Gomez Scarlett L, Glaser Sally L
Northern California Cancer Center, 2201 Walnut Avenue, Suite 300, Fremont, CA 94538, USA.
Cancer Causes Control. 2006 Aug;17(6):771-81. doi: 10.1007/s10552-006-0013-y.
Cancer registry data on race/ethnicity are vital for understanding cancer patterns in population subgroups, as they inform public health policies for allocating resources and form the bases of etiologic hypotheses. However, accuracy of cancer registry data on race/ethnicity has not been systematically evaluated. By comparing race/ethnicity in the Greater Bay Area Cancer Registry to self-reported race/ethnicity for patients from 14 racial/ethnic groups, we determined the accuracy of this variable and the patient and hospital characteristics associated with disagreement. The extent of misclassification (measured by sensitivity and predictive value positive (PV+)) varied across racial/ethnic groups (total n=11,676). Sensitivities and PV+'s were high (exceeding 90%) for non-Hispanic Whites and Blacks, moderate for Hispanics and some Asian subgroups (70-90%), and very low for American Indians (<20%). Overall, registry and interview race/ethnicity disagreed for 11% of the sample. In a multivariate model, disagreement was associated with non-White race/ethnicity, younger age, being married, being foreign-born but preferring to speak English, and diagnosis in a large hospital. Improving data quality for race/ethnicity will be most effectively attempted at the reporting source. We advocate a concerted effort to systematize collection of these patient data across all facilities, which may be more feasible given electronic medical admissions forms.
种族/族裔的癌症登记数据对于了解人群亚组中的癌症模式至关重要，因为它们为分配资源的公共卫生政策提供信息，并构成病因假设的基础。然而，种族/族裔的癌症登记数据的准确性尚未得到系统评估。通过将大湾区癌症登记处的种族/族裔与14个种族/族裔群体患者自我报告的种族/族裔进行比较，我们确定了该变量的准确性以及与不一致相关的患者和医院特征。错误分类的程度（以敏感性和阳性预测值（PV+）衡量）因种族/族裔群体而异（总数n = 11,676）。非西班牙裔白人和黑人的敏感性和PV+较高（超过90%），西班牙裔和一些亚洲亚组中等（70 - 90%），美洲印第安人则非常低（<20%）。总体而言，登记处和访谈中的种族/族裔在11%的样本中存在不一致。在多变量模型中，不一致与非白人种族/族裔、年轻、已婚、外国出生但更喜欢说英语以及在大型医院诊断有关。提高种族/族裔数据质量最有效的方法是在报告源进行尝试。我们主张齐心协力在所有医疗机构中系统地收集这些患者数据，如果有电子医疗入院表格，这可能更可行。
