Abstract 4145655: Association Between Social Vulnerability Index and Heart Failure Mortality in the USA

Background: Heart failure (HF) is a primary cause of hospital readmissions with extremely high projected annual costs. Social Vulnerability Index (SVI) assesses a community's ability to recover from external impacts on public health and provides valuable insights into community-level health outcomes. We aim to identify the association between SVI and HF. Methods: We used a national database (CDC WONDER) to identify the mortality of HF patients in the USA who were ≥25 years in 2020. HF was identified by specific ICD codes I11.0, 13.0, I13.2, and I50. We obtained the SVI per county from the CDC Database. We used the Pearson correlation coefficient (PCC) to examine the association between HF mortality and total SVI overall and within subgroups. Additionally, we employed ANOVA to evaluate the relationship between SVI quartiles and mortality. Results: In 2020, out of 215,907,184 individuals, 113,401 died of HF (40.7% were female and 37% male, 65.9% were of white race, and 8.8% Hispanic). Age-adjusted mortality rate (AAMR) was higher in males (65.3 vs 50.7 in females) and in Black race (75.8 vs 55.8 in White). However, the AAMR was lower in Hispanics (33.5 vs 58.6). Mortality rate increased from 0.044% in 2016 to 0.052% in 2020. The SVI ranged from 2.6045 to 12.7688. Texas, Mississippi, and Georgia were the states with the most counties in the 4th quartile of the SVI. In 2020, Mississippi, Alabama, and Louisiana had the highest AAMR (94.9, 88.6 and 76.4). A weak positive linear relationship was observed between HF mortality and total SVI, with a PCC of 0.2546, p <0.001. When divided into groups, socioeconomic status showed a moderate positive linear relationship, while household showed a weakly positive relationship, with PCC values of 0.32 and 0.27, respectively. For racial and ethnic minorities and house and transportation type, there was a very weak positive relationship (PCC 0.03 and 0.04), not statistically significant. The rate of change of mortality from 2016 to 2020 also had a weak positive linear relationship with SVI (PCC 0.13, p <0.001). When ANOVA was applied, the F-statistic was 24.1 (p <0.001), indicating significant variation between the SVI quartiles, especially for quartile 4. Conclusion: There is a positive association between SVI and HF mortality rates. Counties with greater social vulnerability experience higher mortality rates from HF, suggesting that targeted public health interventions in high-SVI areas could potentially improve outcomes.