Disparities in Acute and Chronic Complications of Diabetes Along the U.S. Rural-Urban Continuum

People living in rural America are 17% more likely to have diabetes (1) and 16% less likely to have adequately controlled diabetes (2). While people in urban communities experienced improvement in diabetes-related mortality since 1999, no such improvement occurred in rural areas (3,4). In fact, the rural-urban diabetes mortality gap tripled in the U.S. between 1999 and 2019 (4). Although differences in prevalence, risk factor control, and mortality in diabetes have been identified along the rural-urban continuum, there are few published data about the rates of acute and chronic diabetes complications in rural compared with urban areas.

Having a better understanding of the burden of diabetes complications in rural areas is an urgent priority, particularly given substantial and growing gaps in primary, specialty, and hospital care in the rural U.S. (5,6). Diabetes and its risk factors have been consistently cited as top rural health priorities. The Rural Healthy People 2020 survey identified nutrition and weight status as the second highest priority for rural America (rated among the top 10 issues facing the rural U.S. by 54.5% of rural health stakeholders), followed closely by diabetes (54.4%), heart disease and stroke (45.3%), and physical activity and health (44.7%) (7). Rural areas have higher rates of poverty, a greater proportion of older residents, and fewer residents completing high school and attending and completing college, all of which are risk factors for diabetes, poorly controlled diabetes, and diabetes complications (8). As a result, age-adjusted mortality in rural areas is higher for all 10 leading causes of death in the U.S., including diabetes and its complications of heart disease, stroke, and kidney disease, and these disparities have been widening over time (9).

Acute and chronic complications of diabetes incur substantial morbidity, disability, mortality, and costs for patients and society (10). Identifying opportunities to improve diabetes care and health outcomes in underserved populations is a growing priority (11). We therefore sought to address the critical knowledge gap in diabetes complications epidemiology by examining the contemporary risks of acute and chronic diabetes complications across the spectrum of rurality among U.S. adults with diabetes.

This retrospective cohort study used deidentified medical and pharmacy claims data from the OptumLabs Data Warehouse (OLDW) (12). OLDW contains longitudinal health information on enrollees in commercial and Medicare Advantage health plans offered by a large national health insurance provider, representing a diverse mixture of ages, racial and ethnic groups, income levels, and geographic regions across the U.S. All data were accessed after statistical deidentification, and the study was exempt from institutional board review (13,14). Results are reported in accordance with Strengthening the Reporting of Observational Studies in Epidemiology guidelines for observational cohort studies (15).

Using Healthcare Effectiveness Data and Information Set (HEDIS) criteria (16), we identified all individuals with diabetes included in OLDW between 1 January 2011 and 31 December 2020 (Supplementary Fig. 1). We required that included individuals have a 12-month period of uninterrupted enrollment following the date they met HEDIS criteria to ensure that we captured patients with established rather than newly diagnosed diabetes and allow baseline covariates to be ascertained during the period of diagnosed diabetes. We set this date (HEDIS date plus 12 months) as the index date for cohort entry (thus, the index date for the cohort was between 1 January 2012 and 31 December 2021). We excluded individuals age <18 years as of the index date. Individuals without available geographic data (0.25% of the cohort) were excluded. All patients were followed until they disenrolled from an OLDW insurance plan or died; follow-up time was calculated from the index date until this censoring date. For each outcome, the censor date was adjusted to the time of the first corresponding event, and the number of follow-up days for each patient was tracked.

Our primary outcomes were acute diabetes complications and acute events that signal incidence or exacerbation of chronic microvascular and macrovascular complications as detailed in Supplementary Table 1. Acute complications include emergency department (ED) visits and hospitalizations for hyperglycemia and hypoglycemia (i.e., diabetic ketoacidosis, hyperglycemic hyperosmolar state). Chronic microvascular complications include a new diagnosis of end-stage kidney disease (ESKD) and new diagnosis of proliferative retinopathy. Chronic macrovascular complications include hospitalizations for acute myocardial infarction and stroke, new diagnosis of atrial fibrillation or flutter, hospitalizations or ED visits for heart failure, lower-extremity amputation, lower-extremity complications other than amputation (i.e., Charcot arthropathy, ulcer, osteomyelitis), and revascularization procedures (i.e., stenting, bypass surgery).

OLDW enrollment files were used to ascertain the zip codes of included individuals’ primary residence and determine rural status, defined using Rural-Urban Commuting Area (RUCA) codes and categorized as urban (RUCA codes 1–3, population >50,000 people), small town (RUCA codes 4–9, population 2,500–50,000 people), and remote (RUCA code 10, population <2,500 people) (17). These definitions of rurality were chosen based on the Federal Office of Rural Health Policy’s (FORHP’s) position that all areas coded as RUCA ≥4 are rural but acknowledge that residents of remote areas (RUCA code 10) have unique travel times to health care that often exceed 30 min (18,19).

Patient age, sex, health plan type, and index year were ascertained from OLDW enrollment files. We classified diabetes as type 1 or 2 using diagnosis codes and medication fills, as previously described (20). Individual race, ethnicity, and income were not included as these data are not available in the OLDW view used for these analyses to preserve patient deidentification.

Baseline comorbidities were ascertained from claims coded during the 12 months preceding the index date (Supplementary Table 2) and included atrial fibrillation/flutter, lower-extremity amputation, chronic kidney disease (CKD) stages 3–4, cerebrovascular disease, coronary artery disease, ESKD, heart failure, hypoglycemia requiring ED/hospital care, hypertension, hyperglycemia requiring ED/hospital care, lower-extremity complications other than amputation, neuropathy, peripheral vascular disease, retinopathy, revascularization procedure, and smoking status. We also included medications that are associated with diabetes complications, as ascertained from pharmacy fills during the 120 days prior to the index date as detailed in Supplementary Table 3. Medications were grouped as metformin, sulfonylureas, glucagon-like peptide 1 receptor agonists, sodium–glucose cotransporter 2 inhibitors, dipeptidyl peptidase 4 inhibitors, thiazolidinediones, intermediate- or long-acting insulin, rapid- or short-acting insulin, glinides, other diabetes drugs, anticoagulants, antiplatelets, lipid-lowering medications, and antihypertensive medications.

We summarized all patient-level covariates by geographic group, reporting frequencies and percentages or means and SDs. We then summarized each of the 11 outcomes by geographic group and calculated the median and interquartile range years of follow-up for each group. Then, to assess whether there were differences in outcome hazards across the three groups, we estimated a series of Cox proportional hazards models, one for each outcome. Each model included indicators for remote and small-town residence, as well as the covariates listed above. Patients who were covered by both commercial insurance and Medicare Advantage plans made up an exceedingly small fraction (<0.01%) of the cohort and, therefore, were considered to have commercial insurance in the models. We report the hazard ratios (HRs) for remote and small-town effects and report 95% CIs for each HR. We performed a secondary analysis excluding patients with type 1 diabetes. CIs excluding 1 were considered statistically significant. For each model, we tested the proportional hazards assumption and report the P value. A two-sided P < 0.05 was considered statistically significant. All analyses were performed using SAS 9.04 (SAS Institute, Cary, NC) and Stata 16 (StataCorp, College Station, TX).

The study population included 2,901,563 adults with diabetes, with a mean age of 63.2 (SD 13.2) years and including 50% women and 90.7% with type 2 diabetes (Table 1). Overall, 2.6% lived in remote areas, 14.1% lived in small towns, and 83.3% lived in cities. People living in remote areas and small towns were older (mean age: remote 64.9 [SD 12.3] years, small town 64.2 [SD 12.5] years) than those living in cities (mean age 62.9 [SD, 13.3] years). People living in cities had a greater prevalence of type 1 diabetes than those living in remote areas or small towns (remote 2.1%, small town 1.9%, city 2.6%). Baseline prevalence of most comorbidities were highest or equivalently high to remote areas for people living in small towns: amputation (remote 1.3%, small town 1.3%, city 1%), cerebrovascular disease (remote 12.2%, small town 13.1%, city 12.4%), CKD stages 3 and 4 (remote 10.6%, small town 11.2%, city 10.4%), coronary artery disease (remote 25.2%, small town 25.7%, city 22.6%), ESKD (remote 1.7%, small town 2%, city 2%), hypertension (remote 83.3%, small town 85%, city 80.1%), heart failure (remote 12.4%, small town 13.1%, city 10.9%), neuropathy (remote 23.7%, small town 25.6%, city 23.8%), other lower-extremity complications (remote 4.6%, small town 4.7%, city 4.4%), hypoglycemia (remote 0.8%, small town 1.0%, city 0.8%), and smoking (remote 13.1%, small town 13.5%, city 10.2%). People in cities had the highest baseline prevalence of retinopathy (remote 12.3%, small town 13.1%, city 13.8%), peripheral vascular disease (remote 13.5%, small town 15.1%, city 15.6%), and severe hyperglycemia (remote 0.6%, small town 0.6%, and city 0.7%), while those living in remote areas had the highest prevalence of revascularization (remote 9.2%, small town 8.9%, city 7.3%) and atrial fibrillation (remote 10.5%, small town 9.6%, city 8.6%). There were also small differences in types of diabetes medications used among patients living in remote, small-town, and urban areas, with the most notable differences in the rates of sulfonylurea use (Table 1).

Remote areas had the highest crude percentage of people experiencing new atrial fibrillation (remote 16.2%, small town 15.4%, city 13.9%) and revascularization (remote 13.5%, small town 13.3%, city 11.0%) (Table 2). Conversely, small towns had the greatest crude percentage of people experiencing new severe hypoglycemia (remote 2.1%, small town 2.4%, city 2.0%), ESKD (remote 3.7%, small town 4.4%, city 4.3%), myocardial infarction (remote 4.7%, small town 5.0%, city 4.2%), heart failure (remote 6.4%, small town 7.1%, city 6.0%), amputation (remote 1.4%, small town 1.5%, city 1.2%), and lower-extremity complications other than amputation (remote 8.6%, small town 8.7%, city 8.2%). Cities had the greatest crude percentage of people experiencing new retinopathy (remote 1.9%, small town 2.0%, city 2.1%) and stroke (remote 3.4%, small town 3.7%, city 3.8%). The crude percentage of people experiencing new severe hyperglycemia was similar in small towns and cities (remote 0.7%, small town 0.9%, city 0.9%).

In multivariable analyses adjusted for age, sex, health plan type, index year, diabetes type, baseline comorbidities, and medication use (Fig. 1 and Supplementary Table 4) and relative to people living in cities, people living in remote areas had a higher hazard of myocardial infarction (HR 1.06 [95% CI 1.02–1.10]) and revascularization (HR 1.04 [1.02–1.06]) but a lower hazard of hyperglycemia (HR 0.90 [0.83–0.98]) and stroke (HR 0.91 [0.88–0.95]). Compared with cities, people living in small towns had a greater hazard of hyperglycemia (HR 1.06 [1.02–1.10]), hypoglycemia (HR 1.15 [1.12–1.18]), ESKD (HR 1.04 [1.03–1.06]), myocardial infarction (HR 1.10 [1.08–1.12]), heart failure (HR 1.05 [1.03–1.06]), amputation (HR 1.05 [1.02–1.09]), other lower-extremity complications (HR 1.02 [1.01–1.03]), and revascularization (HR 1.05 [1.04–1.06]) but a smaller hazard of stroke (HR 0.95 [0.94–0.97]). Compared with small towns, people living in remote areas had a lower hazard of hyperglycemia (HR 0.85 [0.78–0.93]), hypoglycemia (HR 0.92 [0.87–0.97]), and heart failure (HR 0.94 [0.91–0.97]). Hazards of retinopathy and atrial fibrillation/flutter did not vary geographically. When patients with type 1 diabetes were removed from the data set, the results did not markedly change in their magnitude or in statistical significance by P value (Supplementary Table 5).

People with diabetes living in small towns in the U.S. experience a disproportionate burden of acute and chronic diabetes complications. In our study of >2.9 million adults with diabetes across the U.S., people living in small towns had a greater hazard of experiencing 8 of 11 examined complications compared with those living in cities (in descending order of hazard magnitude: severe hypoglycemia, myocardial infarction, severe hyperglycemia, heart failure, amputation, revascularization, ESKD, and other lower-extremity complications). Additionally, people living in remote areas had a lower hazard of 3 of 11 examined complications compared with those living in small towns (from least to greatest hazard: hyperglycemia, hypoglycemia, and heart failure). Compared with people living in cities, those living in remote areas had an increased hazard of 2 of 11 examined complications (myocardial infarction having greater HR than revascularization), but those living in remote areas also had a decreased hazard of 2 of 11 examined complications compared with cities (hyperglycemia having a smaller HR than stroke). These findings underscore the urgent need for understanding the drivers of adverse health outcomes experienced by people living with diabetes in nonurban areas and for greater investment in and prioritization of diabetes prevention and management in small towns and remote areas across the U.S.

Our findings providing evidence of a greater burden of acute and chronic diabetes complications in small towns are consistent with prior studies highlighting lower rates of glycemic control and control of other cardiovascular risk factors, including blood pressure, dyslipidemia, and smoking, in more-rural areas (which included small towns) (2,22). While the magnitude of these differences varied by complication, the 15% increased hazard of severe hypoglycemia is of similar magnitude to differences observed in the prevalence of diabetes and rates of poor glycemic control in prior studies (1,2,7). These differences may be driven by a wide range of structural and socioeconomic factors that increase the risk of developing diabetes, impede diabetes prevention and management efforts, worsen health outcomes, and lead to premature death. These include limited nutritional choices, less access to primary and specialty diabetes care, fewer certified diabetes care and education specialists, higher rates of poverty, and differences in occupational and educational attainment (8). Indeed, people living outside city areas are less likely to receive care from diabetes specialists (22), to receive diabetes self-management education and support (23), and to be screened for diabetes complications (24). Our study builds on this foundational evidence to demonstrate a possible impact of these differences on otherwise potentially preventable diabetes complications.

Although people living in small towns were diagnosed with diabetes complications more frequently than those living in cities, the rates of these complications did not increase predictably along the rural-urban continuum. In fact, people living in small towns were more likely to experience 8 of the 11 examined complications compared with those living in cities and more likely to experience 3 of the 11 examined complications compared with those living in remote areas, while people living in remote areas had higher rates in just 2 of 11 examined categories compared with those living in cities. For context, remote areas are defined as having <2,500 people and where <10% of people commute to a small town or city. Within this unique remote subgroup, which is not always examined separately from a broader rural group that also includes residents of small towns as defined here, it is reasonable to hypothesize that there may be protective factors that allow individuals to live healthier lives than those in small towns. For example, previous studies found that rurally located people who farm are generally healthier than rurally located people who do not farm, and farmers may be disproportionately represented in remote areas (25–27). Notably, the two states with the greatest proportion of residents living in remote areas (Wyoming and Montana) are also two of only three states where people in rural areas have lower mortality than those in urban areas (28,29). However, these findings may also reflect ascertainment bias that cannot be avoided in studies using claims and other health care data sources. Because we relied on health care utilization for each of the complications to capture their occurrence, patients with barriers to accessing care, including those living in areas without ready access to medical facilities or with other structural barriers to seeking care, are less likely to be diagnosed with a health condition, even if it is present. People living in remote areas would be most susceptible to this bias given the well-described lack of access to health care, namely >30-min travel times to a health care facility in some instances (18,19). The same limitation would apply to other data sources, including electronic health records and surveys, such as the National Inpatient Sample. A recent study found that patients in nonurban areas were transported to the ED after an episode of hypoglycemia less often than patients in urban areas (30), and we hypothesize that nonurban residents may be more likely to forego care for other complications as well.

Importantly, there are different ways to define rurality, which must be considered to contextualize our study for ongoing policy and public health priorities (18). We relied on definitions used by FORHP, which defines rural areas as having RUCA codes ≥4 and further delineated remote areas that lack primary commuting flows to towns or cities. In contrast, the Office of Management and Budget considers all areas with <49,999 people as rural, while the Economic Research Service within the U.S. Department of Agriculture uses several different and more granular definitions (18). Prior analyses have shown that different definitions of rural result in vastly different numbers of people and areas considered to be rural, with marked variation in population characteristics and needs (31). Our findings affirm the FORHP’s more broad definition of health care rurality but diverge in finding that more remotely located individuals may require separate evaluation in health care research as others have suggested (19).

To our knowledge, this study is the first to examine contemporary rates of acute and chronic diabetes complications across the rural-urban continuum in a large national cohort of adults with diabetes. However, it has important limitations. We relied on claims data from a single national health insurance provider that administers multiple private and Medicare Advantage health plans with disproportionate representation of urban populations. In our study, 16.7% of people lived in areas defined as rural by the FORHP, whereas 18% of the U.S. population met this definition at the 2010 census (18). While we included underinsured individuals with high-deductible health plans, our data set did not include patients with Medicaid or traditional Medicare fee-for-service insurance and those without any insurance coverage; these patients are disproportionately present in rural areas and are at highest risk for diabetes complications (32). This likely biases our findings toward the null (in addition to missed measured outcomes because of barriers to health care utilization) and reinforces the high burden of diabetes complications on nonurban areas. We were unable to examine intersectionality with income, race, and ethnicity because these data were not available in our data set in order to preserve patient deidentification. Future research is needed to examine these intersections, as prior studies found synergistic disparities in diabetes-related mortality and preventive health screening for minoritized rural populations (33,34). Moreover, as with any observational study, we cannot control for residual confounders for which we do not have biometric or laboratory data. Additionally, we captured data for our independent variable and covariates at baseline, rather than considering time-varying covariates, as our focus was on epidemiology rather than causality. Misclassification due to moving between urban and rural locations, changing medications, or newly developed comorbidities is possible. Finally, our epidemiologic analysis does not explain why small-town areas experience higher rates of diabetes complications or suggest modifiable risk factors most amenable to change.

While more research is needed (8) to better understand the underlying causes of disparate diabetes outcomes along the rural-urban continuum, this study establishes the foundational differences to guide improvement efforts and helps to identify complications with the greatest disparities to which policy interventions may be targeted. In small towns, hazards were relatively high for hospitalizations and ED visits for severe hypoglycemia and hyperglycemia, suggesting a greater need for specialty diabetes care and access to diabetes self-management education. In both small towns and remote areas, the hazard of myocardial infarction was high, calling for programs like the American Heart Association’s Rural Health Care Outcomes Accelerator that provides rural hospitals with free access to Get With the Guidelines education on coronary artery disease and site-specific quality improvement consultations (35).

In summary, compared to people with diabetes living in cities and remote areas, those in small towns experience a greater number of diabetes complications. Additional research is needed to examine the intersectionality of rurality, race and ethnicity, and poverty; examine health outcomes in different settings and populations, including Medicaid and Medicare beneficiaries; and probe for the reasons underlying these disparities.

Funding. This study was funded by National Institute of Diabetes and Digestive and Kidney Diseases grant K23DK114497.

The study contents are the sole responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health.

Duality of Interest. In the past 36 months, R.G.M. received support from the National Institute for Diabetes and Digestive and Kidney Diseases, National Institute on Aging, Patient-Centered Outcomes Research Institute, American Diabetes Association, and National Center for Advancing Translational Sciences for unrelated work. She has also consulted with Emmi on the development of patient education materials related to diabetes. E.M.D. is a member of the U.S. Preventive Services Task Force. No other potential conflicts of interest relevant to this article were reported.

This article does not necessarily represent the views and polices of the U.S. Preventive Services Task Force.

Authors Contributions. K.S. interpreted the results and drafted the manuscript. J.H. analyzed the data and reviewed and edited the manuscript. K.S.S. managed and analyzed the data and reviewed and edited the manuscript. E.M.D. interpreted the results and reviewed and edited the manuscript. R.G.M. designed and supervised the study, interpreted the results, reviewed and edited the manuscript, and secured funding. R.G.M. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented in poster form at the 83rd Scientific Sessions of the American Diabetes Association, San Diego, CA, 23–26 June 2023.

Handling Editors. The journal editors responsible for overseeing the review of the manuscript were Elizabeth Selvin and Alka M. Kanaya.