Pareto Group Instability and the Prediction of Health Care Claims Costs

Abstract

As the cost of health care has increased so have efforts to control costs. An assumption underlying many of these efforts is that health care costs can be predicted. A corollary assumption is that if costs can be predicted, then they can be managed. One fact used to support this approach is that a small group of people typically do create the vast majority of health care costs. The 20/80 rule indicates that within a given population, 20% of the people create 80% of the costs for a year period, with this high-cost subpopulation being called the “pareto group.” If one could predict who the pareto group will be, then the task of managing costs could be directed toward this smaller group rather than to all people in a health care system. This study investigated the stability of heath care costs using a methodology featuring a two-year longitudinal design, stratified random sampling, a large sample size (N = 974), claims system data and self-report survey data, and statistical testing. The typical “regression to the mean” effect was observed, as extreme cases (both high cost and low cost) moved toward the middle during the next year. Almost two-thirds of cases changed their claims cost group status from one year to the next year. The “pareto” group (top 20% of costs in the past year) was the most unstable, with less than 4% still classified at the same highest-cost level the following year. The most striking finding was that 92% of future claims costs could not be predicted, even when using past claims costs and relevant survey data on age, sex, health care visits and psycho-social concerns. Cost control implications of prevention, health promotion, and demand management services (such as employee assistance program counseling and education) are discussed.