Partial recovery of tuberculosis preventive treatment in Brazil after pandemic drawback
Bibliographic record
Abstract
Brazil was heavily affected by COVID-19 both with death toll and economically, with absence of a centralized Federal Government response. Tuberculosis (TB) notifications decreased in 2020 but partial recovery was observed in 2021. We have previously shown a sharp (93%) reduction in TB preventive treatment notifications among five Brazilian cities with more than 1,000 notifications in 2021. We hypothesized TB preventive treatment would also recover. We updated the previous analysis by adding other cities that hold more than a 1,000 notifications until 2022. Data aggregated by 2-week periods were extracted from the Information System for Notifying People Undergoing Treatment for LTBI (IL-TB). Biweekly percentage change (BPC) of notifications until October 2022 and outcomes until July 2022 (in the two weeks of TB preventive treatment initiation) were analyzed using Joinpoint software. A total of 39,701 notifications in 11 cities were included, 66% from São Paulo and Rio de Janeiro, Brazil. We found a significant increase of TB preventive treatment notifications in the beginning of 2021 (BPC range 1.4-49.6), with sustained progression in seven out of the 11 cities. Overall, median completion rates were 65%. In most cities, a gradual and steady decrease of treatment completion rates was found, except for Rio de Janeiro and Manaus (Amazonas State, Brazil), where a BPC of 1.5 and 1.2, respectively, was followed by a sustained increase. Notifications and completion proportions of TB preventive treatment were heterogeneous, which partly reflects the heterogeneity in local response to the pandemic. We found that notifications were recovered, and that the sharp 2021 decrease was no longer observed, which suggests delays in notification. In conclusion, the sharp reductions in TB preventive treatment completion rates in most cities might have been caused by delays in reporting; however, the sustained and progressive decrease are a concern.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".