Accurately estimating the burden of vascular disease using electronic health records
Reliable and detailed estimates of the burden of different diseases on population health are important for the management of healthcare resources. Estimates of the burden of cardiovascular disease in different parts of the UK are largely based on mathematical extrapolation from national disease registries or surveys. Electronic health records that are linked between primary and secondary care have the potential to improve local and national estimates of the burden of vascular disease. They also offers new opportunities for studying the impact of multi-morbidity (the co-existence of more major disease) or multiple risk factors on these estimates. This project will use the Clinical Practice Research Database (CPRD; an anonymised database of the primary care records of five million current UK patients) and the Oxford Research Data Warehouse (a large dataset of electronic health records in Oxfordshire). The specific DPhil project will be subject to further discussion and personal interest, but may include the following areas of work:
- Assess algorithms for diagnosis of types of vascular disease using electronic medical records
- Assess the use of electronic health records to estimate the prevalence of major cardiovascular risk factors
- Investigate spatial distribution and temporal trends in the incidence and prevalence of a range of vascular diseases
- Determine the impact of multi-morbidity on burden of disease estimates for vascular disease
Compare findings to current estimates of the disease burden, such as those produced by the British Heart Foundation and the Global Burden of Disease Study.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
This project will involve detailed analysis and interpretation of existing data. The student will work within a multi-disciplinary team and will gain research experience in literature review, epidemiological and statistical methodology, programming and data analysis. Regular research meetings and workshops will be held which the candidate will be expected to attend and to present research findings.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The project will provide a range of training opportunities in statistical analysis and interpretation and statistical programming. By the end of the DPhil, it is expected that you will be competent to plan, undertake and interpret statistical analysis of large-scale epidemiological data, and to report your findings. The project will be based at the Big Data Institute and the Clinical Trial Service Unit, Nuffield Department of Population Health, which has excellent facilities and a world-class community of statistical and clinical scientists. There will also be opportunities to participate in Health Data Research UK (HDR UK) research meetings and training activities.
Candidates should have a strong background in a mathematical or biomedical discipline and postgraduate training in epidemiology, statistics or public health (or be willing to do the MSc in Global Health Science at Oxford in preparation for such a project). The project will involve large-scale data and statistical analyses. Candidates should therefore have an interest and aptitude in extending these skills as well as a strong interest in non-communicable disease epidemiology.