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Review 2: "The Potential Health and Economic Impacts of New Tuberculosis Vaccines under Varying Delivery Strategies in Delhi and Gujarat, India: A Modelling Study"

Overall, reviewers had divided opinions on the strength of this preprint with some concerns about adherence to the CHEER standards. 

Published onNov 06, 2023
Review 2: "The Potential Health and Economic Impacts of New Tuberculosis Vaccines under Varying Delivery Strategies in Delhi and Gujarat, India: A Modelling Study"
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key-enterThis Pub is a Review of
The potential health and economic impacts of new tuberculosis vaccines under varying delivery strategies in Delhi and Gujarat, India: a modelling study
The potential health and economic impacts of new tuberculosis vaccines under varying delivery strategies in Delhi and Gujarat, India: a modelling study

Abstract Background India has the largest tuberculosis burden globally, but this burden varies nationwide. All-age tuberculosis prevalence in 2021 ranged from 747/100,000 in Delhi to 137/100,000 in Gujarat. Previous modelling has demonstrated the benefits and costs of introducing novel tuberculosis vaccines in India overall. However, no studies have compared the potential impact of tuberculosis vaccines in regions within India with differing tuberculosis disease and infection prevalence. We used mathematical modelling to investigate how the health and economic impact of two potential tuberculosis vaccines, M72/AS01E and BCG-revaccination, could differ in Delhi and Gujarat under varying delivery strategies.Methods We applied a compartmental tuberculosis model separately for Delhi (higher disease and infection prevalence) and Gujarat (lower disease and infection prevalence), and projected epidemiological trends to 2050 assuming no new vaccine introduction. We simulated M72/AS01E and BCG-revaccination scenarios varying target ages and vaccine characteristics. We estimated cumulative cases, deaths, and disability-adjusted life years averted between 2025–2050 compared to the no-new-vaccine scenario and compared incremental cost-effectiveness ratios to three cost-effectiveness thresholds.Results M72/AS01E averted a higher proportion of tuberculosis cases than BCG-revaccination in both regions (Delhi: 16.0% vs 8.3%, Gujarat: 8.5% vs 5.1%) and had higher vaccination costs (Delhi: USD$118 million vs USD$27 million, Gujarat: US$366 million vs US$97 million). M72/AS01E in Delhi could be cost-effective, or even cost-saving, for all modelled vaccine characteristics. M72/AS01E could be cost-effective in Gujarat, unless efficacy was assumed only for those with current infection at vaccination. BCG-revaccination could be cost-effective, or cost-saving, in both regions for all modelled vaccine scenarios.Discussion M72/AS01E and BCG-revaccination could be impactful and cost-effective in Delhi and Gujarat. Differences in impact, costs, and cost-effectiveness between vaccines and regions, were determined partly by differences in disease and infection prevalence, and demography. Age-specific regional estimates of infection prevalence could help to inform delivery strategies for vaccines that may only be effective in people with a particular infection status. Evidence on the mechanism of effect of M72/AS01E and its effectiveness in uninfected individuals, which were important drivers of impact and cost-effectiveness, particularly in Gujarat, are also key to improve estimates of population-level impact.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.



A new TB vaccine or a revaccination with BCG could be impactful and cost-effective in Delhi and Gujarat, two states in India to reduce the burden of disease of TB.

As written it does not follow the iDSI reference case guidelines for reporting or the CHEERS checklist. There are no details on how incremental costs were calculated. Consequently, I don't feel I have enough information to assess how the model estimated impact, and how costs were estimated, and hence the findings and discussion are hard to verify without more information. I checked if there was an appendix that could provide any additional information and did see reference to one in the paper's methods section or as part of the paper I accessed.

Additionally, the papers objectives are not clearly stated. Page 2 par 3-4 jumps from background to using a mathematical model, but never clearly describes the paper’s intent. Therefore, it is hard to follow the conclusions. For example, from the abstract there is a recommendation to obtain age specific regional estimates of infection prevalence, which are inputs to the model. Similarly the authors call for more evidence on effect and effectiveness, which are also inputs to improve estimates of impact. If an objective of the paper is to identify gaps in information for models to inform national vaccine strategies, these findings would make sense. However, these recommendations don't fall out of the model per se, and these recommendations cost money for more research to obtain inputs for mathematical modeling--so not really clear if this is one of the messages of the paper. Alternatively are the authors trying to use mathematical models to provide information to allocate scarce resources more efficiently in a country the size of India, and one that has a high burden of disease with a lot of heterogeneity across states? Clearly defining the research objectives will also help to assess the methods, results and discussion. 

Ashlee Hamilton:

In a similar vein, the authors require additional evidence on the effectiveness and effect of the study, both of which are inputs that can be used to increase estimations of the impact. basketball legends