Implications of missingness in self-reported data for estimating racial and ethnic disparities in Medicaid quality measures

Kimberly Yee, Megan Hoopes, Sophia Giebultowicz, Marc N. Elliott, K. John McConnell

Research output: Contribution to journalArticlepeer-review

Abstract

Objective: To assess the feasibility and implications of imputing race and ethnicity for quality and utilization measurement in Medicaid. Data Sources and Study Setting: 2017 Oregon Medicaid claims from the Oregon Health Authority and electronic health records (EHR) from OCHIN, a clinical data research network, were used. Study Design: 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. Principal Findings: 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.

Original languageEnglish (US)
JournalHealth Services Research
DOIs
StateAccepted/In press - 2022

Keywords

  • Bayesian imputation
  • health care disparities
  • HEDIS
  • Medicaid
  • quality of health care
  • race factors

ASJC Scopus subject areas

  • Health Policy

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