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In epidemiology, prevalence is the proportion of a particular population found to be affected by a medical condition (typically a disease or a risk factor such as smoking or seatbelt use) at a specific time. [1]
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 ...
Prevalence can also be measured with respect to a specific subgroup of a population. 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.
The Plague of Athens (c. 1652 –1654) by Michiel Sweerts, illustrating the devastating epidemic that struck Athens in 430 BC, as described by the historian Thucydides. The United States Centers for Disease Control and Prevention defines epidemic broadly: "Epidemic refers to an increase, often sudden, in the number of cases of a disease above what is normally expected in that population in ...
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
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 positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
Estimating the incidence and prevalence of rare diseases is a complex process due to their wide range of prevalence rates. Rare diseases with higher prevalences can be estimated through a screening panel or patient registries, while diseases which are exceedingly rare may only be able to be estimated through a multi-step nationwide reporting ...