The Allocation of Scarce Resources in Public Health
Scherrer, Christina Robinson
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As health care costs continue to increase at rates higher than the general inflation rate, there is increased focus on controlling health care expenditures in the public and private sectors. In particular, there is a compelling need for more creative and informed allocation decisions for limited government public health funds. This thesis suggests several methods for better forecasting the demand for health care and allocating health care resources more efficiently. First, productivity of dental sealant programs is studied and suggestions are made for increased efficiency. Using simulation and data from several states programs, guidelines are offered for optimal programs based on program size, distance to site, and practice act requirements. We find that under most conditions, it is better to carry an extra dental assistant to every program. The cost of satisfying practice act requirements is also quantified. Second, a model for allocating health resources to Community Health Centers (CHCs) is provided. Using the state of Georgia as a prototype, local estimation is used to forecast county insurance types, disease prevalence, and likelihood of using a clinic. Then, the optimal locations and service portfolios to be offered under financial constraints are determined using a developed mixed-integer programming model. Finally, shortcomings in current Markovian modeling of chronic disease are analyzed. Common forecasting techniques can overestimate or underestimate the population in need of care, as illustrated by analytic results and an example with lung cancer data. The chapter presents suggestions for improving such modeling. Each of these issues affect the planning models for scarce resources in health care, and improving those models can positively impact utilization of those services. Through this research, models are presented that can positively impact public health decisions in coming years, particularly those for growing high-risk and low-income groups.