Occupational use of high-level disinfectants and asthma incidence in early to mid-career nurses: a prospective cohort study
Bibliographic record
Abstract
Background: Occupational use of disinfectants among healthcare workers has been associated with asthma. However, most studies are cross-sectional and prospective evidence for an etiologic role is still needed. To limit the healthy worker effect, it is important to conduct studies among early to mid-career workers. Objectives: We investigated the prospective association between use of disinfectants and asthma incidence in a large cohort of early to mid-career female nurses. Methods: The Nurses’ Health Study 3 is an ongoing, prospective, internet-based cohort of female nurses in the United States and Canada. Analyses included 17,862 participants with no history of asthma in 2010 (baseline; mean age: 34 years, range 19-52) and who had completed ≥1 follow-up questionnaire (2011-2018). During 64,425 person-years of follow-up, 396 nurses reported incident physician-diagnosed asthma. At baseline, use of high-level disinfectants (HLD, eg glutaraldehyde, hydrogen peroxide) was evaluated by questionnaire. We examined the association between HLD use and subsequent asthma development, adjusted for age, race, ethnicity, smoking status, and body mass index. Results: Compared to nurses who reported ≤5 years of HLD use (90%), those with >5 years of HLD use (10%) had increased risk of incident asthma (adjusted hazard ratio [95% CI], 1.40 [1.04-1.87]. In nurses with >5 years of HLD use, the risk of incident asthma was further increased in those reporting current use of ≥2 products (1.98 [1.05-3.73], reference: ≤5 years of HLD use). Conclusions: Use of high-level disinfectants was prospectively associated with increased asthma incidence in early to mid-career nurses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".