The impact of the Brazilian Family Health Strategy and the conditional cash transfer on tuberculosis treatment outcomes in Rio de Janeiro: an individual-level analysis of secondary data
Bibliographic record
Abstract
Background: Unsuccessful tuberculosis outcomes are frequent; bold policies are needed to end the tuberculosis (TB) epidemic to attain the third Sustainable Development Goal (SDG) by 2030. We examined the effect of the Family Health Strategy (FHS) and its interactions with the conditional cash transfer programme (CTP) on TB outcomes in Rio de Janeiro, Brazil. Methods: We performed individual-based analyses of a database resulting from deterministic and probabilistic linkages of the TB information system, FHS registries and CTP payrolls. Patients ≥15 years old treated with the standard RHZE regimen were included. The rates of successful outcomes were analysed according to coverage by FHS. Effects from the CTP and its interactions with the FHS were examined among the poorest. Results: FHS coverage increased the likelihood for successful outcomes by 14% (12-17%) among 13 482 new cases, and by 35% (25-47%) among 1880 retreatment cases. The CTP had an independent effect but no interaction with the FHS among the poorest. Conclusions: This is the first individual-based study to show a relevant protection of poor urban communities regarding patient-important health outcomes by the Brazilian FHS and CTP. These findings support strategies of universal health coverage, primary care strengthening and social protection to achieve a major SDG.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".