The rise in demand for healthcare by an ageing population together with budgetary constraints has put great pressure on the availability of adult social care (ASC). In response, healthcare organisations and researchers have developed practices of care and support, focusing on prolonging functional independence This is done through exploring possible risk factors associated with unplanned outcomes, typically readmissions to hospital or through the use of predictive models to forecast outcomes.
Predictive models are widely used by health care providers in the UK and US due to their potential to inform early interventions. However, equivalent models for predicting new onset of long-term ASC, defined as need for help with tasks of daily living in the community or in care homes, are rare, particularly those using administrative data.
In this study published in Age and Ageing, we describe risk factors for long-term ASC in two inner London boroughs and develop a risk prediction model for long-term ASC. Pseudonymised person-level data from an integrated care dataset were analysed. We used multivariable logistic regression to model associations of demographic factors, and baseline aspects of health status and health service use, with accessing long-term ASC over 12 months.
The cohort comprised 13,394 residents, aged ≥75 years with no prior history of ASC at baseline. Of these, 1.7% became ASC clients over 12 months. Residents were more likely to access ASC if they were older or living in areas with high socioeconomic deprivation. Those with pre-existing mental health or neurological conditions, or more intense prior health service use during the baseline period, were also more likely to access ASC. A prognostic model derived from risk factors had limited predictive power.
Our findings reinforce evidence on known risk factors for residents aged 75 or over, yet even with linked routinely collected health and social care data, it was not possible to make accurate predictions of long-term ASC use for individuals. We propose that a paradigm shift towards more relational, personalised approaches, is needed.