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Applying analytics to sociodemographic disparities in mental health

Nature Mental Health volume 3, pages 124–138 (2025)Cite this article

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Abstract

Mental health services and treatment are unfortunately subject to sociodemographic disparities. To address this issue, recent studies have begun to apply analytics methods—that is, artificial intelligence in general, machine learning and deep learning in particular—toward the identification of such disparities and, where possible, mitigation of bias within models used in mental health research. However, it is difficult to understand the scope and status of such research as it is spread across many journals and contexts of study. Here we conducted an analysis of articles in this area. We identified 40 articles from 2017 to July 2023 related to the use of analytics in the context of sociodemographic disparities in mental health. We find that prediction, clustering/grouping and fairness models were most often applied in the articles analyzed. A number of mental health-related sociodemographic disparities were identified in these articles, for example, associated with race/ethnicity, gender, age and socioeconomic status, but such findings were typically context dependent. Thus, we also provide suggestions in this Analysis on how to both enhance generalizability and embrace context-dependent findings, especially via the identification of heterogeneous treatment effects, model bias mitigation, use of generative artificial intelligence, incorporation of data from devices, and translation of findings into practice.

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Fig. 1: PRISMA-ScR flow diagram.
Fig. 2: Topics and subtopics identified within the corpus.

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Data availability

The data are journal articles obtained from the searches described earlier of Web of Science (https://www.webofscience.com/wos/woscc/basic-search), PubMed (https://pubmed.ncbi.nlm.nih.gov/), IEEE Xeplore (https://ieeexplore.ieee.org/Xplore/home.jsp), PsycInfo (https://psycinfo.apa.org) and Google Scholar (https://scholar.google.com). All articles used in our analyses are referenced in this analysis.

Code availability

Code is available via GitHub at https://github.com/ProfAaronBaird/JournalArticleRepo/tree/main/NatureMHAnalyticsDisparities.

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Acknowledgements

We appreciate the assistance of L. Davordzi in this project, a graduate research assistant completing her Master of Science in Analytics (MSA).

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  1. Institute for Insight, Robinson College of Business, Georgia State University, Atlanta, GA, USA

    Aaron Baird & Yusen Xia

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  1. Aaron Baird
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  2. Yusen Xia
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Contributions

A.B. and Y.X. contributed to the design and implementation of the research, to the analysis of the data, to the development of final results and to the writing of the manuscript.

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Correspondence to Aaron Baird.

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Nature Mental Health thanks Debarshi Datta, Gayatri Kawlra and Emma Stanley for their contribution to the peer review of this work.

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Baird, A., Xia, Y. Applying analytics to sociodemographic disparities in mental health. Nat. Mental Health 3, 124–138 (2025). https://doi.org/10.1038/s44220-024-00359-2

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