Health Care Management Science

, Volume 14, Issue 4, pp 324–337 | Cite as

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

Article
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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.

Keywords

Diabetes Budget allocation Robust optimization Multi-objective optimization 
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Notes

Acknowledgement

The authors thank Tito Homem-de-Mello and Jian Hu for early discussions about the modeling aspects of our budget allocation model. This research is partially supported by NSF grants CMMI-0727532 and CMMI-0928936.

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Industrial Engineering and Management ScienceNorthwestern UniversityEvanstonUSA

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