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F U T U R E  E L D E R L Y
C A N C E R  S P E N D I N G 

26 September 2005  
  

Technological Advances In Cancer 
And Future Spending By The Elderly

Examination of five scenarios to project spending on cancer
among the elderly through 2030.

By Jayanta Bhattacharya, Baoping Shang, Catherine K. Su, and
Dana P. Goldman

ABSTRACT:

This paper forecasts the consequences of scientific progress in cancer for total Medicare spending between 2005 and 2030. Because technological advance is uncertain, widely varying scenarios are modeled. A baseline scenario assumes that year 2000 technology stays frozen. A second scenario incorporates recent cancer treatment advances and their attendant discomfort. Optimistic scenarios analyzed include the discovery of an inexpensive cure, a vaccine that prevents cancer, and vastly improved screening techniques. Applying the Future Elderly Model, we find that no scenario holds major promise for guaranteeing the future financial health of Medicare.

Cancer is largely a disease of old age. In 2000 more than two-thirds of all cancer deaths were among people age sixty-five and older. That year cancer was the second-leading cause of death among the elderly, accounting for 113 deaths per 10,000 elderly people.¹ Looked at another way, of all cancer patients alive in 2001, 61 percent were age sixty-five or older.² Because cancer care is expensive, the treatment of elderly cancer patients places a substantial financing burden on Medicare. In this paper our main purpose is to forecast how technological change in cancer treatment will affect Medicare spending on elderly cancer patients over the next twenty-five years.

It would be foolhardy to claim clairvoyant knowledge of future technological advances in cancer treatment. Such predictions have failed before. In 1971, when President Richard M. Nixon effectively declared “war on cancer,” there was great hope that a focused scientific effort could yield a cure within a reasonable amount of time. Back then we knew much less than we do now about the basic biological mechanisms that cause cancer, so the grand strategy set out to win the war on cancer involved sizable investment in basic laboratory research, alongside investments in clinical trials to test drugs arising out of the labs.³

Both public- and private-sector investments have been substantial. Today the National Cancer Institute (NCI) is the single largest center funded by the National Institutes of Health (NIH), with 2004 spending exceeding $4.7 billion, or nearly 20 percent of the total NIH budget.⁴ Other government entities, such as the Department of Veterans Affairs (VA), the Centers for Disease Control and Prevention (CDC), and the National Institute for Environmental Health Services, contribute another $2 billion to cancer research and prevention efforts. This public investment, large though it be, is still less than pharmaceutical industry spending on cancer research—$7.4 billion in 2003.⁵

Although no cure has been forthcoming, the research effort has paid dividends. Between 1991 and 2000 the age-adjusted cancer death rate decreased 7.2 percent.⁶ Prevention efforts reduced cigarette smoking, while clinical interventions such as early detection are the direct mechanisms responsible for much of the decline in age-specific death rates. Some of the detailed biological knowledge gained from laboratory research has yielded new treatments—for example, sophisticated chemotherapeutic agents that specifically block meiosis and division by cancer cells. In other cases, careful epidemiological research, even in the absence of causal biological knowledge, has been responsible.

Because there is no cure and because these gains have come incrementally, this has led some to argue that we are losing the war on cancer.⁷ The strongest piece of evidence supporting this assessment is that between 1991 and 2000 the annual cancer death rate increased by 38,400 deaths, or 7.4 percent per annum. If we are winning, then why are more people dying each year? The easy answer is that the increase is illusory: It is explained entirely by increases in the size and aging of the U.S. population during this period.⁸ So the gains are real, but given the sizable investment in cancer research, it is a disappointment to see raw death rates high and growing. One counterintuitive, but important, explanation is that cancer death rates are rising because of success in reducing heart disease death rates—people who previously died from heart disease are living to die from cancer.⁹ Nevertheless, it is a disappointment to see the rising social costs of cancer: One estimate places the 2003 direct medical and indirect costs (from lost productivity because of illness or death) at $189.5 billion.¹⁰

We examine one important potential public reward to cancer research: a reduction in Medicare spending arising from improved cancer care. As we have noted, reliable forecasts of new technologies are difficult (some would say impossible). Instead, we take another approach. We look at five widely varying scenarios of technological change. The first assumes that cancer treatment technologies existing in 2000 stay in place over the next twenty-five years. The second incorporates advances in cancer treatment between 2000 and 2004. The third envisions an inexpensive cure for cancer. The fourth envisions a vaccine that inoculates against the development of cancer until death. Unlike the cure, the vaccine requires caring (and paying medical care costs) for the existing cohort of cancer survivors. The fifth envisions major improvements in the early detection of cancer.

Our most important finding is that even if inexpensive and effective, new technologies will not necessarily lead to large declines in total medical spending. Demographic realities trump the ability of technological miracles to seriously address Medicare financing challenges.

Study Data And Methods

Data. Our primary data source for this study is the Medicare Current Beneficiary Survey (MCBS) for the years 1992–2000. The MCBS is a nationally representative sample of aged, disabled, and institutionalized Medicare beneficiaries; it contains comprehensive data on health, health care use, and spending.

The RAND Future Elderly Model (FEM), described below, requires extensive information on health status to account for potential competing risks. Based on MCBS self-reports, we developed indicator variables for the presence of major diseases and functional status limitations that have strong effects on both health care spending and survival. Among these are diabetes, cancer (excluding skin cancer), heart disease (myocardial infarction, heart attack, angina, coronary heart disease, congestive heart failure, or other heart condition), hypertension, stroke, lung disease (emphysema, asthma, or chronic obstructive pulmonary disease), Alzheimer’s disease, osteoarthritis, instrumental activities of daily living (IADLs), activities of daily living (ADLs), and nursing home residency.

For respondents who have or have had cancer, the MCBS also has detailed information on parts of the body where the tumor was found, including lung, colon (colon, rectum, or bowel), breast, uterus, prostate, bladder, ovary, stomach, cervix, brain, kidney, throat, head, back, or other). To ensure a large enough sample size, we grouped cancer into five categories: lung cancer, breast cancer, prostate cancer, colon cancer, and other cancers.

One problem with using an annually collected data set like the MCBS to examine cancer populations is that because cancer is so deadly, long-term cancer survivors are oversampled. We addressed this issue by incorporating information on cancer populations from the Surveillance, Epidemiology, and End Results (SEER) database, which does not have this bias. The SEER data contain important clinical information about cancer patients that the MCBS lacks, including stage of disease. We need both the MCBS and SEER, though, because the MCBS contains information on noncancer health conditions that SEER lacks.

The Future Elderly Model. To simulate the impact of cancer treatment breakthroughs, we use a modified version of the FEM.¹¹ The FEM is a microsimulation model that tracks disease conditions, functional status, and health care spending of the Medicare population. It has three components: a model of health status transitions, a model of health care spending, and a model of future cohorts entering Medicare. For this paper we expanded the basic FEM to include indicators for lung, breast, prostate, colon, and other cancers.

We adjusted the FEM parameters to distinguish early- from late-stage cancers and to account for biases caused by the MCBS sampling scheme. The MCBS does not ask participants with cancer to report stage at diagnosis. Also, because mortality rates among cancer patients are high, the probability of selecting into the MCBS is lower for cancer patients, which makes MCBS cancer prevalence estimates lower than the true prevalence. For similar reasons, early-stage patients are overrepresented in the MCBS. To address these deficiencies, we used SEER data to estimate the relative prevalence of early- and late-stage disease and to adjust the cancer prevalence and mortality estimates to match the population numbers.

The FEM health status transition model consists of a series of flexibly modeled hazard functions, in which the hazard of contracting a disease, entering a new functional status, and dying are functions of demographic characteristics (sex, education, race, region, or urban/rural residence), risk factors (obesity, being underweight, and having ever smoked), and other health conditions (diseases and functional status).¹² We treated most health states as absorbing; that is, once someone contracts an illness, he or she has it forever. This assumption is consistent with the MCBS questionnaire (“Has a doctor ever told you…”) and with the course of most chronic diseases. However, we allowed recoveries from functional limitations. For cancer patients, mortality estimates depended on the stage of disease at diagnosis.

Spending estimates were based on pooled weighted least squares regressions of total Medicare spending on demographic characteristics, risk factors, health conditions, and interactions of disease conditions and functional status. Spending is in 2000 dollars, adjusted using the medical Consumer Price Index (CPI).¹³

For the FEM microsimulation, we drew, with replacement, a sample of 100,000 simulated individuals from the MCBS population in 2000. This simulated population inherited the joint distribution of health status and functional disability from the MCBS, so it reflects the prevalence of disease and disability seen in the population age sixty-five and older in 2000. Using prevalence estimates for breast, prostate, lung, colon, and other cancers from the year 2000 SEER, we randomly assigned late- and early-stage cancer to appropriate subgroups of this population to reflect the prevalence of cancer. We then applied the FEM spending model to estimate year 2000 Medicare costs for this population. Using Census Bureau age-specific population forecasts, we estimated 2000 Medicare costs.

We next applied the FEM disease transition model to the year 2000 simulated population to determine the characteristics of the 2001 simulated population. For each individual in the simulated population, we randomly drew mortality, disease, and disability information in 2001 that depends on the presence of disease and disability in 2000. As our initial sample ages, it becomes less representative of the entire population age sixty-five and older. We therefore replenished our sample each year with a newly entering cohort of sixty-five-year-olds. We used the National Health Interview Survey (NHIS) to predict the health status of these newly entering cohorts. With the newly refreshed 2001 microsimulation population in hand, we again applied the FEM spending model and Census Bureau age-specific population forecasts to estimate 2001 Medicare costs.

We repeated this microsimulation thirty times, progressively aging the simulated population, while refreshing it with incoming sixty-five-year-olds. This yielded forecasts of Medicare costs for 2000–2030. Because the FEM disease transition and spending models reflect year 2000 technology, the cost forecasts reflect the effect of keeping this technology unchanged until 2030. We call these forecasts our “baseline” estimates. The baseline FEM does well in forecasting 2000 Medicare spending, as well as Medicare spending on cancer.¹⁴

A major feature of the FEM is that it permits us to directly model the problem of competing mortality risks. In the FEM, people face an increased mortality risk from a wide and realistic variety of chronic conditions and disability states. This feature is especially important because the scenarios we consider envision radical changes, such as a cancer cure. Improvements in cancer treatment will keep some alive long enough to contract other diseases. If these diseases entail costly treatments, it is quite possible that even inexpensive and effective cancer treatments could lead to increased Medicare spending.

Five scenarios for technological advancement. We considered five scenarios on technological advance in cancer treatment (Exhibit 1). We relied on an expert panel on cancer and the biology of aging to guide us in scenario construction. This panel, which met in 2001, consisted of nationally known clinicians and laboratory researchers.¹⁵ Our motivation in picking these scenarios stemmed in part from fundamental uncertainty about the future of technological progress expressed by this panel. Hence, we considered scenarios that run the gamut from wide-eyed optimism to excessive pessimism.

Optimism comes in several varieties. One scenario envisions a cure for cancer. We assumed that all cancer patients would be eligible for this cure and would be restored to whatever health condition they had before the development of the cancer. Since we wanted this “cure” scenario to be optimistic, we assumed that treating a cancer patient would cost much less than treating a cancer patient today—$10,000 per cancer patient.¹⁶

Our second scenario envisions the development of a cancer vaccine.¹⁷ We assumed that taking this vaccine prevents cancer from ever developing. However, to distinguish this scenario from the “cure” scenario, this vaccine cannot cure patients with existing cancer—such patients receive conventional cancer treatment modalities available in 2000. We further assumed that Medicare would pay for vaccination of the entire population age sixty-five and older (excluding those who have already had cancer) when the vaccine was first disseminated and then subsequently would pay each year to vaccinate incoming cohorts. As suggested by our expert panel, we assumed that this vaccine would cost about $500 per administration, which is similar to the cost of a hepatitis B vaccine.

Our next two scenarios envision large improvements in population screening for cancer. In these scenarios, the probability of developing cancer and the technology of cancer treatment remained unchanged. However, all cancers that develop would be discovered and treated at an early stage. Of all the optimistic scenarios, the expert panel thought these “early detection” scenarios most likely to transpire within the next thirty years (although still unlikely). These scenarios share the same detection technology but are distinguished by cost assumptions. In one, “early detection (zero cost),” we assumed that the costs of detection were free or, alternatively, discovered at no additional cost in the process of routine care. In the second, “early detection (moderate cost),” the costs of early detection roughly equal the annual costs of screening for colon cancer: $100 per elderly person per year.¹⁸ Although these scenarios are certainly optimistic, improved detection will increase the measured prevalence of cancer, as the set of undiagnosed cancers developing to late stage shrinks to null.

Our final scenario is more pessimistic. In the “baseline” scenario, we assumed that the technology of cancer treatment that was extant in 2000 would stay in place unchanged until 2030. This scenario is similar, except that we assumed that the technology of treatment extant in 2004 would stay in place unchanged until 2030. We dubbed this our “drug” scenario because it envisions the adoption of several new pharmaceutical agents into the standard treatment regimen, as well as a wider dissemination of some existing agents. In Exhibit 1 we make explicit our assumptions in this scenario. For example, we assumed that 70 percent of all newly diagnosed early-stage breast cancer patients would receive tamoxifen. Our motivation for including this scenario was to estimate the cost-effectiveness of the technological developments that took place between 2000 and 2004.

We classified the major advances over this period into five categories: (1) monoclonal antibodies; (2) hormonal agents; (3) new cytotoxic agents or existing agents used in new situations; (4) oral agents that are in the same class as existing drugs; and (5) supportive drugs that decrease the side effects of existing therapies. Each of these categories contains new drugs as well as existing drugs that were disseminated more widely. For each drug, we assigned cost, survival, and therapy targeting estimates based on guidance from our expert panel, as well as a search of the clinical literature on cancer between 2000 and 2004.¹⁹

Exhibit 1 summarizes assumptions about cost and survival for each scenario. For the “drug” scenario, the relative risk reduction compared with year 2000 treatment, as well as the patients for whom new treatments apply, are listed. Treatment costs include administration of these supportive drugs when appropriate.²⁰

For each scenario, we adjusted the FEM cost and disease transition probabilities to correspond to scenario parameters. For example, for the “vaccine” scenario, we set the probability of developing cancer to zero, and we increased Medicare spending by about $500 for everyone without cancer. We assumed that the new breakthroughs started to apply in 2005. Therefore, before 2005, all scenarios have the same projections of disease conditions, functional status, and health spending. Projections diverge in 2005, as a large number of interdependent variables are affected by the different technological paths of the scenarios. The affected variables include cancer prevalence, cause-specific death rates, the prevalence of functional status limitations, health spending, and the prevalence of other diseases (indirectly through changes in mortality). We compared outcomes in the “baseline” scenario with outcomes in the “cure,” “vaccine,” “prevention,” and “drug” scenarios to estimate the marginal effect of technological breakthroughs on the prevalence of cancer and on health spending over a twenty-five-year period.

Results

Exhibit 2 shows the evolution of cancer prevalence under the “baseline” and five scenarios. In the baseline scenario, cancer rates decline slowly from 20 percent of the elderly population to about 16 percent in 2015 and then stay there until 2030. The decline reflects the decline in age-adjusted cancer rates that occurred between 1991 and 2000 because of lower smoking rates among newer Medicare beneficiaries.²¹ That is, in 2000 at any given age, there were fewer people with cancer than there were in 1991. Between 2000 and 2015, if technology were to remain fixed at 2000 levels, the healthier cohorts would age into Medicare and replace existing, less healthy, cohorts. By 2015 this process reaches a steady state.

The “cure” and “vaccine” scenarios yield intuitive time profiles of cancer prevalence. Because the cure can be applied whether or not a cancer developed before the 2005 discovery, cancer prevalence would drop sharply to zero. By contrast, our vaccine yields steadily declining cancer prevalence between 2005 and 2030. Under this scenario, there would be no new cancer cases contracted after 2005, so increasing numbers of the incoming Medicare cohorts would be free of cancer. As the existing stock of cancer survivors slowly dies out, cancer prevalence rates would decline toward zero.

Two scenarios yield higher cancer prevalence rates than the baseline. In the “drug” scenario, since new therapies improve cancer survival relative to baseline, the set of people alive with cancer at any given time will be greater. In the “early detection” scenarios, measured cancer prevalence rises over baseline for two reasons: (1) All previously undetected cancers are found at an early stage, and (2) early-stage cancer patients are more likely to survive than late-stage patients.

Exhibit 3 shows projected per capita Medicare costs. Because we are interested in the trade-offs attributable to greater longevity (with its attendant comorbidity), expenditures here include all Medicare spending. One-time adjustment costs of applying new technologies to the whole Medicare population explain the discontinuous jump in 2005 for the “drug,” “cure,” and “vaccine” scenarios. There is no discontinuity in the “baseline” and “early detection” scenarios because there is no pent-up demand for treatments.

Unsurprisingly, the “cure” and “vaccine” scenarios result in the lowest per enrollee costs. At first, costs decline most relative to baseline for the “cure” scenario, but after several years, the cost decline under the “vaccine” scenario dominates. The switch happens because the vaccine does not help existing cancer survivors, and cancer survivors spend more on medical care than others. Ultimately, vaccinating everyone for $100 is cheaper than treating every cancer case for $10,000.

In the short run, whether “early detection” raises or lowers per capita costs relative to “baseline” depends on price. If early detection is free, per capita costs are lowered. Not surprisingly, the costs of treating previously undetected cancers at an early stage are less than the costs of treating at a later stage. If early detection requires spending an additional $100 per enrollee, costs rise in the short run as all previously undetected cancers are found. In the long run, both scenarios reduce per capita total costs.

Finally, Exhibit 3 shows that the 2000–2004 advances in cancer are expensive. The “drug” scenario raises spending per enrollee by nearly $300 in the long run over baseline. The denominator here is the number of Medicare enrollees, not just the number of cancer patients. Thus, $300 is a dramatic increase in Medicare spending for only a few years of new and more widely disseminated cancer technologies.

Exhibit 4 shows the FEM estimates of total Medicare spending. For all scenarios, because the population of elderly Americans will expand dramatically between 2005 and 2030, total medical spending also increases dramatically. One major lesson is that demographic facts can trump the ability of even the most optimistic technological developments to reduce future Medicare spending. Even miraculous technological developments lead to small declines in long-run total Medicare spending. Similarly, vastly improved early detection saves Medicare costs, but not very much.

In addition to demographic trends, these results are driven by competing risks of death. Those saved from cancer by technological miracles will die of other things in expensive ways. Substantial progress in extending the lives of cancer patients will inevitably lead to an increased prevalence of other diseases. For example, in the “cure” scenario, the FEM estimates imply that the prevalence of heart disease in 2030 will increase by 69 cases per 10,000 elderly over the “baseline” scenario. Stroke prevalence will increase by 7 cases per 10,000; hypertension by 45 cases per 10,000; osteoarthritis by 46 cases; and so on. The other scenarios display qualitatively similar, if less dramatic, declines in the average health of the population resulting from improved cancer care.

The “drug” scenario is the highest-cost scenario. In 2030 alone, Medicare spending is forecast to be $20 billion higher than baseline. Further analysis suggests that the increase in costs is largely attributable to the increased use of expensive supportive therapies, such as erythropoietin, the new anti-emetics, and the bisphosponates (which prevent some adverse bone side effects).

Discussion

In one sense, the “drug” scenario cost estimates are a lower bound. If the incremental progress of the past few years continues into the future, elderly medical care costs are likely to increase even beyond levels projected here. Exhibit 5 provides rough cost-effectiveness estimates. In the “drug” scenario, there will be an increase over baseline of 2.79 million life years at a cost to Medicare of $349 billion between 2005 and 2030. This amounts to a cost of nearly $143,000 per life year saved, which is not conventionally seen as cost-effective. However, since much of these costs are attributable to supportive therapies, much of the benefit comes from quality-of-life improvements for cancer patients, which are not measured here.

Exhibit 5 shows that a cure would increase cancer care and screening costs by about $178 billion, saving nearly twenty-seven million life years over the twenty-five-year period. Total medical spending would decline by about $120 billion—a decline of almost $5 billion per year.²² As we have seen, the cost savings from an inexpensive cure are mitigated by competing risks of other diseases.

A cancer vaccine would be an even better bargain: An investment of $59 billion would save $210 billion—about $8.4 billion per year—and more than fifteen million life years over the twenty-five-year period.²³

For our “early detection” scenarios, the estimates suggest that nearly 9.5 million life years would be saved over the next twenty-five years. This is more than one-third the life years saved by a cure. Whether early detection would save or cost Medicare money depends on the price of detection. If screening costs nothing over existing care, Medicare would save $43 billion over twenty-five years. If screening costs an additional annual $100 per enrollee, total Medicare spending would increase $75 billion over twenty-five years—about $3 billion per year.

Although these savings in the optimistic scenarios might seem large, they are small relative to projected Medicare budgets. For example, in 2030 alone, Medicare is projected to spend more than $650 billion in the “baseline” scenario.

In a 1999 essay on the future of medical technology, published in Science, Herbert Pardes and colleagues argued that conventional forecasts of Medicare spending are fundamentally blinkered.²⁴ Among the reasons they cited is their expectation that the nature of future medical technological advances is changing to favor “high technology” over more costly “halfway” and “palliative” technologies.²⁵ They wrote:

Conventional economic thinking suggests that new medical technology for improving the health of seniors often increases costs and absorbs savings as a result of better health. But technological advances are defining new paradigms for medicine to which traditional economic theory may not apply. Improved understanding of human biology at the molecular level may make [costly] invasive surgery, intensive care units, and long-term nursing home care far less necessary.

Our findings suggest that “conventional economic thinking” cannot be dismissed so easily. We considered a broad range of scenarios in this paper because we cannot predict, with any precision, the future of cancer treatment. We can be fairly certain, however, that no technological advance in the treatment of cancer alone, no matter how great the breakthrough, will fundamentally alter the financial future of Medicare.

Principal funding for this study came from the Centers for Medicare and Medicaid Services (CMS Contract no. 500-95-0056), with additional funding from the National Institute on Aging through its support of the RAND Roybal Center for Health Policy Simulation (P30AG024968). The authors are solely responsible for the paper’s contents. No statement in this paper should be construed as being an official position of the CMS.

NOTES

1. National Center for Health Statistics, Health, United States, 2002 (Hyattsville, Md.: National Center for Health Statistics, 2002).
2. National Cancer Institute, “Estimated U.S. Cancer Prevalence,” 2004,
cancercontrol.cancer.gov/ocs/prevalence/prevalence.html (5 May 2005).
3. C. Leaf, “Why We’re Losing the War on Cancer—and How to Win It,” Fortune 149, no. 6 (2004): 76–91, referring to estimates from the Tufts Center for the Study of Drug Development.
4. National Institutes of Health, “Summary of the FY 2004 President’s Budget,” 3 February 2003,
www.nih.gov/news/budgetfy2004/fy2004presidentsbudget.pdf (19 July 2005).
5. Leaf, “Why We’re Losing the War on Cancer.”
6. L. Ries et al., eds., SEER Cancer Statistics Review, 1975–2000 (Bethesda, Md.: NCI, 2003).
7. See J.C. Bailar III and H.L. Gornik, “Cancer Undefeated," New England Journal of Medicine 336, no. 22 (1997): 1569–1574; J.C. Bailar III and E.M. Smith, “Progress against Cancer?” New England Journal of Medicine 314, no. 19 (1986): 1226–1232; and Leaf, “Why We’re Losing the War on Cancer.” Although the NCI no longer calls the research and clinical efforts aimed at finding a solution for cancer the “war on cancer,” this phrase is still commonly used in the popular press and even in the scientific literature.
8. B.K. Edwards et al., “Annual Report to the Nation on the Status of Cancer, 1973–1999, Featuring Implications of Age and Aging on U.S. Cancer Burden,” Cancer 94, no. 10 (2002): 2766–2792.
9. See J. Llorca and M. Delgado-Rodriguez, “Competing Risks using Markov Chains: Impact of Cerebrovascular Disease and Ischaemic Heart Disease in Cancer Mortality,” International Journal of Epidemiology 30, no. 1 (2001): 99–101. This problem of competing mortality risks is a major theme of our paper as well.
10. National Heart, Lung, and Blood Institute, NHLBI Year 2003 Fact Book (Bethesda, Md.: NIH, 2003).
11. The accompanying technical appendix provides substantial detail on the Future Elderly Model (FEM). See content.healthaffairs.org/cgi/content/full/hlthaff.w5.r53/DC2.
12. Respondents are obese if their body mass index (BMI) is greater than 30, and underweight if their BMI is less than 20.
13. We dropped Medicare health maintenance organization (HMO) enrollees. By doing so, we might have overestimated the health care spending if HMOs actually reduce spending, but we expect any bias introduced to be small. We derived stage-specific cancer spending estimates from the results reported in J. Bhattacharya, A. Garber, and M. Miller, “Trends in Medical Expenditures and Survival from Cancer among Medicare Beneficiaries, 1986–2000” (Unpublished paper, Stanford University, 2005). This analysis used SEER data linked to Medicare administrative records to generate estimates.
14. For details on the performance of the FEM in predicting year 2000 Medicare spending, see D.P. Goldman et al., Health Status and Medical Treatment of the Future Elderly: Final Report, Pub. no. TR-169-CMS (Santa Monica, Calif.: RAND, 2004). Because we used SEER data, linked to Medicare administrative records, to scale FEM estimates of Medicare spending on cancer, it is not surprising that the modified version of the FEM that we used in this paper predicts year 2000 Medicare spending on cancer well, such as that reported by M.L. Brown et al., “Estimating Health Care Costs Related to Cancer Treatment from SEER-Medicare Data,” Medical Care 40, no. 8 Supp. (2002): IV-104–IV-117.
15. Our expert panel on cancer and the biology of aging was conducted by Paul Shekelle using a modified version of the RAND/UCLA Appropriateness method. For details, see P.G. Shekelle et al., “Identifying Potential Health Care Innovation for the Elderly of the Future,” Health Affairs, 26 September 2005,
content.healthaffairs.org/cgi/content/abstract/hlthaff.w5.r67; and Goldman et al., Health Status and Medical Treatment.
16. This $10,000 figure is an underestimate of the costs of chemotherapy. The “cure” results should thus be seen as a lower bound on the total spending associated with a cure and an upper bound on cost-effectiveness.
17. According to our expert panel, given the state of current technology, a cancer-preventing vaccine is less likely to arise than an immunogenic agent to treat existing cancer. Confusingly, this latter type of treatment is also often called a cancer vaccine. The target population for these immunogenic agents is typically cancer patients, not the population at large. Because this latter kind of cancer vaccine does not differ in any qualitatively important way from our “cure” scenario, we focused our “vaccine” scenario on a treatment that prevents cancer altogether in the population at large.
18. We derived this annual screening cost from M. Pignone et al., “Cost Effectiveness Analyses of Colorectal Cancer Screening: A Systematic Review for the U.S. Preventive Services Task Force,” Annals of Internal Medicine 137, no. 2 (2002): 96–104. These costs encompass an annual stool guaiac exam and a colonoscopy every five years.
19. We updated the work of our expert panel on cancer and the biology of aging up to 2004 with a detailed review of the clinical literature to construct the “drug” scenario. Some sources we consulted include J.J. Body, “Effectiveness and Cost of Bisphosphonate Therapy in Tumor Bone Disease,” Cancer 97, no. 3 Supp. (2003): 859–865; J.J. Chen, D.G. Frame, and T.J. White, “Efficacy of Ondansetron and Prochlorperazine for the Prevention of Postoperative Nausea and Vomiting after Total Hip Replacement or Total Knee Replacement Procedures: A Randomized, Double-Blind, Comparative Trial,” Archives of Internal Medicine 158, no. 19 (1998): 2124–2128; P.Y. Cremieux et al., “Cost-Minimization Analysis of Once-Weekly versus Thrice-Weekly Epoetin Alfa for Chemotherapy-Related Anemia,” Journal of Managed Care Pharmacy 10, no. 6 (2004): 531–537; E. Elkin et al., “HER-2 Testing and Trastuzumab Therapy for Metastatic Breast Cancer: A Cost-Effectiveness Analysis,” Journal of Clinical Oncology 22, no. 5 (2004): 854–863; R.J. Halbert et al., “Outpatient Cancer Drug Costs: Changes, Drivers, and the Future,” Cancer 94, no. 4 (2002): 1142–1150; S.K. Parsons et al., “Impact of Pharmacy Practices on the Cost of Colony-Stimulating Factor Use in Pediatric Stem Cell Transplantation: An Institution-based Analysis,” Journal of Pediatric Hematology/Oncology 23, no. 1 (2001): 19–24; T. Andre et al., “Oxaliplatin, Fluorouracil, and Leucovorin as Adjuvant Treatment for Colon Cancer,” New England Journal of Medicine 350, no. 23 (2004): 2343–2351; D. Schrag, “The Price Tag on Progress—Chemotherapy for Colorectal Cancer,” New England Journal of Medicine 351, no. 4 (2004): 317–319; “Cancer Drugs: Information on What Cancer Drugs Are Available Now and What Drugs Are in the Pipeline,” TIME, 20 May 2001, www.time.com/time/health/article/0,8599,127226,00.html (5 May 2005); and U.S. Department of Health and Human Services, “Medicare to Extend Access to Certain Drugs for Beneficiaries with Serious and Chronic Illnesses,” Press Release, 24 June 2004, www.hhs.gov/news/press/2004pres/20040624.html (5 May 2005).
20. Body, “Effectiveness and Cost”; Chen et al., “Efficacy of Ondansetron and Prochlorperazine”; Cremieux et al., “Cost-Minimization Analysis”; and Parsons et al., “Impact of Pharmacy Practices.”
21. Ries et al., eds., SEER Cancer Statistics Review, 1975–2000.
22. We adjusted future spending for inflation using the medical care Consumer Price Index. However, we did not adjust for expected annual growth in real medical care spending, nor did we apply a discount rate to these calculations. Real health care spending—adjusted for demographic changes—grew about 4 percent annually during the period spanned by our data. Cost-effectiveness studies often discount future spending by 3 percent. If we discount, then ideally, we should also include a trend for real spending growth. The net effect of including both real growth and discounting in these calculations is not appreciably different from the estimates we present.
23. Compared with the “cure” scenario, fewer lives are saved under than the “vaccine” scenario because those with cancer receive care using existing technologies in the latter case.
24. H. Pardes et al., “Effects of Medical Research on Health Care and Economy,” Science 283, no. 5398 (2004): 36–37.
25. This categorization of technological advance derives from a famous essay by Lewis Thomas. L. Thomas, The Lives of a Cell: Notes from a Biology Watcher (New York: Bantam Books, 1975).

Jay Bhattacharya (jay{at}stanford.edu) is an assistant professor of medicine at Stanford University in Stanford, California. Baoping Shang is a fellow at the Pardee RAND Graduate School in Santa Monica, California. Catherine Su is a radiation oncologist and clinical assistant professor, Department of Radiation Oncology, Stanford University School of Medicine. Dana Goldman is corporate chair and director of health economics at RAND.

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DOI: 10.1377/hlthaff.W5.R53
©2005 Project HOPE–The People-to-People Health Foundation, Inc.