Literature DB >> 19368786

On the use of hemagglutination-inhibition for influenza surveillance: surveillance data are predictive of influenza vaccine effectiveness.

Wilfred Ndifon1, Jonathan Dushoff, Simon A Levin.   

Abstract

The hemagglutination-inhibition (HI) assay is the main tool used by epidemiologists to quantify antigenic differences between circulating influenza virus strains, with the goal of selecting suitable vaccine strains. However, such quantitative measures of antigenic difference were recently shown to have poor predictive accuracy with respect to influenza vaccine effectiveness (VE) in healthy adults. Here, we re-examine those results using a more rigorous criterion for predictive accuracy -- considering only cases when the vaccine (V) and dominant (D) circulating strains are antigenically different -- and greater numbers of HI titers. We find that the Archetti -- Horsfall measure of antigenic difference, which is based on both the normalized HI titer (NHI) of D relative to antisera raised against V and the NHI of V relative to D, predicts VE very well (R(2)=0.62, p=4.1x10(-3)). In contrast, the predictive accuracies of the NHI of D relative to V alone (R(2)=0.01), and two other measures of antigenic difference based on the amino acid sequence of influenza virus hemagglutinin (R(2)=0.03 for both measures) are relatively poor. Furthermore, while VE in the elderly is generally high in cases when D and V are antigenically identical (VE=35%, S.E.=5%), in other cases VE appears to increase with the antigenic difference between D and V (R(2)=0.90, p=2.5x10(-5)). This paradoxical observation could reflect the confounding effects of prior immunity on estimates of VE in the elderly. Together, our results underscore the need for consistently accurate selection of suitable vaccine strains. We suggest directions for further studies aimed at improving vaccine-strain selection and present a large collection of HI titers that will be useful to such studies.

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Year:  2009        PMID: 19368786     DOI: 10.1016/j.vaccine.2009.02.047

Source DB:  PubMed          Journal:  Vaccine        ISSN: 0264-410X            Impact factor:   3.641


  22 in total

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3.  Differential neutralization efficiency of hemagglutinin epitopes, antibody interference, and the design of influenza vaccines.

Authors:  Wilfred Ndifon; Ned S Wingreen; Simon A Levin
Journal:  Proc Natl Acad Sci U S A       Date:  2009-05-13       Impact factor: 11.205

4.  Single hemagglutinin mutations that alter both antigenicity and receptor binding avidity influence influenza virus antigenic clustering.

Authors:  Yang Li; David L Bostick; Colleen B Sullivan; Jaclyn L Myers; Sara B Griesemer; Kirsten Stgeorge; Joshua B Plotkin; Scott E Hensley
Journal:  J Virol       Date:  2013-07-03       Impact factor: 5.103

5.  Characterization of the Candiru antigenic complex (Bunyaviridae: Phlebovirus), a highly diverse and reassorting group of viruses affecting humans in tropical America.

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Journal:  J Virol       Date:  2011-02-02       Impact factor: 5.103

6.  Profiles of influenza A/H1N1 vaccine response using hemagglutination-inhibition titers.

Authors:  Robert M Jacobson; Diane E Grill; Ann L Oberg; Pritish K Tosh; Inna G Ovsyannikova; Gregory A Poland
Journal:  Hum Vaccin Immunother       Date:  2015       Impact factor: 3.452

7.  Large discrepancy between the two-way rNHT distances in hemagglutinin-inhibition assay.

Authors:  Yousong Peng; Dayan Wang; Yuelong Shu; Taijiao Jiang
Journal:  Virol Sin       Date:  2016-10       Impact factor: 4.327

8.  The impact of matching vaccine strains and post-SARS public health efforts on reducing influenza-associated mortality among the elderly.

Authors:  Ta-Chien Chan; Chuhsing Kate Hsiao; Chang-Chun Lee; Po-Huang Chiang; Chuan-Liang Kao; Chung-Ming Liu; Chwan-Chuen King
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9.  Signs of the 2009 influenza pandemic in the New York-Presbyterian Hospital electronic health records.

Authors:  Hossein Khiabanian; Antony B Holmes; Brendan J Kelly; Mrinalini Gururaj; George Hripcsak; Raul Rabadan
Journal:  PLoS One       Date:  2010-09-09       Impact factor: 3.240

10.  Comment on Ndifon et al., "On the use of hemagglutination-inhibition for influenza surveillance: Surveillance data are predictive of influenza vaccine effectiveness".

Authors:  Keyao Pan; Michael W Deem
Journal:  Vaccine       Date:  2009-06-12       Impact factor: 3.641

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