HEALTH TECHNOLOGIES' COSTS AND OUTCOMES IN THE ELDERLY

拟议研究的目标是确定政策， 影响医疗服务的地域准入和提供者的报销 影响治疗模式、卫生保健支出和健康 老年人的结果。 它侧重于五种常见的医疗条件 这占死亡率、发病率和 老年人医疗费用;急性心肌梗死，慢性 心脏病癌症中风和白内障 拟议研究 主要依赖于非常大的纵向数据集，包括 医疗保险覆盖的所有医疗使用信息。 为每个 临床条件下，拟议的研究将： (1)确定与健康状况无关的三个因素对 临床决策和卫生保健利用模式：患者 距离治疗设施的条件;可用性 向供应商提供密集型技术;以及替代供应商付款 施政纲要而 方法将检查这些影响的差异， 社会人口群体。 (2)确定治疗模式变化的后果 由这些因素引起的以下患者结局：生存 时间，随后使用模式中反映的不良健康事件， 和医疗保健的总成本。 该方法将提供无偏 估计替代医学治疗的平均效果， “边缘”患者，即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，很少应用于观察性研究， 生物统计学和流行病学，但可能广泛适用。 应用程序也将探讨在估计和解释 在临床试验中随时间推移的治疗效果， 交叉问题。 拟议研究的一个重要组成部分是 大额医疗索赔中方法的有效性分析 使用各种统计测试和补充数据集 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.