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.

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.

The association between county-level premature cardiovascular mortality related to cardio-kidney-metabolic disease and the social determinants of health in the US.

Cardio-kidney-metabolic (CKM) syndrome is defined by the American Heart Association as the intersection between metabolic, renal and cardiovascular disease. Understanding the contemporary estimates of CKM related mortality and recent trends in the US is essential for developing targeted public interventions. We collected state-level and county-level CKM-associated age-adjusted premature cardiovascular mortality (aaCVM) (2010-2019) rates from the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER). We linked the county-level aaCVM with a multi-component social deprivation metric: the Social Deprivation Index (SDI: range 0-100) and grouped them as follows: I: 0-25, II: 26-50, III: 51-75, and IV: 76-100. We conducted pair-wise comparison of aaCVM between SDI groups with the multiplicity adjusted Wilcoxon test; we compared aaCVM in men versus women, metropolitan versus nonmetropolitan counties, and non-hispanic white versus non-hispanic black residents. In 3101 analyzed counties in the US, the median CKM associated aaCVM was 61 [interquartile range (IQR): 45, 82]/100 000. Mississippi (99/100 000) and Minnesota (33/100 000) had the highest and lowest values respectively. CKM associated aaMR increased across SDI groups [I - 45 (IQR: 36, 55)/100 000, II- 61 (IQR: 49, 77)/100 000, III- 77 (IQR: 61, 94)/100 000, IV- 89 (IQR: 70, 110)/100 000; all pair-wise p-values < 0.001]. Men had higher rates [85 (64, 91)/100 000] than women [41 (28, 58)/100 000](p-value < 0.001), metropolitan counties [54 (40, 72)/100 000] had lower rates than non-metropolitan counties [66 (49, 90)/100 000](p-value < 0.001), and non-Hispanic Black [110 (86, 137)/100 000] had higher aaMR than non-Hispanic White residents [59 (44, 78)/100 000](p-value < 0.001). In the US, CKM mortality remains high and disproportionately occurs in more socially deprived counties and non-metropolitan counties. Our inability to reduce CKM mortality rates over the study period highlights the need for targeted policy interventions to curb the ongoing high burden.

Bayesian Model Projecting Cardiovascular Disease Related Mortality Trends in the United States.

BACKGROUND: As future cardiovascular disease mortality trends have public health implications, we aimed to project ischemic heart disease (IHD), cerebrovascular disease (CeVD), and heart failure (HF) mortality rates for adults (40-79 years). METHODS AND RESULTS: In this population-level study, we linked the yearly mortality rates (per 100 000 US residents) (2000-2019) with IHD, CeVD, or HF as the primary cause of death from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research with the midyear US population estimates (2000-2035) for adults (40-79 years). We calculated the observed age-standardized mortality rates (2000-2019) (per 100 000 residents) (aSMR) and fitted Bayesian age-period-cohort models to project aSMR for IHD, CeVD, and HF up to 2035 in the United States. Between 2019 (last year of observed data) and 2035 (last year of projected results), the US population (40-79 years) will increase by 16% and age. The IHD aSMR will reduce from 111.9 (in 2019) to 81.8 (66.7-96.9) in 2035, an effect observed for all age groups. The CeVD aSMR will remain comparable between 2019 (37.4) and 2035 (38.6 [30.7-46.5]). The HF aSMR will increase from 16.5 in 2019 to reach 30.9 (13-48.8) in 2035; such increases were observed in all age groups. CONCLUSIONS: In the United States, between 2020 and 2035, the aSMR for IHD is expected to decrease, for CeVD will remain stable, and for HF will increase substantially. These data can inform resource allocation for future public health initiatives.

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.

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.