Changes in Incidence Associated With an Influenza Alert System Using Antiviral Dispensing Data From Goto City in Remote Islands of Japan: An Analytical Observational Study.

Authors

Jun Miyata, Laura Skrip, Hirotomo Yamanashi, Jun Koyamatsu, Ippei Shimoshikiryo, Fumiaki Nonaka, Masaaki Sugahara, Masanori Sugahara, Noritaka Ideguchi, Takahiro Maeda

Published in

Journal of epidemiology. Jul 04, 2026. Epub Jul 04, 2026.

Abstract

In late 2015, an automatic influenza alert system was developed in Goto City, Nagasaki Prefecture, Japan using drug-dispensing data to enable earlier provider response to increased case counts. However, changes in incidence after implementation of this system remain unassessed.
An analytical observational study was conducted on influenza surveillance data during 2007-2019. The outcome was the annual cumulative influenza incidence per sentinel site in Nagasaki Prefecture before and after alert system implementation in Goto City in the 2015-2016 season. To evaluate the effect of the system in Goto City, a Poisson generalized linear mixed model was applied. The model included variables for location (Goto City versus other districts in Nagasaki Prefecture), timing (before or after the implementation), and an interaction term between location and timing.
Before 2015, the median annual cumulative incidence was 149 cases (interquartile range: 61-320) in Goto City and 284 (162-474) in other districts. Following implementation, annual incidence was 163 (102-274) in Goto and 302 (197-503) elsewhere. The effect of timing variable (before versus after implementation) on annual incidence significantly differed for Goto City sites versus other sites in Nagasaki Prefecture (p for interaction < 0.001).
Although annual influenza incidence trended upwards, the increase in Goto City was significantly less than in other districts of Nagasaki Prefecture following alert system implementation. This study suggests the potential effect of a real-time surveillance strategy for settings with comprehensive, computerized dispensing data.

PMID:
42402388
Bibliographic data and abstract were imported from PubMed on 06 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.