The association between silica exposure, silicosis and tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: While the association between occupational inhalation of silica dust and pulmonary tuberculosis has been known for over a century, there has never been a published systematic review, particularly of experience in the current era of less severe silicosis and treatable tuberculosis. We undertook a systematic review of the evidence for the association between (1) silicosis and pulmonary tuberculosis, and (2) silica exposure and pulmonary tuberculosis controlling for silicosis, and their respective exposure-response gradients. METHODS: We searched PUBMED and EMBASE, and selected studies according to a priori inclusion criteria. We extracted, summarised and pooled the results of published case-control and cohort studies of silica exposure and/or silicosis and incident active tuberculosis. Study quality was assessed on the Newcastle-Ottawa Scale. Where meta-analysis was possible, effect estimates were pooled using inverse-variance weighted random-effects models. Otherwise narrative and graphic synthesis was undertaken. Confidence regarding overall effect estimates was assessed using the GRADE schema. RESULTS: Nine studies met the inclusion criteria. Meta-analysis of eight studies of silicosis and tuberculosis yielded a pooled relative risk of 4.01 (95% confidence interval (CI) 2.88, 5.58). Exposure-response gradients were strong with a low silicosis severity threshold for increased risk. Our GRADE assessment was high confidence in a strong association. Meta-analysis of five studies of silica exposure controlling for or excluding silicosis yielded a pooled relative risk of 1.92 (95% CI 1.36, 2.73). Exposure-response gradients were observable in individual studies but not finely stratified enough to infer an exposure threshold. Our GRADE assessment was low confidence in the estimated effect owing to inconsistency and use of proxies for silica exposure. CONCLUSIONS: The evidence is robust for a strongly elevated risk of tuberculosis with radiological silicosis, with a low disease severity threshold. The effect estimate is more uncertain for silica exposure without radiological silicosis. Research is needed, particularly cohort studies measuring silica exposure in different settings, to characterise the effect more accurately as well as the silica exposure threshold that could be used to prevent excess tuberculosis risk.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.040 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".