ArticleMedicine, health care, and philosophy2006
The causation of disease - the practical and ethical consequences of competing explanations.
Article in Medicine, health care, and philosophy, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Serum Lipoprotein Profiling by NMR Spectroscopy Reveals Alterations in HDL-1 and HDL-2 Apo-A2 Subfractions in Alzheimer's Disease.International journal of molecular sciences · 2024Article
- Genomics and justice: mitigating the potential harms and inequities that arise from the implementation of genomics in medicine.Human genetics · 2022Article
- Characterising Alzheimer's disease through integrative NMR- and LC-MS-based metabolomics.Metabolism open · 2021Article
- Questions concerning the role of amyloid-β in the definition, aetiology and diagnosis of Alzheimer's disease.Acta neuropathologica · 2018Review
- Community health assessment using self-organizing maps and geographic information systems.International journal of health geographics · 2008Article
- Complex causation and the virtue of pluralism.Medicine, health care, and philosophy · 2006Article
Corrections and comments
- Commented on by
Authors and funding
5 authors.
Funding
Abstract
The prevention, treatment and management of disease are closely linked to how the causes of a particular disease are explained. For multi-factorial conditions, the causal explanations are inevitably complex and competing models may exist to explain the same condition. Selecting one particular causal explanation over another will carry practical and ethical consequences that are acutely relevant for health policy. In this paper our focus is two-fold; (i) the different models of causal explanation that are put forward within current scientific literature for the high and rising prevalence of the common complex conditions of coronary artery disease (CAD) and type 2 diabetes mellitus (T2D); and (ii) how these explanations are taken up (or not) within national health policy guidelines. We examine the causal explanations for these two conditions through a systematic database search of current scientific literature. By identifying different causal explanations we propose a three-tier taxonomy of the most prominent models of explanations: (i) evolutionary, (ii) lifecourse, and (iii) lifestyle and environment. We elaborate this taxonomy with a micro-level thematic analysis to illustrate how some explanations are semantically and rhetorically foregrounded over others. We then investigate the uptake of the scientific causal explanations in health policy documents with regard to the prevention and management recommendations of current National Service Frameworks for CAD and T2D. Our findings indicate a lack of congruence between the complexity and frequent overlap of causal explanations evident in the scientific literature and the predominant focus on lifestyle recommendations found in the mainstream health policy documents.
Indexed as
Identifiers
16937239What OpenQuestion holds
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.