Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures.

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.