Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

Importance: Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality. Objective: To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities. Design, Setting, and Participants: This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (CHD), stroke, and chronic kidney disease (CKD). The study obtained aerial images from GSI covering census tracts in 7 cities (Cleveland, Ohio; Fremont, California; Kansas City, Missouri; Detroit, Michigan; Bellevue, Washington; Brownsville, Texas; and Denver, Colorado). The study used census tract-level data from the US Centers for Disease Control and Prevention's 500 Cities project. The data were originally collected from the Behavioral Risk Factor Surveillance System that surveyed people 18 years and older across the country. Analyses were conducted from February to December 2022. Exposures: GSI images of built environment and cardiometabolic disease prevalence. Main Outcomes and Measures: Census tract-level estimated prevalence of CHD, stroke, and CKD based on image-based built environment features. Results: The study obtained 31 786 aerial images from GSI covering 789 census tracts. Built environment features extracted from GSI using machine learning were associated with prevalence of CHD (R2 = 0.60), stroke (R2 = 0.65), and CKD (R2 = 0.64). The model performed better at distinguishing differences between cardiometabolic prevalence between cities than within cities (eg, highest within-city R2 = 0.39 vs between-city R2 = 0.64 for CKD). Addition of GSI features both outperformed and improved the model that only included age, sex, race, income, education, and composite indices for social determinants of health (R2 = 0.83 vs R2 = 0.76 for CHD; P <.001). Activation maps from the features revealed certain health-related built environment such as roads, highways, and railroads and recreational facilities such as amusement parks, arenas, and baseball parks. Conclusions and Relevance: In this cross-sectional study, a significant portion of cardiometabolic disease prevalence was associated with GSI-based built environment using convolutional neural networks.

Mapping Proximity to Infectious Disease Physicians Across the United States.

Enduring shortages of infectious disease physicians across the United States continue despite efforts to mitigate the problem. The recent fellowship match results underscore the difficulty in rectifying that shortage. Our report sheds light on the current geographic distribution of US infectious disease physicians and highlights the challenges faced by rural communities.

The Urban Environment and Cardiometabolic Health.

Urban environments contribute substantially to the rising burden of cardiometabolic diseases worldwide. Cities are complex adaptive systems that continually exchange resources, shaping exposures relevant to human health such as air pollution, noise, and chemical exposures. In addition, urban infrastructure and provisioning systems influence multiple domains of health risk, including behaviors, psychological stress, pollution, and nutrition through various pathways (eg, physical inactivity, air pollution, noise, heat stress, food systems, the availability of green space, and contaminant exposures). Beyond cardiometabolic health, city design may also affect climate change through energy and material consumption that share many of the same drivers with cardiometabolic diseases. Integrated spatial planning focusing on developing sustainable compact cities could simultaneously create heart-healthy and environmentally healthy city designs. This article reviews current evidence on the associations between the urban exposome (totality of exposures a person experiences, including environmental, occupational, lifestyle, social, and psychological factors) and cardiometabolic diseases within a systems science framework, and examines urban planning principles (eg, connectivity, density, diversity of land use, destination accessibility, and distance to transit). We highlight critical knowledge gaps regarding built-environment feature thresholds for optimizing cardiometabolic health outcomes. Last, we discuss emerging models and metrics to align urban development with the dual goals of mitigating cardiometabolic diseases while reducing climate change through cross-sector collaboration, governance, and community engagement. This review demonstrates that cities represent crucial settings for implementing policies and interventions to simultaneously tackle the global epidemics of cardiovascular disease and climate change.

Cardiovascular disease in Arab Americans: A literature review of prevalence, risk factors, and directions for future research.

Cardiovascular disease (CVD) is the leading cause of mortality worldwide. Recent evidence suggests Arab Americans, individuals with ancestry from Arabic-speaking countries, have an elevated risk for CVD compared to other ethnicities in the US. However, research focusing specifically on CVD in this population is limited. This literature review synthesizes studies investigating CVD prevalence, risk factors, and outcomes in Arab Americans. Multiple studies found higher rates of coronary heart disease, cerebrovascular disease, and hypertension compared to non-Hispanic White participants. The prevalence of type 2 diabetes, a major CVD risk factor, was also markedly higher, ranging from 16 % to 41 % in Arab Americans based on objective measures. Possible explanations include high rates of vitamin D deficiency, genetic factors, and poor diabetes control. Other metabolic factors like dyslipidemia and obesity did not consistently differ from general population estimates. Psychosocial factors may further increase CVD risk, including acculturative stress, discrimination, low health literacy, and barriers to healthcare access. Smoking, especially waterpipe use, was more prevalent in Arab American men. Though heterogenous, Arab Americans overall appear to have elevated CVD risk, warranting tailored screening and management. Culturally appropriate educational initiatives on CVD prevention are greatly needed. Future directions include better characterizing CVD prevalence across Arab American subgroups, delineating genetic and environmental factors underlying increased diabetes susceptibility, and testing culturally tailored interventions to mitigate CVD risks. In summary, this review highlights concerning CVD disparities in Arab Americans and underscores the need for group-specific research and preventive strategies.

Trends in tracheal, bronchial and lung cancer attributed to smoking in South America: Global Burden of Disease analysis 1990-2019.

Objective: To investigate the burden of tracheal, bronchus, and lung (TBL) cancer due to tobacco exposure in the last 30 years in 12 South American countries. Methods: We used the Global Burden of Disease (GBD) 2019 exposure-response function to analyze the total tobacco, smoking, and secondhand smoke exposure-related TBL cancer deaths and disability-adjusted life years (DALYs), for 12 South American countries, between 1990 and 2019. Metrics were described as absolute numbers or rates per 100 000 individuals. The relative change in burden was assessed by comparing the 1990-1994 to 2015-2019 periods. Results: In 2019, the all-ages number of TBL cancer deaths and DALYs associated with tobacco exposure in South America was 29 348 and 658 204 in males and 14 106 and 318 277 in females, respectively. Age-adjusted death and DALYs rates for the region in 2019 were 182.8 and 4035 in males and 50.8 and 1162 in females, respectively. In males, 10/12 countries observed relative declines in TBL death rates attributed to tobacco exposure while only 4 countries reduced their mortality in females. Conclusion: While significant efforts on tobacco control are under place in South America, substantial burden of TBL cancer persists in the region with significant sex-specific disparities. Increased country-specific primary data on TBL cancer and tobacco exposure is needed to optimize healthcare strategies and improve comprehension of regional trends.

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.

Continuous glucose monitoring for glycaemic control and cardiovascular risk reduction in patients with type 2 diabetes not on insulin therapy: A clinical trial.

AIM: To evaluate the impact of the Dexcom G6 continuous glucose monitoring (CGM) device on glycaemic control and cardiometabolic risk in patients with type 2 diabetes mellitus (T2DM) at high cardiovascular risk who are not on insulin therapy. MATERIALS AND METHODS: Adults with T2DM with glycated haemoglobin (HbA1c) >7% and body mass index (BMI) ≥30 kg/m2 not using insulin were enrolled in a two-phase cross-over study. In phase 1, CGM data were blinded, and participants performed standard glucose self-monitoring. In phase 2, the CGM data were unblinded, and CGM, demographic and cardiovascular risk factor data were collected through 90 days of follow-up and compared using paired tests. RESULTS: Forty-seven participants were included (44% women; 34% Black; mean age 63 years; BMI 37 kg/m2; HbA1c 8.4%; 10-year predicted atherosclerotic cardiovascular disease risk 24.0%). CGM use was associated with a reduction in average glucose (184.0 to 147.2 mg/dl, p < .001), an increase in time in range (57.8 to 82.8%, p < .001) and a trend towards lower glucose variability (26.2 to 23.8%). There were significant reductions in HbA1c, BMI, triglycerides, blood pressure, total cholesterol, diabetes distress and 10-year predicted risk for atherosclerotic cardiovascular disease (p < .05 for all) and an increase in prescriptions for sodium-glucose cotransporter 2 inhibitors (36.2 to 83.0%) and glucagon-like peptide-1 receptor agonists (42.5 to 87.2%, p < .001 for both). CONCLUSIONS: Dexcom G6 CGM was associated with improved glycaemic control and cardiometabolic risk in patients with T2DM who were not on insulin. CGM can be a safe and effective tool to improve diabetes management in patients at high risk for adverse cardiovascular outcomes.

Public Health Relevance of US EPA Air Quality Index Activity Recommendations.

Importance: Reducing exposure to fine particulate matter (<2.5 µm [PM2.5]) air pollution improves cardiopulmonary morbidity and mortality. However, the public health relevance of air quality index (AQI) activity guidelines under present-day environmental conditions in the US has not been critically assessed. Objective: To evaluate the public health relevance of following PM2.5 AQI activity guidance in preventing serious atherosclerotic cardiovascular disease (ASCVD) and pulmonary events among adults in the US. Design, Setting, and Participants: This cross-sectional modeling study involved the general adult population and sensitive individuals as designated by the US Environmental Protection Agency (EPA), including adults with preexisting ASCVD or lung disease (asthma or chronic obstructive pulmonary disease). The study was conducted between August 1, 2023, and January 31, 2024. Exposures: Daily AQI strata for PM2.5 and the corresponding activity recommendations. Main Outcomes and Measures: The main outcome was the number needed to treat (NNT) per day by following activity guidance across daily AQI strata to prevent 1 serious ASCVD or pulmonary event among relevant populations. To calculate PM2.5-induced excess disease event rates per day, estimated baseline disease-specific daily event rates for each group were multiplied by the increase in risks due to PM2.5 levels at each AQI stratum. The number of events prevented per day was calculated by multiplying each excess disease event rate by the percentage in exposure reduction plausibly incurred by following population-specific activity guidance at each AQI level. The NNT is the reciprocal of the number of events prevented. Results: The NNT to prevent ASCVD events was high for the general population and for patients with ASCVD across all AQI strata. The range of values was comparatively lower to prevent pulmonary events among adults with lung disease. During most days (96%) when activity recommendations were promulgated due to elevated PM2.5 (AQI, 101-200), the NNT to prevent a serious disease event remained very high for the general population (>18 million), patients with ASCVD (approximately 1.6-5 million), and adults with lung disease (approximately 66 000-202 000). Conclusions and Relevance: These findings suggest that existing PM2.5 AQI activity recommendations are of questionable public health relevance in present-day conditions and merit consideration for updating to improve their potential effectiveness.

Influence of social vulnerability index on Medicare beneficiaries' expenditures upon discharge.

Medicare beneficiaries' healthcare spending varies across geographical regions, influenced by availability of medical resources and institutional efficiency. We aimed to evaluate whether social vulnerability influences healthcare costs among Medicare beneficiaries. Multivariable regression analyses were conducted to determine whether the social vulnerability index (SVI), released by the Centers for Disease Control and Prevention (CDC), was associated with average submitted covered charges, total payment amounts, or total covered days upon hospital discharge among Medicare beneficiaries. We used information from discharged Medicare beneficiaries from hospitals participating in the Inpatient Prospective Payment System. Covariate adjustment included demographic information consisting of age groups, race/ethnicity, and Hierarchical Condition Category risk score. The regressions were performed with weights proportioned to the number of discharges. Average submitted covered charges significantly correlated with SVI (ß = 0.50, p < 0.001) in the unadjusted model and remained significant in the covariates-adjusted model (ß = 0.25, p = 0.039). The SVI was not significantly associated with the total payment amounts (ß = -0.07, p = 0.238) or the total covered days (ß = 0.00, p = 0.953) in the adjusted model. Regional variations in Medicare beneficiaries' healthcare spending exist and are influenced by levels of social vulnerability. Further research is warranted to fully comprehend the impact of social determinants on healthcare costs.

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).

Environmental Exposome and Atrial Fibrillation: Emerging Evidence and Future Directions.

There has been increased awareness of the linkage between environmental exposures and cardiovascular health and disease. Atrial fibrillation is the most common sustained cardiac arrhythmia, affecting millions of people worldwide and contributing to substantial morbidity and mortality. Although numerous studies have explored the role of genetic and lifestyle factors in the development and progression of atrial fibrillation, the potential impact of environmental determinants on this prevalent condition has received comparatively less attention. This review aims to provide a comprehensive overview of the current evidence on environmental determinants of atrial fibrillation, encompassing factors such as air pollution, temperature, humidity, and other meteorologic conditions, noise pollution, greenspace, and the social environment. We discuss the existing evidence from epidemiological and mechanistic studies, critically evaluating the strengths and limitations of these investigations and the potential underlying biological mechanisms through which environmental exposures may affect atrial fibrillation risk. Furthermore, we address the potential implications of these findings for public health and clinical practice and identify knowledge gaps and future research directions in this emerging field.

Primary Prevention of Cardiovascular Disease in Women.

Cardiovascular disease (CVD) remains a leading cause of mortality in women, necessitating innovative primary prevention strategies. Contemporary guidelines on primary prevention of CVD highlight the increasing prevalence of CVD risk factors and emphasize the significance of female-specific risk enhancers that substantially augment the future risk of CVD. These risk factors occur throughout a woman's life cycle, such as hormonal contraception, hypertensive disorders of pregnancy, and menopause, all of which confer an added layer of risk in women beyond the conventional risk factors. Despite this, current methods may not fully capture the nuanced vulnerabilities in women that increase their risk of CVD. In this review, we highlight gender-specific risk enhancers and subsequent prevention as well as strategies to improve primary prevention of CVD in women.

Association of Environmental Injustice and Cardiovascular Diseases and Risk Factors in the United States.

BACKGROUND: While the impacts of social and environmental exposure on cardiovascular risks are often reported individually, the combined effect is poorly understood. METHODS AND RESULTS: Using the 2022 Environmental Justice Index, socio-environmental justice index and environmental burden module ranks of census tracts were divided into quartiles (quartile 1, the least vulnerable census tracts; quartile 4, the most vulnerable census tracts). Age-adjusted rate ratios (RRs) of coronary artery disease, strokes, and various health measures reported in the Prevention Population-Level Analysis and Community Estimates data were compared between quartiles using multivariable Poisson regression. The quartile 4 Environmental Justice Index was associated with a higher rate of coronary artery disease (RR, 1.684 [95% CI, 1.660-1.708]) and stroke (RR, 2.112 [95% CI, 2.078-2.147]) compared with the quartile 1 Environmental Justice Index. Similarly, coronary artery disease 1.057 [95% CI,1.043-1.0716] and stroke (RR, 1.118 [95% CI, 1.102-1.135]) were significantly higher in the quartile 4 than in the quartile 1 environmental burden module. Similar results were observed for chronic kidney disease, hypertension, diabetes, obesity, high cholesterol, lack of health insurance, sleep <7 hours per night, no leisure time physical activity, and impaired mental and physical health >14 days. CONCLUSIONS: The prevalence of CVD and its risk factors is highly associated with increased social and environmental adversities, and environmental exposure plays an important role independent of social factors.

Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.

BACKGROUND AND AIMS: Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities. METHODS: This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). RESULTS: Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence. CONCLUSIONS: In this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.

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.

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.