The Impact of Pregnancy on Tuberculosis Treatment Outcomes: An Analysis of Brazilian National Surveillance Data 2016–2022
Bibliographic record
Abstract
BACKGROUND: More than 200 000 pregnant people fall ill with tuberculosis (TB) annually. Little is known about the impact of pregnancy on TB outcomes. METHODS: This study used surveillance data from Brazil's Ministry of Health. We included women aged 11-49 years newly diagnosed with drug-susceptible TB disease between 2016 and 2022, treated with a first-line anti-TB regimen, and with a known treatment outcome. Using multivariable regression, we estimated the age-stratified effect of pregnancy on (1) loss to follow-up and (2) death during TB treatment. RESULTS: Of 96 868 women with TB, 1870 (1.9%) were pregnant, 79 361 (81.9%) were not pregnant, and 15 637 (16.1%) had unknown pregnancy status. Among pregnant women, 1432 (76.6%) experienced treatment success, 358 (19.1%), lost to follow-up, and 80 (4.3%) died. Among nonpregnant women, 79 262 (83.4%) experienced treatment success, 11 582 (12.2%) were lost to follow-up, and 4154 (4.4%) died. In adolescents, pregnancy conferred higher odds of loss to follow-up (adjusted odds ratio [aOR], 1.78; 95% confidence interval [CI]: 1.29-2.44) and death (aOR, 2.35; 95% CI: 1.27-4.37). Compared to nonpregnant women of the same age, pregnant women aged 20-29 and 30-39 years experienced more loss to follow-up (respectively: aOR, 1.39; 95% CI: 1.17-1.66 and aOR 1.79; 95% CI, 1.42-2.25), while those aged 40-49 years were more likely to die (aOR, 1.66; 95% CI: 1.04-2.66). CONCLUSIONS: Our analysis revealed a significant association between pregnancy and poor TB treatment outcomes, highlighting the need for care providers to offer enhanced support and monitoring for pregnant women undergoing TB treatment. Further research is needed to identify the underlying reasons for these findings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".