Using Bayesian Imputation to Assess Racial and Ethnic Disparities in Pediatric Performance Measures.
Health Serv Res
; 51(3): 1095-108, 2016 06.
Article
em En
| MEDLINE
| ID: mdl-26487321
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. PRINCIPALFINDINGS:
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.Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Pediatria
/
Qualidade da Assistência à Saúde
/
Etnicidade
/
Grupos Raciais
/
Disparidades em Assistência à Saúde
Tipo de estudo:
Prognostic_studies
Limite:
Adolescent
/
Adult
/
Child
/
Child, preschool
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Humans
/
Infant
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article