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PURPOSE OF REVIEW: To examine the concept of time in target range for blood pressure (BP) management, exploring its calculation methods, implications for patient outcomes, and potential use in patient care. RECENT FINDINGS: Recent post-hoc analyses of clinical trials and observational studies highlight the importance of BP time in target range in predicting cardiovascular outcomes. Higher time in target range correlates with reduced risks of major adverse cardiovascular events including heart failure, stroke, myocardial infarction and all-cause mortality. Additionally, longer time in target range decreases the risk of incident atrial fibrillation and risk of developing dementia. BP time in target range is a novel metric offering valuable insights into BP control and its impact on clinical outcomes. Higher time in target range is consistently associated with better cardiovascular outcomes across various patient populations. However, the clinical application of BP time in target range requires further investigation through prospective clinical trials and real-world studies. Integrating wearable devices for continuous BP monitoring could enhance the practical utility of BP time in target range in hypertension management.
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BACKGROUND: Exposure to fine particulate matter (<2.5 um, particulate matter with an aerodynamic diameter <2.5 microns [PM2.5]) has been implicated in atherogenesis. Limited data in animal studies suggest that PM2.5 exposure leads to myocardial fibrosis and increased incidence of heart failure (HF). Whether PM2.5 is associated with adverse outcomes in patients with preexisting HF has not been widely studied. METHODS AND RESULTS: In this retrospective cohort study, Medicare patients hospitalized with first HF between 2013 and 2020 were identified from the Medicare Provider Analysis and Review Part A 100% files. Patients were linked with integrated estimates of ambient PM2.5 obtained at 1×1 km using the zip code of participants' residence. The study outcomes were all-cause death, HF, and all-cause readmissions burden. A total of 2 599 525 patients were included in this study, with 6 321 731 person-years of follow-up. Mean PM2.5 was 7.3±1.7 µg/m3. Each interquartile range of PM2.5 was associated with 0.9% increased hazard of all-cause death, 4.5% increased hazard of first HF readmission, 3.1% increased risk of HF hospitalization burden, and 5.2% increase in all-cause readmission burden, after adjusting for 11 sociodemographic and medical factors. Subgroup analyses showed that the effects were more pronounced at levels <7 µg/m3 and in patients aged <75 years, Asians, and those residing in rural areas. CONCLUSIONS: Ambient air pollution is associated with higher risk of adverse events in Medicare beneficiaries with established HF. These associations persist below the National Air Quality Standards (12 µg/m3), supporting that no threshold effect exists for health effects of air pollution exposure.
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Poluição do Ar , Exposição Ambiental , Insuficiência Cardíaca , Medicare , Material Particulado , Readmissão do Paciente , Humanos , Insuficiência Cardíaca/epidemiologia , Estados Unidos/epidemiologia , Feminino , Masculino , Idoso , Estudos Retrospectivos , Material Particulado/efeitos adversos , Poluição do Ar/efeitos adversos , Idoso de 80 Anos ou mais , Exposição Ambiental/efeitos adversos , Readmissão do Paciente/estatística & dados numéricos , Fatores de Risco , Medição de Risco , Incidência , Poluentes Atmosféricos/efeitos adversos , Causas de MorteRESUMO
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.
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Reanimação Cardiopulmonar , Parada Cardíaca Extra-Hospitalar , Humanos , Parada Cardíaca Extra-Hospitalar/terapia , Parada Cardíaca Extra-Hospitalar/mortalidade , Reanimação Cardiopulmonar/estatística & dados numéricos , Reanimação Cardiopulmonar/métodos , Feminino , Masculino , Pessoa de Meia-Idade , Idoso , Estados Unidos/epidemiologia , Sistema de Registros , Disparidades em Assistência à Saúde/estatística & dados numéricos , Características da Vizinhança/estatística & dados numéricos , Características de Residência/estatística & dados numéricos , Serviços Médicos de Emergência/estatística & dados numéricosRESUMO
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.
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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.
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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 31â¯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.
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Ambiente Construído , Aprendizado Profundo , Humanos , Estudos Transversais , Prevalência , Feminino , Masculino , Pessoa de Meia-Idade , Estados Unidos/epidemiologia , Imagens de Satélites , Doenças Cardiovasculares/epidemiologia , Adulto , Insuficiência Renal Crônica/epidemiologia , Doença das Coronárias/epidemiologia , Acidente Vascular Cerebral/epidemiologia , Cidades/epidemiologia , IdosoRESUMO
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).
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Doenças Cardiovasculares , Chumbo , Adulto , Idoso , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Doenças Cardiovasculares/epidemiologia , Doenças Cardiovasculares/mortalidade , Efeitos Psicossociais da Doença , Anos de Vida Ajustados por Deficiência , Exposição Ambiental/efeitos adversos , Carga Global da Doença , Chumbo/efeitos adversos , Intoxicação por Chumbo/epidemiologia , Intoxicação por Chumbo/diagnóstico , Fatores de Risco , Estados Unidos/epidemiologiaRESUMO
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.
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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.
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Inteligência Artificial , Ambiente Construído , Doença da Artéria Coronariana , Humanos , Estudos Transversais , Doença da Artéria Coronariana/epidemiologia , Prevalência , Masculino , Feminino , Estados Unidos/epidemiologia , Pessoa de Meia-Idade , Cidades/epidemiologiaRESUMO
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.
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Diabetes Mellitus Tipo 2 , Insuficiência Cardíaca , Humanos , Idoso , Diabetes Mellitus Tipo 2/diagnóstico , Diabetes Mellitus Tipo 2/epidemiologia , Fatores de Risco , Insuficiência Cardíaca/diagnóstico , Insuficiência Cardíaca/epidemiologia , Insuficiência Cardíaca/terapia , Pacientes , Privação SocialRESUMO
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.
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Pessoas com Deficiência , Insuficiência Cardíaca , Humanos , Masculino , Feminino , Estados Unidos/epidemiologia , Idoso , Carga Global da Doença , Prevalência , Comportamento Sexual , Saúde Global , Insuficiência Cardíaca/epidemiologiaRESUMO
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.
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Poluentes Atmosféricos , Poluição do Ar , Diabetes Mellitus Tipo 1 , Humanos , Adulto , Adolescente , Poluentes Atmosféricos/efeitos adversos , Poluentes Atmosféricos/análise , Poluição do Ar/efeitos adversos , Poluição do Ar/análise , Dióxido de Nitrogênio/efeitos adversos , Dióxido de Nitrogênio/análise , Pressão Sanguínea , Material Particulado/efeitos adversos , Material Particulado/análise , Exposição Ambiental/efeitos adversos , Exposição Ambiental/análiseRESUMO
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.
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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 79â¯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.
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Aterosclerose , Doenças Cardiovasculares , Infarto do Miocárdio , Acidente Vascular Cerebral , Veteranos , Humanos , Feminino , Idoso , Doenças Cardiovasculares/epidemiologia , Estudos de Coortes , Estudos Longitudinais , Aterosclerose/epidemiologia , Acidente Vascular Cerebral/epidemiologia , Infarto do Miocárdio/epidemiologiaAssuntos
Doenças Cardiovasculares , Insuficiência Renal Crônica , Humanos , Doenças Cardiovasculares/diagnóstico , Doenças Cardiovasculares/epidemiologia , Fatores de Risco , Características de Residência , Fatores de Risco de Doenças Cardíacas , Insuficiência Renal Crônica/diagnóstico , Insuficiência Renal Crônica/epidemiologiaRESUMO
BACKGROUND: Geographical disparities in mortality among Alzheimer`s disease (AD) patients have been reported and complex sociodemographic and environmental determinants of health (SEDH) may be contributing to this variation. Therefore, we aimed to explore high-risk SEDH factors possibly associated with all-cause mortality in AD across US counties using machine learning (ML) methods. METHODS: We performed a cross-sectional analysis of individuals ≥65 years with any underlying cause of death but with AD in the multiple causes of death certificate (ICD-10,G30) between 2016 and 2020. Outcomes were defined as age-adjusted all-cause mortality rates (per 100,000 people). We analyzed 50 county-level SEDH and Classification and Regression Trees (CART) was used to identify specific county-level clusters. Random Forest, another ML technique, evaluated variable importance. CART`s performance was validated using a "hold-out" set of counties. RESULTS: Overall, 714,568 individuals with AD died due to any cause across 2,409 counties during 2016-2020. CART identified 9 county clusters associated with an 80.1% relative increase of mortality across the spectrum. Furthermore, 7 SEDH variables were identified by CART to drive the categorization of clusters, including High School Completion (%), annual Particulate Matter 2.5 Level in Air, live births with Low Birthweight (%), Population under 18 years (%), annual Median Household Income in US dollars ($), population with Food Insecurity (%), and houses with Severe Housing Cost Burden (%). CONCLUSION: ML can aid in the assimilation of intricate SEDH exposures associated with mortality among older population with AD, providing opportunities for optimized interventions and resource allocation to reduce mortality among this population.