Direct, Absenteeism, and Disability Cost Burden of Obesity Among Privately Insured Employees
Bibliographic record
Abstract
OBJECTIVE: To compare obesity-related costs of employees of the healthcare industry versus other major US industries. METHODS: Employees with obesity versus without were identified using the Optum Health Reporting and Insights employer claims database (January, 2010 to March, 2017). Employees working in healthcare with obesity were compared with employees of other industries with obesity for absenteeism/disability and direct cost differences. Multivariate models estimated the association between industries and high costs compared with the healthcare industry. RESULTS: Obesity-related absenteeism/disability and direct costs were higher in several US industries compared with the healthcare industry (adjusted cost differences of $-1220 to $5630). Employees of the government/education/religious services industry (GERS) with obesity (BMI of 30 or greater) had significantly higher odds of direct costs at the 80th percentile and above (odds ratio vs healthcare industry = 2.20; P < 0.05). CONCLUSIONS: Relative to the healthcare industry, employees of other industries, especially GERS, incurred higher obesity-related costs.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".