Development of Hierarchical Neighborhood Data Regarding Cancer in the Elderly

摘要 居住在不同地区的人，由于居民的特点不同，他们的健康状况也可能不同 影响健康的因素（例如，人口统计学属性、社会经济地位、基线发病率）和/或因为 各个地方在影响居民健康的特征方面不同，例如邻近因素（例如，当地 财富、犯罪、住宅稳定性）或医疗服务因素（例如，医生供应、专业医生的提供 护理）。在这里，我们建议收集一个数据集，使我们能够在随后的工作中检查影响 的地方，定义在两个不同的空间分辨率水平，在老年人的癌症过程。我们的首要目标是 建立一个新的，三个层次，分层数据集，关于老年医疗保险受益人的初始队列 在1998-2002年期间，美国11个地区被诊断出患有癌症。将有55万名患者 从SEER癌症登记处在一级，联系人口普查居住区的信息， 社区（N= 8，790）在二级，并通过其邮政编码链接到有关其卫生服务的信息 （N=304）。数据来源包括SEER-医疗保险数据的I级变量，美国 二级变量采用普查数据和其他数据，三级变量采用达特茅斯阿特拉斯系统数据。个人- 水平结果变量包括出现时的癌症分期，接受推荐的抗癌治疗， 和生存数据集还将包含有关个人接受其治疗的医院的信息。 癌症护理我们的第二个目标是检查在两个空间测量的变量之间的关系 水平，以描述，例如，富裕的社区是否倾向于聚集在健康的HSA中， 拥有医疗基础设施。最终，对这一数据集的分析将使我们能够区分 - 来自两个空间嵌套的地理区域的角色的个人（第一级），即，微邻里 在确定癌症患者的病程方面，大卫生服务区（二级）和大卫生服务区（三级）的作用。其他 最后还可以进行分析，以评估质量的可能调解作用。 患者使用的医院在地方和个人结果之间的关系。

Abstract People living in different areas may differ in their health status because the residents differ in characteristics that influence health (e.g., demographic attributes, socioeconomic status, baseline morbidity) and/or because places differ in characteristics that influence the health of residents, such as neighborhood factors (e.g., local wealth, crime, residential stability) or medical service factors (e.g., physician supply, availability of specialized care). Here, we propose to assemble a data set that will allow us, in subsequent work, to examine the impact of place, defined at two different levels of spatial resolution, on cancer course in the elderly. Our first aim is to build a novel, three-level, hierarchical data set about an inception cohort of elderly Medicare beneficiaries diagnosed with cancer in 11 US regions during 1998-2002. There will be 550,000 individual patients drawn from the SEER cancer registry at level I, linked by Census tract of residence to information about their neighborhoods (N=8,790) at level II, and linked by their ZIP code to information about their health service areas (HSAs) (N=304) at level III. Sources of data include the SEER-Medicare data for level I variables, US Census data and other data for level II variables, and Dartmouth Atlas data for level III variables. Individual- level outcome variables include cancer stage at presentation, receipt of recommended anti-cancer therapies, and survival. The data set will also contain information about the hospitals at which individuals received their cancer care. Our second aim is to examine the relationships among variables measured at the two spatial levels in order to describe, for example, whether rich neighborhoods tend to be clustered in HSAs that are well endowed with medical infrastructure. Ultimately, analyses of this data set will allow us to distinguish the role of the individual (level I) from the roles of the two spatially nested geographic areas, i.e., micro neighborhood (level II) and macro health service area (level III) in determining illness course in cancer patients. Other analyses could also ultimately be done in order to evaluate the possible mediating role of the quality of hospitals used by patients in the relationship between place and individual outcomes.