Background Influenza viruses pose significant disease burdens through seasonal outbreaks and unpredictable pandemics. Existing surveillance programs rely heavily on reporting of medically attended influenza (MAI). Continuously monitoring cause‐specific school absenteeism may identify local acceleration of seasonal influenza activity. The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS; Oregon, WI) implements daily school‐based monitoring of influenza‐like illness‐specific student absenteeism (a‐ILI) in kindergarten through Grade 12 schools and assesses this approach for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities. Methods Starting in September 2014, ORCHARDS combines automated reporting of daily absenteeism within six schools and home visits to school children with acute respiratory infection (ARI). Demographic, epidemiological, and symptom data are collected along with respiratory specimens. Specimens are tested for influenza and other respiratory viruses. Household members can opt into a supplementary household transmission study. Community comparisons are possible using a pre‐existing and highly effective influenza surveillance program, based on MAI at five family medicine clinics in the same geographical area. Results Over the first 5 years, a‐ILI occurred on 6634 (0.20%) of 3,260,461 student school days. Viral pathogens were detected in 64.5% of 1728 children with ARI who received a home visit. Influenza was the most commonly detected virus, noted in 23.3% of ill students. Conclusion ORCHARDS uses a community‐based design to detect influenza trends over multiple seasons and to evaluate the utility of absenteeism for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities.

Background Schools are primary venues of influenza amplification with secondary spread to communities. We assessed K-12 student absenteeism monitoring as a means for early detection of influenza activity in the community. Materials and methods Between September 2014 and March 2020, we conducted a prospective observational study of all-cause (a-TOT), illness-associated (a-I), and influenza-like illness–associated (a-ILI) absenteeism within the Oregon School District (OSD), Dane County, Wisconsin. Absenteeism was reported through the electronic student information system. Students were visited at home where pharyngeal specimens were collected for influenza RT-PCR testing. Surveillance of medically-attended laboratory-confirmed influenza (MAI) occurred in five primary care clinics in and adjoining the OSD. Poisson general additive log linear regression models of daily counts of absenteeism and MAI were compared using correlation analysis. Findings Influenza was detected in 723 of 2,378 visited students, and in 1,327 of 4,903 MAI patients. Over six influenza seasons, a-ILI was significantly correlated with MAI in the community (r = 0.57; 95% CI: 0.53–0.63) with a one-day lead time and a-I was significantly correlated with MAI in the community (r = 0.49; 0.44–0.54) with a 10-day lead time, while a-TOT performed poorly (r = 0.27; 0.21–0.33), following MAI by six days. Discussion Surveillance using cause-specific absenteeism was feasible and performed well over a study period marked by diverse presentations of seasonal influenza. Monitoring a-I and a-ILI can provide early warning of seasonal influenza in time for community mitigation efforts.

Introduction:The goal of public health infectious disease surveillance systems is to provide accurate laboratory results in near-real time. When it comes to influenza surveillance, most current systems are encumbered with inherent delays encountered in the real-life chaos of medical practice. To combat this, we implemented and tested near-real-time surveillance using a rapid influenza detection test (RIDT) coupled with immediate, wireless transmission of results to public health entities.Methods

Background: Influenza viruses pose significant disease burdens through annual seasonal outbreaks and unpredictable pandemics. Existing influenza surveillance programs have relied heavily on reporting of medically attended influenza (MAI). Continuously monitoring cause-specific school absenteeism may identify local activity acceleration of seasonal influenza. The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS; Oregon, WI) implements daily school-based monitoring of influenza-like illness-specific student absenteeism (a-ILI) in pre-kindergarten through grade 12 schools and assesses this approach for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities. Methods: Starting in September 2014, ORCHARDS has combined reporting of daily absenteeism though automated processes within 6 schools and home visits to school children with acute respiratory infections (ARI). Demographic, epidemiological, and symptom data are collected along with respiratory specimens. Specimens are tested for influenza and other respiratory viruses. Household members may participate in a supplementary household transmission study. Community comparisons are made possible using a pre-existing, long-standing, and highly effective influenza surveillance program, based on MAI at 5 primary care clinics in the same geographical area. Results: Over the first 5 years, a-ILI occurred on 6,634 (0.20%) of 3,260,461 student school days. Viral pathogens were detected in 64.5% of 1,728 children visited at home with ARI. Influenza was the most commonly detected virus, noted in 23.3% of ill students. Influenza (p<0.001) and adenovirus (P=0.004) were significantly associated with a-ILI. Discussion: ORCHARDS uses a community-based design to detect and evaluate influenza trends over multiple seasons and to evaluate the utility of absenteeism for early detection of accelerated influenza and other respiratory pathogen transmission in schools and surrounding communities. Initial findings suggest the study design is succeeding in collecting appropriate data to achieve study objectives.