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Background: Seasonal influenza leads to an increase in outpatient clinic visits. Timely, accurate, and affordable testing could facilitate improved treatment outcomes. Rapid influenza diagnostic tests (RIDTs) provide results in as little as 15 minutes and are relatively inexpensive, but have reduced sensitivity when compared to RT-PCR. The contributions of multiple factors related to test performance are not well defined for ambulatory care settings. We assessed clinical and laboratory factors that may affect the sensitivity and specificity of Sofia Influenza A+B Fluorescence Immunoassay. Study design: We performed a post-hoc assessment of surveillance data amassed over seven years from five primary care clinics. We analyzed 4,475 paired RIDT and RT-PCR results from specimens collected from patients presenting with respiratory symptoms and examined eleven potential factors with additional sub-categories that could affect RIDT sensitivity. Results: In an unadjusted analysis, greater sensitivity was associated with the presence of an influenza-like illness (ILI), no other virus detected, no seasonal influenza vaccination, younger age, lower cycle threshold value, fewer days since illness onset, nasal discharge, stuffy nose, and fever. After adjustment, presence of an ILI, younger age, fewer days from onset, no co-detection, and presence of a nasal discharge maintained significance. Conclusion: Clinical and laboratory factors may affect RIDT sensitivity. Identifying potential factors during point-of-care testing could aid clinicians in appropriately interpreting negative influenza RIDT results. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Human-to-swine transmission of seasonal influenza viruses has led to sustained human-like influenza viruses circulating in the U.S. swine population. While some reverse zoonotic-origin viruses adapt and become enzootic in swine, nascent reverse zoonoses may result in virus detections that are difficult to classify as "swine-origin" or "human-origin" due to the genetic similarity of circulating viruses. This is the case for human-origin influenza A(H1N1) pandemic 2009 (pdm09) viruses detected in pigs following numerous reverse zoonosis events since the 2009 pandemic. We report the identification of two human infections with A(H1N1)pdm09 viruses originating from swine hosts and classify them as "swine-origin" variant influenza viruses based on phylogenetic analysis and sequence comparison methods. Phylogenetic analyses of viral genomes from two cases revealed these viruses were reassortants containing A(H1N1)pdm09 hemagglutinin (HA) and neuraminidase (NA) genes with genetic combinations derived from the triple reassortant internal gene cassette. Follow-up investigations determined that one individual had direct exposure to swine in the week preceding illness onset, while another did not report swine exposure. The swine-origin A(H1N1) variant cases were resolved by full genome sequence comparison of the variant viruses to swine influenza genomes. However, if reassortment does not result in the acquisition of swine-associated genes and swine virus genomic sequences are not available from the exposure source, future cases may not be discernible. We have developed a pipeline that performs maximum likelihood analyses, a k-mer-based set difference algorithm, and random forest algorithms to identify swine-associated sequences in the hemagglutinin gene to differentiate between human-origin and swine-origin A(H1N1)pdm09 viruses. [ABSTRACT FROM AUTHOR]