Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures. (20th October 2015)

• Record Type: Journal Article
• Authors: Brown, David P.; Knapp, Caprice; Baker, Kimberly; Kaufmann, Meggen
• Is Part Of: Health services research. Volume 51:Number 3(2016:Jun.)
• Page Start: 1095
• Page End: 1108
• Publication Date: 2015-10-20
• Subjects: HEDIS -- disparities -- Bayesian imputation -- racial/ethnic differences -- Medicaid -- children; Medical care -- Periodicals; Medical care -- Evaluation -- Periodicals; Hospital care -- Periodicals; Health services administration -- Periodicals; 362
• DOI: 10.1111/1475-6773.12405
• Languages: English
• ISSNs: 0017-9124

Abstract

Abstract : Objective: To analyze health care disparities in pediatric quality of care measures and determine the impact of data imputation. Data Sources: Five HEDIS measures are calculated based on 2012 administrative data for 145, 652 children in two public insurance programs in Florida. Methods: The Bayesian Improved Surname and Geocoding (BISG) imputation method is used to impute missing race and ethnicity data for 42 percent of the sample (61, 954 children). Models are estimated with and without the imputed race and ethnicity data. Principal Findings: Dropping individuals with missing race and ethnicity data biases quality of care measures for minorities downward relative to nonminority children for several measures. Conclusions: These results provide further support for the importance of appropriately accounting for missing race and ethnicity data through imputation methods.