Aggregating data from multiple informants in child psychiatry epidemiological research

Area of Science:

• Child psychiatric epidemiology
• Adolescent mental health assessment
• Informant discrepancy in diagnostics

Background:

• Multiple informants in child assessments often provide discrepant data.
• Child psychiatric epidemiology faces challenges in aggregating these varied reports.
• Standardizing diagnostic assessments requires addressing informant variability.

Purpose of the Study:

• To evaluate methods for aggregating discrepant reports from multiple informants in child psychiatric assessments.
• To compare statistical approaches with simpler rules for optimal informant identification.
• To address challenges in child and adolescent diagnostic assessment.

Main Methods:

• Utilized data from an epidemiological community survey.
• Applied statistical procedures to identify the optimal informant for symptoms and diagnoses.
• Compared statistical findings with a combinatorial rule (positive if either parent or child reports a symptom).

Main Results:

• Statistical procedures for identifying an "optimal" informant did not demonstrate a clear advantage.
• A simple combinatorial rule, where a symptom is positive if reported by either parent or child, performed comparably.
• Informant aggregation in child psychiatric epidemiology remains complex.

Conclusions:

• Simple combinatorial rules may be as effective as complex statistical methods for aggregating informant data in child psychiatric epidemiology.
• Further research is needed to refine methods for handling discrepant informant reports.
• Improving diagnostic accuracy in child and adolescent mental health assessments is crucial.