Dataset Information

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data.

ABSTRACT:

Objectives

Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services.

Setting

The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013.

Participants

A clinical cohort of 35?509 children and young people (aged 4-17 years).

Design

Multiple government and ethical committees approved the link of clinical mental health service data to Department for Education (DfE) data on education and social care services. Under robust governance protocols, fuzzy and deterministic approaches were used by the DfE to match personal identifiers (names, date of birth and postcode) from National Pupil Database (NPD) and CRIS data sources.

Outcome measures

Risk factors for non-matching to NPD were identified, and the potential impact of non-match biases on International Statistical Classification of Diseases, 10th Revision (ICD-10) classifications of mental disorder, and persistent school absence (<80%?attendance) were examined. Probability weighting and adjustment methods were explored as methods to mitigate the impact of non-match biases.

Results

Governance challenges included developing a research protocol for data linkage, which met the legislative requirements for both National Health Service and DfE. From CRIS, 29 278 (82.5%) were matched to NPD school attendance records. Presenting to services in late adolescence (adjusted OR (aOR) 0.67, 95%?CI 0.59 to 0.75) or outside of school census timeframes (aOR 0.15, 95% CI 0.14 to 0.17) reduced likelihood of matching. After adjustments for linkage error, ICD-10 mental disorder remained significantly associated with persistent school absence (aOR 1.13, 95% CI 1.07 to 1.22).

Conclusions

The work described sets a precedent for education data being used for medical benefit in England. Linkage between health and education records offers a powerful tool for evaluating the impact of mental health on school function, but biases due to linkage error may produce misleading results. Collaborative research with data providers is needed to develop linkage methods that minimise potential biases in analyses of linked data.

SUBMITTER: Downs JM

PROVIDER: S-EPMC6352796 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data.

Downs Johnny M JM Ford Tamsin T Stewart Robert R Epstein Sophie S Shetty Hitesh H Little Ryan R Jewell Amelia A Broadbent Matthew M Deighton Jessica J Mostafa Tarek T Gilbert Ruth R Hotopf Matthew M Hayes Richard R

BMJ open 20190129 1

<h4>Objectives</h4>Creation of linked mental health, social and education records for research to support evidence-based practice for regional mental health services.<h4>Setting</h4>The Clinical Record Interactive Search (CRIS) system was used to extract personal identifiers who accessed psychiatric services between September 2007 and August 2013.<h4>Participants</h4>A clinical cohort of 35 509 children and young people (aged 4-17 years).<h4>Design</h4>Multiple government and ethical committees ...[more]

PMID: 30700480

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Dataset Information

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data.

Objectives

Setting

Participants

Design

Outcome measures

Results

Conclusions

Publications

An approach to linking education, social care and electronic health records for children and young people in South London: a linkage study of child and adolescent mental health service data.

Similar Datasets

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