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Multi-morbidity is generally defined as the co-existence of two or more chronic diseases, and is a growing public health challenge globally. Despite the traditional single disease paradigm on which much research and clinical practice are based, demographic changes (e.g. population aging and declining mortality) and epidemiological shifts (e.g. as a result of public health interventions) have combined to drive an increase in co-occurrence of diseases, with major impacts on mortality, quality of life, clinical management and healthcare utilisation. Although the prevalence of cardiometabolic multi-morbidity is increasing, relatively little is understood about its epidemiology or, as a consequence, about effective approaches to the prevention and management of cardiometabolic multi-morbidity.

The China Kadoorie Biobank (CKB) is a prospective, population-based cohort study of 0.5 million adults recruited from 10 diverse localities in China between 2004 and 2008 (, with extensive lifestyle and health-related data collected at baseline and periodic resurveys. During 10 years of follow-up, large numbers of well-phenotyped incident disease events have been collected through linkage with established disease/death registries and electronic national health insurance databases, and the study currently includes >40,000 individuals with diabetes and >50,000 with major vascular events. This provides a unique resource in which to investigate this emerging field of “syndemics”. 


The aims of this DPhil project, using data from the CKB, will be to:

  • Define multi-morbidity within the context of cardiometabolic diseases;
  • Examine the burden, distribution and characteristics of cardiometabolic multi-morbidity, by age, sex, area and socioeconomic status;
  • Characterise the natural history, determinants, associations, and prognosis of cardiometabolic multi-morbidity, and interactions between constituent diseases.

The student will work within a multi-disciplinary team, and will gain experience in conducting systematic literature reviews, study design and planning, epidemiological and statistical methodology, statistical programming, and data analysis and presentation.


Training opportunities will be offered as required, and attendance at seminars, conferences and courses provided by the Department and the University of Oxford will be encouraged.

By the end of the DPhil, it is expected that the student will be competent to plan, undertake and interpret statistical analysis of large-scale epidemiological data. The student will be expected to publish 3-5 peer-reviewed papers, and to report their findings at relevant national and international meetings. 

prospective candidate

Students should have a good degree in biomedical or life sciences and postgraduate training or experience in epidemiology or statistics.