Development of Hierarchical Neighborhood Data Regarding Cancer in the Elderly

描述（由申请人提供）：生活在不同地区的人的健康状况可能不同，因为居民影响健康的特征不同（例如，人口统计属性、社会经济地位、基线发病率）和/或因为影响居民健康的特征不同，例如社区因素（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、专业护理的可得性）。在这里，我们建议收集一个数据集，这将使我们能够在随后的工作中，以两种不同的空间分辨率来研究地点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颖的，三级的，分层的数据集，关于1998-2002年美国11个地区诊断为癌症的老年医疗保险受益人的初始队列。在第一级将从SEER癌症登记处抽取55万名患者，在第二级将他们的居住地与他们的社区信息（N=8,790）联系起来，在第三级将他们的邮政编码与他们的卫生服务区域（HSAs）信息（N=304）联系起来。数据来源包括一级变量的SEER-Medicare数据，二级变量的US Census数据和其他数据，三级变量的Dartmouth Atlas数据。个体水平的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率。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检查在两个空间水平上测量的变量之间的关系，以便描述，例如，富裕的社区是否倾向于聚集在具有良好医疗基础设施的HSAs中。最终，对该数据集的分析将使我们能够区分个体（第一级）与两个空间嵌套的地理区域（即微观社区（第二级）和宏观卫生服务区域（第三级））在决定癌症患者病程中的作用。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点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相关，因为它将：有助于确定可能出现老年人癌症护理和结果不足的水平（个人、社区和卫生服务区域）；帮助解释健康结果中的种族和经济差异；并解决患者癌症病程的决定因素，这是导致死亡的主要原因。

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.