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

Health Care Manag Sci. 2011 Dec;14(4):324-37. doi: 10.1007/s10729-011-9166-7. Epub 2011 Jun 15.

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.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Budgets
  • Comorbidity
  • Diabetes Mellitus / epidemiology
  • Diabetes Mellitus / mortality
  • Diabetes Mellitus / prevention & control*
  • Health Care Rationing / methods
  • Health Care Rationing / statistics & numerical data*
  • Health Promotion / economics*
  • Healthcare Disparities / ethnology
  • Healthcare Disparities / statistics & numerical data*
  • Humans
  • Models, Econometric
  • Prevalence
  • Principal Component Analysis
  • United States / epidemiology