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.

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.

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.

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.

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.

Air Pollution and Adverse Cardiovascular Events After Coronary Artery Bypass Grafting: A 10-Year Nationwide Study.

Background: Increased particulate matter <2.5 µm (PM2.5) air pollution is associated with adverse cardiovascular outcomes. However, its impact on patients with prior coronary artery bypass grafting (CABG) is unknown. Objectives: The purpose of this study was to evaluate the association between major adverse cardiovascular events (MACE) (defined as myocardial infarction, stroke, or cardiovascular death) and air pollution after CABG. Methods: We linked 26,403 U.S. veterans who underwent CABG (2010-2019) nationally with average annual ambient PM2.5 estimates using residential address. Over a 5-year median follow-up period, we identified MACE and fit a multivariable Cox proportional hazard model to determine the risk of MACE as per PM2.5 exposure. We also estimated the absolute potential reduction in PM2.5 attributable MACE simulating a hypothetical PM2.5 lowered to the revised World Health Organization standard of 5 µg/m3. Results: The observed median PM2.5 exposure was 7.9 µg/m3 (IQR: 7.0-8.9 µg/m3; 95% of patients were exposed to PM2.5 above 5 µg/m3). Increased PM2.5 exposure was associated with a higher 10-year MACE rate (first tertile 38% vs third tertile 45%; P < 0.001). Adjusting for demographic, racial, and clinical characteristics, a 10 µg/m3 increase in PM2.5 resulted in 27% relative risk for MACE (HR: 1.27, 95% CI: 1.10-1.46; P < 0.001). Currently, 10% of total MACE is attributable to PM2.5 exposure. Reducing maximum PM2.5 to 5 µg/m3 could result in a 7% absolute reduction in 10-year MACE rates. Conclusions: In this large nationwide CABG cohort, ambient PM2.5 air pollution was strongly associated with adverse 10-year cardiovascular outcomes. Reducing levels to World Health Organization-recommended standards would result in a substantial risk reduction at the population level.

Historical neighborhood redlining and bystander CPR disparities in out-of-hospital cardiac arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) is associated with low survival rates. Bystander cardiopulmonary resuscitation (CPR) is essential for improving outcomes, but its utilization remains limited, particularly among racial and ethnic minorities. Historical redlining, a practice that classified neighborhoods for mortgage risk in 1930s, may have lasting implications for social and health outcomes. This study sought to investigate the influence of redlining on the provision of bystander CPR during witnessed OHCA. METHODS: We conducted an analysis using data from the comprehensive Cardiac Arrest Registry to Enhance Survival (CARES), encompassing 736,066 non-traumatic OHCA cases across the United States. The Home Owners' Loan Corporation (HOLC) map shapefiles were utilized to categorize census tracts of arrests into four grades (A signifying "best", B "still desirable", C "declining", and D "hazardous"). Multivariable hierarchical logistic regression models were employed to predict the likelihood of CPR provision, adjusting for various factors including age, sex, race/ethnicity, arrest location, calendar year, and state of occurrence. Additionally, we accounted for the percentage of Black residents and residents below poverty levels at the census tract level. RESULTS: Among the 43,186 witnessed cases of OHCA in graded HOLC census tracts, 37.2% received bystander CPR. The rates of bystander CPR exhibited a gradual decline across HOLC grades, ranging from 41.8% in HOLC grade A to 35.8% in HOLC grade D. In fully adjusted model, we observed significantly lower odds of receiving bystander CPR in HOLC grades C (OR 0.89, 95% CI 0.81-0.98, p = 0.016) and D (OR 0.86, 95% CI 0.78-0.95, p = 0.002) compared to HOLC grade A. CONCLUSION: Redlining, a historical segregation practice, is associated with reduced contemporary rates of bystander CPR during OHCA. Targeted CPR training in redlined neighborhoods may be imperative to enhance survival outcomes.

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).

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.

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.

Ameliorating Cardiovascular Risk in Patients with Type 2 Diabetes.

Patients with type 2 diabetes (T2D) are at an increased risk of cardiovascular disease (CVD), which constitutes the most common cause of morbidity and mortality in these patients. Intensive CVD risk factor control can ameliorate the elevated CVD associated with T2D. In this review, we provide an overview of CVD risk factor control, including traditional (blood pressure, glycemia, lipid, thrombosis, and lifestyle modifications) and nontraditional (social and environmental determinants of health) in patients with T2D, including evidence on management and outcomes.

Cardiovascular disease burden attributable to non-optimal temperature: analysis of the 1990-2019 global burden of disease.

AIMS: Extreme temperatures are increasingly experienced as a result of climate change. Both high and low temperatures, impacted by climate change, have been linked with cardiovascular disease (CVD). Global estimates on non-optimal temperature-related CVD are not known. The authors investigated global trends of temperature-related CVD burden over the last three decades. METHODS AND RESULTS: The authors utilized the 1990-2019 global burden of disease methodology to investigate non-optimal temperature, low temperature- and high temperature-related CVD deaths, and disability-adjusted life years (DALYs) globally. Non-optimal temperatures were defined as above (high temperature) or below (low temperature) the location-specific theoretical minimum-risk exposure level or the temperature associated with the lowest mortality rates. Analyses were later stratified by sociodemographic index (SDI) and world regions. In 2019, non-optimal temperature contributed to 1 194 196 (95% uncertainty interval [UI]: 963 816-1 425 090) CVD deaths and 21 799 370 (95% UI: 17 395 761-25 947 499) DALYs. Low temperature contributed to 1 104 200 (95% UI: 897 783-1 326 965) CVD deaths and 19 768 986 (95% UI: 16 039 594-23 925 945) DALYs. High temperature contributed to 93 095 (95% UI: 10 827-158 386) CVD deaths and 2 098 989 (95% UI: 146 158-3 625 564) DALYs. Between 1990 and 2019, CVD deaths related to non-optimal temperature increased by 45% (95% UI: 32-63%), low temperature by 36% (95% UI: 25-48%), and high temperature by 600% (95% UI: -1879-2027%). Non-optimal temperature- and high temperature-related CVD deaths increased more in countries with low income than countries with high income. CONCLUSION: Non-optimal temperatures are significantly associated with global CVD deaths and DALYs, underscoring the significant impact of temperature on public health.

The paper discusses the relationship between non-optimal temperature and cardiovascular disease (CVD) and presents the first-to-date quantification of the global temperature-related CVD burden. Key findings include: Non-optimal temperatures were responsible for a significant proportion of global cardiovascular deaths between 1990 and 2019.People in lower socioeconomic regions were more vulnerable to the effects of non-optimal temperature on CVD than those in higher socioeconomic regions.

The Exposome and Cardiovascular Health.

The study of the interplay between social factors, environmental hazards, and health has garnered much attention in recent years. The term "exposome" was coined to describe the total impact of environmental exposures on an individual's health and well-being, serving as a complementary concept to the genome. Studies have shown a strong correlation between the exposome and cardiovascular health, with various components of the exposome having been implicated in the development and progression of cardiovascular disease. These components include the natural and built environment, air pollution, diet, physical activity, and psychosocial stress, among others. This review provides an overview of the relationship between the exposome and cardiovascular health, highlighting the epidemiologic and mechanistic evidence of environmental exposures on cardiovascular disease. The interplay between various environmental components is discussed, and potential avenues for mitigation are identified.

Historical Neighborhood Redlining and Contemporary Environmental Racism.

To stabilize the housing market during the great depression, the government-sanctioned Home Owners' Loan Corporation (HOLC) created color coded maps of nearly 200 United States cities according to lending risk. These maps were largely driven by racial segregation, with the worst graded neighborhoods colored in red, later termed redlined neighborhoods. We sought to investigate the association between historical redlining, and trends in environmental disparities across the US over the past few decades. We characterized environmental exposures including air pollutants (e.g., NO2 and fine particulate matter), vegetation, noise, and light at night, proximity hazardous emission sources (e.g., hazardous water facilities, wastewater discharge indicator) and other environmental and social indicators harnessed from various sources across HOLC graded neighborhoods and extrapolated census tracts (A [lowest risk neighborhoods] to D [highest risk neighborhoods]). Lower graded areas (C and D) had consistently higher exposures to worse environmental factors. Additionally, there were consistent relative disparities in the exposures to PM2.5 (1981-2018) and NO2 (2005-2019), without significant improvement in the gap compared with HOLC grade A neighborhoods. Our findings illustrate that historical redlining, a form of residential segregation largely based on racial discrimination is associated with environmental injustice over the past 2-4 decades.

Built Environment Features Obtained from Google Street View Are Associated with Coronary Artery Disease Prevalence: A Deep-Learning Framework.

Background: Built environment plays an important role in development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches has been limited. We sought to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in urban 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 cardiometabolic disease. Convolutional neural networks, light gradient boosting machines and activation maps were utilized to predict health outcomes and identify feature associations with coronary heart disease (CHD). The study obtained 0.53 million GSV images covering 789 census tracts in 7 cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO). Analyses were conducted from February 2022 to December 2022. We used census tract-level data from the Centers for Disease Control and Prevention's PLACES dataset. Main outcomes included census tract-level estimated prevalence of CHD based on GSV built environment features. Results: Built environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The ExtraTrees Regressor achieved the best result among all models with the lowest average mean absolute error of 1.11% and Root mean square of error of 1.58. The addition of GSV features outperformed and improved a model that only included census-tract level age, sex, race, income and education. Activation maps from the features revealed a set of neighborhood features represented by buildings and roads associated with CHD prevalence. Conclusions: In this cross-sectional study, a significant portion of CHD prevalence were explained by GSV-based built environment factors analyzed using deep learning, independent of census tract demographics. Machine vision enabled assessment of the built environment could help play a significant role in designing and improving heart-heathy cities.

Risk factors and geographic disparities in premature cardiovascular mortality in US counties: a machine learning approach.

Disparities in premature cardiovascular mortality (PCVM) have been associated with socioeconomic, behavioral, and environmental risk factors. Understanding the "phenotypes", or combinations of characteristics associated with the highest risk of PCVM, and the geographic distributions of these phenotypes is critical to targeting PCVM interventions. This study applied the classification and regression tree (CART) to identify county phenotypes of PCVM and geographic information systems to examine the distributions of identified phenotypes. Random forest analysis was applied to evaluate the relative importance of risk factors associated with PCVM. The CART analysis identified seven county phenotypes of PCVM, where high-risk phenotypes were characterized by having greater percentages of people with lower income, higher physical inactivity, and higher food insecurity. These high-risk phenotypes were mostly concentrated in the Black Belt of the American South and the Appalachian region. The random forest analysis identified additional important risk factors associated with PCVM, including broadband access, smoking, receipt of Supplemental Nutrition Assistance Program benefits, and educational attainment. Our study demonstrates the use of machine learning approaches in characterizing community-level phenotypes of PCVM. Interventions to reduce PCVM should be tailored according to these phenotypes in corresponding geographic areas.

Environmental exposures and blood pressure in adolescents and adults in the T1D exchange clinic registry.

AIMS: To examine the associations between environmental determinants of health and blood pressure and whether age, sex, or race moderated the associations among 18,754 adolescents and adults from the type 1 diabetes (T1D) Exchange Clinic Registry. METHODS: We used multivariable linear regression. Environmental determinants included exposure to ambient fine particulate matter (PM2.5, obtained from an integrated model), nitrogen dioxide (NO2), noise and light pollution, and the normalized difference vegetation index (NDVI, a marker of green space) at the ZIP code level of residence. RESULTS: Higher exposure to PM2.5 and NO2, and lower NDVI, was associated with higher systolic and diastolic blood pressure, and higher light pollution exposure were similarly associated with higher diastolic blood pressure. These associations between environmental exposures and blood pressure remained significant after accounting for other covariates (age, sex, race/ethnicity, BMI, and T1D duration). With aging, the negative association between NDVI and blood pressure weakened. CONCLUSIONS: These findings emphasize the significance of minimizing exposure to environmental pollutants, including PM2.5 and NO2, as well as ensuring access to areas with higher NDVI, to promote cardiovascular health in individuals with T1D.