Factors influencing sensitivity of a rapid influenza diagnostic test in a community-based population of kindergarten through 12th-grade students: Wisconsin 2015-2020.

Rapid influenza diagnostic tests (RIDTs) have variable sensitivity. In a community-based population of kindergarten through 12th-grade (K-12) students, we assessed factors that may influence RIDT performance using 2368 paired results from Sofia® influenza A + B fluorescent immunoassay and reverse transcription polymerase chain reaction (RT-PCR). RIDT sensitivity and specificity were 76.1% (95% CI: 72.8-79.1) and 97.2% (96.2-97.9), respectively. Factors associated with sensitivity included runny nose (OR = 3.0, p < 0.001), nasal congestion (1.59, p = 0.045), days from symptom onset (per day; 0.75; p < 0.001), myalgia (0.61; p = 0.014), age (per 5 years; 0.55; p = 0.001), and detection of another virus (0.50; p = 0.043). Understanding these factors can aid in interpreting negative results.

Surveillance of SARS-CoV-2 in Asymptomatic Faculty and Staff at the University of Wisconsin-Madison.

INTRODUCTION: Surveillance of SARS-CoV-2 among university employees is an important part of mitigation strategies to prevent asymptomatic transmission and ensure a safe learning and work environment. Here, we assess the feasibility and performance of a program that relies on monitored self-collected nasal swabs to detect SARS-CoV-2 among asymptomatic faculty and staff. METHODS: We recruited 1,030 faculty and staff via rolling enrollment who completed the required University of Wisconsin-Madison employee COVID-19 training and reported working on campus. Asymptomatic participants visited a designated location during a specified timeframe each week where they self-collected nasal swabs supervised by study staff. Specimens were stored in a cooler between 2 °C and 8 °C, then transported to the Wisconsin Veterinary Diagnostic Laboratory for polymerase chain reaction testing. Symptomatic participants or participants with a known exposure were advised to test elsewhere and follow quarantine guidelines from the Centers for Disease Control and Prevention. RESULTS: Over the course of 31 weeks, 1,030 participants self-collected 17,323 monitored nasal swabs resulting in high participation (90%). SARS-CoV-2 was detected in 16 specimens. Eight specimens were inconclusive but were treated as positive results because of the implied detection of 1 or more SARS-CoV-2 genes. There were no invalid tests. Weekly SARS-CoV-2 incidence among participants ranged from 0 to 1.54% (x̄ = 0.20%). The SARS-CoV-2 incidence among participants was similar to estimated incidence in the greater university employee population. CONCLUSION: Weekly SARS-CoV-2 surveillance of asymptomatic faculty and staff on campus allowed for estimation of weekly SARS-CoV-2 incidence among on-campus employees. This surveillance protocol presents a low-cost, effective, and scalable option to identify asymptomatic cases of SARS-CoV-2 among university employees.

Cause-specific student absenteeism monitoring in K-12 schools for detection of increased influenza activity in the surrounding community-Dane County, Wisconsin, 2014-2020.

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.