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This study shows that routine developmental surveillance can distinguish temporary from lasting developmental delays in children. Counting unmet milestones offers an accessible method to improve early childhood development monitoring globally.

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Area of Science:

  • Pediatrics
  • Developmental Psychology
  • Public Health Surveillance

Background:

  • Early childhood developmental surveillance is crucial for timely intervention.
  • Current policies struggle to differentiate transient from persistent developmental delays.
  • This can lead to inappropriate referrals or missed support opportunities.

Purpose of the Study:

  • To assess if the Tipat Halav Israeli Surveillance (THIS) developmental scale can differentiate transient from persistent developmental delays.
  • To enhance the precision of early childhood surveillance policies.

Main Methods:

  • Retrospective cohort study using national Israeli maternal and child health data (2014-2022).
  • Included infants born at >=37 weeks gestation, assessed at 9-12 months, with at least one missed milestone.
  • Machine learning models and simpler decision rules were used to predict ongoing milestone attainment failure at 12-24 months.

Main Results:

  • In 529,797 infants, 7.1% showed developmental delays at 9-12 months.
  • Persistent delay was identified in 25.0% of those reassessed.
  • Machine learning models achieved areas under the ROC curve of 0.71-0.77; counting unmet domains showed good sensitivity and specificity.

Conclusions:

  • Routine surveillance data can effectively distinguish transient from persistent developmental delays.
  • Counting unmet developmental domains is a valid and practical approach.
  • This offers an accessible method to improve developmental surveillance and child outcomes worldwide.