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年的全面的、具有全国代表性的社会学数据与包含特定死亡原因的预期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：非洲裔美国人和社会经济地位低的人往往比白人和社会经济地位高的人更难获得社会资源，如好学校。随着时间的推移，在获得这些社会资源方面的差距转化为健康方面的差距。例如，不同群体之间在获得优质教育方面的差距造成职业差距，进而导致收入差距。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、交通和民主参与模式以及许多其他社会制度的机会也较少。早在希波克拉底时代，研究人员和思想家就观察到，这种社会剥夺导致健康差距，但长期以来人们认为，健康差距主要是由于获得救生物质产品的机会较少。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关联，将其作为健康差距的另一个原因。例如，较低的社会资本（例如，信任、团体参与）、社会关系（例如，朋友和家庭关系），以及有害的心理状态（例如，压力，悲观主义）已被确定为种族，民族和社会经济地位的健康差异的贡献者。尽管了解健康差距的根本社会原因的重要性，然而，一个前瞻性的，全面的社会医学数据集从来没有开发。我们提出的解决方案。科学家确实可以获得数据集，从而可以描述健康差异。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教育程度的信息。然而，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社会经济地位的更全面的问题，还需要关于受试者的社交网络、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，可以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、具有国家代表性的、包含社会和心理领域强有力措施的前瞻性数据集将花费数百万美元，并需要多年的后续工作。我们的建议提供了一个快速和高度成本效益的捷径，将产生一个非常强大的数据集，具有出色的后续行动和少数民族人口的过采样。具体来说，我们建议将1977-2007年的综合社会调查（GSS），一个多年期的，横断面调查，丰富的健康和社会学变量，到2008年的国家死亡指数（NDI）数据。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，更深入地了解潜在的机制。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性和广泛使用，我们将1）向更广泛的研究社区发布数据集，2）召集一组领先的跨学科专家，以解决和传播GSS-NDI数据，3）开发一套有用的研究工具，以促进GSS-NDI的采用和推广。这些工具包括定义社会资本等多维概念的手段，调整定性问题解释中群体间差异的手段（即，非洲裔美国人可能比白人更有可能看到这个问题，“人们公平地对待我”，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，以便研究人员的研究结果的政策含义可以更容易理解。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。我们将在一小部分时间内创建这个数据集，并且成本是从头开始生成前瞻性社会医学数据集的一小部分。最后，我们已经做了初步的工作，以确保它的成功，并确保我们的项目准备推出。我们在RC 2（GO）机制下将此提案提交给国家少数民族健康和健康差异中心的健康倡议社会决定因素。我们建议将运行时间最长的社会科学数据集的30多年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.