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利用医疗保险索赔数据区分筛查性和诊断性乳房 X 光检查。

Distinguishing screening from diagnostic mammograms using Medicare claims data.

机构信息

*Departments of Family and Community Medicine, Radiology, and the Center for Healthcare Research and Policy, University of California-Davis, Sacramento, CA †Group Health Research Institute, Seattle, WA ‡Departments of Radiology, Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, CA §Department of Biostatistics, University of Washington, Seattle, WA.

出版信息

Med Care. 2014 Jul;52(7):e44-51. doi: 10.1097/MLR.0b013e318269e0f5.

Abstract

BACKGROUND

Medicare claims data may be a fruitful data source for research or quality measurement in mammography. However, it is uncertain whether claims data can accurately distinguish screening from diagnostic mammograms, particularly when claims are not linked with cancer registry data.

OBJECTIVES

To validate claims-based algorithms that can identify screening mammograms with high positive predictive value (PPV) in claims data with and without cancer registry linkage.

RESEARCH DESIGN

Development of claims-derived algorithms using classification and regression tree analyses within a random half-sample of bilateral mammogram claims with validation in the remaining half-sample.

SUBJECTS

Female fee-for-service Medicare enrollees aged 66 years and older, who underwent bilateral mammography from 1999 to 2005 within Breast Cancer Surveillance Consortium (BCSC) registries in 4 states (CA, NC, NH, and VT), enabling linkage of claims and BCSC mammography data (N=383,730 mammograms obtained from 146,346 women).

MEASURES

Sensitivity, specificity, and PPV of algorithmic designation of a "screening" purpose of the mammogram using a BCSC-derived reference standard.

RESULTS

In claims data without cancer registry linkage, a 3-step claims-derived algorithm identified screening mammograms with 97.1% sensitivity, 69.4% specificity, and a PPV of 94.9%. In claims that are linked to cancer registry data, a similar 3-step algorithm had higher sensitivity (99.7%), similar specificity (62.7%), and higher PPV (97.4%).

CONCLUSIONS

Simple algorithms can identify Medicare claims for screening mammography with high predictive values in Medicare claims alone and in claims linked with cancer registry data.

摘要

背景

医疗保险索赔数据可能是乳腺摄影研究或质量测量的丰富数据源。然而,尚不确定索赔数据是否能准确区分筛查性和诊断性乳房 X 光检查,特别是在索赔数据未与癌症登记数据相关联的情况下。

目的

验证基于索赔的算法,这些算法可以在有和没有癌症登记链接的索赔数据中识别出具有高阳性预测值(PPV)的筛查性乳房 X 光检查。

研究设计

在双边乳房 X 光检查索赔的随机半样本中使用分类和回归树分析开发基于索赔的算法,并在剩余半样本中进行验证。

受试者

1999 年至 2005 年间在 4 个州(加利福尼亚州、北卡罗来纳州、新罕布什尔州和佛蒙特州)的乳腺癌监测联盟(BCSC)登记处接受过双侧乳房 X 光检查的年龄为 66 岁及以上的自费医疗保险参保者,这使索赔和 BCSC 乳房 X 光检查数据能够相关联(从 146346 名女性中获得了 383730 张乳房 X 光检查片)。

测量指标

使用 BCSC 衍生的参考标准,算法指定乳房 X 光检查“筛查”目的的敏感性、特异性和 PPV。

结果

在没有癌症登记链接的索赔数据中,一个 3 步的基于索赔的算法识别出了具有 97.1%敏感性、69.4%特异性和 94.9%PPV 的筛查性乳房 X 光检查。在与癌症登记数据相关联的索赔中,类似的 3 步算法具有更高的敏感性(99.7%)、相似的特异性(62.7%)和更高的 PPV(97.4%)。

结论

简单的算法可以在医疗保险索赔中单独识别出具有高预测值的医疗保险索赔筛查性乳房 X 光检查,并且在与癌症登记数据相关联的索赔中也可以识别。

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