Dynamic microsimulation for health

One full PhD scholarship is available in the School of Geography in 2024/25. This scholarship is open to Home fee rated applicants only and covers fees plus maintenance and a research support fund.

We are looking for a talented and motivated individual to join a large and dynamic multidisciplinary project called Policy Modelling for Health. 

This fully funded PhD place provides an exciting opportunity to pursue postgraduate research in population health modelling, estimating health outcomes and health inequalities which arise from economic policies. You will have the opportunity to work with a multidisciplinary team of geographers, public health experts and policy partners to develop computational models and translate findings in to actionable insights into ways to improve population health. 

Policy Modelling for Health is a thematic pillar of the £35 million Population Health Improvement UK (PHI-UK) network, funded by UK Research and Innovation (UKRI), bringing together expertise and insight from across research, public health and community organisations. Its aim is to find innovative and inclusive ways to improve the health of people, places and communities and reduce health inequalities through the development and evaluation of long-lasting and environmentally sustainable interventions. Policy Modelling for Health comprises experts from six universities, local and national government, agencies, charities and citizen’s groups who will develop computer models to show how tax, welfare, pensions and inheritance policies might affect health inequality outcomes to help policymakers understand their impacts on people in their area. It will incorporate wide-ranging insights into these models to make sure they answer the most pressing questions, inform real world decisions, and are relevant and inclusive across different groups in society. By doing so we will address the economic determinants of health and health inequalities through supporting the development and implementation of high-impact, established and innovative population-level policies using complex systems approaches to policy modelling. Policy Modelling for Health leverages insights and methods developed as part of prior major investments, including the Systems Science in Public Health and Health Economics Research (SIPHER) consortium. 

The PhD will focus on the development of pathway models which capture the health outcomes and health inequalities which arise from economic policies, which will be utilised within an existing dynamic microsimulation framework. These pathways are complex, linking policy interventions with outcomes, for example employment can result in improved health outcomes and reduced inequalities through several mechanisms that are not only representative of changes in income but represent wider social and economic opportunities. To build these models you will make use of longitudinal data, such as the UK Household Longitudinal Survey, to build probability models for individuals, who are evolved over time. The result is a model capable of producing projections under different scenarios. You will work closely with the wider consortium, collaborating with academics and policy partners to develop and model relevant pathways. You will also have the opportunity to collaborate on producing systematic reviews, which will provide evidence for the integration of causal pathways within the models. 

You will be highly numerate, with a strong background in undertaking statistical analysis and writing efficient and well documented code (preferably R and Python). You will ideally have experience of data analytics in a health context. Experience of software development would be an advantage for keeping the model code and documentation up to date. You will have good communication skills, with the ability to work with a large team of academic and professionals and a strong interest in producing quantitative evidence for informing health policy.

For more information and instructions on how to apply, please see the project page on the University of Leeds website.