Dataset Information

Social determinants of health and cardiovascular outcomes in patients with heart failure.

ABSTRACT:

Background

We examined the associations between family income and educational attainment with incident atrial fibrillation (AF), myocardial infarction (MI), stroke and cardiovascular (CV) death among patients with newly-diagnosed heart failure (HF).

Methods

In a nationwide Danish registry of HF patients diagnosed between 2008 and 2018, we established a cohort for each outcome. When examining AF, MI and stroke, respectively, patients with a history of these outcomes at diagnosis of HF were excluded. We used cause-specific proportional hazard models to estimate hazard ratios for tertile groups of family income and three levels of educational attainment.

Results

Among 27,947 AF-free patients, we found no association between income or education and incident AF. Among 27,309 MI-free patients, we found that lower income (hazard ratio 1.28 [95% CI 1.11-1.48] and 1.11 [0.96-1.28] for lower and medium vs. higher income) and education (1.23 [1.04-1.45] and 1.15 [0.97-1.36] for lower and medium vs. higher education) were associated with MI. Among 36,801 stroke-free patients, lower income was associated with stroke (1.38 [1.23-1.56] and 1.27 [1.12-1.44] for lower and medium vs. higher income) but not education. Lower income (1.56 [1.46-1.67] and 1.32 [1.23-1.42] for lower and medium vs. higher income) and education (1.20 [1.11-1.29] and 1.07 [0.99-1.15] for lower and medium vs. higher education) were associated with CV death.

Conclusions

In patients with newly-diagnosed HF, lower family income was associated with higher rates of acute MI, stroke and cardiovascular death. Lower educational attainment was associated with higher rates of acute MI and cardiovascular death. There was no evidence of associations between income and education with incident AF.

SUBMITTER: Vinter N

PROVIDER: S-EPMC9786545 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Social determinants of health and cardiovascular outcomes in patients with heart failure.

Vinter Nicklas N Fawzy Ameenathul M AM Gent David D Ding Wern Yew WY Johnsen Søren Paaske SP Frost Lars L Trinquart Ludovic L Lip Gregory Y H GYH

European journal of clinical investigation 20220815 11

<h4>Background</h4>We examined the associations between family income and educational attainment with incident atrial fibrillation (AF), myocardial infarction (MI), stroke and cardiovascular (CV) death among patients with newly-diagnosed heart failure (HF).<h4>Methods</h4>In a nationwide Danish registry of HF patients diagnosed between 2008 and 2018, we established a cohort for each outcome. When examining AF, MI and stroke, respectively, patients with a history of these outcomes at diagnosis of ...[more]

PMID: 35924957

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Project description:ImportanceInvolvement of a cardiologist in the care of adults during a hospitalization for heart failure (HF) is associated with reduced rates of in-hospital mortality and hospital readmission. However, not all patients see a cardiologist when they are hospitalized for HF.ObjectiveTo determine whether social determinants of health (SDOH) are associated with cardiologist involvement in the management of adults hospitalized for HF.Design, setting, and participantsThis retrospective cohort study used data from the Reasons for Geographic and Racial Difference in Stroke (REGARDS) cohort. Participants included adults who experienced an adjudicated hospitalization for HF between 2009 and 2017 in all 48 contiguous states in the US. Data analysis was performed from November 2022 to January 2023.ExposuresA total of 9 candidate SDOH, aligned with the Healthy People 2030 conceptual model, were examined: Black race, social isolation, social network and/or caregiver availability, educational attainment less than high school, annual household income less than $35 000, living in rural area, living in a zip code with high poverty, living in a Health Professional Shortage Area, and living in a state with poor public health infrastructure.Main outcomes and measuresThe primary outcome was cardiologist involvement, defined as involvement of a cardiologist as the primary responsible clinician or as a consultant. Bivariate associations between each SDOH and cardiologist involvement were examined using Poisson regression with robust SEs.ResultsThe study included 1000 participants (median [IQR] age, 77.8 [71.5-84.0] years; 479 women [47.9%]; 414 Black individuals [41.4%]; and 492 of 876 with low income [56.2%]) hospitalized at 549 unique US hospitals. Low annual household income (<$35 000) was the only SDOH with a statistically significant association with cardiologist involvement (relative risk, 0.88; 95% CI, 0.82-0.95). In a multivariable analysis adjusting for age, race, sex, HF characteristics, comorbidities, and hospital characteristics, low income remained inversely associated with cardiologist involvement (relative risk, 0.89; 95% CI, 0.82-0.97).Conclusions and relevanceThis cohort study found that adults with low household income were 11% less likely than adults with higher incomes to have a cardiologist involved in their care during a hospitalization for HF. These findings suggest that socioeconomic status may bias the care provided to patients hospitalized for HF.

Project description:ImportanceTraditional models for predicting in-hospital mortality for patients with heart failure (HF) have used logistic regression and do not account for social determinants of health (SDOH).ObjectiveTo develop and validate novel machine learning (ML) models for HF mortality that incorporate SDOH.Design, setting, and participantsThis retrospective study used the data from the Get With The Guidelines-Heart Failure (GWTG-HF) registry to identify HF hospitalizations between January 1, 2010, and December 31, 2020. The study included patients with acute decompensated HF who were hospitalized at the GWTG-HF participating centers during the study period. Data analysis was performed January 6, 2021, to April 26, 2022. External validation was performed in the hospitalization cohort from the Atherosclerosis Risk in Communities (ARIC) study between 2005 and 2014.Main outcomes and measuresRandom forest-based ML approaches were used to develop race-specific and race-agnostic models for predicting in-hospital mortality. Performance was assessed using C index (discrimination), regression slopes for observed vs predicted mortality rates (calibration), and decision curves for prognostic utility.ResultsThe training data set included 123 634 hospitalized patients with HF who were enrolled in the GWTG-HF registry (mean [SD] age, 71 [13] years; 58 356 [47.2%] female individuals; 65 278 [52.8%] male individuals. Patients were analyzed in 2 categories: Black (23 453 [19.0%]) and non-Black (2121 [2.1%] Asian; 91 154 [91.0%] White, and 6906 [6.9%] other race and ethnicity). The ML models demonstrated excellent performance in the internal testing subset (n = 82 420) (C statistic, 0.81 for Black patients and 0.82 for non-Black patients) and in the real-world-like cohort with less than 50% missingness on covariates (n = 553 506; C statistic, 0.74 for Black patients and 0.75 for non-Black patients). In the external validation cohort (ARIC registry; n = 1205 Black patients and 2264 non-Black patients), ML models demonstrated high discrimination and adequate calibration (C statistic, 0.79 and 0.80, respectively). Furthermore, the performance of the ML models was superior to the traditional GWTG-HF risk score model (C index, 0.69 for both race groups) and other rederived logistic regression models using race as a covariate. The performance of the ML models was identical using the race-specific and race-agnostic approaches in the GWTG-HF and external validation cohorts. In the GWTG-HF cohort, the addition of zip code-level SDOH parameters to the ML model with clinical covariates only was associated with better discrimination, prognostic utility (assessed using decision curves), and model reclassification metrics in Black patients (net reclassification improvement, 0.22 [95% CI, 0.14-0.30]; P < .001) but not in non-Black patients.Conclusions and relevanceML models for HF mortality demonstrated superior performance to the traditional and rederived logistic regressions models using race as a covariate. The addition of SDOH parameters improved the prognostic utility of prediction models in Black patients but not non-Black patients in the GWTG-HF registry.

Dataset Information

Social determinants of health and cardiovascular outcomes in patients with heart failure.

Background

Methods

Results

Conclusions

Publications

Social determinants of health and cardiovascular outcomes in patients with heart failure.

Similar Datasets

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