Screening for Autism Spectrum Disorder in Toddlers During the 18- and 24-Month Well-Child Visits - Zhang, Ying; Zhou, Zhaoe; Xu, Qiong; Li, Huiping; Lv, Yujing; Zhu, Guowei; Dong, Ping; Li, Dongyun; Wang, Yi; Tang, Xinrui; Xu, Xiu (Frontiers in Psychiatry, 2022)
Objective Early screening contributes to the early detection of children with autism spectrum disorder (ASD). We conducted a longitudinal ASD screening study in a large community setting. The study was designed to investigate the diagnostic rate of ASD screening and determine the effectiveness of ASD screening model in a community-based sample. Methods We enrolled children who attended 18- and 24-month well-child care visits in Shanghai Xuhui District. Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F) and Binomial Observation Test (BOT) were selected as screening instruments. Screen-positive children were referred to a tertiary diagnostic center for comprehensive ASD diagnostic evaluation. Screen-negative children received well-child checkups and follow-up every 3–6 months until age three and were referred if they were suspected of having ASD. Results A total of 11,190 toddlers were screened, and 36 screen-positive toddlers were diagnosed with ASD. The mean age at diagnosis for these children was 23.1 ± 4.55 months, diagnosed 20 months earlier than ASD children not screened. The diagnostic rate of ASD was 0.32% (95% CI: 0.23–0.45%) in this community-based sample. In addition, 12 screen-negative children were diagnosed with ASD during subsequent well-child visit and follow-up. The average diagnostic rate of ASD rose to 0.43% (95% CI: 0.32–0.57%) when toddlers were followed up to 3 years old. The positive predictive values (PPVs) of M-CHAT-R/F, M-CHAT-R high risk, and BOT for ASD were 0.31, 0.43, and 0.38 respectively. Conclusion Our findings provide reliable data for estimating the rate of ASD detection and identifying the validity of community-based screening model. M-CHAT-R/F combined with BOT can be an effective tool for early detection of ASD. This community-based screening model is worth replicating.