Editing Prevalence (section)

==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 }}</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>