Abstract:
: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 we place hypothetical interventions on a spectrum and estimate their effects by emulating trials, either through individual-level data analysis or systems science modeling (Am J Epidemiol. 2020;189(3):167-170). 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 toward 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.
journal_name
Am J Epidemioljournal_title
American journal of epidemiologyauthors
Jackson JW,Arah OAdoi
10.1093/aje/kwz199subject
Has Abstractpub_date
2020-03-02 00:00:00pages
179-182issue
3eissn
0002-9262issn
1476-6256pii
5579818journal_volume
189pub_type
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journal_title:American journal of epidemiology
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更新日期:2007-12-15 00:00:00
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