Project Description
“Levelling up” general practice is a key NHS goal, aiming to reduce health inequalities by ensuring access to primary care. How this is achieved in practice, however, is complex and relies on both adequate staffing and appropriate funding. Core funding is currently based on the global sum allocation formula (Carr-Hill formula) which calculates payment to practices based on population needs. Variables include patient age and sex, additional needs, and rurality, all of which determine payment to practices for service provision. Geographical catchment areas are key to these calculations. Issues with the Carr-Hill formula are widely noted, and in particular, the definition and determination of rural general practice has remained an abiding policy concern throughout the entire history of the NHS. Its ambiguities have shaped financial mechanisms which underpin the contractual delivery of primary care.
Geographical catchment areas, and other routinely-collected data (e.g. prescribing and QOF data), are an underused but important input which can be used to inform statistical analyses that infer trends at a population level. This PhD will use a mixture of geospatial analyses to identify patterns in these data, and ethnographic methodologies to explain them, considering the implications for populations, health inequalities and access to care.
The project will answer the following questions:
- How can geospatial analyses of general practice catchment areas (with an initial focus on rurality) inform aims to “level up” general practice?
- How can geospatial analyses contribute to identifying priorities within the “levelling up” agenda in care provision?
- What are the implications of considering revisions to the Carr-Hill formula for local general practitioners and patients, specifically around access to care?
- How do definitions of locality and catchment affect populations, health inequalities and access to care?
This studentship would suit a candidate who wishes to address important challenges in using data to inform health care service provision. This could be someone with data science skills who wishes to learn applied ethnographic methods in a health setting, or one with ethnographic knowledge wishing to develop data science skills.
Lancaster Medical School has particular strengths in supporting research involving health information, computation and statistics, as well as a strong focus on social and economic inequality in health. Full training on combining approaches to applied data science and ethnographic research will be provided, including the opportunity to take modules from our MSc Health Data Science, and traning will be tailored to candidate expertise.
Application process: Applications should be made in writing to the lead supervisor, Dr Liz Brewster ([Email Address Removed]). You MUST include the following
1. CV (max 2 A4 sides), including details of two academic references
2. A cover letter outlining your qualifications and interest in the studentship (max 2 A4 sides)
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