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Seeking Causal Explanations in Social Epidemiology

Seeking Causal Explanations in Social Epidemiology Abstract Social factors are associated with a wide variety of health outcomes. Social epidemiology has successfully used the traditional methods of surveillance and description to establish consistent relations between social factors and health status. Epidemiology as an etiologic science, however, has been largely ineffective in moving toward causal explanations for these observed patterns. Using the counterfactual approach to causal inference, the authors describe several fundamental problems that often arise when researchers seek to infer explanatory mechanisms from data on social factors. Contrasts that form standard causal effect estimates require implicit unobserved (counterfactual) quantities, because observational data provide only one exposure state for each individual. Although application of counterfactual arguments has successfully advanced etiologic understanding in other observational settings, the particular nature of social factors often leads to logical contradictions or misleading inferences when investigators fail to clearly articulate the counterfactual contrasts that are implied. For example, because social factors are often attributes of individuals and are components of structured social relations, random assignment is not plausible even as a hypothetical experiment, making typical epidemiologic contrasts inappropriate and the inference equivocal at best. Accordingly, more deliberate and creative approaches to causal inference in social epidemiology are required. Infectious disease epidemiology and systems analysis provide examples of approaches to causal inference that can be used when statistical mimicry of simple experimental designs is not tenable. In an era of increasing social inequality, valid approaches for the study of social factors and health are needed more urgently than ever. Am J Epidemiol 1999;150:113–20. causality, confounding factors (epidemiology), epidemiologic methods, observation, social class, social conditions © 1999 by The Johns Hopkins University School of Hygiene and Public Health http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png American Journal of Epidemiology Oxford University Press

Seeking Causal Explanations in Social Epidemiology

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References (51)

Publisher
Oxford University Press
Copyright
© 1999 by The Johns Hopkins University School of Hygiene and Public Health
ISSN
0002-9262
eISSN
1476-6256
DOI
10.1093/oxfordjournals.aje.a009969
Publisher site
See Article on Publisher Site

Abstract

Abstract Social factors are associated with a wide variety of health outcomes. Social epidemiology has successfully used the traditional methods of surveillance and description to establish consistent relations between social factors and health status. Epidemiology as an etiologic science, however, has been largely ineffective in moving toward causal explanations for these observed patterns. Using the counterfactual approach to causal inference, the authors describe several fundamental problems that often arise when researchers seek to infer explanatory mechanisms from data on social factors. Contrasts that form standard causal effect estimates require implicit unobserved (counterfactual) quantities, because observational data provide only one exposure state for each individual. Although application of counterfactual arguments has successfully advanced etiologic understanding in other observational settings, the particular nature of social factors often leads to logical contradictions or misleading inferences when investigators fail to clearly articulate the counterfactual contrasts that are implied. For example, because social factors are often attributes of individuals and are components of structured social relations, random assignment is not plausible even as a hypothetical experiment, making typical epidemiologic contrasts inappropriate and the inference equivocal at best. Accordingly, more deliberate and creative approaches to causal inference in social epidemiology are required. Infectious disease epidemiology and systems analysis provide examples of approaches to causal inference that can be used when statistical mimicry of simple experimental designs is not tenable. In an era of increasing social inequality, valid approaches for the study of social factors and health are needed more urgently than ever. Am J Epidemiol 1999;150:113–20. causality, confounding factors (epidemiology), epidemiologic methods, observation, social class, social conditions © 1999 by The Johns Hopkins University School of Hygiene and Public Health

Journal

American Journal of EpidemiologyOxford University Press

Published: Jul 15, 1999

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