Deciding about Screening

As shown in an accompanying article, the benefits of any broadly based screening program for ovarian cancer are limited more by the low prevalence of disease than by the potential benefits of treating early disease or the performance of currently available tests. Screening for such diseases is best restricted to special populations at markedly increased risk. In low-prevalence populations, false-positive results and the costs and risks of the workups they induce are magnified. In determining the marginal cost-effectiveness ratio of such programs, failure to consider even apparently minor factors can markedly affect the conclusions. In the case of screening for ovarian cancer, factoring in the discomfort and potential inconvenience of venipuncture and transvaginal sonography can eliminate the small benefit of the program. Taking the time and energy required to organize relevant data in constructing a decision analytic model can provide important insights into the usefulness of such screening programs.

Screening for preclinical disease can lead to early intervention and thereby improve health outcomes and possibly save resources. Because screening is rarely free of cost and often carries risk, either from the test itself or from the subsequent workup it can induce, we must decide when and for whom screening is appropriate. A formal decision analysis can help structure the problem, organize data, elucidate tradeoffs, and estimate benefits and costs.

In this issue of Annals, Schapira, Matchar, and Young [1] analyze the effect of screening 40-year-old women for early ovarian cancer. They conclude that screening would, on average, prolong life expectancy of each woman screened by only 14 hours and thus would not be an effective health policy. Although these investigators focused on the characteristics of screening tests and the risks of laparotomy, they correctly identify the crux of the problemthe low prevalence of ovarian cancer. Even assuming perfect test performance …