Breast cancer

The quality of cancer care in the United States is known to be variable, and factors determining quality of cancer care have been insufficiently studied (Hewitt and Simone 1999). The development of methods for using existing databases to study the quality of cancer care would be a major advance (Hewitt and Simone 2000). Methods to permit the use of Medicare administrative databases to study cancer quality of care would be particularly helpful because about 60 percent of persons diagnosed with cancer are aged 65 and older (Hewitt and Simone 2000), and the Medicare claims data represent a nearly population-based source of data.

With respect to breast cancer specifically, several challenges have been identified in the use of Medicare claims in studying the care provided. The use of inpatient Medicare claims to identify incident breast cancer cases offers excellent specificity but poor sensitivity because 30-40 percent of initial breast cancer operations are done on an outpatient basis (Warren et al. 1999; Warren et al. 1996). Inpatient records are also more likely to identify patients undergoing mastectomy for initial therapy than those undergoing breast-conserving surgery (Warren et al. 1996; Cooper et al. 2000). Compared to inpatient data alone, the use of combined inpatient, outpatient, and physician claims increases sensitivity to 80-90 percent (Freeman et al. 2000; Cooper et al. 1999), but decreases specificity (Warren et al. 1999; Freeman et al. 2000). Because only a small percentage of the female Medicare population develops breast cancer in a given year, even small decreases in specificity lead to large decreases in the positive predictive value (PPV) (Freeman et al. 2000).

Our major goal in the development of this algorithm was to identify a cohort of incident breast cancer patients, whose surgical, medical, and follow-up care could be studied over time. Inherent in this goal was a requirement for a high positive predictive value (PPV), ensuring that a high percentage of the cohort was made up of true breast cancer patients. The requirement for a high PPV was considered more important than the algorithm's sensitivity, particularly for the small percentage (6-7 percent) of women not undergoing initial surgical therapy. However, we also considered important the consistency of the algorithm's sensitivity across subgroups defined by geographic location, age, and type of initial surgery undergone (breast-conserving surgery [BCS] or mastectomy.)

The prior work of the other investigators cited had adequately demonstrated that a relatively simple algorithm (generally consisting of the identification of a claim with a coincident breast cancer diagnosis and operative procedure) would not permit us to achieve our goal. Our strategy was to use an interaction of clinical rationale and statistical analysis in developing the four-step algorithm presented herein.

METHODS

Sources of Data

The key data source for this study was the linked SEER-Medicare database (SEER-Medicare Linked Database 2003). This database links information from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) tumor registries and the Centers for Medicare and Medicaid Services (CMS) Medicare claims data. The population-based SEER registries cumulatively represent about 14 percent of the U.S. population, and include information on incident cancer patients, such as demographics, month and year of diagnosis, extent of disease, and initial treatment undergone. The Medicare files required for this study include the Medicare Provider Analysis and Review (MEDPAR) file, which contains inpatient hospital claims; Outpatient file, which contains claims from institutional outpatient providers including hospital ambulatory surgery centers; the Carrier Claims (previously known as Part B Physician/Supplier File), which contains inpatient and outpatient claims from noninstitutional providers such as physicians, as well as stand-alone ambulatory surgical centers; and the Denominator file, which contains beneficiary demographic information and Medicare entitlement and enrollment information. About 94 percent of the SEER registry patients aged 65 and older were successfully linked with their Medicare claims (Potosky et al. 1993). An additional data source was a 5 percent random sample of Medicare beneficiaries residing in the SEER geographic areas, including an indicator for whether the individual linked to the SEER database. When SEER subjects are removed from this sample, it represents nearly a population-based random sample of cancer-free control subjects residing in SEER areas. This study was approved by the Medical College of Wisconsin Human Subjects Research Review Committee.

Training and Validation Datasets

Training Set: Incident Breast Cancer Cases. A cohort of women aged 65 or older at the time of diagnosis of breast cancer in 1995 (according to SEER) was developed. Cases were excluded if the diagnosis was made only at autopsy or by death certificate. Subjects were required to meet the following criteria for the period from January 1995 to March 1996: eligibility for Medicare Parts A and B, not in a Medicare HMO, and to be alive. Eligibility through the first quarter of 1996 was required to capture Medicare treatment information for patients who were diagnosed near the end of 1995, but treated early in 1996. These criteria resulted in a cohort of 7,700 women, whose 1995 Medicare claims comprised the training set for incident breast cancer cases.

Training Set: Cancer-Free Subjects. From the 5 percent random sample of Medicare beneficiaries who resided in SEER areas but who did not link to SEER registry for an incident cancer between 1973 and 1995, a cancer-free cohort was developed. These 71,752 women were required to meet the same eligibility criteria as the breast cancer cases with respect to Medicare eligibility and survival. The 1995 claims of these women comprised the "cancer-free" training set.

Training Set: Other Cancer Cases. Again using the 5 percent random sample and the same eligibility criteria, a cohort was constructed of 4,501 women who were diagnosed with a cancer other than breast cancer between 1973 and 1995. The 1995 claims of these women comprised the "other cancer" training set.

Prevalent Breast Cancer Cases. For the purposes of this article, we use the term prevalence to indicate cancer cases diagnosed prior to the index year and not including the incident cases diagnosed in the index year. From the SEER-Medicare linked data, a cohort was developed of 48,631 women who had breast cancer between 1973 and 1994, according to SEER, and who were alive and eligible for Medicare Parts A and B and not in an HMO during 1995. The 1995 claims for these women were not used to train the algorithm per se, but were used to assess the impact of prevalent cases on the algorithm's specificity.

Validation Sets. Using the same selection criteria as described above for the year 1995, four analogous sets of claims were constructed for calendar year 1994. When evaluating a predictive algorithm, it is important that the validation set be independent of the training set. We defined the training sets to be comprised of claims from 1995, while the validation sets were comprised of independent claims from 1994. We recognized the possibility that some of the individuals generating the claims for the 1995 training set might also have generated claims for the 1994 validation set, particularly among the cancer-free and other cancer groups. In assessing the frequency of such overlap, though, we determined that only 5.5 percent of the individuals whose claims were part of the algorithm's training for steps 2 or 3 (described below under "Algorithm Development") also contributed claims to the validation set at those steps.

Algorithm Development

The algorithm was developed using the 1995 training sets. In constructing the algorithm, consideration was given not only to the presence or absence of breast cancer diagnosis or procedure codes, but also to other related codes (such as historical codes and radiation codes) that might improve the prediction of a case. In addition, variables were evaluated indicating whether a code was in a primary or secondary position on a given claim, and how frequently the code occurred. The algorithm development effort involved an iteratively processed interaction between clinical insight and statistical analysis. The codes actually used in the algorithm are summarized in Table 1.

A four-part algorithm was developed (Figure 1). The input to the algorithm consists of Medicare claims of all women aged 65 and older who were alive and eligible for Medicare Part A and Part B in some index year, including claims for the following three months.