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Burden of schistosomiasis-related deaths in sub-Saharan Africa

  • 8 September 2025 to 2 December 2025
  • Project No: D26002
  • DPhil Project 2026
  • Big Data Institute (BDI) Health Economics Research Centre (HERC)

Background

Schistosomiasis is a parasitic disease affecting mostly individuals in sub-Saharan Africa. An estimated 700 million individuals live in areas with ongoing schistosome transmission, of which an estimated 250m people require treatment. It is assumed schistosomiasis often does not result in death, due to the availability of en masse treatment with praziquantel and slow pathogenesis. Current estimates range from only ~12,000 deaths per year in the Global Burden of Disease Study to ~280,000 deaths per year in outdated but still widely used seminal analyses. Both estimations of mortality rely on the assumption that the community prevalence of infection is associated with deaths. Yet, within the context of repeated treatment and the required long history of infection exposure needed to cause severe diseases, recent evidence from SchistoTrack has shown that current infection cannot be used to approximate actual morbidity burden. Inaccurate assumptions coupled with the lack of data have resulted in the actual burden of mortality due to schistosomiasis remaining poorly understood. 

The aim of this DPhil project is to provide biologically informed estimates of the burden of schistosomiasis-specific mortality. It is anticipated the findings will aid in prioritising emergency care in resource constrained settings, estimating prognosis of individuals, and setting national disease priorities. 

research experience, research methods and skills training

Aims:

  1. To estimate the excess mortality or mortality rates related to schistosomiasis differentiated by species in a systematic meta-analysis.
  2. To measure attributable disability and case fatality rates using SchistoTrack data for severe conditions or complications related to intestinal schistosomiasis to improve estimates of disability weights and develop prognostic indices.
  3. To develop cause-specific death attribution tools using prospective data from SchistoTrack while investigating methods for case ascertainment/adjudication with clinical staff in SchistoTrack.
  4. To validate cause-specific tools from SchistoTrack with publicly available verbal autopsy (OpenMortality) or vital registry (WHO Mortality) data.

Ethical approvals have been obtained for use of SchistoTrack data.

The student will gain skills for systematic literature review, text processing, data cleaning, research presentation, clinical data analysis, health/disease state assignments, and prognostic/mortality modelling.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

This DPhil project may require 1-2 months of fieldwork in rural Uganda. 

PROSPECTIVE STUDENT

The ideal candidate will either have a Bachelor’s or Master’s degree in mathematics, statistics, epidemiology, or a related quantitative discipline, or alternatively be clinically qualified. This post is particularly suited to someone with good programming skills in R. 

research group