The Return on Investment of Scaling Tuberculosis Screening and Preventive Treatment: A Modelling Study in Brazil, Georgia, Kenya, and South Africa
Bibliographic record
Abstract
Summary Background Closing the tuberculosis diagnostic gap and scaling-up tuberculosis preventive treatment (TPT) are two major global priorities to end the tuberculosis epidemic. To help support these efforts, we modeled the impact and return-on-investment (ROI) of a comprehensive intervention to improve tuberculosis screening and prevention in Brazil, Georgia, Kenya, and South Africa—four distinct epidemiological settings. Methods We worked with national tuberculosis programmes (NTP) in each country to define a set of interventions (“the intervention package”) related to tuberculosis screening and TPT in three priority populations: people with HIV, household contacts, and a country-defined high-risk population. We developed transmission models calibrated to tuberculosis epidemiology for each country, and collated cost data related to tuberculosis-related activities and patient costs in 2023 $USD. We compared the intervention package without and with TPT scaled-up to reach priority populations to a status quo scenario based on projected tuberculosis epidemiology over a 27-year time horizon (2024-2050). Outcomes were health system and societal costs, number of tuberculosis episodes, tuberculosis deaths, and disability adjusted life years (DALYs). We performed 1000 simulations and calculated the mean and 95% uncertainty range (95%UR) difference in outcomes between the intervention package and the status quo. We calculated the health system cost per DALY averted and societal return on the health system investment for each country. We did not discount costs or outcomes in the base scenario. Findings Under the status quo, by 2050, tuberculosis incidence is projected to be 39 (95%UR 37-43), 34 (24-50), 204 (186-255), and 208 (124-293) per 100,000 population in Brazil, Georgia, Kenya, and South Africa, respectively. Implementing the intervention package without TPT is projected to reduce tuberculosis incidence by 9.6% (95%UR 9.3-10), 14.4% (11-19.6), 30.3% (29-33.1), and 22.7% (19.4-27.2) in Brazil, Georgia, Kenya, and South Africa, respectively, by 2050. The addition of TPT is projected to further reduce tuberculosis incidence by 9.5% (95%UR 9.3-9.8), 10.9% (9.8-12.3), 19.2% (17.6-20.1), and 13.1% (11.2-14.4%). From the health system perspective, the incremental cost per DALY averted of the intervention package is $771 in Brazil, $1402 in Georgia, $521 in Kenya, and $163 in South Africa. The societal return per $1 invested by the health system is projected to be $10.80, $3.70, $27.40, and $39.00 in Brazil, Georgia, Kenya, and South Africa, respectively. Interpretation Scaling-up interventions related to tuberculosis screening and TPT in priority populations is projected to substantially reduce tuberculosis incidence and provide large returns on investment. Funding World Health Organization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".