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Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
Pathogenesis is the process by which a disease or disorder develops. It can include factors which contribute not only to the onset of the disease or disorder, but also to its progression and maintenance. The word comes from Greek πάθος pathos 'suffering, disease' and γένεσις genesis 'creation'.
A case–control study is a type of observational study in which two existing groups differing in outcome are identified and compared on the basis of some supposed causal attribute. Case–control studies are often used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/disease with patients who do not have the condition/disease but are otherwise similar. They require fewer resources but provide less evidence for causal inference than a randomized controlled trial. A case–control study is often used to produce an odds ratio, which is an inferior measure of strength of association compared to relative risk, but new statistical methods make it possible to use a case-control study to estimate relative risk, risk differences, and other quantities.
The deductive-nomological model of scientific explanation, also known as Hempel's model, the Hempel–Oppenheim model, the Popper–Hempel model, or the covering law model, is a formal view of scientifically answering questions asking, "Why...?". The DN model poses scientific explanation as a deductive structure, one where truth of its premises entails truth of its conclusion, hinged on accurate prediction or postdiction of the phenomenon to be explained.
Gene–environment interaction is when two different genotypes respond to environmental variation in different ways. A norm of reaction is a graph that shows the relationship between genes and environmental factors when phenotypic differences are continuous. They can help illustrate GxE interactions. When the norm of reaction is not parallel, as shown in the figure below, there is a gene by environment interaction. This indicates that each genotype responds to environmental variation in a different way. Environmental variation can be physical, chemical, biological, behavior patterns or life events.
In causal inference, a confounder is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Some notations are explicitly designed to identify the existence, possible existence, or non-existence of confounders in causal relationships between elements of a system.
In the philosophy of science, a causal model is a conceptual model that describes the causal mechanisms of a system. Several types of causal notation may be used in the development of a causal model. Causal models can improve study designs by providing clear rules for deciding which independent variables need to be included/controlled for.
In statistics, a mediation model seeks to identify and explain the mechanism or process that underlies an observed relationship between an independent variable and a dependent variable via the inclusion of a third hypothetical variable, known as a mediator variable. Rather than a direct causal relationship between the independent variable and the dependent variable, a mediation model proposes that the independent variable influences the mediator variable, which in turn influences the dependent variable. Thus, the mediator variable serves to clarify the nature of the relationship between the independent and dependent variables.
In epidemiology, Mendelian randomization is a method using measured variation in genes to interrogate the causal effect of an exposure on an outcome. Under key assumptions, the design reduces both reverse causation and confounding, which often substantially impede or mislead the interpretation of results from epidemiological studies.
Environmental epidemiology is a branch of epidemiology concerned with determining how environmental exposures impact human health. This field seeks to understand how various external risk factors may predispose to or protect against disease, illness, injury, developmental abnormalities, or death. These factors may be naturally occurring or may be introduced into environments where people live, work, and play.
While epidemiology is "the study of the distribution and determinants of states of health in populations", social epidemiology is "that branch of epidemiology concerned with the way that social structures, institutions, and relationships influence health." This research includes "both specific features of, and pathways by which, societal conditions affect health".
Sander Greenland is an American statistician and epidemiologist with many contributions to statistical and epidemiologic methods including Bayesian and causal inference, bias analysis, and meta-analysis. His focus has been the extensions, limitations, and misuses of statistical methods in nonexperimental studies, especially in postmarketing surveillance of drugs, vaccines, and medical devices. He received honors Bachelor's and master's degrees in mathematics from the University of California, Berkeley, where he was Regent's and National Science Foundation Fellow in Mathematics, and then received Master's and Doctoral degrees in epidemiology from the University of California, Los Angeles (UCLA), where he was Regent's Fellow in Epidemiology. After serving as an assistant professor of biostatistics at Harvard, he joined the UCLA Epidemiology faculty in 1980 where he became Professor of Epidemiology in the Fielding School of Public Health in 1989, and Professor of Statistics in the UCLA College of Letters and Science in 1999. He moved to Emeritus status in 2012 and the following year he was awarded an honorary Doctor of Medicine by the University of Aarhus, Denmark.
The Bradford Hill criteria, otherwise known as Hill's criteria for causation, are a group of nine principles that can be useful in establishing epidemiologic evidence of a causal relationship between a presumed cause and an observed effect and have been widely used in public health research. They were established in 1965 by the English epidemiologist Sir Austin Bradford Hill.
In epidemiology and biomedicine, biological plausibility is the proposal of a causal association—a relationship between a putative cause and an outcome—that is consistent with existing biological and medical knowledge.
The French Louis Pasteur (1822–1895) and German Robert Koch (1843–1910) are the two greatest figures in medical microbiology and in establishing acceptance of the germ theory of disease. In 1882, fueled by national rivalry and a language barrier, the tension between Pasteur and the younger Koch erupted into an acute conflict.
In 1995, Jo C. Phelan and Bruce G. Link developed the theory of fundamental causes. This theory seeks to outline why the association between socioeconomic status (SES) and health disparities has persisted over time, particularly when diseases and conditions previously thought to cause morbidity and mortality among low SES individuals have resolved. The theory states that an ongoing association exists between SES and health status because SES "embodies an array of resources, such as money, knowledge, prestige, power, and beneficial social connections that protect health no matter what mechanisms are relevant at any given time." In other words, despite advances in screening techniques, vaccinations, or any other piece of health technology or knowledge, the underlying fact is that those from low SES communities lack resources to protect and/or improve their health.
Cause, also known as etiology and aetiology, is the reason or origination of something.
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.
In statistics, econometrics, epidemiology, genetics and related disciplines, causal graphs are probabilistic graphical models used to encode assumptions about the data-generating process.
The discipline of forensic epidemiology (FE) is a hybrid of principles and practices common to both forensic medicine and epidemiology. FE is directed at filling the gap between clinical judgment and epidemiologic data for determinations of causality in civil lawsuits and criminal prosecution and defense.
References
- ↑ Rothman KJ (1976). "Causes". Am J Epidemiol. 104 (6): 587–92. doi:10.1093/oxfordjournals.aje.a112335. PMID 998606.
- ↑ Rothman, Kenneth J.; Lash, Timothy L.; VanderWeele, Tyler J.; Haneuse, Sebastien (2021). Modern epidemiology (in Spanish). Philadelphia. ISBN 978-1-9751-6628-1. OCLC 1236198056.
{{cite book}}: CS1 maint: location missing publisher (link) - ↑ Rothman, Kenneth J. (2012). Epidemiology : an introduction (2nd ed.). New York, NY: Oxford University Press. p. 24. ISBN 978-0-19-975455-7. OCLC 750986180.
- ↑ Rothman, Kenneth J.; Greenland, Sander (2005). "Causation and Causal Inference in Epidemiology". American Journal of Public Health. 95 (S1): S144–S150. doi:10.2105/AJPH.2004.059204. hdl: 10.2105/AJPH.2004.059204 . ISSN 0090-0036. PMID 16030331.
- ↑ Johnson CY, Howards PP (2013). "Causal pie bingo!". Epidemiology. 24 (2): 331. doi: 10.1097/EDE.0b013e318281e1cf . PMID 23377092.