Development of Hierarchical Neighborhood Data Regarding Cancer in the Elderly

描述（由申请人提供）：居住在不同地区的人的健康状况可能不同，因为居民在影响健康的特征方面存在差异（例如，人口统计学属性、社会经济地位、基线发病率）和/或因为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、专业护理的可用性）。在这里，我们建议组装一个数据集，这将使我们能够在随后的工作中，检查在两个不同的空间分辨率水平上定义的位置对老年人癌症病程的影响。我们的第一个目标是建立一个新的，三个层次，分层数据集的一个初始队列的老年医疗保险受益人诊断为癌症在1998年至2002年期间，在美国11个地区。将有550，000名个体患者从SEER癌症登记处抽取，在I级，通过人口普查居住区与关于他们的社区的信息（N= 8，790）相关联，在II级，通过他们的邮政编码与关于他们的健康服务区（HSA）的信息（N=304）相关联。数据来源包括一级变量的SEER-医疗保险数据、二级变量的美国人口普查数据和其他数据以及三级变量的达特茅斯阿特拉斯数据。个体水平的结果变量包括就诊时的癌症分期、接受推荐的抗癌治疗和生存期。该数据集还将包含有关个人接受癌症治疗的医院的信息。我们的第二个目标是研究在两个空间水平上测量的变量之间的关系，以描述，例如，是否富裕的社区往往聚集在具有良好的医疗基础设施的HSAs。最终，对这一数据集的分析将使我们能够区分个人的作用（第一级）和两个空间嵌套的地理区域的作用，即，微观社区（二级）和宏观卫生服务区（三级）在确定癌症患者的病程。最终还可以进行其他分析，以评估患者使用的医院质量在地方和个人结果之间的关系中可能发挥的中介作用。这项基于全国癌症患者样本的工作与公共卫生有关，因为它将：帮助定位可能出现癌症护理和老年人结果不足的水平（个人与社区与卫生服务区）;帮助解释健康结果的种族和经济差异;并解决患者癌症过程的决定因素，这是死亡的主要原因。

DESCRIPTION (provided by applicant): 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. This work based on a national sample of cancer patients is relevant to public health since it will: help localize the level (individual versus neighborhood versus health service area) at which deficiencies in cancer care and outcomes in the elderly may arise; help explain racial and economic disparities in health outcomes; and address determinants of the course of patients' cancer, a leading cause of death.