Incidence and Risk Factors Associated with Latent Tuberculosis Infection and Pulmonary Tuberculosis among People Deprived of Liberty in Colombian Prisons
Bibliographic record
Abstract
People deprived of liberty (PDL) are at high risk of acquiring Mycobacterium tuberculosis infection (latent tuberculosis infection [LTBI]) and progressing to active tuberculosis (TB). We sought to determine the incidence rates and factors associated with LTBI and active TB in Colombian prisons. Using information of four cohort studies, we included 240 PDL with two-step tuberculin skin test (TST) negative and followed them to evaluate TST conversion, as well as, 2,134 PDL that were investigated to rule out active TB (1,305 among people with lower respiratory symptoms of any duration, and 829 among people without respiratory symptoms and screened for LTBI). Latent tuberculosis infection incidence rate was 2,402.88 cases per 100,000 person-months (95% CI 1,364.62-4,231.10) in PDL with short incarceration at baseline, and 419.66 cases per 100,000 person-months (95% CI 225.80-779.95) in individuals with long incarceration at baseline (who were enrolled for the follow after at least 1 year of incarceration). The TB incidence rate among PDL with lower respiratory symptoms was 146.53 cases/100,000 person-months, and among PDL without respiratory symptoms screened for LTBI the incidence rate was 19.49 cases/100,000 person-months. History of Bacillus Calmette-Guerin vaccination decreased the risk of acquiring LTBI among PDL who were recently incarcerated. Female sex, smoked drugs, and current cigarette smoking were associated with an increased risk of developing active TB. This study shows that PDL have high risk for LTBI and active TB. It is important to perform LTBI testing at admission to prison, as well as regular follow-up to control TB in prisons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".