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background

Diabetes now affects over 100 million adults in China, accounting for over a quarter of the diabetes population worldwide. Individuals with diabetes are at a higher risk of developing infections, but most evidence comes from studies in Western populations, with little reliable data from low- and middle-income countries, including China, where differences in detection and management of diabetes, patterns of infectious diseases and genetic susceptibility, compared with Western populations, may have important implications for the burden of diabetes-associated infectious diseases.

The China Kadoorie Biobank (CKB) is a prospective cohort study of 0.5 million adults recruited from 10 diverse localities in China between 2004 and 2008 (http://www.ckbiobank.org/), with extensive data collected by questionnaire and physical measurements, and with long-term storage of blood samples. At baseline about 6% of participants had previously diagnosed or screen-detected diabetes, and after 10-years of follow-up, about 12,000 infectious disease events and 14,000 incident diabetes events have been recorded among study participants, and these data are complemented by genome-wide SNP data and assays of blood-related infectious markers (~45 antigens/antibodies from ~20 pathogens).  

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The aims of this DPhil project, based on the CKB study population, will be to:

  • Characterise the frequency and distribution of incident infectious diseases by age, sex, area and socioeconomic status;
  • Examine the association of diabetes with prevalence of seropositivity of ~20 pathogens in a subset of the CKB population;
  • Examine the association for diabetes with incident infectious diseases and how these associations are modified by other factors, for example, smoking;
  • Explore effect modification by genetic susceptibility (e.g. HLA) on the association of diabetes with prevalence of seropositivity and incidence of infectious 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.

field work, secondments, industry placements and training

Training opportunities will be offered as required, for example, in advanced statistical and epidemiological methods and programming. 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 meetings. 

prospective candidate

Students should have a good degree in the biomedical or life sciences and training or experience in epidemiology or statistics, including programming.

Supervisors