Date Published: April 3, 2014
Publisher: Public Library of Science
Author(s): Joseph T. Wu, Kathy Leung, Ranawaka A. P. M. Perera, Daniel K. W. Chu, Cheuk Kwong Lee, Ivan F. N. Hung, Che Kit Lin, Su-Vui Lo, Yu-Lung Lau, Gabriel M. Leung, Benjamin J. Cowling, J. S. Malik Peiris, Neil M. Ferguson.
Seroprevalence survey is the most practical method for accurately estimating infection attack rate (IAR) in an epidemic such as influenza. These studies typically entail selecting an arbitrary titer threshold for seropositivity (e.g. microneutralization [MN] 1∶40) and assuming the probability of seropositivity given infection (infection-seropositivity probability, ISP) is 100% or similar to that among clinical cases. We hypothesize that such conventions are not necessarily robust because different thresholds may result in different IAR estimates and serologic responses of clinical cases may not be representative. To illustrate our hypothesis, we used an age-structured transmission model to fully characterize the transmission dynamics and seroprevalence rises of 2009 influenza pandemic A/H1N1 (pdmH1N1) during its first wave in Hong Kong. We estimated that while 99% of pdmH1N1 infections became MN1∶20 seropositive, only 72%, 62%, 58% and 34% of infections among age 3–12, 13–19, 20–29, 30–59 became MN1∶40 seropositive, which was much lower than the 90%–100% observed among clinical cases. The fitted model was consistent with prevailing consensus on pdmH1N1 transmission characteristics (e.g. initial reproductive number of 1.28 and mean generation time of 2.4 days which were within the consensus range), hence our ISP estimates were consistent with the transmission dynamics and temporal buildup of population-level immunity. IAR estimates in influenza seroprevalence studies are sensitive to seropositivity thresholds and ISP adjustments which in current practice are mostly chosen based on conventions instead of systematic criteria. Our results thus highlighted the need for reexamining conventional practice to develop standards for analyzing influenza serologic data (e.g. real-time assessment of bias in ISP adjustments by evaluating the consistency of IAR across multiple thresholds and with mixture models), especially in the context of pandemics when robustness and comparability of IAR estimates are most needed for informing situational awareness and risk assessment. The same principles are broadly applicable for seroprevalence studies of other infectious disease outbreaks.
Severity of influenza infection is defined as the probability of severe complications (e.g. hospitalization or death) if infected . Timely and accurate estimates of severity are extremely valuable for informing decisions about the scale and targeting of response to an emerging pandemic . In 2011, the International Health Regulations Review Committee highlighted the lack of “a consistent, measurable and understandable depiction of severity” as a major shortcoming of global response to the 2009 influenza pandemic . Real-time serial cross-sectional or longitudinal seroprevalence studies can address this shortcoming in future pandemics by providing direct estimates of infection attack rate (IAR) as the denominator for severity .
We hypothesized that influenza seroprevalence studies might substantially underestimate IARs if ISP is ignored or based on data from patients presenting to healthcare providers with clinically overt disease. We substantiated this hypothesis with pdmH1N1 seroprevalence data from Hong Kong. To further examine the validity of this conjecture, we performed crude analyses of published pdmH1N1 seroprevalence data from other countries to examine the robustness of their IAR estimates across different seropositivity thresholds and ISP adjustments (see Text S1). In a study in Germany with HI1∶40 threshold and no ISP adjustments, ΔSHI 1∶40/ΔS HI 1∶20 was around 0.9, 0.7, and 0.4 among unvaccinated individuals of age 18–32, 33–52 and >52 . Similarly, in a study in New Zealand with HI1∶40 threshold and no ISP adjustments, ΔSHI 1∶40/ΔS HI 1∶20 was around 0.7, 0.9, and 0.6 among individuals of age 1–4, 5–19 and 20–59 . Therefore, IAR have probably been underestimated in these studies, especially among older adults.