TY - JOUR AU - Schultze, Anna AU - Bates, Chris J. AU - Cockburn, Jonathan AU - Mackenna, Brian AU - Nightingale, Emily S. AU - Curtis, Helen J. AU - Hulme, William AU - Morton, Caroline E. AU - Croker, Richard AU - Bacon, Sebastian C.J. AU - McDonald, Helen I. AU - Rentsch, Christopher T. AU - Bhaskaran, Krishnan AU - Mathur, Rohini AU - Tomlinson, Laurie A. AU - Williamson, Elizabeth J. AU - Forbes, Harriet AU - Tazare, John AU - Grint, Daniel J. AU - Walker, Alex J. AU - Inglesby, Peter AU - DeVito, Nicholas J. AU - Mehrkar, Amir AU - Hickman, George AU - Davy, Simon AU - Ward, Tom AU - Fisher, Louis AU - Evans, David AU - Wing, Kevin AU - Wong, Angel Y.S. AU - McManus, Robert AU - Parry, John AU - Hester, Frank AU - Harper, Sam AU - Evans, Stephen J.W. AU - Douglas, Ian J. AU - Smeeth, Liam AU - Eggo, Rosalind M. AU - Goldacre, Ben PY - 2021 DA - April TI - Identifying Care Home Residents in Electronic Health Records - An OpenSAFELY Short Data Report JO - Wellcome Open Research VL - 6 DO - https://doi.org/10.12688/wellcomeopenres.16737.1 AB - 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. PB - F1000 Research UR - https://www.research.ed.ac.uk/en/publications/a2d9fdf9-2394-4f07-9fa3-25a536886c83 KW - Care homes KW - Coronavirus (COVID-19) KW - Digital health and technology ER