Identifying Care Home Residents in Electronic Health Records - An OpenSAFELY Short Data Report

Anna Schultze, Chris Bates, Jonathan Cockburn, Brian MacKenna, Emily Nightingale, Helen J. Curtis, William J. Hulme, Caroline E. Morton, Richard Croker, Seb Bacon, Helen I. McDonald, Christopher T. Rentsch, Krishnan Bhaskaran, Rohini Mathur, Laurie A. Tomlinson, Elizabeth J. Williamson, Harriet Forbes, John Tazare, Daniel J. Grint, Alex J. WalkerPeter Inglesby, Nicholas J. DeVito, Amir Mehrkar, George Hickman, Simon Davy, Tom Ward, Louis Fisher, David Evans, Kevin Wing, Angel Y.S. Wong, Robert McManus, John Parry, Frank Hester, Sam Harper, Stephen J.W. Evans, Ian J. Douglas, Liam Smeeth, Rosalind M. Eggo, Ben Goldacre

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

Background: Care home residents have been severely affected by the COVID-19 pandemic. Electronic Health Records (EHR) hold significant potential for studying the healthcare needs of this vulnerable population; however, identifying care home residents in EHR is not straightforward. We describe and compare three different methods for identifying care home residents in the newly created OpenSAFELY-TPP data analytics platform. Methods: Working on behalf of NHS England, we identified individuals aged 65 years or older potentially living in a care home on the 1st of February 2020 using (1) a complex address linkage, in which cleaned GP registered addresses were matched to old age care home addresses using data from the Care and Quality Commission (CQC); (2) coded events in the EHR; (3) household identifiers, age and household size to identify households with more than 3 individuals aged 65 years or older as potential care home residents. Raw addresses were not available to the investigators. Results: Of 4,437,286 individuals aged 65 years or older, 2.27% were identified as potential care home residents using the complex address linkage, 1.96% using coded events, 3.13% using household size and age and 3.74% using either of these methods. 53,210 individuals (32.0% of all potential care home residents) were classified as care home residents using all three methods. Address linkage had the largest overlap with the other methods; 93.3% of individuals identified as care home residents using the address linkage were also identified as such using either coded events or household age and size. Conclusion: We have described the partial overlap between three methods for identifying care home residents in EHR, and provide detailed instructions for how to implement these in OpenSAFELY-TPP to support research into the impact of the COVID-19 pandemic on care home residents.

Original languageEnglish
Article number90
JournalWellcome Open Research
Volume6
DOIs
Publication statusPublished - 27 Apr 2021
Externally publishedYes

Keywords

  • Address Linkage
  • Care Homes
  • Electronic Health Records

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Biochemistry, Genetics and Molecular Biology(all)

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