A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data
Phillips MT., Meiring JE., Voysey M., Warren JL., Baker S., Basnyat B., Clemens JD., Dolecek C., Dunstan SJ., Dougan G., Gordon MA., Heyderman RS., Holt KE., Qadri F., Pollard AJ., Pitzer VE.
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>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.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>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.</jats:p></jats:sec>