Abstract：
The availability and accuracy of data on a patient\'s race/ethnicity varies across databases. Discrepancies in data quality can negatively impact attempts to study health disparities.
We conducted a systematic review to organize information on the accuracy of race/ethnicity data stratified by database type and by specific race/ethnicity categories.
The review included 43 studies. Disease registries showed consistently high levels of data completeness and accuracy. EHRs frequently showed incomplete and/or inaccurate data on the race/ethnicity of patients. Databases had high levels of accurate data for White and Black patients but relatively high levels of misclassification and incomplete data for Hispanic/Latinx patients. Asians, Pacific Islanders, and AI/ANs are the most misclassified. Systems-based interventions to increase self-reported data showed improvement in data quality.
Data on race/ethnicity that is collected with the purpose of research and quality improvement appears most reliable. Data accuracy can vary by race/ethnicity status and better collection standards are needed.
We conducted a systematic review to organize information on the accuracy of race/ethnicity data stratified by database type and by specific race/ethnicity categories.
The review included 43 studies. Disease registries showed consistently high levels of data completeness and accuracy. EHRs frequently showed incomplete and/or inaccurate data on the race/ethnicity of patients. Databases had high levels of accurate data for White and Black patients but relatively high levels of misclassification and incomplete data for Hispanic/Latinx patients. Asians, Pacific Islanders, and AI/ANs are the most misclassified. Systems-based interventions to increase self-reported data showed improvement in data quality.
Data on race/ethnicity that is collected with the purpose of research and quality improvement appears most reliable. Data accuracy can vary by race/ethnicity status and better collection standards are needed.
摘要：
背景：患者种族/民族数据的可用性和准确性因数据库而异。数据质量的差异可能会对研究健康差异的尝试产生负面影响。
方法：我们进行了系统评价，以组织有关按数据库类型和特定种族/种族类别分层的种族/种族数据准确性的信息。
结果：该综述包括43项研究。疾病登记处始终显示出高水平的数据完整性和准确性。EHR经常显示患者种族/民族的不完整和/或不准确数据。对于白人和黑人患者，数据库具有高水平的准确数据，但是对于西班牙裔/拉丁裔患者，错误分类和不完整的数据相对较高。亚洲人,太平洋岛民,和AI/AN是最错误的分类。增加自我报告数据的基于系统的干预措施显示数据质量有所改善。
结论：以研究和质量改进为目的收集的有关种族/民族的数据似乎最可靠。数据准确性可能因种族/族裔状况而异，因此需要更好的收集标准。
方法：我们进行了系统评价，以组织有关按数据库类型和特定种族/种族类别分层的种族/种族数据准确性的信息。
结果：该综述包括43项研究。疾病登记处始终显示出高水平的数据完整性和准确性。EHR经常显示患者种族/民族的不完整和/或不准确数据。对于白人和黑人患者，数据库具有高水平的准确数据，但是对于西班牙裔/拉丁裔患者，错误分类和不完整的数据相对较高。亚洲人,太平洋岛民,和AI/AN是最错误的分类。增加自我报告数据的基于系统的干预措施显示数据质量有所改善。
结论：以研究和质量改进为目的收集的有关种族/民族的数据似乎最可靠。数据准确性可能因种族/族裔状况而异，因此需要更好的收集标准。
