Thinking about Uncertainty

It has been argued that the health system should try to maximize the amount of health gain with available resources.

This means that decisions about efficient reimbursement strategies need to consider how much health various strategies produce, and their associated costs. If a unit of health (an extra year of healthy life, for example) can be produced more cheaply using one strategy versus another, it is said to be a cost-effective use of resources. A health care intervention may also produce more health than another strategy at additional costs. At this point, a decision maker will have to decide whether or not these additional costs are justifiable in a health care budget.

Even under tightly controlled conditions, measuring health effects and health care resources can never be done with absolute certainty. This means decisions considering this information can never be certain. Analysts can support decision makers by examining the effects of statistical uncertainty or “known unknowns” on their decisions.

A popular graphical method of doing this is the cost-effective acceptability curve (CEAC). A CEAC is a visual representation of uncertainty. It shows the probability that any one intervention is cost-effective, depending on how much a decision maker values an additional unit of health.

In the visual example on this page, given the uncertainties from the data obtained, intervention B leads to the best trade-off of health for resources unless there is a willingness to pay of more than $45,000 per life-year gained. At this point, intervention E is most likely to be cost-effective.

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