Assessing the long-term ambient temperature variability and the associated health risk
Extreme temperature events are becoming more common as climate change is intensifying. While heat waves and cold snaps will certainly be leading to excess deaths, recent epidemiological studies have shown even moderately high and low temperatures may lead to excess death, with some studies showing each degree above or below the optimum temperature associated with 1.7% and 1.1% excess mortality risks, respectively. However, there were also observations that mortality rates did not seem to differ between US cities located in south and those in the north, suggesting people tend to adapt well to the usual local temperatures. Emerging evidence shows that variability of temperature (i.e. temporal changes in temperature) may be more relevant than the mean temperature alone in understanding and quantifying temperature-related health risks.
A few published studies have focused on the health risk due to short-term fluctuations in temperatures (intra- or inter-day). These time-series or case-crossover studies have provided insights into the immediate (or short-term delayed) impact of abrupt changes in ambient temperatures, but are not suited to address the research question on whether life expectancy is reduced as a result of prolonged exposure to higher temperature variability, which would only be possible by means of a prospective cohort design.
CKB is a large prospective cohort study of >0.5 million adults recruited from 10 diverse localities in China between 2004 and 2008. Extensive questionnaire and physical measurements data have been collected from all participants. Regional daily ambient temperature data for the study areas is also available. To date, >55,000 deaths and >1.2 million ICD-10 coded episodes of hospitalisation of >5000 different disease types have been recorded among participants through electronic linkage to health insurance agencies and death registries.
In addition, CKB has also collected high-frequency personal temperature exposure data using wearable sensors, together with time-activity diaries and objective physical activity measurements in a subset of participants (n=450). This offers a unique opportunity to compare temperature variability at ambient and personal levels.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The precise project will depend on the student’s interests and aptitude, but will likely to include the following components:
- To assess the trends of temperature variability across the CKB study sites
- To examine the relationship between different timescales of long-term temperature variability (e.g. monthly, sub-seasonal, seasonal, inter-annual) and health risks
- To explore the 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 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 some 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, part of the Nuffield Department of Population Health and based 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. There may be opportunities to work with external partners from industry and other research institutions.
A candidate with a good degree in quantitative science subject, 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.