Interpreting Seroepidemiologic Studies of Influenza in a Context of Nonbracketing Sera

Abstract

Background In influenza epidemiology, analysis of paired sera collected from people before and after influenza seasons has been used for decades to study the cumulative incidence of influenza virus infections in populations. However, interpretation becomes challenging when sera are collected after the start or before the end of an epidemic, and do not neatly bracket the epidemic. Methods Serum samples were collected longitudinally in a community-based study. Most participants provided their first serum after the start of circulation of influenza A(H1N1)pdm09 virus in 2009. We developed a Bayesian hierarchical model to correct for non-bracketing sera and estimate the cumulative incidence of infection from the serological data and surveillance data in Hong Kong. Results We analysed 4843 sera from 2097 unvaccinated participants in the study, collected from April 2009 through December 2010. After accounting for non-bracketing, we estimated that the cumulative incidence of H1N1pdm09 virus infection was 45.1% (95% credible interval, CI: 40.2%, 49.2%), 16.5% (95% CI: 13.0%, 19.7%) and 11.3% (95% CI: 5.9%, 17.5%) for children 0–18y, adults 19–50y and older adults >50y respectively. Including all available data substantially increased precision compared to a simpler analysis based only on sera collected at 6-month intervals in a subset of participants. Conclusions We developed a framework for the analysis of antibody titers that accounted for the timing of sera collection with respect to influenza activity and permitted robust estimation of the cumulative incidence of infection during an epidemic.

Individual and Population Trajectories of Influenza Antibody Titers Over Multiple Seasons in a Tropical Country

“…Previous research has shown that delayed collection of blood samples might lead to underascertainment of influenza infections ( 8 ). A straightforward approach to accounting for the timing of serological samples was to use a suitable proxy for influenza levels in the community, such as extraction of the rate of influenza-like illness from an influenza surveillance program ( 4 ).…”

“…We developed a statistical model with which to characterize the evolution of antibody titers against influenza virus infection using a series of HAI assays collected over multiple influenza seasons in the community in Singapore, as well as supplementary real-time polymerase chain reaction (RT-PCR) data collected from various subpopulations. Conventionally, a 4-fold rise in antibody titers in paired serum samples is indicative of infection ( 7 , 8 ), but this measure has low sensitivity ( 9 ). Therefore, we synthesized information from RT-PCR data in addition to repeated serological sampling to obtain information on the temporal evolution of HAI titers in the immediate aftermath of infection; we also estimated the risk of infection without the restriction of assuming a 4-fold rise.…”

Incidence of influenza A(H3N2) virus infections in Hong Kong in a longitudinal sero-epidemiological study, 2009-2015

“…Influenza A(H3N2) viruses have also evolved more rapidly in more recent years, and between January 2000 and January 2015 there were 9 changes in the recommended A(H3N2) component of influenza vaccines compared to 4 changes in the A(H1N1) component including the A(H1N1)pdm09 pandemic virus [ 8 ]. Many serologic studies were done during and after the 2009 influenza pandemic, to estimate the cumulative incidence of influenza A(H1N1)pdm09 virus infections in the first [ 9 , 10 ] and subsequent waves [ 5 , 11 ], and the development of population immunity [ 12 ]. In comparison, there are relatively few population-based estimates of influenza A(H3N2) virus infections [ 5 , 13 ].…”

Reconstructing antibody dynamics to estimate the risk of influenza virus infection

“…For example, our model suggests that the Child 1 in Figure 1B that has a 2-fold rise in epidemic 1 was likely infected with probability 0.81. Due to irregular seasonality in Hong Kong and unpredictable timing of in uenza circulation [12,[19][20][21], the pre-epidemic titer may be missing and infection may have occurred before the collection of the rst sample (Figure S1). For example, Adult 1 in Figure 1B has HAI titer equal to 3 in log 2 scale in the rst and second sample but we infer that this individual has an 80% chance to have been infected.…”

“…For more than 70 years, scientists have relied on an ad-hoc rule whereby a 4-fold or greater rise in hemagglutination-inhibiting (HAI) titers in paired sera is considered as evidence of in uenza virus infection [9,10]. Although this can capture some asymptomatic and subclinical infections, there are a number of known limitations to this heuristic, such as misclassi cation due to measurement error [11], and 'non-bracketing' issue that the rst serum is collected after infection [12]. Consequently it can be di cult to estimate infection rates in communities [11,12], and characterize disease severity [13], disease burden [1,14] and risk factors for infection [15] since all of these require accurate classi cation of infected vs uninfected persons.…”