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. (...)
Mots-clés Pascal : Etats Unis, Amérique du Nord, Amérique, Epidémiologie, Homme, Santé, Classe sociale, Méthodologie, Analyse sociologique, Déterminant
Mots-clés Pascal anglais : United States, North America, America, Epidemiology, Human, Health, Social class, Methodology, Social analysis, Determinant
Notice produite par :
Inist-CNRS - Institut de l'Information Scientifique et Technique
Cote : 99-0393104
Code Inist : 002B30A01A1. Création : 22/03/2000.