Short-term predictions of the total medical costs of California counties

Gary Kleinman, Dinesh Pai, Kenneth D. Lawrence

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


The aim of this research is to develop a model to forecast short-term health cost changes. The motivation for producing such a model is to provide local decision makers with a tool to predict short-term health-care costs in their localities. In order to achieve this objective, we collected data on total health-care expenditures and demographic data for California counties from 2000 to 2007. We then used various statistical methods to better understand the data and developed a regression model. Each year's prediction model was then used to forecast the following year's total health-care expenditure. The model developed adequately predicted health-care costs for the years on which the model was developed (2000-2006), and adequately forecast health-care costs for the holdout year, 2007. The average adjusted R2 value was 0.57, with an average mean absolute deviation score of 34. The best predictors of total health-care expenditures were county population, the number of county health-care facilities, and county per capita personal income. The practical implications of the model are that it will provide public and private decision makers with a useful tool for forecasting short-term demand for health-care services, enabling better planning for health-care manpower, facility planning, and financial planning needs. The contribution of this paper contrasts with the earlier work in that it supports shortterm operational, not strategic, planning needs. The paper's limitation is that it relies on data from one state. It should be tested in other, dissimilar, areas of the United States.

Original languageEnglish
Title of host publicationAdvances in Business and Management Forecasting
EditorsKenneth Lawrence, Ronald Klimberg
Number of pages9
StatePublished - 2011

Publication series

NameAdvances in Business and Management Forecasting
ISSN (Print)1477-4070


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