Public Transportation and Tuberculosis Transmission in a High Incidence Setting
Bibliographic record
Abstract
BACKGROUND: Tuberculosis (TB) transmission may occur with exposure to an infectious contact often in the setting of household environments, but extra-domiciliary transmission also may happen. We evaluated if using buses and/or minibuses as public transportation was associated with acquiring TB in a high incidence urban district in Lima, Peru. METHODS: Newly diagnosed TB cases with no history of previous treatment and community controls were recruited from August to December 2008 for a case-control study. Crude and adjusted odd ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression to study the association between bus/minibus use and TB risk. RESULTS: One hundred forty TB cases and 80 controls were included. The overall use of buses/minibuses was 44.9%; 53.3% (72/135) among cases and 30.4% (24/79) among controls [OR: 3.50, (95% CI: 1.60-7.64)]. In the TB group, 25.7% (36/140) of subjects reported having had a recent household TB contact, and 13% (18/139) reported having had a workplace TB contact; corresponding figures for controls were 3.8% (3/80) and 4.1% (3/73), respectively[OR: 8.88 (95% CI: 2.64-29.92), and OR: 3.89 (95% CI: 1.10-13.70)]. In multivariate analyses, age, household income, household contact and using buses/minibuses to commute to work were independently associated with TB [OR for bus/minibus use: 11.8 (95% CI: 1.45-96.07)]. CONCLUSIONS: Bus/minibus use to commute to work is associated with TB risk in this high-incidence, urban population in Lima, Peru. Measures should be implemented to prevent TB transmission through this exposure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".