Abstract
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.
Highlights
Current estimates of typhoid fever incidence serve as a basis for decision-making around typhoid control
Facility-based cases of blood-culture-confirmed typhoid fever are considerably lower than the true number of those with the disease1 because the reported numbers do not account for individuals with typhoid fever who do not seek healthcare, fail to receive a diagnostic test, or falsely test negative for typhoid (Figure 1)
We developed a framework within the Bayesian setting to integrate data from multiple sources to estimate the population-based incidence of typhoid fever based on passive surveillance in Malawi, Nepal, and Bangladesh, three typhoid-endemic countries with different demographics, healthcare systems, and access to diagnostics
Summary
Current estimates of typhoid fever incidence serve as a basis for decision-making around typhoid control. Facility-based cases of blood-culture-confirmed typhoid fever are considerably lower than the true number of those with the disease because the reported numbers do not account for individuals with typhoid fever who do not seek healthcare, fail to receive a diagnostic test, or falsely test negative for typhoid (Figure 1). Typhoid fever is estimated to cause 11 to 18 million infections and 100 000 to 200 000 deaths, but there is considerable uncertainty in these estimates. Studies suggest that somewhere between 60% and 90% individuals with typhoid fever do not receive adequate medical attention, in part because they do not to seek formal treatment.. Even if a blood culture test is recommended and laboratory facilities are available, not all patients will consent. Clinical opinion on the cause of fever can affect the likelihood of blood being drawn for culture.
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