Using Monte Carlo Simulation to Determine Combination Vaccine Price Distributions for Childhood Diseases

Abstract

The Recommended Childhood Immunization Schedule provides guidelines that allow pediatricians to administer childhood vaccines in an efficient and effective manner. Research by vaccine manufacturers has resulted in the development of new vaccines that protect against a growing number of diseases. This has created a dilemma for how to insert such new vaccines into an already crowded immunization schedule, and prompted vaccine manufacturers to develop vaccine products that combine several individual vaccines into a single injection. Such combination vaccines permit new vaccines to be inserted into the immunization schedule without requiring children to be exposed to an unacceptable number of injections during a single clinic visit. Given this advantage, combination vaccines merit an economic premium. The purpose of this paper is to describe how Monte Carlo simulation can be used to assess and quantify this premium by studying four combination vaccines that may become available for distribution within the United States. Each combination vaccine is added to twelve licensed vaccine products for six childhood diseases (diphtheria, tetanus, pertussis, haemophilus influenzae type B, hepatitis B, and polio). Monte Carlo simulation with an integer programming model is used to determine the (maximal) inclusion price distribution of four combination vaccines, by randomizing the cost of an injection. The results of this study suggest that combination vaccines warrant price premiums based on the cost assigned to administering an injection, and that further developments and innovations in this area by vaccine manufacturers may provide significant economic and societal benefits.