Dataset Information
Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures.
Ontology highlight
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.
SUBMITTER: Brown DP
PROVIDER: S-EPMC4874818 | biostudies-literature | 2016 Jun
REPOSITORIES: biostudies-literature
Publications
Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures.
Health services research 20151020 3
<h4>Objective</h4>To analyze health care disparities in pediatric quality of care measures and determine the impact of data imputation.<h4>Data sources</h4>Five HEDIS measures are calculated based on 2012 administrative data for 145,652 children in two public insurance programs in Florida.<h4>Methods</h4>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 a ...[more]