Mycobacterium tuberculosis Infection in Close Childhood Contacts of Adults with Pulmonary Tuberculosis is Increased by Secondhand Exposure to Tobacco
Bibliographic record
Abstract
Tobacco use is a major risk factor for tuberculosis (TB). Secondhand smoke (SHS) is also a risk factor for TB and to a lesser extent, Mycobacterium tuberculosis infection without disease. We investigated the added risk of M. tuberculosis infection due to SHS exposure in childhood contacts of TB cases in The Gambia. Participants were childhood household contacts aged ≤ 14 years of newly diagnosed pulmonary TB (PTB) cases. The intensity of exposure to the case was categorized according to whether contacts slept in the same room, same house, or a different house as the case. Contacts were tested with an enzyme-linked immunospot interferon gamma release assay. In multivariate regression models, M. tuberculosis infection was associated with increasing exposure to a case (odds ratios [OR]: 3.9, 95% confidence interval [CI]: 2.11–71.4, P < 0.001]) and with male gender (OR: 1.5 [95% CI: 1.12–2.11], P = 0.008). Tobacco use caused a 3-fold increase in the odds of M. tuberculosis infection in children who slept closest to a case who smoked within the same home compared with a nonsmoking case (OR: 8.0 [95% CI: 2.74–23.29] versus 2.4 [95% CI: 1.17–4.92], P < 0.001). SHS exposure as an effect modifier appears to greatly increase the risk of M. tuberculosis infection in children exposed to PTB cases. Smoking cessation campaigns may be important for reducing transmission of M. tuberculosis to children within households.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".