Variation in First-line Type 2 Diabetes Treatment due to eGFR and Provider Preferences: A Novel Statistical Analysis

Abstract Introduction The decision between metformin and a DPP-4 inhibitor or sulfonylurea for first-line type 2 diabetes treatment relies on many factors, including estimated glomerular filtration rate (eGFR), history of heart failure, age, sex, and even provider preferences. This study evaluates variation in this treatment decision across two factors: eGFR and provider preferences. Research Design and Methods Using health insurance claims data, we defined a cohort based on observation prior to first-line treatment, availability of eGFR results, and no type 1 or gestational diabetes (n=10,643). We performed a chi-squared test to verify the association between eGFR and treatment. The cohort was then restricted to providers with at least 10 patients (n=2,271 patients). We conducted a novel statistical analysis to assess variation across providers. We fitted two models to predict treatment—one using only patient characteristics (age, eGFR, sex, history of heart failure, and treatment date) and another using both patient characteristics and provider-specific random effects. With these models, we performed a generalized likelihood ratio test (GLRT) to assess whether including provider-specific random effects improved fit. Results The chi-squared test confirmed significant association between treatment and eGFR (p < 0.0001). The GLRT in our novel statistical analysis found significant variation existed across providers even after accounting for patient characteristics (p < 0.0001). Visualizations of the observed treatment decisions and treatment policy models show that most of this variation across providers occurred at low eGFR levels, where the level of kidney damage at which metformin should be contraindicated is unclear. Conclusions While some variation in first-line type 2 diabetes treatment was associated with eGFR, some variation may be due to provider preferences that cannot be explained by treatment guidelines. Further studies can elucidate whether such variation across providers is appropriate. Our approach can be applied to other treatment decisions to improve diabetes management. Key Messages What is already known on this topic Guidelines for first-line type 2 diabetes treatments recommend metformin unless there are contraindications, such as kidney damage indicated by low estimated glomerular filtration rate (eGFR). What this study adds This study uses a health insurance claims dataset to verify that first-line treatment is significantly associated with eGFR levels. Then, we propose a novel statistical analysis to assess whether significant variation exists across providers even after accounting for patient age, eGFR, sex, history of heart failure, and treatment date. By fitting two random effects models—one with only patient characteristics and one that also utilizes provider-specific random effects—and comparing the likelihoods of the observed treatment decisions under the two models, we find that the treatment decisions can be explained significantly better when accounting for differences among providers in treatment preferences and eGFR considerations. How this study might affect research, practice, or policy Our results suggest future studies about whether the significant variation across providers found in our analysis is appropriate may help improve first-line type 2 diabetes treatment decisions, and our novel statistical approach can be applied to evaluate variation across providers throughout the diabetes management process.

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