Risk factors for clustering of tuberculosis cases: a systematic review of population-based molecular epidemiology studies.
Bibliographic record
Abstract
BACKGROUND: Many molecular epidemiology studies have been conducted to identify risk factors for clustering of tuberculosis (TB) cases in the population. OBJECTIVE: To estimate the impact of commonly investigated risk factors on TB clustering. METHODS: Ten electronic databases were searched up to January 2006 along with a hand search of the International Journal of Tuberculosis and Lung Disease and bibliographies of review articles. Meta-analyses of odds ratios (ORs) for various risk factors were conducted using random effect models, stratified by TB incidence. Meta-regressions were employed to account for the heterogeneity in clustering proportions and the magnitudes of risk. FINDINGS: The TB clustering proportion varied greatly (7.0-72.3%) among 36 studies in 17 countries. In multiple meta-regression analyses, high TB incidence, mean cluster size and conventional contact tracing were significantly associated with higher clustering. The pooled ORs (95%CIs) for low and high/intermediate TB incidence studies, using a cut off of 25/100000 per year, were 3.4 (2.7- 4.2) and 1.6 (1.3-2.1) for local-born status, 1.6 (1.5-1.7) and 1.7 (1.3-2.2) for pulmonary TB and 1.2 (1.1-1.3) and 1.3 (1.1-1.7) for smear-positive cases, respectively. Male sex, local birth, alcohol abuse and injection drug use were significantly higher risks in low TB incidence studies than in the high/intermediate ones. INTERPRETATION: Meta-analyses yielded significant estimates of ORs for several risk factors across both levels of TB incidence. Alcohol abuse, injection drug use and homelessness--all characteristics of marginalized populations--were found to be consistently significant in populations of low TB incidence. More research is needed to better understand TB transmission dynamics in high-burden countries.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".