Journal of medical economics
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The objective of this study was to apply quantile regression (QR) methodology to a population from a large representative health insurance plan with known skewed healthcare utilization attributes, co-morbidities, and costs in order to identify predictors of increased healthcare costs. Further, this study provides comparison of the results to those obtained using ordinary least squares (OLS) regression methodology. ⋯ These results demonstrate that QR provides additional insight compared to traditional OLS regression modeling, and may be more useful for informing resource allocation to patients most likely to benefit from interventions. This study highlights that the impact of clinical and demographic characteristics on the economic burden of the disease vary across the continuum of healthcare costs. Understanding factors that drive costs on an individual patient level provide important insights that will help in ameliorating the clinical, humanistic, and economic burden of diabetes.