Affiliations of authors: A. Mariotto, E. J. Feuer, L. C. Harlan, L.-M. Wun (Division of Cancer Control and Population Sciences), K. A. Johnson (Division of Cancer Prevention), J. Abrams (Division of Cancer Treatment and Diagnosis), National Cancer Institute, National Institutes of Health, Bethesda, MD.
Correspondence to: Angela Mariotto, Ph.D., National Cancer Institute, 6116 Executive Blvd., Suite 504 MSC 8317, Bethesda, MD 208928317 (e-mail: mariotta{at}mail.nih.gov).
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ABSTRACT |
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INTRODUCTION |
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The benefits of adjuvant systemic therapy for early-stage breast cancer, in terms of the relative reduction of the annual odds of death, have proven to be as large as 27% and 47% for multi-agent chemotherapy and tamoxifen, respectively (6,7). An increase in the use of adjuvant systemic therapy in the community setting could be expected to translate into longer survival for early-stage breast cancer patients and a decrease in breast cancer mortality rates. Recent publications (8,9) have shown a decrease in breast cancer-related deaths in recent years in most western countries, suggesting that this decrease is chiefly due to changes in the way breast cancer is diagnosed and treated.
Delineating the time needed for the use of adjuvant systemic therapy to disseminate into general practice is essential for understanding the relationship between medical research findings and for translating these results into general practice. Appreciation of the pace at which research results are adopted by health practitioners may predict the speed with which future medical technologies will be accepted. Knowledge of dissemination trends can be used to estimate the impact on the general population of the survival benefits achieved in clinical trials. Finally, the ultimate motivation for this work is that dissemination patterns of adjuvant therapy, together with survival benefits measured in clinical trials, can be used as input for microsimulation models designed to quantify the contribution of adjuvant systemic therapy to the decline of breast cancer mortality after 1991 in the United States. One such effort is the Cancer Intervention Surveillance Modeling Network (CISNET) (http://srab.cancer.gov/cisnet.html), a cooperative agreement funded by the NCI to use modeling techniques to study the impact of interventions (i.e., primary prevention, screening, and treatment) on population-based cohorts of patients with breast, colorectal, prostate, or lung cancer. Thus, estimates of the dissemination of adjuvant systemic therapy use for breast cancer in the general population is an important input to model and quantify the effect of adjuvant therapy on the observed mortality trends.
Although there are studies that have reported levels of use of breast cancer adjuvant systemic therapy over short periods (1012), no data sources exist that completely describe the dissemination patterns over the last two decades. The objective of this article is to estimate the complete history of the dissemination of adjuvant systemic therapy for breast cancer from 1975 through 1999. The method for the analysis combines two data sources: reasonable assumptions about the dissemination process and information from the literature supplemented by discussions with experts in this subject matter about when the dissemination of adjuvant therapy first began.
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MATERIALS AND METHODS |
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Data sources used are the Surveillance, Epidemiology, and End Results (SEER)1 Program (13) and data from a series of special projects, Patterns of Care (POC) studies (12), which sampled case subjects from SEER registries from 1987 through 1991 and again in 1995. The SEER Program has routinely collected incidence data, as well as information about pathologic characteristics, therapy, and vital status from population-based registries in the United States. In this analysis we used data from eight registries: Atlanta, Connecticut, Detroit, Iowa, New Mexico, San Francisco/Oakland, Seattle/Puget Sound, and Utah from 1975 through 1999. SEER data are collected primarily from hospital medical records. Because treatment provided in outpatient settings is not always reported in the hospital record, SEER information on the use of adjuvant systemic therapy is incomplete and thus underreports use of adjuvant therapy. To correct this bias, information from the POC studies was used to supplement the SEER data.
The population-based POC studies were conducted from 1987 through 1991 and again in 1995 by sampling cases from the eight SEER registries listed above (10,12). Women with early-stage breast cancer were randomly sampled from the SEER breast cancer patients diagnosed from 1987 through 1991 and in 1995. Women younger than 51 years were oversampled in all years and, in 1995, African-Americans and Hispanics were also oversampled. The treating physicians were asked to verify whether chemotherapy and/or hormonal agents were administered. Although POC data are subject to more variability because of smaller sample sizes, the treatment verification provides more accurate estimates of the actual use of adjuvant systemic therapy than are available in the SEER data.
The case subjects from the POC studies were eliminated from the SEER data collected from 1975 through 1999 that were used in this analysis. Women with a previous diagnosis of cancer, other than non-melanoma skin cancer, were excluded from both the SEER and POC data, as were women diagnosed with breast cancer at autopsy or on the death certificate. Patients who did not undergo primary surgery (15 in POC and 15 497 in SEER) were excluded from the analysis, because they were not eligible for adjuvant systemic therapy. Patients with an unknown stage of cancer (495 in POC and 46 506 in SEER) were also excluded. The analysis included 7116 patients participating in the POC studies and 217 508 patients from the SEER database, not including the POC study population.
Stage
The American Joint Committee on Cancer (AJCC) staging system (14) provides a strategy for grouping patients with respect to prognosis. In SEER, staging is based on the extent of disease (EOD) (15) codes, which contain information about the extension of primary tumor, size of primary tumor, and lymph node involvement. EOD codes were combined to approximate the AJCC stages as closely as possible. Different EOD coding schemes were used in the time periods 19731982, 19831987, and 1988 onward. The coding schemes for 19831987 and 1988 onward are very similar, with only subtle differences. Differences between the 19731982 coding scheme and the later schemes are greater. One difference is the coding of tumor size. In the 19731982 period, tumor size was coded into categories (<0.5, 0.50.9, 1.01.9, 2.02.9...cm) instead of by actual size, as in coding by single millimeters used in the two more recent coding schemes. The 19731982 categorization does not allow the exact classification of tumors into the AJCC tumor size categories of 2 cm, >2 cm to
5 cm, and >5 cm. Thus, for the first period, a special stage classification as compatible as possible with the AJCC staging system was used. To evaluate the effect of the different EOD coding scheme used in 19731982, we back-coded tumors diagnosed from 1983 through 1987 to the EOD coding scheme used from 1973 through 1982. We then compared the AJCC classifications obtained from the two EOD coding schemes used in 19731982 and 19831987. For tumors staged as I, II, and III, 90% were classified in the same stages using the 19731982 codes. The largest difference is that 8% of tumors classified as stage I according to the 19831987 EOD coding scheme would have been classified as stage II if the 19731982 coding scheme had been used. Despite these differences, the comparison by stage of survival for patients diagnosed from 1983 through 1987 classified using the 19731982 and 19831987 codes was very similar.
For the analysis, we have considered four disease stages: stage I, stage II lymph node-negative (II), stage II lymph node-positive (II+), and stage IIIA, based on the AJCC stage variables. Stage IIIA represents patients with stage III breast cancer, whose tumors have not spread to the chest wall, skin, or internal mammary lymph nodes and who are thus eligible to receive adjuvant systemic therapy. Because the numbers for stage IIIA were small and the observed proportions using adjuvant systemic therapy in SEER and POC data for stage II+ and stage IIIA were similar, we have further combined patients in stages II+ and IIIA into one category (II+/IIIA).
Modeling the Dissemination Patterns of Adjuvant Therapy
Before 1988, SEER recorded chemotherapy treatment in two categories: chemotherapy receivedtype not otherwise specified (NOS), and chemotherapy recommendedunknown if received. Beginning in 1988, chemotherapy was coded into four categories: chemotherapytype NOS, chemotherapy recommendedunknown if received, multi-agent chemotherapy, and single-agent chemotherapy. The variable used for measuring the use of any chemotherapy in SEER was obtained by summing all the categories available in each of the two time periods. In so doing, we are assuming that all patients with "chemotherapy recommended" actually received it. SEER recorded hormonal therapy in three categories: hormones including NOS and antihormones, endocrine surgery and/or endocrine radiation, and hormones recommendedunknown if received. Thus, SEER does not distinguish between tamoxifen and other types of hormonal treatment. To estimate the proportion of patients in SEER who use hormonal therapy, we summed the three categories, assuming that patients with "hormones recommended" actually received it. The SEER proportions are incomplete due to underreporting of treatment in the hospital medical records and are therefore biased. Patients with unknown information (<1%) were excluded from the analyses.
The exact agents received by each patient were obtained from POC data for 19871991 and for 1995, and this data source represents a gold standard. SEER considers prednisone/steroids as hormonal therapy. Therefore, when calculating the proportion of patients using hormonal therapy in the POC data, we included those using prednisone/steroids. Patients with unknown information on a specific treatment were excluded from the respective proportions. On average, 7.2% of the patients had unknown information on multi-agent chemotherapy or tamoxifen in the POC data. The proportions of patients estimated from the POC data as using adjuvant therapy were weighted to reflect the SEER population from which the sample was drawn, with weights calculated as the inverse of the sampling proportions for each sampling stratum (defined by age, race, stage, and registry). The standard errors of the proportions adjusted for the finite population were calculated from the SAS procedure PROC SURVEYMEANS (SAS Institute, Cary, NC).
Two steps were used to model the dissemination of multi-agent chemotherapy use, tamoxifen use, and the combined use of the two from 1975 through 1999. The first step was to correct the SEER bias due to underreporting by using variables that both SEER and POC data have in common, i.e., the proportion of patients receiving chemotherapy, hormonal therapy, and the combination of the two. In the second step, these adjusted estimates of the proportion receiving any chemotherapy, any hormonal therapy, and the combination of the two were used to estimate the proportion of those receiving the specific treatments of interest (i.e., multi-agent chemotherapy, tamoxifen, and the combination of the two). Details of the two steps are described below.
Details of the Statistical Methodology
In mathematical terms for agestage group k and year i (i = 1975, ..., 1999), let bik be the inflation factor, which corrects for SEER underreporting, and let aik be the proportion of the specific treatment among the more general treatment, i.e., multi-agent chemotherapy among all chemotherapy, tamoxifen among hormonal therapy, and the combination of multi-agent chemotherapy and tamoxifen among all chemotherapy and hormonal therapy. These two quantities are estimated and combined to estimate the final dissemination curves for multi-agent chemotherapy, tamoxifen, and the combination of the two. The SEER proportions of women in agestage group k year i using, respectively, the general treatment and the specific treatment are denoted, respectively, by SGik (SEER data on general treatment) and SSik (SEER data on specific treatment). Since SS is not directly observed in SEER we will estimate it by modeling using the equation
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Adjusting for underreporting of proportion receiving general therapy in SEER (bk).
Let PGik (POC general treatment) be the proportion of women in POC at agestage group k and year i (i = 1987, ..., 1991, and 1995) using the general treatmentfor example, any chemotherapy. The same procedure is repeated for hormonal therapy and for the combined use of hormonal and any-chemotherapy. The general model describes the underreporting of the SEER proportion of the general treatment as a linear function of time
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where E is the expected value. Because in all groups the coefficient k was not statistically significant (P
.05), we opted for the simpler model E[PGik/SGik] = bk, where the underreporting does not vary by year. Another reason for choosing the simpler model is that bk is extrapolated beyond available data and the linear model could have produced unrealistic estimates beyond the available data.
The weights are the inverse of the variance of PGik/SGik. By using Taylor expansion, it is possible to show that variance Var(PGik /SGik) can be approximated by
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where Cov is the covariance.
Because POC is a random sample of SEER and we have excluded from SEER women participating in the POC studies, then Cov(PGik, SGik) = 0, and the above formula reduces to
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where Nik and Mik are, respectively, the SEER and POC sample sizes of patients diagnosed in year i at agestage group k. The SEER-adjusted proportions of the general treatments are then calculated for all the years from 1975 through 1999 using the estimated bias, bk, as SG*ik = SGikbk, i = 1975, ..., 1999.
Apportioning specific treatments from the respective general class of treatments (aik). Over the years an increasing proportion of adjuvant chemotherapy and hormonal therapy has included multi-agent chemotherapy and tamoxifen, respectively, because these treatments have proven to be the most beneficial forms of therapy for early-stage breast cancer.
Apportioning specific treatments consists of estimating the proportion of multi-agent chemotherapy among chemotherapy treatment and tamoxifen among hormonal treatments. These proportions can be calculated directly from the POC data for 19871991 and 1995, using the equation aik = PSik /PGik, where PSik is the proportion of women in POC receiving the specific treatment and PGik is the proportion of women in POC receiving the general treatment. For the years prior to 1987, we synthesized information from data and the literature to determine the point at which dissemination of the specific treatment first began (starting point) and when they became the prominent therapy within the treatment class (saturation point). To extrapolate for the years before 1987, we fitted a logistic model using the start and saturation points when needed and the values calculated from the POC data. Based on assumptions (from classical diffusion theory) about the mechanism by which new technologies are accepted and used by physicians in the classical diffusion theory, it is possible to show that the diffusion of new medical technologies typically results in an S-shaped curve (16,17). S-shaped function implies a slow period of early adoption, followed by a rapid period of dissemination and finally by a slowing as the dissemination reaches a saturation point. We used a logistic regression model, which is S-shaped, to model what proportion of all chemotherapy is multi-agent, what proportion of all hormonal therapy is tamoxifen, and what proportion of patients who received both chemotherapy and hormonal therapy received multi-agent chemotherapy and tamoxifen.
From 1975 through 1976, the use of any chemotherapy reported by breast cancer patients in SEER rose from 8% to 17%. We consider that this rise was due to an increase in the use of multi-agent chemotherapy as a result of the publication of Bonadonnas work in 1976 (18). Thus, we assume that in 1976, the proportion (a1976k) of multi-agent chemotherapy among any chemotherapy was the excess of chemotherapy compared with that in 1975 (a1975k), such that
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After 1983, almost all chemotherapy administered to patients with late-stage breast cancer involved multi-agent chemotherapy (19). Thus we assume 1983 as the saturation year and set a1983k = a1987k, where the latter quantity is estimated directly using the POC data. The logistic model, defined as
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was fit using the SAS procedure PROC NLIN (SAS Institute) on data aik (i = 1975, 1983, 19871991, 1995). Using the parameter estimates of k and
k, we can calculate the estimated apportioning proportions, âik, for all the years from 1975 through 1999 and agestage combination k.
Tamoxifen was not widely used until the late 1970s and early 1980s, when clinical trials, such as the studies of the Nolvadex Adjuvant Trial Organization (the NATO studies) (2022), demonstrated its effectiveness. Thus, for tamoxifen we assume a1978k = 0.005. Because we know from the POC data that the proportion of tamoxifen among hormonal therapy was still increasing in 1987, the logistic function was fit using aik for i = 1978, 19871991, and 1995. The 0.005 value representing the start of the dissemination is arbitrary. In the case most sensitive to this assumptionwomen diagnosed with stages II+/IIIA breast cancer in the earlier years and aged 70 years and overthe proportion of tamoxifen use among users of hormonal therapy, âik, changed from 0.53 to 0.58 (9%) in 1983 for starting values between 0.001 and 0.01. However, the effect of the starting values on the final proportion of tamoxifen use is small. For example, by multiplying the above proportions of tamoxifen among hormonal by the 1983 proportion of women receiving hormonal therapy (0.33), we estimated that the proportion receiving tamoxifen was 0.17 and 0.19 for starting values between 0.001 and 0.01.
Because the combined use of tamoxifen and multi-agent chemotherapy is relatively recent and the POC-observed proportions for the use of both was still very low in 1987, no starting point was used, and the logistic function was fit using aik for i = 19871991, and 1995.
It should be noted that the final proportions of patients receiving multi-agent chemotherapy, tamoxifen, and both are not mutually exclusive. The proportion of patients receiving multi-agent chemotherapy also includes patients receiving tamoxifen; the converse is true as well. The proportion of patients receiving multi-agent chemotherapy exclusively is calculated by subtracting the proportion of those receiving both.
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RESULTS |
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Fig. 3 illustrates the association between the dissemination of systemic adjuvant therapy and benchmarks in disseminating the results of clinical trials of systemic adjuvant therapy. The curves represented in this figure are smoothed and are not mutually exclusive. Thus a woman receiving both treatments is represented in both tamoxifen and multi-agent chemotherapy curves. The letters represent the main results and/or recommendations from the respective publication, conference, or announcement. This figure shows a decrease in the use of tamoxifen in recent years, especially for postmenopausal women with stage II+/IIIA breast cancer. Some of this decline may be explained by the results of clinical trials in the early 1990s demonstrating that tamoxifen is associated with an increased risk of endometrial cancer (26) but also by results showing that tamoxifen is only effective with ER-positive tumors (27).
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DISCUSSION |
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The dissemination patterns described by our model suggest that the results of clinical trials are disseminated fairly rapidly to community-based physicians and their patients. Rapid dissemination likely reflects the importance that physicians and patients attach to the latest results from clinical trials. It also emphasizes that research results are being transmitted in a timely fashion and suggests that the mechanisms for wide dissemination, such as medical journals, scientific meetings, NCI clinical announcements, and consensus conferences, are effective in reaching practicing physicians.
In those instances in which treatment use is less than 50% (i.e., patients with stage I breast cancer), the patterns reflect a lower risk of relapse and the more equivocal benefits of current adjuvant systemic therapies in this population. Indeed, the 1990 NIH Consensus Conference suggested that patients with lymph node-negative breast cancer whose tumors are smaller than 1 cm do not need treatment beyond surgery. Due to the increased use of mammography screening, this group represented 36% of stage I cases in 1983.
A major factor in determining the effectiveness of tamoxifen is ER status; however, the dissemination curves presented here did not consider ER status. Although it is possible to estimate the dissemination of adjuvant systemic therapy while controlling for ER status, SEER has collected ER status data only since 1990. Thus, the models would not be able to capture special features of the dissemination before 1987, such as the substitution of multi-agent chemotherapy for tamoxifen from 1983 through 1986. Also, on average, 33% and 19% of cases in SEER and POC, respectively, have no information on ER status and, consequently, more variability in the estimates would be introduced because cases with no information on ER status would be eliminated from the analysis.
For the purpose of estimating the impact of adjuvant systemic therapy on breast cancer mortality in the general population, these dissemination curves will have to be paired with the impact of the respective therapies on survival, usually expressed as the reduction in the annual odds of death (6,7). The survival benefits of tamoxifen are usually expressed in terms of ER status; however, they are also reported for the observed mixture of patients in trials with respect to ER status. For example, 85%95% of women 6069 years old in clinical trials have ER-positive tumors (7). In the SEER and POC data, respectively, we estimate that 79% and 80% of women 6069 years old have ER-positive tumors. Although the proportion of women in the population with ER-positive tumors is slightly lower than that in clinical trials, the overall benefits from clinical trials can be used, allowing for a small correction factor. A more difficult issue to overcome is the fact that tamoxifen has proven to be more effective when administered for 5 years rather than 1 or 2 years (7), and no data are available on the amount of time tamoxifen is administered in the general population.
Although improvements in outcome observed in clinical trials provide a benchmark, they represent only one piece of the puzzle in estimating the ultimate impact of treatment advances on population mortality. Another key piece, the dissemination of new therapies to the general population, is more difficult to reconstruct. In this article, we have demonstrated how statistical models can be created that bring together disparate data sources while accounting for inherent biases in the different sources. Estimation of dissemination of state-of-the-art treatments is crucial in monitoring the performance of the health care system to ensure that every individual in a community has access to proven advances in medical treatment.
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NOTES |
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We thank Timothy McNeel (Information Management Systems, Silver Spring, MD) for assistance in preparing the data.
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REFERENCES |
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Manuscript received October 9, 2001; revised July 30, 2002; accepted August 27, 2002.
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