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

Abstract

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.

Defining diabetes status using medication groups in Medicare data: Trends in prescribing diabetes medications to patients without a diabetes diagnosis over time

“…However, several longitudinal cohort studies 7,8 define diabetes using a combination of ICD codes and medications as well as in analyses using electronic medical record (EMR) data. 8,12 Varying definitions of diabetes can lead to different estimated impacts on outcomes 12 in studies using EMR data, and findings that suggest substantial differences in patterns of prescribing diabetes medications by Medicare plan type. 6 It is important to point out that our examination of the data is not without limitations and should only serve as a prompt for awareness for other investigators.…”

“…Many studies using Medicare claims data define diabetes only using ICD codes 2,3 or ICD code-derived algorithms, 4,5 and thus may not be sensitive to the changing indications for the use of diabetesrelated medications over time. However, several longitudinal cohort studies 7,8 define diabetes using a combination of ICD codes and medications as well as in analyses using electronic medical record (EMR) data. 8,12 Varying definitions of diabetes can lead to different estimated impacts on outcomes 12 in studies using EMR data, and findings that suggest substantial differences in patterns of prescribing diabetes medications by Medicare plan type.…”

“…Typically, people are classified as having diabetes using a diabetes diagnosis code or-for studies which have access to prescription claims data-using a combination of International Classification of Disease (ICD) codes and medications. 7,8 The advent of new frontline medications to treat diabetes, such as glucagon-like peptide-1 receptor agonists (GLP-1RAs) and sodiumglucose cotransporter-2 (SGLT2) inhibitors, has resulted in improvements in other heath conditions, particularly obesity, kidney disease, and congestive heart failure, which has ushered in the prescription of diabetes-related medications for new US Food and Drug Administration-approved indications as well as off-label and/or nondiabetes-specific use. This is important to consider when using administrative data since definitions of diabetes in these data often use a combination of ICD codes and/or use of specific diabetes medications.Since the prescribing patterns of these diabetes medications have changed over time-not only among people with diabetes, but among people without diabetes 9,10 as well-how investigators define diabetes when using medication lists should be revisited, particularly when dealing with data from 2010 and beyond.…”

Mediation of an association between neighborhood socioeconomic environment and type 2 diabetes through the leisure-time physical activity environment in an analysis of three independent samples

“… 4–8 In an analysis of three study samples in the USA, greater socioeconomic disadvantage was generally associated with higher relative risk of T2D across study samples in most community types ranging from rural to urban in the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network. 9 Stark geographic disparities in the incidence and prevalence of T2D observed across regions, counties, and neighborhoods may be attributable to community-level structural causes. 10–14 …”

“…Epidemiologic studies have found graded associations between greater area-level socioeconomic disadvantage, despite differences in measurement, and higher risk of type 2 diabetes (T2D) across a variety of study populations and designs 4–8. In an analysis of three study samples in the USA, greater socioeconomic disadvantage was generally associated with higher relative risk of T2D across study samples in most community types ranging from rural to urban in the Diabetes Location, Environmental Attributes, and Disparities (LEAD) Network 9. Stark geographic disparities in the incidence and prevalence of T2D observed across regions, counties, and neighborhoods may be attributable to community-level structural causes 10–14…”