The HCUP SID Imputation Project: Improving Statistical Inferences for Health Disparities Research by Imputing Missing Race Data

Yan Ma, Wei Zhang, Stephen Lyman, Yihe Huang

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Objective: To identify the most appropriate imputation method for missing data in the HCUP State Inpatient Databases (SID) and assess the impact of different missing data methods on racial disparities research. Data Sources/Study Setting: HCUP SID. Study Design: A novel simulation study compared four imputation methods (random draw, hot deck, joint multiple imputation [MI], conditional MI) for missing values for multiple variables, including race, gender, admission source, median household income, and total charges. The simulation was built on real data from the SID to retain their hierarchical data structures and missing data patterns. Additional predictive information from the U.S. Census and American Hospital Association (AHA) database was incorporated into the imputation. Principal Findings: Conditional MI prediction was equivalent or superior to the best performing alternatives for all missing data structures and substantially outperformed each of the alternatives in various scenarios. Conclusions: Conditional MI substantially improved statistical inferences for racial health disparities research with the SID.

Original languageEnglish
Pages (from-to)1870-1889
Number of pages20
JournalHealth Services Research
Volume53
Issue number3
DOIs
Publication statusPublished - Jun 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Health Policy

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