Associations of Four Community Factors With Longitudinal Change in Hemoglobin A1c Levels in Patients With Type 2 Diabetes

Paving a Path to Equity in Cardiorenal Care

Cardiorenal syndrome encompasses a dynamic interplay between cardiovascular and kidney disease, and its prevention requires careful examination of multiple predisposing underlying conditions. The unequal distribution of diabetes, heart failure, hypertension, and kidney disease requires special attention because of the influence of these conditions on cardiorenal disease. Despite growing evidence regarding the benefits of disease-modifying agents (e.g., sodium-glucose cotransporter 2 inhibitors) for cardiovascular, kidney, and metabolic (CKM) disease, significant disparities remain in access to and utilization of these essential therapeutics. Multilevel barriers impeding their use require multisector interventions that address patient, provider, and health system-tailored strategies. Burgeoning literature also describes the critical role of unequal social determinants of health, or the sociopolitical contexts in which people live and work, in cardiorenal risk factors, including heart failure, diabetes, and chronic kidney disease. This review outlines (i) inequality in the burden and treatment of hypertension, type 2 diabetes, and heart failure; (ii) disparities in the use of key disease-modifying therapies for CKM diseases; and (iii) multilevel barriers and solutions to achieve greater pharmacoequity in the use of disease-modifying therapies. In addition, this review provides summative evidence regarding the role of unequal social determinants of health in cardiorenal health disparities, further outlining potential considerations for future research and intervention. As proposed in the 2023 American Heart Association presidential advisory on CKM health, a paradigm shift will be needed to achieve cardiorenal health equity. Through a deeper understanding of CKM health and a commitment to equity in the prevention, detection, and treatment of CKM disease, we can achieve this critical goal.

Predictors of lack of glycemic control in persons with type 2 diabetes

BACKGROUND

Professional guidelines recommend an HbA1c < 7% for most people with diabetes and < 8.5% for those with relaxed glycemic goals. However, many people with type 2 diabetes mellitus (T2DM) are unable to achieve the desired HbA1c goal. This study evaluated factors associated with lack of improvement in HbA1c over 3 years.

METHODS

All patients with T2DM treated within a major academic healthcare system during 2015-2020, who had at least one HbA1c value > 8.5% within 3 years from their last HbA1c were included in analysis. Patients were grouped as improved glycemic control (last HbA1c ≤ 8.5%) or lack of improvement (last HbA1c > 8.5%). Multivariate logistic regression analysis was performed to assess independent predictors of lack of improvement in glycemic control.

RESULTS

Out of 2,232 patients who met the inclusion criteria, 1,383 had an improvement in HbA1c while 849 did not. In the fully adjusted model, independent predictors of lack of improvement included: younger age (odds ratio, 0.89 per 1-SD [12 years]; 95% CI, 0.79-1.00), female gender (1.30, 1.08-1.56), presence of hypertension (1.29, 1.08-1.55), belonging to Black race (1.32, 1.04-1.68, White as reference), living in low income area (1.86,1.28-2.68, high income area as reference), and insurance coverage other than Medicare (1.32, 1.05-1.66). Presence of current smoking was associated with a paradoxical improvement in HbA1c (0.69, 0.47-0.99). In a subgroup analysis, comparing those with all subsequent HbA1c values > 8.5% (N = 444) to those with all subsequent HbA1c values < 8.5% (N = 341), similar factors were associated with lack of improvement, but smoking was no longer significant.

CONCLUSION

We conclude that socioeconomic factors like race, type of insurance coverage and living in low-income areas are associated with lack of improvement in HbA1c over a period of 3-years in people with T2DM. Intervention strategies focused on low-income neighborhoods need to be designed to improve diabetes management.

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.

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.

A decade of socioeconomic inequality in type 2 diabetes area-level prevalence: an unshakeable status quo?

AIMS

It is well known that there is a socioeconomic gradient in the prevalence of many chronic diseases, including type 2 diabetes (T2DM). We present a simple assessment of the macro-level association between area socioeconomic disadvantage and the area-level prevalence of T2DM in Danish municipalities and the development in this relationship over the last decade.

METHODS

We used readily available public data on the socioeconomic composition of municipalities and T2DM prevalence to illustrate this association and report the absolute and relative summary measures of socioeconomic inequality over the time period 2008-2018.

RESULTS

The results show a persistent relationship between municipality socioeconomic disadvantage and T2DM prevalence across all analyses, with a modelled gap in T2DM prevalence between the most and least disadvantaged municipalities, the slope index of inequality, of 1.23 [0.97;1.49] in 2018.

CONCLUSIONS

These results may be used to indicate areas with specific needs, to encourage systematic monitoring of socioeconomic gradients in health, and to provide a descriptive backdrop for a discussion of how to tackle these socioeconomic and geographic inequalities, which seem to persist even in the context of the comprehensive welfare systems in Scandinavia.

Associations between cardiometabolic disease severity, social determinants of health (SDoH), and poor COVID-19 outcomes

OBJECTIVE

This study aimed to determine the ability of retrospective cardiometabolic disease staging (CMDS) and social determinants of health (SDoH) to predict COVID-19 outcomes.

METHODS

Individual and neighborhood SDoH and CMDS clinical parameters (BMI, glucose, blood pressure, high-density lipoprotein, triglycerides), collected up to 3 years prior to a positive COVID-19 test, were extracted from the electronic medical record. Bayesian logistic regression was used to model CMDS and SDoH to predict subsequent hospitalization, intensive care unit (ICU) admission, and mortality, and whether adding SDoH to the CMDS model improved prediction was investigated. Models were cross validated, and areas under the curve (AUC) were compared.

RESULTS

A total of 2,873 patients were identified (mean age: 58 years [SD 13.2], 59% were female, 45% were Black). CMDS, insurance status, male sex, and higher glucose values were associated with increased odds of all outcomes; area-level social vulnerability was associated with increased odds of hospitalization (odds ratio: 1.84, 95% CI: 1.38-2.45) and ICU admission (odds ratio 1.98, 95% CI: 1.45-2.85). The AUCs improved when SDoH were added to CMDS (p < 0.001): hospitalization (AUC 0.78 vs. 0.82), ICU admission (AUC 0.77 vs. 0.81), and mortality (AUC 0.77 vs. 0.83).

CONCLUSIONS

Retrospective clinical markers of cardiometabolic disease and SDoH were independently predictive of COVID-19 outcomes in the population.

Evaluating the Implementation of the Diabetes Self-Management Program in a Rural Population

The evidence-based Diabetes Self-Management Program (DSMP) has been shown to improve a variety of health-related outcomes, but the program has been challenging to implement in rural areas, and rural dissemination has been low. The purpose of this project was to evaluate the effect of implementing the DSMP on self-reported outcomes in a rural region. Through a collaboration with multiple partners, the Living Well program delivered 28 DSMP workshops from 2017 to 2019. Data were collected to determine whether there were post-intervention changes in patient-reported outcomes on measures of diabetes distress, self-management, and patient activation. In addition, secondary analysis of A1C was abstracted from the medical records of participants with type 2 diabetes who completed at least four sessions of a DSMP workshop between 2017 and 2019 and whose medical records had an A1C value in the year before the program and at least one A1C value >3 months after the program. Statistically significant improvements were seen for the Diabetes Distress Scale (P = 0.0017), the Diabetes Self-Management Questionnaire (P <0.0001) and the 10-item Patient Activation Measure (P <0.0001). There was no evidence of change in A1C over time in analyses of all participants (P = 0.5875), but a consistent though nonsignificant (P = 0.1087) decline in A1C was seen for a subset of participants with a baseline A1C ≥8%. This evaluation provides preliminary support for implementing the DSMP as part of a comprehensive treatment and self-management plan for people living with diabetes in rural areas.

Association of community types and features in a case-control analysis of new onset type 2 diabetes across a diverse geography in Pennsylvania

OBJECTIVES

To evaluate associations of community types and features with new onset type 2 diabetes in diverse communities. Understanding the location and scale of geographic disparities can lead to community-level interventions.

DESIGN

Nested case-control study within the open dynamic cohort of health system patients.

SETTING

Large, integrated health system in 37 counties in central and northeastern Pennsylvania, USA.

PARTICIPANTS AND ANALYSIS

We used electronic health records to identify persons with new-onset type 2 diabetes from 2008 to 2016 (n=15 888). Persons with diabetes were age, sex and year matched (1:5) to persons without diabetes (n=79 435). We used generalised estimating equations to control for individual-level confounding variables, accounting for clustering of persons within communities. Communities were defined as (1) townships, boroughs and city census tracts; (2) urbanised area (large metro), urban cluster (small cities and towns) and rural; (3) combination of the first two; and (4) county. Community socioeconomic deprivation and greenness were evaluated alone and in models stratified by community types.

RESULTS

Borough and city census tract residence (vs townships) were associated (OR (95% CI)) with higher odds of type 2 diabetes (1.10 (1.04 to 1.16) and 1.34 (1.25 to 1.44), respectively). Urbanised areas (vs rural) also had increased odds of type 2 diabetes (1.14 (1.08 to 1.21)). In the combined definition, the strongest associations (vs townships in rural areas) were city census tracts in urban clusters (1.41 (1.22 to 1.62)) and city census tracts in urbanised areas (1.33 (1.22 to 1.45)). Higher community socioeconomic deprivation and lower greenness were each associated with increased odds.

CONCLUSIONS

Urban residence was associated with higher odds of type 2 diabetes than for other areas. Higher community socioeconomic deprivation in city census tracts and lower greenness in all community types were also associated with type 2 diabetes.

The Diabetes Location, Environmental Attributes, and Disparities Network: Protocol for Nested Case Control and Cohort Studies, Rationale, and Baseline Characteristics

BACKGROUND

Diabetes prevalence and incidence vary by neighborhood socioeconomic environment (NSEE) and geographic region in the United States. Identifying modifiable community factors driving type 2 diabetes disparities is essential to inform policy interventions that reduce the risk of type 2 diabetes.

OBJECTIVE

This paper aims to describe the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network, a group funded by the Centers for Disease Control and Prevention to apply harmonized epidemiologic approaches across unique and geographically expansive data to identify community factors that contribute to type 2 diabetes risk.

METHODS

The Diabetes LEAD Network is a collaboration of 3 study sites and a data coordinating center (Drexel University). The Geisinger and Johns Hopkins University study population includes 578,485 individuals receiving primary care at Geisinger, a health system serving a population representative of 37 counties in Pennsylvania. The New York University School of Medicine study population is a baseline cohort of 6,082,146 veterans who do not have diabetes and are receiving primary care through Veterans Affairs from every US county. The University of Alabama at Birmingham study population includes 11,199 participants who did not have diabetes at baseline from the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a cohort study with oversampling of participants from the Stroke Belt region.

RESULTS

The Network has established a shared set of aims: evaluate mediation of the association of the NSEE with type 2 diabetes onset, evaluate effect modification of the association of NSEE with type 2 diabetes onset, assess the differential item functioning of community measures by geographic region and community type, and evaluate the impact of the spatial scale used to measure community factors. The Network has developed standardized approaches for measurement.

CONCLUSIONS

The Network will provide insight into the community factors driving geographical disparities in type 2 diabetes risk and disseminate findings to stakeholders, providing guidance on policies to ameliorate geographic disparities in type 2 diabetes in the United States.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/21377.

The Longitudinal Influence of Social Determinants of Health on Glycemic Control in Elderly Adults With Diabetes

OBJECTIVE

This study aimed to understand the longitudinal relationship between financial, psychosocial, and neighborhood social determinants and glycemic control (HbA1c) in older adults with diabetes.

RESEARCH DESIGN AND METHODS

Data from 2,662 individuals with self-reported diabetes who participated in the Health and Retirement Study (HRS) were used. Participants were followed from 2006 through 2014. Financial hardship, psychosocial, and neighborhood-level social determinant factors were based on validated surveys from the biennial core interview and RAND data sets. All social determinant factors and measurements of HbA1c from the time period were used and treated as time varying in analyses. SAS PROC GLIMMIX was used to fit a series of hierarchical linear mixed models. Models controlled for nonindependence among the repeated observations using a random intercept and treating each individual participant as a random factor. Survey methods were used to apply HRS weighting.

RESULTS

Before adjustment for demographics, difficulty paying bills (β = 0.18 [95% CI 0.02, 0.24]) and medication cost nonadherence (0.15 [0.01, 0.29]) were independently associated with increasing HbA1c over time, and social cohesion (-0.05 [-0.10, -0.001]) was independently associated with decreasing HbA1c over time. After adjusting for both demographics and comorbidity count, difficulty paying bills (0.13 [0.03, 0.24]) and religiosity (0.04 [0.001, 0.08]) were independently associated with increasing HbA1c over time.

CONCLUSIONS

Using a longitudinal cohort of older adults with diabetes, this study found that financial hardship factors, such as difficulty paying bills, were more consistently associated with worsening glycemic control over time than psychosocial and neighborhood factors.

Identifying neighborhood characteristics associated with diabetes and hypertension control in an urban African-American population using geo-linked electronic health records

For health care providers, information on community-level social determinants of health is most valuable when it is specific to the populations and health outcomes for which they are responsible. Diabetes and hypertension are highly prevalent conditions whose management requires an interplay of clinical treatment and behavioral modifications that may be sensitive to community conditions. We used geo-linked electronic health records from 2016 of African American patients of a network of federally qualified health centers in Philadelphia, PA to examine cross-sectional associations between characteristics of patients' residential neighborhoods and hypertension and diabetes control (n = 1061 and n = 2633, respectively). Hypertension and diabetes control were defined to align with the Health Resources and Services Administration (HRSA) Uniform Data System (UDS) reporting requirements for HRSA-funded health centers. We examined associations with nine measures of neighborhood socioeconomic status (poverty, education, deprivation index), social environment (violent crime, perceived safety and social capital, racial segregation), and built environment (land-use mix, intersection density). In demographics-adjusted log-binomial regression models accounting for neighborhood-level clustering, poor diabetes and hypertension control were more common in highly segregated neighborhoods (i.e., high proportion of African American residents relative to the mean for Philadelphia; prevalence ratio = 1.27 [1.02-1.57] for diabetes, 1.22 [1.12-1.33] for hypertension) and less common in more walkable neighborhoods (i.e., higher retail land use). Neighborhood deprivation was also weakly associated with poor hypertension control. An important consideration in making geographic information actionable for providers is understanding how specific community-level determinants affect the patient population beyond individual-level determinants.

Sleep and Environmental Factors Affecting Glycemic Control in People with Type 2 Diabetes Mellitus

PURPOSE OF REVIEW

Sleep and environmental factors both impact glycemic control in persons with type 2 diabetes mellitus (T2DM). This narrative article aims to review research within the past 5 years, focusing on chronotype, light, noise, and neighborhood disparities in relation to sleep in people with T2DM.

RECENT FINDINGS

Sleep quality and duration have been shown to impact glycemic control in patients with T2DM. Later chronotype can lead to poorer glycemic control due to disruption of circadian rhythms. Light exposure also has similar effects, likely due to its inherent influence on sleep quality. Environmental determinants, were associated with lower T2DM incidence, and noise and air pollution were associated with increased risks for T2DM. Findings were mixed; while most studies found that later chronotype, light/noise exposure, and neighborhood disadvantages were associated with poorer glycemic control in patients with T2DM, other environmental factors, such as green space, were not significantly associated with diabetes outcomes.

Are exposures to ready-to-eat food environments associated with type 2 diabetes? A cross-sectional study of 347 551 UK Biobank adult participants

BACKGROUND

Rapid urbanisation and associated socioeconomic transformations have modified current lifestyles, shifting dietary preferences towards ready-to-eat, calorie-dense food of poor nutritional quality. The effect of ready-to-eat food environments that sell food for instant consumption on the risk of type 2 diabetes has received scant attention. We therefore aimed to examine the association between exposure to ready-to-eat food environments and type 2 diabetes in a large and diverse population sample.

METHODS

We conducted a cross-sectional study of adult male and female participants from the baseline phase of the UK Biobank cohort. Participants in this cohort were aged 37-73 years and resided in one of 21 cities in the UK. Ready-to-eat food environments, which we determined from a modelled and linked built environment database, were objectively measured within 1-km catchment areas of the residential streets of participants and were expressed as metrics of density and proximity to the participants' homes. We used logistic regression models to examine the associations between exposure to ready-to-eat food environments and the odds of type 2 diabetes, adjusting for individual covariates such as physical activity. As sensitivity analyses, we investigated the associations between the street distance to the nearest ready-to-eat food outlet and type 2 diabetes. We also tested post hoc for effect modification by sex, income, body-mass index, and location of the UK Biobank collection centre.

FINDINGS

Of 502 635 UK Biobank participants enrolled between March 13, 2006, and Oct 1, 2010, the sample analysed included 347 551 (69·1%) participants. The density of ready-to-eat food environments within a 1-km catchment area was associated with higher odds of type 2 diabetes for participants in the groups with highest exposure to restaurants and cafeterias (odss ratio 1·129, 95% CI 1·05-1·21; p=0·0007) and a composite measure of ready-to-eat outlet density (1·112, 1·02-1·21; p=0·0134) compared with those with no exposure. Exposure to hot and cold takeaways was only significantly associated with higher odds of type 2 diabetes at the second highest exposure category that we examined (1·076, 1·01-1·14; p=0·0171), representing a density of 0·75-2·15 units per km². A protective effect with distance decay was observed: participants in the highest quintile of street distance to nearest ready-to-eat food outlet reported lower odds of type 2 diabetes than those in the lowest quintile (0·842, 0·78-0·91; p<0·0001 for restaurants and cafeterias; and 0·913, 0·85-0·98; p=0·0173 for hot and cold takeaways). These effects were most pronounced in overweight participants (p=0·0329), but there was no evidence of interaction by sex, income, or UK Biobank collection centre.

INTERPRETATION

Access to ready-to-eat food environments was positively associated with type 2 diabetes. Top-down policies aimed at minimising unhealthy food access could potentially reduce unhealthy consumption and risks of chronic diseases. Further long-term studies are needed to effectively guide such interventions.

FUNDING

University of Hong Kong, UK Biobank, and UK Economic & Social Research Council.