title: Influenza infection rates, measurement errors and the interpretation of paired serology.
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.
authors.affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom. s.cauchemez@imperial.ac.uk
authors.forename: Simon
authors.initials: S
authors.lastname: Cauchemez
authors.forename: Peter
authors.initials: P
authors.lastname: Horby
authors.forename: Annette
authors.initials: A
authors.lastname: Fox
authors.forename: Le Quynh
authors.initials: le Q
authors.lastname: Mai
authors.forename: Le Thi
authors.initials: le T
authors.lastname: Thanh
authors.forename: Pham Quang
authors.initials: PQ
authors.lastname: Thai
authors.forename: Le Nguyen Minh
authors.initials: le NM
authors.lastname: Hoa
authors.forename: Nguyen Tran
authors.initials: NT
authors.lastname: Hien
authors.forename: Neil M
authors.initials: NM
authors.lastname: Ferguson
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000914:Antibodies, Viral / Q000097:blood / Q000276:immunology*; D015331:Cohort Studies; D005260:Female; D019267:Hemagglutinin Glycoproteins, Influenza Virus / Q000097:blood / Q000276:immunology*; D006801:Humans; D007251:Influenza, Human / Q000097:blood / Q000453:epidemiology* / Q000276:immunology*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D016036:Seroepidemiologic Studies; D012698:Serologic Tests; D014744:Vietnam / Q000453:epidemiology
chemical_list: D000914:Antibodies, Viral; D019267:Hemagglutinin Glycoproteins, Influenza Virus