Factors associated with the rapid implementation process of the fixed-dose combination RHZE tuberculosis regimen in brazil: an ecological study
Bibliographic record
Abstract
BACKGROUND: The Brazilian National Tuberculosis Control Program (NTCP) recommended the fixed-dose four-drug combination (FDC-RHZE) regimen to treat new tuberculosis cases in December 2009, expecting to increase adherence and avoid resistance. We evaluated factors associated with the speed of the new regimen implementation process in this continent-sized country. METHODS: We conducted an ecological study based on the Brazilian Case Notification Database (SINAN) having the Brazilian municipalities as the analytical unit. Municipalities with at least one case reported from December 2009 to March 2011 were considered eligible. The association of rapid (≤ 3 months) implementation of the new regimen with demographic, epidemiological and operational health service characteristics, such as compliance with NTCP recommendations (supervised treatment, bacteriological confirmation of the diagnosis and monthly bacteriological monitoring), was analyzed. We used the adjusted odds ratios (OR) and their 95% confidence interval (CI) to assess the association of independent variables with the outcome in a multiple logistic regression model. RESULTS: Rapid implementation of the new regimen in municipalities was associated with small populations (OR=25.5, 95% CI= 19.1-34.1), low population density (OR=2.3, 95% CI= 1.9-2.9), low tuberculosis incidence rates (OR=8.8, 95% CI= 6.7-11.4) and good compliance with other NTCP recommendations. CONCLUSIONS: We showed that SINAN secondary data analysis is feasible and useful to learn lessons from. Municipalities with high tuberculosis burden and large populations need special attention for implementing new recommendations. This is particularly important considering the Global Alliance pipeline for new tuberculosis treatment regimens.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".