Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking
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Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking Michal Shimonovich1 · Anna Pearce1 · Hilary Thomson1 · Katherine Keyes2 · Srinivasa Vittal Katikireddi1 Received: 23 June 2020 / Accepted: 2 December 2020 © The Author(s) 2020
Abstract The nine Bradford Hill (BH) viewpoints (sometimes referred to as criteria) are commonly used to assess causality within epidemiology. However, causal thinking has since developed, with three of the most prominent approaches implicitly or explicitly building on the potential outcomes framework: directed acyclic graphs (DAGs), sufficient-component cause models (SCC models, also referred to as ‘causal pies’) and the grading of recommendations, assessment, development and evaluation (GRADE) methodology. This paper explores how these approaches relate to BH’s viewpoints and considers implications for improving causal assessment. We mapped the three approaches above against each BH viewpoint. We found overlap across the approaches and BH viewpoints, underscoring BH viewpoints’ enduring importance. Mapping the approaches helped elucidate the theoretical underpinning of each viewpoint and articulate the conditions when the viewpoint would be relevant. Our comparisons identified commonality on four viewpoints: strength of association (including analysis of plausible confounding); temporality; plausibility (encoded by DAGs or SCC models to articulate mediation and interaction, respectively); and experiments (including implications of study design on exchangeability). Consistency may be more usefully operationalised by considering an effect size’s transportability to a different population or unexplained inconsistency in effect sizes (statistical heterogeneity). Because specificity rarely occurs, falsification exposures or outcomes (i.e., negative controls) may be more useful. The presence of a dose-response relationship may be less than widely perceived as it can easily arise from confounding. We found limited utility for coherence and analogy. This study highlights a need for greater clarity on BH viewpoints to improve causal assessment. Keywords Causal inference · Bradford Hill · Directed acyclic graphs · Sufficient component cause models · GRADE
Introduction Causal assessment is fundamental to epidemiology as it may inform policy and practice to improve population health. A leading figure in epidemiology, Sir Austin Bradford Hill, suggested the goal of causal assessment is to understand if there is “any other way of explaining the set of facts before us … any other answer equally, or more, likely than cause and effect” [1]. Causal assessment may be applied to a body of evidence or a single study to interrogate the “set of facts” underlying a relationship. Bradford Hill notably laid out a * Michal Shimonovich [email protected] 1
MRC/CSO Social and Public Health Sciences Unit, University of Glasgow, Glasgow, UK
Mailman School of Public Health, Columbia University, New York, NY, USA
2
set of such facts. Alth
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