Longitudinal child data: What can be gained by linking administrative data and cohort data?

Area of Science:

• Population Health
• Data Linkage Methodologies
• Child Development Research

Background:

• Linked administrative data are valuable for population health research.
• Previous methods often overlooked children, especially those at financial risk.
• Linking tax data to child cohorts is underexplored.

Purpose of the Study:

• To describe a probabilistic linkage of two Canadian child cohorts with administrative tax data.
• To establish a longitudinal dataset for studying long-term outcomes in children.
• To facilitate research on factors influencing children's financial well-being.

Main Methods:

• Probabilistic linkage by Statistics Canada.
• Utilized Montreal Longitudinal Experimental Study (MLES) and Quebec Longitudinal Study of Kindergarten Children (QLSKC) data.
• Linked survey cohorts with administrative tax data from 1992-2012.

Main Results:

• Achieved an 84% linkage rate between cohort files and tax records.
• Demonstrated acceptable rates for false positives, false negatives, sensitivity, and specificity.
• Created a valuable linked dataset for future analysis.

Conclusions:

• Innovative longitudinal datasets are crucial for child well-being research.
• Enables examination of long-term outcomes from early childhood factors.
• Provides an evidence base for interventions promoting positive child development.