Association between Non-Dietary Cardiovascular Health and Expenditures Related to Acute Coronary Syndrome in the US between 2008–2018
Bibliographic record
Abstract
Abstract Background Acute Coronary Syndrome (ACS) causes the most deaths in the United States and accounts for the highest amount of healthcare spending. Cardiovascular Health (CVH) metrics have been widely used in primary prevention, but their benefits in secondary prevention on total healthcare expenditures related to ACS are largely unknown. This study aims to quantify the potential significance of ideal CVH scores as a tool in secondary cardiovascular disease prevention. Methods In a cross-sectional analytical study, ten years of Medical Expenditure Panel Survey (MEPS) data from 2008 to 2018 were pooled, comparing ACS to non-ACS subgroups, utilizing a Two-part model with log link and gamma distribution, since our sample had both positive and zero costs. Conditional on positive expenditure, healthcare expenditure amounts were measured as a function of ACS status, socio-demographics, and CVH while controlling for relevant covariates. Finally, interactions of ACS with CVH metrics and other key variables were included to allow for variations in the effect of these variables on the two subgroups. Results Improvements in CVH scores tended to reduce annual expenditures to a greater degree percentage-wise among ACS subjects compared to non-ACS groups, even though subjects with an ACS diagnosis tended to have approximately twice as big expenditures as similar subjects without an ACS diagnosis. Meanwhile, the financial impact of an ACS event on total expenditure would be approximately $88,500 ([95% CI, $70,200–106,900; p < 0.001]), and a unit improvement in CVH management score would generate savings of approximately $4160 ([95% CI, $5390–2950; p < 0.001]) in total health expenditures. Conclusion Effective secondary preventive measures through targeted behavioral endeavors and improved health factors, especially the normalization of hypertension, diabetes mellitus, body mass index, and smoking cessation, have the potential to reduce medical spending for ACS subgroups.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".