Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights

Sanjay Mehrotra*, Kibaek Kim

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

We consider the problem of outcomes based budget allocations to chronic disease prevention programs across the United States (US) to achieve greater geographical healthcare equity. We use Diabetes Prevention and Control Programs (DPCP) by the Center for Disease Control and Prevention (CDC) as an example. We present a multi-criteria robust weighted sum model for such multi-criteria decision making in a group decision setting. The principal component analysis and an inverse linear programming techniques are presented and used to study the actual 2009 budget allocation by CDC. Our results show that the CDC budget allocation process for the DPCPs is not likely model based. In our empirical study, the relative weights for different prevalence and comorbidity factors and the corresponding budgets obtained under different weight regions are discussed. Parametric analysis suggests that money should be allocated to states to promote diabetes education and to increase patient-healthcare provider interactions to reduce disparity across the US.

Original languageEnglish (US)
Pages (from-to)324-337
Number of pages14
JournalHealth Care Management Science
Volume14
Issue number4
DOIs
StatePublished - Nov 1 2011

Keywords

  • Budget allocation
  • Diabetes
  • Multi-objective optimization
  • Robust optimization

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Health Professions(all)

Fingerprint Dive into the research topics of 'Outcome based state budget allocation for diabetes prevention programs using multi-criteria optimization with robust weights'. Together they form a unique fingerprint.

Cite this