What are we optimizing for in autism screening? Examination of algorithmic changes in the M-CHAT

Autism Res. 2022 Feb;15(2):296-304. doi: 10.1002/aur.2643. Epub 2021 Nov 26.

Abstract

The present study objectives were to examine the performance of the new M-CHAT-R algorithm to the original M-CHAT algorithm. The main purpose was to examine if the algorithmic changes increase identification of children later diagnosed with ASD, and to examine if there is a trade-off when changing algorithms. We included 54,463 screened cases from the Norwegian Mother and Child Cohort Study. Children were screened using the 23 items of the M-CHAT at 18 months. Further, the performance of the M-CHAT-R algorithm was compared to the M-CHAT algorithm on the 23-items. In total, 337 individuals were later diagnosed with ASD. Using M-CHAT-R algorithm decreased the number of correctly identified ASD children by 12 compared to M-CHAT, with no children with ASD screening negative on the M-CHAT criteria subsequently screening positive utilizing the M-CHAT-R algorithm. A nonparametric McNemar's test determined a statistically significant difference in identifying ASD utilizing the M-CHAT-R algorithm. The present study examined the application of 20-item MCHAT-R scoring criterion to the 23-item MCHAT. We found that this resulted in decreased sensitivity and increased specificity for identifying children with ASD, which is a trade-off that needs further investigation in terms of cost-effectiveness. However, further research is needed to optimize screening for ASD in the early developmental period to increase identification of false negatives.

Keywords: children; early detection; psychometrics.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Autism Spectrum Disorder* / diagnosis
  • Autistic Disorder* / diagnosis
  • Checklist
  • Child
  • Cohort Studies
  • Female
  • Humans
  • Infant
  • Mass Screening / methods
  • Mothers