Funded PhD – Estimating population-level impacts of public health evaluations using multiple data sources

(Students Worldwide) About the Project About the project To improve population health, policymakers need robust evidence about which interventions and policies will translate into meaningful impacts when implemented at scale and to inform subsequent assessments of cost-effectiveness and return on investment. However, estimating population-level impacts of complex public health interventions is challenging. Evaluations, whether based on randomised, natural experimental or other designs, can provide valuable evidence on effectiveness but are often limited by trade-offs between internal validity, representativeness, geographical coverage and follow-up timescales, making it difficult to determine whether findings will generalise to wider populations over time. Population-level data sources such as routine administrative datasets, surveillance and national surveys provide longer-term, representative data, but rarely collect all the information needed for evaluation purposes. Combining evidence from multiple data sources offers the potential to address this challenge by drawing on the strengths of different data sources while mitigating their limitations. Aims and objectives The aim of this project is to develop and apply Bayesian statistical methods that combine evidence from public health evaluations with larger population-level datasets to improve the transportability and generalisability of findings and support estimation of population-level impacts. There are three objectives To develop a Bayesian framework for combining evidence from public health evaluations and population-level datasets. To apply and evaluate these methods using simulation studies and real-world public health evaluations, assessing their performance and their ability to support estimation of realistic population-level impacts. To support the wider adoption of these methods through the creation of accessible guidance, tutorials, open-source code and effective communication of findings to non-technical audiences. Methods This project will use Bayesian methods to develop a framework for combining data from multiple sources, which provides a flexible framework that can incorporate external information and fully account for uncertainty. The methodological development will be informed by the practical needs and challenges of real-world public health evaluation, with access to data from existing projects within the Centre for Public Health that cover a range of evaluation designs and application areas, such as physical activity, diet and mental health. Identifying the specific statistical methods to be used will form part of the PhD and be guided by both the student's interests and the practical requirements of the applications. Potential approaches include hierarchical and mixed-effects models, methods for combining aggregate and individual-level data, quantitative bias analysis, synthetic controls and causal inference methods. The project will involve adapting and extending existing methods to address challenges in estimating population-level impacts. Simulation studies will evaluate the performance of proposed methods under realistic scenarios, to examine bias, precision and uncertainty, and identify characteristics of studies and data sources that support reliable population-level inference. Methods will then be applied to public health evaluations and national datasets. This will include exploring how previous evaluations, expert knowledge and qualitative evidence can be incorporated as informative prior distributions. A key feature of the project is its translational focus to ensure that approaches can be implemented and understood by public health researchers and decision makers. The project will therefore explore how these methods can be made more accessible and how findings can be communicated effectively to policymakers, practitioners and other non-technical audiences. Potential outputs including methodological and interpretation guidance for researchers and policy makers, tutorials, open-source software and reusable analytical tools. This PhD offers an exciting opportunity for someone with a background in statistics, data science or a related quantitative discipline who wishes to apply advanced analytical skills to public health. It would also suit applicants with a background in epidemiology or public health with strong quantitative skills who wish to develop these further. How to apply Before applying, please check the entry requirements . Submit your application via the University of Bristol portal . Search for and then select 'Population Health Sciences (PhD)'. Click 'Apply' for September 2027 start. You must provide the following in your application: A CV Two referees (add to the Reference section of your application form) A personal statement highlighting your motivation for applying for this PhD, and any relevant experience and/or skills (maximum of one side of A4, font size 11). We are keen to support applicants from minority and under-represented backgrounds (based on protected characteristics ) and those who have experienced other challenges or disadvantages. Please use your personal statement to ensure we can take these factors into account. Certificates and transcripts Enter the project title and the supervisor names as they appear on this advert in the Research section. A research statement/proposal is not required. You should upload a blank document in this section. In the funding section, choose 'University of Bristol Scholarship'. Incomplete applications will not be considered . Read the list above carefully and ensure you have uploaded all required documents by the deadline. If you have yet to complete your degree, please supply an interim transcript where possible. You can find more information on how to prepare your application and what we are looking for at the "PhD Application Workshop" which will be held on Teams on Monday 23rd November at 2pm (GMT): PhD Application Workshop. Applications will be shortlisted in January 2027 with interviews in February 2027. The anticipated start date for this scholarship is September 2027. Please visit our website for more information on PhD opportunities in the Faculty of Health and Life Sciences. Further enquiries Project-specific queries: Ruth.Salway@bristol.ac.uk Application queries: uob-pgradmissions@bristol.ac.uk Funding Notes This is a four-year scholarship covering tuition fees, a UKRI-rate stipend (£21,805 in 2026/27, updated annually), and £2,100 annual research costs. The scholarship is available to UK and International applicants. International candidates (including EU) will be required to cover the cost of their student visa, healthcare surcharge and other costs of moving to the UK. References https://doi.org/10.1136/bmj.l2239 https://doi.org/10.1016/j.artmed.2021.102108