Introduction
Health care systems around the world struggle with high prices for new, innovative cancer drugs. In the U.S., for example, median annual costs of new cancer drugs are now above $150,000 [
1]. They contribute to about one third of projected peak worldwide annual sales of new drugs [
2]. From a manufacturer’s perspective, high prices are justified because largely fixed costs for research and development (R&D) need to be distributed over a small cancer patient population. In addition, high prices provide an incentive to continue to invest in R&D and obtain future innovations [
3]. However, when setting launch prices of cancer drugs in the U.S. over a “typical duration of treatment” in relation to survival gains, the resulting cost-effectiveness ratios are now, on average, above $200,000 per life year gained [
4]. Thus, ratios are higher than the willingness-to-pay threshold commonly applied in the U.S. academic literature, which is in the range of $50,000 to $100,000 per quality-adjusted life year (QALY) gained [
5]. On the other hand, cancer drugs currently account for less than 20% of total drug costs in Western countries [
6]. Hence, even dramatic increases in cancer drug costs will have little impact on total health expenditures in the foreseen future. At least in countries with generous public health insurance coverage total expenditures for cancer drugs thus still seem affordable. The contrast between high prices and relatively low budget impact has led to diverging recommendations by experts and policymakers in countries with generous public health insurance coverage, ranging from ‘wait and see’ to considerable price cuts [
7]. In terms of affordability, the U.S. is somewhat of an outlier: although it spends less as a percentage of total drug costs than many other Western countries [
6], affordability is more of an issue. The reason is that cancer drugs need to be covered to a considerable degree by private means, i.e., out-of-pocket payments [
8,
9].
The purpose of this study was to contribute to the ongoing debate on policy implications of high cancer drug prices. To this end, I conducted a
gedankenexperiment (thought experiment) that envisioned in a deliberately chosen extreme-case scenario a cure for cancer through pharmaceutical treatment. I calculated how much health expenditures would change if such cure were made available. This cancer cure was conceived to eliminate both cancer deaths and the underlying morbidity burden of cancer as well as new cancers in cancer survivors. Furthermore, I hypothesized that a cancer cure would arrive in incremental steps (consistent with past technology diffusion) but at infinitesimally small time intervals (resulting, effectively, in an immediate cure). This follows the idea that small incremental gains in survival conferred by single drugs translate into large improvements in survival when drugs are given in combination [
10,
11].
I determined the price for a cure based on the current ‘exchange rate’ between money and health. This calculation was based on the following argument: If we are willing to pay, based on the current exchange rate, X euros for a small incremental survival gain, then we should be willing to pay a proportionally larger amount for the sum of all incremental survival gains. That is, if we are willing to pay X euros to increase survival by Y/n years, then we should be willing to pay n · X euros to increase survival by n · Y/n years. Conversely, if we are willing to pay Z euros to increase survival by m years, then we should be willing to pay (no more than) Z/n euros to increase survival by m/n years. These calculations assume i) the absence of a budget for health care expenditures, ii) an extra-welfarist perspective, which is commonly interpreted to include health (e.g., life years) in its evaluation space but to exclude the utility of life years (in contrast, adopting a welfarist perspective would require assuming a constant marginal benefit of life years), and iii) profit-maximizing behavior of pharmaceutical manufacturers.
Needless to say that our scenario of an immediate cure is unrealistic: When extrapolating the mortality decline over the past 20 years [
12] into the future, it would take approximately 44 years for a final cure to arrive and obtain a normal remaining life expectancy for cancer patients (at present, the 5-year mortality rate of cancer patients compared to the normal U.S. population is increased by 31% [
12]). Nevertheless, the scenario of an immediate cure is useful in order to analyze whether current drug prices are justified. Likewise, the absence of budget restrictions cannot be considered realistic. Yet, assuming it is helpful in laying out the consequences of spending on cancer care at current prices.
Results
Based on the cause-elimination life-table approach curing cancer in Germany yields an increase in life expectancy at birth by 3.25 life years. The average gain in the total population is 2.66 years (see Table
1). The resulting increase in lifetime health expenditures in the total population is small, however (€10,028). The reason is that life extension costs from eliminating cancer mortality are almost offset by savings from eliminating cancer morbidity.
Table 1
Health expenditure and life years per person over remaining lifetime (without consideration of the cost of drug treatment)
Base case |
Current care | 135,303 | 39.11 | | |
Cancer cure | 145,331 | 41.77 | 10,028 | 2.66 |
3% discount rate |
Current care | 54,798 | 18.11 | | |
Cancer cure | 59,317 | 19.14 | 4519 | 1.03 |
Nevertheless, when adding the cost of drug treatment, the picture changes completely. The current exchange rate between money and health is on average €101,493 per life year gained (€39,751/0.39 life years). Thus, we would need to pay on average €270,469 (€101,493 · 2.66) for the cancer cure itself in order to obtain the gains in life expectancy from cancer elimination (accounting for the age structure of the population). Subtracting savings from eliminating cancer morbidity and adding costs of life extension increases the total to €280,497. Dividing the latter figure by the current remaining lifetime health expenditures (again adjusted for the age structure of the population) yields a ratio of 2.07, which represents a multiplier of current health expenditures. The multiplier changes only little (to 1.99) after discounting of costs and life years but more substantially when accounting for generic/biosimilar entry (to 1.35). When accounting for a 25% reduction in the incremental costs of new drugs, the multiplier falls in-between the two estimates (1.57). Based on the annual cancer incidence in Germany, the drug cost per cancer patient treated and cured is €704,099 even accounting for generic/biosimilar entry.
Discussion
This study shows that eliminating cancer at the current exchange rate between money and health would increase total health expenditures in Germany 3.07-fold or by 207% in the base-case analysis and 2.35-fold accounting for generic/biosimilar entry. The underlying gain in life expectancy from cancer elimination is in line with the results of other studies. For example, the gain for female and male newborns in the Netherlands was reported to be 3.6 and 4.1 years, respectively, based on data from 2009 [
29], whereas in the U.S. the gain for newborns was estimated to lie between 2 and 3 years in the period between 2001 and 2008 [
30].
Based on the
gedankenexperiment the percentage of income spent on SHI in Germany would grow from currently 15.7% (which includes an average supplementary premium of 1.1% [
31]) to 37% even considering generic/biosimilar entry. Disregarding the macroeconomic implications of such labor cost increase (e.g., in terms of competitiveness of German goods and products in the international market), the question appears whether the German population would support the necessary drastic reduction of non-health consumption. Also, the reduction of non-health consumption could reduce the survival benefit of eliminating cancer. This will happen if the negative health impact of spending less on nutrition, hygiene, better social conditions, and so forth outweighs any positive impact such as a reduction in the use of cars.
Even a 50% discount from current prices for new cancer drugs in conjunction with the consideration of generic/biosimilar entry would still imply that 27% of income in Germany is spent on health care. To reduce this share to let’s say 20% of income it would be necessary to command an 83% discount from current prices. This implies not only to bend the ‘price curve’ but a much more drastic reversal of the current trend of increasing drug prices. Hence, it may be fair to say that, taken to an extreme, the R&D cost argument as the fundamental justification for today’s prices does not align well with the presumed willingness to pay of the German SHI. It seems at least questionable that insurees would be willing to pay this amount for a cancer cure in order to account for R&D costs, once the portion of their income spent on health care has reached a certain threshold and significantly cuts into their non-health spending. But if the extrapolated price for a cure lacks justification as implied in this study, then it appears that current prices even for small steps towards the cure (the small gains in life expectancy) need reconsideration as well. One may invoke the notions of diminishing marginal benefit of additional life years and diminishing severity of cancer here, on the basis of which the willingness to pay for more distant steps towards the cure would be lower than for the initial steps. This stands in contrast to what is implied by the concept of diminishing marginal benefit of R&D, however, which is that later market entrants are justified in commanding higher prices. The latter principle thus suggests that current prices cannot be easily compensated by sufficiently large discounts for products entering the market later.
One may counterargue that such discounts may even be possible when envisioning a single-step cure because R&D costs of such a drug would be distributed over a large patient population. In fact, in a similar
gedankenexperiment to this one, Bhattacharya et al. [
32] assumed a single-step cure at a cost of just $10,000 per cancer patient, which was deliberately chosen to be optimistically low even at the time of their publication. But a single cure would, of course, deviate from the past history of small incremental gains in life expectancy, which the present study uses as a basis for its calculation in order to test the plausibility of the R&D cost argument. Hence, such a miracle drug seems unrealistic, at least when it comes to curing cancer as such (acknowledging that for specific types of cancer or patient subgroups a cure may be both conceivable and affordable). One may counterargue that obtaining any immediate cure – be it through a miracle drug or the sum of small incremental innovations – is unlikely and purely hypothetical. Therefore, the
gedankenexperiment would fail. Yet, similar hypothetical scenarios and thought experiments are common in the health economics literature. Consider, e.g., the question posed by the time trade-off (TTO) questionnaire, which elicits quality-of-life weights and underlies one of the most common health-related quality-of-life questionnaires used in clinical research, the EuroQol five dimensions questionnaire (EQ-5D): The TTO questionnaire asks for the number of remaining life years one is willing to give up in order to be cured from, say, cancer. This is very similar to the trade-off raised by this article, viz., how much non-health consumption (in monetary terms) we as a society are willing to give up in order to be cured from cancer. That is, in both cases we capture a trade-off involving a hypothetical cure for cancer.
In addition, one may criticize the logic of taking high prices for small incremental innovations to an extreme. But again, such linear extrapolation is common in the health economics literature. For example, when eliciting the willingness to pay for a QALY in the general public by a survey, the estimate is obtained only for a fraction of a QALY in order to avoid hitting an income constraint [
33,
34]. The willingness-to-pay value is then extrapolated to match a full QALY [
33,
34]. Similarly, the calculation of the ICER extrapolates the cost of gaining less than one QALY to a full QALY using linear extrapolation. The only difference is that this study extrapolates to more than one unit of health outcome whereas the former approaches extrapolate to exactly one unit of health outcome.
Furthermore, one may counter that a growing economy would be able to accommodate future cancer drug expenditure increases. However, while price levels may be sustainable, their justification based on R&D costs fails as shown in the extreme-case scenario envisioned in this study. Hence, there is a difference between what we are able to pay on the one hand and what we are willing to pay considering the opportunity costs on the other hand.
As a word of caution, modeling studies such as this one are rarely perfect due to constraints of resources, time, and information availability. On the one hand, our model even underestimates the costs of a cancer cure because costs of drug-related AEs and drug-related services are ignored and costs of cancer treatment are limited to a period of 1 year. That is, I do not account for the fact that some cancers have a chronic course, thus mandating treatment for more than 1 year. Also, costs of curing cancer do not include a potential premium for eliminating anxiety associated with cancer (cf. [
35]). Fully accounting for these aspects would increase the costs of a cancer cure and support the conclusions of this paper. Furthermore, cancer survivors are at increased risk for cardiovascular disease [
36]. It remains to be investigated, however, whether an increase in expenditure for cardiovascular disease (and thus the costs of a cancer cure) is offset by less spending on non-cardiovascular disease due to earlier death. On the other hand, costs of a cancer cure are overestimated because separate modelling of expenditure data for survivors and decedents as opposed to using age-specific average cost data would decrease life extension costs associated with the elimination of cancer mortality cf. [
37]. Also, the survival benefit is underestimated as it is confined to the trial period [
18]. Therefore, the current exchange rate between money and health is overestimated and so is the cost of a cancer cure. Some of the biases mentioned in this and the previous paragraph may cancel out, however.
Arguably, a more comprehensive assessment of the health gain from cancer elimination could be obtained through the QALY metric, which combines survival with a valuation of health-related quality of life [
38]. I did not calculate QALYs, however, due to a lack of aggregated data on cancer-related quality of life. If currently available treatments reduced the morbidity and mortality burden of cancer to the same degree, results would be exactly the same as for the calculation of life years (because each life year gained would be associated with a proportional quality-of-life improvement both for the current and the remaining burden reduction). Yet, if the impact of current cancer drugs were smaller on the morbidity burden, a cancer cure would result in even higher expenditure when using the QALY metric. The reason is that the remaining morbidity burden that would need to be eliminated by a cure would become larger, resulting in more QALYs gained by a cure and higher expenditures based on the fixed exchange rate between money and health. In any case, even the QALY metric is not able to fully capture an elimination of cancer-associated anxiety.
Transferability of the results from a German setting to other jurisdictions depends, among others, on how the population burden of cancer, costs of new cancer drugs, and health care expenditures as a percentage of income compare to Germany. Taking the U.S. as an example, spending on cancer drugs as a percentage of total drug expenditures is lower than in Germany (11.5% vs. 15.9%) [
6] but health care expenditures as a percentage of Gross Domestic Product (17.2% vs, 11.3%) and incidence of malignant neoplasms (318 vs. 284 per 100,000) are higher [
39]. Therefore, given that these differences cancel out to some degree, the results of this study may also apply to other jurisdictions such as the U.S.
Conclusions
This article has scrutinized the justification of high cancer drug prices for small gains in life expectancy. When taking high prices for small gains in life expectancy to an extreme, they do not seem to be aligned with the presumed willingness to pay by German social health insurees. From this perspective current prices do not seem justifiable, while acknowledging that they are sustainable at least in the medium turn, presuming that economic growth in Germany will return to pre-pandemic levels.
As stated in the introduction, high prices of new cancer drugs may not only be seen as a manufacturer’s compensation for past R&D but also as an incentive for future R&D. If the current exchange rate between money and health were decreased by stricter price control, would it disincentivize cancer research? The relationship between pharmaceutical sales and innovation is complex and while there is evidence for a positive effect of pharmaceutical sales on the number of clinical trials conducted at a national level, basic research activities may not be affected, at least not in the U.S. [
40,
41]. As the German market is much smaller in size, the impact of stricter price control on innovation is expected to be even less tangible. Nevertheless, stricter price control by payers will not be a straightforward panacea in face of potential market entry delays or even market withdrawals.
As an alternative solution to incentivizing R&D of future cancer drugs, it may be more efficient, from a policy perspective, to reallocate funds to preventive oncology. In fact, increasing prices for new cancer drugs makes prevention more cost-effective due to larger savings from avoiding cancer. Nevertheless, prevention may suffer from a problem analogous to that of a small target population, which is a potentially high number of patients that need to be enrolled in a prevention program to avoid one cancer death. In addition, the presence of life extensions costs from reducing cancer-associated mortality sets limits on efficiency gains. Needless to say, the cost-effectiveness of cancer prevention may also depend on the type of cancer. Therefore, the search for pragmatic solutions for the conundrum identified in this article needs to continue.
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