Health inequalities and communicable disease: projecting future public health challenges

Background

Population demography is always changing. As a pressing example, the number of people aged 80 and over in the UK is forecast to double in the next 40 years to over 6 million. As populations change, so too does the public health burden. This project will investigate how multiple sociodemographic factors impact health outcomes, such as rates of mortality, hospitalisation, and infection. Then, via dynamic modelling methods, the project will forecast how this is likely to change public health demand in the next 50 years, and what interventions could be most effective in tackling this increased health demand.

An initial scoping review will identify which health inequalities to prioritise and analyse. The student will have scope to focus on particular inequalities or health challenges of interest. Example research questions include:

• What are the forecast number of annual hospitalisations due to seasonal respiratory viruses under different population age projections?
• How do health inequalities (e.g. socioeconomic status, ethnicity, air quality) interact with ageing demographics to shape vulnerability to seasonal health shocks?
• What other age-sensitive conditions, such as heat-stress and multimorbidity, are likely to drive parallel increases in hospital burden, and how should they be prioritised?
• What are the specific mechanisms by which broad measures such as deprivation indices influence disease outcomes?
• Could investment in preventative strategies, such as vaccination programmes or public health campaigns, be more cost-effective than expanding hospital capacity?

This project will engage with disciplines across the Pandemic Sciences Institute’s remit and provide opportunity for significant collaboration with groups across the clinical and social health sciences. Applicants selecting this project can be considered for an Oxford-Moh Family Foundation Global Health Scholarship alongside other departmental scholarships they may be eligible for.

research experience, research methods and skills training

The project offers the DPhil candidate the opportunity to gain experience in dynamic compartmental modelling, data analysis, and broader statistical modelling techniques. Should data allow, there is scope for upskilling in machine learning methods to identify risk factors for health outcomes. The project will provide the opportunity for building networks across the public health sciences.

PROSPECTIVE STUDENT

The ideal candidate will have a master’s degree in a relevant field (e.g. statistics, epidemiology, public health) or a bachelor’s degree with researcher experience.