A dataset for the study of the social determinants of health.

描述（由申请人提供）：我们提出了一种方法，通过将30年来全面的、具有全国代表性的社会学数据与包含具体死亡原因的前瞻性死亡率数据联系起来，来回答有关健康和健康差异的社会决定因素的关键问题。问题是：与白人和高社会经济地位的人相比，非洲裔美国人和低社会经济地位（SES）的人往往更少获得社会资源，比如好学校。随着时间的推移，在获得这种社会资源方面的差距转化为健康差距。例如，群体之间在获得优质教育方面的差异导致职业差异，进而导致收入差异。那些受教育程度和收入较低的人不仅获得医疗保险的机会较低，而且获得银行服务、交通、民主参与模式以及许多其他社会制度的机会也较低。早在希波克拉底（Hippocrates）时代，研究人员和思想家就观察到，这种社会剥夺会导致健康差异，但长期以来，人们一直认为，健康差异主要是由于获得救命物资的机会较少。在20世纪，研究人员将注意力转向社会剥夺和心理社会占有之间的联系，作为健康差异的另一个原因。例如，较低的社会资本（如信任、群体参与）、社会联系（如朋友和家庭关系）和有害的心理状态（如压力、悲观）已被确定为造成种族、民族和社会经济地位之间的健康差异的因素。然而，尽管了解健康差异的根本社会原因很重要，但从未开发出前瞻性的、全面的社会医学数据集。我们提出的解决方案。科学家们确实可以访问允许描述健康差异的数据集。这些数据集包括一些重要的医疗风险因素和结果，以及有关受试者收入和受教育程度的信息。然而，这些数据集不允许对这些差异的社会和心理原因进行更深入的分析。这样的数据集不仅需要包含关于受试者童年和成年SES的更全面的问题，还需要关于受试者的社会网络、思想、感情、态度、信仰和参与公民社会的问题。简而言之，科学家需要一个数据集，以便探索健康差异的假定社会和心理原因。显然，开发一个跨学科的、具有全国代表性的、包含社会和心理领域可靠测量的前瞻性数据集将花费数百万美元，并需要多年的随访。我们的建议提供了一个快速和高成本效益的捷径，将产生一个非常强大的数据集，具有出色的跟踪和少数民族人口的过采样。具体而言，我们建议将1977-2007年综合社会调查（GSS）与2008年之前的国家死亡指数（NDI）数据联系起来，GSS是一项多年的、具有丰富健康和社会学变量的横断面调查。该数据集将推动健康差异的社会流行病学研究，而不仅仅是识别和描述，以更深入地了解潜在机制。这将带来高度针对性的政策，以解决群体之间的“健康差距”。为了促进该数据集的实用和广泛使用，我们将1)向更广泛的研究社区发布该数据集，2)召集一组领先的跨学科专家来解决和传播GSS-NDI数据，以及3)开发一套有用的研究工具，以促进GSS-NDI的采用和推广。这些工具包括一种定义诸如社会资本等多维概念的方法，一种在解释定性问题时调整群体间差异的方法（例如，非裔美国人可能比白人更有可能看到“人们对我很公平”这一问题）。（因为有种族成分），最后，一种将死亡率差异转化为预期寿命差异的方法，以便更容易理解研究人员发现的政策含义。简而言之，我们的项目将创建健康科学中缺失的前瞻性社会医学数据集，该数据集能够极大地促进我们对健康的非医学决定因素及其与健康差异的关系的理解。我们将在很短的时间内创建这个数据集，并且花费的成本是从头开始生成一个前瞻性社会医学数据集的一小部分。最后，我们已经完成了初步工作，以确保它的成功，并确保我们的项目为推出做好了准备。我们在RC2 （GO）机制下将这一建议转发给国家少数民族健康和健康差异中心的健康社会决定因素倡议。我们建议将运行时间最长的社会科学数据集三十多年的数据与死亡率数据联系起来，创建第一个主要的前瞻性社会医学数据集。该数据集将极大地促进对疾病和健康差异的社会原因的科学理解。

DESCRIPTION (provided by applicant): We propose a method for answering critical questions concerning the social determinants of health and health disparities by linking 30 years of comprehensive, nationally-representative sociological data to prospective mortality data containing specific causes of death. The problem: African-Americans and persons of low socio-economic status (SES) tend to have less access to social resources, such as good schools, than do whites and persons of high SES, respectively. Over time, disparities in access to such social resources translate into health disparities. For instance, disparities in access to quality education between groups results in occupational disparities that lead to income disparities. Those with less education and income have not only lower access to health insurance, but also less access to banking services, transportation, and modes of democratic engagement, as well as a host of other social systems. Researchers and thinkers dating back to Hippocrates have observed that such social deprivations lead to health disparities, but it was long thought that health disparities primarily arose from lower access to lifesaving material goods. In the twentieth century, researchers turned their attention to the association between social deprivations and psychosocial possesses as additional causes of health disparities. For instance, lower social capital (e.g., trust, group participation), social ties (e.g., friends and family relationships), and harmful psychological states (e.g., stress, pessimism) have been identified as contributors to health disparities by race, ethnicity, and SES. Despite the importance of understanding the root social causes of health disparities, however, a prospective, comprehensive sociomedical dataset has never been developed. Our proposed solution. Scientists do have access to datasets that allow for the description of health disparities. These datasets consist of a number of important medical risk factors and outcomes alongside information about the subjects' income and educational attainment. However, the datasets do not permit a deeper analysis of social and psychological causes of these disparities. Such a dataset would not only need to contain more comprehensive questions about the subjects' childhood and adult SES, they would also require questions about subjects' social networks, thought, feelings, attitudes, beliefs, and participation in civil society. In short, scientists require a dataset that allows exploration of putative social and psychological causes of health disparities. Clearly, development of an interdisciplinary, nationally-representative, prospective dataset containing robust measures in social and psychological domains would cost millions of dollars and would require many years of follow-up. Our proposal provides a rapid and highly cost-effective shortcut that will produce an extremely robust dataset with outstanding follow-up and oversampling of minority populations. Specifically, we propose to link the 1977-2007 General Social Survey (GSS), a multiple-year, cross-sectional survey rich in health and sociological variables, to National Death Index (NDI) data through 2008. This dataset will advance social epidemiologic studies of health disparities beyond mere identification and description to a deeper understanding of the underlying mechanisms. This will usher in highly targeted policies to address "health gaps" between groups. To catalyze the utility and the widespread use of this dataset, we will 1) release the dataset to the wider research community, 2) convene a group of leading transdisciplinary experts in order to troubleshoot and disseminate the GSS-NDI data, and 3) develop a set of useful research tools that will facilitate the adoption and reach of the GSS-NDI. These tools include a means for defining multi-dimensional concepts such as social capital, a means for adjusting for between group differences in the interpretation of qualitative questions (i.e., African Americans may be more likely than whites to see the question, "People treat me fairly," as having a racial component), and, finally, a means for translating mortality differences into life expectancy differences so that the policy implications of researchers' findings can be more easily understood. In short, our project will create the prospective sociomedical dataset that has been missing in the health sciences, a dataset capable of greatly advancing our understanding of the non-medical determinants of health and their relationship to health disparities. We will create this dataset in a fraction of the time and at a fraction of the cost of generating a prospective sociomedical dataset from scratch. Finally, we have done preliminary work to both ensure its success and to ensure that our project is ready for rollout. We forward this proposal under the RC2 (GO) mechanism to the National Center on Minority Health and Health Disparities' Social Determinants of Health Initiative. We propose to link over thirty years of data from the longest running social science dataset to mortality data, creating the first major prospective sociomedical dataset. This dataset will greatly advance scientific understanding of the social causation of disease and health disparities.