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In science, prevalence describes a proportion (typically expressed as a percentage). For example, the prevalence of obesity among American adults in 2001 was estimated by the U. S. Centers for Disease Control (CDC) at approximately 20.9%. [5] Prevalence is a term that means being widespread and it is distinct from incidence.
Incidence is usually more useful than prevalence in understanding the disease etiology: for example, if the incidence rate of a disease in a population increases, then there is a risk factor that promotes the incidence. For example, consider a disease that takes a long time to cure and was widespread in 2002 but dissipated in 2003.
The term epidemiology is now widely applied to cover the description and causation of not only epidemic, infectious disease, but of disease in general, including related conditions. Some examples of topics examined through epidemiology include as high blood pressure, mental illness and obesity. Therefore, this epidemiology is based upon how the ...
For example, if the control group, using no treatment at all, had their own base rate of 1/20 recoveries within 1 day and a treatment had a 1/100 base rate of recovery within 1 day, we see that the treatment actively decreases the recovery. The base rate is an important concept in statistical inference, particularly in Bayesian statistics. [2]
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]
An example of the base rate fallacy is the false positive paradox (also known as accuracy paradox).This paradox describes situations where there are more false positive test results than true positives (this means the classifier has a low precision).
In medical research, epidemiology, social science, and biology, a cross-sectional study (also known as a cross-sectional analysis, transverse study, prevalence study) is a type of observational study that analyzes data from a population, or a representative subset, at a specific point in time—that is, cross-sectional data. [definition needed]
From a mathematical point of view, by taking values between 0 and 1 or 0% and 100%, CFRs are actually a measure of risk (case fatality risk) – that is, they are a proportion of incidence, although they do not reflect a disease's incidence. They are neither rates, incidence rates, nor ratios (none of which are limited to the range 0–1). They ...