The cost effectiveness of occupational health interventions: Prevention of silicosis
Bibliographic record
Abstract
BACKGROUND: The failure to recognize occupational health as an economic phenomenon limits the effectiveness of interventions ostensibly designed to prevent disease and injury. Hence, consideration of economic efficiency is essential in the evaluations of interventions to reduce hazardous working conditions. In this paper, we present an analysis of the cost effectiveness of alternative means of preventing silicosis. METHODS: To evaluate the cost effectiveness of specific interventions for the prevention of occupationally induced silicosis, we have used the simulation models based on the generalized cost-effectiveness analysis (GCEA) developed by the WHO-CHOICE initiative for two representative subregions namely AMROA (Canada, United States of America), and WPROB1 (China, Korea, Mongolia). RESULTS: In both of the two subregions, engineering controls are the most cost effective with ratios varying from 105.89 dollars per healthy year or disability adjusted life year saved in AMROA to approximately 109 dollars in WPROB1. In the two subregions, the incremental cost-effectiveness ratio of engineering controls (EC) looks most attractive. Although dust masks (DM) look attractive in terms of cost, the total efficacy is extremely limited. CONCLUSIONS: To the extent that this analysis can be generalized across other subregions, it suggests that engineering control programs would be cost effective in both developed and developing countries for reducing silica exposure to save lives. Note that this analysis understates health benefits since only silicosis and not all silica-related diseases are considered.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".