Causality in epidemiology
Abstract
Epidemiology represents an interesting and unique example of cross-fertilization between social and natural sciences. Epidemiology has evolved from a monocausal to a multicausal concept of the "web of causation", thus mimicking a similar and much earlier shift in the social sciences. However, in comparison with the social sciences epidemiology is both more sensitive to underlying biological models (which condition the interpretation of population findings), and more prone to a simplification of the causal pathways. Paradoxically, epidemiology has developed more sophisticated theoretical models for bias and confounding than the social sciences did, but for the practical purpose of identifying single preventable risk factors. Epidemiology makes use more often of study designs that simulate experimentation, than of surveys in the general population.
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If the relative risk is a long way from 1, the association is less likely to be due to confounding.
“If the relative risk is a long way from 1 it is less likely that the association is due to confounding [ 164 , 165 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [165]
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- Authors
- Vineis, P.
- Affiliations
- Azienda Ospedaliero Universitaria San Giovanni Battista (IT)
- Corresponding author
- Vineis, Paolo
- ORCID
- Vineis, Paolo
- Venue
- Sozial- und Präventivmedizin
- Published
- 2003-04
- Volume
- 48
- Issue
- 2
- Pages
- 80-87
- ISSN
- 0303-8408 (ISSN-L)
- 1420-911X
- OpenAlex
- W2155544610
- Cited by
- 49
- References
- 11
- Citations by year
- 2024: 2 · 2022: 3 · 2021: 1 · 2020: 1 · 2019: 4 · 2018: 2 · 2017: 3 · 2015: 1 · 2014: 2 · 2013: 2
- FWCI
- 3.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Sozial- und Präventivmedizin (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Sociology and Political Science › Historical and modern epidemiology studies
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Keywords
- causality, multicausality, confounding, bias, preventable risk factors, causal pathways, experimental design, social sciences and natural sciences, history of epidemiology
- MeSH terms
- Humans, Models, Biological, Models, Theoretical, Population Surveillance, Risk Factors, Epidemiologic Factors, Bias, Causality
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