Predictors of care home and hospital admissions and their costs for older people with Alzheimer's disease: findings from a large London case register

Objectives: To examine links between clinical and other characteristics of people with Alzheimer's disease living in the community, likelihood of care home or hospital admission, and associated costs.

Design: Observational data extracted from clinical records using natural language processing and Hospital Episode Statistics. Statistical analyses examined effects of cognition, physical health, mental health, sociodemographic factors and living circumstances on risk of admission to care home or hospital over 6 months and associated costs, adjusting for repeated observations.

Setting: Catchment area for South London and Maudsley National Health Service Foundation Trust, provider for 1.2 million people in Southeast London.

Participants: Every individual with diagnosis of Alzheimer's disease seen and treated by mental health services in the catchment area, with at least one rating of cognition, not resident in care home at time of assessment (n=3075).

Interventions: Usual treatment.

Main outcome measures: Risk of admission to, and days spent in three settings during 6-month period following routine clinical assessment: care home, mental health inpatient care and general hospital inpatient care.

Results: Predictors of probability of care home or hospital admission and/or associated costs over 6 months include cognition, functional problems, agitation, depression, physical illness, previous hospitalisations, age, gender, ethnicity, living alone and having a partner. Patterns of association differed considerably by destination.

Conclusions: Most people with dementia prefer to remain in their own homes, and funding bodies see this as cheaper than institutionalisation. Better treatment in the community that reduces health and social care needs of Alzheimer's patients would reduce admission rates. Living alone, poor living circumstances and functional problems all raise admission rates, and so major cuts in social care budgets increase the risk of high-cost admissions which older people do not want. Routinely collected data can be used to reveal local patterns of admission and costs.