Using the Bayesian Improved Surname Geocoding Method (BISG) to Create a Working Classification of Race and Ethnicity in a Diverse Managed Care Population: A Validation Study
Using the Bayesian Improved Surname Geocoding Method (BISG) to Create a Working Classification of Race and Ethnicity in a Diverse Managed Care Population: A Validation Study
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DOI：
10.1111/1475-6773.12089
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发表时间：
2014-02-01
影响因子：
3.4
通讯作者：
Omer, Saad B.
中科院分区：
文献类型：
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作者：
Adjaye-Gbewonyo, Dzifa;Bednarczyk, Robert A.;Omer, Saad B.
目的验证基于贝叶斯改进姓氏地理编码方法（BISG）的种族/民族分类，并评估性别和年龄的有效性差异。数据来源/研究设置Kaiser Permanente格鲁吉亚（一家综合管理式医疗机构）成员的二级数据，截至2010年。研究设计对于191，494名自我报告种族/民族的成员，属于从BISG算法预测的六个种族/民族类别中的每一个的概率被用于在大于0.50的概率的截止值范围内将个体分配到种族/民族类别。计算总体以及性别和年龄分层的敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）。产生受试者工作特征（ROC）曲线，并用于确定最佳cutoffs的种族/ethnicationassignment.Principal FindingsThe总体cutoffs分配，优化的灵敏度和特异性范围从0.50至0.57的四个主要种族/民族类别（白色，黑色，亚洲/太平洋岛民，西班牙裔）。相应的敏感性、特异性、PPV和NPV分别为64.4%至81.4%、80.8%至99.7%、75.0%至91.6%和79.4%至98.0%。分配的准确性是更好的男性和个人的65岁或以上。ConclusionsBISG可能是有用的分类种族/民族的健康计划成员时，需要的医疗保健研究。
ObjectiveTo validate classification of race/ethnicity based on the Bayesian Improved Surname Geocoding method (BISG) and assess variations in validity by gender and age.Data Sources/Study SettingSecondary data on members of Kaiser Permanente Georgia, an integrated managed care organization, through 2010.Study DesignFor 191,494 members with self‐reported race/ethnicity, probabilities for belonging to each of six race/ethnicity categories predicted from the BISG algorithm were used to assign individuals to a race/ethnicity category over a range of cutoffs greater than a probability of 0.50. Overall as well as gender‐ and age‐stratified sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Receiver operating characteristic (ROC) curves were generated and used to identify optimal cutoffs for race/ethnicity assignment.Principal FindingsThe overall cutoffs for assignment that optimized sensitivity and specificity ranged from 0.50 to 0.57 for the four main racial/ethnic categories (White, Black, Asian/Pacific Islander, Hispanic). Corresponding sensitivity, specificity, PPV, and NPV ranged from 64.4 to 81.4 percent, 80.8 to 99.7 percent, 75.0 to 91.6 percent, and 79.4 to 98.0 percent, respectively. Accuracy of assignment was better among males and individuals of 65 years or older.ConclusionsBISG may be useful for classifying race/ethnicity of health plan members when needed for health care studies.