Secondary Cancer Rates Following Breast Cancer Diagnosis

Transcription

1 Copyright E 2007 Journal of Insurance Medicine J Insur Med 2007;39: ORIGINAL RESEARCH Secondary Cancer Rates Following Breast Cancer Diagnosis David Wesley, MD Life table analysis and other mortality methods apply well to end points other than mortality. This paper demonstrates the application of mortality experience methodology to the recurrence of breast cancer and/or new primaries in women previously diagnosed with primary breast cancer. Specific recurrence rates are broken out by duration as well as attributes available at the time of primary breast cancer diagnosis: stage, histology, age band, and year of diagnosis. Use of attained age is demonstrated to control for the effect of aging over long durations. Breast cancer recurrence is shown to drop to a relatively low rate compared with the rate of new primary occurrence. Address: Transamerica Reinsurance, 401 North Tryon Street, Suite 700, Charlotte, NC 28202; ph: ; fax: ; david.wesley. md@transamerica.com. Correspondent: David Wesley, MD, Vice President, Medical Research & Development. Key words: Breast cancer, SEER, cancer recurrence, mortality methodology. Received: January 10, 2007 Accepted: March 13, 2007 INTRODUCTION Breast cancer is a common cancer in women worldwide. In the United States, it is estimated that 1 in 8 women will develop breast cancer during their lifetime. Breast cancer is the leading cause of death in American women aged 40 to 55, and it was only recently surpassed by lung cancer as the leading cause of cancer deaths at all ages. See Richie and Swanson for an excellent literature review. 1 Breast cancer is also known for relapsing disease; local, regional, and/or distant relapse of the index tumor is common, as is the recurrence of a second breast primary. Women with breast cancer histories are also at risk of developing primary cancers originating from other organs. Breast cancer treatment is expensive, and the medical cost of recurrent disease is often greater than that of the original treatment. The Surveillance Epidemiology and End- Results (SEER) is a program managed by the National Cancer Institute (NCI). SEER data has been collected since 1973 from 7 founding cancer registries in SF-Oakland; Connecticut; Detroit; Hawaii; Iowa; New Mexico and Utah. Seattle was added in 1974 and Atlanta in More recent additions were in 1992 (Alaska, San Jose, Los Angeles and rural Georgia) and 2000 (greater California, Kentucky, Louisiana and New Jersey). The data includes de-identified but otherwise seriatim data with details about newly diagnosed cancer cases and follow-up infor- 98

2 WESLEY BREAST CANCER Table 1. Age Distribution of Observations ( ) Age Band Count Percent Cum % % 0.08% % 0.68% % 2.71% , % 7.30% , % 15.4% , % 26.2% , % 37.6% , % 49.2% , % 61.1% , % 73.1% , % 84.3% , % 93.8% , % 100% Total 357, % mation including date and cause of death. This data is available to qualified researchers in the form of re-coded public use data files. I was recently asked by my Japanese colleagues to provide summary data from the US Surveillance Epidemiology and End- Results (SEER) database that could be used to price a medical cover for the Japan market. The proposed product would cover women with a history of breast cancer, who have no evidence of disease (NED) at the time of underwriting. The coverage would pay for medical expenses incurred upon the development of any form of secondary cancer: recurrence (second breast primary) or new cancer. For pricing, one needs to know the annual rate of failures (secondary cancers), not simply the proportion of patients that would eventually fail. My initial reaction was that it could not be done. There are no fields in the SEER tables that flag recurrences, new tumors, or even the achievement of NED status. Also, to my knowledge, no such study has been reported in the insurance medicine literature. Upon further review, it was determined that a satisfactory study could be done. Furthermore, it is possible to provide summary data to pricing colleagues in a format that does not compromise SEER rules for public-use data. The summary data can be viewed in a variety of ways using pivot-table or crosstabulation technology. This paper highlights both the methodology and a few of the initial insights that it has provided. METHODS NCI provides a statistical software package named SEER-Stat for analyzing SEER data. SEER-Stat has grown from an in-house NCI tool with a quirky user interface to an easy-to-use program with a sophisticated interface. One of the SEER-Stat features that is attractive to medical directors is the ability to perform life table analysis that includes comparative mortality based on appropriate population mortality tables. The vast majority of analyses performed by medical directors are based on this tool. Unfortunately, SEER-Stat does not allow for the study of recurrence rates, so a professional statistics software package, Stata 9, was needed to restructure the data in the SEER public-use ASCII files. 2,3 Evaluation of the 86 fields provided in the public-use data allowed selection of observations with the following characteristics: N Sex 5 female N Age at diagnosis $20,,85 N Initial breast cancer diagnosis is first primary, both within and outside of SEER database N Initial diagnosis made in 1979 or later N Initial tumor shows invasive behavior N No concomitant tumor of any other organ system N Time to failure greater than 0 months N Did not die of breast cancer within first year of diagnosis Patient ID numbers are frequently duplicated between the 17 different SEER registries, but never within the same site. Thus, a combination of site ID and patient ID provides a unique identifier. Each cancer diagnosis for a given patient is assigned 99

3 Table 2. Surgical and Radiation Treatments ( ) Surgery Radiation Treatment None Ext Beam Other Rad Unknown Total Biopsy Lumpectomy 16,326 53, ,756 Modified Radical 105,986 13, ,972 Radical Other 25, ,517 Unknown 53,250 58, ,293 Total 207, ,846 14, ,091 a sequence number. Through analysis of these sequence numbers and other flags, one is able to identify secondary as well as primary cancer diagnoses. Time to failure is simply the difference between the time of diagnosis for the secondary cancer and that for the primary. Failure for this study was defined as a secondary cancer of any type: recurrent breast (second primary) or primary of any other tissue. In addition to failure, on-study exposure to risk ended for any of the following conditions: end of study (December 31, 2003), death (any cause), and lost to follow-up. Additionally, follow-up for this study was limited to the first 20 durations (yearly intervals after diagnosis). The finest unit of time available in the SEER ASCII files is the month; used for both time of diagnosis and end of study. Months of on-study exposure to risk were calculated for each observation, and the defined failures were flagged. The attributes of interest in this study (age band, year of diagnosis, histologic type, stage, surgery type, radiation therapy) were retained for each observation. Individual exposures were then sub-divided by duration (interval year after diagnosis) and calendar year of exposure. Exposures, and failures where applicable, were then summed over observations with the same attributes and a new, fully de-identified database was formed. These summary rec- Table 3. Histologic Type Distribution by Stage ( ) Histology SEER Stage A Localized Regional Distant Unknown Total Intraductal 156,397 88, ,761 Lobular 15, ,315 Mixed 13, ,062 Adenocarcinoma Mucinous Comedo Medullary Tubular Paget s Inflammatory Papillary Adenocystic NOS Total 216, ,985 15, ,

4 WESLEY BREAST CANCER Failure rates by duration (annual intervals after diagnosis). ords lend themselves to rapid pivot-table analysis. The stage attribute is based on historic SEER Stage A, as defined in the SEER User File Documentation. It is produced by collapsing all available extent of disease information into the following categories: N Localized An invasive neoplasm confined entirely to the organ of origin. N Regional A neoplasm that has extended beyond the limits of the organ of origin directly into surrounding organs or tissues; into regional lymph nodes by way of the lymphatic system; or by a combination of extension and regional lymph nodes. N Distant A neoplasm that has spread to parts of the body remote from the primary tumor either by direct extension or by discontinuous metastasis to distant organs, issues, or via the lymphatic system to distant lymph nodes. Histologic types are also defined in the SEER documentation and in ICD-O-2. Surgery and radio-therapy categories are defined in the SEER User File Documentation. Confidence intervals were calculated using the binomial distribution when the count of failures exceeded 100, and using the exact Poisson distribution otherwise. RESULTS There were 357,091 patients who qualified under the constraints detailed in Methods. Their average age was 59.9 years, and they were all females. For this group, there were 39,236 failures experienced during 2,417,694 person-years of exposure. Thus, the average follow-up was 6.8 years and the average failure rate was 16.2 per 1000 person-years. Tables 1-3 give further details about the study group. In Tables 4 9, all failure rates are in units of 1/1000 person-years. All confidence intervals are 95%. The trends in Table 4 can be best appreciated if displayed graphically. Figure 1 shows the occurrence rates for both Table 4. Failure Rates by (Annual Intervals after Diagnosis) All Failures Breast CA only Other Cancers rate 95% CI Failures rate 95% CI Failures rate 95% CI Failures , , ,

5 Table 5a. Failure rates by Stage and All Failure Types Stage Localized Regional Distant Unknown breast cancer and all other cancers during each of the first 20 durations after diagnosis with primary breast cancer. In Tables 5 9, exposure and failures are broken out by a variety of attributes of interest and the corresponding rates (per 1000 personyears) are calculated for each category. DISCUSSION The restructured database from which Tables 4 9 are derived contains 828,414 records many more than the number of women in the study. At the same time, each record is a summary of exposure contributions from many different women. This seeming paradox is due to the restructured data representing slices of exposure, which are further split according to the various attributes of interest. The data fields for each record are: failure type, year of diagnosis, age band, histology, stage, surgery, radiation, year of exposure, attained age at exposure, duration of exposure, person-years of exposure, and number of failures. Most of these records (795,479) had no failures; 28,280 had a single failure; 3519 had 2 failures; and so on down to 2 records with 10 failures. At their atomic level, these individual records are fairly meaningless. Significance comes from summation over a small number of higher-level attributes as shown in the results section. The tables shown are only a few of the possible ways to analyze this particular restructuring of the SEER data. Not only are there many other possible analyses of this database, other databases can be formed from the SEER data based on other attributes of breast cancer cases, as well as databases for other cancers. By choosing a different end-point, this methodology applies to mortality study as well. In fact, the methods are derived from those used by life insurers to report their Table 5b. Failure Rates by Stage and Breast Cancer Failures Stage Localized Regional Distant Unknown

6 WESLEY BREAST CANCER Table 5c. Failure Rates by Stage and Non-Breast Cancer Failures Stage Localized Regional Distant Unknown Table 6a. Failure Rates by Year of Diagnosis and All Failure Types Year of diagnosis mortality experience. What is truly unique to this study is the demonstration that secondary cancers can be identified within the SEER data and time to failure for this end-point can be studied in the same way as mortality. We have been taught that breast cancer has a long tail with significant excess mortality occurring as many as 15 years post diagnosis. Review of Table 4 shows that after the first year, breast cancer recurrence rates drop Table 6b. Failure Rates by Year of Diagnosis and Breast Cancer Recurrences Only Year of diagnosis

7 Table 7. Failure Rates by Histology and All Failure Types Histology Intraductal Lobular Mixed Adenocarcinoma Mucinous Comedo Medullary Tubular Paget s Inflammatory Adenocystic NOS

8 WESLEY BREAST CANCER Table 8. Failure Rates by Age at Diagnosis and All Failure Types Age

9 Table 9. Failure Rates by Attained Age at Time of Exposure and All Failure Types Attained Age to a stable level lower than that for developing new primaries in other organs. Further study of long-term breast cancer mortality is indicated. While the rate of all secondary cancers appears to increase with duration post diagnosis (Tables 4, 5b and 6b), there is something else occurring. These patients are growing older, and significantly so by durations 15 or 20. Note the second through fifth intervals in Table 8. It is clear that the rate of secondary cancers is greater at older ages. A way to counter this aging effect and isolate the durational significance is to use attained age as in Table 9. This table shows that after the first year, there is no significant change in secondary cancer rates over the following durations. Further study is under way that will apply these analytic methods to mortality in breast cancer, as well as relapsed disease and mortality in other cancers. REFERENCES 1. Richie RC, Swanson JO. Breast Cancer: A Review of the Literature. J Insur Med. 2003;35: StataCorp LP. College Station, TX, Available at: 3. Surveillance, Epidemiology, and End Results (SEER) Program. Public-Use Data ( ), National Cancer Institute, DCCPS, Surveillance Research Program, Cancer Statistics Branch. Available at: Released on: April