TY - JOUR
T1 - How multi-scale modeling can help examine social determinants of health and resulting disparities
AU - Yoshida, Kyoko
AU - Pienaar, Elsje
AU - Bynum, Shalanda A.
AU - Chesler, Naomi
AU - Colebank, Mitchel J.
AU - Heneghan, Jessie
AU - Tyus, Nadra
AU - Miller-Kleinhenz, Jasmine
AU - Lee, Bruce Y.
N1 - Publisher Copyright:
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
PY - 2025/7
Y1 - 2025/7
N2 - Social determinants of health (SDOH) are the conditions in which people live, work, and play, and the wider set of factors (e.g., social and economic systems and policies) that shape a person’s daily life. SDOH can differ significantly across communities and populations, having positive impacts for some and negative impacts for others. Ultimately, this results in differences in health and disease distribution, that are known as health disparities. Despite the known impacts of SDOH and calls to characterize, address, reduce, and eliminate health disparities, they persist and, in some cases, have worsened. To address this challenge, a session at the Interagency Modeling and Analysis Group Multiscale Modeling Meeting held on the National Institutes of Health campus from June 28th to 29th, 2023, considered potential ways that multiscale modeling can help characterize adverse SDOH and resulting health disparities. This perspective summarizes and synthesizes the session discussions as a call to action to promote and strengthen interdisciplinary science that merges the unique perspectives, experiences, and expertise of the SDOH and multiscale modeling scientific communities in the pursuit of knowledge to improve population health. Specifically, we identify current challenges and ways in which multiscale modeling is uniquely suited to address the challenges, as well as identify what is necessary to facilitate the successful application of multiscale modeling in SDOH research. We conclude with a discussion on the future of multiscale modeling in SDOH and health disparities research.
AB - Social determinants of health (SDOH) are the conditions in which people live, work, and play, and the wider set of factors (e.g., social and economic systems and policies) that shape a person’s daily life. SDOH can differ significantly across communities and populations, having positive impacts for some and negative impacts for others. Ultimately, this results in differences in health and disease distribution, that are known as health disparities. Despite the known impacts of SDOH and calls to characterize, address, reduce, and eliminate health disparities, they persist and, in some cases, have worsened. To address this challenge, a session at the Interagency Modeling and Analysis Group Multiscale Modeling Meeting held on the National Institutes of Health campus from June 28th to 29th, 2023, considered potential ways that multiscale modeling can help characterize adverse SDOH and resulting health disparities. This perspective summarizes and synthesizes the session discussions as a call to action to promote and strengthen interdisciplinary science that merges the unique perspectives, experiences, and expertise of the SDOH and multiscale modeling scientific communities in the pursuit of knowledge to improve population health. Specifically, we identify current challenges and ways in which multiscale modeling is uniquely suited to address the challenges, as well as identify what is necessary to facilitate the successful application of multiscale modeling in SDOH research. We conclude with a discussion on the future of multiscale modeling in SDOH and health disparities research.
UR - https://www.scopus.com/pages/publications/105010681761
UR - https://www.scopus.com/pages/publications/105010681761#tab=citedBy
U2 - 10.1371/journal.pcbi.1013284
DO - 10.1371/journal.pcbi.1013284
M3 - Article
C2 - 40663536
AN - SCOPUS:105010681761
SN - 1553-734X
VL - 21
JO - PLoS computational biology
JF - PLoS computational biology
IS - 7 July
M1 - e1013284
ER -