Additional file 1 of Cost-effectiveness analysis of typhoid conjugate vaccines in an outbreak setting: a modeling study
Creators: name: Phillips, Maile T.
name: Antillon, Marina
name: Bilcke, Joke
name: Bar-Zeev, Naor
name: Limani, Fumbani
name: Debellut, Frédéric
name: Pecenka, Clint
name: Neuzil, Kathleen M.
name: Gordon, Melita A.
name: Thindwa, Deus
name: Paltiel, A. David
name: Yaesoubi, Reza
name: Pitzer, Virginia E.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: Cancer
subject: Science Policy
subject: Infectious Diseases
subject: FOS: Health sciences
subject: Computational Biology

description: Additional file 1: Table S1. Options for disability weights assigned to different healthcare use groups. Disability weights presented for the two options are for infectious disease, acute, and for the specified level of typhoid episode (mild, moderate, or severe). Table S2. Input parameters for transmission model and cost-effectiveness analysis, with distributions. Typhoid incidence and age distribution, mortality, antimicrobial resistance, healthcare use, treatment cost, vaccine-related costs, and disability-adjusted life-year uncertainty distributions and their page location in the text are shown. Table S3. Predicted disease and economic burden in the absence of vaccination. The median (95% credible interval) estimates for predicted cases, deaths, DALYs, and treatment costs per 100,000 people over 10 years are shown for each scenario in the absence of vaccination. Table S4. Predicted vaccine impact with randomized outbreak timing, pre-outbreak incidence and post-outbreak incidence per 100,000 individuals. The median (95% credible interval) estimates for averted cases, deaths, DALYs, treatment costs, costs of vaccinations and net costs per 100,000 people are shown for each vaccination strategy compared to no vaccination for randomized outbreak timing (“randomized”), pre-outbreak incidence (“pre”), and post-outbreak incidence (“post”). Vaccination strategies include routine vaccination at nine months of age in year 0 with or without a catchup campaign up to 15 years of age. Reactive routine vaccination strategies (with a catchup campaign) include delays of 1, 6, 12, and 24 months to deployment (“1 m”, “6 m”, “12 m”, “24 m”, respectively). All values are presented as incidence per 100,000 people. R = routine vaccination; RC = routine vaccination plus a catchup campaign. Table S5. Predicted vaccine impact with fixed outbreak timing incidence per 100,000 individuals. The median (95% credible interval) estimates for averted cases, deaths, DALYs, treatment costs, costs of vaccinations and net costs per 100,000 people are shown for each vaccination strategy compared to no vaccination with fixed outbreak timing. vaccination strategies include routine vaccination at nine months of age in year 0 with or without a catchup campaign up to 15 years of age. Preventive strategy results are shown for 10, 5, 2, and 1 year(s) (“10y”, “5y”, “2y”, “1y”) before the outbreak starts. Reactive routine vaccination strategies (with a catchup campaign) include delays of 1, 6, 12, and 24 months to deployment (“1 m”, “6 m”, “12 m”, “24 m”, respectively). All values are presented as incidence per 100,000 people. R = routine vaccination; RC = routine vaccination plus a catchup campaign. Table S6. Predicted vaccine impact with WHO-CHOICE cost of illness data: randomized outbreak timing, pre-outbreak incidence and post-outbreak incidence. The median (95% credible interval) estimates for averted treatment costs and net costs per 100,000 people are shown (averted cases, deaths, DALYs, and costs of vaccinations are the same as the main scenarios) for each vaccination strategy compared to no vaccination for randomized outbreak timing (“randomized”), pre-outbreak incidence (“pre”), and post-outbreak incidence (“post”) using previous WHO-CHOICE cost of illness data. Vaccination strategies include routine vaccination at nine months of age in year 0 with or without a catchup campaign up to 15 years of age. Reactive routine vaccination strategies (with a catchup campaign) include delays of 1, 6, 12, and 24 months to deployment (“1 m”, “6 m”, “12 m”, “24 m”, respectively). All values are presented as costs per 100,000 people. R = routine vaccination; RC = routine vaccination plus a catchup campaign. Fig S1. Ordinary differential equations and corresponding dynamic compartmental model for typhoid disease dynamics. Black compartments and text indicate the scenario in which there is no vaccination, and the blue compartments and text indicate the added scenarios in which vaccination is introduced. Note that this model is also age-structured, though not shown. Fig S2. Observed and modeled vaccine efficacy over time. Observed data from a phase 3, double-blind, randomized active-controlled clinical trial of single-dose TCV in Blantyre, Malawi at 3 time points (12, 18, and 24 months) (black points with 95% confidence interval error bars) and the modeled vaccine efficacy after vaccination (median estimate: solid blue line; 95% credible interval in shaded light blue) are shown. Fig S3. Sensitivity and specificity of outbreak identification threshold definitions. The sensitivity (purple) and specificity (green) are shown for each outbreak identification definition (x-axis; ranging from 6–16 standard deviations above the mean monthly reported typhoid fever cases). Fig S4. Estimated specificity of outbreak identification thresholds 6 to 16 standard deviations above the monthly mean reported typhoid fever cases. The median outbreak identification date (dot) and 95% credible interval (line) is shown for outbreak identification thresholds of 6–16 standard deviations above the monthly mean number of typhoid cases for 1000 simulations of the dynamic model. The black dashed line represents the “true” start date of the outbreak, and the shaded grey area represents 0–18 months after the outbreak started (sensitivity window). Fig S5. Observed and fitted proportion of typhoid infections that are resistant to antimicrobial treatment in Blantyre, Malawi from 1995–2025. Observed data points of the yearly proportion of antimicrobial resistant typhoid fever infections over time are shown in black dots, while the fitted estimates from the beta regression model are shown in the dashed blue line and the prediction intervals are shown in the turquoise dotted lines. Fig S6. Predicted and observed weekly blood-culture confirmed typhoid fever cases in the absence of vaccination. The 1000 stochastic realizations of weekly blood-culture confirmed typhoid fever case incidence per 100,000 people in the absence of vaccination from the dynamic transmission model are show in purple. The observed (reported) typhoid fever cases used to fit the dynamic model is represented by the bold black line, while the observed incidence collected after model fitting is represented by the dashed red line. Fig S7. Observed versus fitted age distribution of reported typhoid cases. The proportion of observed cases in each age group are denoted by light blue bars, while the model-predicted age distribution is shown in darker blue. Fig S8. Predicted weekly blood-culture confirmed typhoid infections in Scenario 1 for reactive vaccination strategies. The 1000 simulated predictions for weekly blood-culture confirmed typhoid infections are shown in purple, with the median of all stochastic realizations shown in orange for each reactive vaccination strategy. Four situations are shown, representing the four different delays in timing to implement vaccination once the outbreak is identified (1, 6, 12, and 24 months). The median typhoid infections in the absence of vaccination from 1000 realizations is shown in black, and the median date of vaccination deployment for each situation is denoted by the vertical dashed green line. Fig S9. Predicted weekly blood-culture confirmed typhoid fever cases in Scenario 1 for preventive vaccination strategies. The 1000 stochastic realizations of weekly typhoid cases are shown in purple, with the median of all simulations shown in orange for each preventive vaccination strategy. Eight situations are shown, representing each preventive routine vaccination timing strategy (10, 5, 2, and 1 year(s) before the outbreak for routine vaccination at 9 months of age with and without a catchup campaign up to 15 years of age). The median number of typhoid infections in the absence of vaccination from 1,000 realizations is shown in black, and the date of vaccination deployment for each situation is denoted by the vertical dashed green line. Fig S10. Heatmap of optimal intervention strategy and its estimated uncertainty across a range of willingness to pay values for each strategy comparison and a range of deployment delays, and years before the outbreak. Each column in a single panel shows the preferred strategy (i.e. the strategy that yields the highest average net benefit) for one cost-effectiveness analysis comparing no vaccination (grey), preventive routine vaccination (purple), preventive routine vaccination with a catch-up campaign (green), and reactive vaccination with a catch-up campaign (orange) for delays of 1, 6, 12, or 24 months after the outbreak has been identified (x-axis). The y-axis represents willingness-to-pay (WTP) values ranging from $0-$1000 (USD 2020). The shading represents the probability that the preferred strategy yields the highest net benefit (lighter: lower probability; darker: higher probability). Results are plotted for whether preventive vaccination is introduced 10 years (top panel) or 1 year (bottom panel) before the outbreak for a 20-year time horizon. Note that preventive routine vaccination without a catchup campaign is never a preferred strategy, and as a result does not appear in the plots. Fig S11. Cost-effectiveness planes and acceptability frontiers for sensitivity analysis assuming WHO-CHOICE treatment costs. The cost-effectiveness planes (left) and cost-effectiveness acceptability frontiers (CEAFs; right) are plotted for (A-B) Scenario 1 (randomized outbreak timing), (C-D) Scenario 2 (no outbreak, assuming pre-outbreak incidence), and (E–F) Scenario 3 (outbreak has already occurred). In the cost-effectiveness planes, each dot represents the incremental costs (in 2020 USD) and DALYs averted for one simulation when compared with the base case strategy of no vaccination. The bold Xs denote the expected additional cost and DALYs averted for each vaccination strategy with respect to no vaccination. Strategies are indicated by the color of the dot or X (purple: preventive routine vaccination; green: preventive routine vaccination plus a catch-up campaign up to age 15; or orange: reactive routine vaccination plus a catch-up campaign to age 15—for Scenario 1 only). In the CEAFs, the preferred strategy (i.e. the strategy that yielded the highest average net benefit) for each willingness-to-pay threshold ($0–1,000 per DALY averted; x-axis, 2020 USD) is indicated by the color of the line (black: no vaccination; and same strategy colors as other panels), while the proportion of samples in which that strategy yielded the highest net benefit is indicated by the value on the y-axis; this can be interpreted as our certainty in the optimal strategy. Fig S12. Expected value of partially perfect information for differing delays in vaccination deployment for reactive strategies with randomized outbreak timing (Scenario 1). The expected value of partial perfect information (EVPPI) for each parameter is shown for a range of willingness-to-pay values. Results are shown for 5000 parameter samples (in 2020 USD). Each panel shown represents the EVPPI for one cost-effectiveness analysis comparing 4 strategies: no vaccination (base case), preventive routine vaccination at 9 months, preventive routine vaccination with a catch-up campaign up to 15 years, and reactive routine vaccination with a catch-up campaign. The four panels (left to right) correspond to different delays in the reactive strategy (1-, 6-, 12-, or 24-month delays). The grey vertical dashed line corresponds to the 2020 gross domestic product per capita for Malawi. Fig S13. Expected value of partially perfect information for Scenario 2 (pre-outbreak incidence). The expected value of partial perfect information (EVPPI) for each parameter is shown for a range of willingness-to-pay values. Results are shown for 5000 parameter samples in 2020 USD. Fig S14. Expected value of partially perfect information for Scenario 3 (post-outbreak incidence). The expected value of partial perfect information (EVPPI) for each parameter is shown for a range of willingness-to-pay values. Results are shown for 5000 parameter samples in 2020 USD.