Tuberculosis among correctional facility workers: A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Prison inmates can transmit tuberculosis, including drug-resistant strains, to correctional facility workers and the community. In this systematic literature review, we investigated the magnitude of active and latent tuberculosis infection (LTBI) and associated risk factors among correctional facility workers. METHODS: We searched MEDLINE, EMBASE, LILACS, Cochrane CENTRAL, ISI Web of Science, CINAHL, and SCOPUS databases (January 1, 1989-December 31, 2017) for studies with the MeSH terms "prison" (and similar) AND "tuberculosis", without language restriction. We searched for gray literature in Google Scholar and conference proceedings. Stratified analyses according to tuberculosis burden were performed. RESULTS: Of the 974 titles identified, 15 (nine good, six fair quality) fulfilled the inclusion criteria (110,393 correctional facility workers; six countries; 82,668 active tuberculosis; 110,192 LTBI). Pooled LTBI prevalence and incidence rates were 26% (12-42, I2 = 99.0%) and 2% (1-3, I2 = 98.6%), respectively. LTBI prevalence reached 44% (12-79, I2 = 99.0%) in high-burden countries. Active tuberculosis was reported only in low-burden countries (incidence range, 0.61-450/10,000 correctional facility workers/year). LTBI-associated risk factors included job duration, older age, country of birth, current tobacco smoking, reported contact with prisoners, and BCG vaccination. CONCLUSION: Despite the risk of bias and high heterogeneity, LTBI was found to be prevalent in correctional facility workers, mainly in high-burden countries. LTBI risk factors suggest both occupational and community exposure. Active tuberculosis occurrence in low-burden countries suggests higher vulnerability from recent infection among correctional facility workers in these countries. Systematic surveillance and infection control measures are necessary to protect these highly vulnerable workers.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".