The effectiveness, cost-effectiveness, budget impact, and return on investment of scaling up tuberculosis screening and preventive treatment in Brazil, Georgia, Kenya, and South Africa: a modelling study
Bibliographic record
Abstract
BACKGROUND: Closing the tuberculosis diagnostic gap and scaling up tuberculosis preventive treatment (TPT) are two global priorities to end tuberculosis. We aimed to estimate the cost-effectiveness, budget impact, and societal return on investment of a comprehensive intervention to improve tuberculosis screening and prevention in Brazil, Georgia, Kenya, and South Africa-four distinct epidemiological settings. METHODS: In this modelling study, in partnership with national tuberculosis programmes we defined 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 (people deprived of liberty [Brazil], people accessing care for injection drug use [Georgia], people in informal settlements in nine districts with a high prevalence of tuberculosis [Kenya], and people in the 22 subdistricts with the highest prevalence of tuberculosis [South Africa]). We developed transmission models calibrated to country-specific epidemiology and collated cost data for tuberculosis-related activities and patient costs in 2023 US dollars (US$). We compared the intervention package scaled up to reach all priority populations by 2030 to a status quo scenario based on projected tuberculosis epidemiology over a 27-year time horizon (Jan 1, 2024, to Dec 31, 2050); to delineate the impact of intervention components, we also evaluated the intervention package without TPT. Outcomes were health system and societal costs, number of tuberculosis episodes, tuberculosis deaths, and disability-adjusted life years (DALYs). We calculated the budget impact, health system cost per DALY averted, and societal return on the health system investment for each country. Outcomes were discounted at 3% per annum. FINDINGS: With the status quo scenario, by 2050, tuberculosis incidence is projected to be 41 per 100 000 population (95% uncertainty range 32-53) in Brazil, 45 per 100 000 population (36-60) in Georgia, 214 per 100 000 population (146-266) in Kenya, and 261 per 100 000 population (133-406) in South Africa. The percentage of all tuberculosis episodes prevented by implementing the intervention package in all priority populations is projected to be 15·0% (12·8-17·5) in Brazil, 14·3% (13·1-15·8) in Georgia, 21·3% (15·2-27·6) in Kenya, and 26·4% (21·1-31·8) in South Africa by 2050. If implemented without TPT (ie, tuberculosis disease screening alone), corresponding reductions were lower at 10·4% (8·6-12·2) in Brazil, 10·2% (9·5-11·2) in Georgia, 12·6% (9·5-15·9) in Kenya, and 16·8% (13·0-20·4) in South Africa. In 2030, the percentage of the national tuberculosis programme budget required for the intervention package was 62% in Brazil, 10% in Georgia, 67% in Kenya, and 44% South Africa. The incremental cost per DALY averted of the intervention package compared with the status quo in all priority populations is $386 in Brazil, $491 in Georgia, $53 in Kenya, and $160 in South Africa. The corresponding societal return per health system dollar invested is projected to be $51 in Brazil, $8 in Georgia, $27 in Kenya, and $54 in South Africa. INTERPRETATION: Scaling up tuberculosis screening and TPT requires substantial investment but is projected to be cost-effective compared with the status quo, to greatly reduce tuberculosis incidence, and to provide large returns on investment. FUNDING: World Health Organization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".