Volicer B J, Romagnoli D M
Department of Health, College of Health Professions, University of Lowell, MA 01854.
Health Serv Res. 1988 Dec;23(5):669-82.
The purpose of the project was to develop a model for predicting costs for potential new HMO subscribers, using available cost data from fiscal year 1985 for current enrollees of a large HMO. Regression analysis of aggregated clinic, referral, and hospital cost data using a log transformation of cost indicated that 20 percent of the variation in cost could be explained by sex and coverage type of the subscriber, compared with 7 percent explainable by a simple comparison of costs for single versus family subscribers. Subscriber age, while by itself a significant and nonlinear predictor of cost, was not significant when controlled for coverage type. Application of the model to 28 large companies yielded predicted costs well correlated with observed costs (r = .75, p less than .01). Prediction was significantly better for companies with low observed mean costs than for companies with high observed mean costs.
该项目的目的是利用一家大型健康维护组织（HMO）当前参保人1985财年的可用成本数据，开发一个预测潜在新HMO参保人成本的模型。对汇总的诊所、转诊和医院成本数据进行回归分析，采用成本的对数变换，结果表明，参保人的性别和保险类型可解释20%的成本变化，而单参保人与家庭参保人成本的简单比较只能解释7%的成本变化。参保人的年龄虽然本身是成本的一个显著且非线性的预测因素，但在控制保险类型后并不显著。将该模型应用于28家大公司，得出的预测成本与观察到的成本高度相关（r = 0.75，p < 0.01）。对于观察到的平均成本较低的公司，预测效果明显优于观察到的平均成本较高的公司。
