Vaccine Approvals and Mandates Under Uncertainty: Some Simple Analytics (WP-14-29)
Charles F. Manski
Social interactions make communicable disease a core concern of public health policy. A prevalent problem is scarcity of empirical evidence that is informative about how interventions affect population behavior and illness. Randomized trials, which have been important to evaluation of treatments for non-infectious diseases, are less informative about treatment of communicable diseases because they do not shed light on population-wide disease transmission. In particular, trials do not reveal the indirect preventive (herd immunity) effect of vaccination on persons who are not vaccinated or who are unsuccessfully vaccinated. This paper studies the decision problems faced by health planners who must choose whether to approve a new vaccine or mandate an approved one, but who do not know the indirect effect of vaccination. Manski studies vaccine approval as a choice between a zero vaccination rate (rejection of the new vaccine) and whatever vaccination rate the health-care system will yield if the vaccine is approved. He studies the decision to mandate an approved vaccine as a choice between vaccinating the entire population (the mandate) and the vaccination rate that would be generated by decentralized health-care decisions. Considering decision making with partial knowledge, he shows that it might be possible to determine optimal policies in some cases where the planner can only bound the indirect effect of vaccination. Considering settings where optimal policy is indeterminate, he poses several criteria for decision-making—expected utility, minimax, and minimaxregret— and derives the policies they yield. He suggests that performance of formal decision analysis can improve prevailing vaccine approval and mandate procedures.