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Review 2: "Cost-effectiveness of End-game Strategies Against Sleeping Sickness across the Democratic Republic of Congo"

Reviewers found the methodology used in this analysis to be robust and reliable in general though one reviewer had concerns that limitations were not discussed in enough detail. 

Published onApr 30, 2024
Review 2: "Cost-effectiveness of End-game Strategies Against Sleeping Sickness across the Democratic Republic of Congo"
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Cost-effectiveness of end-game strategies against sleeping sickness across the Democratic Republic of Congo
Cost-effectiveness of end-game strategies against sleeping sickness across the Democratic Republic of Congo
Description

Gambiense human African trypanosomiasis (gHAT) is marked for elimination of transmission (EoT) by 2030. We examined the cost-effectiveness (CE) of EoT in the Democratic Republic of Congo, which has the highest global gHAT burden. In 166 health zones (HZs), we modelled the transmission dynamics, health outcomes, and economic costs of six strategies during 2024-40, including the cessation of activities after case reporting reduces to zero. Uncertainty in CE was assessed within the net monetary framework, presented as the optimal strategies at a range of willingness-to-pay (WTP) values, denominated in costs per disability-adjusted life-year averted. Status quo strategies, CE strategies (WTP=$500), and strategies with a high probability of EoT by 2030 are predicted to yield EoT by 2030 in 117 HZs, 130 HZs, and 138 HZs respectively, at a cost by 2040 of \$159M (82M--266M), $175M ($98M-$285M), $206M ($114M-$339M). A more lenient timeline of EoT by 2040 could lead to EoT in 153 HZs at a cost of $189M ($105M-$311M), leaving 13 HZs shy of the goal. Investing in EoT by 2030 is predicted to reduce gHAT deaths from 34,770 (14,113-71,118) with status quo strategies to 8,214 (3,284-18,507).

RR:C19 Evidence Scale rating by reviewer:

  • Reliable. The main study claims are generally justified by its methods and data. The results and conclusions are likely to be similar to the hypothetical ideal study. There are some minor caveats or limitations, but they would/do not change the major claims of the study. The study provides sufficient strength of evidence on its own that its main claims should be considered actionable, with some room for future revision.

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Review: In this preprint, the authors claim that elimination of gHAT by 2030 is a realistic goal for the majority of the Democratic Republic of Congo. However, this will require several regions to adopt more intensive strategies that are currently within their toolkit but will necessitate further financial investment. A substantial return on investment is anticipated by 2040.

This paper presents an impressive analysis of the cost-effectiveness of strategies for elimination of gHAT in the DRC. The conclusions drawn by the authors are well-supported by the research outlined in the manuscript. The authors have employed sophisticated ensemble modeling techniques to illustrate disease transmission and have utilized probabilistic models to address uncertainty effectively. The methodology is robust and transparent, which lends credibility to findings. The study is strengthened by the involvement of local stakeholders in the selection of strategies. This participatory approach enhances the relevance and suitability of strategies for the DRC context, and it increases the potential for successful implementation of recommendations. The net monetary benefit framework used in the analysis is appropriate for the calculation of return on investment, a helpful metric for informing decision-making.

However, there are certain shortcomings of the model's design that may have had an impact on findings. As noted by the authors in the limitations section, the model assumes perfect application of available tool in the strategies it considers. Further, the imperfect treatment of detected cases is not accounted for. The potential implications of these limitations on model results are not described in detail. The study could benefit from a few sensitivity analyses to address these assumptions and estimate how outcomes would differ in non-ideal scenarios. At a minimum, I recommend authors discuss in further detail how frequent/common these shortcomings are in the DRC (drug stock-outs, loss to follow up for treatment etc.), and discuss how study results may change if they were included in the model design.

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