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Epidemiological studies are aimed, where possible, at revealing unbiased relationships between exposures such as alcohol or smoking, biological agents, stress, or chemicals to mortality or morbidity. The identification of causal relationships between these exposures and outcomes is an important aspect of epidemiology.
Epidemiological (and other observational) studies typically highlight associations between exposures and outcomes, rather than causation. While some consider this a limitation of observational research, epidemiological models of causation (e.g. Bradford Hill criteria) [7] contend that an entire body of evidence is needed before determining if an association is truly causal. [8]
The science of epidemiology has had enormous growth, particularly with charity and government funding. Many researchers have been trained to conduct studies, requiring multiple skills ranging from liaising with clinical staff to the statistical analysis of complex data, such as using Bayesian methods.
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]
From a user's perspective, the most important functions of Epi Info are the ability to rapidly develop a questionnaire, customize the data entry process, quickly enter data into that questionnaire, and then analyze the data. For epidemiological uses, such as outbreak investigations, being able to rapidly create an electronic data entry screen ...
An epidemic curve, also known as an epi curve or epidemiological curve, is a statistical chart used in epidemiology to visualise the onset of a disease outbreak. It can help with the identification of the mode of transmission of the disease. It can also show the disease's magnitude, whether cases are clustered or if there are individual case ...
When comparing data from a specific country or region, using a standard population from that country or region means that the age-adjusted rates are similar to the true population rates. [6] On the other hand, standardizing data using a widely used standard such as the WHO standard population allows for easier comparison with published statistics.
In epidemiology, case fatality rate (CFR) – or sometimes more accurately case-fatality risk – is the proportion of people who have been diagnosed with a certain disease and end up dying of it. Unlike a disease's mortality rate, the CFR does not take into account the time period between disease onset and death. A CFR is generally expressed ...