Promoting Physical Activity and Exercise JACC Health Promotion Series

Social determinants of health (SDoH), which encompass the economic, social, environmental, and psychosocial factors that influence health, play a significant role in the development of cardiovascular disease (CVD) risk factors as well as CVD morbidity and mortality. The COVID-19 pandemic and the current social justice movement sparked by the death of George Floyd have laid bare long-existing health inequities in our society driven by SDoH. Despite a recent focus on these structural drivers of health disparities, the impact of SDoH on cardiovascular health and CVD outcomes remains understudied and incompletely understood. To further investigate the mechanisms connecting SDoH and CVD, and ultimately design targeted and effective interventions, it is important to foster interdisciplinary efforts that incorporate translational, epidemiological, and clinical research in examining SDoH-CVD relationships. This review aims to facilitate research coordination and intervention development by providing an evidence-based framework for SDoH rooted in the lived experiences of marginalized populations. Our framework highlights critical structural/socioeconomic, environmental, and psychosocial factors most strongly associated with CVD and explores several of the underlying biologic mechanisms connecting SDoH to CVD pathogenesis, including excess stress hormones, inflammation, immune cell function, and cellular aging. We present landmark studies and recent findings about SDoH in our framework, with careful consideration of the constructs and measures utilized. Finally, we provide a roadmap for future SDoH research focused on individual, clinical, and policy approaches directed towards developing multilevel community-engaged interventions to promote cardiovascular health. 

Social Determinants of Cardiovascular Disease

Physical and Social Environment Relationship With Sleep Health and Disorders.

Cardiovascular disease (CVD) continues to be the leading cause of death and a major source of health disparities in the Unites States and globally. Efforts to reduce CVD risk and eliminate cardiovascular health disparities have increasingly emphasized the importance of the social determinants of health. Neighborhood environments have emerged as a possible target for prevention and policy efforts. Hence there is a need to better understand the role of neighborhood environments in shaping cardiovascular risk. The MESA (Multi-Ethnic Study of Atherosclerosis) Neighborhood Study provided a unique opportunity to build a comprehensive place-based resource for investigations of associations between specific features of neighborhood physical and social environments and cardiovascular risk factors and outcomes. This review summarizes the approaches used to characterize residential neighborhood environments in the MESA cohort, provides an overview of key findings to date, and discusses challenges and opportunities in neighborhood health effects research. Results to date suggest that neighborhood physical and social environments are related to behavioral and biomedical risk factors for CVD and that cardiovascular prevention efforts may benefit from taking neighborhood context into account.

https://globalheartjournal.com/articles/10.1016/j.gheart.2016.08.002/galley/368/download/

The Impact of Neighborhoods on CV Risk.

Until now, much of the work on machine learning and health has focused on processes inside the hospital or clinic. However, this represents only a narrow set of tasks and challenges related to health; there is greater potential for impact by leveraging machine learning in health tasks more broadly. In this Perspective we aim to highlight potential opportunities and challenges for machine learning within a holistic view of health and its influences. To do so, we build on research in population and public health that focuses on the mechanisms between different cultural, social and environmental factors and their effect on the health of individuals and communities. We present a brief introduction to research in these fields, data sources and types of tasks, and use these to identify settings where machine learning is relevant and can contribute to new knowledge. Given the key foci of health equity and disparities within public and population health, we juxtapose these topics with the machine learning subfield of algorithmic fairness to highlight specific opportunities where machine learning, public and population health may synergize to achieve health equity. Algorithmic solutions to improve treatment are starting to transform health care. Mhasawade and colleagues discuss in this Perspective how machine learning applications in population and public health can extend beyond clinical practice. While working with general health data comes with its own challenges, most notably ensuring algorithmic fairness in the face of existing health disparities, the area provides new kinds of data and questions for the machine learning community.

/pdf/machine-learning-and-algorithmic-fairness-in-public-and-1t4taoh9qw.pdf

Machine learning and algorithmic fairness in public and population health

The ways in which a neighborhood environment may affect depression and depressive symptoms have not been thoroughly explored. This study used longitudinal data from 5475 adults in the Multi-Ethnic Study of Atherosclerosis to investigate associations of time-varying depressive symptoms between 2000 and 2012 (measured using the 20-item Center for Epidemiologic Studies Depression Scale (CES-D)) with survey-based measures of neighborhood safety and social cohesion (both individual-level perceptions and neighborhood-level aggregates) and densities of social engagement destinations. Linear mixed models were used to examine associations of baseline cross-sectional associations and cumulative exposures with changes over time in CES-D. Econometric fixed effects models were utilized to investigate associations of within-person changes in neighborhood exposures with within-person changes in CES-D. Adjusting for relevant covariates, higher safety and social cohesion and greater density of social engagement destinations were associated with lower CES-D at baseline. Greater cumulative exposure to these features was not associated with progression of CES-D over 10 years. Within-person increases in safety and in social cohesion were associated with decreases in CES-D, although associations with cohesion were not statistically significant. Social elements of neighborhoods should be considered by community planners and public health practitioners to achieve optimal mental health.

Neighborhood Social Resources and Depressive Symptoms: Longitudinal Results from the Multi-Ethnic Study of Atherosclerosis

Longitudinal associations between neighborhood characteristics and body mass index (BMI; weight (kg)/height (m)2) were assessed from 2000 to 2011 among 5,919 participants in the Multi-Ethnic Study of Atherosclerosis The perceived availability of healthy food and walking environment were assessed via surveys, and 1-mile (16-km) densities of supermarkets, fruit-and-vegetable stores, and recreational facilities were obtained through a commercial database Econometric fixed-effects models were used to estimate the association between within-person changes in neighborhood characteristics and within-person change in BMI In fully adjusted models, a 1-standard-deviation increase in the healthy food environment index was associated with a 016-kg/m2 decrease in BMI (95% confidence interval (CI): -027, -006) among participants with obesity at baseline A 1-standard-deviation increase in the physical activity environment index was associated with 013-kg/m2 (95% CI: -024, -002) and 014-kg/m2 (95% CI: -027, -001) decreases in BMI for participants who were overweight and obese at baseline, respectively Paradoxically, increases in the physical activity index were associated with BMI increases in persons who were normal-weight at baseline This study provides preliminary longitudinal evidence that favorable changes in neighborhood physical environments are related to BMI reductions in obese persons, who comprise a substantial proportion of the US population

/pdf/neighborhood-physical-environment-and-changes-in-body-mass-1q4e239daj.pdf

Neighborhood Physical Environment and Changes in Body Mass Index: Results From the Multi-Ethnic Study of Atherosclerosis.

Inter-individual variability in blood pressure (BP) is influenced by both genetic and non-genetic factors including socioeconomic and psychosocial stressors. A deeper understanding of the gene-by-socioeconomic/psychosocial factor interactions on BP may help to identify individuals that are genetically susceptible to high BP in specific social contexts. In this study, we used a genomic region-based method for longitudinal analysis, Longitudinal Gene-Environment-Wide Interaction Studies (LGEWIS), to evaluate the effects of interactions between known socioeconomic/psychosocial and genetic risk factors on systolic and diastolic BP in four large epidemiologic cohorts of European and/or African ancestry. After correction for multiple testing, two interactions were significantly associated with diastolic BP. In European ancestry participants, outward/trait anger score had a significant interaction with the C10orf107 genomic region (p = 0.0019). In African ancestry participants, depressive symptom score had a significant interaction with the HFE genomic region (p = 0.0048). This study provides a foundation for using genomic region-based longitudinal analysis to identify subgroups of the population that may be at greater risk of elevated BP due to the combined influence of genetic and socioeconomic/psychosocial risk factors.

https://www.mdpi.com/1660-4601/14/12/1596/pdf

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies

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Neighborhood Social Resources and Depressive Symptoms: Longitudinal Results from the Multi-Ethnic Study of Atherosclerosis

Neighborhood Physical Environment and Changes in Body Mass Index: Results From the Multi-Ethnic Study of Atherosclerosis.

Gene-by-Psychosocial Factor Interactions Influence Diastolic Blood Pressure in European and African Ancestry Populations: Meta-Analysis of Four Cohort Studies