TY - JOUR
T1 - Identifying Care Home Residents in Electronic Health Records - An OpenSAFELY Short Data Report
AU - Schultze, Anna
AU - Bates, Chris
AU - Cockburn, Jonathan
AU - MacKenna, Brian
AU - Nightingale, Emily
AU - Curtis, Helen J.
AU - Hulme, William J.
AU - Morton, Caroline E.
AU - Croker, Richard
AU - Bacon, Seb
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
N1 - Publisher Copyright:
© 2021 Schultze A et al.
PY - 2021/4/27
Y1 - 2021/4/27
N2 - 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.
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.
KW - Address Linkage
KW - Care Homes
KW - Electronic Health Records
UR - http://www.scopus.com/inward/record.url?scp=85110666937&partnerID=8YFLogxK
U2 - 10.12688/wellcomeopenres.16737.1
DO - 10.12688/wellcomeopenres.16737.1
M3 - Article
AN - SCOPUS:85110666937
SN - 2312-0541
VL - 6
JO - Wellcome Open Research
JF - Wellcome Open Research
M1 - 90
ER -