A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health

Uses of Social Determinants of Health Data to Address Cardiovascular Disease and Health Equity: A Scoping Review

Background Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Prior research suggests that social determinants of health have a compounding effect on health and are associated with cardiovascular disease. This scoping review explores what and how social determinants of health data are being used to address cardiovascular disease and improve health equity. Methods and Results After removing duplicate citations, the initial search yielded 4110 articles for screening, and 50 studies were identified for data extraction. Most studies relied on similar data sources for social determinants of health, including geocoded electronic health record data, national survey responses, and census data, and largely focused on health care access and quality, and the neighborhood and built environment. Most focused on developing interventions to improve health care access and quality or characterizing neighborhood risk and individual risk. Conclusions Given that few interventions addressed economic stability, education access and quality, or community context and social risk, the potential for harnessing social determinants of health data to reduce the burden of cardiovascular disease remains unrealized.

Longitudinal profiles of neighborhood socioeconomic vulnerability influence blood pressure changes across the female midlife period

PURPOSE

To examine whether longitudinal exposure to neighborhood socioeconomic vulnerability influences blood pressure changes throughout midlife in a racially, ethnically, and geographically-diverse cohort of women transitioning through menopause.

METHODS

We used longitudinal data on 2738 women (age 42-52 at baseline) living in six United States cities from The Study of Women's Health Across the Nation. Residential histories, systolic blood pressures (SBP), and diastolic blood pressures (DBP) were collected annually for ten years. We used longitudinal latent profile analysis to identify patterns of neighborhood socioeconomic vulnerability occurring from 1996 to 2007 in participant neighborhoods. We used linear mixed-effect models to determine if a woman's neighborhood profile throughout midlife was associated with blood pressure changes.

RESULTS

We identified four unique profiles of neighborhood socioeconomic vulnerability - differentiated by residential socioeconomic status, population density, and vacant housing conditions - which remained stable across time. Women residing in the most socioeconomically vulnerable neighborhoods experienced the steepest increase in annual SBP growth by 0.93 mmHg/year (95% CI: 0.65-1.21) across ten-year follow-up.

CONCLUSIONS

Neighborhood socioeconomic vulnerability was significantly associated with accelerated SBP increases throughout midlife among women.

The association of accelerated epigenetic age with all-cause mortality in cardiac catheterization patients as mediated by vascular and cardiometabolic outcomes

BACKGROUND

Epigenetic age is a DNA methylation-based biomarker of aging that is accurate across the lifespan and a range of cell types. The difference between epigenetic age and chronological age, termed age acceleration (AA), is a strong predictor of lifespan and healthspan. The predictive capabilities of AA for all-cause mortality have been evaluated in the general population; however, its utility is less well evaluated in those with chronic conditions. Additionally, the pathophysiologic pathways whereby AA predicts mortality are unclear. We hypothesized that AA predicts mortality in individuals with underlying cardiovascular disease; and the association between AA and mortality is mediated, in part, by vascular and cardiometabolic measures.

METHODS

We evaluated 562 participants in an urban, three-county area of central North Carolina from the CATHGEN cohort, all of whom received a cardiac catheterization procedure. We analyzed three AA biomarkers, Horvath epigenetic age acceleration (HAA), phenotypic age acceleration (PhenoAA), and Grim age acceleration (GrimAA), by Cox regression models, to assess whether AAs were associated with all-cause mortality. We also evaluated if these associations were mediated by vascular and cardiometabolic outcomes, including left ventricular ejection fraction (LVEF), blood cholesterol concentrations, angiopoietin-2 (ANG2) protein concentration, peripheral artery disease, coronary artery disease, diabetes, and hypertension. The total effect, direct effect, indirect effect, and percentage mediated were estimated using pathway mediation tests with a regression adjustment approach.

RESULTS

PhenoAA (HR = 1.05, P < 0.0001), GrimAA (HR = 1.10, P < 0.0001) and HAA (HR = 1.03, P = 0.01) were all associated with all-cause mortality. The association of mortality and PhenoAA was partially mediated by ANG2, a marker of vascular function (19.8%, P = 0.016), and by diabetes (8.2%, P = 0.043). The GrimAA-mortality association was mediated by ANG2 (12.3%, P = 0.014), and showed weaker evidence for mediation by LVEF (5.3%, P = 0.065).

CONCLUSIONS

Epigenetic age acceleration remains strongly predictive of mortality even in individuals already burdened with cardiovascular disease. Mortality associations were mediated by ANG2, which regulates endothelial permeability and angiogenic functions, suggesting that specific vascular pathophysiology may link accelerated epigenetic aging with increased mortality risks.

Neighborhood Socioeconomic Environment and Risk of Type 2 Diabetes: Associations and Mediation Through Food Environment Pathways in Three Independent Study Samples

OBJECTIVE

We examined whether relative availability of fast-food restaurants and supermarkets mediates the association between worse neighborhood socioeconomic conditions and risk of developing type 2 diabetes (T2D).

RESEARCH DESIGN AND METHODS

As part of the Diabetes Location, Environmental Attributes, and Disparities Network, three academic institutions used harmonized environmental data sources and analytic methods in three distinct study samples: 1) the Veterans Administration Diabetes Risk (VADR) cohort, a national administrative cohort of 4.1 million diabetes-free veterans developed using electronic health records (EHRs); 2) Reasons for Geographic and Racial Differences in Stroke (REGARDS), a longitudinal, epidemiologic cohort with Stroke Belt region oversampling (N = 11,208); and 3) Geisinger/Johns Hopkins University (G/JHU), an EHR-based, nested case-control study of 15,888 patients with new-onset T2D and of matched control participants in Pennsylvania. A census tract-level measure of neighborhood socioeconomic environment (NSEE) was developed as a community type-specific z-score sum. Baseline food-environment mediators included percentages of 1) fast-food restaurants and 2) food retail establishments that are supermarkets. Natural direct and indirect mediating effects were modeled; results were stratified across four community types: higher-density urban, lower-density urban, suburban/small town, and rural.

RESULTS

Across studies, worse NSEE was associated with higher T2D risk. In VADR, relative availability of fast-food restaurants and supermarkets was positively and negatively associated with T2D, respectively, whereas associations in REGARDS and G/JHU geographies were mixed. Mediation results suggested that little to none of the NSEE-diabetes associations were mediated through food-environment pathways.

CONCLUSIONS

Worse neighborhood socioeconomic conditions were associated with higher T2D risk, yet associations are likely not mediated through food-environment pathways.

The association between neighborhood social and economic environment and prevalent diabetes in urban and rural communities: The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study

Objective

The association between neighborhood disadvantage and health is well-documented. However, whether these associations may differ across rural and urban areas is unclear. This study examines the association between a multi-item neighborhood social and economic environment (NSEE) measure and diabetes prevalence across urban and rural communities in the US.

Methods

This study included 27,159 Black and White participants aged ≥45 years at baseline (2003-2007) from the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Each participant's residential address was geocoded. NSEE was calculated as the sum of z-scores for six US Census tract variables (% of adults with less than high school education; % of adults unemployed; % of households earning <$30,000 per year; % of households in poverty; % of households on public assistance; and % of households with no car) and within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). NSEE was categorized as quartiles, with higher NSEE quartiles reflecting more disadvantage. Prevalent diabetes was defined as fasting blood glucose ≥126 mg/dL or random blood glucose ≥200 mg/dL or use of diabetes medication at baseline. Multivariable adjusted Poisson regression models were used to estimate prevalence ratios (PR) and 95% confidence intervals (CI) for the association between NSEE and prevalent diabetes across community types.

Results

The mean age was 64.8 (SD=9.4) years, 55% were women, 40.7% were non-Hispanic Black adults. The overall prevalence of diabetes was 21% at baseline and was greatest for participants living in higher density urban areas (24.5%) and lowest for those in suburban/small town areas (18.5%). Compared with participants living in the most advantaged neighborhood (NSEE quartile 1, reference group), those living in the most disadvantaged neighborhoods (NSEE quartile 4) had higher diabetes prevalence in crude models. After adjustment for sociodemographic factors, the association remained statistically significant for moderate density community types (lower density urban quartile 4 PR=1.50, 95% CI=1.29, 1.75; suburban/small town quartile 4 PR=1.54, 95% CI=1.24, 1.92). These associations were also attenuated and of smaller magnitude for those living in higher density urban and rural communities.

Conclusion

Participants living in the most disadvantaged neighborhoods had a higher diabetes prevalence in each urban/rural community type and these associations were only partly explained by individual-level sociodemographic factors. In addition to addressing individual-level factors, identifying neighborhood characteristics and how they operate across urban and rural settings may be helpful for informing interventions that target chronic health conditions.

Environmental Health Literacy as Knowing, Feeling, and Believing: Analyzing Linkages between Race, Ethnicity, and Socioeconomic Status and Willingness to Engage in Protective Behaviors against Health Threats

This study investigates the relationships between environmental health literacy, the characteristics of people (race, ethnicity, and socioeconomic status) associated with health disparities, and people's willingness to engage in protective behaviors against environmental health threats. Environmental health literacy is a framework for capturing the continuum between the knowledge of environmental impacts on public health, and the skills and decisions needed to take health-protective actions. We pay particular attention to three dimensions of environmental health literacy: factual knowledge (knowing the facts), knowledge sufficiency (feeling ready to decide what to do), and response efficacy (believing that protective behaviors work). In June 2020, we collected survey data from North Carolina residents on two topics: the viral infection COVID-19 and industrial contaminants called per- and polyfluoroalkyl substances (PFAS). We used their responses to test stepwise regression models with willingness to engage in protective behaviors as a dependent variable and other characteristics as independent variables, including environmental health literacy. For both topics, our results indicated that no disparities emerged according to socioeconomic factors (level of education, household income, or renting one's residence). We observed disparities in willingness according to race, comparing Black to White participants, but not when comparing White to American Indian, Alaska Native, Asian, Native Hawaiian, or Pacific Islander participants nor Hispanic to non-Hispanic participants. The disparities in willingness between Black and White participants persisted until we introduced the variables of environmental health literacy, when the difference between these groups was no longer significant in the final regression models. The findings suggest that focusing on environmental health literacy could bridge a gap in willingness to protect oneself based on factors such as race/ethnicity and socioeconomic status, which have been identified in the environmental health literature as resulting in health disparities.

Associations between neighborhood socioeconomic cluster and hypertension, diabetes, myocardial infarction, and coronary artery disease within a cohort of cardiac catheterization patients

Background

Neighborhood-level socioeconomic status (SES) is associated with health outcomes, including cardiovascular disease and diabetes, but these associations are rarely studied across large, diverse populations.

Methods

We used Ward’s Hierarchical clustering to define eight neighborhood clusters across North Carolina using 11 census-based indicators of SES, race, housing, and urbanicity and assigned 6992 cardiac catheterization patients at Duke University Hospital from 2001 to 2010 to clusters. We examined associations between clusters and coronary artery disease index > 23 (CAD), history of myocardial infarction, hypertension, and diabetes using logistic regression adjusted for age, race, sex, body mass index, region of North Carolina, distance to Duke University Hospital, and smoking status.

Results

Four clusters were urban, three rural, and one suburban higher-middle-SES (referent). We observed greater odds of myocardial infarction in all six clusters with lower or middle-SES. Odds of CAD were elevated in the rural cluster that was low-SES and plurality Black (OR 1.16, 95% CI 0.94-1.43) and in the rural cluster that was majority American Indian (OR 1.31, 95% CI 0.91-1.90). Odds of diabetes and hypertension were elevated in two urban and one rural low- and lower-middle SES clusters with large Black populations.

Conclusions

We observed higher prevalence of cardiovascular disease and diabetes in neighborhoods that were predominantly rural, low-SES, and non-White, highlighting the importance of public health and healthcare system outreach into these communities to promote cardiometabolic health and prevent and manage hypertension, diabetes and coronary artery disease.

Accelerated epigenetic age as a biomarker of cardiovascular sensitivity to traffic-related air pollution

BACKGROUND

Accelerated epigenetic age has been proposed as a biomarker of increased aging, which may indicate disruptions in cellular and organ system homeostasis and thus contribute to sensitivity to environmental exposures.

METHODS

Using 497 participants from the CATHGEN cohort, we evaluated whether accelerated epigenetic aging increases cardiovascular sensitivity to traffic-related air pollution (TRAP) exposure. We used residential proximity to major roadways and source apportioned air pollution models as measures of TRAP exposure, and chose peripheral arterial disease (PAD) and blood pressure as outcomes based on previous associations with TRAP. We used Horvath epigenetic age acceleration (AAD) and phenotypic age acceleration (PhenoAAD) as measures of age acceleration, and adjusted all models for chronological age, race, sex, smoking, and socioeconomic status.

RESULTS

We observed significant interactions between TRAP and both AAD and PhenoAAD. Interactions indicated that increased epigenetic age acceleration elevated associations between proximity to roadways and PAD. Interactions were also observed between AAD and gasoline and diesel source apportioned PM_2.5.

CONCLUSION

Epigenetic age acceleration may be a biomarker of sensitivity to air pollution, particularly for TRAP in urban cohorts. This presents a novel means by which to understand sensitivity to air pollution and provides a molecular measure of environmental sensitivity.

Relationship Between Multiple Measures of Financial Hardship and Glycemic Control in Older Adults With Diabetes

Aim: To examine the relationship between multiple measures of financial hardship and glycemic control in older adults with diabetes. Methods: Using data from Health and Retirement Study (HRS), we investigated four measures of financial hardship: difficulty paying bills, ongoing financial strain, decreasing food intake due to money, and taking less medication due to cost. Using linear regression models, we investigated the relationship between each measure, and a cumulative score of hardships per person, on glycemic control (HbA1c). Results: After adjustment, a significant relationship existed with each increasing number of hardships associated with increasing HbA1c (0.09, [95%CI 0.04, 0.14]). Difficulty paying bills (0.25, [95%CI 0.14, 0.35]) and decreased medication usage due to cost (0.17, [95%CI 0.03, 0.31]) remained significantly associated with HbA1c. Conclusion: In older adults, difficulty paying bills and cost-related medication nonadherence is associated with glycemic control, and every additional financial hardship was associated with an increased HbA1c by nearly 0.1%.

Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM2.5 and Ozone

Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM2.5 and O3, and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application ("app") that determines seven tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM2.5 and O3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.

Neighborhood sociodemographic effects on the associations between long-term PM2.5 exposure and cardiovascular outcomes and diabetes

Supplemental Digital Content is available in the text.

Exposure to PM_2.5 air pollution and neighborhood-level sociodemographic characteristics are associated with cardiovascular disease and possibly diabetes mellitus. However, the joint effect of sociodemographics and PM_2.5 on these outcomes is uncertain.

Neighborhood Environments and Diabetes Risk and Control

PURPOSE OF REVIEW

The objective of this review is to highlight the evidence on the association between contextual characteristics of residential environments and type 2 diabetes, to provide an overview of the methodological challenges and to outline potential topics for future research in this field.

RECENT FINDINGS

The link between neighborhood socioeconomic status or deprivation and diabetes prevalence, incidence, and control is robust and has been replicated in numerous settings, including in experimental and quasi-experimental studies. The association between characteristics of the built environment that affect physical activity, other aspects of the built environment, and diabetes risk is robust. There is also evidence for an association between food environments and diabetes risk, but some conflicting results have emerged in this area. While the evidence base on the association of neighborhood socioeconomic status and built and physical environments and diabetes is large and robust, challenges remain related to confounding due to neighborhood selection. Moreover, we also outline five paths forward for future research on the role of neighborhood environments on diabetes.

Modeling individual exposures to ambient PM2.5 in the diabetes and the environment panel study (DEPS)

Air pollution epidemiology studies of ambient fine particulate matter (PM_2.5) often use outdoor concentrations as exposure surrogates, which can induce exposure error. The goal of this study was to improve ambient PM_2.5 exposure assessments for a repeated measurements study with 22 diabetic individuals in central North Carolina called the Diabetes and Environment Panel Study (DEPS) by applying the Exposure Model for Individuals (EMI), which predicts five tiers of individual-level exposure metrics for ambient PM_2.5 using outdoor concentrations, questionnaires, weather, and time-location information. Using EMI, we linked a mechanistic air exchange rate (AER) model to a mass-balance PM_2.5 infiltration model to predict residential AER (Tier 1), infiltration factors (F_{inf_home}, Tier 2), indoor concentrations (C_in, Tier 3), personal exposure factors (F_pex, Tier 4), and personal exposures (E, Tier 5) for ambient PM_2.5. We applied EMI to predict daily PM_2.5 exposure metrics (Tiers 1-5) for 174 participant-days across the 13 months of DEPS. Individual model predictions were compared to a subset of daily measurements of F_pex and E (Tiers 4-5) from the DEPS participants. Model-predicted F_pex and E corresponded well to daily measurements with a median difference of 14% and 23%; respectively. Daily model predictions for all 174 days showed considerable temporal and house-to-house variability of AER, F_{inf_home}, and C_in (Tiers 1-3), and person-to-person variability of F_pex and E (Tiers 4-5). Our study demonstrates the capability of predicting individual-level ambient PM_2.5 exposure metrics for an epidemiological study, in support of improving risk estimation.