
Novel Method for Identifying Care Home Residents in England: A Validation Study
Author(s) -
Filipe Oliveira dos Santos,
Stefano Conti,
Arne Wolters
Publication year - 2021
Publication title -
international journal of population data science
Language(s) - English
Resource type - Journals
ISSN - 2399-4908
DOI - 10.23889/ijpds.v5i4.1666
Subject(s) - primary care , medicine , computer science , data science , psychology , family medicine
The ability to identify residents of care homes in routinely collected health care data is key to informing healthcare planning decisions and delivery initiatives targeting the older and frail population. Health-care planning and delivery implications at national level concerning this population subgroup have considerably and suddenly grown in urgency following the onset of the COVID-19 pandemic, which has especially hit care homes. The range of applicability of this information has widened with the increased availability in England of retrospectively collected administrative databases, holding rich patient-level details on health and prognostic status who have made or are in contact with the National Health Service. In practice lack of a national registry of care homes residents in England complicates assessing an individual's care home residency status, which has been typically identified via manual address matching from pseudonymised patient-level healthcare databases linked with publicly availably care home address information.ObjectivesTo examine a novel methodology based on linking unique care home address identifiers with primary care patient registration data, enabling routine identification of care home residents in health-care data.MethodsThis study benchmarks the proposed strategy against the manual address matching standard approach through a diagnostic assessment of a stratified random sample of care home post codes in England.ResultsDerived estimates of diagnostic performance, albeit showing a non-insignificant false negative rate (21.98%), highlight a remarkable true negative rate (99.69%) and positive predictive value (99.35%) as well as a satisfactory negative predictive value (88.25%).ConclusionsThe validation exercise lends confidence to the reliability of the novel address matching method as a viable and general alternative to manual address matching.