Long-term ambient temperature variability and the associated health risks
Extreme temperature events are becoming more common with intensifying climate change. While heat waves and cold snaps are clearly harmful, even moderately high and low temperatures may also carry health risks, with certain studies showing each degree above or below the optimum temperature to be associated with 1-2% excess mortality risks. Emerging evidence from case-crossover studies or short-term studies of abrupt changes in ambient temperatures also suggests that temperature variability may be more important than the mean temperature in increasing health risks. Long-term prospective cohort studies are needed to reliably assess the long-term health risks associated with variability of ambient temperature.
In the prospective China Kadoorie Biobank (CKB) of more than 0.5 million adults, daily ambient temperature data were collected from each area of 10 diverse localities during the baseline survey (2004-08) and subsequent follow-up (2008-2020), along with extensive questionnaire and physical measurements data from all participants. To date, CKB has recorded more than 80,000 deaths and more than 2 million ICD-10 coded episodes of hospitalisation of more than 5000 different disease types. Moreover, high-frequency personal temperature exposure data were also collected in a subset of participants using wearable sensors, together with time-activity diaries and objective physical activity measurements. These data offer a unique opportunity to assess temperature variability and associated health risks at ambient and personal levels.
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 assess the long-term trends of temperature variability across the CKB study sites and their associations with certain physical traits (e.g. blood pressure);
- to examine the relationships of different timescales of long-term temperature variability (e.g. monthly, sub-seasonal, seasonal, inter-annual) with mortality and morbidity risks of different diseases;
- to explore potential effect modification by activity and location.
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 programing 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 within the CKB research group 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 first degree (2.1) in quantitative science discipline, with strong interest and background in environmental epidemiology, exposure science or a related discipline. Previous postgraduate training or experience in epidemiology or statistics is necessary.