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external supervisor

Simon Stanworth, Radcliffe Department of Medicine


Although maternal anaemia is known to be associated with substantial short-term maternal health impacts, prevention and treatment remains poor, with up to 25% of women becoming anaemic at some point in their pregnancy. Evidence on diagnostic thresholds and associations with maternal and infant outcomes is inconsistent. Evidence is conflicting concerning the long-term impacts of maternal anaemia on both maternal health and infant and child development, which may partly explain why the condition is poorly managed. With the advent of big data, there exists the potential to link health and laboratory datasets (for example Hospital Episode Statistics, NHS Blood and Transplant data) with national school data (such as the National Pupil Database) to investigate in depth long-term outcomes for both mother and child. There is also the opportunity to work within the context of a large randomised trial of anaemia prevention to undertake additional analyses investigating outcomes in relation to different measures of iron status and diagnostic thresholds or to undertake primary data collection on infant developmental outcomes. There is scope for two DPhil students to cover different aspects (diagnostic thresholds and short-term outcomes versus long-term infant and child outcomes).


The project(s) will involve detailed analysis and interpretation of existing data, as well as primary data collection and analysis and experience within a large (11,500 participants) randomised controlled trial. The student(s) will work within a multi-disciplinary team and will gain research experience in literature review, epidemiological and statistical methodology, study design, data collection and analysis. Regular research meetings and workshops will be held which the candidate will be expected to attend and to present research findings.


The project(s) will be based at the National Perinatal Epidemiology Unit (NPEU), Nuffield Department of Population Health. The project(s) will provide a range of training opportunities in study design, primary data collection, data linkage and statistical analysis and interpretation of large datasets. 

prospective student

Candidates should have a background in a biomedical or mathematical discipline and postgraduate training in epidemiology, statistics or public health. Candidates should have an interest in advancing their quantitative skills as well as an interest in maternal and child health, haematology or public health.