Differentiating Transient From Persistent Developmental Delays in a Nationwide Infant Cohort

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.