Penalized Regression Methods for Modelling Rare Events Data with Application to Occupational Injury Study
Bibliographic record
Abstract
Occupational injuries are a serious public health concern for workers around the world. Among all occupational injuries reported to the Workers' Compensation Board of Saskatchewan (WCB-SK) from 2007-2016, 177 (0.06%) out of 280,704 injury claims were fatal. Although work-related injuries are relatively rare, they have tremendous impact on the workers, their family, as well as a company's overall productivity, hiring/training costs, and insurance premiums. To help inform prevention of fatal claims, this study identified factors that increase the probability of fatal injury claims in Saskatchewan. WCB Saskatchewan's administrative occupational injury claims data from 2007-2016 was used to extract fatal and non-fatal occupational events. Potential covariates included worker characteristics (age, gender, occupation) and incident characteristics (source of injury, cause of injury, part of body). Given the fatality being rare in this study, conventional logistic regression including multiple categorical covariates with over 40 parameters yielded biased parameter estimates. Penalized logistic regression methods, such as bias-correction method, i.e. Firth's method as well as the model selection methods, i.e., lasso and elastic net were compared to identify an optimal modelling strategy for calculating the odds ratio (OR) and 95% confidence intervals (CI) for probability of a WCB claim being fatal (vs. non-fatal). Based on the best-fitting model, i.e., Firth's logistic regression of the selected variables under the elastic net method, odds of a claim being fatal was 5.5 (95% CI: 2.77,12.46) times higher among men than women and was 6.59 (95% CI: 3.59,12.20) times higher for seniors aged 65-85 as compared with those who are aged 14-24. Odds of a claim being fatal among those who work in primary industry is 2.85 (95% CI: 1.07,9.39) higher than those working in social sciences. The odds of injury being fatal for machinery sources is 51 (95% CI: 10.38,505.38) times higher than chemical products as the source. Men workers are at higher risk of a claim being fatal (vs non-fatal). With respect to age, result of analysis showed that the middle-aged workers are at a lower risk, and the young workers are at a higher risk than middle aged workers. The risk of a claim being fatal increased sharply as age increased from 45 to 85. Primary industry sector and machinery have a disproportionate share of fatal claims. This knowledge can improve workplace safety by learning from past incidents, identifying significant risk factors, and implementing targeted prevention strategies. Through development of effective interventions, we hope to prevent fatal injuries in Saskatchewan.
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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 | 0.056 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".