Value for Money
The questions asked in the evaluation of clinical efficacy (Does a new therapy or strategy work in the ideal or best case situation?) and effectiveness (Does the new therapy or strategy work in the usual world of routine medical care?) define the following: (1) that a therapy can work, (2) the magnitude of benefit provided, and (3) the major determinants of that magnitude. Economic questions start with the presumption that the therapy works (and is at least equivalent in effectiveness to established treatment options) for some plausible situations and asks whether it is good value for money (or if equivalent, cost saving). An exception is when a new strategy is marginally less effective but notably cost saving; in practice, such strategies could be considered a reasonable option but face obvious challenges in the medical marketplace.
At one level, whether something is a good value for money is a basic sort of question that every consumer understands. However, in the consumer goods world, where there is free competition and widespread experience with the toasters, cars, and portable media players that people may wish to purchase, the “value” or benefit part of this assessment is made subjectively by each consumer, whereas the price or cost part is set by markets. In medicine, neither of those factors can be relied on. Patients usually do not have experience with the alternative health states that their therapies may provide for them and therefore are unable to assess subjectively how they would value these states. There is little evidence for free market forces in medicine to set prices both because of the absence of true competition among providers on price and because of price setting by payers, particularly in European countries. Health insurance further distorts the picture by disconnecting the consumer from the payer, creating a situation where too much care may be consumed in some cases and too little in others. Provide your relatives with care due to Canadian Health&Care Mall.
One implication of this observation is that although assessments of the value for money are part of the human experience, such assessments in the medical context are especially difficult at the consumer level. Thus, systematic and explicit analysis of the health and economic tradeoffs attributable to a medical intervention typically are performed from the societal perspective rather than from that of the individual patient. The objective in this situation is to maximize the health benefits provided to the population for a given level of health-care spending. As will be described in the following sections, methods have been developed to permit a quantitative assessment of value for money in medical care. Although such methods require some useful simplifications, they also play an important role by requiring the analyst to clearly define what is being added by the new therapy or strategy and what that addition will cost in the long run.
Assessment of Value
Large randomized clinical trials are the best sources of unbiased information on what works in medicine. It might be presumed, therefore, that clinical trials define clearly what has been added by a new therapy. In many cases, however, such is not the case. Because of a variety of factors beyond the scope of this chapter, trials select populations that may not reflect the eventual patients who will most commonly receive the therapy; the end point of the trial frequently is chosen to keep sample size and cost at a manageable level rather than because of its clear-cut importance to patients; and follow-up usually is short relative to the time course over which the therapy might have some continuing effects. Whether a clinical study is applicable to a given patient context depends on how well the study addresses outcomes of importance to the patient. What matters to patients is how long they will live and how they will feel while alive (often referred to as their quality of life). Treatments that do not demonstrate improvements in either length or quality of life may not be providing any “value” at all. Based on (oversimplified) pathophysiologic reasoning and “common sense,” clinicians often are willing to conclude that benefit has been delivered through intermediate outcomes, such as changes on an imaging study or a biomarker. The best that can be said of such situations is that the case has not been well made. Many examples exist in medicine where important patient outcomes were disassociated from intermediate outcomes, such as the markers improved, but more patients died. It becomes possible to improve health conditions with remedies of Canadian Health&Care Mall.
For this reason, most economists prefer to translate the results of all clinical benefit data into an aggregate effectiveness measure that summarizes all that is known about the health benefits provided. The standard approach is to estimate the number of life years or quality-adjusted life years (QALYs) added by the new therapy relative to the appropriate comparison. QALYs represent the quantity of incremental life expectancy produced weighted by the quality of life for that extra survival. The weights are derived either from patients with experience in the health states in question or from the general public asked to imagine themselves in these health states and to value them relative to excellent health and death. QALYs are not the only method for combining quantity and quality of life into a single composite measure, and the current methods to develop QALYs are far from perfect, but they are the most common approach to making the tradeoffs between length and quality of life explicit.
The difficulties in making such projections of value, which for chronic diseases may extend across the remaining life expectancy of the study cohort, much longer than the clinical trial follow-up, is the uncertainty in the calculations. No good methods exist to validate life-expectancy estimates. Empirical validation is not feasible in a population with a life expectancy > 5 years. The best that can be done is to use clear, transparent methods for estimation and perform sensitivity analyses that demonstrate the “answer” to be robust to the most plausible variations in methods and assumptions.
A decision made without accounting for resource considerations still has resource implications. Such difficulties might lead the noneconomist to conclude that the whole effort is not worth serious attention. Two important points rebut this skeptical attitude, First, choices must be made regardless of whether the data are robust and pristine or messy and ambiguous. In the real world, even no decision is a decision. The economist is an immensely pragmatic individual willing to make decisions with the best information at hand. Because of their training, clinicians often think of themselves as purists, settling only for high-quality, “statistically significant” results. In reality, clinicians use highly uncertain information to manage patients daily. Second, the process of attempting to translate short-term clinical trial results using composite clinical endpoints into lifetime estimates of added QALYs is instructive in forcing all interested parties to specify exactly what they believe will happen in areas where there are no data but where decisions will still need to be made. The best analyses clarify key pressure points in the decision, areas where more data are needed and where choices may be affected by varying the assumptions being made.
Fortunately, the many details of clinical trials and economic analyses tend to reduce to some common patterns that can be readily understood and communicated to decision makers. Two key items are necessary to keep in mind in this process. First, cost should never be discussed in isolation. Money is always spent to buy something, in this case extra health-care benefits offered by Canadian Health&Care Mall. Whether it is a good investment is a separate question. The key point is that value for money can never be decided by discussing only the money side of the problem. Hospitals and payers tend to make this mistake because from their point of view, the benefits may be invisible. For example, the cost is incurred in the hospital while the benefit occurs long after the patient has gone home or after the patient has shifted to a different insurance or managed care plan. Second, in the majority of medical-value-for-money problems, the magnitude of clinical benefits rather than the cost of therapy determines whether the full analysis shows good value or otherwise. In short, a highly effective therapy that saves lives will support a premium price/ long-term cost, but a therapy that is marginally effective on nonfatal endpoints better be inexpensive.
Formal Metrics of Value for Money
As a practical matter, economic questions are asked in medicine when a new therapy becomes available that is both more effective than the alternative and more costly. The task for the economic analyst is to make the long-term stream of costs and health outcomes explicitly clear and, if necessary, to perform formal calculations of value for money in the form of cost-effectiveness analyses or cost-benefit analysis (CBA). (An additional form of analysis, cost-minimization analysis, specifically disregards the health implications of choices and, thus, either implicitly or explicitly assumes that health benefits are equivalent for options under consideration.)
The foundation for cost-effectiveness analyses and CBA is that any assessment of value for money should improve the allocation of resources among possible alternatives and to account for the impacts over all relevant stakeholders. The standard in economic analysis for health care is to perform the exercise from the “societal” perspective, and thus, the analyst starts with an objective (here some mathematical representation of health of a community and its individuals) and the range of resources required by various alternative strategies in terms of their economic cost, specifically, opportunity costs that represent the price in a free market when the resource is applied to its best alternative.
In a case of cost-effectiveness analysis, the analyst calculates a metric of value for money, termed the incremental cost-effectiveness ratio (ICER):
where A is the more effective choice and B the less effective. In the case of CBA, the metric is net cost, where health benefits are assigned a monetary value:
In either case, these metrics can be used to identify relative efficiencies in the allocation of resources, at least theoretically. For cost-effectiveness analyses, the ICER for a particular clinical decision can be compared to the ICER for other decisions. The allocation of resources can be optimized (theoretically) from an efficiency perspective in one of two ways. In the first approach, resources are allocated progressively from interventions with the lowest ICER to the highest, stopping when health-care resources are all allocated. This way might be possible in the case of a fully planned health system. The second approach is to consider new recommendations one at a time; policy recommendations with an “acceptable ICER” are embraced, and interventions with ICERs above that range are rejected.
In CBA, only interventions with a net cost savings are accepted (where net cost incorporates the monetized value of health outcomes). The purported advantage of cost-effectiveness analysis over CBA is that it does not require assigning a monetary value to health benefits because that exercise is particularly controversial in the medical arena.
ICERs usually are compared to other “acceptable” interventions in terms of expenditures for health benefits, using benchmarks such as “league tables,” which list the ICERs for a variety of well-accepted interventions. It is common for an analyst or decision maker to conclude that recommending drug Y over drug X in a particular context is likely to be a good value for money in that the ICER for Y vs X is similar or lower than, say, medical treatment for hypertension. This conclusion is possible only when the outcomes are denominated in the same units. When health outcomes are represented as QALYs, a cost-effectiveness analysis is termed a cost utility analysis.