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Comparative Study
. 2016 May;23(3):627-34.
doi: 10.1093/jamia/ocv156. Epub 2015 Dec 11.

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Simon J Craddock Lee  1 , James E Grobe  2 , Jasmin A Tiro  3
Affiliations
Comparative Study

Assessing race and ethnicity data quality across cancer registries and EMRs in two hospitals

Simon J Craddock Lee et al. J Am Med Inform Assoc. 2016 May.

Abstract

Background: Measurement of patient race/ethnicity in electronic health records is mandated and important for tracking health disparities.

Objective: Characterize the quality of race/ethnicity data collection efforts.

Methods: For all cancer patients diagnosed (2007-2010) at two hospitals, we extracted demographic data from five sources: 1) a university hospital cancer registry, 2) a university electronic medical record (EMR), 3) a community hospital cancer registry, 4) a community EMR, and 5) a joint clinical research registry. The patients whose data we examined (N = 17 834) contributed 41 025 entries (range: 2-5 per patient across sources), and the source comparisons generated 1-10 unique pairs per patient. We used generalized estimating equations, chi-squares tests, and kappas estimates to assess data availability and agreement.

Results: Compared to sex and insurance status, race/ethnicity information was significantly less likely to be available (χ(2 )> 8043, P < .001), with variation across sources (χ(2 )> 10 589, P < .001). The university EMR had a high prevalence of "Unknown" values. Aggregate kappa estimates across the sources was 0.45 (95% confidence interval, 0.45-0.45; N = 31 276 unique pairs), but improved in sensitivity analyses that excluded the university EMR source (κ = 0.89). Race/ethnicity data were in complete agreement for only 6988 patients (39.2%). Pairs with a "Black" data value in one of the sources had the highest agreement (95.3%), whereas pairs with an "Other" value exhibited the lowest agreement across sources (11.1%).

Discussion: Our findings suggest that high-quality race/ethnicity data are attainable. Many of the "errors" in race/ethnicity data are caused by missing or "Unknown" data values.

Conclusions: To facilitate transparent reporting of healthcare delivery outcomes by race/ethnicity, healthcare systems need to monitor and enforce race/ethnicity data collection standards.

Keywords: cancer registry; data quality; electronic medical record; race and ethnicity.

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Figures

Figure 1:
Figure 1:
Proportion of patients with availability of race/ethnicity, sex, and health insurance variables across the five data sources. Panel 1: Race/Ethnicity. Panel 2: Sex. Panel 3: Health Insurance. Each vertical bar represents point estimates for a specific source, with 95% confidence intervals denoted by the error bars. Source 5 did not record insurance information. *** Denotes that data availability for a given source is significantly different from the other sources (P < .001).
Figure 2:
Figure 2:
Disagreement analyses stratified by race-ethnicity category, excluding pairs containing an “Unknown” value. 95% CI values denote error bars.
See this image and copyright information in PMC

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