: an evidence-based review of systematic reviews and meta-analyses. Mol Autism 2017;8:13. ArticlePubMedPMCPDF
• 7. Chaste P, Leboyer M. Autism risk factors: genes, environment, and gene-environment interactions. Dialogues Clin Neurosci 2012;14:281−92. ArticlePubMedPMC
• 8. Kanner L. Autistic disturbances of affective contact. Acta Paedopsychiatr 1968;35:100−36. PubMed
• 9. Yang L, Chen F, He X, et al. Global burden and inequality of autism spectrum disorders: based on data from the 2019 Global Burden of Disease study. Prev Med Rep 2023;36:102511. ArticlePubMedPMC
• 10. Hossain MD, Ahmed HU, Jalal Uddin MM, et al. Autism spectrum disorders (ASD) in South Asia: a systematic review. BMC Psychiatry 2017;17:281. ArticlePubMedPMCPDF
• 11. Sauer AK, Stanton JE, Hans S, et al. Autism spectrum disorders: etiology and pathology. In: Grabrucker AM, editor. Autism spectrum disorders. Exon Publications; 2021. p. 1–16.
• 12. Narzisi A, Posada M, Barbieri F, et al. Prevalence of autism spectrum disorder in a large Italian catchment area: a school-based population study within the ASDEU project. Epidemiol Psychiatr Sci 2018;29:e5. ArticlePubMedPMC
• 13. Talantseva OI, Romanova RS, Shurdova EM, et al. The global prevalence of autism spectrum disorder: a three-level meta-analysis. Front Psychiatry 2023;14:1071181. ArticlePubMedPMC
• 14. Maenner MJ, Rice CE, Arneson CL, et al. Potential impact of DSM-5 criteria on autism spectrum disorder prevalence estimates. JAMA Psychiatry 2014;71:292−300. ArticlePubMedPMC
• 15. Sensi C, Ricca V, Gravestock S, et al. DSM-5 criteria for feeding and eating disorders. In: Bertelli MO, Deb S, Munir K, et al., editors. Textbook of psychiatry for intellectual disability and autism spectrum disorder. Springer; 2022. p. 661.
• 16. Neggers YH. Increasing prevalence, changes in diagnostic criteria, and nutritional risk factors for autism spectrum disorders. ISRN Nutr 2014;2014:514026. ArticlePubMedPMCPDF
• 17. Tonge BJ, Brereton AV, Bertelli MO. Somatic symptom and related disorders. In: Bertelli MO, Deb S, Munir K, et al., editors. Textbook of psychiatry for intellectual disability and autism spectrum disorder. Springer; 2022. p. 609–23.
• 18. Leigh JP, Du J. Brief report: forecasting the economic burden of autism in 2015 and 2025 in the United States. J Autism Dev Disord 2015;45:4135−9. ArticlePubMed
• 19. Imm P, White T, Durkin MS. Assessment of racial and ethnic bias in autism spectrum disorder prevalence estimates from a US surveillance system. Autism 2019;23:1927−35. ArticlePubMedPMCPDF
• 20. Higgins JP, Thomas J, Chandler J, et al. Cochrane handbook for systematic reviews of interventions version 6.2 [Internet]. Cochrane training; 2021 [cited 2022 Jan 24]. Available from: https://training.cochrane.org/handbook/archive/v6.1.
• 21. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. ArticlePubMedPMC
• 22. Ouzzani M, Hammady H, Fedorowicz Z, et al. Rayyan: a web and mobile app for systematic reviews. Syst Rev 2016;5:210. ArticlePubMedPMCPDF
• 23. Thorlund K, Imberger G, Johnston BC, et al. Evolution of heterogeneity (I²) estimates and their 95% confidence intervals in large meta-analyses. PLoS One 2012;7:e39471. ArticlePubMedPMC
• 24. Kocovska E, Biskupsto R, Carina Gillberg I, et al. The rising prevalence of autism: a prospective longitudinal study in the Faroe Islands. J Autism Dev Disord 2012;42:1959−66. ArticlePubMedPDF
• 25. Nygren G, Cederlund M, Sandberg E, et al. The prevalence of autism spectrum disorders in toddlers: a population study of 2-year-old Swedish children. J Autism Dev Disord 2012;42:1491−7. ArticlePubMedPDF
• 26. Morales-Hidalgo P, Roige-Castellvi J, Hernandez-Martinez C, et al. Prevalence and characteristics of autism spectrum disorder among Spanish school-age children. J Autism Dev Disord 2018;48:3176−90. ArticlePubMedPDF
• 27. Fernell E, Gillberg C. Autism spectrum disorder diagnoses in Stockholm preschoolers. Res Dev Disabil 2010;31:680−5. ArticlePubMed
• 28. Skonieczna-Zydecka K, Gorzkowska I, Pierzak-Sominka J, et al. The prevalence of autism spectrum disorders in West Pomeranian and Pomeranian Regions of Poland. J Appl Res Intellect Disabil 2017;30:283−9. ArticlePubMedPDF
• 29. Idring S, Lundberg M, Sturm H, et al. Changes in prevalence of autism spectrum disorders in 2001-2011: findings from the Stockholm youth cohort. J Autism Dev Disord 2015;45:1766−73. ArticlePubMedPDF
• 30. Saemundsen E, Magnusson P, Georgsdottir I, et al. Prevalence of autism spectrum disorders in an Icelandic birth cohort. BMJ Open 2013;3:e002748. ArticlePubMedPMC
• 31. Posserud M, Lundervold AJ, Lie SA, et al. The prevalence of autism spectrum disorders: impact of diagnostic instrument and non-response bias. Soc Psychiatry Psychiatr Epidemiol 2010;45:319−27. ArticlePubMedPDF
• 32. Isaksen J, Diseth TH, Schjolberg S, et al. Observed prevalence of autism spectrum disorders in two Norwegian counties. Eur J Paediatr Neurol 2012;16:592−8. ArticlePubMed
• 33. Mattila ML, Kielinen M, Linna SL, et al. Autism spectrum disorders according to DSM-IV-TR and comparison with DSM-5 draft criteria: an epidemiological study. J Am Acad Child Adolesc Psychiatry 2011;50:583−92. ArticlePubMed
• 34. van Bakel MM, Delobel-Ayoub M, Cans C, et al. Low but increasing prevalence of autism spectrum disorders in a French area from register-based data. J Autism Dev Disord 2015;45:3255−61. ArticlePubMedPDF
• 35. Bachmann CJ, Gerste B, Hoffmann F. Diagnoses of autism spectrum disorders in Germany: time trends in administrative prevalence and diagnostic stability. Autism 2018;22:283−90. ArticlePubMedPDF
• 36. Baron-Cohen S, Scott FJ, Allison C, et al. Prevalence of autism-spectrum conditions: UK school-based population study. Br J Psychiatry 2009;194:500−9. ArticlePubMed
• 37. Hansen SN, Schendel DE, Parner ET. Explaining the increase in the prevalence of autism spectrum disorders: the proportion attributable to changes in reporting practices. JAMA Pediatr 2015;169:56−62. ArticlePubMed
• 38. Parner ET, Schendel DE, Thorsen P. Autism prevalence trends over time in Denmark: changes in prevalence and age at diagnosis. Arch Pediatr Adolesc Med 2008;162:1150−6. ArticlePubMed
• 39. Thomaidis L, Mavroeidi N, Richardson C, et al. Autism spectrum disorders in Greece: nationwide prevalence in 10-11 year-old children and regional disparities. J Clin Med 2020;9:2163. ArticlePubMedPMC
• 40. van Balkom ID, Bresnahan M, Vogtlander MF, et al. Prevalence of treated autism spectrum disorders in Aruba. J Neurodev Disord 2009;1:197−204. ArticlePubMedPMCPDF
• 41. Suren P, Bakken IJ, Aase H, et al. Autism spectrum disorder, ADHD, epilepsy, and cerebral palsy in Norwegian children. Pediatrics 2012;130:e152−8. ArticlePubMedPMCPDF
• 42. Fuentes J, Basurko A, Isasa I, et al. The ASDEU autism prevalence study in northern Spain. Eur Child Adolesc Psychiatry 2021;30:579−89. ArticlePubMedPDF
• 43. Boilson AM, Staines A, Ramirez A, et al. Operationalisation of the European Protocol for Autism Prevalence (EPAP) for autism spectrum disorder prevalence measurement in Ireland. J Autism Dev Disord 2016;46:3054−67. ArticlePubMedPDF
• 44. Linnsand P, Gillberg C, Nilses A, et al. A high prevalence of autism spectrum disorder in preschool children in an immigrant, multiethnic population in Sweden: challenges for health care. J Autism Dev Disord 2021;51:538−49. ArticlePubMedPMCPDF
• 45. Taylor B, Jick H, Maclaughlin D. Prevalence and incidence rates of autism in the UK: time trend from 2004-2010 in children aged 8 years. BMJ Open 2013;3:e003219. ArticlePubMedPMC
• 46. Williams E, Thomas K, Sidebotham H, et al. Prevalence and characteristics of autistic spectrum disorders in the ALSPAC cohort. Dev Med Child Neurol 2008;50:672−7. ArticlePubMed
• 47. Scattoni ML, Fatta LM, Micai M, et al. Autism spectrum disorder prevalence in Italy: a nationwide study promoted by the Ministry of Health. Child Adolesc Psychiatry Ment Health 2023;17:125. ArticlePubMedPMCPDF
• 48. Heys M, Gibbons F, Haworth E, et al. The estimated prevalence of autism in school-aged children living in rural Nepal using a population-based screening tool. J Autism Dev Disord 2018;48:3483−98. ArticlePubMedPMCPDF
• 49. Raina SK, Chander V, Bhardwaj AK, et al. Prevalence of autism spectrum disorder among rural, urban, and tribal children (1-10 years of age). J Neurosci Rural Pract 2017;8:368−74. ArticlePubMedPMC
• 50. Akhter S, Hussain AH, Shefa J, et al. Prevalence of autism spectrum disorder (ASD) among the children aged 18-36 months in a rural community of Bangladesh: a cross sectional study. F1000Res 2018;7:424. ArticlePubMedPMCPDF
• 51. Rudra A, Belmonte MK, Soni PK, et al. Prevalence of autism spectrum disorder and autistic symptoms in a school-based cohort of children in Kolkata, India. Autism Res 2017;10:1597−605. ArticlePubMedPMCPDF
• 52. Chaaya M, Saab D, Maalouf FT, et al. Prevalence of autism spectrum disorder in nurseries in Lebanon: a cross sectional study. J Autism Dev Disord 2016;46:514−22. ArticlePubMedPDF
• 53. Huang JP, Cui SS, Han Y, et al. Prevalence and early signs of autism spectrum disorder (ASD) among 18-36 month-old children of Tianjin in China. Biomed Environ Sci 2014;27:453−61. ArticlePubMed
• 54. GBD 2015 Child Mortality Collaborators. Global, regional, national, and selected subnational levels of stillbirths, neonatal, infant, and under-5 mortality, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388:1725−74. ArticlePubMedPMC
• 55. Raina SK, Kashyap V, Bhardwaj AK, et al. Prevalence of autism spectrum disorders among children (1-10 years of age): findings of a mid-term report from Northwest India. J Postgrad Med 2015;61:243−6. ArticlePubMedPMC
• 56. Davidovitch M, Hemo B, Manning-Courtney P, et al. Prevalence and incidence of autism spectrum disorder in an Israeli population. J Autism Dev Disord 2013;43:785−93. ArticlePubMedPDF
• 57. Chien IC, Lin CH, Chou YJ, et al. Prevalence and incidence of autism spectrum disorders among national health insurance enrollees in Taiwan from 1996 to 2005. J Child Neurol 2011;26:830−4. ArticlePubMedPDF
• 58. Kim YS, Leventhal BL, Koh YJ, et al. Prevalence of autism spectrum disorders in a total population sample. Am J Psychiatry 2011;168:904−12. ArticlePubMed
• 59. Perera H, Wijewardena K, Aluthwelage R. Screening of 18-24-month-old children for autism in a semi-urban community in Sri Lanka. J Trop Pediatr 2009;55:402−5. ArticlePubMed
• 60. Sun X, Allison C, Matthews FE, et al. Exploring the underdiagnosis and prevalence of autism spectrum conditions in Beijing. Autism Res 2015;8:250−60. ArticlePubMedPMC
• 61. Jin Z, Yang Y, Liu S, et al. Prevalence of DSM-5 autism spectrum disorder among school-based children aged 3-12 years in Shanghai, China. J Autism Dev Disord 2018;48:2434−43. ArticlePubMedPDF
• 62. Al-Zahrani A. Prevalence and clinical characteristics of autism spectrum disorders in school-age children in Taif-KSA. Int J Med Sci Public Health 2013;2:578−82. Article
• 63. Poovathinal SA, Anitha A, Thomas R, et al. Prevalence of autism spectrum disorders in a semiurban community in south India. Ann Epidemiol 2016;26:663−5. ArticlePubMed
• 64. Li N, Chen G, Song X, et al. Prevalence of autism-caused disability among Chinese children: a national population-based survey. Epilepsy Behav 2011;22:786−9. ArticlePubMed
• 65. Al-Mamri W, Idris AB, Dakak S, et al. Revisiting the prevalence of autism spectrum disorder among Omani children: a multicentre study. Sultan Qaboos Univ Med J 2019;19:e305−9. ArticlePubMedPMC
• 66. Alshaban F, Aldosari M, Al-Shammari H, et al. Prevalence and correlates of autism spectrum disorder in Qatar: a national study. J Child Psychol Psychiatry 2019;60:1254−68. ArticlePubMedPMC
• 67. Zhou H, Xu X, Yan W, et al. Prevalence of autism spectrum disorder in China: a nationwide multi-center population-based study among children aged 6 to 12 years. Neurosci Bull 2020;36:961−71. ArticlePubMedPMCPDF
• 68. Lagunju IA, Bella-Awusah TT, Omigbodun OO. Autistic disorder in Nigeria: profile and challenges to management. Epilepsy Behav 2014;39:126−9. ArticlePubMed
• 69. Nicholas JS, Carpenter LA, King LB, et al. Autism spectrum disorders in preschool-aged children: prevalence and comparison to a school-aged population. Ann Epidemiol 2009;19:808−14. ArticlePubMed
• 70. Kogan MD, Vladutiu CJ, Schieve LA, et al. The prevalence of parent-reported autism spectrum disorder among US children. Pediatrics 2018;142:e20174161. ArticlePubMedPMCPDF
• 71. Baio J, Wiggins L, Christensen DL, et al. Prevalence of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill Summ 2018;67:1−23. ArticlePubMed
• 72. Christensen DL, Maenner MJ, Bilder D, et al. Prevalence and characteristics of autism spectrum disorder among children aged 4 years: early autism and developmental disabilities monitoring network, seven sites, United States, 2010, 2012, and 2014. MMWR Surveill Summ 2019;68:1−19. ArticlePubMedPMC
• 73. Durkin MS, Maenner MJ, Baio J, et al. Autism spectrum disorder among US children (2002-2010): socioeconomic, racial, and ethnic disparities. Am J Public Health 2017;107:1818−26. ArticlePubMedPMC
• 74. Fombonne E, Marcin C, Manero AC, et al. Prevalence of autism spectrum disorders in Guanajuato, Mexico: the Leon survey. J Autism Dev Disord 2016;46:1669−85. ArticlePubMedPDF
• 75. Nicholas JS, Charles JM, Carpenter LA, et al. Prevalence and characteristics of children with autism-spectrum disorders. Ann Epidemiol 2008;18:130−6. ArticlePubMed
• 76. Diallo FB, Fombonne E, Kisely S, et al. Prevalence and correlates of autism spectrum disorders in Quebec: Prévalence et corrélats des troubles du spectre de l'autisme au Québec. Can J Psychiatry 2018;63:231−9. ArticlePubMedPMCPDF
• 77. Dekkers LM, Groot NA, Diaz Mosquera EN, et al. Prevalence of autism spectrum disorders in Ecuador: a pilot study in Quito. J Autism Dev Disord 2015;45:4165−73. ArticlePubMedPMCPDF
• 78. Montiel-Nava C, Pena JA. Epidemiological findings of pervasive developmental disorders in a Venezuelan study. Autism 2008;12:191−202. ArticlePubMed
• 79. Christensen DL, Baio J, Van Naarden Braun K, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2012. MMWR Surveill Summ 2016;65:1−23. ArticlePubMedPMC
• 80. Maenner MJ, Warren Z, Williams AR, et al. Prevalence and characteristics of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 2023;72:1−14. ArticlePubMed
• 81. Shaw KA, Bilder DA, McArthur D, et al. Early identification of autism spectrum disorder among children aged 4 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2020. MMWR Surveill Summ 2023;72:1−15. ArticlePubMed
• 82. Zeglam AM, Maound AJ. Prevalence of autistic spectrum disorders in Tripoli, Libya: the need for more research and planned services. East Mediterr Health J 2012;18:184−8. ArticlePubMed
• 83. Hewitt A, Hall-Lande J, Hamre K, et al. Autism spectrum disorder (ASD) prevalence in Somali and non-Somali children. J Autism Dev Disord 2016;46:2599−608. ArticlePubMedPDF
• 84. May T, Brignell A, Williams K. Autism spectrum disorder prevalence in children aged 12-13 years from the longitudinal study of Australian children. Autism Res 2020;13:821−7. ArticlePubMedPDF
• 85. Bowden N, Thabrew H, Kokaua J, et al. Autism spectrum disorder/Takiwātanga: an integrated data infrastructure-based approach to autism spectrum disorder research in New Zealand. Autism 2020;24:2213−27. ArticlePubMedPMCPDF
• 86. Randall M, Sciberras E, Brignell A, et al. Autism spectrum disorder: presentation and prevalence in a nationally representative Australian sample. Aust N Z J Psychiatry 2016;50:243−53. ArticlePubMed
• 87. Al-Farsi YM, Al-Sharbati MM, Al-Farsi OA, et al. Brief report: prevalence of autistic spectrum disorders in the Sultanate of Oman. J Autism Dev Disord 2011;41:821−5. ArticlePubMedPDF
• 88. Sun X, Allison C, Wei L, et al. Autism prevalence in China is comparable to Western prevalence. Mol Autism 2019;10:7. ArticlePubMedPMCPDF
• 89. Raz R, Weisskopf MG, Davidovitch M, et al. Differences in autism spectrum disorders incidence by sub-populations in Israel 1992-2009: a total population study. J Autism Dev Disord 2015;45:1062−9. ArticlePubMedPMCPDF
• 90. Maenner MJ, Shaw KA, Baio J, et al. Prevalence of autism spectrum disorder among children aged 8 years: autism and developmental disabilities monitoring network, 11 sites, United States, 2016. MMWR Surveill Summ 2020;69:1−12. Article
• 91. Peters DH, Garg A, Bloom G, et al. Poverty and access to health care in developing countries. Ann N Y Acad Sci 2008;1136:161−71. ArticlePubMed
• 92. DuBay M, Lee H, Palomo R. Evidence map of Spanish language parent- and self-report screening and diagnostic tools for autism spectrum disorder. Res Autism Spectr Disord 2023;102:102117. Article
• 93. Santos CL, Barreto II, Floriano I, et al. Screening and diagnostic tools for autism spectrum disorder: systematic review and meta-analysis. Clinics (Sao Paulo) 2024;79:100323. ArticlePubMedPMC
• 94. Lordan R, Storni C, De Benedictis CA. Autism spectrum disorders: diagnosis and treatment. In: Grabrucker AM, editor. Autism spectrum disorders. Exon Publications; 2021. p. 17–32.
• 95. Pugsley K, Scherer SW, Bellgrove MA, et al. Environmental exposures associated with elevated risk for autism spectrum disorder may augment the burden of deleterious de novo mutations among probands. Mol Psychiatry 2022;27:710−30. ArticlePubMedPMCPDF
• 96. Karimi P, Kamali E, Mousavi SM, et al. Environmental factors influencing the risk of autism. J Res Med Sci 2017;22:27. ArticlePubMedPMC
• 97. Yenkoyan K, Mkhitaryan M, Bjorklund G. Environmental risk factors in autism spectrum disorder: a narrative review. Curr Med Chem 2024;31:2345−60. ArticlePubMedPDF
• 98. Fombonne E. Epidemiology of pervasive developmental disorders. Pediatr Res 2009;65:591−8. ArticlePubMed
• 99. Bauer K, Morin KL, Renz III TE, et al. Autism assessment in low-and middle-income countries: Feasibility and usability of western tools. Focus Autism Other Dev Disabl 2022;37:179−88. ArticlePDF
• 100. Loomes R, Hull L, Mandy WP. What is the male-to-female ratio in autism spectrum disorder?: a systematic review and meta-analysis. J Am Acad Child Adolesc Psychiatry 2017;56:466−74. ArticlePubMed
• 101. Ebrahimi Meimand S, Amiri Z, Shobeiri P, et al. Burden of autism spectrum disorders in North Africa and Middle East from 1990 to 2019: a systematic analysis for the Global Burden of Disease Study 2019. Brain Behav 2023;13:e3067. ArticlePubMedPMC
• 102. van Steensel FJ, Heeman EJ. Anxiety levels in children with autism spectrum disorder: a meta-analysis. J Child Fam Stud 2017;26:1753−67. ArticlePubMedPMCPDF
• 103. Sue S, Yan Cheng JK, Saad CS, et al. Asian American mental health: a call to action. Am Psychol 2012;67:532−44. ArticlePubMed
• 104. Lopez SR, Barrio C, Kopelowicz A, et al. From documenting to eliminating disparities in mental health care for Latinos. Am Psychol 2012;67:511−23. ArticlePubMed
• 105. Office of the Surgeon General (US); Center for Mental Health Services (US); National Institute of Mental Health (US). Mental health: culture, race, and ethnicity: a supplement to mental health: a report of the surgeon general. Substance Abuse and Mental Health Services Administration (US); 2001.
• 106. Stroth S, Tauscher J, Wolff N, et al. Phenotypic differences between female and male individuals with suspicion of autism spectrum disorder. Mol Autism 2022;13:11. ArticlePubMedPMCPDF
• 107. Napolitano A, Schiavi S, La Rosa P, et al. Sex differences in autism spectrum disorder: diagnostic, neurobiological, and behavioral features. Front Psychiatry 2022;13:889636. ArticlePubMedPMC
• 108. Barr M, Duncan J, Dally K. Parent experience of the national disability insurance scheme (NDIS) for children with hearing loss in Australia. Disabil Soc 2021;36:1663−87. Article
• 109. Veli-Gold S, Gilroy J, Wright W, et al. The experiences of people with disability and their families/carers navigating the NDIS planning process in regional, rural and remote regions of Australia: scoping review. Aust J Rural Health 2023;31:631−47. ArticlePubMedPDF
• 110. Gilroy J, Veli-Gold S, Wright W, et al. Disability workforce and the NDIS planning process in regional, rural and remote regions of Australia: scoping review. Aust J Rural Health 2023;31:839−54. ArticlePubMedPDF
• 111. Townsend C, White P, Cullen J, et al. Making every Australian count: challenges for the National Disability Insurance Scheme (NDIS) and the equal inclusion of homeless Aboriginal and Torres Strait Islander Peoples with neurocognitive disability. Aust Health Rev 2018;42:227−9. ArticlePubMed
• 112. Shelby-James T, Duncan A, Rattray M, et al. National disability insurance scheme access: what evidence do you need to provide for psychosocial disability? Australas Psychiatry 2023;31:174−7. ArticlePubMedPMCPDF

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