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. [1]
In 1996, David Fredricks and David Relman remarked on Hill's criteria in their pivotal paper on microbial pathogenesis. [2]
In 1965, the English statistician Sir Austin Bradford Hill proposed a set of nine criteria to provide epidemiologic evidence of a causal relationship between a presumed cause and an observed effect. (For example, he demonstrated the connection between cigarette smoking and lung cancer.) The list of the criteria is as follows: [1]
Some authors [3] consider, also, Reversibility: If the cause is deleted then the effect should disappear as well.
All scientific work is incomplete. All scientific work is liable to be upset or modified by advancing knowledge. That does not confer upon us a freedom to ignore the knowledge we already have, or to postpone the action that it appears to demand at a given time.
Bradford Hill's criteria had been widely accepted as useful guidelines for investigating causality in epidemiological studies but their value has been questioned because they have become somewhat outdated. [5]
In addition, their method of application is debated.[ citation needed ] Some proposed options how to apply them include:
An argument against the use of Bradford Hill criteria as exclusive considerations in proving causality is that the basic mechanism of proving causality is not in applying specific criteria—whether those of Bradford Hill or counterfactual argument—but in scientific common sense deduction. [11] Others argue that the specific study from which data has been produced is important, and while the Bradford Hill criteria may be applied to test causality in these scenarios, the study type may rule out deducing or inducing causality, and the criteria are only of use in inferring the best explanation of this data. [12]
Debate over the scope of application of the criteria includes, whether they can be applied to social sciences. [13] The argument proposes that there are different motives behind defining causality; the Bradford Hill criteria applied to complex systems such as health sciences are useful in prediction models where a consequence is sought; explanation models as to why causation occurred are deduced less easily from Bradford Hill criteria because the instigation of causation, rather than the consequence, is needed for these models.[ citation needed ]
Researchers have applied Hill’s criteria for causality in examining the evidence in several areas of epidemiology, including connections between exposures to molds and infant pulmonary hemorrhage, [14] ultraviolet B radiation, vitamin D and cancer, [15] [16] vitamin D and pregnancy and neonatal outcomes, [17] alcohol and cardiovascular disease outcomes, [18] infections and risk of stroke, [19] nutrition and biomarkers related to disease outcomes, [20] foods and nutrients related to cardiovascular disease and diabetes [21] and sugar-sweetened beverage consumption and the prevalence of obesity and obesity-related diseases. [22] They have also been used in non-human epidemiological studies, such as on the effects of neonicotinoid pesticides on honey bees. [23] Their use in quality improvement of health care services has been proposed, highlighting how quality improvement methods can be used to provide evidence for the criteria. [24]
Since the description of the criteria, many methods to systematically evaluate the evidence supporting a causal relationship have been published, for example the five evidence-grading criteria of the World Cancer Research Fund (Convincing; Probable; Limited evidence – suggestive; Limited evidence – no conclusion; Substantial effect on risk unlikely). [25]
Causality is an influence by which one event, process, state, or object (acause) contributes to the production of another event, process, state, or object (an effect) where the cause is at least partly responsible for the effect, and the effect is at least partly dependent on the cause. The cause of something may also be described as the reason for the event or process.
The phrase "correlation does not imply causation" refers to the inability to legitimately deduce a cause-and-effect relationship between two events or variables solely on the basis of an observed association or correlation between them. The idea that "correlation implies causation" is an example of a questionable-cause logical fallacy, in which two events occurring together are taken to have established a cause-and-effect relationship. This fallacy is also known by the Latin phrase cum hoc ergo propter hoc. This differs from the fallacy known as post hoc ergo propter hoc, in which an event following another is seen as a necessary consequence of the former event, and from conflation, the errant merging of two events, ideas, databases, etc., into one.
Epidemiology is the study and analysis of the distribution, patterns and determinants of health and disease conditions in a defined population.
Koch's postulates are four criteria designed to establish a causal relationship between a microbe and a disease. The postulates were formulated by Robert Koch and Friedrich Loeffler in 1884, based on earlier concepts described by Jakob Henle, and the statements were refined and published by Koch in 1890. Koch applied the postulates to describe the etiology of cholera and tuberculosis, both of which are now ascribed to bacteria. The postulates have been controversially generalized to other diseases. More modern concepts in microbial pathogenesis cannot be examined using Koch's postulates, including viruses and asymptomatic carriers. They have largely been supplanted by other criteria such as the Bradford Hill criteria for infectious disease causality in modern public health and the Molecular Koch's postulates for microbial pathogenesis.
The science of epidemiology has matured significantly from the times of Hippocrates, Semmelweis and John Snow. The techniques for gathering and analyzing epidemiological data vary depending on the type of disease being monitored but each study will have overarching similarities.
In pathology, 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 Ancient 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 the condition with patients who do not have the condition 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. Some statistical methods make it possible to use a case–control study to also estimate relative risk, risk differences, and other quantities.
Sir Austin Bradford Hill was an English epidemiologist who pioneered the modern randomised clinical trial and, together with Richard Doll, demonstrated the connection between cigarette smoking and lung cancer. Hill is widely known for pioneering the "Bradford Hill" criteria for determining a causal association.
Wesley Charles Salmon was an American philosopher of science renowned for his work on the nature of scientific explanation. He also worked on confirmation theory, trying to explicate how probability theory via inductive logic might help confirm and choose hypotheses. Yet most prominently, Salmon was a realist about causality in scientific explanation, although his realist explanation of causality drew ample criticism. Still, his books on scientific explanation itself were landmarks of the 20th century's philosophy of science, and solidified recognition of causality's important roles in scientific explanation, whereas causality itself has evaded satisfactory elucidation by anyone.
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 metaphysics, 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 epidemiology, Mendelian randomization is a method using measured variation in genes to examine 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.
Causal analysis is the field of experimental design and statistics pertaining to establishing cause and effect. Typically it involves establishing four elements: correlation, sequence in time, a plausible physical or information-theoretical mechanism for an observed effect to follow from a possible cause, and eliminating the possibility of common and alternative ("special") causes. Such analysis usually involves one or more artificial or natural experiments.
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.
Nutritional epidemiology examines dietary and nutritional factors in relation to disease occurrence at a population level. Nutritional epidemiology is a relatively new field of medical research that studies the relationship between nutrition and health. It is a young discipline in epidemiology that is continuing to grow in relevance to present-day health concerns. Diet and physical activity are difficult to measure accurately, which may partly explain why nutrition has received less attention than other risk factors for disease in epidemiology.
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 study 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.
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.
Causal analysis is the field of experimental design and statistical analysis pertaining to establishing cause and effect. Exploratory causal analysis (ECA), also known as data causality or causal discovery is the use of statistical algorithms to infer associations in observed data sets that are potentially causal under strict assumptions. ECA is a type of causal inference distinct from causal modeling and treatment effects in randomized controlled trials. It is exploratory research usually preceding more formal causal research in the same way exploratory data analysis often precedes statistical hypothesis testing in data analysis
{{cite journal}}
: CS1 maint: multiple names: authors list (link){{cite journal}}
: CS1 maint: multiple names: authors list (link)