By Steven Coughlin
This anthology of articles on causal inference and medical paradigms in epidemiology covers numerous vital issues together with the hunt for causal causes, the strengths and boundaries of causal standards, quantitative methods for assessing causal relationships which are appropriate to epidemiology and rising paradigms in epidemiologic examine. as a way to supply old context, an summary of philosophical and historic advancements correct to causal inference in epidemiology and public health and wellbeing is additionally supplied. numerous theoretical and utilized features of causal inference are handled. the purpose of this book isn't just to summarize very important advancements in causal inference in epidemiology but additionally to spot attainable how one can increase the hunt for causal factors for illnesses and accidents. Examples are supplied from such fields as power sickness epidemiology, Veterans wellbeing and fitness, and environmental epidemiology. a specific objective of the book is to supply principles for strengthening causal inference in epidemiology within the context of sophisticated learn paradigms. those issues are very important as the result of epidemiologic reviews give a contribution to generalizable wisdom through clarifying the reasons of ailments, by means of combining epidemiologic facts with details from different disciplines (for instance, psychology and business hygiene), by means of comparing the consistency of epidemiologic info with etiological hypotheses approximately causation, and by way of supplying the root for comparing methods for future health merchandising and prevention and public overall healthiness practices Read more...
1. Foundations --
2. informal inference in epidemiologic examine --
3. towards a brand new examine paradigm in epidemiology --
4. study paradigms in epidemiology and causal inference.
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Additional resources for Causal inference and scientific paradigms in epidemiology
In defense of black box epidemiology. Epidemiology 1994;5:550-2. Skrabanek P. The emptiness of the black box. Epidemiology 1994;5:553-5. Krieger N. Epidemiology and the web of causation: has anyone seen the spider? Soc Sci Med 1994;39:887-903. Krieger N, Zierler S. What explains the public’s health: a call for epidemiologic theory. Epidemiology 1995;7:107-9. Susser M, Susser E. Choosing a future for epidemiology. I. Eras and paradigms. Am J Public Health 1996;86:668-73. Susser M, Susser E. Choosing a future for epidemiology.
Causation in epidemiology. J Epidemiol Commun Health 2001;55:905-12. Olsen J. What characterizes a useful concept of causation in epidemiology? J Epidemiol Commun Health 2003;57:86-88. Hernan MA, Cole SR. Causal diagrams and measurement bias. Am J Epidemiol 2009;170:959-62. Atkins D, Best D, Briss PA, et al. Grading quality of evidence and strength of recommendations. Br Med J 2004;328:1490. VanderWeele TJ, Hernandez-Diaz S, Hernan MA. Case-only gene-environment interaction studies: when does association imply mechanistic interaction?
For example, an editorial about the DRD2 gene, smoking, and lung cancer  asked: “Why do some individuals smoke and others do not? Why can some smokers quit, while others cannot? ” Genetic factors explain why some individuals infected with the human immunodeficiency virus (HIV) do not progress to AIDS . Nevertheless, an improved understanding of how genes contribute to cancer, AIDS, and other diseases for which the sick have sometimes been unfairly blamed is unlikely to do away with concerns about personal (or societal) responsibility for smoking, HIV infection, or other factors that interact with genetic susceptibility factors for disease.
Causal inference and scientific paradigms in epidemiology by Steven Coughlin