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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".