The protozoan pathogen Cryptosporidium is an important cause of diarrhoeal disease, but in many contexts its burden remains uncertain. The Global Waterborne Pathogen model for Cryptosporidium (GloWPa-Crypto) predicts oocyst concentrations in surface water at 0.5 by 0.5° (longitude by latitude) resolution, allowing us to assess the burden specifically associated with the consumption of contaminated surface water at a large scale. In this study, data produced by the GloWPa-Crypto model were used in a quantitative microbial risk assessment (QMRA) for sub-Saharan Africa, one of the regions most severely affected by diarrhoeal disease. We first estimated the number of people consuming surface water in this region and assessed both direct consumption and consumption from a piped (treated) supply. The disease burden was expressed in disability adjusted life years (DALYs). We estimate an annual number of 4.3 × 10 7 (95% uncertainty interval [UI] 7.4 × 10 6 –5.4 × 10 7 ) cases which represent 1.6 × 10 6 (95% UI 3.2 × 10 5 –2.3 × 10 6 ) DALYs. Relative disease burden (DALYs per 100,000 persons) varies widely, ranging between 1.3 (95% UI 0.1–5.7) for Senegal and 1.0 × 10 3 (95% UI 4.2 × 10 2 –1.4 × 10 4 ) for Eswatini. Countries that carry the highest relative disease burden are primarily located in south and south-east sub-Saharan Africa and are characterised by a relatively high HIV/AIDS prevalence. Direct surface water consumption accounts for the vast majority of cases, but the results also point towards the importance of stable drinking water treatment performance. This is, to our knowledge, the first study to utilise modelled data on pathogen concentrations in a large scale QMRA. It demonstrates the potential value of such data in epidemiological research, particularly regarding disease aetiology.
|Journal||International Journal of Hygiene and Environmental Health|
|Early online date||16 Apr 2019|
|Publication status||Published - Jun 2019|
- Disease burden
- Quantitative microbial risk assessment (QMRA)
- Surface water