Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNIFESP |
Texto Completo: | http://dx.doi.org/10.1186/s12976-017-0069-2 https://repositorio.unifesp.br/handle/11600/54002 |
Resumo: | Background: 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 cases |
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Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databasesHepatitis CMathematical modelsNotifications system incidencePrevalenceBackground: 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 casesa prevalence of non-notified cases that varied between 1,433,638 and 1,446,771and 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.Univ Sao Paulo, Fac Med, Hosp Clin LIM01, Sao Paulo, SP, BrazilUniv Fed Sao Paulo, Hosp Sao Paulo, Escola Paulista Med, Sao Paulo, SP, BrazilUniv Strathclyde, Dept Math & Stat, Glasgow, Lanark, ScotlandFlorida Int Univ, Ctr Internet Augmented Res & Assessment, Miami, FL 33199 USALondon Sch Hyg & Trop Med, London, EnglandUniv Fed Sao Paulo, Hosp Sao Paulo, Escola Paulista Med, Sao Paulo, SP, BrazilWeb of ScienceLIM01-HCFMUSPCNPqBrazilian Ministry of Health [TED 27/2015]FAPESPLeverhulme Trust from a Leverhulme Research Fellowship [RF-2015-88]British Council, Malaysia from the Dengue Tech Challenge [DTC 16022]Science Without Borders Program for a Special Visiting Fellowship (CNPq) [30098/2014-7]CNPqBrazilian Ministry of Health [TED 27/2015]CNPq [30098/2014-7]Biomed Central Ltd2020-07-02T18:52:19Z2020-07-02T18:52:19Z2017info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersion-http://dx.doi.org/10.1186/s12976-017-0069-2Theoretical Biology And Medical Modelling. London, v. 14, p. -, 2017.10.1186/s12976-017-0069-2WOS000417868000001.pdf1742-4682https://repositorio.unifesp.br/handle/11600/54002WOS:000417868000001engTheoretical Biology And Medical ModellingLondoninfo:eu-repo/semantics/openAccessAmaku, MarcosBurattini, Marcelo Nascimento [UNIFESP]Chaib, EleazarBezerra Coutinho, Francisco AntonioGreenhalgh, DavidLopez, Luis FernandezMassad, Eduardoreponame:Repositório Institucional da UNIFESPinstname:Universidade Federal de São Paulo (UNIFESP)instacron:UNIFESP2022-02-03T11:55:37Zoai:repositorio.unifesp.br/:11600/54002Repositório InstitucionalPUBhttp://www.repositorio.unifesp.br/oai/requestbiblioteca.csp@unifesp.bropendoar:34652022-02-03T11:55:37Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP)false |
dc.title.none.fl_str_mv |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
title |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
spellingShingle |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases Amaku, Marcos Hepatitis C Mathematical models Notifications system incidence Prevalence |
title_short |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
title_full |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
title_fullStr |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
title_full_unstemmed |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
title_sort |
Estimating the prevalence of infectious diseases from under-reported age-dependent compulsorily notification databases |
author |
Amaku, Marcos |
author_facet |
Amaku, Marcos Burattini, Marcelo Nascimento [UNIFESP] Chaib, Eleazar Bezerra Coutinho, Francisco Antonio Greenhalgh, David Lopez, Luis Fernandez Massad, Eduardo |
author_role |
author |
author2 |
Burattini, Marcelo Nascimento [UNIFESP] Chaib, Eleazar Bezerra Coutinho, Francisco Antonio Greenhalgh, David Lopez, Luis Fernandez Massad, Eduardo |
author2_role |
author author author author author author |
dc.contributor.author.fl_str_mv |
Amaku, Marcos Burattini, Marcelo Nascimento [UNIFESP] Chaib, Eleazar Bezerra Coutinho, Francisco Antonio Greenhalgh, David Lopez, Luis Fernandez Massad, Eduardo |
dc.subject.por.fl_str_mv |
Hepatitis C Mathematical models Notifications system incidence Prevalence |
topic |
Hepatitis C Mathematical models Notifications system incidence Prevalence |
description |
Background: 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 cases |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2020-07-02T18:52:19Z 2020-07-02T18:52:19Z |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1186/s12976-017-0069-2 Theoretical Biology And Medical Modelling. London, v. 14, p. -, 2017. 10.1186/s12976-017-0069-2 WOS000417868000001.pdf 1742-4682 https://repositorio.unifesp.br/handle/11600/54002 WOS:000417868000001 |
url |
http://dx.doi.org/10.1186/s12976-017-0069-2 https://repositorio.unifesp.br/handle/11600/54002 |
identifier_str_mv |
Theoretical Biology And Medical Modelling. London, v. 14, p. -, 2017. 10.1186/s12976-017-0069-2 WOS000417868000001.pdf 1742-4682 WOS:000417868000001 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Theoretical Biology And Medical Modelling |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
- |
dc.coverage.none.fl_str_mv |
London |
dc.publisher.none.fl_str_mv |
Biomed Central Ltd |
publisher.none.fl_str_mv |
Biomed Central Ltd |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UNIFESP instname:Universidade Federal de São Paulo (UNIFESP) instacron:UNIFESP |
instname_str |
Universidade Federal de São Paulo (UNIFESP) |
instacron_str |
UNIFESP |
institution |
UNIFESP |
reponame_str |
Repositório Institucional da UNIFESP |
collection |
Repositório Institucional da UNIFESP |
repository.name.fl_str_mv |
Repositório Institucional da UNIFESP - Universidade Federal de São Paulo (UNIFESP) |
repository.mail.fl_str_mv |
biblioteca.csp@unifesp.br |
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1814268288467730432 |