Scaling up investigation and treatment of household contacts of tuberculosis patients in Brazil: a cost-effectiveness and budget impact analysis
Bibliographic record
Abstract
Background: In Brazil, investigation and treatment of tuberculosis infection (TBI) in households contacts (HHC) of TB patients is not a priority. We estimated the cost-effectiveness and budget-impact of scaling-up an enhanced HHC management in Brazil. Methods: and two enhanced strategies for management of HHC focusing on: (1) only tuberculosis disease (TBD) detection and, (2) TBD and TBI detection and treatment. Effectiveness was the number of HHC diagnosed with TBD and completing TBI treatment. Proportions in the cascades-of-care were derived from a meta-analysis. Health-system costs (2019 US$) were based on literature and official data from Brazil. The impact of enhanced strategies was extrapolated using reported data from 2019. Findings: , 0 (95% uncertainty interval: 0-1) HHC are diagnosed with TBD and 2 (0-16) complete TBI treatment. With strategy(1), an additional 15 (3-45) HHC would be diagnosed with TBD at a cost of US$346 each. With strategy(2), 81 (19-226) additional HHC would complete TBI treatment at a cost of US$84 each. A combined strategy, implemented nationally to enhance TBD detection and TBI treatment would result in an additional 9,711 (845-28,693) TBD being detected, and 51,277 (12,028-143,495) more HHC completing TBI treatment each year, utilizing 10.9% and 11.6% of the annual national tuberculosis program budget, respectively. Interpretation: Enhanced detection and treatment of TBD and TBI among HHC in Brazil can be achieved at a national level using current tools at reasonable cost. Funding: None.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".