Evidence map›Paper›PMID 29228966›Full record

ArticleTheoretical biology & medical modelling2017

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

Marcos Amaku, Marcelo Nascimento Burattini, Eleazar Chaib, Francisco Antonio Bezerra Coutinho, David Greenhalgh, Luis Fernandez Lopez, Eduardo Massad

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Article in Theoretical biology & medical modelling, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Marcos AmakuLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil.
Marcelo Nascimento BurattiniLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil.
Eleazar ChaibLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil.
Francisco Antonio Bezerra CoutinhoLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil.
David GreenhalghDepartment of Mathematics and Statistics, The University of Strathclyde, Glasgow, Scotland, UK.
Luis Fernandez LopezLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil.
Eduardo MassadLIM01-Hospital de Clínicas, Faculdade de Medicina Universidade de São Paulo, São Paulo, SP, Brazil. edmassad@dim.fm.usp.br.

Funding

CNPq 30098/2014-7CNPq Grant TED 27/2015Leverhulme Research Fellowship RF-2015-88
6 · The paper itself

Abstract

backgroundNational 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.

methodsIn 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.

resultsThe 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; a prevalence of non-notified cases that varied between 1,433,638 and 1,446,771; and a total prevalence of infections that varied between 1,597,540 and 1,616,153 cases.

conclusionsWe 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.

Indexed as

Age FactorsCommunicable DiseasesDatabases, FactualDisease NotificationHumansPrevalenceHepatitis CMathematical modelsNotifications system incidencePrevalence

Identifiers

PMID29228966
PMCPMC5725986

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.