The relationship between cleaning product exposure and respiratory and skin symptoms among healthcare workers in a hospital setting: A systematic review and meta‐analysis
Bibliographic record
Abstract
Background and Aims: Several studies from multiple work settings have reported an increase in asthma and asthma-like respiratory symptoms in workers exposed to cleaning or disinfecting agents. Hospital workers perform many cleaning and disinfecting activities and may be vulnerable to respiratory and skin symptoms caused by these agents. This systematic review and meta-analysis aim to quantify the risk of asthma and asthma-like symptoms in hospital workers exposed to cleaning/disinfecting agents. A secondary aim is to assess associated risks of skin symptoms in those studies. Methods: MEDLINE, EMBASE, CDSR, CENTRAL, CINAHL databases, and references of relevant review articles were searched. NHLBI quality assessment tools were used to assess the quality of the included studies. A total of 2550 articles were retrieved and 34 studies met criteria to be included. The software R version 4.0.5 was used to perform the meta-analysis. The random-effects model was used to pool the results due to within-studies heterogeneity. Results: Meta-analysis of 10 studies evaluating the association between occupational cleaning exposures and asthma demonstrated a 35% increased risk in exposed hospital workers (meta-RR = 1.35, 95% CI: 1.09-1.68). The risk of asthma increased when workers were exposed to bleach compared with nonexposed workers (meta-RR = 1.51, 95% CI: 0.54-4.18), but was not statistically significant. Two studies investigated the relationship between respiratory and skin symptoms and produced mixed results. Conclusions: The results suggest a need for preventive practices to reduce the risk of asthma and asthma-like symptoms in hospital workers exposed to occupational cleaning/disinfecting agents. Trial registration number: CRD42020137804.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".