The Impact of Weight Gain or Loss on Health Care Costs for Employees at the Johnson & Johnson Family of Companies
Bibliographic record
Abstract
OBJECTIVE: To quantify the impact of weight gain or weight loss on health care costs. METHODS: Employees completing at least two health risk assessments during 2002 to 2008 were classified as adding, losing, or staying at high/low risk for each of the nine health risks including overweight and obesity. Models for each risk were used to compare cost trends by controlling for employee characteristics. RESULTS: Employees who developed high risk for obesity (n = 405) experienced 9.9% points higher annual cost increases (95% confidence interval: 3.0%-16.8%) than those who remained at lower risk (n = 8015). Employees who moved from high to lower risk for obesity (n = 384), experienced annual cost increases that were 2.3% points lower (95% confidence interval: -7.4% to 2.8%) than those who remained high risk (n = 1699). CONCLUSIONS: Preventing weight gain through effective employee health promotion programs is likely to result in cost savings for employers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".