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Influenza infection rates, measurement errors and the interpretation of paired serology.
Cauchemez, Simon; Horby, Peter; Fox, Annette; Mai, Le Quynh; Thanh, Le Thi; Thai, Pham Quang; Hoa, Le Nguyen Minh; Hien, Nguyen Tran; Ferguson, Neil M.
Afiliação
  • Cauchemez S; MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. s.cauchemez@imperial.ac.uk
PLoS Pathog ; 8(12): e1003061, 2012.
Article em En | MEDLINE | ID: mdl-23271967
ABSTRACT
Serological studies are the gold standard method to estimate influenza infection attack rates (ARs) in human populations. In a common protocol, blood samples are collected before and after the epidemic in a cohort of individuals; and a rise in haemagglutination-inhibition (HI) antibody titers during the epidemic is considered as a marker of infection. Because of inherent measurement errors, a 2-fold rise is usually considered as insufficient evidence for infection and seroconversion is therefore typically defined as a 4-fold rise or more. Here, we revisit this widely accepted 70-year old criterion. We develop a Markov chain Monte Carlo data augmentation model to quantify measurement errors and reconstruct the distribution of latent true serological status in a Vietnamese 3-year serological cohort, in which replicate measurements were available. We estimate that the 1-sided probability of a 2-fold error is 9.3% (95% Credible Interval, CI 3.3%, 17.6%) when antibody titer is below 10 but is 20.2% (95% CI 15.9%, 24.0%) otherwise. After correction for measurement errors, we find that the proportion of individuals with 2-fold rises in antibody titers was too large to be explained by measurement errors alone. Estimates of ARs vary greatly depending on whether those individuals are included in the definition of the infected population. A simulation study shows that our method is unbiased. The 4-fold rise case definition is relevant when aiming at a specific diagnostic for individual cases, but the justification is less obvious when the objective is to estimate ARs. In particular, it may lead to large underestimates of ARs. Determining which biological phenomenon contributes most to 2-fold rises in antibody titers is essential to assess bias with the traditional case definition and offer improved estimates of influenza ARs.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glicoproteínas de Hemaglutininação de Vírus da Influenza / Influenza Humana / Anticorpos Antivirais Tipo de estudo: Clinical_trials / Diagnostic_studies / Etiology_studies / Guideline / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: PLoS Pathog Ano de publicação: 2012 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Glicoproteínas de Hemaglutininação de Vírus da Influenza / Influenza Humana / Anticorpos Antivirais Tipo de estudo: Clinical_trials / Diagnostic_studies / Etiology_studies / Guideline / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Female / Humans / Male / Middle aged País/Região como assunto: Asia Idioma: En Revista: PLoS Pathog Ano de publicação: 2012 Tipo de documento: Article