Giuseppe
Emeritus
[Source: PLoS Pathogens, full text: (LINK). Abstract, edited.]
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Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology
Simon Cauchemez<SUP>1</SUP><SUP>*</SUP>, Peter Horby<SUP>2</SUP>, Annette Fox<SUP>2</SUP>, Le Quynh Mai<SUP>3</SUP>, Le Thi Thanh<SUP>3</SUP>, Pham Quang Thai<SUP>3</SUP>, Le Nguyen Minh Hoa<SUP>2</SUP>, Nguyen Tran Hien<SUP>3</SUP>, Neil M. Ferguson<SUP>1</SUP>
<SUP></SUP>
1 MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom, 2 Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Hanoi, Vietnam, 3 National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
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.
Author Summary
Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a ?signature? in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza ? and the nature of infection.
Citation: Cauchemez S, Horby P, Fox A, Mai LQ, Thanh LT, et al. (2012) Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology. PLoS Pathog 8(12): e1003061. doi:10.1371/journal.ppat.1003061
Editor: Ron A. M. Fouchier, Erasmus Medical Center, Netherlands
Received: August 9, 2012; Accepted: October 14, 2012; Published: December 13, 2012
Copyright: ? 2012 Cauchemez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by research grants from the Wellcome Trust (grants 081613/Z/06/Z and 077078/Z/05/Z), the NIH MIDAS program, EU FP7 EMPERIE and PREDEMICS projects and the MRC. SC also thanks Research Council UK. SC received consulting fees from Sanofi Pasteur MSD for a project on the modelling of varicella zoster virus transmission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: SC received consulting fees from Sanofi Pasteur MSD for a project on the modelling of the transmission of varicella zoster virus (i.e. different subject than submission). This does not alter our adherence to all PLOS Pathogens policies on sharing data and materials.
* E-mail: s.cauchemez@imperial.ac.uk
-Simon Cauchemez<SUP>1</SUP><SUP>*</SUP>, Peter Horby<SUP>2</SUP>, Annette Fox<SUP>2</SUP>, Le Quynh Mai<SUP>3</SUP>, Le Thi Thanh<SUP>3</SUP>, Pham Quang Thai<SUP>3</SUP>, Le Nguyen Minh Hoa<SUP>2</SUP>, Nguyen Tran Hien<SUP>3</SUP>, Neil M. Ferguson<SUP>1</SUP>
<SUP></SUP>
1 MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom, 2 Oxford University Clinical Research Unit - Wellcome Trust Major Overseas Programme, Hanoi, Vietnam, 3 National Institute of Hygiene and Epidemiology, Hanoi, Vietnam
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.
Author Summary
Each year, seasonal influenza is responsible for about three to five million severe illnesses and about 250,000 to 500,000 deaths worldwide. In order to assess the burden of disease and guide control policies, it is important to quantify the proportion of people infected by an influenza virus each year. Since infection usually leaves a ?signature? in the blood of infected individuals (namely a rise in antibodies), a standard protocol consists in collecting blood samples in a cohort of subjects and determining the proportion of those who experienced such rise. However, because of inherent measurement errors, only large rises are accounted for in the standard 4-fold rise case definition. Here, we revisit this 70 year old and widely accepted and applied criterion. We present innovative statistical techniques to better capture the impact of measurement errors and improve our interpretation of the data. Our analysis suggests that the number of people infected by an influenza virus each year might be substantially larger than previously thought, with important implications for our understanding of the transmission and evolution of influenza ? and the nature of infection.
Citation: Cauchemez S, Horby P, Fox A, Mai LQ, Thanh LT, et al. (2012) Influenza Infection Rates, Measurement Errors and the Interpretation of Paired Serology. PLoS Pathog 8(12): e1003061. doi:10.1371/journal.ppat.1003061
Editor: Ron A. M. Fouchier, Erasmus Medical Center, Netherlands
Received: August 9, 2012; Accepted: October 14, 2012; Published: December 13, 2012
Copyright: ? 2012 Cauchemez et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by research grants from the Wellcome Trust (grants 081613/Z/06/Z and 077078/Z/05/Z), the NIH MIDAS program, EU FP7 EMPERIE and PREDEMICS projects and the MRC. SC also thanks Research Council UK. SC received consulting fees from Sanofi Pasteur MSD for a project on the modelling of varicella zoster virus transmission. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: SC received consulting fees from Sanofi Pasteur MSD for a project on the modelling of the transmission of varicella zoster virus (i.e. different subject than submission). This does not alter our adherence to all PLOS Pathogens policies on sharing data and materials.
* E-mail: s.cauchemez@imperial.ac.uk
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