User guide for Social Determinants of Health Survey data in the All of Us Research Program

Author:

Koleck Theresa A1ORCID,Dreisbach Caitlin23ORCID,Zhang Chen2,Grayson Susan1,Lor Maichou4ORCID,Deng Zhirui1,Conway Alex1,Higgins Peter D R5,Bakken Suzanne678ORCID

Affiliation:

1. School of Nursing, University of Pittsburgh , Pittsburgh, PA 15261, United States

2. School of Nursing, University of Rochester , Rochester, NY 14620, United States

3. Goergen Institute for Data Science, University of Rochester , Rochester, NY 14627, United States

4. School of Nursing, University of Wisconsin-Madison , Madison, WI 53705, United States

5. School of Medicine, University of Michigan , Ann Arbor, MI 48109, United States

6. School of Nursing, Columbia University , New York, NY 10032, United States

7. Department of Biomedical Informatics, Columbia University , New York, NY 10032, United States

8. Data Science Institute, Columbia University , New York, NY 10027, United States

Abstract

Abstract Objectives 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. Target Audience 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. Scope 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.

Funder

National Institute of Nursing Research

National Institutes of Health

Publisher

Oxford University Press (OUP)

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