Estimated costs of tuberculosis services in Brazil, 2023
Bibliographic record
Abstract
INTRODUCTION: To eliminate tuberculosis (TB) in Brazil, scaling up screening and prevention strategies will be essential. We estimated costs of TB services in Brazil to support budgeting, cost-effectiveness analysis and inform the implementation of these strategies. METHODS: We leveraged databases from five large cities in Brazil (Manaus, Recife, Porto Alegre, São Paulo and Rio de Janeiro) to estimate costs of TB services in 2023 US dollars. We estimated mean costs and 95% uncertainty ranges (95% UR) for specific components and combined these components according to national algorithms for TB diagnosis and treatment in adults and children to estimate costs for different services. We leveraged these outputs to estimate the costs of household contact investigation. RESULTS: We estimated the mean (95% UR) cost of testing children for TB infection with a tuberculin skin test (TST) or interferon-gamma release assay and providing 3 months of once-weekly isoniazid and rifapentine (3HP) was US$48 ($25-$82) and US$67 ($43-$101), respectively. Providing 6 months of treatment for drug-susceptible tuberculosis (DS-TB) to children was US$557 ($163-$1195). In adults, costs were similar to the cost of TST and 3HP costing US$49 ($25-$86) and 6 months of DS-TB treatment being $583 ($175-$1252). For both children and adults, costs of newer, 6-month treatment regimens for rifampin-resistant tuberculosis (RR-TB) were less expensive than 18-month regimens. In children, the cost was US$4807 ($1559-$10 066) for the 6-month regimen and US$9212 ($2756-$19 567) for the 18-month regimen. Corresponding costs in adults were US$3518 ($1169-$7330) and US$7910 ($2533-$16 717). Across 10 000 households with an index TB patient, we estimated use of a TST and 3HP for TB infection screening and treatment and 6-month regimens for DS-TB and RR-TB disease to cost $1 093 531 (95% UR $409 349-$2 217 054). CONCLUSION: There are important cost differences in TB services depending on diagnostic and treatment choices. These data are essential inputs for budgeting and cost-effectiveness.
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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 | 0.012 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".