ArticleClinical epidemiology2018
Attributing diseases to multiple pathways: a causal-pie modeling approach.
Article in Clinical epidemiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Are contributory causes of death in part 2 of the death certificate mediators of chains of morbid events leading to death?Population health metrics · 2025Article
- Multidisciplinary perspectives on cumulative impact assessment for vulnerable communities: expert elicitation using a Delphi method.Integrated environmental assessment and management · 2025Article
- Evaluation of Nationwide Oral Mucosal Screening Program for Oral Cancer Mortality among Men in Taiwan.International journal of environmental research and public health · 2022Article
- Assessing causality in epidemiology: revisiting Bradford Hill to incorporate developments in causal thinking.European journal of epidemiology · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Characterizing the relations between exposures and diseases is the central tenet of epidemiology. Researchers may want to evaluate exposure-disease causation by assessing whether the disease under concern is induced by the various exposures - the so-called "attribution". In this paper, the authors propose a method to attribute diseases to multiple pathways based on the causal-pie model. The method can also be used to evaluate the potential impact of an intervention strategy and to allocate responsibility in tort-law liability issues.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.