Year: 2014
Type: article
Abstract: Something is missing in VanderWeele and Knol's extensive and remarkable review of the assessment and interpretation of interactions (VanderWeele and Knol, 2015).At which point does the analysis of interaction become incompatible with epidemiology?In an extreme scenario in which multiple exposures interact with one another, including all higher degree interactions, that is, in a model that is saturated with interactions, there is a different solution for each individual, and the basic tools of epidemiology, population thinking, and group comparisons, breakdown.The density of interactions is an indicator of complexity and complex hypotheses are something that epidemiology struggles with (Diez Roux, 2011).We don't need to resort to extreme scenarios to show the historically diffident attitude of epidemiology toward interactions, and more generally complexity.Isn't it striking that in VanderWeele and Knol's review (VanderWeele and Knol, 2015) all the examples deal with situations in which there is only one extra variable, interacting with (or modifying) the effect of one exposure?True, simplicity is a key feature of a didactic tutorial, but in this commentary, my aim is to show that the limitation to the simple case in which all the heterogeneity of an exposure effect is explained by splitting the studied population according to a third binary variable essentially is a reflection of epidemiology's conflictive relationship with the concept of interaction.This commentary broadly reviews what has been the role of interaction in epidemiologic thinking.In the seventeenth century, ignoring interaction was one of the fundamental conditions that permitted the emergence of population thinking (Morabia, 2013b).The scientists who, throughout the eighteenth and nineteenth centuries, steered away from interactions, adhering to simple causal frameworks limited to one exposure and one outcome (eventually with some confounders) are presently viewed as precursors of epidemiology, whereas those who had a complex vision of disease causation sometimes met dramatic failure.The interest of epidemiology for interaction resurfaced after 1945, when the so-called chronic disease epidemiologists timidly formulated hypotheses involving two-way interactions and evaluated them by stratification.Today, we may have reached the point at which the relation of epidemiology to interaction can be relaxed.The potential outcome framework may be more apt to deal with complexity than traditional modern epidemiologic approaches.
Source: Epidemiologic Methods
DOI: 10.1515/em-2014-0017
Author: Alfredo Morabia
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W2045786937