Influenza newspaper reports and the influenza epidemic: An observational study in Fukuoka City, Japan

Akihito Hagihara, Daisuke Onozuka, Shougo Miyazaki, Takeru Abe

研究成果: Contribution to journalArticle査読

2 被引用数 (Scopus)

抄録

Objectives: We examined whether the weekly number of newspaper articles reporting on influenza was related to the incidence of influenza in a large city. Design: Prospective, non-randomised, observational study. Setting: Registry data of influenza cases in Fukuoka City, Japan. Participants: A total of 83 613 cases of influenza cases that occurred between October 1999 and March 2007 in Fukuoka City, Japan. Main outcome measure: A linear model with autoregressive time series errors was fitted to time series data on the incidence of influenza and the accumulated number of influenza-related newspaper articles with different time lags in Fukuoka City, Japan. In order to obtain further evidence that the number of newspaper articles a week with specific time lags is related to the incidence of influenza, Granger causality was also tested. Results: Of the 16 models including 'number of newspaper articles' with different time lags between 2 and 17 weeks (Xt-2 to t-17), the β coefficients of 'number of newspaper articles' at time lags between t-5 and t-13 were significant. However, the β coefficients of 'number of newspaper articles' that are significant with respect to the Granger causality tests ( p<0.05) were the weekly number of newspaper articles at time lags between t-6 and t-10 (time shift of 10 weeks, β= -0.301, p<0.01; time shift of 9 weeks, β=-0.200, p<0.01; time shift of 8 weeks, β=-0.156, p<0.01; time shift of 7 weeks, β=-0.122, p<0.05; time shift of 6 weeks, β=-0.113, p<0.05). Conclusions: We found that the number of newspaper articles reporting on influenza in a week was related to the incidence of influenza 6-10 weeks after media coverage in a large city in Japan.

本文言語英語
論文番号e009900
ジャーナルBMJ open
5
12
DOI
出版ステータス出版済み - 2015

All Science Journal Classification (ASJC) codes

  • Medicine(all)

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