Published Online: December 23, 2013
Robert D. Lieberthal, PhD
Background: Creating the value proposition for innovations in personalized genomic medicine requires generation of evidence-based demonstrations of clinical utility and cost-effectiveness.
Objectives: To assess economic studies of genomic testing for women with breast cancer and to understand the value of genomic testing for multiple stakeholders.
Study Design: Literature review.
Methods: A structured review of the literature was conducted to identify and synthesize available evidence regarding economic analyses of genomic testing for breast cancer. A search was conducted using PubMed and Google Scholar for articles published between January 1, 2005, and December 31, 2010. The search was then expanded to include articles as far back as 1981. In addition, snowball methodology was used to identify and include additional articles based on frequency of author publication and frequency of citation in the literature.
Results: Of the articles reviewed, a subset of 9 articles describing specific economic analysis studies were included in a more in-depth, side-by-side comparison. This review of the literature on the economics of genomic testing for women with breast cancer found that most of the economic evidence relied on modeling rather than clinical trial data.
Conclusions: Facilitating the diffusion of new technology will require more data to satisfy the payer, provider, and societal perspectives. Conversely, willingness by payers and clinicians to consider economic modeling data as part of their evaluation of new technologies can help facilitate the diffusion of newly developed genomic tests.
Am J Manag Care. 2013;19(12):1024-1031
Many studies document the scientific basis for genomic testing for breast cancer, but few tools allow payers to assess the value of these genomic tests.
Economic research on genomic testing for breast cancer has not involved randomized controlled trials or other direct trial data.
Research on the economics of genomic testing will consist primarily of modeling studies for the foreseeable future.
Payers should use economic models as the best available evidence for which genomic tests should be reimbursed.
Payers should demand more funding for high-quality prospective trials of genomic tests with an economic evaluation.
In women, breast cancer accounts for nearly 1 in 4 cancers. It is the most common cancer in women aside from skin cancer. It has the second-greatest mortality rate for women, exceeded only by lung cancer.1 Treatment pathways for breast cancer recurrence after primary surgical and radiation therapy are complex and are based on multiple known risk factors. Women are offered adjuvant chemotherapy if the risk of recurrence is above a certain threshold.2
While the benefit of adjuvant chemotherapy is well established in patients with early-stage breast cancer that is estrogen receptor ER) positive and lymph node negative, 65% of women diagnosed with invasive breast cancer have lymph node–negative disease; of these, 15% are expected to die or have distant metastasis in 10 years.3 Currently, novel genomic evidence about the diversity of patients is challenging scientists to develop and validate more personalized approaches to treatment.
Advances in genomic diagnostics and personalized treatment have the potential to improve health outcomes, reduce mortality, and increase quality of life for cancer patients. Genomic tests analyze the genetic profiles of patients’ tumors by generating a “recurrence score” based on a particular algorithm. The recurrence score predicts the likelihood of cancer recurrence and informs decision making.
Several types of genomic tests are available today in the United States for breast cancer, including Oncotype DX, MammaPrint, and CancerTYPE ID. Oncotype DX is a 21-gene assay test that gives patients with stage 1 or 2 ER-positive, lymph node-negative breast cancer a recurrence score between 0 and 100.4 MammaPrint is a 70-gene assay to determine the risk of cancer recurrence for ER-positive or ER-negative, lymph node–negative patients with early-stage breast cancer (stages 1 and 2).5,6 CancerTYPE ID measures a 92-gene expression taken from a tumor biopsy to determine 30 different types of tumors. A component of the test, Breast Cancer Index, can be ordered if the tumor is the suspected primary source.6 CancerTYPE ID is not as widely used as the other 2 tests in breast cancer.
ECONOMICS OF BREAST CANCER
The total cost of breast cancer includes the financial burdens for the patients, their families, and society. The National Cancer Institute estimates the national annual financial cost of breast cancer care at $13.9 billion.7 Breast cancer care represents the largest portion of all cancer care expenditures. As the healthcare system works to find ways to pay for genomic technology and healthcare costs continue to trend upward, the potential cost savings and productivity gains associated with advances in genomic diagnostics and personalized treatment that avoid unnecessary and ineffective treatments are appealing.8
Economic analysis provides a framework for assessing the value of clinical outcomes, as well as for determining how that value might differ by population.9 This literature review improves upon previously published literature reviews of genomic testing for breast cancer by focusing on the economic evidence. We are not aware of other structured reviews on this topic that utilize the economic perspective. Our key findings are that research in this area has been limited and is likely to consist primarily of modeling studies for the foreseeable future. Additional funding, better outcomes data, and regulation are identified as barriers to adoption of this new technology.
An economic evaluation prioritizes the efficient use of scarce resources. As a result, economic evaluation leads to the test that best balances the trade-offs among available alternatives rather than simply selecting the cheapest or most valid test.10 While data from randomized controlled trials are a gold standard, an economic analysis uses models to combine costs and outcomes. The combination of both types of data in a model that allows for the evaluation of the cost-effectiveness of a particular type of technology results in information that maximizes efficiency.11
This framework guided the snowball methodology, described in further detail in the eAppendix (available at www.ajmc.com). In order to appear in our literature review, a study had to cover the clinical area of breast cancer, with the scientific application of a genomic test. Further, the analysis had to include a cost-effectiveness analysis broadly defined. A costeffectiveness analysis uses 2 basic building blocks—economic cost and clinical effectiveness—to assess the value of a test, drug, or other healthcare product. Value comes from calculating the incremental improvement derived from a particular test over existing technology.12
This conceptual framework resulted in a review of 9 studies as described in the section titled Published Studies on Economic Outcomes. Due to the small number of studies available for each topic area of the review, we present studies on an individual basis rather than presenting summary results across studies.
PUBLISHED STUDIES ON ECONOMIC OUTCOMES
Several studies have been published utilizing economic modeling methods to analyze the benefits and costs of genomic testing for breast cancer. The costs of genomic testing are 2-fold: the cost of the genomic test itself and the costs associated with false-negative or false-positive results. For a complete listing of cost variables, please see the Ishikawa Fishbone Analysis13,14 depicted in the Figure. The Jefferson Population Health Continuing Professional Education Collaborative created the diagram based on the references cited in the article. Thus, the Figure is an original work based on the source material from this literature review.
The direct cost of genomic tests is clear and varies by test and payer. The evidence report commissioned by the Evaluation of Genomic Application in Practice and Prevention Working Group and the Agency for Healthcare Research and Quality identified the 3 genomic tests for women with breast cancer that are clinically available in the United States (MammaPrint, Oncotype DX, and CancerTYPE ID).2,9 The cost is generally regarded as $3460, with a range of $1960 to $4860. The cost for a particular patient depends on factors such as insurance coverage policy and regional variability.15
Economic modeling studies have focused primarily on the direct costs associated with breast cancer treatment and recurrence, while failing to fully address indirect costs. For patients, these indirect costs play a significant role in treatment decision making. The false-negative cost mainly includes the cost of nonfatal recurrences and mortality during recurrence or chemotherapy, and is estimated at $51,000.16 Medical costs and related nonmedical costs of unnecessary chemotherapy are high: treatment costs varied from $10,000 to $23,000 in a prior study that was included in our side-byside comparison.17 In another study included in our comparison, adverse events related to chemotherapy were found to cost more than $2000 per patient.18 In a third study included in our comparison, Bacchi and colleagues19 stated that the 18% difference in cost between use and nonuse of an assay could be attributed to the cost of medications for prophylaxis and treatment of side effects of chemotherapy.4
There is limited mention of indirect costs (eg, productivity loss) in any of the published studies we reviewed. There have been attempts to estimate the burden of side effects in terms of quality of life or other patient-reported outcome measures. However, there is no systematic study that translates indirect costs into monetary terms. Data used in a payer perspective study that we reviewed focused on the avoidance of costs of long-term adjuvant chemotherapy such as infertility and second primary tumors.20 A study by Hornberger and colleagues21 in 2011 that was included in our comparison also did not translate indirect costs into monetary terms.
The most significant benefit expected from tailoring treatment using genomic testing is a reduction in adverse drug effects for subpopulations that may not benefit from chemotherapy. The adverse effects of chemotherapy include nausea, vomiting, alopecia, fatigue, vasomotor symptoms, pain, and risk of infection.22 McArdle and colleagues23 found that 85% of patients receiving chemotherapy experienced nausea and vomiting and suffered distress, 36% of patients lost their hair, and 39% of patients developed mucosal ulceration. They also reported that depression and anxiety occurred in patients receiving chemotherapy, although psychiatric morbidity was present for some of these patients even prior to invasive surgery.
PDF is available on the last page.