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Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures
Author(s) -
Brown David P.,
Knapp Caprice,
Baker Kimberly,
Kaufmann Meggen
Publication year - 2016
Publication title -
health services research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.706
H-Index - 121
eISSN - 1475-6773
pISSN - 0017-9124
DOI - 10.1111/1475-6773.12405
Subject(s) - geocoding , imputation (statistics) , missing data , ethnic group , bayesian probability , public health insurance , medicine , health care , statistics , demography , geography , health insurance , mathematics , political science , sociology , remote sensing , law
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.