Using directed acyclic graphs to guide analyses of neighbourhood health effects: an introduction.
BACKGROUND
Directed acyclic graphs, or DAGs, are a useful graphical tool in epidemiologic research that can help identify appropriate analytical strategies in addition to potential unintended consequences of commonly used methods such as conditioning on mediators. The use of DAGs can be particularly informative in the study of the causal effects of social factors on health.


METHODS
The authors consider four specific scenarios in which DAGs may be useful to neighbourhood health effects researchers: (1) identifying variables that need to be adjusted for in estimating neighbourhood health effects, (2) identifying the unintended consequences of estimating "direct" effects by conditioning on a mediator, (3) using DAGs to understand possible sources and consequences of selection bias in neighbourhood health effects research, and (4) using DAGs to identify the consequences of adjustment for variables affected by prior exposure.


CONCLUSIONS
The authors present simplified sample DAGs for each scenario and discuss the insights that can be gleaned from the DAGs in each case and the implications these have for analytical approaches.
Fleischer
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Center for Social Epidemiology and Population Health, 3rd Floor SPH Tower, 109 Observatory St, Ann Arbor, MI 48109-2029, USA. adiezrou@umich.edu
Diez Roux
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D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006304:Health Status; D006801:Humans; D012111:Residence Characteristics; D015983:Selection Bias; D012931:Social Environment; D012959:Socioeconomic Factors