HEALTH TECHNOLOGIES' COSTS AND OUTCOMES IN THE ELDERLY

拟议研究的目标是确定政策如何 影响医疗服务的地域可获得性和提供者报销 影响治疗模式、医疗保健支出和健康 老年人的结果。它重点介绍了五种常见的医学状况 这在死亡率、发病率和 老年人的医疗费用；急性心肌梗死，慢性 心脏病、癌症、中风和白内障。拟议的研究 主要依赖于非常大的纵向数据集，其中包括 有关联邦医疗保险覆盖的所有医疗用途的信息。对于每个 临床情况下，拟议的研究将： (1)找出三个与健康状况无关的因素对 临床决策与卫生保健利用模式：患者 距离治疗设施的距离；条件的可用性 向提供商提供密集技术；以及替代提供商支付 政策。方法将检查这些影响的差异。 社会人口统计群体。 (2)确定治疗模式变化的后果 这些因素对以下患者预后的影响：生存 时间，反映在随后的使用模式中的不良健康事件， 以及医疗保健的总成本。这些方法将提供公正的 年替代疗法的平均效果估计 “边缘”患者，治疗受这些因素影响的患者 与健康状况无关。这些方法被称为工具变量 在计量经济学中，很少被应用于#年的观测研究 生物统计学和流行病学，但可能广泛适用。 在估计和解释中也将探索应用 依从性或依从性的临床试验中随时间的治疗效果 交叉问题。拟议研究的一个重要组成部分是 大额医疗索赔处理方法的有效性分析 数据集，使用各种统计检验和补充数据集 有更多的临床细节。

The goal of the proposed research is to determine how policies that affect geographic access to medical services and provider reimbursement influence treatment patterns, health care expenditures, and health outcomes for the elderly. It focuses on five common medical conditions that account for a substantial portion of mortality, morbidity, and medical expenditures in the elderly; acute myocardial infarction, chronic heart disease, cancer, stroke, and cataracts. The proposed research relies primarily on very large, longitudinal datasets that include information on all medical utilization covered by Medicare. For each clinical condition, the proposed research will: (1) Identify the effects of three factors unrelated to health status on clinical decision making and health care utilization patterns: patient distance to treatment facilities for the condition; the availability of intensive technologies to providers; and alternative provider payment policies. Methods will examine differences in these effects across sociodemographic groups. (2) Determine the consequences of variations in treatment patterns induced by these factors for the following patient outcomes: survival time, adverse health events reflected in subsequent utilization patterns, and overall costs of health care. The methods will provide unbiased estimates of the average effects of alternative medical treatments in 'marginal" patients, patients whose treatment is affected by the factors unrelated to health status. The methods, known as instrumental variables in econometrics, have rarely been applied in observational studies in biostatistics and epidemiology but may be widely applicable. Applications will also be explored in the estimation and interpretation of treatment effects over time in clinical trials with compliance or crossover problems. An important component of the proposed research is an analysis of the validity of the methods in large medical claims datasets, using a variety of statistical tests and supplementary datasets with much more clinical detail.