Gold Heather T, Do Huong T
Department of Public Health, Weill Medical College of Cornell University, 411 E, 69th Street, New York, NY 10021, USA.
Health Serv Res. 2007 Oct;42(5):2056-69. doi: 10.1111/j.1475-6773.2007.00705.x.
To test the validity of three published algorithms designed to identify incident breast cancer cases using recent inpatient, outpatient, and physician insurance claims data.
The Surveillance, Epidemiology, and End Results (SEER) registry data linked with Medicare physician, hospital, and outpatient claims data for breast cancer cases diagnosed from 1995 to 1998 and a 5 percent control sample of Medicare beneficiaries in SEER areas.
We evaluate the sensitivity and specificity of three algorithms applied to new data compared with original reported results. Algorithms use health insurance diagnosis and procedure claims codes to classify breast cancer cases, with SEER as the reference standard. We compare algorithms by age, stage, race, and SEER region, and explore via logistic regression whether adding demographic variables improves algorithm performance.
The sensitivity of two of three algorithms is significantly lower when applied to newer data, compared with sensitivity calculated during algorithm development (59 and 77.4 percent versus 90 and 80.2 percent, p<.00001). Sensitivity decreases as age increases, and false negative rates are higher for cases with in situ, metastatic, and unknown stage disease compared with localized or regional breast cancer. Substantial variation also exists by SEER registry. There was potential for improvement in algorithm performance when adding age, region, and race to an indicator variable for whether the algorithm determined a subject to be a breast cancer case (p<.00001).
Differential sensitivity of the algorithms by SEER region and age likely reflects variation in practice patterns, because the algorithms rely on administrative procedure codes. Depending on the algorithm, 3-5 percent of subjects overall are misclassified in 1998. Misclassification disproportionately affects older women and those diagnosed with in situ, metastatic, or unknown-stage disease. Algorithms should be applied cautiously to insurance claims databases to assess health care utilization outside SEER-Medicare populations because of uneven misclassification of subgroups that may be understudied already.
使用近期住院、门诊及医生保险理赔数据，检验三种已发表的用于识别新发乳腺癌病例的算法的有效性。
监测、流行病学和最终结果（SEER）登记数据与1995年至1998年诊断为乳腺癌的病例的医疗保险医生、医院及门诊理赔数据以及SEER地区5%的医疗保险受益人的对照样本相链接。
与原始报告结果相比，我们评估应用于新数据的三种算法的敏感性和特异性。算法使用健康保险诊断和程序理赔代码对乳腺癌病例进行分类，以SEER作为参考标准。我们按年龄、分期、种族和SEER地区比较算法，并通过逻辑回归探索添加人口统计学变量是否能改善算法性能。
与算法开发期间计算的敏感性相比，三种算法中的两种应用于更新数据时敏感性显著降低（分别为59%和77.4%，而开发期间为90%和80.2%，p<0.00001）。敏感性随年龄增加而降低，与局限性或区域性乳腺癌相比，原位癌、转移性癌及分期不明的病例假阴性率更高。SEER登记处也存在显著差异。当在算法是否判定某个体为乳腺癌病例的指标变量中添加年龄、地区和种族时，算法性能有改善的潜力（p<0.00001）。
算法在SEER地区和年龄方面的敏感性差异可能反映了实践模式的差异，因为算法依赖于行政程序代码。根据算法，1998年总体上有3%至5%的个体被错误分类。错误分类对老年女性以及诊断为原位癌、转移性癌或分期不明疾病的女性影响尤为严重。由于可能已研究不足的亚组存在不均衡的错误分类情况，在将算法应用于保险理赔数据库以评估SEER - 医疗保险人群以外的医疗保健利用情况时应谨慎。
