What Is Cost-Effectiveness Analysis?

A key skill for emergency physicians is effective integration of new interventions into existing clinical practice. Physicians have classically been trained and have become comfortable with more traditional methodologies of evaluating new interventions and treatment modalities, such as randomized controlled trials, but are often less comfortable in applying techniques such as cost-effectiveness analysis (CEA). As health care resources become more limited, costs rise, and treatment options expand, interventions and treatments are increasingly being compared and evaluated on the basis of cost.¹

CEA falls under the broad umbrella of comparative effectiveness research, which refers to any study that compares the effectiveness of two or more strategies with or without regard to cost. CEA is a method for evaluating and comparing the outcomes and costs of interventions designed to improve health.² Although many methodologies fall under the umbrella of CEA, they all share the common goal of reporting outcomes as a cost per achieving a unit of health effect (i.e., lives saved, year of life gained, quality-adjusted year of life gained). CEA tracks costs and effectiveness for a number of strategies by using both real and modeled data, compares the cost-effectiveness of the strategies, and then accounts for uncertainty in the data through sensitivity analyses.³

How Does Cost-Effectiveness Analysis Affect the Emergency Physician?

CEA is becoming an increasingly important input into health policy decisions; however, the methodology does have limitations. CEA cannot incorporate all aspects of a decision, and the results are only as good as the data available to input into the analyses. Economic analyses such as CEA cannot capture every input necessary to make health care decisions and should not be reported as scientific fact, but models and their results may be reported as aids in decision making.⁴ An additional criticism of CEA is that although the economic models have become increasingly complex to better represent reality, they have also become increasingly opaque to the lay reader, thus making it difficult for those without a background in economics to personally interpret the results.¹ Despite these limitations, CEA offers the benefit of adding perspective to difficult questions regarding treatment choices in the setting of limited resources.^2,5,6 If emergency physicians are to act as decision makers in our changing practice environment, it is essential to have an understanding of the basic principles of CEA.

Methodology in Cost-Effectiveness Analysis

Inputs

Perspective

An important characteristic of any CEA is the perspective from which the analysis is conducted. The cost and effectiveness of an intervention may vary depending on the perspective. Common perspectives include those of the patient, the insurer, the employer, the hospital, or society, to name a few. For instance, from the perspective of an insurer, the cost of an intervention for a patient whom it insures is simply the amount of money paid to physicians and hospitals. However, from the perspective of an employer who provides health care insurance, the cost of an intervention additionally includes loss of productivity while the employee is ill and undergoing the intervention. The choice of perspective is of critical importance in performing and interpreting a CEA because it determines the relevance of the study for decision making.

Although one can perform a CEA from a vast number of perspectives, the most common is the societal perspective, which will be used for our example. In a CEA conducted from the societal perspective, the analyst considers all parties affected by the intervention and all costs related to the intervention, regardless of who experiences these costs and effects. Because the societal perspective includes all costs and health effects, it does not necessarily give an individual group the information necessary to make decisions on which interventions to implement. However, the societal perspective is used most commonly because it has several benefits.³ It is standardized and therefore allows comparison of various interventions across a broad spectrum of disciplines in medicine and society when making policy decisions. It is fair: if all decisions in health care were made from a societal standpoint, resources would be allocated to provide the most benefit to the most people. Moreover, although other perspectives may be more useful for individual groups, these perspectives do not necessarily take into account the cost or harm seen by those outside the sphere of the analyst’s perspective that the societal perspective takes into account.

Costs

Costs may be gathered from several different sources: institutional data, Medicare or other payment records, or elsewhere in the published literature. In our example study, the costs associated with the hospital stay, such as the cost of an exercise stress test and hospital admission, could be taken from institutional data. Because the study is conducted from a societal perspective, the true cost of these interventions to the hospital (as opposed to hospital charges) would need to be calculated. Other costs included in the study, such as the cost of a day of missed work and the cost of outpatient medical treatment of coronary artery disease (CAD), could be taken from the economic or medical literature. When evaluating a CEA, it is important to verify that the authors included all relevant costs for the stated perspective of the study and that these costs are plausible.

Effectiveness

In CEA, each strategy has an associated effectiveness. The effectiveness (sometimes referred to in the economic literature as utility) can be expressed with a number of different metrics. The most common measure is “life-years gained” or “lives saved.” Traditionally, most medical and public health studies use “life-years,” whereas CEAs in transportation and environmental policy use “lives saved.”⁷

Even though life-years may be used alone as an effectiveness measure, they are generally adjusted to account for quality of life or disability. Without adjustment, two interventions would have the same effectiveness based on life-years even if one substantially increased quality of life without any extension in life expectancy. The most common adjustment unit is the quality-adjusted life-year (QALY), although other units such as disability-adjusted life-year are sometimes used.⁸

Several methods can be used for quality-adjusting life-years to arrive at QALYs. All methods seek to find the value placed on various disease states among a group representing the interests of either a patient population or society. One commonly used method is known as the time trade-off. In a time trade-off, subjects are asked to choose between remaining in a particular health state for a fixed length of time or “trading off” that time for some shorter amount of time in perfect health. The ratio of time traded gives the QALY equivalent for the health state. For instance, if respondents would choose to equate 6 months of perfect health with 1 year in a health state, each year in that state would be worth 0.5 QALYs. Another commonly used method for quality adjustment is the standard gamble. Respondents choose between remaining in a health state or taking a gamble on a treatment that will either kill them or restore them to perfect health. If a patient in a particular health state is willing to take a 50-50 chance of dying or being restored to perfect health, that state is valued at 0.5 QALYs.

These methodologies for quality adjustment have limitations. Not all patients will value quality of life and disease states the same. The life-year–to-QALY conversion is not standard or universal, and when evaluating a CEA, one should verify that the adjustments seem appropriate. Controversy exists regarding the most appropriate derivation method, and the different approaches to derive QALYs may deliver different results even with the same respondent.

In our example model we could track the effectiveness of several outcomes. Our first outcome could be a disease-free outcome; this would represent the average quality-adjusted life span of CAD-free patients following discharge from the ED after a negative work-up. Another outcome would be a true diagnosis of CAD. Patients found to have CAD in the hospital would probably have a lower quality-adjusted life expectancy than those without CAD but would benefit from treatment of the disease. A third outcome would be patients discharged from the ED after a false-negative work-up; that is, those who have CAD but are sent home thinking that they are well. These patients would probably have the shortest life expectancy.

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