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Closing the UK care home data gap - methodological challenges and solutions

Lookup NU author(s): Professor Barbara Hanratty

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2020 The Authors.UK care home residents are invisible in national datasets. The COVID-19 pandemic has exposed data failings that have hindered service development and research for years. Fundamental gaps, in terms of population and service demographics coupled with difficulties identifying the population in routine data are a significant limitation. These challenges are a key factor underpinning the failure to provide timely and responsive policy decisions to support care homes. In this commentary we propose changes that could address this data gap, priorities include: (1) Reliable identification of care home residents and their tenure; (2) Common identifiers to facilitate linkage between data sources from different sectors; (3) Individual-level, anonymised data inclusive of mortality irrespective of where death occurs; (4) Investment in capacity for large-scale, anonymised linked data analysis within social care working in partnership with academics; (5) Recognition of the need for collaborative working to use novel data sources, working to understand their meaning and ensure correct interpretation; (6) Better integration of information governance, enabling safe access for legitimate analyses from all relevant sectors; (7) A core national dataset for care homes developed in collaboration with key stakeholders to support integrated care delivery, service planning, commissioning, policy and research. Our suggestions are immediately actionable with political will and investment. We should seize this opportunity to capitalise on the spotlight the pandemic has thrown on the vulnerable populations living in care homes to invest in data-informed approaches to support care, evidence-based policy making and research.


Publication metadata

Author(s):

Publication type: Article

Publication status: Published

Journal: International Journal of Population Data Science

Year: 2020

Volume: 5

Issue: 4

Print publication date: 28/09/2020

Online publication date: 15/12/2020

Acceptance date: 30/10/2020

Date deposited: 21/07/2021

ISSN (electronic): 2399-4908

Publisher: Swansea University

URL: https://doi.org/10.23889/IJPDS.V5I4.1391

DOI: 10.23889/IJPDS.V5I4.1391


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