Association of cardiovascular risk profile with premature all-cause and cardiovascular mortality in US adults: findings from a national study.

OBJECTIVE: To assess the association between cardiovascular risk factor (CRF) profile and premature all-cause and cardiovascular disease (CVD) mortality among US adults (age < 65). METHODS: This study used data from the National Health Interview Survey from 2006 to 2014, linked to the National Death Index for non-elderly adults aged < 65 years. A composite CRF score (range = 0-6) was calculated, based on the presence or absence of six established cardiovascular risk factors: hypertension, diabetes, hypercholesterolemia, smoking, obesity, and insufficient physical activity. CRF profile was defined as "Poor" (≥ 3 risk factors), "Average" (1-2), or "Optimal" (0 risk factors). Age-adjusted mortality rates (AAMR) were reported across CRF profile categories, separately for all-cause and CVD mortality. Cox proportional hazard models were used to evaluate the association between CRF profile and all-cause and CVD mortality. RESULTS: Among 195,901 non-elderly individuals (mean age: 40.4 ± 13.0, 50% females and 70% Non-Hispanic (NH) White adults), 24.8% had optimal, 58.9% average, and 16.2% poor CRF profiles, respectively. Participants with poor CRF profile were more likely to be NH Black, have lower educational attainment and lower income compared to those with optimal CRF profile. All-cause and CVD mortality rates were three to four fold higher in individuals with poor CRF profile, compared to their optimal profile counterparts. Adults with poor CRF profile experienced 3.5-fold (aHR: 3.48 [95% CI: 2.96, 4.10]) and 5-fold (aHR: 4.76 [3.44, 6.60]) higher risk of all-cause and CVD mortality, respectively, compared to those with optimal profile. These results were consistent across age, sex, and race/ethnicity subgroups. CONCLUSIONS: In this population-based study, non-elderly adults with poor CRF profile had a three to five-fold higher risk of all-cause and CVD mortality, compared to those with optimal CRF profile. Targeted prevention efforts to achieve optimal cardiovascular risk profile are imperative to reduce the persistent burden of premature all-cause and CVD mortality in the US.

Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi-Ethnic Study of Atherosclerosis.

BACKGROUND: Social determinants of health (SDoH) are associated with cardiovascular risk factors and outcomes; however, they are absent from risk prediction models. We aimed to assess if the addition of SDoH improves the predictive ability of the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score. METHODS AND RESULTS: This was a community-based prospective population cohort study that enrolled 6286 men and women, ages 45-84 years, who were free of clinical coronary heart disease (CHD) at baseline. Data from 10-year follow-up were examined for CHD events, defined as myocardial infarction, fatal CHD, resuscitated cardiac arrest, and revascularization in cases of anginal symptoms. Participants included 53% women with average age of 62 years. When adjusting for traditional cardiovascular risk factors, SDoH, and coronary artery calcium, economic strain, specifically low family income, was associated with a greater risk of CHD events (hazard ratio [HR], 1.42 [95% CI, 1.17-1.71], P value<0.001). Area under the curve of risk prediction with SDoH was 0.822, compared with 0.816 without SDoH. The calibration slope was 0.860 with SDoH and 0.878 in the original model. CONCLUSIONS: Significant associations were found between economic/financial SDoH and CHD risk factors and outcomes. Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.

Multidimensional Poverty and Risk of Atherosclerotic Cardiovascular Disease: A U.S. National Study.

Background: Poverty is associated with atherosclerotic cardiovascular disease (ASCVD). While poverty can be evaluated using income, a unidimensional poverty metric inadequately captures socioeconomic adversity. Objectives: The aim of the study was to examine the association between a multidimensional poverty measure and ASCVD. Methods: Survey data from the National Health Interview Survey was analyzed. Four poverty dimensions were used: income, education, self-reported health, and health insurance status. A weighted deprivation score (c i ) was calculated for each person. The multidimensional poverty index was computed for various cutoffs, k, for total population, and by ASCVD status. The association between multidimensional poverty and ASCVD was examined using Poisson regression. Area under receiver operator characteristics curve analysis was performed to compare the multidimensional poverty measure with the income poverty measure as a classification tool for ASCVD. Results: Among the 328,164 participants, 55.0% were females, the mean age was 46.3 years, 63.1% were non-Hispanic Whites, and 14.1% were non-Hispanic Blacks. Participants with ASCVD (7.95%) experienced greater deprivation at each multidimensional poverty cutoff, k, compared to those without ASCVD. In adjusted models, higher burden of multidimensional poverty was associated with up to 2.4-fold increased prevalence of ASCVD (c i  = 0.25, adjusted prevalence ratio [aPR] = 1.66, P < 0.001; c i  = 0.50, aPR = 1.99; c i  = 0.75, aPR = 2.29; P < 0.001; c i  = 1.00, aPR = 2.38, P < 0.001). Multidimensional poverty exhibited modestly higher discriminant validity, compared to income poverty (area under receiver operator characteristics = 0.62 vs 0.58). Conclusions: There is an association between the multidimensional poverty and ASCVD. Multidimensional poverty index demonstrates slightly better discriminatory power than income alone. Future validation studies are warranted to redefine poverty's role in health outcomes.