Acute and chronic respiratory diseases in diverse populations: patterns, trends, and risk factors

BACKGROUND

Worldwide respiratory diseases cause >9 million deaths each year. Despite the health burden, there remain large knowledge gaps about the epidemiology of respiratory diseases in different populations. This project will utilise comprehensive data from the prospective China Kadoorie Biobank (CKB) and the UK Biobank (UKB), each of 0.5 million adults. Both studies have collected extensive exposure (eg smoking, lung function, environmental exposures) and long-term health outcome (eg more than 3 million hospital episodes of more than 5000 different conditions in CKB) data. In addition, we host other large cohort studies, for example the Mexico City Prospective Study (150,000 participants), which could potentially be included to examine, compare and understand the epidemiology of different respiratory diseases in multi-ethnic populations.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The precise project will be developed according to the student’s interests and aptitude, but will likely to cover some of the following components:

• to examine and compare the incidence, patterns, and trends of different (acute and chronic) respiratory diseases in CKB and UKB; 
• to characterise individuals with occurrence of multiple respiratory diseases and their long-term prognosis; 
• to assess the contribution of various modifiable (eg smoking, adiposity, air pollution, ambient temperature, pathogenic infections) and non-modifiable risk factors with risks of different respiratory diseases; 
• to explore the potential role of co-existing chronic conditions in the susceptibility to respiratory diseases (and vice versa); 
• to explore the causal relevance of specific exposures for disease risks and the associated biological mechanisms, using multi-omics data.

The student will work within a multi-disciplinary team, and will gain experience and in-house training in systematic literature review, study design and planning, data analysis and scientific writing. 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/Oxford Population Health Building. There are excellent facilities and a world-class community of population health, data science and genomic medicine researchers.

PROSPECTIVE  STUDENT

The ideal candidate will have a good first degree (2.1) and master’s degree in epidemiology, statistics, biomedical science, or a related discipline, with a strong interest in respiratory disease.