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Background Persistent racial/ethnic disparities in cardiovascular disease (CVD) mortality are partially explained by healthcare access and socioeconomic, demographic, and behavioral factors. Little is known about the association between race/ethnicity-specific CVD mortality and county-level factors. Methods and Results Using 2017 county-level data, we studied the association between race/ethnicity-specific CVD age-adjusted mortality rate (AAMR) and county-level factors (demographics, census region, socioeconomics, CVD risk factors, and healthcare access). Univariate and multivariable linear regressions were used to estimate the association between these factors; R 2 values were used to assess the factors that accounted for the greatest variation in CVD AAMR by race/ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic/Latinx individuals). There were 659 740 CVD deaths among non-Hispanic White individuals in 2698 counties; 100 475 deaths among non-Hispanic Black individuals in 717 counties; and 49 493 deaths among Hispanic/Latinx individuals across 267 counties. Non-Hispanic Black individuals had the highest mean CVD AAMR (320.04 deaths per 100 000 individuals), whereas Hispanic/Latinx individuals had the lowest (168.42 deaths per 100 000 individuals). The highest CVD AAMRs across all racial/ethnic groups were observed in the South. In unadjusted analyses, the greatest variation ( R 2 ) in CVD AAMR was explained by physical inactivity for non-Hispanic White individuals (32.3%), median household income for non-Hispanic Black individuals (24.7%), and population size for Hispanic/Latinx individuals (28.4%). In multivariable regressions using county-level factor categories, the greatest variation in CVD AAMR was explained by CVD risk factors for non-Hispanic White individuals (35.3%), socioeconomic factors for non-Hispanic Black (25.8%), and demographic factors for Hispanic/Latinx individuals (34.9%). Conclusions The associations between race/ethnicity-specific age-adjusted CVD mortality and county-level factors differ significantly. Interventions to reduce disparities may benefit from being designed accordingly.

Background The aim of this study was to determine whether frailty is associated with increased admission and mortality risk in the setting of heart failure. Methods and Results This retrospective cohort analysis included patients treated within the Veterans Affairs Health System who had International Classification of Diseases, Ninth Revision ( ICD-9 ) codes for heart failure on 2 or more dates over a 2-year period. The clinical variables identifiable in claims data, such as demographic variables and markers of physical and cognitive dysfunction, were used to identify patients meeting the frailty phenotype. Of 388 785 extracted patients with coding of heart failure between 2015 and 2018, 163 085 patients (41.9%) with ejection fraction (EF) measurement were included in the present analysis (38.3% with reduced EF and 61.7% with preserved EF). There were 16 660 patients (10.2%) who were identified as frail (9.1% in heart failure with reduced EF and 10.9% in heart failure with preserved EF). Frail patients were older, more often depressed, and were likely to have been admitted in the previous year. One-year all-cause mortality rate was 9.7% and 28.1%, and admission rate was 58.1% and 79.5% for nonfrail and frail patients, respectively. Frailty was associated with mortality and admission risk compared with the nonfrail group (adjusted odds ratio [OR], 1.71; 95% CI, 1.65-1.77 for mortality; adjusted OR, 1.29; 95% CI, 1.24-1.34 for admission) independent of EF. Conclusions Frailty based on diagnostic coding was associated with particularly higher risk of mortality despite adjustment for known clinical variables. Our findings underscore the importance of nontraditional parameters in the prognostic assessment.

Background Although 30-day readmission is thought to be an important quality indicator in patients with hospitalized heart failure, its prognostic impact and comparison of patients who were readmitted beyond 30 days has not been investigated. We assessed early (0-30 days) versus midrange (31-90 days) readmission in terms of incidence and distribution, and elucidated whether the timing of readmission could have a different prognostic significance. Methods and Results We examined patients with hospitalized heart failure registered in the WET-HF (West Tokyo Heart Failure) registry. The primary outcomes analyzed were all-cause death and HF readmission. Data of 3592 consecutive patients with hospitalized heart failure (median follow-up, 2.0 years [interquartile range, 0.8-3.1 years]; 39.6% women, mean age 73.9±13.3 years) were analyzed. Within 90 days after discharge, HF readmissions occurred in 11.1% patients. Of them, patients readmitted within 30 and 31 to 90 days after discharge accounted for 43.1% and 56.9%, respectively. Independent predictors of 30- and 90-day readmission were almost identical, and after adjustment, readmission for HF within 90 days (including both early and midrange readmission) was an independent predictor of subsequent all-cause death (hazard ratio, 2.36; P <0.001). Among 90-day readmitted patients, the time interval from discharge to readmission was not significantly associated with subsequent all-cause death. Conclusions Among patients readmitted within 90 days after index hospitalization discharge, ≈60% of readmission events occurred beyond 30 days. Patients readmitted within 90 days had a higher risk of long-term mortality, regardless of the temporal proximity of readmission to the index hospitalization.