Evaluating the cost-effectiveness of vaccination programmes: a dynamic perspective.
Although there are many models which are used to calculate the health benefits (and thus the cost-effectiveness) of vaccination programmes, they can be divided into two groups: those which assume a constant force of infection, that is a constant per-susceptible rate of infection; and those which assume that the force of infection (at time t) is a function of the number of infectious individuals in the population at that time (dynamic models). In constant force of infection models the per-susceptible rate of infection is not altered, whereas in dynamic models mass immunization results in fewer infectious individuals in the community and thus a lower force of infection acting on those who were not immunized. We take an example of each of these types of model, examine their underlying assumptions and compare their predictions of the cost-effectiveness of a mass immunization programme against a hypothetical close contact infection, such as measles. We show that if cases of infection are the outcome of interest then the constant force of infection model will always underestimate the cost-effectiveness of the immunization programme except at the extremes when no one or everyone is immunized. However, unlike the constant force of infection model, the dynamic model predicts an increase in the average age at infection after immunization which could impact on the estimate of the cost-effectiveness of the programme if the risk of developing serious disease is a function of the age at infection (as, for instance, is the case for congenital rubella syndrome). Taking cases of infection as the outcome measure and using the dynamic model, the undiscounted cost-effectiveness ratio will tend to decline over time and approach a constant value, as the system moves from pre- to post-immunization equilibrium. We go on to show how the cost-effectiveness of a fixed-term immunization programme might change over time, and discuss why, under most circumstances, decision makers should not assume that elimination (permitting termination of mass immunization) will occur.
Edmunds
W J
WJ
Department of Biological Sciences, University of Warwick, Coventry, CV4 7AL, U.K.
Medley
G F
GF
Nokes
D J
DJ
D015331:Cohort Studies; D003140:Communicable Disease Control / Q000191:economics* / Q000706:statistics & numerical data; D003362:Cost-Benefit Analysis; D003657:Decision Making; D006801:Humans; D015994:Incidence; D008954:Models, Biological; D018803:Models, Economic; D011157:Population Dynamics; D014611:Vaccination / Q000191:economics* / Q000706:statistics & numerical data