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The American Journal of Managed Care May 2016
Greater Potential Cost Savings With Biosimilar Use
Benjamin Yu, PharmD
Implementing a Hybrid Approach to Select Patients for Care Management: Variations Across Practices
Christine Vogeli, PhD; Jenna Spirt, MPH; Richard Brand, PhD; John Hsu, MD, MPH; Namita Mohta, MD; Clemens Hong, MD, MPH; Eric Weil, MD; and Timothy G. Ferris, MD, MPH
Medicaid Managed Care Penetration and Drug Utilization for Patients With Serious Mental Illness
Aaron L. Schwartz, PhD; Jacqueline Pesa, PhD, MPH; Dilesh Doshi, PharmD; John Fastenau, PhD, MPH; Seth A. Seabury, PhD; Eric T. Roberts, PhD; and David C. Grabowski, PhD
Clinical Interventions Addressing Nonmedical Health Determinants in Medicaid Managed Care
Laura M. Gottlieb, MD, MPH; Kim Garcia, MPH; Holly Wing, MA; and Rishi Manchanda, MD, MPH
Physician Perceptions of Choosing Wisely and Drivers of Overuse
Carrie H. Colla, PhD; Elizabeth A. Kinsella, BA; Nancy E. Morden, MD, MPH; David J. Meyers, MPH; Meredith B. Rosenthal, PhD; and Thomas D. Sequist, MD, MPH
Potential of Risk-Based Population Guidelines to Reduce Cardiovascular Risk in a Large Integrated Health System
Galina Inzhakova, MPH; Hui Zhou, PhD, MS; Macdonald Morris, PhD; Megan I. Early, MD, MPH; Anny H. Xiang, PhD; Steven J. Jacobsen, MD, PhD; and Stephen F. Derose, MD, MSHS
Enhanced Primary Care and Impact on Quality of Care in Massachusetts
Asaf Bitton, MD, MPH; Amy W. Baughman, MD, MPH; Sara Carlini, BA; Joel S. Weissman, PhD; and David W. Bates, MD, MSc
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Breast Cancer Multigene Testing Trends and Impact on Chemotherapy Use
G. Thomas Ray, MBA; Jeanne Mandelblatt, MD; Laurel A. Habel, PhD; Scott Ramsey, MD, PhD; Lawrence H. Kushi, ScD; Yan Li, MD; and Tracy A. Lieu, MD, MPH
Referring Wisely: Orthopedic Referral Guidelines at an Academic Institution
Maria E. Otto, MD; Carlin Senter, MD; Ralph Gonzales, MD, MSPH; and Nathaniel Gleason, MD

Breast Cancer Multigene Testing Trends and Impact on Chemotherapy Use

G. Thomas Ray, MBA; Jeanne Mandelblatt, MD; Laurel A. Habel, PhD; Scott Ramsey, MD, PhD; Lawrence H. Kushi, ScD; Yan Li, MD; and Tracy A. Lieu, MD, MPH
A multigene test for breast cancer recurrence risk was used in a minority of eligible patients, yet was associated with a decrease in chemotherapy use.

A 21-gene test that predicts recurrence risk among women with hormone receptor positive (HR+), localized breast cancer was nationally recommended in 2007, but we know little about its subsequent impact. We evaluated: a) patient characteristics associated with test use, b) correlations between Recurrence Score (RS) and chemotherapy, and c) whether test introduction was associated with a reduction in chemotherapy use.

Study Design: Retrospective cohort study.

Methods: The Kaiser Permanente Northern California tumor registry and electronic health records from 2005 to 2012 were used to identify HR+, human epidermal growth factor receptor 2 negative, node-negative cancers. Analyses used logistic regression with propensity score matching and 2-level logistic regression.

Results: Of the 7004 patients who met guidelines for testing, 22% were tested and 26% had chemotherapy. Test use was more likely in younger women (for ages 40-49 years vs 50-64 years: odds ratio [OR], 1.22; 95% CI, 1.04-1.44), in women with tumors sized 1.0 to 2.0 cm versus >2 cm (OR, 1.20; 95% CI, 1.03-1.40), and in women from higher-income neighborhoods (for each $10,000 increase in area median income: OR, 1.05; 95% CI, 1.03-1.07). Among patients with low RS, 8% had chemotherapy versus 72% among patients with high RS (P <.01). In propensity score-matched analyses, testing was associated with an absolute reduction of 6.2% in the proportion of women receiving chemotherapy (95% CI, 2.9%-9.5%); the 2-level model showed a similar but nonsignificant (P = .14) association.

Conclusions: The 21-gene test is used in a minority of eligible patients in this integrated plan. Its use appears to be associated with a modest decrease in overall chemotherapy use.

Am J Manag Care. 2016;22(5):e153-e160
Take-Away Points

In this retrospective cohort study, we evaluated the use of a multigene test that predicts recurrence risk in localized breast cancer, and associated trends in chemotherapy use in eligible patients.
  • The multigene test was used in fewer than 1 in 3 eligible patients, with use plateauing in 2010 through 2012.
  • Recurrence risk scores on the test were highly, but not perfectly, correlated with the use of chemotherapy. 
  • Introduction of the test was associated with a modest decrease in overall chemotherapy use.
Genetic testing of tumor cells has the potential to revolutionize the care of patients with breast cancer and to accelerate the benefits of personalized medicine.1 Several studies have observed that these tests have been incorporated into clinical practice and seem to influence chemotherapy decisions.2-6 Recent US studies of claims data and the National Cancer Institute’s Surveillance Epidemiology and End Results (SEER) data found that only 20% to 30% of eligible women were being tested, with the claims data study observing that reimbursement by insurers has increased slowly.4,5 It is unknown whether acceptance and use of this test have been more complete in integrated, fully capitated systems where the costs of care are covered and decisions are less likely to be affected by financial incentives for or against test or chemotherapy use. These tests are potentially costly, and may be marketed directly to patients, as well as physicians.5,7 As genetic testing in cancer grows more common, healthcare systems need to systematically evaluate how consistently such tests are being used, and how much incremental benefit they add to baseline clinical practices.8,9

Breast cancer genetic testing provides a useful paradigm for evaluating the impact of such tests at a population level. Decisions about the use of adjuvant chemotherapy, for women with early-stage breast tumors that are estrogen or progesterone receptor positive (ER/PR+), can be especially difficult, as most will never experience a recurrence, even without adjuvant chemotherapy. Currently, several gene-expression profiling tests are being marketed to clinicians and patients as tools to enhance the accuracy of predicting recurrence risk and the likelihood of realizing benefit from adjuvant chemotherapy. The 21-gene Oncotype DX (Genomic Health, Inc, Redwood City, California) breast cancer assay has been validated in clinical trials to predict risk of distant recurrence in patients with early-stage, node-negative, ER/PR+, human epidermal growth factor receptor 2 negative (HER2–) cancers.10-13 Guidelines for incorporating Oncotype DX testing into treatment decisions were published in 200714,15; however, little is known about how this test and other genomic tests are being incorporated into real-world oncology practice and how they affect patterns of care.

This study’s overall objective was to assess how the 21-gene test is being used among patients with early-stage breast cancer in a large integrated health delivery system. Among patients who met current guidelines for use of this test, our aims were to: a) compare the demographic and clinical characteristics of patients who had the test with those who did not; b) describe chemotherapy use among women with test results that indicate low, intermediate, and high risk of recurrence; and c) evaluate whether the introduction of the test was associated with a change in chemotherapy use.


Kaiser Permanente Northern California (KPNC) is a nonprofit integrated healthcare delivery system that currently provides care to more than 3.9 million members. Within KPNC, essentially all primary and specialty care, and the vast majority of emergency and hospital care, is delivered by providers working within a single care system for patients of a single health plan.16

Identification of Breast Cancers and Cancer Characteristics

The KPNC tumor registry—a contributor to the SEER program of cancer registries—was used to identify all female KPNC members diagnosed with invasive, nonmetastatic, incident breast cancer between September 1, 2005 (when significant use of Oncotype DX began at KPNC), and June 30, 2012. A member’s first, newly diagnosed breast cancer during this period was included. The tumor registry includes patient age, sex, diagnosis date, tumor size, node involvement, ER/PR status, stage, and initial chemotherapy treatment. HER2 status was determined using the results of Immunohistochemistry and Fluorescence In Situ Hybridization tests.

In 2007, both the National Comprehensive Cancer Network (NCCN) and the American Society of Clinical Oncology included Oncotype DX testing in their guidelines.14,15 Following NCCN guidelines, we selected women for whom Oncotype DX was to be considered: those with ER/PR+, HER2–, stage I and stage II breast cancers having primary tumors ≥0.51 cm with either no lymph node involvement or only ≤2 mm axillary node micro-metastases.14 The Oncotype DX assay analyzes the expression of 21 genes to provide a Recurrence Score (RS) corresponding to the risk of distant recurrence at 10 years among tamoxifen-treated patients not treated with chemotherapy.12,13 The RS is classified into 3 categories based on likelihood of distant recurrence: low risk (RS <18), intermediate risk (RS 18-30), and high risk (RS ≥31). Low RS has been shown to predict little benefit from chemotherapy, whereas high RS predicts greater benefit.13 The routine approach in this medical group was to follow NCCN recommendations; thus, other genetic tests for breast cancer were not commonly used.

Due to the timing of SEER registry reporting requirements, there is some underascertainment of the chemotherapy treatment status in the tumor registry. As part of an ongoing prospective study of newly diagnosed cases of breast cancer,17 a subset of cases were reviewed to validate the chemotherapy treatment status. Of 7004 eligible cancers, 62% were reviewed. Of 1071 patients validated to have used chemotherapy, 86.18% were correctly classified as such in the registry, and of 3240 patients validated not to have used chemotherapy, 99.97% were correctly classified in the registry. The chemotherapy status used was the validated one, if it existed; otherwise, the registry status was used.

Patient Characteristics

Patients were assigned to a census block group (defined by the US 2010 Census) based on their home address at the time of cancer diagnosis. Block group income and education were based on the 2006 to 2010 American Community Survey.18,19 We used data from the year before the cancer diagnosis to create a modified Deyo version of the Charlson comorbidity index.20 From administrative databases, we extracted (from the year before the cancer diagnosis) the following additional variables for each patient for use in the propensity score matching: primary medical center used for care, number of clinic visits and hospital days, and their associated cost.


To identify the predictors of receiving 21-gene testing, we used logistic regression, in which receipt of the test was the dependent variable. Independent variables were calendar year of cancer diagnosis (as a categorical variable), age group (5 categories: aged <40, 40 to <50, 50 to <65, 65 to <75, and ≥75 years), race/ethnicity (Asian, black, white Hispanic, white non-Hispanic, and other/unknown), tumor size (3 categories: 0.5 cm to ≤1.0 cm, >1.0 cm to ≤2.0 cm, and >2.0 cm), comorbidity (3 categories: 0, 1-2, and ≥3 comorbidities), census block group median income, and proportion of adults in the block group with less than a high school degree.

To assess the potential impact of 21-gene testing on receipt of chemotherapy, we used 2 different analytic approaches. The first approach directly evaluated whether women who received the test were more or less likely to receive chemotherapy compared with women who were not tested. We ran a logistic regression in which the dependent variable was whether the woman was tested, and the independent variables were the same as those listed above, plus the following (to increase further the similarity of the matched cohorts): primary medical center for care, patient’s age-squared (for potential nonlinear age effects), costs of clinic and hospital services, as well as the number of clinic visits and hospital days in the year before cancer diagnosis. Model calibration and discrimination were good (C statistic = 0.81; Hosmer-Lemeshow goodness of fit test: P = .32, with nonsignificance reflecting adequate fit). The resulting predicted probability of receiving the test was the patient’s propensity score.

We selected patients who received the 21-gene test and matched them 1-to-1 to patients who were not tested. Matching was performed using the Mahalanobis metric matching within calipers, defined as one-fourth of the standard deviation of the logit of the propensity score.21 Using this methodology, 93% (n = 1462) of tested women were matched to a nontested woman, and after matching, there were no significant differences between the cohorts with regard to the variables used in the propensity score calculation. However, women in the matched cohorts were younger and had fewer comorbidities than the general pool of women from whom they were drawn. This reflects the fact that among the entire cohort of patients with cancer, tested women tended to be different from nontested women.

Using the propensity-matched samples, we calculated the percent of women in each sample receiving chemotherapy, and the corresponding ratio of the odds of receiving chemotherapy among women tested, to the odds of receiving chemotherapy among matched women not tested. As a sensitivity analysis, the matched analysis was repeated among the subset of patients with validated chemotherapy use.

The second analytical approach was to treat the overall percent of women who received testing as a predictive variable for receiving chemotherapy. This approach is similar to “ecological” regression, wherein aggregates are used either in place of, or in addition to, individual-level predictors.22 Unlike an interrupted time-series analysis in which differences before and after some change in practice are assessed, this approach does not require us to arbitrarily define time periods as “before” or “after” the introduction of the test, given that testing was phased in over time. We started with an analytic data set with 1 record per woman. For each calendar year and age group, we calculated the percent of women who received the test, and added this variable to the analytic data set. Thus, a woman diagnosed with cancer in 2011 who was aged 50 to <65 years, had a variable added to her record that reflected the percent of women in her age group in 2011 who were tested. Using these patient-level records, we ran a logistic regression in which receipt of chemotherapy was the dependent variable, and the independent variable of interest was the percent of women in the strata who were tested. The woman’s own test status is not included in the model. Other independent variables included in the model were the same as those used in the model identifying predictors of testing. The results of this model were then used to predict the percent of women receiving chemotherapy, assuming 1 of 2 scenarios: 1) 0% of women in her age group and year were tested, and 2) 30% were tested (which was about the maximum percent of women who received the 21-gene test in any given year).

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