Explaining Variations in End-of-Life Care Intensity

医疗保健支出占美国国内生产总值的17.3%，并且不成比例地分配给生命末期（EOL）的护理。这主要是由于在生命的最后几个月里大量使用服务（住院治疗、机械通气）造成的。然而，数据显示，接受过 积极的终末期护理比其他患者的生活质量更差。需要进行研究，以确定导致EOL护理强度的因素。 大量的证据表明，在整个美国的EOL护理和医疗保健支出的强度有很大的区域差异研究也表明了种族/民族和卫生系统的EOL护理的显着差异。现有数据没有足够的细节、变化或大小来评估患者、医生和卫生系统因素对区域变化的解释程度，也没有足够的细节、变化或大小来评估EOL护理中的种族/民族差异是否可以通过患者信仰和其他因素的差异来解释。特征、医生的实践风格、接受护理的医院或区域实践模式。 我们将使用来自癌症护理结果和研究监测（CanCORS）联盟的数据，这是一项多地区前瞻性研究，旨在检查2003-2005年期间向超过10，000名诊断为肺癌或结直肠癌的患者提供的基于人口和卫生系统的队列的护理。我们将使用CanCORS患者调查数据、医疗记录数据和医生调查数据，以及来自医疗保险、私人健康计划、医疗补助和退伍军人事务部的管理数据，来检查2012年随访的4,000多名晚期肺癌或结直肠癌患者的EOL护理强度。我们将使用分层模型来评估影响EOL护理强度的患者、医生、医院和区域因素。具体来说，我们将：1。第三次回顾性达特茅斯措施的终末期支出与措施的护理强度和支出的前瞻性确定的晚期癌症患者。2.了解导致EOL护理强度区域水平变化的因素，包括患者和肿瘤特征（例如，人口统计学、共病、转移部位），患者信念（例如， 对延长生命护理的偏好，关于晚期癌症化疗有效性的信念），医师实践风格和信念（例如，晚期癌症患者更多地使用化疗，自我报告的EOL讨论的时间和舒适度，如果患绝症，个人对临终关怀的偏好），医院特征和实践模式，以及服务可用性。3.在区域内，评估患者、医生和医院差异在多大程度上解释了EOL护理强度的种族/民族差异。4.了解整个卫生系统（按服务收费的医疗保险、医疗保险管理的医疗保健、VA）中EOL护理强度的差异，并评估整个系统中患者和医生的特征和信念。

DESCRIPTION (provided by applicant): Health care expenditures account for 17.3% of gross domestic product in the United States and are disproportionately allocated to care at the end-of-life (EOL). Much of this results from intensive use of services (hospitalizations, mechanical ventilation) in the last months of life. Yet, data suggest that terminally-ill patients who receive aggressive EOL care have worse quality of life than other patients. Research is needed to determine the factors that contribute to the intensity of EOL care. A large body of evidence demonstrates substantial regional variations in intensity of care and health care spending at the EOL across the U.S. Studies have also demonstrated notable differences in EOL care by race/ethnicity and health systems. Available data have not had sufficient detail, variation, or siz to assess the extent to which area variations are explained by patient, physician, and health system factors, nor to assess if racial/ethnic disparities in EOL care can be explained by differences in patients' beliefs and other. characteristics, physicians' practice styles, the hospitals where care is received, or area practice patterns. We will use data from the Cancer Care Outcomes and Research Surveillance (CanCORS) Consortium, a multi-regional prospective study examining care delivered to population and health-system based cohorts of more than 10,000 patients diagnosed with lung or colorectal cancer during 2003-2005. We will use CanCORS patient survey data, medical record data and physician survey data linked with administrative data from Medicare, private health plans, Medicaid, and the VA to examine the intensity of EOL care among over 4,000 patients with advanced lung or colorectal cancer followed through 2012. We will use hierarchical models to assess the patient, physician, hospital, and area factors influencing intensity of EOL care. Specifically, we will: 1. Validate th retrospective Dartmouth measures of EOL spending with measures of care intensity and expenditures for prospectively identified patients with advanced cancer. 2. Understand the factors contributing to area-level variations in intensity of EOL care, including patient and tumo characteristics (e.g., demographics, comorbid illness, site of metastases), patient beliefs (e.g., preferences for life-prolonging care, beliefs about the effectiveness of chemotherapy for advanced cancer), physician practice style and beliefs (e.g., greater use of chemotherapy for advanced cancer patients, self- reported timing and comfort with EOL discussions, personal preference for hospice if terminally-ill), hospital characteristics and practice patterns, and are service availability. 3. Within areas, assess to what extent racial/ethnic differences in intensityof EOL care are explained by patient, physician, and hospital differences. 4. Understand differences in intensity of EOL care across health system (fee-for-service Medicare, Medicare managed care, VA), and assess patient and physician characteristics and beliefs across systems.