TY - JOUR
T1 - Complexity in Epidemiology and Public Health. Addressing Complex Health Problems Through a Mix of Epidemiologic Methods and Data
AU - Rod, Naja Hulvej
AU - Broadbent, Alex
AU - Rod, Morten Hulvej
AU - Russo, Federica
AU - Arah, Onyebuchi A.
AU - Stronks, Karien
N1 - Funding Information: This work has been made possible by generous fellowship grants to Naja Hulvej Rod and Morten Hulvej Rod (visiting Fellows in Spring 2022) from the Institute of Advanced Studies at Amsterdam University. Publisher Copyright: © 2023 Lippincott Williams and Wilkins. All rights reserved.
PY - 2023/7/1
Y1 - 2023/7/1
N2 - Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.
AB - Public health and the underlying disease processes are complex, often involving the interaction of biologic, social, psychologic, economic, and other processes that may be nonlinear and adaptive and have other features of complex systems. There is therefore a need to push the boundaries of public health beyond single-factor data analysis and expand the capacity of research methodology to tackle real-world complexities. This article sets out a way to operationalize complex systems thinking in public health, with a particular focus on how epidemiologic methods and data can contribute towards this end. Our proposed framework comprises three core dimensions-patterns, mechanisms, and dynamics-along which complex systems may be conceptualized. These dimensions cover seven key features of complex systems-emergence, interactions, nonlinearity, interference, feedback loops, adaptation, and evolution. We relate this framework to examples of methods and data traditionally used in epidemiology. We conclude that systematic production of knowledge on complex health issues may benefit from: formulation of research questions and programs in terms of the core dimensions we identify, as a comprehensive way to capture crucial features of complex systems; integration of traditional epidemiologic methods with systems methodology such as computational simulation modeling; interdisciplinary work; and continued investment in a wide range of data types. We believe that the proposed framework can support the systematic production of knowledge on complex health problems, with the use of epidemiology and other disciplines. This will help us understand emergent health phenomena, identify vulnerable population groups, and detect leverage points for promoting public health.
KW - Complex systems
KW - Epidemiology
KW - Methods
KW - Public health
KW - Theory
UR - http://www.scopus.com/inward/record.url?scp=85160876321&partnerID=8YFLogxK
U2 - https://doi.org/10.1097/EDE.0000000000001612
DO - https://doi.org/10.1097/EDE.0000000000001612
M3 - Article
C2 - 37042967
SN - 1044-3983
VL - 34
SP - 505
EP - 514
JO - Epidemiology (Cambridge, Mass.)
JF - Epidemiology (Cambridge, Mass.)
IS - 4
ER -