Prevalence of Antibodies to 2009 Pandemic Influenza A (H1N1) Virus in German Adult Population in Pre- and Post-Pandemic Period

Dudareva, Sandra; Schweiger, Brunhilde; Thamm, Michael; Höhle, Michael; Stark, Klaus; Krause, Gérard; Buda, Silke; Haas, Walter

doi:10.1371/journal.pone.0021340

Cited by 27 publications

(39 citation statements)

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“…These are in line with available data on population immunity against A(H1N1)pdm09 following the pandemic season 2009/10 in the UK, indicating a very high immunity among elderly and low immunity among children [52]. However, in Germany post-pandemic antibody titres against A(H1N1)pdm09 were comparably low among elderly, although in the subsequent season the lowest infection rates were also detected among elderly [53]. This inconsistency might be caused from pre-existing immunity not measurable through cross-reactive antibodies [53], such that our estimated susceptibilities provide a rough assessment of the actual pre-existing immunity.…”

Section: Discussionsupporting

confidence: 84%

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

et al. 2017

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BackgroundTo reduce the burden of severe influenza, most industrialized countries target specific risk-groups with influenza vaccines, e.g. the elderly or individuals with comorbidities. Since children are the main spreaders, some countries have recently implemented childhood vaccination programs to reduce overall virus transmission and thereby influenza disease in the whole population. The introduction of childhood vaccination programs was often supported by modelling studies that predicted substantial incidence reductions. We developed a mathematical transmission model to examine the potential impact of childhood influenza vaccination in Germany, while also challenging established modelling assumptions.MethodsWe developed an age-stratified SEIR-type transmission model to reproduce the epidemic influenza seasons between 2003/04 and 2013/14. The model was built upon German population counts, contact patterns, and vaccination history and was fitted to seasonal data on influenza-attributable medically attended acute respiratory infections (I-MAARI) and strain distribution using Bayesian methods. As novelties we (i) implemented a stratified model structure enabling seasonal variability and (ii) deviated from the commonly assumed mass-action-principle by employing a phenomenological transmission rate.ResultsAccording to the model, by vaccinating primarily the elderly over ten seasons 4 million (95% prediction interval: 3.84 – 4.19) I-MAARI were prevented which corresponds to an 8.6% (8.3% – 8.9%) reduction compared to a no-vaccination scenario and a number-needed-to-vaccinate (NNV) to prevent one I-MAARI of 37.1 (35.5 – 38.7). Additional vaccination of 2-10 year-old children at 40% coverage would have led to an overall I-MAARI reduction of 17.8% (17.1 – 18.7%) mostly due to indirect effects with a NNV of 20.7 (19.6 – 21.6). When employing the traditional mass-action-principle, the model predicted a more than 3-fold higher I-MAARI reduction (55.6%) due to childhood vaccination.ConclusionIn Germany, the introduction of routine childhood influenza vaccination could considerably reduce I-MAARI among all age-groups and improve the NNV. However, the predicted impact is much lower compared to previous studies, which is primarily caused by our phenomenological approach to modelling influenza virus transmission.Electronic supplementary materialThe online version of this article (doi:10.1186/s12879-017-2344-6) contains supplementary material, which is available to authorized users.

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Section: Discussionsupporting

confidence: 84%

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

et al. 2017

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“…In European countries serial seroepidemiological studies were conducted in United Kingdom [6], [12], where a considerable increase in HI antibody titers in children living in metropolitan areas from 1.8% to 23% (0 to 4 years) and from 3.7% to 46% (5 to 14 years), was shown, and similar results were found in Scotland [44]. In Germany [8]–[10], [15], [16], in Greece Maltezou, 2011 2205/id}, France [17], Sweden [18] and Norway [11] results showed increasing antibody titers mostly in younger age groups, but also in the elderly [11]. With regard to overseas countries, similar serial seroprevalence studies were conducted in the US where an overall increase of seroprevalence from 6% to 21% was found with the highest prevalence observed among children aged 0 to 19 years, followed by over- 80-year-olds, while no increase in seroprevalence was observed among the 70- to 79-year-olds.…”

Section: Discussionmentioning

confidence: 76%

Pandemic Influenza A/H1N1pdm in Italy: Age, Risk and Population Susceptibility

et al. 2013

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BackgroundA common pattern emerging from several studies evaluating the impact of the 2009 A/H1N1 pandemic influenza (A/H1N1pdm) conducted in countries worldwide is the low attack rate observed in elderly compared to that observed in children and young adults. The biological or social mechanisms responsible for the observed age-specific risk of infection are still to be deeply investigated.MethodsThe level of immunity against the A/H1N1pdm in pre and post pandemic sera was determined using left over sera taken for diagnostic purposes or routine ascertainment obtained from clinical laboratories. The antibody titres were measured by the haemagglutination inhibition (HI) assay. To investigate whether certain age groups had higher risk of infection the presence of protective antibody (≥1∶40), was calculated using exact binomial 95% CI on both pre- and post- pandemic serological data in the age groups considered. To estimate age-specific susceptibility to infection we used an age-structured SEIR model.ResultsBy comparing pre- and post-pandemic serological data in Italy we found age- specific attack rates similar to those observed in other countries. Cumulative attack rate at the end of the first A/H1N1pdm season in Italy was estimated to be 16.3% (95% CI 9.4%-23.1%). Modeling results allow ruling out the hypothesis that only age-specific characteristics of the contact network and levels of pre-pandemic immunity are responsible for the observed age-specific risk of infection. This means that age-specific susceptibility to infection, suspected to play an important role in the pandemic, was not only determined by pre-pandemic levels of H1N1pdm antibody measured by HI.ConclusionsOur results claim for new studies to better identify the biological mechanisms, which might have determined the observed pattern of susceptibility with age. Moreover, our results highlight the need to obtain early estimates of differential susceptibility with age in any future pandemics to obtain more reliable real time estimates of critical epidemiological parameters.

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“…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 HI 1∶40 threshold and no ISP adjustments, Δ S HI 1∶40 /Δ S HI 1∶20 was around 0.9, 0.7, and 0.4 among unvaccinated individuals of age 18–32, 33–52 and >52 [8]. Similarly, in a study in New Zealand with HI 1∶40 threshold and no ISP adjustments, Δ S HI 1∶40 /Δ S HI 1∶20 was around 0.7, 0.9, and 0.6 among individuals of age 1–4, 5–19 and 20–59 [6].…”

Section: Discussionmentioning

confidence: 96%

Inferring Influenza Infection Attack Rate from Seroprevalence Data

Leung

Perera

et al. 2014

PLoS Pathog

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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.

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Prevalence of Antibodies to 2009 Pandemic Influenza A (H1N1) Virus in German Adult Population in Pre- and Post-Pandemic Period

Cited by 27 publications

References 29 publications

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

Is the impact of childhood influenza vaccination less than expected: a transmission modelling study

Pandemic Influenza A/H1N1pdm in Italy: Age, Risk and Population Susceptibility

Inferring Influenza Infection Attack Rate from Seroprevalence Data

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