Predicting who will use intensive social care: Case finding tools based on linked health and social care data

Martin Bardsley, John Billings, Jennifer Dixon, Theo Georghiou, Geraint Hywel Lewis, Adam Steventon

Research output: Contribution to journalArticlepeer-review


Background: the costs of delivering health and social care services are rising as the population ages and more people live with chronic diseases. Objectives: to determine whether predictive risk models can be built that use routine health and social care data to predict which older people will begin receiving intensive social care. Design: analysis of pseudonymous, person-level, data extracted from the administrative data systems of local health and social care organisations.Setting: five primary care trust areas in England and their associated councils with social services responsibilities.Subjects: people aged 75 or older registered continuously with a general practitioner in five selected areas of England (n = 155,905). Methods: multivariate statistical analysis using a split sample of data. Results: it was possible to construct models that predicted which people would begin receiving intensive social care in the coming 12 months. The performance of the models was improved by selecting a dependent variable based on a lower cost threshold as one of the definitions of commencing intensive social care. Conclusions: predictive models can be constructed that use linked, routine health and social care data for case finding in social care settings.

Original languageEnglish (US)
Article numberafq181
Pages (from-to)265-270
Number of pages6
JournalAge and Ageing
Issue number2
StatePublished - Mar 2011


  • Algorithms
  • Elderly
  • Residential facilities
  • Risk assessment/methods
  • Risk assessment/standards
  • Risk factors

ASJC Scopus subject areas

  • Aging
  • Geriatrics and Gerontology


Dive into the research topics of 'Predicting who will use intensive social care: Case finding tools based on linked health and social care data'. Together they form a unique fingerprint.

Cite this