Comparison of Patients with Diabetes in Rural and Urban Populations

A comparison of rural and urban diabetes patients’ data over twenty years shows where a patient resides can impact their diabetes care and comorbidities.  

Socioeconomic status has always had an impact on patient outcomes. “Although national mortality rates have generally declined in the past few decades, the urban-rural disparity in life expectancy has widened, with greater improvements in urban areas than rural” (Mercado et al.). Complications from diabetes such as microvascular and macrovascular diseases increase disability and mortality. In addition, people with diabetes who live in rural areas are less likely to adhere to preventative measures such as annual foot and eye exams than their urban counterparts. However, there has been no clinical research comparing metrics, such as ABCS, to provide data to support that statement.  

For this study, Mercado et al. analyzed data from patients with diabetes in rural and urban areas from 1999-to 2018. They specifically focused on four parameters: A1c, blood pressure, cholesterol, and smoking history (ABCS). For Mercado’s study, “poor ABCS was A1C >9.0% (>75 mmol/mol), BP ≥140/90 mmHg (systolic BP ≥140 mmHg or diastolic BP ≥90 mmHg), non-HDL cholesterol ≥160 mg/dL (≥4.1 mmol/L) (equivalent to LDL ≥130 mg/dL, ≥3.4 mmol/L), and being a current smoker.” Mercado et al. collected data for this study from The National Health and Nutrition Examination Survey (NHANES). NHANES collects information every two years, and Mercado et al. used ten cycles for this study. Mercado et al. considered patient demographics were age, sex, and race/ethnicity. Other data points were education, poverty-to-income ratio, health insurance coverage, time since diabetes diagnosis, age at diabetes diagnosis, and BMI.   

The study results found that overall, from 1999-2018, there were no significant differences between the rural and urban population data. “However, there were significant socioeconomic and clinical disparities in the association between poor ABCS management and rural/urban residence” (Mercado et al., 2021). This study used weighted regression models for each location (rural and urban) to examine the correlation between location and poor ABCS measures. Mercado et al. made several models with covariates such as age, sex, and race/ethnicity. The fifth model encompassed all the covariates from the previous four models. Patients with diabetes who live in an urban area were more likely to have an A1c >9.0%. Additionally, patients with diabetes tended to have a higher percentage of smokers in rural areas than urban areas. According to Mercado, “Adults diagnosed diabetes in urban areas experienced greater BP and non-HDL cholesterol improvements from 1999–2006 to 2013–2018 than their rural counterparts.”  

The limitations of this study are that these patients were self-reported. Therefore, the sample was not representative of the general population and was hard to generalize. Also, patients were selected for inclusion only if there was no missing data from the NHANES survey. In each survey cycle, missing values ranged from 3% to 12% in the data set. That significantly decreased the available sample size. 

Additionally, when this data was gathered, response to NHANES surveys decreased by 80%. Another limitation of this study was that there were only two classifications for participants: urban and highly rural. In addition, a limited number of participants lived in rural areas and were diagnosed with diabetes. Finally, this study was conducted with a cross-sectional design. While that provides population-level associations, it did not allow for cohort-specific research. There could have been individual improvement with ABCS metrics, but Mercado et al. could not extrapolate it given the data set.  

The information gained from this survey showed a disparity within diabetes-specific criteria; while not significant, it was evident. The data from Mercado’s study highlighted significant sociodemographic differences, emphasizing the multi-faceted complexity of diabetes management. The results varied among subgroups for which geographical location and residential status were more likely to be associated with poor ABCS, but there were correlations. According to Mercado, “Clinical and public health professionals need to incorporate evidence-based approaches in addressing sociodemographic barriers to achieve better ABCS measures across the nation but especially in disproportionately affected groups living in urban and rural areas.” There was also a disparity when comparing comprehensive care criteria. Providers should emphasize care for co-occurring diseases to optimize care from a provider perspective.  

Practice Pearls:  

• Comprehensive care is vital when treating patients from all backgrounds. 
• Access to healthcare and beneficial resources is a social determinant of health.  
• Diabetes care can and often does involve other co-morbid disease care.  

Luo, Huabin, et al. “Rural‐Urban Differences in Uptake of Diabetes SELF‐MANAGEMENT Education AMONG MEDICARE Beneficiaries: Results from the 2016 to 2018 MEDICARE Current Beneficiary Survey.” The Journal of Rural Health, 2021, doi:10.1111/jrh.12588.  

Mercado, Carla I., et al. “Differences in U.s. Rural-Urban Trends in Diabetes Abcs, 1999–2018.” Diabetes Care, vol. 44, no. 8, 2021, pp. 1766–1773..  

Grace Behr, PharmD/MBA Candidate, Creighton University