Forecasting long-term care demand with incomplete information: A grey modelling approach

Long-term care (LTC) consists of the services and support given to patients with complex needs due to illness, disability or a mental condition and is typically provided to those aged 65 and above. Projections of future demand and cost are crucial in supporting regional LTC planers commission services yet existing methodologies frequently require data beyond the scope of local datasets. In this paper we present an investigation into the suitability of using a Grey inspired forecasting methodology to predict future levels of LTC expenditure using routinely collected data from LTC activity in London. Our results are based on data on formal LTC in two London regions between 2008 and 2009. We find that grey modelling can outperform traditional industrial techniques in a number of cases and identify areas for future work.