Objectives: Residents of elderly facilities are vulnerable to COVID-19 outbreaks. However, since May 8, 2023, timely recognition of outbreaks in elderly facilities by public health centers in Japan has been difficult following the Japanese government’s discontinuation of aggressive COVID-19 countermeasures. Under these circumstances, the Facility for Elderly Surveillance System (FESSy) was expected to improve information collection. This study examined the effectiveness of FESSy, ultimately measured by outbreak size.
Methods: Public health centers detected outbreaks in facilities through three modes: 1) AI detection (FESSy AI), 2) manual detection by public health center staff (FESSy staff), and 3) direct reporting from facilities to the public health center (direct report). Thereafter, the effectiveness of FESSy was evaluated using regression of eventual outbreak size on dummy variables representing the three detection routes, the number of patients at detection, and dummy variables for diseases or symptoms. The estimation procedure incorporated an individual effects model and heteroscedasticity among facilities. The study period extended from June 1, 2023, to July 2024.
Results: The estimation results revealed that FESSy AI was less effective than FESSy staff but more effective than direct reporting.
Discussion and Conclusion: FESSy staff detection was the most effective route. In addition, FESSy was superior to direct reporting from facilities and was the only available detection route in FESSy-inactivated areas. Because FESSy enables more timely detection and response to outbreaks, it is expected to reduce the burden on public health centers and improve work efficiency.
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