Abstract
BACKGROUND: Autism prevalence in the West is approximately 1% of school age children. Autism prevalence in China has been reported to be lower than in the West. This is likely due to at least two reasons: (1) most studies in China only included the special school population, overlooking the mainstream school population; and (2) most studies in China have not used contemporary screening and diagnostic methods. To address this, we tested total autism prevalence (mainstream and special schools) in Jilin City, and mainstream school autism prevalence in Jiamusi and Shenzhen cities. METHODS: The study included a three-step process: (1) screening; (2) clinical assessment of 'screen positives' plus controls; and (3) research diagnostic assessment of those meeting clinical threshold for concerns at step 2. Prevalence estimates per 10,000 children aged 6-10 years old were weighted for study design using diagnostic criteria applied at the research assessment stage. RESULTS: In Jilin City, 77 cases of autism were identified from a total population of 7258, equating to a prevalence of 108 per 10,000 (95% confidence interval (CI) 89, 130). In Shenzhen City: 21,420 children were screened and 35 cases of autism were identified, resulting in a mainstream prevalence of 42 per 10,000 (95% CI 20-89). In Jiamusi City, 16,358 children were screened, with 10 autism cases being identified, with a mainstream prevalence of 19 per 10,000 (95% CI 10-38). CONCLUSIONS: Results from Jilin City, where both mainstream and special school data were available, revealed a similar prevalence of autism in China to the West, at around 1%. Results from Shenzhen and Jiamusi cities, where only mainstream data were available, prevalence is also in line with Western estimates. In all three cities, new cases of autism were identified by the study in mainstream schools, reflecting current under-diagnosis. Non-significant variation across different cities is seen indicating the need to explore potential variation of autism across diverse Chinese regions with large sample sizes to achieve a fully robust national picture.