Open science and phenotyping in UK administrative health, education and social care data: the ECHILD phenotype code list repository
Administrative health data, such as the Hospital Episode Statistics (HES), can be used to identify groups of people with a particular target condition, a process known as phenotyping. Clinical phenotypes are useful as exposures, covariates and outcomes in research studies using administrative data,...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Swansea University
2025-05-01
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| Series: | International Journal of Population Data Science |
| Subjects: | |
| Online Access: | https://ijpds.org/article/view/2943 |
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| Summary: | Administrative health data, such as the Hospital Episode Statistics (HES), can be used to identify groups of people with a particular target condition, a process known as phenotyping. Clinical phenotypes are useful as exposures, covariates and outcomes in research studies using administrative data, including health data linked to other sources such as the Education and Child Health Insights from Linked Data (ECHILD) project. ECHILD brings together HES and other national health datasets with the National Pupil Database and children's social care data for all of England as a data asset that can be accessed by researchers at UK institutions. Because using linked administrative data is complex, the ECHILD team has created additional resources to improve the accessibility of ECHILD. One such initiative is the ECHILD Phenotype Code List Repository. The Repository is a fully open and searchable website containing phenotype code lists that can be used in ECHILD and beyond. As well as a primer on phenotyping, it includes summaries of each code list and R and Stata implementation scripts. The Repository was designed according to a set of principles to ensure that finding and using code lists is easy and standardised. The ECHILD Phenotype Code List Repository is a step forward in the findability and use of phenotype code lists in ECHILD and its constituent datasets.
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| ISSN: | 2399-4908 |