Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases

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dc.citation.volume14
dc.contributor.authorAmaku, Marcos
dc.contributor.authorBurattini, Marcelo Nascimento [UNIFESP]
dc.contributor.authorChaib, Eleazar
dc.contributor.authorBezerra Coutinho, Francisco Antonio
dc.contributor.authorGreenhalgh, David
dc.contributor.authorLopez, Luis Fernandez
dc.contributor.authorMassad, Eduardo
dc.coverageLondon
dc.date.accessioned2020-07-02T18:52:19Z
dc.date.available2020-07-02T18:52:19Z
dc.date.issued2017
dc.description.abstractBackground: National or local laws, norms or regulations (sometimes and in some countries) require medical providers to report notifiable diseases to public health authorities. Reporting, however, is almost always incomplete. This is due to a variety of reasons, ranging from not recognizing the diseased to failures in the technical or administrative steps leading to the final official register in the disease notification system. The reported fraction varies from 9 to 99% and is strongly associated with the disease being reported. Methods: In this paper we propose a method to approximately estimate the full prevalence (and any other variable or parameter related to transmission intensity) of infectious diseases. The model assumes incomplete notification of incidence and allows the estimation of the non-notified number of infections and it is illustrated by the case of hepatitis C in Brazil. The method has the advantage that it can be corrected iteratively by comparing its findings with empirical results. Results: The application of the model for the case of hepatitis C in Brazil resulted in a prevalence of notified cases that varied between 163,902 and 169,382 casesen
dc.description.abstracta prevalence of non-notified cases that varied between 1,433,638 and 1,446,771en
dc.description.abstractand a total prevalence of infections that varied between 1,597,540 and 1,616,153 cases. Conclusions: We conclude that the model proposed can be useful for estimation of the actual magnitude of endemic states of infectious diseases, particularly for those where the number of notified cases is only the tip of the iceberg. In addition, the method can be applied to other situations, such as the well-known underreported incidence of criminality (for example rape), among others.en
dc.description.affiliationUniv Sao Paulo, Fac Med, Hosp Clin LIM01, Sao Paulo, SP, Brazil
dc.description.affiliationUniv Fed Sao Paulo, Hosp Sao Paulo, Escola Paulista Med, Sao Paulo, SP, Brazil
dc.description.affiliationUniv Strathclyde, Dept Math & Stat, Glasgow, Lanark, Scotland
dc.description.affiliationFlorida Int Univ, Ctr Internet Augmented Res & Assessment, Miami, FL 33199 USA
dc.description.affiliationLondon Sch Hyg & Trop Med, London, England
dc.description.affiliationUnifespUniv Fed Sao Paulo, Hosp Sao Paulo, Escola Paulista Med, Sao Paulo, SP, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipLIM01-HCFMUSP
dc.description.sponsorshipCNPq
dc.description.sponsorshipBrazilian Ministry of Health [TED 27/2015]
dc.description.sponsorshipFAPESP
dc.description.sponsorshipLeverhulme Trust from a Leverhulme Research Fellowship [RF-2015-88]
dc.description.sponsorshipBritish Council, Malaysia from the Dengue Tech Challenge [DTC 16022]
dc.description.sponsorshipScience Without Borders Program for a Special Visiting Fellowship (CNPq) [30098/2014-7]
dc.description.sponsorshipIDCNPq
dc.description.sponsorshipIDBrazilian Ministry of Health [TED 27/2015]
dc.description.sponsorshipIDCNPq [30098/2014-7]
dc.format.extent-
dc.identifierhttp://dx.doi.org/10.1186/s12976-017-0069-2
dc.identifier.citationTheoretical Biology And Medical Modelling. London, v. 14, p. -, 2017.
dc.identifier.doi10.1186/s12976-017-0069-2
dc.identifier.fileWOS000417868000001.pdf
dc.identifier.issn1742-4682
dc.identifier.urihttps://repositorio.unifesp.br/handle/11600/54002
dc.identifier.wosWOS:000417868000001
dc.language.isoeng
dc.publisherBiomed Central Ltd
dc.relation.ispartofTheoretical Biology And Medical Modelling
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectHepatitis Cen
dc.subjectMathematical modelsen
dc.subjectNotifications system incidenceen
dc.subjectPrevalenceen
dc.titleEstimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databasesen
dc.typeinfo:eu-repo/semantics/article
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