An Expanded Dataset to Study Psychosocial Influences on Health Disparities

描述（由申请人提供）：一些研究人员认为，健康的非医疗决定因素比医疗因素（如获得医疗保健）重要得多。这些非医学决定因素中的关键是假定的健康风险因素，如社会地位低（无论是客观测量还是感知），物质匮乏，邻里特征，以及消极的想法，感受或信仰。由于弱势群体从定义上讲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年综合社会调查-国家死亡指数（2008年GSS-NDI）出现之前，没有可用于研究健康的非医疗决定因素的具有全国代表性的数据集。在公开发布后的短短20个月内，该数据集已被下载数千次，并且富有成效，探索了广泛的研究问题。2008年国家死亡率普查包括1978年至2002年与2008年死亡率数据相关的国家死亡率普查。我们现在建议扩大2008年GSS-NDI，以包括2004-2012年GSS调查数据，以及到2013年的详细死因（2013年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，用于评估当地政策对健康的影响。2013年全球抽样调查-国家发展指数和2013年全球抽样调查-国家发展指数-全球环境展望将提供大量额外的社会学研究问题，扩大统计能力，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。这两个新的数据集将包含35年的前瞻性死亡率数据和地理信息。随着新数据的统计能力和时空范围的扩大，有可能执行年龄-时期-队列分析，运行工具变量分析，采用分层模型，并利用政策实施中的时空变化，同时消除生存偏差。因此，我们的数据集允许用户设计比使用传统的前瞻性数据集更复杂，因果导向的统计模型。因此，我们的数据集不仅提供了400个与健康、物质环境、社会学现象、信仰、态度和思想有关的额外变量，还为创新的流行病学分析打开了大门。上一个2008年全球安全系统NDI需要大量的前期工作，因为必须从仓库中检索载有识别资料的纸质记录，并人工输入数据库。目前的工作力度将小得多，因为关于早期记录的前期工作已经完成（较近期的记录已经采用电子格式），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.