Series on epidemiology
Causation in Epidemiology

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Prevailing Models of Causation in Epidemiology

There are a multitude of definitions and models of causation in epidemiology, yet space precludes a thorough presentation of this material herein; fortunately, this topic has been covered in adequate detail elsewhere. Many of the currently debated definitions and models have their roots in the purely deterministic model of disease causation described by Henle and Koch in the late 19th century.1 They describe 4 criteria necessary to establish a casual relationship between a microbe and a

Integrating Causation into Epidemiologic Study Design and Analysis

The component-cause model serves as a framework on which issues of confounding and interaction (or effect modification) can be better incorporated into epidemiologic study design and analysis. Directed acyclic graphs operationalize the causal “pies” into causal diagrams that depict the functional relationships between various hypothesized component causes of a disease (Figure 1). Directed acyclic graphs are more complex than can be appreciated within the confines of this editorial, and the

Approaches to Causal Inference in Epidemiology

It should be noted that there are other definitions and models of causation in epidemiology. These models are not necessarily competing approaches, yet they offer different perspectives on the issue. The more scientific or logical definitions of causation seek to explain the world, whereas a goal of epidemiology is more practical: that is, to inform public health efforts to reduce morbidity and mortality. For example, it was possible to diminish the epidemic of cholera in London by identifying

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There are more references available in the full text version of this article.

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