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BAckground

An estimated 250 million people worldwide have schistosomiasis infections, of which 80% are in sub-Saharan Africa. In 2017, schistosomiasis caused at least 1.43 million disability-adjusted life years lost. Blood flukes (parasitic worms) cause schistosomiasis. Transmission to humans is via contact with freshwater sources that are contaminated through open defecation/urination and that harbour competent intermediate snail hosts. Water, sanitation, and hygiene (WASH) interventions are promoted to reduce pathogen transmission.

Consistently identifying effective WASH interventions that reduce schistosomiasis infection risks is challenging. One issue entails the heterogeneity in human activities that puts individuals at risk of infection. Exposure to parasites varies based on the water-related activity, time of day, duration in the water, location, and individual characteristics. Another issue is the lack of a systematic framework for identifying water-related exposures. There is a need to effectively combine diverse types of data to construct robust infection risk indicators that can be utilized across study settings and data collection methods.

Survey-based approaches or direct observations are common methods for recording schistosomiasis-related exposures. Wearable cameras offer an alternative to these approaches to enable the measurement of water-related activities from first person perspectives. Previously unrecognized activities that may contribute to parasite exposure may be identifiable with wearable cameras.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

This project will use data from ongoing studies in rural villages in Uganda. Ethics approval will be obtained prior to the start of this project. Depending on restrictions due to the ongoing pandemic, it is expected that this data will be collected prior to the start of this DPhil project. Otherwise, this data will be collected within the first year of the DPhil project. The study designs are as follows. A cross-sectional survey of 600 individuals in 300 households from 10 villages will be completed. To measure infection status/intensity, one child (aged 5+ years) and one adult (aged 18+ years) will be sampled from each household. For 300 of these individuals, wearable cameras that capture an image every 1-3 minutes will be worn for one day. WASH data will be collected on every person aged 1+ years within sampled households (approx. 1500 individuals). To directly observe water contact patterns at the village level, an observational study will be conducted, whereby trained water site observers will record information on every person entering a pre-identified lake site (including the time of day, duration, and activity). Contact sites will be observed for one week. The DPhil candidate will have the opportunity to contribute to the existing fieldwork study designs through follow-up studies.

Epidemiological analyses (Aims 1-3) will focus on data from household surveys and records of directly observed water contact from a trained water site observer.

Aims

  1. Establish a systematic framework for identifying schistosomiasis-related exposures.
  2. Construct and validate exposure/activity-based indices
  3. Determine the cross-sectional association of exposures with schistosomiasis infection prevalence and intensity
  4. Explore the dimensions of exposures captured by wearable cameras as compared to other approaches

The student will gain skills in literature review, study design, primary data collection, epidemiological data analysis, statistical programming, data annotation, and research presentation. Training in schistosomiasis epidemiology and fieldwork will be provided. Protocols for wearable camera data exploration and annotation are available.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

This project requires approximately 1-2 months of fieldwork in rural Uganda over the course of the DPhil project. The study is in close collaboration with the Uganda Ministry of Health. Experienced field teams from Uganda will co-lead the primary data collection with the primary supervisor.

prospective candidate

Candidates ideally will have postgraduate training in global health, epidemiology, or a related discipline as well as experience in statistical/quantitative analyses of health data. Interest in parasitic infections and good communication skills are necessary.

Supervisors