Identifying treatment gaps and health system inequalities in the use of community prescribed medications

BACKGROUND

The Clinical Practice Research Datalink (CPRD) is a comprehensive patient-level dataset that covers a substantial proportion of English general practices. The CPRD collects primary care records, including consultations, prescriptions, and patient- and practice-level data. Additionally, the CPRD can be linked with hospital data from the NHS Hospital Episode Statistics (HES), as well as area-level deprivation data. 

This project aims to examine trends and variations in prescribing practices and their implications for population health and inequalities in England, using economic measures such as the concentration index. The use of large-scale administrative data allows for the application of innovative big data and econometric methods (e.g., using variations in prescription practices across England as exogenous variation to identify the impact of different therapies on a range of health outcomes). The results of the project are expected to have policy relevance, and the successful applicant will be encouraged to explore other potential uses of the CPRD to address important policy questions, such as the extent to which closing treatment gaps can reduce health inequalities in England.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in Health Economics research. This studentship will provide training in literature review methods, data management, and statistical methods for handling large datasets. The selected individual will develop a deep understanding of CPRD data and will enhance their programming, statistical, econometric, and big data analysis skills through their work on this project. This includes developing skills in R, Stata, and potentially SQL and Python. Additionally, there will be opportunities to collaborate with external researchers across the UK and internationally.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Training in using CPRD data, econometrics, programming, and scientific writing will be provided either formally within the department or informally through the supervisory team. Attendance at HERC seminars, workshops, and departmental and University courses will be encouraged. There will be opportunities to present research work at relevant international and national conferences, as well as internally.

PROSPECTIVE  STUDENT

The ideal candidate will have a background in economics, health economics, or health policy at the Master's level, with a proven record of quantitative skills (mathematics, statistics, economics). The candidate is expected to have good programming and data analysis skills, particularly in statistical/econometric methods, and the ability to further improve these skills over the course of their DPhil. Experience with Stata and/or R or similar statistical software, and some experience working with complex observational datasets or electronic health record databases, is required.