Neighborhood Deprivation Predicts Heart Failure Risk in a Low-Income Population of Blacks and Whites in the Southeastern United States

Abstract

Background Recent data suggest that neighborhood socioeconomic environment predicts heart failure (HF) hospital readmissions. We investigated whether neighborhood deprivation predicts risk of incident HF beyond individual socioeconomic status (SES) in a low-income population. Methods and Results Participants were 27,078 whites and blacks recruited during 2002-2009 in the Southern Community Cohort Study, who had no history of HF and were utilizing Centers for Medicare or Medicaid services (CMS). Incident HF diagnoses through December 31, 2010 were ascertained using ICD-9 codes 428.x via linkage with CMS research files. Participant residential information was geocoded and census tract determined by a spatial join to the US Census Bureau's TIGER/Line Shapefiles. The neighborhood deprivation index (NDI) was constructed using principal components analysis based on census tract-level socioeconomic variables. Cox models with Huber-White cluster sandwich estimator of variance were utilized to investigate the association between NDI and HF risk. The study sample was predominantly middle-aged (mean 55.5 years), black (69%), female (63%), low-income (70% earned < $15,000/year), and >50% of participants lived in the most deprived neighborhoods (3rd NDI tertile). Over median follow-up of 5.2 years, 4,300 participants were diagnosed with HF. After adjustment for demographic, lifestyle and clinical factors, a 1 interquartile increase in NDI was associated with a 12% increase in risk of HF [HR= 1.12; 95% CI: 1.07-1.18], and 4.8% of the variance in HF risk [ICC = 4.8; 95% CI: 3.6-6.4] was explained by neighborhood deprivation. Conclusions In this low-income population, scant neighborhood resources compound the risk of HF above and beyond individual SES and traditional cardiovascular risk factors. Improvements in community resources may be a significant axis for curbing the burden of HF.

Is Home Where the Heart Is?

“…In fact, the inclusion of individual income and education in the models reported in the current article showed only a modest attenuation of the direct effect of more severe neighborhood deprivation and increased risk of heart failure incidence. The careful and unique population prospective cohort approach taken by Akwo et al 4 further establishes that there indeed is something unique about neighborhoods.…”

“…5 The American Heart Association metrics for cardiovascular health incorporate 2 key domains: health factors (blood glucose, blood pressure, and total cholesterol) and health behaviors (diet, physical activity, smoking, and body mass index). Although there is a strong scientific basis for the impact of these individual-level factors on cardiovascular health, work such as that of Akwo et al 4 provides an argument of additional contribution of contextual metrics such as neighborhood conditions, particularly in underserved populations. It seems reasonable to consider that to achieve bold goals such as a 20% improvement in cardiovascular health in all Americans, a better understanding of the elements of one's neighborhood that contribute to these health factors and behaviors is severely needed.…”

“…One might argue that neighborhood metrics themselves could be added to future definitions of cardiovascular health. An important methodologic challenge addressed by Akwo et al 4 is how best to measure and define neighborhood. Akwo et al 4 used census tracts as proxies for neighborhood, a reasonable and commonly used approach.…”

“…An important methodologic challenge addressed by Akwo et al 4 is how best to measure and define neighborhood. Akwo et al 4 used census tracts as proxies for neighborhood, a reasonable and commonly used approach. Previous studies have found that census tracts are a robust indicator of the contextual components relating to health and that they provide distinct, complementary information to individual-level indicators.…”

“…As accurately noted by Akwo et al 4 , using medical claims data to classify heart failure events has limitations in terms of validity and in its ability to differentiate heart failure subtypes. However, this method provides an efficient method of capturing events diagnosed in the outpatient setting, where a considerable proportion of events are identified, treated, and managed.…”