Neighborhood Walkability Is Associated With Lower Burden of Cardiovascular Risk Factors Among Cancer Patients.

Background: Modifiable cardiovascular risk factors constitute a significant cause of cardiovascular disease and mortality among patients with cancer. Recent studies suggest a potential link between neighborhood walkability and favorable cardiovascular risk factor profiles in the general population. Objectives: This study aimed to investigate whether neighborhood walkability is correlated with favorable cardiovascular risk factor profiles among patients with a history of cancer. Methods: We conducted a cross-sectional study using data from the Houston Methodist Learning Health System Outpatient Registry (2016-2022) comprising 1,171,768 adults aged 18 years and older. Neighborhood walkability was determined using the 2019 Walk Score and divided into 4 categories. Patients with a history of cancer were identified through International Classification of Diseases-10th Revision-Clinical Modification codes (C00-C96). We examined the prevalence and association between modifiable cardiovascular risk factors (hypertension, diabetes, smoking, dyslipidemia, and obesity) and neighborhood walkability categories in cancer patients. Results: The study included 121,109 patients with a history of cancer; 56.7% were female patients, and 68.8% were non-Hispanic Whites, with a mean age of 67.3 years. The prevalence of modifiable cardiovascular risk factors was lower among participants residing in the most walkable neighborhoods compared with those in the least walkable neighborhoods (76.7% and 86.0%, respectively). Patients with a history of cancer living in very walkable neighborhoods were 16% less likely to have any risk factor compared with car-dependent-all errands neighborhoods (adjusted OR: 0.84, 95% CI: 0.78-0.92). Sensitivity analyses considering the timing of events yielded similar results. Conclusions: Our findings demonstrate an association between neighborhood walkability and the burden of modifiable cardiovascular risk factors among patients with a medical history of cancer. Investments in walkable neighborhoods may present a viable opportunity for mitigating the growing burden of modifiable cardiovascular risk factors among patients with a history of cancer.

Social Phenotyping for Cardiovascular Risk Stratification in Electronic Health Registries.

PURPOSE OF REVIEW: Evaluation of social influences on cardiovascular care requires a comprehensive analysis encompassing economic, societal, and environmental factors. The increased utilization of electronic health registries provides a foundation for social phenotyping, yet standardization in methodology remains lacking. This review aimed to elucidate the primary approaches to social phenotyping for cardiovascular risk stratification through electronic health registries. RECENT FINDINGS: Social phenotyping in the context of cardiovascular risk stratification within electronic health registries can be separated into four principal approaches: place-based metrics, questionnaires, ICD Z-coding, and natural language processing. These methodologies vary in their complexity, advantages and limitations, and intended outcomes. Place-based metrics often rely on geospatial data to infer socioeconomic influences, while questionnaires may directly gather individual-level behavioral and social factors. Z-coding, a relatively new approach, can capture data directly related to social determinant of health domains in the clinical context. Natural language processing has been increasingly utilized to extract social influences from unstructured clinical narratives-offering nuanced insights for risk prediction models. Each method plays an important role in our understanding and approach to using social determinants data for improving population cardiovascular health. These four principal approaches to social phenotyping contribute to a more structured approach to social determinant of health research via electronic health registries, with a focus on cardiovascular risk stratification. Social phenotyping related research should prioritize refining predictive models for cardiovascular diseases and advancing health equity by integrating applied implementation science into public health strategies.

Houston Methodist cardiovascular learning health system (CVD-LHS) registry: Methods for development and implementation of an automated electronic medical record-based registry using an informatics framework approach.

Objectives: To investigate the potential value and feasibility of creating a listing system-wide registry of patients with at-risk and established Atherosclerotic Cardiovascular Disease (ASCVD) within a large healthcare system using automated data extraction methods to systematically identify burden, determinants, and the spectrum of at-risk patients to inform population health management. Additionally, the Houston Methodist Cardiovascular Disease Learning Health System (HM CVD-LHS) registry intends to create high-quality data-driven analytical insights to assess, track, and promote cardiovascular research and care. Methods: We conducted a retrospective multi-center, cohort analysis of adult patients who were seen in the outpatient settings of a large healthcare system between June 2016 - December 2022 to create an EMR-based registry. A common framework was developed to automatically extract clinical data from the EMR and then integrate it with the social determinants of health information retrieved from external sources. Microsoft's SQL Server Management Studio was used for creating multiple Extract-Transform-Load scripts and stored procedures for collecting, cleaning, storing, monitoring, reviewing, auto-updating, validating, and reporting the data based on the registry goals. Results: A real-time, programmatically deidentified, auto-updated EMR-based HM CVD-LHS registry was developed with ∼450 variables stored in multiple tables each containing information related to patient's demographics, encounters, diagnoses, vitals, labs, medication use, and comorbidities. Out of 1,171,768 adult individuals in the registry, 113,022 (9.6%) ASCVD patients were identified between June 2016 and December 2022 (mean age was 69.2 ± 12.2 years, with 55% Men and 15% Black individuals). Further, multi-level groupings of patients with laboratory test results and medication use have been analyzed for evaluating the outcomes of interest. Conclusions: HM CVD-LHS registry database was developed successfully providing the listing registry of patients with established ASCVD and those at risk. This approach empowers knowledge inference and provides support for efforts to move away from manual patient chart abstraction by suggesting that a common registry framework with a concurrent design of data collection tools and reporting rapidly extracting useful structured clinical data from EMRs for creating patient or specialty population registries.

Social Determinants of Health Framework to Identify and Reduce Barriers to Imaging in Marginalized Communities.

Social determinants of health (SDOH) are conditions influencing individuals' health based on their environment of birth, living, working, and aging. Addressing SDOH is crucial for promoting health equity and reducing health outcome disparities. For conditions such as stroke and cancer screening where imaging is central to diagnosis and management, access to high-quality medical imaging is necessary. This article applies a previously described structural framework characterizing the impact of SDOH on patients who require imaging for their clinical indications. SDOH factors can be broadly categorized into five sectors: economic stability, education access and quality, neighborhood and built environment, social and community context, and health care access and quality. As patients navigate the health care system, they experience barriers at each step, which are significantly influenced by SDOH factors. Marginalized communities are prone to disparities due to the inability to complete the required diagnostic or screening imaging work-up. This article highlights SDOH that disproportionately affect marginalized communities, using stroke and cancer as examples of disease processes where imaging is needed for care. Potential strategies to mitigate these disparities include dedicating resources for clinical care coordinators, transportation, language assistance, and financial hardship subsidies. Last, various national and international health initiatives are tackling SDOH and fostering health equity.

Association of cardiovascular risk profile with premature all-cause and cardiovascular mortality in US adults: findings from a national study.

OBJECTIVE: To assess the association between cardiovascular risk factor (CRF) profile and premature all-cause and cardiovascular disease (CVD) mortality among US adults (age < 65). METHODS: This study used data from the National Health Interview Survey from 2006 to 2014, linked to the National Death Index for non-elderly adults aged < 65 years. A composite CRF score (range = 0-6) was calculated, based on the presence or absence of six established cardiovascular risk factors: hypertension, diabetes, hypercholesterolemia, smoking, obesity, and insufficient physical activity. CRF profile was defined as "Poor" (≥ 3 risk factors), "Average" (1-2), or "Optimal" (0 risk factors). Age-adjusted mortality rates (AAMR) were reported across CRF profile categories, separately for all-cause and CVD mortality. Cox proportional hazard models were used to evaluate the association between CRF profile and all-cause and CVD mortality. RESULTS: Among 195,901 non-elderly individuals (mean age: 40.4 ± 13.0, 50% females and 70% Non-Hispanic (NH) White adults), 24.8% had optimal, 58.9% average, and 16.2% poor CRF profiles, respectively. Participants with poor CRF profile were more likely to be NH Black, have lower educational attainment and lower income compared to those with optimal CRF profile. All-cause and CVD mortality rates were three to four fold higher in individuals with poor CRF profile, compared to their optimal profile counterparts. Adults with poor CRF profile experienced 3.5-fold (aHR: 3.48 [95% CI: 2.96, 4.10]) and 5-fold (aHR: 4.76 [3.44, 6.60]) higher risk of all-cause and CVD mortality, respectively, compared to those with optimal profile. These results were consistent across age, sex, and race/ethnicity subgroups. CONCLUSIONS: In this population-based study, non-elderly adults with poor CRF profile had a three to five-fold higher risk of all-cause and CVD mortality, compared to those with optimal CRF profile. Targeted prevention efforts to achieve optimal cardiovascular risk profile are imperative to reduce the persistent burden of premature all-cause and CVD mortality in the US.

Unfavorable social determinants of health and risk of mortality in adults with diabetes: findings from the National Health Interview Survey.

INTRODUCTION: Understanding the role of social determinants of health as predictors of mortality in adults with diabetes may help improve health outcomes in this high-risk population. Using population-based, nationally representative data, this study investigated the cumulative effect of unfavorable social determinants on all-cause mortality in adults with diabetes. RESEARCH DESIGN AND METHODS: We used data from the 2013-2018 National Health Interview Survey, linked to the National Death Index through 2019, for mortality ascertainment. A total of 47 individual social determinants of health were used to categorize participants in quartiles denoting increasing levels of social disadvantage. Poisson regression was used to report age-adjusted mortality rates across increasing social burden. Multivariable Cox proportional hazards models were used to assess the association between cumulative social disadvantage and all-cause mortality in adults with diabetes, adjusting for traditional risk factors. RESULTS: The final sample comprised 182 445 adults, of whom 20 079 had diabetes. In the diabetes population, mortality rate increased from 1052.7 per 100 000 person-years in the first quartile (Q1) to 2073.1 in the fourth quartile (Q4). In multivariable models, individuals in Q4 experienced up to twofold higher mortality risk relative to those in Q1. This effect was observed similarly across gender and racial/ethnic subgroups, although with a relatively stronger association for non-Hispanic white participants compared with non-Hispanic black and Hispanic subpopulations. CONCLUSIONS: Cumulative social disadvantage in individuals with diabetes is associated with over twofold higher risk of mortality, independent of established risk factors. Our findings call for action to screen for unfavorable social determinants and design novel interventions to mitigate the risk of mortality in this high-risk population.

Race, Social Determinants of Health, and Risk of All-Cause and Cardiovascular Mortality in the United States.

OBJECTIVE: To examine the independent and interdependent effects of race and social determinants of health (SDoH) and risk of all-cause and cardiovascular disease (CVD) mortality in the US. DATA SOURCE/STUDY DESIGN: Secondary analysis of pooled data for 252,218 participants of the 2006-2018 National Health Interview Survey, linked to the National Death Index. METHODS: Age-adjusted mortality rates (AAMR) were reported for non-Hispanic White (NHW) and non-Hispanic Black (NHB) individuals overall, and by quintiles of SDoH burden, with higher quintiles representing higher cumulative social disadvantage (SDoH-Qx). Survival analysis was used to examine the association between race, SDoH-Qx, and all-cause and CVD mortality. FINDINGS: AAMRs for all-cause and CVD mortality were higher for NHB and considerably higher at higher levels of SDoH-Qx, however, with similar mortality rates at any given level of SDoH-Qx. In multivariable models, NHB experienced 20-25% higher mortality risk relative to NHW (aHR = 1.20-1.26); however, no association was observed after adjusting for SDoH. In contrast, higher SDoH burden was associated with up to nearly threefold increased risk of all-cause (aHR, Q5 vs Q1 = 2.81) and CVD mortality (aHR, Q5 vs Q1 = 2.90); the SDoH effect was observed similarly for NHB (aHR, Q5:all-cause mortality = 2.38; CVD mortality = 2.58) and NHW (aHR, Q5:all-cause mortality = 2.87; CVD mortality = 2.93) subgroups. SDoH burden mediated 40-60% of the association between NHB race and mortality. CONCLUSIONS: These findings highlight the critical role of SDoH as upstream drivers of racial inequities in all-cause and CVD mortality. Population level interventions focused on addressing adverse SDoH experienced by NHB individuals may help mitigate persistent disparities in mortality in the US.

Unfavorable social determinants of health and mortality risk by cardiovascular disease status: Findings from a National Study of United States Adults.

BACKGROUND: The association between cumulative burden of unfavorable social determinants of health (SDoH) and all-cause mortality has not been assessed by atherosclerotic cardiovascular disease (ASCVD) status on a population level in the United States. METHODS: We assessed the association between cumulative social disadvantage and all-cause mortality by ASCVD status in the National Health Interview Survey, linked to the National Death Index. RESULTS: In models adjusted for established clinical risk factors, individuals experiencing the highest level of social disadvantage (SDoH-Q4) had over 1.5 (aHR = 1.55; 95%CI = 1.22, 1.96) and 2-fold (aHR = 2.21; 95% CI = 1.91, 2.56) fold increased risk of mortality relative to those with the most favorable social profile (SDoH-Q1), respectively for adults with and without ASCVD; those experiencing co-occurring ASCVD and high social disadvantage had up to four-fold higher risk of mortality (aHR = 3.81; 95%CI = 3.36, 4.32). CONCLUSIONS: These findings emphasize the importance of a healthcare model that prioritizes efforts to identify and address key social and environmental barriers to health and wellbeing, particularly in individuals experiencing the double jeopardy of clinical and social risk.

Polysocial Risk Scores: Implications for Cardiovascular Disease Risk Assessment and Management.

PURPOSE OF REVIEW: To review current evidence, discuss key knowledge gaps and identify opportunities for development, validation and application of polysocial risk scores (pSRS) for cardiovascular disease (CVD) risk prediction and population cardiovascular health management. RECENT FINDINGS: Limited existing evidence suggests that pSRS are promising tools to capture cumulative social determinants of health (SDOH) burden and improve CVD risk prediction beyond traditional risk factors. However, available tools lack generalizability, are cross-sectional in nature or do not assess social risk holistically across SDOH domains. Available SDOH and clinical risk factor data in large population-based databases are under-utilized for pSRS development. Recent advances in machine learning and artificial intelligence present unprecedented opportunities for SDOH integration and assessment in real-world data, with implications for pSRS development and validation for both clinical and healthcare utilization outcomes. pSRS presents unique opportunities to potentially improve traditional "clinical" models of CVD risk prediction. Future efforts should focus on fully utilizing available SDOH data in large epidemiological databases, testing pSRS efficacy in diverse population subgroups, and integrating pSRS into real-world clinical decision support systems to inform clinical care and advance cardiovascular health equity.

ChatGPT and Artificial Intelligence in Hospital Level Research: Potential, Precautions, and Prospects.

Rapid advancements in artificial intelligence (AI) have revolutionized numerous sectors, including medical research. Among the various AI tools, OpenAI's ChatGPT, a state-of-the-art language model, has demonstrated immense potential in aiding and enhancing research processes. This review explores the application of ChatGPT in medical hospital level research, focusing on its capabilities for academic writing assistance, data analytics, statistics handling, and code generation. Notably, it delves into the model's ability to streamline tasks, support decision making, and improve patient interaction. However, the article also underscores the importance of exercising caution while dealing with sensitive healthcare data and highlights the limitations of ChatGPT, such as its potential for erroneous outputs and biases. Furthermore, the review discusses the ethical considerations that arise with AI use in health care, including data privacy, AI interpretability, and the risk of AI-induced disparities. The article culminates by envisioning the future of AI in medical research, emphasizing the need for robust regulatory frameworks and guidelines that balance the potential of AI with ethical considerations. As AI continues to evolve, it holds promising potential to augment medical research in a manner that is ethical, equitable, and patient-centric.

Cumulative social disadvantage and health-related quality of life: national health interview survey 2013-2017.

BACKGROUND: Evidence for the association between social determinants of health (SDoH) and health-related quality of life (HRQoL) is largely based on single SDoH measures, with limited evaluation of cumulative social disadvantage. We examined the association between cumulative social disadvantage and the Health and Activity Limitation Index (HALex). METHODS: Using adult data from the National Health Interview Survey (2013-2017), we created a cumulative disadvantage index by aggregating 47 deprivations across 6 SDoH domains. Respondents were ranked using cumulative SDoH index quartiles (SDoH-Q1 to Q4), with higher quartile groups being more disadvantaged. We used two-part models for continuous HALex scores and logistic regression for poor HALex (< 20th percentile score) to examine HALex differences associated with cumulative disadvantage. Lower HALex scores implied poorer HRQoL performance. RESULTS: The study sample included 156,182 respondents, representing 232.8 million adults in the United States (mean age 46 years; 51.7% women). The mean HALex score was 0.85 and 17.7% had poor HALex. Higher SDoH quartile groups had poorer HALex performance (lower scores and increased prevalence of poor HALex). A unit increase in SDoH index was associated with - 0.010 (95% CI [-0.011, -0.010]) difference in HALex score and 20% higher odds of poor HALex (odds ratio, OR = 1.20; 95% CI [1.19, 1.21]). Relative to SDoH-Q1, SDoH-Q4 was associated with HALex score difference of -0.086 (95% CI [-0.089, -0.083]) and OR = 5.32 (95% CI [4.97, 5.70]) for poor HALex. Despite a higher burden of cumulative social disadvantage, Hispanics had a weaker SDoH-HALex association than their non-Hispanic White counterparts. CONCLUSIONS: Cumulative social disadvantage was associated with poorer HALex performance in an incremental fashion. Innovations to incorporate SDoH-screening tools into clinical decision systems must continue in order to accurately identify socially vulnerable groups in need of both clinical risk mitigation and social support. To maximize health returns, policies can be tailored through community partnerships to address systemic barriers that exist within distinct sociodemographic groups, as well as demographic differences in health perception and healthcare experience.

Body Mass Index and All-Cause and Cardiovascular Mortality in United States Adults With and Without Atherosclerotic Cardiovascular Disease: Findings from the National Health Interview Survey.

In a nationally representative population-based study of US adults, the authors sought to examine the association between body mass index (BMI) and all-cause and cardiovascular disease (CVD) mortality in a nationally representative sample of adults with and without atherosclerotic cardiovascular disease (ASCVD), and further stratified by age, sex, and race/ethnicity. The study used data from 2006 to 2015 National Health Interview Survey and categorized participants into the following BMI categories: normal weight (20-24.9), overweight (25-29.9), obesity class 1 (30-34.9), obesity class 2 (35-39.9), and obesity class 3 (≥40 kg/m2). Multivariable Cox proportional hazards models were used to assess the risk of all-cause and CVD mortality across successively increasing BMI categories overall, and by sociodemographic subgroups. A total of 210,923 individuals were included in the final analysis. In the population without ASCVD, the risk of all-cause and CVD mortality was lower in overweight and higher in obesity classes 2 and 3, compared with normal weight, with the highest risk observed in the young adult (age 18-39) population. Elderly adults (65 and above) and populations with ASCVD exhibited a BMI-mortality paradox. In addition, Hispanic individuals did not show a relationship between BMI and mortality compared with non-Hispanic White and Black adults. In conclusion, being overweight was associated with decreased risk, whereas obesity class 3 was consistently associated with increased risk of all-cause and CVD mortality in adults without ASCVD, particularly young adults. BMI-mortality paradox was noted in ASCVD, elderly, and non-Hispanic adults.

Low educational attainment is associated with higher all-cause and cardiovascular mortality in the United States adult population.

INTRODUCTION: Educational attainment is an important social determinant of health (SDOH) for cardiovascular disease (CVD). However, the association between educational attainment and all-cause and CVD mortality has not been longitudinally evaluated on a population-level in the US, especially in individuals with atherosclerotic cardiovascular disease (ASCVD). In this nationally representative study, we assessed the association between educational attainment and the risk of all-cause and cardiovascular (CVD) mortality in the general adult population and in adults with ASCVD in the US. METHODS: We used data from the 2006-2014 National Death Index-linked National Health Interview Survey for adults ≥ 18 years. We generated age-adjusted mortality rates (AAMR) by levels of educational attainment (< high school (HS), HS/General Education Development (GED), some college, and ≥ College) in the overall population and in adults with ASCVD. Cox proportional hazards models were used to examine the multivariable-adjusted associations between educational attainment and all-cause and CVD mortality. RESULTS: The sample comprised 210,853 participants (mean age 46.3), representing ~ 189 million adults annually, of which 8% had ASCVD. Overall, 14.7%, 27%, 20.3%, and 38% of the population had educational attainment < HS, HS/GED, Some College, and ≥ College, respectively. During a median follow-up of 4.5 years, all-cause age-adjusted mortality rates were 400.6 vs. 208.6 and 1446.7 vs. 984.0 for the total and ASCVD populations for < HS vs ≥ College education, respectively. CVD age adjusted mortality rates were 82.1 vs. 38.7 and 456.4 vs 279.5 for the total and ASCVD populations for < HS vs ≥ College education, respectively. In models adjusting for demographics and SDOH, < HS (reference = ≥ College) was associated with 40-50% increased risk of mortality in the total population and 20-40% increased risk of mortality in the ASCVD population, for both all-cause and CVD mortality. Further adjustment for traditional risk factors attenuated the associations but remained statistically significant for < HS in the overall population. Similar trends were seen across sociodemographic subgroups including age, sex, race/ethnicity, income, and insurance status. CONCLUSIONS: Lower educational attainment is independently associated with increased risk of all-cause and CVD mortality in both the total and ASCVD populations, with the highest risk observed for individuals with < HS education. Future efforts to understand persistent disparities in CVD and all-cause mortality should pay close attention to the role of education, and include educational attainment as an independent predictor in mortality risk prediction algorithms.

Social Determinants of Cardiovascular Risk, Subclinical Cardiovascular Disease, and Cardiovascular Events.

Background Although there is research on the impact of social determinants of health (SDOHs) on cardiovascular health, most existing evidence is based on individual SDOH components. We evaluated the impact of cumulative SDOH burden on cardiovascular risk factors, subclinical atherosclerosis, and incident cardiovascular disease events. Methods and Results We included 6479 participants from the MESA (Multi-Ethnic Study of Atherosclerosis). A weighted aggregate SDOH score representing the cumulative number of unfavorable SDOHs, identified from 14 components across 5 domains (economic stability, neighborhood and physical environment, community and social context, education, and health care system access) was calculated and divided into quartiles (quartile 4 being the least favorable). The impact of cumulative SDOH burden on cardiovascular risk factors (hypertension, diabetes, dyslipidemia, smoking, and obesity), systemic inflammation, subclinical atherosclerosis, and incident cardiovascular disease was evaluated. Increasing social disadvantage was associated with increased odds of all cardiovascular risk factors except dyslipidemia. Smoking was the risk factor most strongly associated with worse SDOH (odds ratio [OR], 2.67 for quartile 4 versus quartile 1 [95% CI, 2.13-3.34]). Participants within SDOH quartile 4 had 33% higher odds of increased high-sensitivity C-reactive protein (OR, 1.33 [95% CI, 1.11-1.60]) and 31% higher risk of all cardiovascular disease (hazard ratio, 1.31 [95% CI, 1.03-1.67]), yet no greater burden of subclinical atherosclerosis (OR, 1.01 [95% CI, 0.79-1.29]), when compared with those in quartile 1. Conclusions Increasing social disadvantage was associated with more prevalent cardiovascular risk factors, inflammation, and incident cardiovascular disease. These findings call for better identification of SDOHs in clinical practice and stronger measures to mitigate the higher SDOH burden among the socially disadvantaged to improve cardiovascular outcomes.

Favorable Neighborhood Walkability is Associated With Lower Burden of Cardiovascular Risk Factors Among Patients Within an Integrated Health System: The Houston Methodist Learning Health System Outpatient Registry.

This is the first study to investigate the relationship between neighborhood walkability and cardiovascular (CV) risk factors in the United States using a large population-based database. Cross-sectional study using data from 1.1 million patients over the age of 18 in the Houston Methodist Learning Health System Outpatient Registry (2016-2022). Using the 2019 WalkScore, patients were assigned to one of the 4 neighborhood walkability categories. The burden of CV risk factors (hypertension, diabetes, obesity, dyslipidemia, and smoking) was defined as poor, average, or optimal (>3, 1-2, 0 risk factors, respectively). We included 887,654 patients, of which 86% resided in the two least walkable neighborhoods. The prevalence of CV risk factors was significantly lower among participants in the most walkable neighborhoods irrespective of ASCVD status. After adjusting for age, sex, race/ethnicity, and socioeconomic factors, we found that adults living in the most walkable neighborhoods were more likely to have optimal CV risk profile than those in the least walkable ones (RRR 2.77, 95% CI 2.64-2.91). We observed an inverse association between neighborhood walkability and the burden of CV risk factors. These findings support multilevel health system stakeholder engagements and investments in walkable neighborhoods as a viable tool for mitigating the growing burden of modifiable CV risk factors.

Neighborhood deprivation and morbid obesity: Insights from the Houston Methodist Cardiovascular Disease Health System Learning Registry.

This study examined the relationship between a validated measure of socioeconomic deprivation, such as the Area Deprivation Index (ADI), and morbid obesity. We used cross-sectional data on adult patients (≥18 years) in the Houston Methodist Cardiovascular Disease Health System Learning Registry (located in Houston, Texas, USA) between June 2016 and July 2021. Each patient was grouped by quintiles of ADI, with higher quintiles signaling greater deprivation. BMI was calculated using measured height and weight with morbid obesity defined as ≥ 40 kg/m2. Multivariable logistic regression models were used to examine the association between ADI and morbid obesity adjusting for demographic (age, sex, and race/ethnicity) factors. Out of the 751,174 adults with an ADI ranking included in the analysis, 6.9 % had morbid obesity (n = 51,609). Patients in the highest ADI quintile had a higher age-adjusted prevalence (10.9 % vs 3.3 %), and about 4-fold odds (aOR, 3.8; 95 % CI = 3.6, 3.9) of morbid obesity compared to the lowest ADI quintile. We tested for and found interaction effects between ADI and each demographic factor, with stronger ADI-morbid obesity association observed for patients that were female, Hispanic, non-Hispanic White and 40-65 years old. The highest ADI quintile also had a high prevalence (44 %) of any obesity (aOR, 2.2; 95 % CI = 2.1, 2.2). In geospatial mapping, areas with higher ADI were more likely to have higher proportion of patients with morbid obesity. Census-based measures, like the ADI, may be informative for area-level obesity reduction strategies as it can help identify neighborhoods at high odds of having patients with morbid obesity.