ECONOMICS OF CANCER

描述：拟议研究的目的是：1)评估 索赔数据在估计发病率、治疗和费用方面的有效性 以及老年人肺癌、结肠癌和前列腺癌的预后；2)分析 在筛查、初始治疗、随访监测和治疗方面的趋势 这些癌症的复发；3)衡量政策因素的影响， 包括癌症机构的类型、州法律和竞争压力； 4)估计这些替代治疗模式对成本和 护理的结果；以及5)从 估计政策和治疗后果的经验研究结果 影响美国癌症管理的变化。 第一个目标是解决国家癌症研究所的项目 宣布，“使用基于健康声明的数据系统进行癌症监测”。 这些研究问题将使用一个大型的国家数据库进行审查 和工具变量(IV)技术，以控制遗漏的变量 偏见。分析分为三个部分。第一部分重点介绍了 用于流行病学和经济学研究的医疗保险索赔数据的可靠性 癌症方面。第二个分析集中在医疗方面的趋势。 模式、支出和成果，以及政策和人口因素 影响着这些趋势。第三部分的分析使用了工具 变量(即与治疗选择相关的变量，但 与未观察到的影响健康结果的因素无关，例如 患者健康状况)以确定治疗的边际效果 结果。 研究人员已将待研究的癌症数量从6种减少到 三种：肺癌、前列腺癌和结肠癌。确诊时的癌症分期为 分为两个基本类别：局限型和转移型。癌 治疗分为四个阶段：检测、初始治疗、随访 对局限性癌症患者的监测和可能的后期治疗 与反复发作有关。治疗仅限于：侵入性筛查 手术方式(前列腺癌和结肠癌)，手术的侵袭性程度 管理和使用更广泛的积极措施(手术、 化疗和/或放射治疗)。主要成果衡量标准将 生存时间和无治疗生存月数(无 住院或癌症治疗)。支出措施将包括 保险报销总额(以及患者免赔额和自付费用)和 预计成本。(第42页的表8总结了操作定义 分析中的变量。) 拟议的分析方法建立在调查人员的方法之上 心导管术的效果分析。因为病人 不是随机分配给治疗的，调查人员担心 这忽略了变量，例如患者健康的未观察到的维度， 可能会影响治疗选择和患者结局。他们的方法 是标识一个变量，例如访问提供 特殊的处理技术，作为实际应用的“工具” 分析中的处理方法。如果获得治疗的机会与选择相关 治疗，但不是用未观察到的患者健康措施，那么 比较不同访问级别患者的治疗结果 可以对不同治疗方法的效果提供公正的估计。 结果。

DESCRIPTION: The purpose of the proposed study is to: 1) Assess the validity of claims data for estimating incidence, treatment, expenditures and outcomes for lung, colon and prostate cancer in the elderly; 2) Analyze trends in screening, initial therapy, followup monitoring and treatment of recurrences for these cancers; 3) Measure the effects of policy factors, including types of cancer facilities, state laws and competitive pressures; 4) Estimate the effects of these alternative treatment patterns on costs and outcomes of care; and 5) Develop comprehensive simulation models from the empirical findings to estimate the consequences of policy and treatment changes affecting the management of cancer in the U.S. The first objective addresses a National Cancer Institute Program Announcement, "Cancer Surveillance Using Health Claims-Based Data Systems". These research questions will be examined using a large, national data base and instrumental variable (IV) techniques to control for omitted variables bias. The analysis is in three parts. The first part focuses on the reliability of Medicare claims data for epidemiologic and economic research on cancer. The second analysis focuses on trends in medical treatment patterns, expenditures and outcomes, and the policy and demographic factors influencing those trends. The third part of the analysis uses instrumental variables (i.e., variables that are correlated with choice of treatment, but uncorrelated with unobserved factors affecting health outcomes, such as patient health status) to identify the marginal effect of treatments on outcomes. The investigators have reduced the number of cancers to be studied from six to three: lung, prostate and colon cancer. Stage of cancer at diagnosis is divided into two basic categories: localized and metastatic. Cancer treatment is divided into four phases: detection, initial therapy, followup surveillance for patients with localized cancers and later treatment likely associated with recurrence. Treatments are limited to: invasive screening procedures (for prostate and colon cancer), degree of aggressive surgical management and the use of a broader range of aggressive measures (surgery, chemotherapy and/or radiation therapy). The principal outcome measures will be survival time and treatment-free survival months (months without a hospitalization or a cancer treatment). Expenditure measures will include total insured reimbursement (and patient deductibles and copayments) and estimated costs. (Table 8 on page 42 summarizes the operational definition of variables in the analysis.) The proposed analytic methods build on the investigators' approach to analysis of the effectiveness of cardiac catheterization. Because patients are not assigned randomly to treatments, the investigators are concerned that omitted variables, such as unobserved dimensions of patient health, might influence both treatment choice and patient outcomes. Their approach is to identify a variable, such as access to facilities that offer particular treatment technologies, to stand in as an "instrument" for actual treatment in the analysis. If access to treatment is correlated with choice of treatment, but not with unobserved measures of patient health, then comparing treatment outcomes for patients with different levels of access can provide an unbiased estimate of the effect of different treatments on outcomes.