Estimating racial and ethnic healthcare quality disparities using exploratory item response theory and latent class item response theory models.

Abstract

Healthcare quality metrics refer to a variety of measures used to characterize what should have been done or not done for a patient or the health consequences of what was or was not done. When estimating healthcare quality, many metrics are measured and combined to provide an overall estimate either at the patient level or at higher levels, such as the provider organization or insurer. Racial and ethnic disparities are defined as the mean difference in quality between minorities and Whites not justified by underlying health conditions or patient preferences. Several statistical features of healthcare quality data have been ignored: quality is a theoretical construct not directly observable; quality metrics are measured on different scales or, if measured on the same scale, have different baseline rates; the construct may be multidimensional; and metrics are correlated within-individuals. Balancing health differences across race and ethnicity groups is challenging due to confounding. We provide an approach addressing these features, utilizing exploratory multidimensional item response theory (IRT) models and latent class IRT models to estimate quality, and optimization-based matching to adjust for confounding among the race and ethnicity groups. Quality metrics measured on 93,000 adults with schizophrenia residing in five US states illustrate approaches.