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

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.