Modelling future demand for long-term care

Abstract

This research was jointly funded by the Economic and Social Research Council (ESRC) and the Engineering and Physical Sciences Research Council (EPSRC). As such, its underpinning and innovative aim was to explore the use of Operational Research (OR) techniques, a research area traditionally associated with the EPSRC, to address key societal problems traditionally associated with the ESRC.
The ageing population presents many significant challenges for social care services at both a national and local level, one of which is to meet the demand for long-term care. The population of people aged over 65 will continue to grow for some time as the ?baby boom? generation ages. The concern for policy planners is whether there will be enough resources in place to handle the expected strain on the system in the future. The research presented in this thesis addresses this key issue, and was carried out in collaboration with the Adult Services Department of Hampshire County Council (HCC). The overarching aim of this thesis was to develop computer models (using data local to Hampshire) which would be of practical use in estimating the future demand and planning the supply of long-term care in Hampshire.
A cell-based model was built to forecast the demand for long-term care in Hampshire from people aged 65 and over for the period 2009 to 2026. An important part of this research was to understand the main drivers of future demand for long-term care and to predict the future number of people with a disability. Hampshire County Council has already tried to address these issues of demographic change through a modernisation programme. Part of this has been the establishment of a contact centre called Hantsdirect. A discrete-event simulation model of the contact centre was developed. The two models were combined to explore the short- and long-term performance of the contact centre in the light of demographic change. This hybrid model has enabled HCC to explore the short- and long-term performance of the contact centre.
This study combines OR with Gerontology, Demography and Social Policy. This research is novel as it iteratively combines a compartmental population model with a discrete-event simulation model. From an OR perspective, the aim was not only to explore the use of modelling in social care (where, unlike healthcare, there has not hitherto been a lot of research), but also to investigate the potential for combining different modelling approaches in order to obtain additional value from the modelling. This novel approach in a social care setting is one of the main contributions of this thesis.

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Identifiers

ORCID for S.C. Brailsford: orcid.org/0000-0002-6665-8230

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