Industrial Injury Hospitalizations Billed to Payers Other Than Workers' Compensation: Characteristics and Trends by State
Bibliographic record
Abstract
OBJECTIVE: To describe characteristics of industrial injury hospitalizations, and to test the hypothesis that industrial injuries were increasingly billed to non-workers' compensation (WC) payers over time. DATA SOURCES: Hospitalization data for 1998-2009 from State Inpatient Databases, Healthcare Cost and Utilization Project, and Agency for Healthcare Research and Quality. STUDY DESIGN: Retrospective secondary analyses described the distribution of payer, age, gender, race/ethnicity, and injury severity for injuries identified using industrial place of occurrence codes. Logistic regression models estimated trends in expected payer. PRINCIPAL FINDINGS: There was a significant increase over time in the odds of an industrial injury not being billed to WC in California and Colorado, but a significant decrease in New York. These states had markedly different WC policy histories. Industrial injuries among older workers were more often billed to a non-WC payer, primarily Medicare. CONCLUSIONS: Findings suggest potentially dramatic cost shifting from WC to Medicare. This study adds to limited, but mounting evidence that, in at least some states, the burden on non-WC payers to cover health care for industrial injuries is growing, even while WC-related employer costs are decreasing-an area that warrants further research.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".