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Title: Modelling health and healthcare for an ageing population
Author: Youn, Ji Hee
ISNI:       0000 0004 5992 0637
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2016
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Population ageing has received much attention as a contributing cause of spiralling healthcare expenditure. This study primarily aims to estimate the impact of population ageing on key diseases, and to develop a flexible modelling framework that can inform policy decisions. This research provides a proof-of-concept model where individual Discrete Event Simulation models for three diseases (heart disease, Alzheimer’s disease, and osteoporosis) were extended from existing published models to simulate the general UK population aged 45 years and older, and combined within a single model. Using external population projection data incorporating potential demographic changes, the methods for projecting future healthcare expenditures for the three diseases were demonstrated and the relative benefits of improving treatment of each of the diseases evaluated. Secondary outcomes include the development of a pragmatic literature search method which can be used for literature within diffuse topic areas, and a literature repository for future researchers to explore the existing literature on ageing and healthcare expenditure. Expenditure for the three diseases is projected to increase from £16 billion in 2012 to £28 billion in 2037. A key finding from this work is that the estimates of costs, quality-adjusted life years (QALYs), and the projected expenditure for healthcare services can differ when multiple diseases are modelled in a single model compared with the summed results from single disease models. This implies that policy decisions on the allocation and planning of healthcare resources based on the results from individual disease models can be different from those based on linked models. The novel approach of linking multiple disease models with correlations incorporated provides a new methodological option primarily for modellers who undertake research on comorbidities. It also has potential for wider applications in informing decisions on commissioning of healthcare services and long-term priority setting across diseases and healthcare programmes, hence ultimately contributing to the improvement of population health.
Supervisor: Stevenson, Matt ; Goddard, Maria ; Thokala, Praveen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available