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Investments in water and sanitation systems are believed to have led to the decline in typhoid fever in developed countries, such that most cases now occur in regions lacking adequate clean water and sanitation. Exploring seasonal and long-term patterns in historical typhoid mortality in the United States can offer deeper understanding of disease drivers. We fit modified Time-series Susceptible-Infectious-Recovered models to city-level weekly mortality counts to estimate seasonal and long-term typhoid transmission. We examined seasonal transmission separately by city and aggregated by water source. Typhoid transmission peaked in late summer/early fall. Seasonality varied by water source, with the greatest variation occurring in cities with reservoirs. We then fit hierarchical regression models to measure associations between long-term transmission and annual financial investments in water and sewer systems. Overall historical $1 per capita ($16.13 in 2017) investments in the water supply were associated with approximately 5% (95% confidence interval: 3-6%) decreases in typhoid transmission, while $1 increases in the overall sewer system investments were associated with estimated 6% (95% confidence interval: 4-9%) decreases. Our findings aid in the understanding of typhoid transmission dynamics and potential impacts of water and sanitation improvements, and can inform cost-effectiveness analyses of interventions to reduce the typhoid burden.

Background. Decisions about typhoid fever prevention and control are based on current estimates of typhoid incidence and their uncertainty, which can be difficult to measure. Limits of using facility-based estimates alone- the lack of specific clinical diagnostic criteria, poorly sensitive and specific diagnostic tests, and scarcity of accurate and complete datasets- contribute to difficulties in calculating population-level incidence of typhoid. Methods. Using data from the Strategic Alliance across Africa & Asia (STRATAA) programme, we integrated information from demographic censuses, healthcare utilization surveys, facility-based passive surveillance, and serological surveillance from sites in Malawi, Nepal, and Bangladesh in order to adjust crude incidence estimates to account for under-detection. We developed an approach using a Bayesian framework that adjusts the count of reported blood-culture-positive cases of typhoid for each of the following phases: healthcare seeking, blood culture collection, and blood culture detection. We estimated the proportion of "true" typhoid cases occurring in the population under surveillance captured at each phase by combining information from the observed cases from the STRATAA datasets and estimates from prior published studies. We confirmed that the model was correctly formulated by comparing to simulated data. Results. The ratio between the observed and adjusted incidence rates was 8.2 (95% CI: 6.4-13.3) in Malawi, 13.8 (95% CI: 8.8-23.0) in Nepal, and 7.0 (95% CI: 5.5-9.1) in Bangladesh, and varied by age across the three sites. The probability of having blood drawn for culture led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most to adjustment factors in Nepal and Bangladesh. Adjusted incidence rates were mostly within the limits of the seroincidence rate of typhoid infection determined by serological data. Conclusion. Passive surveillance of blood culture-confirmed typhoid fever without adjustment for case ascertainment, sample collection and diagnostic sensitivity results in considerable underestimation of the true incidence of typhoid in the population. Our approach allows each phase of the typhoid reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty in this estimate, which can inform decision-making for typhoid prevention and control.

Decisions about typhoid fever prevention and control are based on estimates of typhoid incidence and their uncertainty. Lack of specific clinical diagnostic criteria, poorly sensitive diagnostic tests, and scarcity of accurate and complete datasets contribute to difficulties in calculating age‐specific population‐level typhoid incidence. Using data from the Strategic Typhoid Alliance across Africa and Asia program, we integrated demographic censuses, healthcare utilization surveys, facility‐based surveillance, and serological surveillance from Malawi, Nepal, and Bangladesh to account for under‐detection of cases. We developed a Bayesian approach that adjusts the count of reported blood‐culture‐positive cases for blood culture detection, blood culture collection, and healthcare seeking—and how these factors vary by age—while combining information from prior published studies. We validated the model using simulated data. The ratio of observed to adjusted incidence rates was 7.7 (95% credible interval [CrI]: 6.0‐12.4) in Malawi, 14.4 (95% CrI: 9.3‐24.9) in Nepal, and 7.0 (95% CrI: 5.6‐9.2) in Bangladesh. The probability of blood culture collection led to the largest adjustment in Malawi, while the probability of seeking healthcare contributed the most in Nepal and Bangladesh; adjustment factors varied by age. Adjusted incidence rates were within or below the seroincidence rate limits of typhoid infection. Estimates of blood‐culture‐confirmed typhoid fever without these adjustments results in considerable underestimation of the true incidence of typhoid fever. Our approach allows each phase of the reporting process to be synthesized to estimate the adjusted incidence of typhoid fever while correctly characterizing uncertainty, which can inform decision‐making for typhoid prevention and control.