Neighborhood Disadvantage, Poor Social Conditions, and Cardiovascular Disease Incidence Among African American Adults in the Jackson Heart Study

Introduction: Racial residential segregation results in increased exposure to adverse neighborhood environments for African Americans; however, the impact of segregation on ideal cardiovascular health (CVH) has not been examined in large, socioeconomically diverse African American samples. Using a novel spatial measure of neighborhood-level racial residential segregation, we examined the association between segregation and ideal CVH in the Jackson Heart Study (JHS). Hypothesis: Racial residential segregation will be associated with worse cardiovascular health among African American adults. Methods: The sample included 4,354 men and women ages 21-93 from the baseline exam of the JHS (2000-2004). Racial residential segregation was assessed at the census-tract level. Data on racial composition (% African American) from the 2000 US Census was used to calculate the local G i * statistic- a spatially-weighted z-score that represents how much a neighborhood’s racial/ethnic composition deviates from the larger metropolitan area. Ideal CVH was assessed using the AHA Life’s Simple Seven (LS7) index which includes 3 behavioral (nutrition, physical activity, and smoking) and 4 biological (systolic BP, glucose, BMI, and cholesterol) metrics of CVH. Multivariable regression models were used to test associations between segregation and the LS7 index continuously (range: 0-14) and categorically (Inadequate: 0-4; Average: 5-9; and Optimal: 10-14). Covariates included age, sex, income, education, and insurance status. Results: The average LS7 summary score was 7.03 (±2.1) and was lowest in the most racially segregated neighborhood environments (High Segregation: 6.88 ±2.1 vs. Low Segregation: 7.55 ±2.1). The prevalence of inadequate CVH was higher in racially segregated neighborhoods (12.3%) compared to neighborhoods that were the least segregated (6.9%). After adjusting for key socio-demographic characteristics, racial residential segregation was inversely associated with ideal CVH (B=-0.041 ±0.02, p=0.0146). Moreover, a 1-SD unit increase in segregation was associated with a 6% increased odds of having inadequate CVH (OR: 1.06, 95% CI: 1.00-1.12, p=0.0461). Conclusion: In conclusion, African Americans in racially segregated neighborhoods are less likely to achieve ideal CVH even after accounting for individual-level factors. Policies aimed at restricting housing segregation/discrimination and/or structural interventions designed to improve neighborhood environments may be viable strategies to improving CVH in this at-risk population.

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The Epidemiology of Coping in African American Adults in the Jackson Heart Study (JHS)

Journal of Racial and Ethnic Health Disparities ◽

10.1007/s40615-017-0445-y ◽

2017 ◽

Vol 5 (5) ◽

pp. 978-994 ◽

Cited By ~ 6

Author(s):

Allison B. Brenner ◽

Ana V. Diez-Roux ◽

Samson Y. Gebreab ◽

Amy J. Schulz ◽

Mario Sims

Keyword(s):

African American ◽

Jackson Heart Study ◽

African American Adults ◽

Heart Study

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Using ECG Machine Learning for Detection of Cardiovascular Disease in African American Men and Women: the Jackson Heart Study

10.1101/2020.07.02.20145128 ◽

2020 ◽

Author(s):

James Pollard ◽

Kazi T Haq ◽

Katherine Lutz ◽

Nichole Rogovoy ◽

Kevin Paternostro ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

African American ◽

African American Men ◽

Random Effect ◽

Detection Methods ◽

Jackson Heart Study ◽

Men And Women ◽

Heart Study ◽

Free Women

Background: Almost half of African American (AA) men and women have cardiovascular disease (CVD). Detection of prevalent CVD in barbershops would facilitate secondary prevention of CVD. We sought to investigate the cross-sectional association of prevalent CVD and sex with global electrical heterogeneity (GEH) and develop a tool for CVD detection. Methods: Participants from the Jackson Heart Study (JHS) with analyzable ECGs (n=3,679; age, 62 ± 12 years; 36% men) were included. QRS, T, and spatial ventricular gradient (SVG) vectors magnitude and direction, and traditional metrics were measured on 12-lead ECG. Linear regression and mixed linear models with random intercept were adjusted for cardiovascular risk factors, sociodemographic and anthropometric characteristics, type of median beat, and mean RR intervals. Random forests, convolutional neural network, and lasso models were developed in 80%, and validated in 20% samples. Results: In fully adjusted models, women had a smaller spatial QRS-T angle (-12.2(-19.4 to-5.1) ° ; P=0.001), SAI QRST (-29.8(-39.3 to -20.3) mV*ms; P<0.0001), and SVG elevation (-4.5(-7.5 to -1.4) ° ; P=0.004) than men, but larger SVG azimuth (+16.2(10.5-21.9) ° ; P<0.0001), with a significant random effect between families (+20.8(8.2-33.5) ° ; P=0.001). SAI QRST was larger in women with CVD as compared to CVD-free women or men (+15.1(3.8-26.4) mV*ms; P=0.009). Men with CVD had smaller T area [by 5.1 (95%CI 1.2-9.0) mV*ms] than CVD-free men, but there were no differences when comparing women with CVD to CVD-free women. Machine-learning detected CVD with ROC AUC 0.69-0.74; plug-in-based model included only age and QRS-T angle. Conclusions: GEH varies by sex. Sex modifies an association of GEH with CVD. Automated CVD detection is feasible.

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