Evaluation of three algorithms to identify incident breast cancer in medicare claims data
Evaluation of three algorithms to identify incident breast cancer in medicare claims data
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DOI：
10.1111/j.1475-6773.2007.00705.x
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发表时间：
2007-10-01
影响因子：
3.4
通讯作者：
Do, Huong T.
中科院分区：
文献类型：
--
作者：
Gold, Heather T.;Do, Huong T.
客观的。测试三种已发布算法的有效性，这些算法旨在使用最近的住院患者、门诊患者和医生保险索赔数据来识别乳腺癌病例。数据。监测、流行病学和最终结果 (SEER) 登记数据与 1995 年至 1998 年诊断的乳腺癌病例的 Medicare 医生、医院和门诊索赔数据以及 SEER 地区 Medicare 受益人 5% 的对照样本相关联。研究设计。与原始报告的结果相比，我们评估了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索赔代码对乳腺癌病例进行分类，并以 SEER 作为参考标准。我们按年龄、阶段、种族和 SEER 区域比较算法，并通过逻辑回归探索添加人口统计变量是否可以提高算法性能。主要发现。与算法开发过程中计算的灵敏度相比，三种算法中的两种的灵敏度在应用于较新的数据时显着降低（分别为 59% 和 77.4% 与 90% 和 80.2%，p
Objective. To test the validity of three published algorithms designed to identify incident breast cancer cases using recent inpatient, outpatient, and physician insurance claims data.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.Study Design. 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.Principal Findings. 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