Assessing Disparities Through Missing Race and Ethnicity Data: Results from a Juvenile Arthritis Registry

Abstract

Ensuring high quality race and ethnicity data within the electronic health record (EHR) and across linked systems, such as patient registries, is necessary to achieve a goal of inclusion of racial and ethnic minorities in scientific research and detect disparities associated with race and ethnicity. The project goal was to improve race and ethnicity data completion within the Pediatric Rheumatology Care Outcomes Improvement Network (PR-COIN) and assess impact of improved data completion on conclusions drawn from the registry. The project consisted of 5 parts: (1) Identifying baseline missing race and ethnicity data, (2) REDCap survey of current collection and entry, (3) Data completion through audit and feedback cycles, (4) Assessment of impact on outcome measures, and (5) Participant interviews and thematic analysis. REDCap survey (Supplementary Materials A) and participant interviews (Supplementary Materials B) are available in the supplementary materials. Across 6 participating centers, 29% of patients were missing race and 31% were missing ethnicity, with most patients missing both. Rates of missingness varied by data entry method (electronic vs manual). Recovered data had a higher percentage of patients with Other race or Hispanic/Latino ethnicity compared to patients with non-missing race and ethnicity at baseline. Black patients had a significantly higher odds ratio of having a clinical juvenile arthritis disease activity score (cJADAS10) of ≥5 at first follow up compared to White patients. There was no significant change in odds of cJADAS10 ≥5 for race and ethnicity after data completion. Patients missing race and ethnicity were more likely to be missing cJADAS values which may affect the ability to detect changes in odds of cJADAS ≥5 after completion. About 1/3 of patients in a pediatric rheumatology registry were missing race and ethnicity data. After three audit and feedback cycles, centers decreased missing data by 94%, primarily via data recovery from the EHR. In this sample, completion of missing data did not change the findings related to differential outcomes by race. Recovered data was not uniformly distributed compared to those with non-missing race and ethnicity at baseline, suggesting that differences in outcomes after completing race and ethnicity data may be seen with larger sample sizes.