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

Where Patients Live Matter in Emergency Department Visits in Home Health Care: Rural/Urban Status and Neighborhood Socioeconomic Status

An increasing body of evidence highlights the importance of an individual's place of residence on their health and functional outcomes. This study is based on Outcome and Assessment Information Set data to assess the differences in emergency department visits among Medicare home health care patients by patients' residence location (rural/urban status and neighborhood socioeconomic status). Compared to urban patients, a disproportionately higher proportion of rural patients lived in more or most disadvantaged neighborhoods (83.9% vs. 41.3%). Using linear probability regression models, patients in rural areas (coefficient = .02, p < .001) and disadvantaged neighborhoods (less disadvantaged: coefficient = .02, p < .001; more disadvantaged: coefficient = .034, p < .001; most disadvantaged: coefficient = .042, p < .001) were more likely to experience emergency department visits. Policymakers should consider utilizing area-based target interventions to mitigate gaps in home health care. Also, given that the majority of rural patients reside in disadvantaged neighborhoods, neighborhood characteristics should be considered in addressing rural-urban disparities and improving home health care.

Cardiovascular Risk Associated With Social Determinants of Health at Individual and Area Levels

Importance

The benefit of adding social determinants of health (SDOH) when estimating atherosclerotic cardiovascular disease (ASCVD) risk is unclear.

Objective

To examine the association of SDOH at both individual and area levels with ASCVD risks, and to assess if adding individual- and area-level SDOH to the pooled cohort equations (PCEs) or the Predicting Risk of CVD Events (PREVENT) equations improves the accuracy of risk estimates.

Design, Setting, and Participants

This cohort study included participants data from 4 large US cohort studies. Eligible participants were aged 40 to 79 years without a history of ASCVD. Baseline data were collected from 1995 to 2007; median (IQR) follow-up was 13.0 (9.3-15.0) years. Data were analyzed from September 2023 to February 2024.

Exposures

Individual- and area-level education, income, and employment status.

Main outcomes and measures

ASCVD was defined as the composite outcome of nonfatal myocardial infarction, death from coronary heart disease, and fatal or nonfatal stroke.

Results

A total of 26 316 participants were included (mean [SD] age, 61.0 [9.1] years; 15 494 women [58.9%]; 11 365 Black [43.2%], 703 Chinese American [2.7%], 1278 Hispanic [4.9%], and 12 970 White [49.3%]); 11 764 individuals (44.7%) had at least 1 adverse individual-level SDOH and 10 908 (41.5%) had at least 1 adverse area-level SDOH. A total of 2673 ASCVD events occurred during follow-up. SDOH were associated with increased risk of ASCVD at both the individual and area levels, including for low education (individual: hazard ratio [HR], 1.39 [95% CI, 1.25-1.55]; area: HR, 1.31 [95% CI, 1.20-1.42]), low income (individual: 1.35 [95% CI, 1.25-1.47]; area: HR, 1.28 [95% CI, 1.17-1.40]), and unemployment (individual: HR, 1.61 [95% CI, 1.24-2.10]; area: HR, 1.25 [95% CI, 1.14-1.37]). Adding area-level SDOH alone to the PCEs did not change model discrimination but modestly improved calibration. Furthermore, adding both individual- and area-level SDOH to the PCEs led to a modest improvement in both discrimination and calibration in non-Hispanic Black individuals (change in C index, 0.0051 [95% CI, 0.0011 to 0.0126]; change in scaled integrated Brier score [IBS], 0.396% [95% CI, 0.221% to 0.802%]), and improvement in calibration in White individuals (change in scaled IBS, 0.274% [95% CI, 0.095% to 0.665%]). Adding individual-level SDOH to the PREVENT plus area-level social deprivation index (SDI) equations did not improve discrimination but modestly improved calibration in White participants (change in scaled IBS, 0.182% [95% CI, 0.040% to 0.496%]), Black participants (0.187% [95% CI, 0.039% to 0.501%]), and women (0.289% [95% CI, 0.115% to 0.574%]).

Conclusions and Relevance

In this cohort study, both individual- and area-level SDOH were associated with ASCVD risk; adding both individual- and area-level SDOH to the PCEs modestly improved discrimination and calibration for estimating ASCVD risk for Black individuals, and adding individual-level SDOH to PREVENT plus SDI also modestly improved calibration. These findings suggest that both individual- and area-level SDOH may be considered in future development of ASCVD risk assessment tools, particularly among Black individuals.

Age and sex differences in the association between neighborhood socioeconomic environment and incident diabetes: Results from the diabetes location, environmental attributes and disparities (LEAD) network

Objective

Worse neighborhood socioeconomic environment (NSEE) may contribute to an increased risk of type 2 diabetes (T2D). We examined whether the relationship between NSEE and T2D differs by sex and age in three study populations.

Research design and methods

We conducted a harmonized analysis using data from three independent longitudinal study samples in the US: 1) the Veteran Administration Diabetes Risk (VADR) cohort, 2) the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort, and 3) a case-control study of Geisinger electronic health records in Pennsylvania. We measured NSEE with a z-score sum of six census tract indicators within strata of community type (higher density urban, lower density urban, suburban/small town, and rural). Community type-stratified models evaluated the likelihood of new diagnoses of T2D in each study sample using restricted cubic splines and quartiles of NSEE.

Results

Across study samples, worse NSEE was associated with higher risk of T2D. We observed significant effect modification by sex and age, though evidence of effect modification varied by site and community type. Largely, stronger associations between worse NSEE and diabetes risk were found among women relative to men and among those less than age 45 in the VADR cohort. Similar modification by age group results were observed in the Geisinger sample in small town/suburban communities only and similar modification by sex was observed in REGARDS in lower density urban communities.

Conclusions

The impact of NSEE on T2D risk may differ for males and females and by age group within different community types.

The role of neighborhood inequalities on diabetes prevention care: a mini-review

An emerging research niche has focused on the link between social determinants of health and diabetes mellitus, one of the most prevalent non-communicable diseases in modern society. The aim of the present mini-review is to explore and summarize current findings in this field targeting high-income countries. In the presence of disadvantaged neighborhood factors (including socioeconomic status, food environment, walkability and neighborhood aesthetics), diabetes prevention and care are affected at a multidimensional level. The vast majority of the included studies suggest that, besides individual risk factors, aggregated neighborhood inequalities should be tackled to implement effective evidence-based policies for diabetes mellitus.

Demographic, social and geographic factors associated with glycaemic control among US Veterans with new onset type 2 diabetes: a retrospective cohort study

OBJECTIVES

This study evaluated whether a range of demographic, social and geographic factors had an influence on glycaemic control longitudinally after an initial diagnosis of diabetes.

DESIGN, SETTING AND PARTICIPANTS

We used the US Veterans Administration Diabetes Risk national cohort to track glycaemic control among patients 20-79-year old with a new diagnosis of type 2 diabetes.

PRIMARY OUTCOME AND METHODS

We modelled associations between glycaemic control at follow-up clinical assessments and geographic factors including neighbourhood race/ethnicity, socioeconomic, land use and food environment measures. We also adjusted for individual demographics, comorbidities, haemoglobin A1c (HbA1c) at diagnosis and duration of follow-up. These factors were analysed within strata of community type: high-density urban, low-density urban, suburban/small town and rural areas.

RESULTS

We analysed 246 079 Veterans who developed a new type 2 diabetes diagnosis in 2008-2018 and had at least 2 years of follow-up data available. Across all community types, we found that lower baseline HbA1c and female sex were strongly associated with a higher likelihood of within-range HbA1c at follow-up. Surprisingly, patients who were older or had more documented comorbidities were more likely to have within-range follow-up HbA1c results. While there was variation by community type, none of the geographic measures analysed consistently demonstrated significant associations with glycaemic control across all community types.

Assessing the association between food environment and dietary inflammation by community type: a cross-sectional REGARDS study

BACKGROUND

Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors.

OBJECTIVE

This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US.

METHODS

Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003-2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together.

RESULTS

Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was - 0.004 (SD = 2.5; min = - 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas.

CONCLUSIONS

The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options.

The Impact of Neighborhoods on Diabetes Risk and Outcomes: Centering Health Equity

Neighborhood environments significantly influence the development of diabetes risk factors, morbidity, and mortality throughout an individual's life. The social, economic, and physical environments of a neighborhood all affect the health risks of individuals and communities and also affect population health inequities. Factors such as access to healthy food, green spaces, safe housing, and transportation options can impact the health outcomes of residents. Social factors, including social cohesion and neighborhood safety, also play an important role in shaping neighborhood environments and can influence the development of diabetes. Therefore, understanding the complex relationships between neighborhood environments and diabetes is crucial for developing effective strategies to address health disparities and promote health equity. This review presents landmark findings from studies that examined associations between neighborhood socioeconomic, built and physical, and social environmental factors and diabetes-related risk and outcomes. Our framework emphasizes the historical context and structural and institutional racism as the key drivers of neighborhood environments that ultimately shape diabetes risk and outcomes. To address health inequities in diabetes, we propose future research areas that incorporate health equity principles and place-based interventions.

Socioeconomic Level and the Relationship in Glycemic Behavior in the Mexican Population. A Nutritional Alternative Focused on Vulnerable Populations

In this study, numerical approximations were generated to analyze the behavior of glycemic variations in the non-diabetic population of the Mexican republic. The main objective of this work is to obtain an overview of the glycemic variations in the non-diabetic population from different socioeconomic statuses in Mexico (Medium-high, medium, and low). Thus, evaluating the effect on the glucose level under a healthy diet considering the socioeconomic capabilities of the population. Through the national health and nutrition survey of Mexico 2020 and the Mexican food base, 1420 virtual patients were proposed (522 low status, 485 medium status and 413 Medium-High status) focused on simulating the glycemic behavior in each of the survey participants. Considering that the average food expenditure of the Mexican population is $107.00 MXN, and the cost of a healthy diet is $66.50 MXN, the economic sustainability of the Mexican population to adopt a healthy diet is revealed. The particularity of this work is focused on obtaining diverse data that are difficult to access in the development of population analyses. Such is the case of the approach proposed for different socioeconomic statuses. In this way, the proposed methodology provides a framework for complementary research contributions to the subject.