Portfolio decision analysis for population health

Mara Airoldi, Alec Morton

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Citations (Scopus)

Abstract

In this chapter, we discuss the application of Multi-Criteria Portfolio Decision Analysis in healthcare. We consider the problem of allocating a limited budget to healthcare for a defined population, where the healthcare planner needs to take into account both the state of ill-health of the population, and the costs and benefits of providing different healthcare interventions. To date, two techniques have been applied widely to combine these two perspectives: Generalized Cost Effectiveness Analysis and Program Budgeting and Marginal Analysis. We describe these two approaches and present a case study to illustrate how a simple, formal Multi-Criteria Portfolio Decision Analysis model can help structure this sort of resource allocation problem. The case study highlights challenges for the research community around the use of disease models, capturing preferences relating to health inequalities, unrelated future costs, the appropriate balance between acute and preventive interventions, and the quality of death.
Original languageEnglish
Title of host publicationPortfolio Decision Analysis
Subtitle of host publicationImproved Methods for Resource Allocation
EditorsAhti Salo, Jeffrey Keisler, Alec Morton
PublisherSpringer-Verlag
Pages359-381
Number of pages23
ISBN (Print)9781441999429
DOIs
Publication statusPublished - 2011

Publication series

NameInternational Series in Operations Research & Management Science
PublisherSpringer
Volume162
ISSN (Print)0884-8289

Keywords

  • population health
  • portfolio decision analysis

Fingerprint Dive into the research topics of 'Portfolio decision analysis for population health'. Together they form a unique fingerprint.

  • Cite this

    Airoldi, M., & Morton, A. (2011). Portfolio decision analysis for population health. In A. Salo, J. Keisler, & A. Morton (Eds.), Portfolio Decision Analysis: Improved Methods for Resource Allocation (pp. 359-381). (International Series in Operations Research & Management Science; Vol. 162). Springer-Verlag. https://doi.org/10.1007/978-1-4419-9943-6_15