Abstract 12757: Impact of Social Vulnerability on Cardio-Oncology Mortality in the United States
Bibliographic record
Abstract
Introduction: Racial and social disparities affect cancer and CVD-related mortality. The relationship between social vulnerability and concomitant cancer and CVD (cardio-oncology) related mortality remains understudied. Methods: We used the CDC Wide-Ranging OnLine Data for Epidemiologic Research (WONDER) database to determine the association between deaths attributed to the presence of concomitant CVD and cancer (ICD-10 codes I00-I09, I11, I13, I20-I51 and C00-C96), and the county-level social-vulnerability index (SVI). SVI measures a community’s vulnerability based on socioeconomic status, household composition, disability, minority status, language, and transportation. We aggregated counties by SVI quartiles (1st: most favorable = 0.00 to 0.25; 4th: least favorable = 0.75 to 1.00) and compared age-adjusted mortality rates (AAMRs) across SVI quartiles. Results: Between 2014 and 2018, the AAMR due to concomitant cancer and CVD was 47.75 (95% CI, 47.66- 47.85) per 100,000 person-years with higher mortality in areas with a higher SVI (Figure). Similarly, CVD and cancer-related mortality was also significantly greater in counties with the highest SVI [(CVD: 1.287 (95% CI 1.284-1.290); Cancer: 1.087 (95% CI 1.084-1.091)]. Moreover, the proportional increase in cardio-oncology mortality between the highest and lowest SVI counties was greater than that observed for CVD or cancer-associated mortality alone (p<0.001). This difference was most striking in adults < 45 years, females, Asian and Pacific Islanders, and Hispanics. Conclusion: A graded increase in cardio-oncology mortality is observed in counties with higher social vulnerability. The incremental impact of SVI was greater for cardio-oncology mortality than for cancer or CVD mortality alone, particularly in certain demographic groups. These findings highlight the need for targeted resource allocation and public health interventions to address social inequities in cardio-oncology.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".