Abstract 4371293: Missed Opportunities for Cardiovascular Prevention in the United States: A Simulation-Based Analysis Using NHANES 2021–2022
Bibliographic record
Abstract
Background: Despite clear guidelines, many high-risk individuals in the U.S. remain untreated for ASCVD prevention. We quantified the prevalence of missed treatment opportunities, identified sociodemographic predictors, and modeled long-term outcomes using a Markov framework. Methods: We analyzed 9,215 adults aged ≥18 years from NHANES 2021–2022. Adults were eligible for preventive therapy if they had ASCVD, diabetes, cardiometabolic burden score ≥3, or were current smokers aged ≥40. Treatment was defined by use of any cardiovascular medication. A missed opportunity was defined as treatment-eligible but untreated. We used multivariable logistic regression to identify predictors and modeled ASCVD cases, deaths, QALYs, and costs over 5 years using a 3-state Markov model (Healthy, ASCVD, Death). Results: Of 2,756 eligible adults, 1,083 (39.3%) were not receiving any cardiovascular therapy. A missed opportunity was more likely among uninsured adults (OR 2.13, 95% CI: 1.61–2.84), those with income below the federal poverty line (OR 1.82, 95% CI: 1.31–2.52), and adults younger than 50 years (OR 1.94, 95% CI: 1.42–2.65) (Figure 1). Markov simulation projected 132 ASCVD cases and 88 deaths over 5 years in this untreated cohort, compared to 93 ASCVD cases and 60 deaths with preventive therapy (Figure 2). As shown in Figure 3, treatment preserved more individuals in the healthy state and slowed progression to ASCVD and death. Overall, preventive therapy resulted in 114 QALYs gained and $954,000 in cost savings per 1,083 adults over 5 years . When extrapolated nationally, over 1 million ASCVD events and $20 billion in costs could be prevented within 5 years. Conclusions: More than one-third of eligible U.S. adults are not receiving ASCVD preventive therapy. These missed opportunities represent not only lost years of life but also substantial cost and equity gaps that can be addressed through policy and population-level interventions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".