We propose a 4-step algorithm for the use of Medicare claims data to identify women with surgically treated incident breast cancer. This algorithm has a sensitivity of about 80 percent overall, with a sensitivity of 82–87 percent for stages 1 and 2 disease. The algorithm has a specificity above 99.9 percent, and a positive predictive value of 89 percent, using a SEER gold standard. The PPV is greater than 93 percent based on the SEER plus High Likelihood gold standard.
The algorithm development process described herein illustrates several major issues with respect to the use of Medicare claims to identify breast cancer cases. One is the relationship of specificity to positive predictive value. Because only a minority of women, even in the Medicare age group, develop breast cancer in a given year, an exceedingly high specificity (>99.9 percent) is necessary to have a positive predictive value of 90 percent. The dramatic decline in PPV that occurs with only small decreases in specificity can be seen by comparing the results of this algorithm with prior proposed algorithms (). Given that the procedures used to treat breast cancer may also be used to identify or treat benign breast disease, and given occasional inaccuracies in the use of a breast cancer diagnosis, it is challenging to reach the necessary level of specificity.
Comparison of Algorithms Using Medicare Claims to Identify Breast Cancer Subjects in Tumor Registries
A major goal of this algorithm was to maintain a high specificity while including cases treated in the ambulatory surgical setting. This algorithm achieves a PPV similar to that reported by Warren and colleagues (Warren et al. 1999
) for inpatient claims, while providing improved sensitivity (). Although the sensitivity is not as high as that reported by Freeman and colleagues (Freeman et al. 2000
), the PPV is much higher.
Another major issue with this and prior algorithms is the presence of prevalent cases. Because women with breast cancer often live for many years, the number of prevalent cases in a dataset greatly exceeds the number of incident cases. Women with prevalent disease undergo at times the same breast procedures as women with initial disease to diagnose or rule out recurrent or new breast disease, and also may carry diagnostic codes of primary breast cancer for years after initial disease. Since local disease recurrences occur most frequently within the first few years after diagnosis, our approach was to assume that algorithm-identified cases with a history of breast cancer within the prior three years had recurrent disease. This led to a decrease in sensitivity from about 85 percent to 80 percent, but maintained the high specificity of the algorithm.
In attempting to maximize the PPV of the algorithm, we accepted a moderate sensitivity of about 80 percent. Therefore, this algorithm may have limited utility for determining breast cancer incidence. The key uses for this algorithm are likely to be for aspects of care not well captured by SEER or other state tumor registries. The study of survivor care, for example, studies of mammography (Schapira, McAuliffe, and Nattinger 2000
) or other health care utilization and physician care (Nattinger et al. 2002
) among survivors, is well suited to claims analysis. Patterns of care studies with respect to geographic variation and rural areas not well represented by SEER appear feasible given the consistency of the algorithm in different geographic areas. Studies might examine pre-morbid care for older breast cancer patients, such as use of mammography or other preventive care interventions. Although some of the studies mentioned could be performed using the limited number of available linked tumor registry–Medicare databases, the need for greater geographic representation or larger sample sizes might favor the use of Medicare-derived samples. Given that almost half of all breast cancer cases occur in women aged 65 and older, the algorithm could be applied to 100 percent state Medicare databases for identifying providers with possible quality problems, such as low levels of medical oncology consultation, poor follow-up care, and poor preventive care practices. An algorithm that is less than perfect may still provide a valid assessment of patterns of care (Kahn et al. 1996
A limitation we encountered is the fact that about 5 percent of women identified by SEER as having an incident breast cancer, and who linked to the Medicare claims data, did not even pass the screening step. Based on it appears that some of these women do not undergo initial surgical therapy. Perhaps some women undergo surgery but are covered by employer-based insurance, which pays for their care in preference to Medicare. In any event, this problem does cause a limitation on the sensitivity that can be achieved by the algorithm, even if steps 2, 3, and 4 could be further optimized. Another limitation is that women who underwent radiotherapy are somewhat overrepresented compared to those who did not, limiting the ability to use this algorithm to study patterns of care for radiotherapy.
We are not able to state which of the two “gold standards” represents a more accurate definition of an incident breast cancer case. Although the SEER tumor registry program has excellent case ascertainment, all registry programs likely miss occasional cancer cases. In the case of this study, an incident cancer patient could also have been classified as a cancer-free control subject due to failure to link with the Medicare beneficiary files, or due to moving into a SEER area shortly after disease diagnosis. For these reasons, we developed and presented the “SEER plus High Likelihood” gold standard, which followed a decision rule created initially by manual inspection of the claims histories for certain control subjects who seemed to have a high likelihood of having breast cancer. We were convinced that these subjects likely had incident breast cancer by the lack of prior claims suggesting prevalent disease, and by the multiple claims during the training year that consistently suggested an operation for breast cancer (surgical claims, pathology claims, anesthesiology claims, etc.). Since we did not have access to patient identifiers or charts, we could not confirm that these patients had breast cancer. However, Warren and colleagues (1999)
have previously demonstrated that some cases identified by their algorithm using Medicare claims actually had breast cancer but failed to link to SEER when the linkage was conducted. In addition, the number of high-likelihood cases identified by our algorithm within the 5 percent control sample is very close to the number that one would expect given a 94 percent linkage rate between SEER and Medicare. For example, in 1994, a 6 percent failure to link would translate into 456 unlinked breast cancer cases. We would expect 5 percent of these (23 cases) to be found in the 5 percent control sample. We would further expect the high-likelihood definition to identify 75 percent (17 cases). In fact, the high-likelihood definition did identify 19 cases in the 5 percent control cohort that year (), very close to the expected number.
As has been shown in a number of other disease areas, Medicare claims data offer unique advantages for cancer quality of care and health services research (Hewitt and Simone 1999
; McNeil 2001
). These data are essentially population-based, and minimize selection bias with respect to geographic region, urban versus rural location, and socioeconomic status. Each of these factors is an important predictor of cancer treatment, a fact that limits analyses of databases from more restricted populations (Nattinger et al. 1992
; Guadagnoli et al. 1998
; Gilligan et al. 2002
). The possibility of using Medicare data more widely to assess patterns of cancer practice and related outcomes offers a potential that is worthy of further exploration.