BACKGROUND: Typhoid fever incidence estimates are central to policy decisions on vaccine introduction and investments in non-vaccine prevention and control but are often unavailable. We explored whether prevalence metrics from sentinel studies of community-onset bloodstream infections could accurately predict local Salmonella enterica serovar Typhi (Salmonella Typhi) incidence. METHODS: Using a previous systematic review (January 2018-December 2024), we identified studies reporting both typhoid incidence and prevalence of community-onset bloodstream infections from sentinel sites. From authors, we requested data on blood culture isolates and analysed four metrics: (i) Salmonella Typhi prevalence among probable pathogens, (ii) Salmonella Typhi rank order, (iii) Salmonella Typhi to Escherichia coli ratio, and (iv) Salmonella Typhi to 'stably endemic' organisms ratio. Typhoid incidence was categorized as low (100) per 100,000 person-years. We used univariate ordinal regression to assess the association between each metric and typhoid incidence level. The model performance was evaluated by the c-statistic, sensitivity, and specificity. RESULTS: Analysis of 29 study sites (20 Africa, 9 Asia) yielded 4625 probable pathogens. The median (IQR) typhoid incidence was 140 (28-319) per 100,000 person-years. All metrics were associated with increased typhoid incidence level: for each 1% increase in Salmonella Typhi prevalence OR 1.07 (95%CI 1.02-1.15); for each unit increase in rank order OR 0.25 (95%CI 0.06-0.64); for each unit increase in the log Salmonella Typhi to E. coli ratio OR 2.88 (95%CI 1.48-7.39) for each unit increase in the log Salmonella Typhi to 'stably endemic' organisms ratio OR 3.74 (95%CI 1.80-10.7). A parsimonious model using Salmonella Typhi prevalence alone achieved c-statistics of 0.87 (0.58-0.97), 0.76 (0.51-0.91), and 0.88 (0.69-0.96) for low, medium, and high incidence, respectively. CONCLUSION: Sentinel prevalence metrics from bloodstream infections, particularly Salmonella Typhi prevalence among probable pathogens, could be useful for inferring local typhoid fever incidence where direct data are unavailable.
Hagedoorn et al. (Sat,) studied this question.