The Application of the First Year Inventory for ASD Screening in China

J Pediatr Nurs. 2019 Jan-Feb;44:e72-e78. doi: 10.1016/j.pedn.2018.11.004. Epub 2018 Nov 23.

Abstract

Purpose: The First Year Inventory (FYI) is a parent-report instrument, and is developed to assess behaviors of 12-month-old infants that could suggest risk for an eventual diagnosis of autism. This study was designed to examine the application of the FYI in the Chinese community.

Design and methods: FYIs were completed at a community health center by 541 families during the child's physical examination at 12 months of age from 2013 to 2015. The weighted risk scores used in this study were based on US norms, and compared the FYI differences between China and the U.S.

Results: The total risk scores ranged from 5 to 42 points; the 95th percentile cutoff was 27.00(9.8 points higher than the 95th percentile cutoff in the US), the 98th percentile cutoff was 29.66(7.04 points higher than the 98th percentile cutoff in the US), and the 99th percentile cutoff was 31.83. Higher risk scores were found for boys than girls. Mothers with a junior college education reported significantly higher FYI risk scores than other three groups including high school, college graduates and post-graduates.

Conclusions: There were no significant effects of birth parity, investigator, or investigation year on risk scores. Large-scale longitudinal research is encouraged in the future to develop an early detection model of autism in China.

Keywords: ASD; China; Community; First year inventory; Screening.

MeSH terms

  • Adult
  • Autism Spectrum Disorder / diagnosis*
  • China
  • Community Health Centers / organization & administration
  • Early Diagnosis*
  • Educational Status
  • Female
  • Health Education / organization & administration
  • Humans
  • Infant
  • Male
  • Mass Screening / organization & administration*
  • Parents / education
  • Parents / psychology*
  • Patient Compliance
  • Retrospective Studies
  • Risk Assessment
  • Sex Factors
  • Socioeconomic Factors
  • Statistics, Nonparametric