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Modelling disease progression and treatment pathways for depression

Brice, Syaribah Noor 2019. Modelling disease progression and treatment pathways for depression. PhD Thesis, Cardiff University.
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Abstract

Depression is a global public health issue which affects and is affected by many life factors. The burden of depression has an impact on an individual's quality of life as well as on healthcare costs. Studies have shown that the condition is complex and healthcare providers are struggling to meet the demand. Building on the existing studies of model-based economic evaluation and a stepped care treatment recommendation, the study aims to develop a model which incorporates disease progression and treatment pathways. It seeks to investigate: the impact of depression on healthcare services; the relationship between different levels of service provision and depression progression, and its related burden of disease. The literature review shows there is a gap in the application of a hybrid simulation in mental health care. This research endeavours to all that gap by combining Agent Based Modelling and System Dynamics approaches to describe depression progression and related treatment pathways. Data obtained from different sources inform the parameters for running the experimentation at different service levels. The results indicate that an increase in service provision tends to reduce inpatient care use, the deterioration of depression, and relapse cases. Such an increase in service use may also increase healthcare costs, however treating more people with depression could avoid a detrimental effect. The research addresses the development of a hybrid simulation model applied in a healthcare problem where disease progression and treatment pathways are important elements that cannot be separated. The developed model can be used to answer questions relating to disease progression, resource utilisation, and implications for the burden of disease and health policy. Further research should consider a multi disciplinary study including experts from different fields: Operational Research, Data Science, and Public Health.

Item Type: Thesis (PhD)
Date Type: Completion
Status: Unpublished
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Funders: EPSRC
Date of First Compliant Deposit: 20 February 2020
Last Modified: 21 Feb 2020 10:03
URI: http://orca.cf.ac.uk/id/eprint/129861

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