IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v13y2016i10p1017-d80716.html
   My bibliography  Save this article

High Vaccination Coverage among Children during Influenza A(H1N1)pdm09 as a Potential Factor of Herd Immunity

Author

Listed:
  • Toshihiko Matsuoka

    (Department of Epidemiology, Infectious Disease Control and Prevention, Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan)

  • Tomoki Sato

    (Department of Epidemiology, Infectious Disease Control and Prevention, Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan)

  • Tomoyuki Akita

    (Department of Epidemiology, Infectious Disease Control and Prevention, Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan)

  • Jiturou Yanagida

    (Hiroshima City Funairi Citizens Hospital, Hiroshima 730-0844, Japan)

  • Hiroki Ohge

    (Department of Infectious Diseases, Hiroshima University Hospital, Hiroshima 734-8551, Japan)

  • Masao Kuwabara

    (Hiroshima Prefectural Center for Disease Control and Prevention, Hiroshima 734-0007, Japan)

  • Junko Tanaka

    (Department of Epidemiology, Infectious Disease Control and Prevention, Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima 734-8551, Japan)

Abstract

The objective of this study was to identify factors related to the expansion of infection and prevention of influenza A(H1N1)pdm09. A retrospective non-randomized cohort study (from June 2009 to May 2010) on influenza A(H1N1)pdm09 was conducted in a sample of residents from Hiroshima Prefecture, Japan. The cumulative incidence of the influenza A(H1N1)pdm09 and the pandemic vaccine effectiveness (VE) were estimated. The response rate was 53.5% (178,669/333,892). Overall, the odds ratio of non-vaccinated group to vaccinated group for cumulative incidence of influenza A(H1N1)pdm09 was 2.18 (95% confidence interval (CI): 2.13–2.23) and the VE was 43.9% (CI: 42.8–44.9). The expansion of infection, indicating the power of transmission from infected person to susceptible person, was high in the 7–15 years age groups in each area. In conclusion, results from this survey suggested that schoolchildren-based vaccination rate participates in determining the level of herd immunity to influenza and children might be the drivers of influenza transmission. For future pandemic preparedness, vaccination of schoolchildren may help to prevent disease transmission during influenza outbreak.

Suggested Citation

  • Toshihiko Matsuoka & Tomoki Sato & Tomoyuki Akita & Jiturou Yanagida & Hiroki Ohge & Masao Kuwabara & Junko Tanaka, 2016. "High Vaccination Coverage among Children during Influenza A(H1N1)pdm09 as a Potential Factor of Herd Immunity," IJERPH, MDPI, vol. 13(10), pages 1-17, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:1017-:d:80716
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/13/10/1017/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/13/10/1017/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. T. Britton, 1998. "Estimation in multitype epidemics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(4), pages 663-679.
    2. Simon Cauchemez & Alain-Jacques Valleron & Pierre-Yves Boëlle & Antoine Flahault & Neil M. Ferguson, 2008. "Estimating the impact of school closure on influenza transmission from Sentinel data," Nature, Nature, vol. 452(7188), pages 750-754, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Robert Susło & Piotr Pobrotyn & Lidia Brydak & Łukasz Rypicz & Urszula Grata-Borkowska & Jarosław Drobnik, 2021. "Seasonal Influenza and Low Flu Vaccination Coverage as Important Factors Modifying the Costs and Availability of Hospital Services in Poland: A Retrospective Comparative Study," IJERPH, MDPI, vol. 18(10), pages 1-15, May.
    2. Zewei Li & James C. Spall, 2022. "Discrete Stochastic Optimization for Public Health Interventions with Constraints," SN Operations Research Forum, Springer, vol. 3(4), pages 1-20, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kozhaya, Mireille, 2022. "The double burden: The impact of school closures on labor force participation of mothers," Ruhr Economic Papers 956, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    2. Jérôme Adda, 2016. "Economic Activity and the Spread of Viral Diseases: Evidence from High Frequency Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 891-941.
    3. Han, Lili & Song, Sha & Pan, Qiuhui & He, Mingfeng, 2023. "The impact of multiple population-wide testing and social distancing on the transmission of an infectious disease," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Eiji Yamamura & Yoshiro Tsustsui, 2021. "The impact of closing schools on working from home during the COVID-19 pandemic: evidence using panel data from Japan," Review of Economics of the Household, Springer, vol. 19(1), pages 41-60, March.
    5. Rakowski, Franciszek & Gruziel, Magdalena & Bieniasz-Krzywiec, Łukasz & Radomski, Jan P., 2010. "Influenza epidemic spread simulation for Poland — a large scale, individual based model study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3149-3165.
    6. Casey B. Mulligan, 2021. "The incidence and magnitude of the health costs of in-person schooling during the COVID-19 pandemic," Public Choice, Springer, vol. 188(3), pages 303-332, September.
    7. T Déirdre Hollingsworth & Don Klinkenberg & Hans Heesterbeek & Roy M Anderson, 2011. "Mitigation Strategies for Pandemic Influenza A: Balancing Conflicting Policy Objectives," PLOS Computational Biology, Public Library of Science, vol. 7(2), pages 1-11, February.
    8. Yakir Berchenko & Jonathan D. Rosenblatt & Simon D. W. Frost, 2017. "Modeling and analyzing respondent‐driven sampling as a counting process," Biometrics, The International Biometric Society, vol. 73(4), pages 1189-1198, December.
    9. Judith Legrand & Joseph R Egan & Ian M Hall & Simon Cauchemez & Steve Leach & Neil M Ferguson, 2009. "Estimating the Location and Spatial Extent of a Covert Anthrax Release," PLOS Computational Biology, Public Library of Science, vol. 5(1), pages 1-9, April.
    10. Auliya A. Suwantika & Neily Zakiyah & Ajeng Diantini & Rizky Abdulah & Maarten J. Postma, 2020. "The Role of Administrative and Secondary Data in Estimating the Costs and Effects of School and Workplace Closures due to the COVID-19 Pandemic," Data, MDPI, vol. 5(4), pages 1-11, October.
    11. Charles Stoecker & Nicholas J. Sanders & Alan Barreca, 2015. "Success is Something to Sneeze at: Influenza Mortality in Regions that Send Teams to the Super Bowl," Working Papers 1501, Tulane University, Department of Economics.
    12. Demiris, Nikolaos & Kypraios, Theodore & Smith, L. Vanessa, 2012. "On the epidemic of financial crises," MPRA Paper 46693, University Library of Munich, Germany.
    13. Eiji Yamamura & Yoshiro Tsutsui, 2021. "Changing views about remote working during the COVID-19 pandemic: Evidence using panel data from Japan," Papers 2101.08480, arXiv.org.
    14. Huggins, Richard M. & Yip, Paul S. F. & Lau, Eric H. Y., 2004. "A note on the estimation of the initial number of susceptible individuals in the general epidemic model," Statistics & Probability Letters, Elsevier, vol. 67(4), pages 321-330, May.
    15. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    16. Christopher Avery & William Bossert & Adam Clark & Glenn Ellison & Sara Fisher Ellison, 2020. "An Economist's Guide to Epidemiology Models of Infectious Disease," Journal of Economic Perspectives, American Economic Association, vol. 34(4), pages 79-104, Fall.
    17. Shoko Kawano & Masayuki Kakehashi, 2015. "Substantial Impact of School Closure on the Transmission Dynamics during the Pandemic Flu H1N1-2009 in Oita, Japan," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-15, December.
    18. Saki Saito & Mariko I. Ito & Takaaki Ohnishi, 2022. "Fluctuations in the Number of Stores by Industry During the COVID-19 Pandemic Based on Japanese Phone Book Entries," The Review of Socionetwork Strategies, Springer, vol. 16(2), pages 545-557, October.
    19. Elena Raffetti & Giuliano Di Baldassarre, 2022. "Do the Benefits of School Closure Outweigh Its Costs?," IJERPH, MDPI, vol. 19(5), pages 1-8, February.
    20. Georgios Marinos & Dimitrios Lamprinos & Panagiotis Georgakopoulos & Evangelos Oikonomou & Georgios Zoumpoulis & Nikolaos Garmpis & Anna Garmpi & Eirini Tzalavara & Gerasimos Siasos & Georgios Rachiot, 2022. "Evaluation of Knowledge, Attitudes and Practices Related to Self-Testing Procedure against COVID-19 among Greek Students: A Pilot Study," IJERPH, MDPI, vol. 19(8), pages 1-11, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:13:y:2016:i:10:p:1017-:d:80716. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.