Koleck Theresa A, Dreisbach Caitlin, Zhang Chen, Grayson Susan, Lor Maichou, Deng Zhirui, Conway Alex, Higgins Peter D R, Bakken Suzanne
School of Nursing, University of Pittsburgh, Pittsburgh, PA 15261, United States.
School of Nursing, University of Rochester, Rochester, NY 14620, United States.
J Am Med Inform Assoc. 2024 Dec 1;31(12):3032-3041. doi: 10.1093/jamia/ocae214.
Integration of social determinants of health into health outcomes research will allow researchers to study health inequities. The All of Us Research Program has the potential to be a rich source of social determinants of health data. However, user-friendly recommendations for scoring and interpreting the All of Us Social Determinants of Health Survey are needed to return value to communities through advancing researcher competencies in use of the All of Us Research Hub Researcher Workbench. We created a user guide aimed at providing researchers with an overview of the Social Determinants of Health Survey, recommendations for scoring and interpreting participant responses, and readily executable R and Python functions.
This user guide targets registered users of the All of Us Research Hub Researcher Workbench, a cloud-based platform that supports analysis of All of Us data, who are currently conducting or planning to conduct analyses using the Social Determinants of Health Survey.
We introduce 14 constructs evaluated as part of the Social Determinants of Health Survey and summarize construct operationalization. We offer 30 literature-informed recommendations for scoring participant responses and interpreting scores, with multiple options available for 8 of the constructs. Then, we walk through example R and Python functions for relabeling responses and scoring constructs that can be directly implemented in Jupyter Notebook or RStudio within the Researcher Workbench. Full source code is available in supplemental files and GitHub. Finally, we discuss psychometric considerations related to the Social Determinants of Health Survey for researchers.
将健康的社会决定因素纳入健康结果研究,将使研究人员能够研究健康不平等问题。“我们所有人”研究计划有可能成为健康社会决定因素数据的丰富来源。然而,需要有用户友好型的关于对“我们所有人”健康社会决定因素调查进行评分和解读的建议,以便通过提升研究人员使用“我们所有人”研究中心研究工作台的能力,为社区带来价值。我们创建了一份用户指南,旨在为研究人员提供健康社会决定因素调查的概述、对参与者回答进行评分和解读的建议,以及易于执行的R和Python函数。
本用户指南面向“我们所有人”研究中心研究工作台的注册用户,该工作台是一个基于云的平台,支持对“我们所有人”的数据进行分析,这些用户目前正在或计划使用健康社会决定因素调查进行分析。
我们介绍了作为健康社会决定因素调查一部分进行评估的14个结构,并总结了结构的操作化。我们提供了30条基于文献的关于对参与者回答进行评分和解读分数的建议,其中8个结构有多种选项。然后,我们逐步讲解用于重新标记回答和对结构进行评分的示例R和Python函数,这些函数可以在研究工作台中的Jupyter Notebook或RStudio中直接实现。完整的源代码可在补充文件和GitHub中获取。最后,我们为研究人员讨论与健康社会决定因素调查相关的心理测量学考虑因素。