Statistical Approaches for Quantifying the Quality of Neurosurgical Care

Abstract

Quantifying quality of health care can provide valuable information to patients, providers, and policy makers. However, the observational nature of measuring quality complicates assessments. We describe a conceptual model for defining quality and its implications about the data collected, how to make inferences about quality, and the assumptions required to provide statistically valid estimates. Twenty-one binary or polytomous quality measures collected from 101,051 adult Medicaid beneficiaries aged 18-64 years with schizophrenia from 5 U.S. states show methodology. A categorical principal components analysis establishes dimensionality of quality, and item response theory models characterize the relationship between each quality measure and a unidimensional quality construct. Latent regression models estimate racial/ethnic and geographic quality disparities. More than 90% of beneficiaries filled at least 1 antipsychotic prescription and 19% were hospitalized for schizophrenia during a 12-month observational period in our multistate cohort with approximately 2/3 nonwhite beneficiaries. Four quality constructs emerged: inpatient, emergency room, pharmacologic/ambulatory, and ambulatory only. Using a 2-parameter logistic model, pharmacologic/ambulatory care quality varied from -2.35 to 1.26 (higher= better quality). Black and Latinx beneficiaries had lower pharmacologic/ambulatory quality compared with whites. Race/ethnicity modified the association of state and pharmacologic/ambulatory care quality in latent regression modeling. Average quality ranged from -0.28 (95% confidence interval, -2.15 to 1.04) for blacks in New Jersey to 0.46 [95% confidence interval, -0.89 to 1.40] for whites in Michigan. By combining multiple quality measures using item response theory models, a composite measure can be estimated that has more statistical power to detect differences among subjects than the observed mean per subject.