Evaluating Race and Ethnicity Reported in Hospital Discharge Data and Its Impact on the Assessment of Health Disparities

Med Care. 2020 Mar;58(3):280-284. doi: 10.1097/MLR.0000000000001259.

Abstract

Background: Improving the collection and quality of race and ethnicity reported in hospital data is a key step in identifying disparities in health service utilization and outcomes and opportunities for quality improvement.

Objective: The objective of this study was to assess the quality of race/ethnicity reported in hospital discharge data and examine the impact on the identification of disparities in select health outcomes in New York City.

Research design: Using the birth certificate as a gold standard, we examined the quality of hospital discharge race/ethnicity and estimated the impact of misclassification on racial/ethnic disparities in severe maternal morbidity and preventable hospitalizations.

Subjects: Delivery hospitalizations from the New York State hospital discharge data (Statewide Planning and Research Cooperative System) linked with 2015 New York City birth certificates.

Measures: Sensitivity and positive predictive value (PPV).

Results: The non-Hispanic white and black race had relatively high sensitivity and PPV. Hispanic ethnicity and Asian race had moderate sensitivity and high PPV, but were often misclassified as "Other." As a result, health disparities may be underestimated for those of Hispanic ethnicity and Asian race, particularly for indicators that use population denominators drawn from another source.

Conclusions: The quality of hospital discharge data varies by race/ethnicity and may underestimate disparities in some groups. Future research should validate findings with other data sources, identify driving factors, and evaluate progress over time.

MeSH terms

  • Adult
  • Birth Certificates
  • Ethnicity / statistics & numerical data*
  • Female
  • Health Status Disparities*
  • Humans
  • Male
  • New York City
  • Patient Discharge / statistics & numerical data*
  • Racial Groups / statistics & numerical data*