Five models for child and adolescent data linkage in the UK: a review of existing and proposed methods

Area of Science:

• Child and Adolescent Health
• Mental Health Research
• Data Science

Background:

• Advances in technology and data offer new insights into child and adolescent health.
• Multifactorial influences on child and adolescent development require integrated data approaches.
• Existing adolescent mental health research often fails to leverage data linkage potential.

Purpose of the Study:

• To clarify methods for linking health, education, social care, and research datasets.
• To enable advanced analytics for identifying and addressing child and adolescent mental health needs.
• To present models for overcoming data linkage challenges in adolescent research.

Main Methods:

• Review and presentation of five existing and proposed models for adolescent data linkage.
• Analysis of data linkage challenges including confidentiality, sampling, matching, and scalability.
• Focus on facilitating clinical benefits through data integration.

Main Results:

• Data linkage facilitates epidemiological research from population to patient levels.
• Advanced analytics can improve identification, conceptualization, and treatment of adolescent needs.
• Five models are presented to address key data linkage challenges.

Conclusions:

• Effective integration of diverse datasets is crucial for understanding, preventing, and treating child and adolescent mental disorders.
• Overcoming data linkage challenges will unlock significant clinical benefits.
• This work aims to advance the field of adolescent mental health research through improved data utilization.