Influenza Vaccination Coverage among Registered Nurses in China during 2017–2018: An Internet Panel Survey
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Influenza Vaccination Coverage among Registered Nurses in China during 2017–2018: An Internet Panel Survey

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English
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Details:

  • Alternative Title:
    Vaccines (Basel)
  • Personal Author:
  • Description:
    Influenza vaccination is recommended for nurses in China but is not mandatory or offered free of charge. The main objective of this study was to determine influenza vaccination coverage and the principal factors influencing influenza vaccination among nurses in China. During 22 March-1 April 2018, we conducted an opt-in internet panel survey among registered nurses in China. Respondents were recruited from an internet-based training platform for nurses. Among 22,888 nurses invited to participate, 4706 responded, and 4153 were valid respondents. Overall, 257 (6%) nurses reported receiving the seasonal influenza vaccine during the 2017/2018 season. Vaccination coverage was highest among nurses working in Beijing (10%, | < 0.001) and nurses working in primary care (12%, | = 0.023). The top three reasons for not being vaccinated were lack of time (28%), not knowing where and when to get vaccinated (14%), and lack of confidence in the vaccine's effectiveness (12%). Overall, 41% of nurses reported experiencing at least one episode of influenza-like illness (ILI) during the 2017/2018 season; 87% of nurses kept working while sick, and 25% of nurses reported ever recommending influenza vaccination to patients. Compared with nurses who did not receive influenza vaccination in the 2017/2018 season, nurses who received influenza vaccination were more likely to recommend influenza vaccination to patients (67% vs. 22%, | < 0.001). Influenza vaccination coverage among nurses was low, and only a small proportion recommended influenza vaccine to patients. Our findings highlight the need for a multipronged strategy to increase influenza vaccination among nurses in China.
  • Subjects:
  • Source:
    Vaccines (Basel). 2019; 7(4)
  • Pubmed ID:
    31569475
  • Pubmed Central ID:
    PMC6963313
  • Document Type:
    Journal Article
  • Funding:
    7192136/Beijing Natural Science Foundation/
  • Place as Subject:
    China
  • Volume:
    7
  • Issue:
    4
  • Collection(s):
  • Main Document Checksum:
  • Download URL:
  • File Type:

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