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BACKGROUND: Typhoid fever, or enteric fever, is a highly fatal infectious disease that affects over 9 million people worldwide each year, resulting in more than 110,000 deaths. Reduction in the burden of typhoid in low-income countries is crucial for public health and requires the implementation of feasible water, sanitation, and hygiene (WASH) interventions, especially in densely populated urban slums. OBJECTIVE: In this study, conducted in Mirpur, Bangladesh, we aimed to assess the association between household WASH status and typhoid risk in a training subpopulation of a large prospective cohort (n=98,087), and to evaluate the performance of a machine learning algorithm in creating a composite WASH variable. Further, we investigated the protection associated with living in households with improved WASH facilities and in clusters with increasing prevalence of such facilities during a 2-year follow-up period. METHODS: We used a machine learning algorithm to create a dichotomous composite variable ("Better" and "Not Better") based on 3 WASH variables: private toilet facility, safe drinking water source, and presence of water filter. The algorithm was trained using data from the training subpopulation and then validated in a distinct subpopulation (n=65,286) to assess its sensitivity and specificity. Cox regression models were used to evaluate the protective effect of living in "Better" WASH households and in clusters with increasing levels of "Better" WASH prevalence. RESULTS: We found that residence in households with improved WASH facilities was associated with a 38% reduction in typhoid risk (adjusted hazard ratio=0.62, 95% CI 0.49-0.78; P

Original publication




Journal article


JMIR Public Health Surveill

Publication Date





Bangladesh, LMIC, WASH, algorithm, algorithms, bacteria, bacterial, bacterial infection, contaminated, contamination, enteric, enteric fever, epidemiological, epidemiology, hygiene, hygienic, incidence, infection control, low income, low- and middle-income countries, machine learning, model, poverty, prevalence, protection, recursive partitioning, risk, salmonella, sanitary, sanitation, slum, slums, typhoid, typhoid fever, typhus, water, water, sanitation and hygiene, Humans, Water, Sanitation, Typhoid Fever, Bangladesh, Prospective Studies, Poverty Areas, Hygiene