Pesticide exposure and lung function: a systematic review and meta-analysis
Bibliographic record
Abstract
Epidemiological studies have reported associations between pesticide exposure and respiratory health, but the specific causal agents and the quantitative impact on lung function are unclear. To address this, we undertook a systematic review of the available literature reporting on pesticide exposures and lung function. Aim: To examine, systematically and by meta-analysis, available literature on the relationship between pesticide exposures and lung function. Methods: MEDLINE, EMBASE, and Web of Science electronic databases were searched to 1 October 2017. We searched by a combination of MeSH terms and free text for pesticide exposures and lung function using our protocol registered in PROSPERO. We assessed the quality of the reports using a modified Newcastle-Ottawa scale. We converted outcome measures to standard mean differences (SMD) and undertook meta-analyses by the metan command in Stata 15 with fixed-effect models where I² from test for heterogeneity<50%. Results: We retrieved 2,356 articles; of these, 56 met our criteria for inclusion and review; 19, 22 and 22 papers were pooled in meta-analyses of FEV1/FVC, FVC and FEV1 respectively. We found no effect for paraquat exposure on FEV1/FVC (SMD=0.05; 95%CI 0.04, 0.15). Cholinesterase (ChE) inhibiting pesticides showed a significant negative effect on FEV1/FVC (SMD=-0.27; 95%CI -0.39, -0.14). Conclusion: Respiratory surveillance should be enhanced in those exposed to ChE-inhibiting pesticides which reduce FEV1/FVC according to the meta-analysis. Our review is limited by heterogeneity between studies from variable exposure assessments and limited adjustment for potential confounders. Further studies with more accurate exposure assessment are indicated.
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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 | 0.021 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".