Making Causal Inference More Social and (Social) Epidemiology More Causal

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  • John W Jackson, Departments of Epidemiology and Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland., USA
  • Onyebuchi A Arah

A society's social structure and the interactions of its members determine when key drivers of health occur, for how long they last, and how they operate. Yet, it has been unclear whether causal inference methods can help us find meaningful interventions on these fundamental social drivers of health. Galea and Hernán propose that we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Galea & Hernán. Am J Epidemiol. 2019;XX(XX):XXXX-XXXX)). In this commentary, by way of example in health disparities research, we probe this "closer engagement of social epidemiology with formal causal inference approaches." The formidable, but not insurmountable, tensions call for causal reasoning and effect estimation in social epidemiology that should always be enveloped by a thorough understanding of how systems and the social exposome shape risk factor and health distributions. We argue that one way towards progress is a true partnership of social epidemiology and causal inference with bilateral feedback aimed at integrating social epidemiologic theory, causal identification and modeling methods, systems thinking, and improved study design and data. To produce consequential work, we must make social epidemiology more causal and causal inference more social.

OriginalsprogEngelsk
Artikelnummerkwz199
TidsskriftAmerican Journal of Epidemiology
Vol/bind189
Nummer3
Sider (fra-til)179-182
Antal sider4
ISSN0002-9262
DOI
StatusUdgivet - 2020

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© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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