Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures.
Health Serv Res
; 57(6): 1370-1378, 2022 12.
Article
em En
| MEDLINE
| ID: mdl-35802064
ABSTRACT
OBJECTIVE:
To assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid. DATA SOURCES AND STUDYSETTING:
2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used. STUDYDESIGN:
We cross-sectionally assessed Hispanic-White, Black-White, and Asian-White disparities in 22 quality and utilization measures, comparing self-reported race and ethnicity to imputed values from the Bayesian Improved Surname Geocoding (BISG) algorithm. DATA COLLECTION Race and ethnicity were obtained from self-reported data and imputed using BISG. PRINCIPALFINDINGS:
42.5%/4.9% of claims/EHR were missing self-reported data; BISG estimates were available for >99% of each and had good concordance (0.87-0.95) with Asian, Black, Hispanic, and White self-report. All estimated racial and ethnic disparities were statistically similar in self-reported and imputed EHR-based measures. However, within claims, BISG estimates and incomplete self-reported data yielded substantially different disparities in almost half of the measures, with BISG-based Black-White disparities generally larger than self-reported race and ethnicity data.CONCLUSIONS:
BISG imputation methods are feasible for Medicaid claims data and reduced missingness to <1%. Disparities may be larger than what is estimated using self-reported data with high rates of missingness.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Etnicidade
/
Medicaid
Tipo de estudo:
Prognostic_studies
Limite:
Humans
País/Região como assunto:
America do norte
Idioma:
En
Revista:
Health Serv Res
Ano de publicação:
2022
Tipo de documento:
Article
País de afiliação:
Estados Unidos