Abstract 15416: High Neighbourhood-Level Material Deprivation is Associated With Increased Rates of Cardiovascular Outcomes in Patients With ASCVD: A Population-Based Cohort Study
Bibliographic record
Abstract
Background: Disparities in atherosclerotic cardiovascular disease (ASCVD) may persist even in jurisdictions with universal health care. The aim of this study was to examine the relationship between material deprivation and cardiovascular (CV) events in a population with established ASCVD. Methods: This population-based cohort study identified individuals in Ontario, Canada ≥66 years old as of January 1, 2019, with an ASCVD event in the prior 10 years. The primary exposure was neighbourhood-level material deprivation, denoting the inability to attain basic material needs, categorized into quintiles, from Q1 (least deprived) to Q5 (most deprived). Cause-specific hazard models estimated the association between material deprivation and CV outcomes over 3 years, adjusted for baseline characteristics. Trend tests across deprivation quintiles were performed. Results: Among 195,742 individuals with established ASCVD (median age 76 years, 37.3% female), individuals in the most deprived neighbourhoods (Q5) had higher rates of co-morbid conditions and CV disease, including myocardial infarction (MI), angina, peripheral artery disease, and coronary artery bypass grafts, compared to those in the least deprived neighbourhoods (Q1). Q5 residents had higher hazards of the composite outcome of all-cause death, MI, or stroke (hazard ratio [HR], 1.20 [95% CI, 1.16-1.24]), and component outcomes: all-cause death (HR, 1.23 [95% CI, 1.19-1.28]), MI (HR, 1.20 [95% CI, 1.11-1.31]), stroke (HR, 1.13 [95% CI, 1.03-1.23]), and heart failure (HR, 1.22 [95% CI, 1.16-1.28]), compared to Q1 residents. There were no significant differences for Q5 versus Q1 for coronary revascularization (HR, 0.97 [95% CI, 0.90-1.04]). We observed a progressive increase in risk across each quintile of deprivation (P-trend <0.05 for all outcomes). Conclusion: Despite universal health care, increasing deprivation was independently associated with higher rates of CV outcomes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".