Hornbrook M C, Goodman M J
Center for Health Research, Kaiser Permanente, Portland, OR 97227-1098, USA.
Health Serv Res. 1996 Aug;31(3):283-307.
The goal of this study was to develop unbiased risk-assessment models to be used for paying health plans on the basis of enrollee health status and use propensity. We explored the risk structure of adult employed HMO members using self-reported morbidities, functional status, perceived health status, and demographic characteristics.
DATA SOURCES/STUDY SETTING: Data were collected on a random sample of members of a large, federally qualified, prepaid group practice, hospital-based HMO located in the Pacific Northwest.
Multivariate linear nonparametric techniques were used to estimate risk weights on demographic, morbidity, and health status factors at the individual level. The dependent variable was annual real total health plan expense for covered services for the year following the survey. Repeated random split-sample validation techniques minimized outlier influences and avoided inappropriate distributional assumptions required by parametric techniques.
DATA COLLECTION/EXTRACTION METHODS: A mail questionnaire containing an abbreviated medical history and the RAND-36 Health Survey was administered to a 5 percent sample of adult subscribers and their spouses in 1990 and 1991, with an overall 44 percent response rate. Utilization data were extracted from HMO automated information systems. Annual expenses were computed by weighting all utilization elements by standard unit costs for the HMO.
Prevalence of such major chronic diseases as heart disease, diabetes, depression, and asthma improve prediction of future medical expense; functional health status and morbidities are each better than simple demographic factors alone; functional and perceived health status as well as demographic characteristics and diagnoses together yield the best prediction performance and reduce opportunities for selection bias. We also found evidence of important interaction effects between functional/perceived health status scales and disease classes.
Self-reported morbidities and functional health status are useful risk measures for adults. Risk-assessment research should focus on combining clinical information with social survey techniques to capitalize on the strengths of both approaches. Disease-specific functional health status scales should be developed and tested to capture the most information for prediction.
本研究的目标是开发无偏风险评估模型，以便根据参保人的健康状况和使用倾向来向健康计划支付费用。我们利用自我报告的发病率、功能状态、感知健康状况和人口统计学特征，探究了成年在职健康维护组织（HMO）成员的风险结构。
数据来源/研究背景：数据收集自位于太平洋西北部的一家大型、获得联邦资格认证的预付团体医疗、以医院为基础的HMO的成员随机样本。
采用多变量线性非参数技术在个体层面估计人口统计学、发病率和健康状况因素的风险权重。因变量是调查后一年涵盖服务的年度实际健康计划总费用。重复随机分割样本验证技术将异常值影响降至最低，并避免了参数技术所需的不适当分布假设。
数据收集/提取方法：1990年和1991年，向5%的成年订阅者及其配偶样本发放了一份包含简略病史和兰德36健康调查的邮寄问卷，总体回复率为44%。利用数据从HMO自动化信息系统中提取。年度费用通过将所有利用要素按HMO的标准单位成本加权来计算。
心脏病、糖尿病、抑郁症和哮喘等主要慢性病的患病率能改善对未来医疗费用的预测；功能健康状况和发病率各自比单纯的人口统计学因素更好；功能和感知健康状况以及人口统计学特征和诊断共同产生最佳预测性能，并减少选择偏倚的机会。我们还发现了功能/感知健康状况量表与疾病类别之间重要交互作用的证据。
自我报告的发病率和功能健康状况是成年人有用的风险指标。风险评估研究应侧重于将临床信息与社会调查技术相结合，以利用两种方法的优势。应开发并测试针对特定疾病的功能健康状况量表，以获取最多预测信息。
