Development of Hierarchical Neighborhood Data Regarding Cancer in the Elderly

摘要 居住在不同地区的人可能会因为居民的特征不同而健康状况不同 影响健康(例如，人口属性、社会经济状况、基线发病率)和/或因为 影响居民健康的特征不同，例如邻里因素(例如，当地 财富、犯罪、居住稳定性)或医疗服务因素(例如医生供应、专科医生的可获得性 关爱)。在这里，我们建议汇编一个数据集，使我们能够在后续工作中检查影响 在两个不同的空间分辨率水平上定义的位置，在老年人的癌症过程中。我们的首要目标是 建立一个关于老年医疗保险受益人的初始队列的新的、三个级别的分层数据集 1998-2002年间，在美国11个地区被诊断患有癌症。将抽出55万名个人患者 从SEER癌症登记的I级，通过居住地人口普查链接到关于他们的信息 II级社区(N=8,790)，并通过邮政编码链接到有关其卫生服务的信息 三级区域(HSA)(N=304)。数据来源包括美国一级变量的SEER-Medicare数据 第二级变量的普查数据和其他数据，以及第三级变量的达特茅斯地图集数据。个人- 水平结果变量包括提出时的癌症阶段，接受推荐的抗癌治疗， 和生存。数据集还将包含有关个人在哪些医院接受治疗的信息 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 例如，为了描述富裕社区是否倾向于聚集在健康状况良好的HSA中 具有医疗基础设施。最终，对这一数据集的分析将使我们能够区分 来自两个空间嵌套地理区域的角色的个体(水平I)，即微邻域 (II级)和宏观卫生服务区(III级)用于确定癌症患者的病程。其他 最终也可以进行分析，以评估质量的可能中介作用 医院中患者所使用的场所与个体结局的关系。

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.