Editing Prevalence

{{Short description|Number of disease cases in a given population at a specific time}}
[[File:DefofPrevalence (cropped).jpg|thumb|upright=1.8|A depiction of prevalence]]
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.<ref>{{cite web|url=https://www.health-ni.gov.uk/articles/prevalence-statistics#:~:text=Prevalence%20is%20a%20measure%20of,within%20a%20particular%20time%20period).|title=Prevalence statistics|date=26 August 2015 |access-date=15 March 2022}}</ref> It is derived by comparing the number of people found to have the condition with the total number of people studied and is usually expressed as a fraction, a percentage, or the number of cases per 10,000 or 100,000 people. Prevalence is most often used in [[questionnaire]] studies.

==Difference between prevalence and incidence==
{{See also|Incidence (epidemiology)#Incidence vs. prevalence}}

Prevalence is the number of disease cases ''present ''in a particular population at a given time, whereas [[incidence (epidemiology)|incidence]] is the number of new cases that ''develop ''during a specified time period.<ref>{{Cite web|url=https://www.medicinenet.com/script/main/art.asp?articlekey=11697|title=Definition of Prevalence|website=MedicineNet|language=en|access-date=2019-12-03}}</ref> Prevalence answers "How many people have this disease right now?" or "How many people have had this disease during this time period?". Incidence answers "How many people acquired the disease [during a specified time period]?". However, mathematically, prevalence is proportional to the product of the incidence and the average duration of the disease. In particular, when the prevalence is low (<10%), the relationship can be expressed as:<ref name=":0">{{Cite book |author=Bruce, Nigel |title=Quantitative methods for health research : a practical interactive guide to epidemiology and statistics|author2=Pope, Daniel |author3=Stanistreet, Debbi |date=29 November 2017|isbn=978-1-118-66526-8|edition=Second |location=Hoboken, NJ|page=16|oclc=992438133}}</ref>

:<math>Prevalence = incidence \times duration</math>

Caution must be practiced as this relationship is only applicable when the following two conditions are met: 1) prevalence is low and 2) the duration is constant (or an average can be taken).<ref name=":0" /> A general formulation requires [[differential equation]]s.<ref>{{Cite journal|language=en|author=Brinks, Ralph|title=Illness-Death Model in Chronic Disease Epidemiology: Characteristics of a Related, Differential Equation and an Inverse Problem|journal=Computational and Mathematical Methods in Medicine |year=2018 |volume=2018 |pages=1–6 |doi=10.1155/2018/5091096 |pmid=30275874 |pmc=6157110 |doi-access=free }}</ref>

==Examples and utility==
In science, ''prevalence'' describes a [[ratio|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 and Prevention|Centers for Disease Control (CDC)]] at approximately 20.9%.<ref>{{cite web |url=https://www.cdc.gov/nccdphp/dnpa/obesity/trend/prev_reg.htm |archive-url=https://web.archive.org/web/20060624145234/http://www.cdc.gov/nccdphp/dnpa/obesity/trend/prev_reg.htm |access-date=2017-09-10 |archive-date=2006-06-24 |title=Overweight and Obesity: Obesity Trends &#124; DNPA &#124; CDC }}</ref>

Prevalence is a term that means being widespread and it is distinct from [[incidence (epidemiology)|incidence]]. Prevalence is a measurement of ''all'' individuals affected by the disease at a particular time, whereas incidence is a measurement of the number of ''new'' individuals who contract a disease during a particular period of time. Prevalence is a useful parameter when talking about long-lasting diseases, such as [[HIV]], but incidence is more useful when talking about diseases of short duration, such as [[chickenpox]]. {{Citation needed|date=October 2018}}

==Uses==

===Lifetime prevalence===
'''Lifetime prevalence'''<!--Redirects here, bolded per MOS:BOLD--> ('''LTP''') is the proportion of individuals in a population that at some point in their life (up to the time of assessment) have experienced a "case" (e.g., a disease, a traumatic event, or, a behavior, such as committing a crime). Often, a 12-month prevalence (or some other type of "period prevalence") is provided in conjunction with lifetime prevalence. ''Point prevalence'' is the prevalence of disorder at a specific point in time (a month or less). ''Lifetime morbid risk'' is "the proportion of a population that might become afflicted with a given disease at any point in their lifetime."<ref name="Rothman2012">{{cite book|author=Kenneth J. Rothman|title=Epidemiology: An Introduction|url=https://books.google.com/books?id=tKs7adtH-_IC&pg=PA53|date=21 June 2012|publisher=Oxford University Press|isbn=978-0-19-975455-7|page=53}}</ref><ref>{{Cite book| publisher = Oxford University Press| isbn = 978-0-19-937806-7| others = S. Charles Schulz, Michael Foster Green, Katharine J. Nelson (eds.)| title = Schizophrenia and psychotic spectrum disorders|chapter=Chapter 1: Overview of schizophrenia and treatment approaches|last1=Kruse|first1=Matthew|last2=Schulz|first2=S. Charles|page=7|location = New York|date = 2016}}</ref>

===Period prevalence===
'''Period prevalence'''<!--Redirects here, bolded per MOS:BOLD--> is the proportion of the population with a given disease or condition over a specific period of time. It could describe how many people in a population had a cold over the cold season in 2006, for example.{{cn|date=May 2023}} It is expressed as a percentage of the population and can be described by the following formula:

Period prevalence (proportion) = Number of cases that existed in a given period ÷ Number of people in the population during this period{{citation needed|date=June 2022}}

The relationship between incidence (rate), point prevalence (ratio) and period prevalence (ratio) is easily explained via an analogy with photography. Point prevalence is akin to a flashlit photograph: what is happening at this instant frozen in time. Period prevalence is analogous to a long exposure (seconds, rather than an instant) photograph: the number of events recorded in the photo whilst the camera shutter was open. In a movie each frame records an instant (point prevalence); by looking from frame to frame one notices new events (incident events) and can relate the number of such events to a period (number of frames); see [[Incidence (epidemiology)|incidence rate]].{{citation needed|date=June 2022}}

===Point prevalence===
'''Point prevalence'''<!--Redirects here, bolded per MOS:BOLD--> is a measure of the proportion of people in a population who have a disease or condition at a particular time, such as a particular date. It is like a snapshot of the disease in time. It can be used for statistics on the occurrence of [[chronic diseases]]. This is in contrast to period prevalence which is a measure of the proportion of people in a population who have a disease or condition over a specific period of time, say a season, or a year. Point prevalence can be described by the formula: Prevalence = Number of existing cases on a specific date ÷ Number of people in the population on this date <ref>
{{cite book|author=Gerstman, B.B.|year=2003|title=Epidemiology Kept Simple: An Introduction to Traditional and Modern Epidemiology (2nd ed.)|location=Hoboken, NJ|publisher=Wiley-Liss}}</ref>

==Limitations==
It can be said that a very small error applied over a very large number of individuals (that is, those who are ''not affected'' by the condition in the general population during their lifetime; for example, over 95%) produces a relevant, non-negligible number of subjects who are incorrectly classified as having the condition or any other condition which is the object of a survey study: these subjects are the so-called false positives; such reasoning applies to the 'false positive' but not the 'false negative' problem where we have an error applied over a relatively very small number of individuals to begin with (that is, those who are ''affected'' by the condition in the general population; for example, less than 5%). Hence, a very high percentage of subjects who seem to have a history of a disorder at interview are false positives for such a medical condition and apparently never developed a fully clinical [[syndrome]].{{citation needed|date=July 2021}}

A different but related problem in evaluating the public health significance of psychiatric conditions has been highlighted by [[Robert Spitzer (psychiatrist)|Robert Spitzer]] of [[Columbia University]]: fulfillment of [[diagnostic criteria]] and the resulting [[medical diagnosis|diagnosis]] do not necessarily imply need for treatment.<ref name=Spitzer1998>{{cite journal | first = Robert | last = Spitzer | date = February 1998 | title = Diagnosis and need for treatment are not the same | journal = Archives of General Psychiatry | volume = 55 | issue = 2 | pages = 120 | url = http://archpsyc.ama-assn.org/cgi/pmidlookup?view=long&pmid=9477924 | doi = 10.1001/archpsyc.55.2.120 | pmid = 9477924 | url-status = dead | archive-url = http://archive.wikiwix.com/cache/20110705210403/http://archpsyc.ama-assn.org/cgi/pmidlookup?view=long&pmid=9477924 | archive-date = 2011-07-05 | url-access = subscription }}</ref>

A well-known statistical problem arises when ascertaining rates for disorders and conditions with a relatively low population prevalence or [[base rate]]. Even assuming that lay interview diagnoses are highly accurate in terms of [[Sensitivity (tests)|sensitivity]] and [[Specificity (tests)|specificity]] and their corresponding area under the [[ROC curve]] (that is, [[Area under the curve|AUC]], or area under the [[receiver operating characteristic]] curve), a condition with a relatively low prevalence or base-rate is bound to yield high [[Type I and type II errors|false positive]] rates, which exceed [[Type I and type II errors|false negative]] rates; in such a circumstance a limited [[positive predictive value]], PPV, yields high [[false positive]] rates even in presence of a specificity which is very close to 100%.<ref name=Baldessarini1993>{{cite journal | first = Ross J. | last = Baldessarini |author2=Finklestein S. |author3=Arana G. W. |date=May 1983 | title =The predictive power of diagnostic tests and the effect of prevalence of illness | journal = Archives of General Psychiatry | volume = 40 | issue = 5 | pages = 569–73 |pmid=6838334 | doi=10.1001/archpsyc.1983.01790050095011}}</ref>

==See also==
* [[Denominator data]]
* [[Rare disease]]
* [[Base rate fallacy]]

==References==
{{Reflist}}

== External links ==
{{Wiktionary|prevalence}}
* [http://www.plusnews.org/country-profile.aspx PlusNews, the UN's HIV/AIDS news service provides HIV prevalence rates for nearly 60 countries worldwide]
* [https://web.archive.org/web/20071117234209/http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371%2Fjournal.pmed.0020146 Synopsis of article on "How Prevalent Is Schizophrenia?" from Public Library of Science]
* [https://www.europeanreview.org/article/20379 Prevalence of COVID-19 outbreak]
* https://www.prevalenceuk.com/

{{Concepts in infectious disease}}
{{Medical research studies}}
{{Authority control}}

[[Category:Epidemiology]]
[[Category:Medical statistics]]
[[Category:Statistical ratios]]