Genetic testing has many promising applications, including the possibilities of assessing predisposition to disease and predicting drug efficacy or toxicity in individuals with specific genetic profiles.1,2 Determining how best to use these technologies will require consideration of the clinical benefits and costs, both to individuals and to society.3- 5 Cost-effectiveness analysis is increasingly being used to weigh these factors and thus to determine the relative value of new technologies.
The fundamental principle of cost-effectiveness analysis is that choices must be made between alternative uses of limited resources. A cost-effectiveness analysis can illustrate the relationship between the net resources used and the net health benefits gained for a specific clinical intervention (such as genetic testing) compared with an alternative (such as phenotypic testing). It can illustrate the tradeoffs with different policy choices and can provide quantitative insight into the relative importance of different parameters, thus helping to determine which variables are most important to measure in clinical research.
Genetic tests will generally be used to identify susceptibility to disease or to a certain type of drug response, rather than to confirm the presence of disease.6 The cost-effectiveness of genetic testing will depend on the value of this information to patients and to society. Susceptibility is determined by the risk of disease among gene carriers (i.e., gene penetrance), which may vary substantially between high-risk families and the general population. Therefore, the most critical parameters in cost-effectiveness analyses of genetic testing will be the target population, the prevalence of the mutation, and gene penetrance.4
Genetic tests for the detection of variant genes are typically very accurate for the detection of the presence or absence of a mutation. For example, consider a woman identified with a BRCA-1 mutation, which is associated with familial breast cancer. If she has the mutation, the probability of a positive test result is close to 100% (i.e., sensitivity of 1.0). However, the appropriate measures of sensitivity and specificity for use in a cost-effectiveness analysis—the clinically meaningful test characteristics—should reflect the degree of association between the genotype and the clinical phenotype. For a cost-effectiveness analysis to be useful in informing policy, it should consider all clinical and economic events triggered by the positive test result. For a woman who tests positive for the BRCA-1 mutation but is not destined to develop breast cancer, the benefits of a prophylactic mastectomy would be negligible but she would still bear the huge costs of the psychological anxiety and health care resources associated with lifelong screening. From the perspective of a genetic screening program, this should be considered a "false-positive" result.
A genetic testing strategy is more likely to be cost-effective when the genotype and clinical phenotype are tightly linked, when the next best alternative is less effective or more costly, and when there is an effective intervention that can be implemented on the basis of the genetic information. It is less likely to be cost-effective when penetrance is incomplete, when effective alternative tests exist, and when there is no treatment for the disease.4,5 We will describe several specific aspects of cost-effectiveness analysis that are particularly relevant to genetic screening.
The perspective of a cost-effectiveness analysis dictates which costs and which health benefits should be counted.7 For studies that affect the broad allocation of health care resources, a societal perspective is recommended. This means that all costs and all health effects should be incorporated regardless of who incurs the costs and who obtains the clinical benefits. This is particularly relevant for genetic testing because unlike other medical tests, genetic tests reveal information not only about patients but also about their relatives. For example, in addition to direct medical costs borne by the patient, a cost-effectiveness analysis of BRCA-1 testing should include costs related to any consequences (e.g., psychological harms) experienced by family members. Analyses that adopt other perspectives are no less valid, but serve different goals. For instance, from the patient's perspective, the most relevant costs might include the future medical costs borne out of pocket due to loss of health insurance.
The time horizon of an analysis should also be long enough to incorporate all relevant future effects of an intervention.5 In most cases, modeling will be required to extend the analysis beyond the original time frame of the primary data to estimate longer-term outcomes.7 Thus, there will be inevitable assumptions with respect to data extrapolation and imputation. For example, data may be available for the prevalence of a genetic variant and the corresponding risk of cancer in the gene carrier. To estimate life expectancy, the analysis would need to combine data on cancer incidence, treatment efficacy, and the probability of survival conditional on the stage of disease. This process will involve the specification of survival parameters, the choice of disease-specific or total mortality data, and the decision to represent certain event probabilities as conditional upon patient characteristics, such as age, sex, risk factors, stage of disease, and prior morbid events. The implications of these assumptions will need to be explicitly described when reporting cost-effectiveness results.
Genetic testing may affect a person's health-related quality of life in both positive and negative ways.1,3 For example, there are emotions aroused by learning that one is—or is not—likely to develop a serious disease, and reliable methods to measure this psychological impact are still needed. The usual approach to incorporate both the prolongation and quality of life in cost-effectiveness analyses is to express clinical benefits in terms of quality-adjusted life years (QALYs). QALYs represent the benefit of a health intervention in terms of time in a series of health states, which are assigned a weight that reflects the desirability of living in the state, typically from "perfect" health (weighted 1.0) to dead (weighted 0.0). Once the quality weights are obtained for each state, they are multiplied by the time spent in the state; these products are summed to obtain the total number of QALYs. These quality weights reflect the fact that people with similar abilities to function, or in similar current health, may value that level of health differently. For example, 2 individuals with identical health status and the same variant of the familial adenomatous polyposis gene might very well perceive colectomy differently and thereby place different quality weights on this health state. Thus, even if they faced identical life expectancies their quality-adjusted life expectancies would differ by virtue of their individual preferences.
While measures of health outcomes are included in the denominator of the cost-effectiveness ratio, all relevant costs related to the intervention itself (e.g., counseling) and the downstream events triggered by different test results (e.g., screening) should be included in the numerator.7 These include direct health care costs (e.g., testing, medication, procedures), direct non-health care costs (e.g., transportation costs for clinic appointments), and patient time costs. Other costs likely to be important in the context of genetic testing include those needed for public health education efforts, training of genetic counselors, privacy safeguards in health-care settings, and anticipated litigation.
While a genetic test may be costly, the long-term consequences may make it an efficient use of resources (i.e., cost-effective). For example, genotyping can detect mutations associated with resistance to antiretroviral drugs for HIV.This information can then be used to optimize the choice of antiretroviral therapy. Although the test costs more than $500, genotyping for resistant mutations has been found to be cost-effective.8
The results of a cost-effectiveness analysis are summarized using an incremental cost-effectiveness ratio, which represents the incremental price of obtaining a unit health effect (usually dollars per QALY) as a result of a given clinical intervention when compared to the next best alternative. Because cost-effectiveness analyses are always incremental, the intervention of interest (eg, genetic testing) must be compared to all reasonable alternative strategies. If all relevant options are not included, there is a risk that genetic testing will erroneously be found to be cost-effective. For example, an analysis to evaluate the cost-effectiveness of detecting cytochrome p450 mutations (which are associated with poor metabolism of warfarin) would need to assess the additional costs and benefits of genotyping compared with the relevant phenotypic test (e.g., routine monitoring of the international standardized ratio).5
The uncertainty in a cost-effectiveness analysis is evaluated by sensitivity analysis, which involves testing the stability of the conclusions over a range of parameter estimates and structural assumptions. In the context of genetic testing, special attention should be paid to understanding the implications of varying parameters governing the frequency and severity of the clinical and economic consequences of the disease, the phenotypic expression of genetic variation, and the genetic test characteristics.
Advances in genetic science will undoubtedly influence clinical medicine, public health, and health policy. Developing sound policy for questions related to genetic testing must take into account issues wider than the health of the patient because the consequences extend to other related individuals, as well as to society at large. As a result of the pace at which specific genes are being implicated in disease processes and drug metabolism, there is a risk that genetic testing policy could be made prematurely. It is important to ensure that clinical recommendations do not outpace the rate at which the effectiveness, the balance between risks and benefits, and the cost-effectiveness of genetic testing can be rigorously evaluated.
Goldie SJ, Levin AR. Genomics in Medicine and Public Health: Role of Cost-effectiveness Analysis. JAMA. 2001;286(13):1637-1638. doi:10.1001/jama.286.13.1637-JMS1003-5-1