An Analysis of Pediatric Vaccine Formulary Selection Problems

作     者：Hall, Shane N. Jacobson, Sheldon H. Sewell, Edward C. 

作者机构：USAF Inst Technol Dept Operat Sci Wright Patterson AFB OH 45433 USA Univ Illinois Dept Comp Sci Simulat & Optimizat Lab Urbana IL 61801 USA So Illinois Univ Dept Math & Stat Edwardsville IL 62026 USA 

出 版 物：《OPERATIONS RESEARCH》 (运筹学)

年 卷 期：2008年第56卷第6期

页      面：1348-1365页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：National Science Foundation [DMI-0457176, DMI-0456945] Air Force Office of Scientific Research [FA9550-07-1-0232] 

主　　题：analysis of algorithms: computational complexity health care: pediatric immunization programming: integer algorithms heuristics dynamic vaccines 

摘      要：Vaccination against infectious disease is hailed as one of the great achievements in public health. However, the United States Recommended Childhood Immunization Schedule is becoming increasingly complex as it is expanded to cover additional diseases. Moreover, biotechnology advances have allowed vaccine manufacturers to create combination vaccines that immunize against several diseases in a single injection. All these factors are creating a combinatorial explosion of alternatives and choices (each with a different cost) for public health policy makers, pediatricians, and parents/guardians (each with a different perspective). The General Vaccine Formulary Selection Problem (GVFSP) is introduced to model general childhood immunization schedules that can be used to illuminate these alternatives and choices by selecting a vaccine formulary that minimizes the cost of fully immunizing a child and the amount of extraimmunization. Both exact algorithms and heuristics for GVFSP are presented. A computational comparison of these algorithms and heuristics is presented for the 2006 Recommended Childhood Immunization Schedule, as well as several randomly generated childhood immunization schedules that are likely to be representative of future childhood immunization schedules. The results reported here provide both fundamental insights into the structure of the GVFSP models and algorithms and practical value for the public health community.