Health effects of long-term exposure to non-optimal ambient temperatures and variability

BACKGROUND

Climate change increases global average temperature, extreme temperature events, and temperature variability. While heat waves and cold snaps are clearly harmful, even moderately high and low temperatures may also carry health risks, and emerging evidence suggest that temperature variability may be more important than the mean temperature in increasing health risks. However, as most existing studies focused on the short-term (in days) health effects, there is a major knowledge gaps about the longer-term mortality and morbidity impact of non-optimal temperatures and temperature variability.

In the prospective China Kadoorie Biobank (CKB) of >0.5 million adults, daily ambient temperature data were collected from each 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 ~100,000 deaths and ~3 million ICD-10 coded episodes of hospitalisation of >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 be likely to include the following components:

• to assess the long-term trends of ambient temperature and its 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 and temperature variability (e.g. monthly, sub-seasonal, seasonal, inter-annual) with mortality and morbidity risks of different diseases;
• to explore potential effect modification by lifestyle factors, medical history, activity, and location.

The student will work within a multi-disciplinary team, and will gain in-house training in systematic literature review, 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. 

PROSPECTIVE  STUDENT

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.