Evaluation of Three Algorithms to Identify Incident Breast Cancer in Medicare Claims Data

Researchers studying the quality of cancer care in the United States have noted disparities by geography, race/ethnicity, and socioeconomic status. Prior studies to examine these differences have relied on large secondary databases and chart abstraction (Wennberg et al. 1987; Nattinger and Goodwin 1994; Harlan et al. 1995; Ayanian and Guadagnoli 1996; Michalski and Nattinger 1997; Earle et al. 2002; Smedley, Stith, and Nelson 2003; Gilligan 2005; Neuss et al. 2005). The disadvantages of these data sources are that chart abstraction is costly and time-consuming, and large administrative databases often have limited generalizability, making it expensive or difficult to analyze national patterns of care. The National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) population-based cancer registry is considered the reference standard for cancer case ascertainment in the United States, and collects limited treatment information. Linking these data with Medicare claims data further restricts the available study subjects to those ages 65 and older (Potosky et al. 1993). Researchers attempting to obtain data on a broader age and geographic range of subjects are often limited to data sets covering a single state (Ayanian et al. 1993; McClish et al. 1997; Hodgson et al. 2003; McClish, Penberthy, and Pugh 2003; McClish and Penberthy 2004; Penberthy et al. 2005), smaller, localized populations (Elston et al. 2005), or areas covered by passive surveillance systems, which may have lower rates of case ascertainment or incomplete data (Brewster et al. 1997; Yoo et al. 2002; Greenberg et al. 2003; Wang et al. 2005). Owing to these limitations, several researchers (Warren et al. 1999; Freeman et al. 2000; Nattinger et al. 2004; Ramsey et al. 2004) have developed algorithms to identify cancer cases using Medicare claims data to determine if broad cancer incidence and patterns-of-care studies can be performed solely with administrative claims data sources. Reliable methods to identify incident breast cancer using administrative data would permit the study of patterns and quality of care without the time and cost requirements of chart abstraction or linkage of claims and cancer registry data sources, and allow researchers to study populations not covered by existing surveillance systems. Health insurance claims data are available across the United States wherever health insurance is used. Thus, an effective algorithm would allow study of patterns and costs of care in larger and more diverse insured populations, including subjects under age 65, members of health maintenance organizations (HMOs), and those in previously understudied racial/ethnic populations or regions. For this study, we use the linked SEER-Medicare data to evaluate three published algorithms designed to identify incident breast cancer cases using inpatient, outpatient, and physician insurance claims data. We assess algorithm validity based on more recent claims data by population subgroup (i.e., by age, race, stage, and region). We implement these algorithms and compare them based on standard diagnostic characteristics, including Receiver-Operating Characteristic (ROC) curve analysis, sensitivity, and specificity.