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。在这些非医学决定因素中，关键是假定的健康风险因素，如社会地位低下（无论是客观衡量还是感知）、物质匮乏、邻里特征以及消极的思想、感觉或信仰。由于弱势群体根据定义缺乏获得物质和社会资源的机会，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-全国死亡指数（2008 GSS-NDI）出现之前，没有一个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，并且很有成效，探索了一系列广泛的研究问题。2008年GSS- ndi由1978年至2002年与2008年死亡率数据相关的GSS调查组成。我们现在建议将2008年GSS- ndi扩大到包括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，该数据集与各州数据相关联，将用于评估地方政策对健康的影响。2013年GSS-NDI和2013年GSS-NDI- geo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社会心理变量，如静脉注射吸毒。这两个新的数据集将包含35年的预期死亡率数据和地理信息。随着新数据的扩展统计能力和时空范围，可以执行年龄-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出比使用传统前瞻性数据集更复杂的、因果导向的统计模型。因此，我们的数据集不仅提供了与健康、物质环境、社会现象、信仰、态度和想法有关的400个额外变量，还为创新的流行病学分析打开了大门。之前的2008 GSS-NDI需要大量的前期工作，因为必须从仓库中检索包含标识符的纸质记录并手动输入数据库。目前的工作将不那么密集，因为早期记录的前期工作已经完成（最近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.