128 Assessing potential health risks from multi-exposure to chemicals in U.S. workplaces: findings from the OSHA database
Bibliographic record
Abstract
Abstract Introduction The combined effects of occupational exposure to multiple chemicals on health can be substantial. However, the most prevalent multi-exposure situations and their toxic effects remain understudied. We assessed the health risks from multi-exposure to chemicals in U.S. workplaces using the Occupational Safety and Health Administration’s (OSHA) measurement database. Methods We analysed personal air measurements for the period 1971-2021 summarized by workplace situation (WS), corresponding to measurements taken for the same job title, within a company, within a year. We calculated hazard quotients (HQ) by dividing the agents’ concentrations by their ACGIH® threshold limit value. We calculated the mixtures’ hazard indices (HI) by summing the HQs of agents by combination of WS and toxicological class (n=24) using the MiXie tool, which identifies classes of toxic effects for >700 chemicals. Results We extracted 609,233 measurements of 206 chemicals from 162,473 WSs. Workers in 58,252 WSs were exposed to ≥2 agents, of which 21,563 had an HI>1, indicating overexposure for at least one toxicological class. Toxicological classes with the highest HIs among multi-exposed WSs were lower airway damage (median 0.41; interquartile interval 0.05-1.9; percentage of overexposed WSs 35%), ototoxicity (0.28; 0.04-1.1; 26%) and central nervous system (CNS) damage (0.26; 0.02-1.2; 28%). For these three classes respectively, the most frequent multi-exposures leading to high values of HI were manganese-iron oxides, toluene-xylene and manganese-lead. Conclusions Although the OSHA database does not necessarily represent a random sample of U.S. workplaces, our approach provides insights into the health risks of occupational exposures to prevalent chemical mixtures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".