Assessing the impact of measurement error, bias, and confounding in air pollution epidemiology using prospective cohort studies in diverse population [MRC PHRU]
Ambient air pollution is considered a leading risk factor of morbidity and mortality worldwide. Despite decades of research, however, there remains important uncertainties on the health effects of air pollution, primarily due to the reliance on ecological exposure proxies and the limited prospective evidence in diverse populations beyond North America and Europe.
Measurement error, bias, and confounding are three fundamental challenges in observational epidemiology studies. In air pollution epidemiology, there has been insufficient in-depth investigation on the extent of these challenges and their impact on epidemiological associations. Recent advancement in wearable sensor technologies and increasing availability of large population-based cohort studies offer a unique opportunity to tackle these longstanding questions.
The China Kadoorie Biobank (CKB) has collected an extensive range of epidemiological and health outcomes data in >500,000 Chinese adults. In addition to area-level environmental exposure data, CKB has collected high-frequency personal and indoor air pollution exposure data in a subset of participants using wearable sensors, together with time-activity diaries. The Richard Doll Consortium (RDC) is a network of >30 large cohort studies across diverse populations worldwide. Together, CKB and RDC offer a unique opportunity to understand the issue of error, bias, and confounding in air pollution epidemiology.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The precise project will be developed according to candidate’s interests and aptitude, but will likely to include the following components:
- To systematically reviewed the existing literature on the association of ambient air pollution with major chronic disease and build causal network models to identify key sources of measurement error, bias, and confounding;
- To quantify the impact of measurement error, bias, and confounding in observational associations, using both simulated and real-life data from population-based cohort studies;
- To develop study design and analytical approaches to mitigate the impact of the above challenges and improve disease burden estimation associated with air pollution.
The student will work within a multi-disciplinary team, and will gain research experience in systematic literature review, study design and planning, data analysis and scientific writing. There will also be in-house training in epidemiology and statistical programming and, if necessary, attendance at relevant courses. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large and high dimensional datasets, and to report research findings, including publications as the lead author in peer-reviewed journals and presentation at national/international conferences.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The project will be based primarily within the CTSU in the Big Data Institute. There are excellent facilities and a world-class community of population health and data science researchers. There may be opportunities to work with external partners from other research institutions.
A candidate with a good degree in quantitative science subject, with strong interest and background in environmental epidemiology, exposure science, statistics, or a related discipline. Previous postgraduate training or experience in epidemiology or statistics is necessary.