Healthcare back matter

We consider the problem of outcomes based budget allocations to prevention programs across different states 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 robust weighted sum model for outcome adjusted budget allocation. An inverse linear programming technique is presented and used to identify the weights used by decision-makers in such models. Our results show that the CDC budget allocation process for the DPCPs is not likely to be model based. Relative weights for different risk factors and corresponding budgets obtained under different weight regions are discussed. Parametric analysis shows that, when all possible weights are allowed in our robust multi-objective model, frequency of checking HbA1c, risk for limb amputation, diabetes retinopathy or diabetes education can all be used as a surrogate marker for allocating DPCP budgets.