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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.160 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".