County-Level Socio-Environmental Factors Associated With Stroke Mortality in the United States: A Cross-Sectional Study.

We used machine learning methods to explore sociodemographic and environmental determinants of health (SEDH) associated with county-level stroke mortality in the USA. We conducted a cross-sectional analysis of individuals aged ≥15 years who died from all stroke subtypes between 2016 and 2020. We analyzed 54 county-level SEDH possibly associated with age-adjusted stroke mortality rates/100,000 people. Classification and Regression Tree (CART) was used to identify specific county-level clusters associated with stroke mortality. Variable importance was assessed using Random Forest analysis. A total of 501,391 decedents from 2397 counties were included. CART identified 10 clusters, with 77.5% relative increase in stroke mortality rates across the spectrum (28.5 vs 50.7 per 100,000 persons). CART identified 8 SEDH to guide the classification of the county clusters. Including, annual Median Household Income ($), live births with Low Birthweight (%), current adult Smokers (%), adults reporting Severe Housing Problems (%), adequate Access to Exercise (%), adults reporting Physical Inactivity (%), adults with diagnosed Diabetes (%), and adults reporting Excessive Drinking (%). In conclusion, SEDH exposures have a complex relationship with stroke. Machine learning approaches can help deconstruct this relationship and demonstrate associations that allow improved understanding of the socio-environmental drivers of stroke and development of targeted interventions.

Quantifying lead-attributable cardiovascular disease burden in the United States.

Lead exposure has been linked to a myriad of cardiovascular diseases. Utilizing data from the 2019 Global Burden of Disease Study, we quantified age-standardized lead exposure-related mortality and disability-adjusted life years (DALYs) in the United States between 1990 and 2019. Our analysis revealed a substantial reduction in age-standardized cardiovascular disease (CVD) mortality attributable to lead exposure by 60 % (from 7.4 to 2.9 per 100,000), along with a concurrent decrease in age-standardized CVD DALYs by 66 % (from 143.2 to 48.7 per 100,000).

Associations of diabesity with all-cause and cardiovascular disease mortality: Findings from the Mexico City Prospective Study.

AIM: To investigate the joint associations of diabetes and obesity with all-cause and cardiovascular disease (CVD) mortality in the Mexico City Prospective Study. MATERIALS AND METHODS: In total, 154 128 participants (67.2% women) were included in this prospective analysis. Diabetes was self-reported, while body mass index was used to calculate obesity. Using diabetes and obesity classifications, six groups were created: (a) normal (no diabetes and normal weight); (b) normal weight and diabetes; (c) overweight but not diabetes (overweight); (d) overweight and diabetes (prediabesity); (e) obesity but not diabetes (obesity); and (f) obesity and diabetes (diabesity). Associations between these categories and outcomes were investigated using Cox proportional hazard models adjusted for confounder factors. RESULTS: During 18.3 years of follow-up, 27 197 (17.6%) participants died (28.5% because of CV causes). In the maximally adjusted model, participants those with the highest risk {hazard ratio (HR): 2.37 [95% confidence interval (CI): 2.24-2.51]}, followed by those with diabesity [HR: 2.04 (95% CI: 1.94-2.15)]. Similar trends of associations were observed for CVD mortality. The highest CV mortality risk was observed in individuals with diabesity [HR: 1.80 (95% CI: 1.63-1.99)], followed by normal weight and diabetic individuals [HR: 1.78 (95% CI: 1.60-1.98)]. CONCLUSION: This large prospective study identified that diabetes was the main driver of all-cause and CVD mortality in all the categories studied, with diabesity being the riskiest. Given the high prevalence of both conditions in Mexico, our results reinforce the importance of initiating prevention strategies from an early age.

Impact of Residential Social Deprivation on Prediction of Heart Failure in Patients With Type 2 Diabetes: External Validation and Recalibration of the WATCH-DM Score Using Real World Data.

BACKGROUND: Patients with type 2 diabetes are at risk of heart failure hospitalization. As social determinants of health are rarely included in risk models, we validated and recalibrated the WATCH-DM score in a diverse patient-group using their social deprivation index (SDI). METHODS: We identified US Veterans with type 2 diabetes without heart failure that received outpatient care during 2010 at Veterans Affairs medical centers nationwide, linked them to their SDI using residential ZIP codes and grouped them as SDI <20%, 21% to 40%, 41% to 60%, 61% to 80%, and >80% (higher values represent increased deprivation). Accounting for all-cause mortality, we obtained the incidence for heart failure hospitalization at 5 years follow-up; overall and in each SDI group. We evaluated the WATCH-DM score using the C statistic, the Greenwood Nam D'Agostino test χ2 test and calibration plots and further recalibrated the WATCH-DM score for each SDI group using a statistical correction factor. RESULTS: In 1 065 691 studied patients (mean age 67 years, 25% Black and 6% Hispanic patients), the 5-year incidence of heart failure hospitalization was 5.39%. In SDI group 1 (least deprived) and 5 (most deprived), the 5-year heart failure hospitalization was 3.18% and 11%, respectively. The score C statistic was 0.62; WATCH-DM systematically overestimated heart failure risk in SDI groups 1 to 2 (expected/observed ratios, 1.38 and 1.36, respectively) and underestimated the heart failure risk in groups 4 to 5 (expected/observed ratios, 0.95 and 0.80, respectively). Graphical evaluation demonstrated that the recalibration of WATCH-DM using an SDI group-based correction factor improved predictive capabilities as supported by reduction in the χ2 test results (801-27 in SDI groups I; 623-23 in SDI group V). CONCLUSIONS: Including social determinants of health to recalibrate the WATCH-DM score improved risk prediction highlighting the importance of including social determinants in future clinical risk prediction models.

County-level socio-environmental factors and obesity prevalence in the United States.

AIMS: To investigate high-risk sociodemographic and environmental determinants of health (SEDH) potentially associated with adult obesity in counties in the United States using machine-learning techniques. MATERIALS AND METHODS: We performed a cross-sectional analysis of county-level adult obesity prevalence (body mass index ≥30 kg/m2) in the United States using data from the Diabetes Surveillance System 2017. We harvested 49 county-level SEDH factors that were used in a classification and regression trees (CART) model to identify county-level clusters. The CART model was validated using a 'hold-out' set of counties and variable importance was evaluated using Random Forest. RESULTS: Overall, we analysed 2752 counties in the United States, identifying a national median (interquartile range) obesity prevalence of 34.1% (30.2%, 37.7%). The CART method identified 11 clusters with a 60.8% relative increase in prevalence across the spectrum. Additionally, seven key SEDH variables were identified by CART to guide the categorization of clusters, including Physically Inactive (%), Diabetes (%), Severe Housing Problems (%), Food Insecurity (%), Uninsured (%), Population over 65 years (%) and Non-Hispanic Black (%). CONCLUSION: There is significant county-level geographical variation in obesity prevalence in the United States, which can in part be explained by complex SEDH factors. The use of machine-learning techniques to analyse these factors can provide valuable insights into the importance of these upstream determinants of obesity and, therefore, aid in the development of geo-specific strategic interventions and optimize resource allocation to help battle the obesity pandemic.

Air pollution exposure and cardiometabolic risk.

The Global Burden of Disease assessment estimates that 20% of global type 2 diabetes cases are related to chronic exposure to particulate matter (PM) with a diameter of 2·5 µm or less (PM2·5). With 99% of the global population residing in areas where air pollution levels are above current WHO air quality guidelines, and increasing concern in regard to the common drivers of air pollution and climate change, there is a compelling need to understand the connection between air pollution and cardiometabolic disease, and pathways to address this preventable risk factor. This Review provides an up to date summary of the epidemiological evidence and mechanistic underpinnings linking air pollution with cardiometabolic risk. We also outline approaches to improve awareness, and discuss personal-level, community, governmental, and policy interventions to help mitigate the growing global public health risk of air pollution exposure.

Association of five diet scores with severe NAFLD incidence: A prospective study from UK Biobank.

AIM: This study aimed to contrast the associations of five common diet scores with severe non-alcoholic fatty liver disease (NAFLD) incidence. MATERIALS AND METHODS: In total, 162 999 UK Biobank participants were included in this prospective population-based study. Five international diet scores were included: the 14-Item Mediterranean Diet Adherence Screener (MEDAS-14), the Recommended Food Score (RFS), the Healthy Diet Indicator (HDI), the Mediterranean Diet Score and the Mediterranean-DASH Intervention for Neurodegenerative Delay score. As each score has different measurements and scales, all scores were standardized and categorized into quartiles. Cox proportional hazard models adjusted for confounder factors investigated associations between the standardized quartiles and severe NAFLD incidence. RESULTS: Over a median follow-up of 10.2 years, 1370 participants were diagnosed with severe NAFLD. When the analyses were fully adjusted, participants in quartile 4 using the MEDAS-14 and RFS scores, as well as those in quartiles 2 and 3 using the HDI score, had a significantly lower risk of severe incident NAFLD compared with those in quartile 1. The lowest risk was observed in quartile 4 for the MEDAS-14 score [hazard ratio (HR): 0.76 (95% confidence interval (CI): 0.62-0.94)] and the RFS score [HR: 0.82 (95% CI: 0.69-0.96)] and as well as in quartile 2 in the HDI score [HR: 0.80 (95% CI: 0.70-0.91)]. CONCLUSION: MEDAS-14, RFS and HDI scores were the strongest diet score predictors of severe NAFLD. A healthy diet might protect against NAFLD development irrespective of the specific approach used to assess diet. However, following these score recommendations could represent optimal dietary approaches to mitigate NAFLD risk.

Sex-specific heart failure burden across the United States: Global burden of disease 1990-2019.

BACKGROUND: Heart failure (HF) has unique aspects that vary by biological sex. Thus, understanding sex-specific trends of HF in the US population is crucial to develop targeted interventions. We aimed to analyze the burden of HF in female and male patients across the US, from 1990 to 2019. METHODS: Using the Global Burden of Disease (GBD) study data from 2019, we performed an analysis of the burden of HF from 1990-2019, across US states and regions. The GBD defined HF through studies that used symptom-based criteria and expressed the burden of HF as the age-adjusted prevalence and years lived with disability (YLDs) rates per 100,000 individuals. RESULTS: The age-adjusted prevalence of HF for the US in 2019 was 926.2 (95% UI [799.6, 1,079.0]) for females and 1,291.2 (95% UI [1,104.1, 1,496.8]) for males. Notably, our findings also highlight cyclic fluctuations in HF prevalence over time, with peaks occurring in the mid-1990s and around 2010, while reaching their lowest points in around 2000 and 2018. Among individuals >70 years of age, the absolute number of individuals with HF was higher in females, and this age group doubled the absolute count between 1990 and 2019. Comparing 1990-1994 to 2015-2019, 10 states had increased female HF prevalence, while only 4 states increased male prevalence. Overall, Western states had the greatest relative decline in HF burden, in both sexes. CONCLUSION: The burden of HF in the US is high, although the magnitude of this burden varies according to age, sex, state, and region. There is a significant increase in the absolute number of individuals with HF, especially among women >70 years, expected to continue due to the aging population.

Trends in Cardiovascular Disease Mortality by County-Level Social Vulnerability Index in the United States.

Background Although cardiovascular mortality (CVM) rates in the United States have been declining overall, our study evaluated whether this holds true for areas with increased social deprivation. Methods and Results We used county-level cross-sectional age-adjusted CVM rates (aa-CVM) (2000-2019) linked to the Centers for Disease Control and Prevention Social Vulnerability Index (SVI-2010). We grouped counties as per SVI (Groups I 0-0.2, II 0.21-0.4, III 0.41-0.6, IV 0.61-0.8, and V 0.81-1) and calculated the relative change in the aa-CVM between 2000 to 2003 and 2016 to 2019. We used adjusted linear regression analyses to explore the association between a higher SVI and temporal aa-CVM improvement; we studied this temporal change in aa-CVM across subgroups of race, sex, and location. The median aa-CVM rate (per 100 000) was 272.6 (interquartile range [IQR]: 237.5-311.7). The aa-CVM was higher in men (315.6 [IQR: 273.4-363.9]) than women (221.3 [IQR: 189.6-256.7]), and in Black residents (347.2 [IQR: 301.1-391.1]; P<0.001) than White residents (258.9 [IQR: 226-299.1]; P<0.001). The aa-CVM for SVI I (233.6 [IQR: 214.8-257.0]) was significantly lower than that of group V (323.6 [IQR: 277.2-359.2]; P<0.001). The relative reduction in CVM was significantly higher for SVI group I (32.2% [IQR: 24.2-38.4]) than group V (27.2% [IQR: 19-34.1]) counties. After multivariable adjustment, a higher SVI index was associated with lower relative improvement in the age-adjusted CVM (model coefficient -3.11 [95% CI, -5.66 to -1.22]; P<0.001). Conclusions Socially deprived counties in the United States had higher aa-CVM rates, and the improvement in aa-CVM over the past 20 years was lower in these counties.

Validating the SMART2 Score in a Racially Diverse High-Risk Nationwide Cohort of Patients Receiving Coronary Artery Bypass Grafting.

Background We tested the potential of the Secondary Manifestations of Arterial Disease (SMART2) risk score for use in patients undergoing coronary artery bypass grafting. Methods and Results We conducted an external validation of the SMART2 score in a racially diverse high-risk national cohort (2010-2019) that underwent isolated coronary artery bypass grafting. We calculated the preoperative SMART2 score and modeled the 5-year major adverse cardiovascular event (cardiovascular mortality+myocardial infarction+stroke) incidence. We evaluated SMART2 score discrimination at 5 years using c-statistic and calibration with observed/expected ratio and calibration plots. We analyzed the potential clinical benefit using decision curves. We repeated these analyses in clinical subgroups, diabetes, chronic kidney disease, and polyvascular disease, and separately in White and Black patients. In 27 443 (mean age, 65 years; 10% Black individuals) US veterans undergoing coronary artery bypass grafting (2010-2019) nationwide, the 5-year major adverse cardiovascular event rate was 25%; 27% patients were in high predicted risk (>30% 5-year major adverse cardiovascular events). SMART2 score discrimination (c-statistic: 64) was comparable to the original study (c-statistic: 67) and was best in patients with chronic kidney disease (c-statistic: 66). However, it underpredicted major adverse cardiovascular event rates in the whole cohort (observed/expected ratio, 1.45) as well as in all studied subgroups. The SMART2 score performed better in White than Black patients. On decision curve analysis, the SMART2 score provides a net benefit over a wide range of risk thresholds. Conclusions The SMART2 model performs well in a racially diverse coronary artery bypass grafting cohort, with better predictive capabilities at the upper range of baseline risk, and can therefore be used to guide secondary preventive pharmacotherapy.

Geographically Weighted Modeling to Explore Social and Environmental Factors Affecting County-Level Cardiovascular Mortality in People With Diabetes in the United States: A Cross-Sectional Analysis.

Disparities exist in the cardiovascular mortality rates among people with type 2 diabetes (T2D). Research has established that these disparities are often related to the environmental and social determinations of health. This study explores the spatial variation between air pollution, social determinants of health and T2D related age-adjusted cardiovascular mortality (aa-CVM) in the United States. We obtained county-level T2D related to aa-CVM (per 100,000 residents) from Centers for Disease Control and Prevention WONDER (Wide-ranging Online Data for Epidemiologic Research) (2010 to 2019). We fit a geographically weighted linear regression with aa-CVM as the outcome and the following covariates (ambient air pollution [particulate matter of 2.5 µm size], median annual household income, racial/ethnic minorities, higher education, rurality, food insecurity, and primary health care access) were included. Overall, the median aa-CVM rate was 92.9 and highest in the South (102.2). In the West, aa-CVM was significantly associated with particulate matter of 2.5 µm size, annual median household income, racial minority status and primary health care access. Food insecurity was the most significant exposure in the Midwest and Northeast, while in the South, annual median household income and food insecurity were significant. In conclusion, this study demonstrated a substantial regional variation of exposure to determinants of T2D related aa-CVM in the United States. These findings should be considered in policy frameworks and interventions as part of community-level approaches to addressing T2D related aa-CVM, and within broader state and national discussions of the importance of population health.

Association Between Historical Neighborhood Redlining and Cardiovascular Outcomes Among US Veterans With Atherosclerotic Cardiovascular Diseases.

Importance: In the 1930s, the government-sponsored Home Owners' Loan Corporation (HOLC) established maps of US neighborhoods that identified mortgage risk (grade A [green] characterizing lowest-risk neighborhoods in the US through mechanisms that transcend traditional risk factors to grade D [red] characterizing highest risk). This practice led to disinvestments and segregation in neighborhoods considered redlined. Very few studies have targeted whether there is an association between redlining and cardiovascular disease. Objective: To evaluate whether redlining is associated with adverse cardiovascular outcomes in US veterans. Design, Setting, and Participants: In this longitudinal cohort study, US veterans were followed up (January 1, 2016, to December 31, 2019) for a median of 4 years. Data, including self-reported race and ethnicity, were obtained from Veterans Affairs medical centers across the US on individuals receiving care for established atherosclerotic disease (coronary artery disease, peripheral vascular disease, or stroke). Data analysis was performed in June 2022. Exposure: Home Owners' Loan Corporation grade of the census tracts of residence. Main Outcomes and Measures: The first occurrence of major adverse cardiovascular events (MACE), comprising myocardial infarction, stroke, major adverse extremity events, and all-cause mortality. The adjusted association between HOLC grade and adverse outcomes was measured using Cox proportional hazards regression. Competing risks were used to model individual nonfatal components of MACE. Results: Of 79 997 patients (mean [SD] age, 74.46 [10.16] years, female, 2.9%; White, 55.7%; Black, 37.3%; and Hispanic, 5.4%), a total of 7% of the individuals resided in HOLC grade A neighborhoods, 20% in B neighborhoods, 42% in C neighborhoods, and 31% in D neighborhoods. Compared with grade A neighborhoods, patients residing in HOLC grade D (redlined) neighborhoods were more likely to be Black or Hispanic with a higher prevalence of diabetes, heart failure, and chronic kidney disease. There were no associations between HOLC and MACE in unadjusted models. After adjustment for demographic factors, compared with grade A neighborhoods, those residing in redlined neighborhoods had an increased risk of MACE (hazard ratio [HR], 1.139; 95% CI, 1.083-1.198; P < .001) and all-cause mortality (HR, 1.129; 95% CI, 1.072-1.190; P < .001). Similarly, veterans residing in redlined neighborhoods had a higher risk of myocardial infarction (HR, 1.148; 95% CI, 1.011-1.303; P < .001) but not stroke (HR, 0.889; 95% CI, 0.584-1.353; P = .58). Hazard ratios were smaller, but remained significant, after adjustment for risk factors and social vulnerability. Conclusions and Relevance: In this cohort study of US veterans, the findings suggest that those with atherosclerotic cardiovascular disease who reside in historically redlined neighborhoods continue to have a higher prevalence of traditional cardiovascular risk factors and higher cardiovascular risk. Even close to a century after this practice was discontinued, redlining appears to still be adversely associated with adverse cardiovascular events.

Association Between the AHA Life's Essential 8 Score and Incident All-Cause Dementia: A Prospective Cohort Study from UK Biobank.

This study aimed to investigate the association between the Life's Essential 8 (LE8) score and incident all-cause dementia (including Alzheimer's disease [AD] and vascular dementia) in UK Biobank. A total of 259,718 participants were included in this prospective study. Smoking, non-HDL cholesterol, blood pressure, body mass index, HbA1c, physical activity, diet, and sleep were used to create the Life's Essential 8 (LE8) score. Associations between the score (both continuous and as quartiles) and outcomes were investigated using adjusted Cox proportional hazard models. The potential impact fractions of 2 scenarios and the rate advancement periods were also calculated. Over a median follow-up of 10.6 years, 4958 participants were diagnosed with any dementia. Higher LE8 scores were associated with lower risk of all-cause and vascular dementia in an exponential decay pattern. Compared with individuals in the healthiest quartile, those in the least healthy quartile had a higher risk of all-cause dementia (HR: 1.50 [95% CI: 1.37-1.65] and vascular dementia (HR: 1.86 [1.44-2.42]). A targeted intervention that increased the score by 10-points among individuals in the lowest quartile could have prevented 6.8% of all-cause dementia cases. Individuals in the least healthy LE8 quartile might develop all-cause dementia 2.45 years earlier than their counterparts. In conclusion, individuals with higher LE8 scores had lower risk of all-cause and vascular dementia. Because of nonlinear associations, interventions targeted at the least healthy individuals might produce greater population-level benefits.

Neighbourhood-level social deprivation and the risk of recurrent heart failure hospitalizations in type 2 diabetes.

BACKGROUND: The importance of type 2 diabetes mellitus (T2D) in heart failure hospitalizations (HFH) is acknowledged. As information on the prevalence and influence of social deprivation on HFH is limited, we studied this issue in a racially diverse cohort. METHODS: Linking data from US Veterans with stable T2D (without prevalent HF) with a zip-code derived population-level social deprivation index (SDI), we grouped them according to increasing SDI as follows: SDI: group I: ≤20; II: 21-40; III: 41-60; IV: 61-80; and V (most deprived) 81-100. Over a 10-year follow-up period, we identified the total (first and recurrent) number of HFH episodes for each patient and calculated the age-adjusted HFH rate [per 1000 patient-years (PY)]. We analysed the incident rate ratio between SDI groups and HFH using adjusted analyses. RESULTS: In 1 012 351 patients with T2D (mean age 67.5 years, 75.7% White), the cumulative incidence of first HFH was 9.4% and 14.2% in SDI groups I and V respectively. The 10-year total HFH rate was 54.8 (95% CI: 54.5, 55.2)/1000 PY. Total HFH increased incrementally from SDI group I [43.3 (95% CI: 42.4, 44.2)/1000 PY] to group V [68.6 (95% CI: 67.8, 69.9)/1000 PY]. Compared with group I, group V patients had a 53% higher relative risk of HFH. The negative association between SDI and HFH was stronger in Black patients (SDI × Race pinteraction < .001). CONCLUSIONS: Social deprivation is associated with increased HFH in T2D with a disproportionate influence in Black patients. Strategies to reduce social disparity and equalize racial differences may help to bridge this gap.