Background While a range of interventions exist for tuberculosis prevention, screening, diagnosis, and treatment, their potential population impact and cost-effectiveness are seldom directly compared, or evaluated between settings with different background TB epidemiology and structural drivers. Methods We calibrated a deterministic TB model to epidemiological indicators in Brazil, India, and South Africa. We implemented seven interventions across countries focusing on prevention, screening and diagnosis, and treatment of TB, as well as TB screening in prisons in Brazil and nutritional supplementation in India. We standardised scale-up (2025-2030), coverage (80% of target population), and strength of evidence for epidemiological impact using published efficacy data. We estimated epidemiological impact and incremental cost-effectiveness ratios (ICERs), expressed as costs per disability-adjusted life year (DALY) averted by 2050. Results Only three interventions prevented >10% of incident TB episodes by 2050: vaccination (median 15-28% across countries), symptom-agnostic community-wide screening (32-38%) and screening in prisons (23%). The impact of other interventions was more limited, ranging from 0% (shortened drug-susceptible treatment) to 5% (nutritional supplementation). ICERs varied widely by intervention and setting. Shortened drug-resistant treatment was cost-saving across settings, with the next lowest ICERs for prison screening in Brazil (72 USD/DALY) and nutritional supplementation in India (167 USD/DALY). Within each country, both low-cost community-wide screening and TB vaccine campaigns had lower USD/DALY than TB preventive treatment. Conclusion Interventions with meaningful epidemiological impact can also be cost-effective, but need to target populations beyond clinic-diagnosed individuals or their households. Achieving such potential requires a priority shift in funding, policy and product development.

The authors have declared no competing interest.

This paper is published within the context of FAST-TB, a program supported through CRDF Global with funding from the National Institutes of Health (NIH), National Institute of Allergy and Infectious Diseases (NIAID) and National Science Foundation (NSF) through agreement number INT-9531011. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH and/or CRDF Global.

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All information, data and code to replicate these analyses are contained in the supplementary materials and the GitHub repository

https://github.com/lshtm-tbmg/NIH2