Abstract 15192: Neighborhood Social Vulnerability Predicts Cardiovascular Disease Outcomes Through Pro-Inflammatory Risk Factors
Bibliographic record
Abstract
Introduction: Emerging evidence suggests that neighborhood adverse characteristics are linked to nationwide disparities in cardiovascular disease (CVD) incidence and mortality. However, it is unclear if this association is linked to disease progression and outcomes in people with established CVD. Hypothesis: We hypothesized that in patients with CVD, neighborhood social adversity, measured as social vulnerability index (SVI) predicts incident cardiovascular events at least partly through pro-inflammatory. Methods: A total of 4150 participants enrolled in the Emory Cardiovascular Biobank, residential addresses were geocoded according to the census tract and SVI was determined using the Center for Disease Control data. Serum hs-C-reactive protein (hsCRP) levels were measured during enrollment and participants were followed up for incident myocardial infarction (MI) and cardiovascular death. Subdistribution hazard models were used to investigate the association between SVI and the study endpoint. A mediation analysis was performed to evaluate whether hsCRP levels underly this association. Results: Mean age was 63 years, 24% Black, and 64% were women. During a median 5 year follow-up, there were 904 (22%) adverse events. SVI and hsCRP were correlated (r=0.12, P<0.001). After adjustment for age, race, sex,hypertension, diabetes mellitus, body mass index, prior MI, prior heart failure, renal function, smoking, statin and aspirin use, participants in the highest SVI quartile (most socially vulnerable), had a 31% (95%CI, 4%,64%) higher risk of CV death/MI compared to those in the lowest quartile. The risk was attenuated and became insignificant after adjusting for the hsCRP 23% (CI -3%, 54%). Serum hsCRP levels mediated 61% of the association between SVI and adverse events. Conclusions: Neighborhood social vulnerability is an independent risk factor for adverse outcomes in CVD, that is at least in part mediated through increased systemic inflammation. Neighborhood social vulnerability may explain some of the residual risk observed in patients with CVD.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".