The impact of the COVID-19 pandemic in tuberculosis preventive treatment in Brazil: a retrospective cohort study using secondary data
Bibliographic record
Abstract
Background: Disruptions in tuberculosis services have been reported around the world since the emergence of the COVID-19 pandemic. However, the pandemic's effect on tuberculosis preventive treatment (TPT) has been poorly explored. We compared TPT-notified prescriptions and outcomes before and during the pandemic in Brazil. Methods: Retrospective cohort using secondary data from the Brazilian TPT information system in five cities with over 1000 notifications. The number of TPT prescriptions was analysed from 6 months after healthcare workers' training, in 2018, to July 2021. The proportion of TPT outcomes by the date of treatment initiation was analysed up to the end of 2020, as most outcomes of TPT started in 2021 were still unknown in July 2021. Joinpoint regression was used to evaluate trends. Findings: 14,014 TPT prescriptions were included, most from São Paulo (8032) and Rio de Janeiro (3187). Compared to the same epidemiological weeks in 2019, the number of TPT prescribed in 2020 increased in Rio de Janeiro (82%) and São Paulo (14%) and decreased in Recife (65%), Fortaleza (31%) and Manaus (44%). In 2021, however, there was a 93% reduction in TPT prescriptions in all cities. The proportion of completed TPT remained constant (median = 74%). Interpretation: The COVID-19 pandemic in Brazil was associated with a dramatic decrease in TPT prescriptions in 2021. Treatment adherence remained constant, suggesting that health services were able to keep people on treatment but did not perform well in providing opportunities for people to enter care. Efforts are needed to expand access to TPT. Funding: Brazilian Ministry of Science, Technology and Innovation, CNPq.
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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 | 0.005 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".