Prevalence analysis and trend prediction of the burden of autism spectrum disorder

Abstract

Objective To compare and analyze the current burden status of autism spectrum disorders (ASD) in China and globally, to extract the epidemiological characteristics and to predict the burden in 2030, based on the analysis of the global burden of disease (GBD) database, so as to provide reference for the prevention and control of the disease burden caused by ASD. Methods Prevalence and disability-adjusted life years (DALYs) data from 1990 to 2019 for ASD in China and globally were retrieved from the GBD database. Joinpoint regression model was used to analyze the changes in ASD disease burden and calculate average annual percentage change (AAPC) over time. Auto regressive integrated moving average model(ARIMA) was constructed to predict the trend of ASD disease burden in China and globally from 2020 to 2030. Results The age standardized prevalence rate and age standardized DALYs rate in China showed an upward trend from 1990 to 2019, with an average annual increase of 0.14% and 0.15%, respectively, and the increase was higher than the global average level (AAPC=-0.03%, P<0.001, AAPC=-0.03%, P<0.001). It is predicted that the age standardized prevalence rate and age standardized DALYs rate of ASD in China will both show an upward trend by 2030, reaching 376.08/10⁵ and 57.90/10⁵, respectively. In comparison, the global age standardized prevalence rate and age standardized DALYs rate of ASD, which are 369.47/10⁵ and 56.28/10⁵, respectively, and the trend is relatively stable. The burden of ASD is biggest for male children under the age of 5 in China and globally. Conclusion Over the past three decades, the burden of ASD in China has persistently escalated and may exceed the global average in the future, making it particularly urgent to implement targeted prevention and control measures.

Keywords

autism spectrum disorder / global burden of disease / disability-adjusted life years / Joinpoint regression model / auto regressive integrated moving average model