Abstract
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 × 107 (95% uncertainty interval [UI] 7.4 × 106-5.4 × 107) cases which represent 1.6 × 106 (95% UI 3.2 × 105-2.3 × 106) 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 × 103 (95% UI 4.2 × 102-1.4 × 103) 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.
Highlights
Cryptosporidium is increasingly recognised as a leading cause of diarrhoeal disease (Checkley et al, 2015; Shirley et al, 2012)
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
This study demonstrates the use of modelled concentration data to explore the disease burden associated with Cryptosporidium in consumed surface water for sub-Saharan Africa
Summary
Cryptosporidium is increasingly recognised as a leading cause of diarrhoeal disease (Checkley et al, 2015; Shirley et al, 2012). The recently developed Global Waterborne Pathogen model for Cryptosporidium (GloWPa-Crypto model) produces global estimates of Cryptosporidium oocyst concentrations in surface water at a 0.5 by 0.5° (longitude by latitude) resolution (Vermeulen et al, 2018). This has created the opportunity to assess the risk and disease burden due to Cryptosporidium in surface water used as drinking water at a larger scale than previously possible. Development and application of largescale QMRA models, utilising data such as those produced by the GloWPa model, was identified as a priority towards gaining a greater understanding of the risks and disease burden associated with waterborne pathogens (Hofstra et al, 2019)
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