改善职业健康差异研究：测试一种在工作人群中估计种族和族裔的方法。
Improving Occupational Health Disparity Research: Testing a method to estimate race and ethnicity in a working population.
作者信息
Smith Caroline K, Bonauto David K
机构信息
Safety and Health Assessment and Research for Prevention (SHARP) Program, Washington State Department of Labor and Industries, Olympia, Washington.
出版信息
Am J Ind Med. 2018 Apr 2. doi: 10.1002/ajim.22850.
BACKGROUND
Race and ethnicity data are often absent from administrative and health insurance databases. Indirect estimation methods to assign probability scores for race and ethnicity to insurance records may help identify occupational health inequities.
METHODS
We compared race and ethnicity estimates from the Bayesian Improved Surname Geocoding (BISG) formula to self-reported race and ethnicity from 1132 workers.
RESULTS
The accuracy of the BISG using gender stratified regression models adjusted for worker age and industry were excellent for White and Latino males and Latino females, good for Black and Asian Pacific Islander males and White and Asian Pacific Islander females. American Indian/Alaskan Native and those who indicated they were "Other" or "More than one race" were poorly identified.
CONCLUSION
The BISG estimation method was accurate for White, Black, Latino, and Asian Pacific Islanders in a sample of workers. Using the BISG in administrative datasets will expand research into occupational health disparities.
背景
行政和医疗保险数据库中常常缺少种族和族裔数据。为保险记录分配种族和族裔概率分数的间接估计方法可能有助于识别职业健康不平等现象。
方法
我们将贝叶斯改进姓氏地理编码（BISG）公式得出的种族和族裔估计值与1132名工人自我报告的种族和族裔进行了比较。
结果
使用针对工人年龄和行业进行调整的性别分层回归模型，BISG对白种男性、拉丁裔男性和拉丁裔女性的准确率极高，对黑人和亚太岛民男性以及白人和亚太岛民女性的准确率良好。美洲印第安人/阿拉斯加原住民以及表示自己是“其他”或“不止一个种族”的人识别效果较差。
结论
在一个工人样本中，BISG估计方法对白种人、黑人、拉丁裔和亚太岛民的准确率较高。在行政数据集中使用BISG将扩大对职业健康差异的研究。
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