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Improving Hospital Reporting of Patient Race and Ethnicity--Approaches to Data Auditing.

  • Author(s): Zingmond, David S
  • Parikh, Punam
  • Louie, Rachel
  • Lichtensztajn, Daphne Y
  • Ponce, Ninez
  • Hasnain-Wynia, Romana
  • Gomez, Scarlett Lin
  • et al.

Published Web Location

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4545337/
No data is associated with this publication.
Abstract

Objective

To investigate new metrics to improve the reporting of patient race and ethnicity (R/E) by hospitals.

Data sources

California Patient Discharge Database (PDD) and birth registry, 2008-2009, Healthcare and Cost Utilization Project's State Inpatient Database, 2008-2011, cancer registry 2000-2008, and 2010 US Census Summary File 2.

Study design

We examined agreement between hospital reported R/E versus self-report among mothers delivering babies and a cancer cohort in California. Metrics were created to measure root mean squared differences (RMSD) by hospital between reported R/E distribution and R/E estimates using R/E distribution within each patient's zip code of residence. RMSD comparisons were made to corresponding "gold standard" facility-level measures within the maternal cohort for California and six comparison states.

Data collection

Maternal birth hospitalization (linked to the state birth registry) and cancer cohort records linked to preceding and subsequent hospitalizations. Hospital discharges were linked to the corresponding Census zip code tabulation area using patient zip code.

Principal findings

Overall agreement between the PDD and the gold standard for the maternal cohort was 86 percent for the combined R/E measure and 71 percent for race alone. The RMSD measure is modestly correlated with the summary level gold standard measure for R/E (r = 0.44). The RMSD metric revealed general improvement in data agreement and completeness across states. "Other" and "unknown" categories were inconsistently applied within inpatient databases.

Conclusions

Comparison between reported R/E and R/E estimates using zip code level data may be a reasonable first approach to evaluate and track hospital R/E reporting. Further work should focus on using more granular geocoded data for estimates and tracking data to improve hospital collection of R/E data.

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