Medication use and contextual factors associated with meeting guideline-based glycemic levels in diabetes among a...

Based on the long-lasting diabetes management challenges in the United States, the objective was to examine glycemic levels among a nationally representative sample of people with diabetes stratified by prescribed antihyperglycemic treatment regimens and contextual factors.

This serial cross-sectional study used United States population-based data from the 2015 to March 2020 National Health and Nutrition Examination Surveys (NHANES). The study included non-pregnant adults (≥20 years old) with non-missing A1C and self-reported diabetes diagnosis from NHANES. Using A1C lab values, we dichotomized the outcome of glycemic levels into <7% versus ≥7% (meeting vs. not meeting guideline-based glycemic levels, respectively). We stratified the outcome by antihyperglycemic medication use and contextual factors (e.g., race/ethnicity, gender, chronic conditions, diet, healthcare utilization, insurance, etc.) and performed multivariable logistic regression analyses.

The 2042 adults with diabetes had a mean age of 60.63 (SE = 0.50), 55.26% (95% CI = 51.39-59.09) were male, and 51.82% (95% CI = 47.11-56.51) met guideline-based glycemic levels. Contextual factors associated with meeting guideline-based glycemic levels included reporting an "excellent" versus "poor" diet (aOR = 4.21, 95% CI = 1.92-9.25) and having no family history of diabetes (aOR = 1.43, 95% CI = 1.03-1.98). Contextual factors associated with lower odds of meeting guideline-based glycemic levels included taking insulin (aOR = 0.16, 95% CI = 0.10-0.26), taking metformin (aOR = 0.66, 95% CI = 0.46-0.96), less frequent healthcare utilization [e.g., none vs. ≥4 times/year (aOR = 0.51, 95% CI = 0.27-0.96)], being uninsured (aOR = 0.51, 95% CI = 0.33-0.79), etc.

Meeting guideline-based glycemic levels was associated with medication use (taking vs. not taking respective antihyperglycemic medication classes) and contextual factors. The timely, population-based estimates can inform national efforts to optimize diabetes management.