Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Jul 22, 2022
Date Accepted: Nov 25, 2022
Monitoring school absenteeism for influenza-like illness surveillance: A systematic review and meta-analysis
ABSTRACT
Background:
Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear.
Objective:
To determine the potential of monitoring school absenteeism as a surveillance tolls of influenza.
Methods:
We conducted a systematic review of the published literature on relationship between school absenteeism and influenza activity in community. We categorized the types of school absenteeism, and influenza activity in community, to determine the impact on the correlation between these two data streams.
Results:
We identified 35 studies including 9826 person-years. Of these, 22, 12 and 8 studies monitored all-cause, illness-specific and influenza-like illness (ILI) specific absents respectively, and 16 used quantitative approaches and provided 33 estimates on temporal correlation between school absenteeism and influenza activity in community. The pooled estimate of this correlation was 0.44 (95% CI: 0.34, 0.53). The correlation between influenza activity in community and ILI-specific absenteeism were higher than all-cause absenteeism. Among 19 studies used qualitative approaches, 15 studies concluded that school absenteeism was concordant, coincided or associated with community surveillance. Only six studies attempted to predict influenza activity in community from school absenteeism surveillance.
Conclusions:
There was a moderate correlation between school absenteeism and influenza activity in community. Using ILI-specific absenteeism was preferred since it could monitor influenza activity more closely, but the required resource or willingness of school participation may require careful consideration. Further explorations are required to utilize and optimize the use of school absenteeism to predict influenza activity.
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