An Expanded Dataset to Study Psychosocial Influences on Health Disparities

描述(申请人提供)：一些研究人员认为，健康的非医疗决定因素比医疗因素重要得多，例如获得医疗保健的机会。这些非医学决定因素中的关键因素是假定的健康风险因素，如社会地位低(无论是客观衡量还是感知)、物质匮乏、邻里特征以及消极的想法、感觉或信念。由于从定义上讲，弱势群体无法获得物质和社会资源，健康的非医疗决定因素也可能是造成健康差距的主要因素。然而，在2008年一般社会调查--国家死亡指数(2008年GSS-NDI)出现之前，一直没有具有全国代表性的数据集可用于研究健康的非医学决定因素。在公开发布后的短短20个月里，这个数据集已经被下载了数千次，并取得了丰硕的成果，探索了广泛的研究问题。2008年GSS-NDI由1978年至2002年的GSS调查组成，与2008年的死亡率数据相关联。我们现在建议扩大2008年GSS-NDI，以包括2004-2012年GSS调查数据和截至2013年的详细死因(2013 GSS-NDI)。此外，我们建议创建一个地理编码的GSS-NDI数据集，与州数据相关联，用于评估地方政策对卫生的影响。2013年GSS-NDI和2013 GSS-NDI-GEO将提供丰富的额外社会学研究问题，扩大统计权力，并将包含额外的健康和心理社会变量，如静脉注射毒品使用。这两个新数据集将包含35年的预期死亡率数据和地理信息。随着新数据的统计能力和时空覆盖面的扩大，有可能进行年龄-时期-队列分析，运行工具变量分析，使用分层模型，并在政策执行中利用时空差异，同时几乎消除生存偏见。因此，我们的数据集允许用户设计出比使用传统预期数据集更复杂、更注重因果关系的统计模型。因此，我们的数据集不仅提供了与健康、物质环境、社会学现象、信仰、态度和思想有关的400个额外变量，而且还为创新的流行病学分析打开了大门。以前的2008年全球数据安全系统-NDI需要大量前期工作，因为必须从仓库中检索包含身份识别符的纸质记录，并手动将其输入数据库。目前的工作将不那么密集，因为以前的记录的前期工作已经完成(较新的记录已经是电子格式)，只需要少量资金。

DESCRIPTION (provided by applicant): Some researchers believe that non-medical determinants of health are much more important than medical factors, such as access to health care. Key among these non-medical determinants are putative health risk factors such as low social status (whether objectively measured or perceived), material deprivations, neighborhood characteristics, and negative thoughts, feelings, or beliefs. Because disadvantaged populations by definition lack access to material and social resources, the non-medical determinants of health may also be major contributors to health disparities. However, until the advent of the 2008 General Social Survey-National Death Index (2008 GSS-NDI), there has been no nationally-representative dataset that could be used to study the non-medical determinants of health. In the short 20 months since its public release, this dataset has been downloaded thousands of times and has been productive, exploring a wide array of research questions. The 2008 GSS-NDI consists of GSS surveys from 1978 to 2002 linked to mortality data through 2008. We now propose to expand the 2008 GSS-NDI to include the 2004-2012 GSS survey data, and detailed cause of death through 2013 (2013 GSS-NDI). In addition, we propose to create a geographically-coded GSS-NDI dataset linked with state data that will be used to assess the effect of local policies on health. The 2013 GSS-NDI and 2013 GSS-NDI-GEO will provide a wealth of additional sociological research questions, expanded statistical power, and will contain additional health and psychosocial variables, such as intravenous drug use. These two new datasets will contain 35 years of prospective mortality data and geographic information. With the extended statistical power and spatiotemporal reach of the new data, it is possible to execute age-period-cohort analyses, run instrumental variable analyses, employ hierarchical models, and exploit spatiotemporal variation in policy implementation, while all but eliminating survival bias. Our datasets therefore allow users to devise much more sophisticated, causally-oriented, statistical models than is possible using traditional prospective datasets. Thus, our datasets not only offer a 400 additional variables pertaining to health, material circumstances, sociological phenomena, beliefs, attitudes, and ideas, they also open the door to innovative epidemiological analyses. The previous 2008 GSS-NDI required a large up front effort because paper records containing identifiers had to be retrieved from warehouses and manually entered into databases. The present effort will be much less intensive because this up front work on the earlier records has already been done (and more recent records are already in electronic format), requiring only a small amount of funding.