Differences in COVID-19 Hospitalizations by Self-Reported Race and Ethnicity in a Hospital in Honolulu, Hawaii

Brendan K. Seto, Laura Nishizaki, Gerard Akaka, Jo Ann Kimura,Todd B. Seto

PREVENTING CHRONIC DISEASE(2022)

引用 2|浏览10
暂无评分
摘要
Introduction The true extent of racial and ethnic disparities in COVID-19 hos-pitalizations may be hidden by misclassification of race and ethni-city. This study aimed to quantify this inaccuracy in a hospital's electronic medical record (EMR) against the gold standard of self -identification and then project data onto state-level COVID-19 hospitalizations by self-identified race and ethnicity.Methods To identify misclassification of race and ethnicity in the EMRs of a hospital in Honolulu, Hawaii, research and quality improvement staff members surveyed all available patients (N = 847) in 5 co-horts in 2007, 2008, 2010, 2013, and 2020 at randomly selected hospital and ambulatory units. The survey asked patients to self -identify up to 12 races and ethnicities. We compared these data with data from EMRs. We then estimated the number of COVID-19 hospitalizations by projecting racial misclassifications onto publicly available data. We determined significant differences via simulation-constructed medians and 95% CIs.Results EMR-based and self-identified race and ethnicity were the same in 86.5% of the sample. Native Hawaiians (79.2%) were signific-antly less likely than non-Native Hawaiians (89.4%) to be cor-rectly classified on initial analysis; this difference was driven by Native Hawaiians being more likely than non-Native Hawaiians to be multiracial (93.4% vs 30.3%). When restricted to multiracial patients only, we found no significant difference in accuracy (P = .32). The number of COVID-19-related hospitalizations was 8.7% higher among Native Hawaiians and 3.9% higher among Pacific Islanders when we projected self-identified race and ethnicity rather than using EMR data.Conclusion Using self-identified rather than hospital EMR data on race and ethnicity may uncover further disparities in COVID-19 hospitaliz-ations.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要