JMIR Publications

ABSTRACT

Background:

The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity is collected and structured by healthcare organizations.

Objective:

To evaluate and describe variations in how healthcare systems collect and report information about the race and ethnicity of their patients, and to assess how well these data are integrated when aggregated into a large clinical database.

Methods:

At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 healthcare institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Enclave by analyzing its conformance to healthcare standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by healthcare institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The non-conforming data was also cross-tabulated by three factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the non-conforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused”; “Multiple Race” was grouped with “Two or more races” and “Multiracial”, etc.

Results:

“No matching category” was the second largest harmonized racial group in the N3C. 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were over-represented in the non-conforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African-American and Hispanic/Latino patients were over-represented in this category.

Conclusions:

Differences in how race and ethnicity data is conceptualized and encoded by healthcare institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data has been transformed can help users make accurate analyses and inferences, and eventually better guide clinical care and public policy.