Mental Health and US Attitudes Toward Social Determinants of Health Policies

Introduction: Research has suggested that individual health may influence fl uence policy attitudes, yet the relationship between mental health and policy support is understudied. Clarifying this relationship may help inform policies that can improve the population mental health. To address this gap, this study measures national support for 5 social determinants of health policy priorities and their relation to mental health and political affiliation. fi liation. Methods: This study assessed support for 5 policy priorities related to the social determinants of health using a nationally representative survey of US adults (n=2,430) conducted in March-April - April 2023. Logistic regression was used to estimate the predicted probability of identifying each priority as important, test differences in support by self-rated mental health, and evaluate whether partisanship modified fi ed these relationships. Analyses were conducted in 2023. Results: The majority of US adults, across partisan identities, supported 5 policy priorities related to improving the economy (84%), healthcare affordability (77%), improving K-12 education (76%), housing affordability (68%), and childcare affordability (61%). Worse mental health predicted significantly fi cantly greater support for addressing housing affordability (73.9% vs 66.2%), and partisanship modified fi ed the relationship between mental health and support for improving the economy, improving K-12 education, and housing affordability. Conclusions: In 2023, there was substantial bipartisan support for federal policy action to address the social determinants of health, and worse mental health was related to greater policy support, particularly among Democrats. Federal policymakers have a broad consensus to take action to address the social determinants of health, which may improve the population mental health. Am J Prev Med 2024;67(3):350-359. - 359. (c) 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.