The Need for Systems Approaches for Precision Communications in Public Health

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Abstract

A major challenge in communicating health-related information is the involvement of multiple complex systems from the creation of the information to the sources and channels of dispersion to the information users themselves. To date, public health communications approaches have often not adequately accounted for the complexities of these systems to the degree necessary to have maximum impact. The virality of COVID-19 misinformation and disinformation has brought to light the need to consider these system complexities more extensively. Unaided, it is difficult for humans to see and fully understand complex systems. Luckily, there are a range of systems approaches and methods, such as systems mapping and systems modeling, that can help better elucidate complex systems. Using these methods to better characterize the various systems involved in communicating public health-related information can lead to the development of more tailored, precise, and proactive communications. Proceeding in an iterative manner to help design, implement, and adjust such communications strategies can increase impact and leave less opportunity for misinformation and disinformation to spread.

Acknowledgments

The authors would like to acknowledge Danielle John, a Scientific Coordinator for the NYC Pandemic Response Institute (PRI) at the CUNY Graduate School of Public Health and Health Policy and an Analyst for PHICOR, for her assistance in proofreading and editing the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Agency for Healthcare Research and Quality (AHRQ) via grant 1R01HS028165-01, the National Institute of General Medical Sciences as part of the Models of Infectious Disease Agent Study network under grants R01GM127512 and 3R01GM127512-01A1S1, and by the National Science Foundation via proposal number 2054858, the National Center for Advancing Translational Sciences of the National Institutes of Health via award number U54TR004279, and by the City University of New York (CUNY) in support of the NYC Pandemic Response Institute (PRI). Statements in the manuscript do not necessarily represent the official views of, or imply endorsement by NIH, AHRQ, the US Department of Health and Human Services, CUNY, or the NYC PRI.