Abstract P285: Potential Direct Medical Cost-Savings Attributable to Physical Activity in Major Disease Categories
Bibliographic record
Abstract
Introduction: Physical activity (PA) is known to be effective in treating and preventing many lifestyle diseases including CVD, stroke, depression, type II diabetes, Alzheimer’s disease, as well as breast and colon cancer. To date the direct medical cost-savings of PA as a medical intervention are poorly understood. Hypothesis: We hypothesized that a 10% increase in the proportion of US citizens who meet the minimum weekly exercise requirements of 150 minutes per week would lead to savings in direct medical costs (DMC) and cases prevented, as related to the above diseases. Methods: Population Attributable Risk (PAR) was calculated as PAR= (1+Prf x (RR-1))/(Prf x(RR-1)), where Prf is the percentage of the U.S. population not meeting minimum exercise requirements and RR is the relative risk of disease for sedentary versus physically active individuals. Prf and RR data were retrieved from the most recent and comprehensive meta-analyses and systematic reviews. PAR was calculated for each disease under two conditions; first, Prf was equal to the current percent (9.6%) of the population estimated to achieve the minimum weekly PA requirements. Second, Prf was equal to the initial Prf plus 10 percent (19.6%). For each condition the following were calculated: Attributable DMC=(PAR x DMC), preventable cases=(PAR x Prevalence) and Savings=(Condition 2- Condition 1). Results: The Prevalence, RR, PAR and DMC are provided in Table 1. This table also describes the potential savings in DMC and new cases by improving the Prf by 10%. A 10% increase in US citizens who meet the minimum weekly exercise requirements could lead to a total savings of 10.78 billion USD in DMC and 2.1 million cases prevented related to the studied diseases. Conclusion: A healthcare system directed PA intervention that effectively leads to a 10% increase in US citizens that meet minimum weekly exercise requirements and costs less than 10.78 billion dollars has the potential to be cost-effective, and prevent and treat, millions of cases in the United States.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.014 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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".