A Mortality Risk Analysis for OSHA’s COVID-19 Emergency Regulations
Bibliographic record
Abstract
In 2021, the Occupational Safety and Health Administration (OSHA) issued two emergency temporary standard regulations related to COVID-19 hazards in US workplaces. One regulation covered healthcare sector workers, while the second regulation would have covered workers at firms with 100 or more employees. This paper conducts an original mortality risk analysis for these regulations. Mortality risk analysis evaluates the increase or decrease in expected mortality associated with a new policy, such as a rule or regulation, taking into account economic factors like lost income due to regulatory costs. If we accept OSHA’s cost and health benefit estimates at face value, we find that the first regulation related to COVID-19 hazards in the healthcare sector reduces risk initially but increases risk over a longer time horizon. We find that the second regulation would reduce risk according to OSHA’s main estimates but may not reduce risk after including some ancillary costs and adjusting the agency’s prevented hospitalizations estimate based on more reasonable assumptions. Moreover, OSHA’s economic analysis for the two regulations in question does not purport to comprehensively evaluate costs; ergo, our mortality risk estimates probably underestimate countervailing mortality risks stemming from these regulations. We review some of OSHA’s underlying assumptions that could change the outcomes of our mortality analysis. These estimates demonstrate that OSHA would benefit from more comprehensive consideration of costs in its economic analysis.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".