The Oregon Child Absenteeism Due to Respiratory Disease Study (ORCHARDS): Rationale, objectives, and design

Jonathan L Temte, Shari Barlow, Maureen Goss, Emily Temte, Cristalyne Bell, Cecilia He, Caroline Hamer, Amber Schemmel, Bradley Maerz, Lily Comp, Mitchell Arnold, Kimberly Breunig, Sarah Clifford, Erik Reisdorf, Peter Shult, Mary Wedig, Thomas Haupt, James Conway, Ronald Gangnon, Ashley Fowlkes, Amra Uzicanin, Jonathan L Temte, Shari Barlow, Maureen Goss, Emily Temte, Cristalyne Bell, Cecilia He, Caroline Hamer, Amber Schemmel, Bradley Maerz, Lily Comp, Mitchell Arnold, Kimberly Breunig, Sarah Clifford, Erik Reisdorf, Peter Shult, Mary Wedig, Thomas Haupt, James Conway, Ronald Gangnon, Ashley Fowlkes, Amra Uzicanin

Abstract

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.

Keywords: absenteeism; children; influenza; population surveillance; school.

© 2021 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.

Figures

FIGURE 1
FIGURE 1
Theoretical framework of ORCHARDS demonstrating the relationships between influenza in school‐aged children, K‐12 school absenteeism, and medically attended influenza in the community. The relatedness of the four components (C1–C4) of ORCHARDS and the three primary hypotheses (H1–H3) are provided
FIGURE 2
FIGURE 2
Flow diagram of absenteeism data from telephone reporting by parents/guardians, to entry into the student information system at the Oregon School District, to data transfer to the ORCHARDS research team
FIGURE 3
FIGURE 3
Daily counts of all absent students (a‐TOT: Panel A), students for whom an illness is reported (a‐I: Panel B), and students absent with influenza‐like illness (a‐ILI: Panel C) occurring over five consecutive school years at the Oregon School District, Wisconsin, USA, from September 2014 through June 2019. Vertical white bars demonstrate the timing of July 1 (thin bars) and January 1 (heavy bars)

References

    1. CDC . Disease burden of influenza. . Accessed 6/22/2020.
    1. Putri WCWS, Muscatello DJ, Stockwell MS, Newall AT. Economic burden of seasonal influenza in the United States. Vaccine. 2018. Jun 22;36(27):3960‐3966. 10.1016/j.vaccine.2018.05.057 Epub 2018 May 22
    1. Monto AS, Fukuda K. Lessons from influenza pandemics of the last 100 years. Clin Infect Dis. 2019. Aug 17. pii:;70(5):951, ciz803‐957. 10.1093/cid/ciz803
    1. Qualls N, Levitt A, Kanade N, et al. Community mitigation guidelines to prevent pandemic influenza—United States, 2017. MMWR Recomm Rep. 2017. Apr 21;66(1):1‐34. 10.15585/mmwr.rr6601a1
    1. Lowe L, Dopson SA, Budd AP. Pandemic influenza readiness report on laboratory and epidemiology capacity—United States and territories, 2015. Health Secur. 2018. Jul/Aug;16(4):239‐243. 10.1089/hs.2018.0021
    1. Meltzer MI, Gambhir M, Atkins CY, Swerdlow DL. Standardizing scenarios to assess the need to respond to an influenza pandemic. Clin Infect Dis. 2015. May 1;60(Suppl 1):S1‐S8. 10.1093/cid/civ088
    1. CDC . Weekly U.S. Influenza Surveillance Report. . Accessed 8/30/2019.
    1. Fowlkes A, Dasgupta S, Chao E, et al. Estimating influenza incidence and rates of influenza‐like illness in the outpatient setting. Influenza Other Respi Viruses. 2013;7(5):694‐700.
    1. Sasaki A, Hoen AG, Ozonoff A, et al. Evidence‐based tool for triggering school closures during influenza outbreaks, Japan. EID. 2009;15(11):1841‐1843.
    1. Egger JR, Hoen AG, Brownstein JS, et al. Usefulness of school absenteeism data for predicting influenza outbreaks, United States. Letter to Editor. EID. August 2012;18(8).
    1. Uscher‐Pines L, Schwartz HL, Ahmed F, et al. School practices to promote social distancing in K‐12 schools: review of influenza pandemic policies and practices. BMC Public Health. 2018. Mar 27;18(1):406. 10.1186/s12889-018-5302-3
    1. Temte JL, Meiman JG, Gangnon RE. School sessions are correlated with seasonal outbreaks of medically attended respiratory infections: electronic health record time series analysis, Wisconsin 2004–2011. Epidemiol Infect Jan. 147:e127. 10.1017/S0950268818003424
    1. National Center for Education Statistics . Number of public school districts and public and private elementary and secondary schools: selected years, 1869–70 through 2010–11. . Accessed 10/20/2019.
    1. Best K‐12 Student Information Systems. . Accessed 10/20/2019.
    1. Williams NJ, Ghosh TS, Bisgard KM, Vogt RL. Comparison of 3 school‐based influenza surveillance indicators: lessons learned from 2009 pandemic influenza A (H1N1)‐Denver Metropolitan Region, Colorado. J Public Health Manag Pract. 2013;19(2):119‐125.
    1. Besculides M, Heffernan R, Mostashari F, Weiss D. Evaluation of school absenteeism data for early outbreak detection. New York City BMC Public Health. 2005;5(1):105.
    1. Schmidt WP, Pebody R, Mangtani P. School absence data for influenza surveillance: a pilot study in the United Kingdom. Euro Surveill. 2010;15(3) pii::19467.
    1. Aldridge RW, Hayward AC, Field N, et al. Decipher My Data project and schools. Are school absences correlated with influenza surveillance data in England? Results from Decipher My Data—a research project conducted through scientific engagement with schools. PLoS ONE. 2016;11(3):e0146964. 10.1371/journal.pone.0146964
    1. Oregon School District . . Accessed 10/20/2019.
    1. United States Census . Quick Facts. . Accessed 10/20/2019.
    1. Wisconsin Department of Public Instruction . Wisconsin Information System for Education data Dashboard. . Accessed 10/20/2019.
    1. Thomas RE. Is influenza‐like illness a useful concept and an appropriate test of influenza vaccine effectiveness? Vaccine. 2014. Apr 17;32(19):2143‐2149. 10.1016/j.vaccine.2014.02.059
    1. Casalegno JS, Eibach D, Valette M, et al. Performance of influenza case definitions for influenza community surveillance: based on the French influenza surveillance network GROG, 2009–2014. Euro Surveill. 2017. Apr 6;22(14) pii::30504. 10.2807/1560-7917.ES.2017.22.14.30504
    1. Infinite Campus . . Accessed 10/27/2017.
    1. U.S. Department of Education . Family Educational Rights and Privacy Act (FERPA). . Accessed 8/30/2019.
    1. Jackson GG, Dowling HF, Spiesman IG, Boand AV. Transmission of the common cold to volunteers under controlled conditions. Arch Intern Med. 1958;101(2):267‐278.
    1. Jackson GG, Dowling HF, Anderson TO, Riff L, Saporta J, Turck M. Susceptibility and immunity to common upper respiratory viral infections—the common cold. Ann Intern Med. 1960;55:719‐738.
    1. Jackson GG, Dowling HF, Muldoon RL. Present concepts of the common cold. Am J Public Health. 1962;52(6):940‐945.
    1. Ng S, Lopez R, Kuan G, et al. The timeline of influenza virus shedding in children and adults in a household transmission study of influenza in Managua, Nicaragua. Pediatr Infect Dis J. 2016;35(5):583‐586. 10.1097/INF.0000000000001083
    1. Lau MS, Cowling BJ, Cook AR, Riley S. Inferring influenza dynamics and control in households. Proc Natl Acad Sci U S A. 2015;112(29):9094‐9099. 10.1073/pnas.1423339112 Epub 2015 Jul 6
    1. Tsang TK, Lau LLH, Cauchemez S, Cowling BJ. Household transmission of influenza virus. Trends Microbiol. 2016. Feb;24(2):123‐133. 10.1016/j.tim.2015.10.012 Epub 2015 Nov 21
    1. Quidel . Sofia Influenza A + B FIA. . Accessed 10/20/2019.
    1. CDC . CDC Human Influenza Virus Real‐time RT‐PCR Diagnostic Panel. Package Insert LB‐029, R‐0. 2011.
    1. Luminex . NxTAG® Respiratory Pathogen Panel. . Accessed 10/20/2019.
    1. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina Sequence Data. Bioinformatics. 2014;30(15):2114‐2120.
    1. Kearse M, Moir R, Wilson A, et al. Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28(12):1647‐1649.WGS.
    1. Wisconsin Department of Health Services . Wisconsin Immunization Registry. . Accessed 10/20/2019.
    1. Grohskopf LA, Alyanak E, Broder KR, Walter EB, Fry AM, Jernigan DB. Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices—United States, 2019–20 influenza season. MMWR Recomm Rep. 2019;68(RR‐3):1‐21. 10.15585/mmwr.rr6803a
    1. UW Institute for Clinical and Translational Research . REDCap. . Accessed 10/20/2019.
    1. Fowlkes A, Steffens A, Temte J, et al. Influenza Incidence Surveillance Project Working Group. Incidence of medically attended influenza during pandemic and post‐pandemic seasons through the Influenza Incidence Surveillance Project, 2009–13. Lancet Respir Med. 2015. Sep;3(9):709‐718.
    1. Rolfes MA, Foppa IM, Garg S, et al. Annual estimates of the burden of seasonal influenza in the United States: a tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018;12(1):132‐137.
    1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population‐based surveillance data in the United States. PLoS ONE. 2015;10(3):e0118369.
    1. McLean HQ, Peterson SH, King JP, Meece JK, Belongia EA. School absenteeism among school‐aged children with medically attended acute viral respiratory illness during three influenza seasons, 2012–2013 through 2014–2015. Influenza Other Respi Viruses. 2017;11(3):220‐229. 10.1111/irv.12440
    1. Cheng CK, Cowling BJ, Lau EH, Ho LM, Leung GM, Ip DK. Electronic school absenteeism monitoring and influenza surveillance, Hong Kong. Emerg Infect Dis. 2012;18(5):885‐887. 10.3201/eid1805.111796
    1. Allena JD, Rossa TM. H3N2 influenza viruses in humans: viral mechanisms, evolution, and evaluation. Hum Vaccin Immunother. 2018;14(8):1840‐1847. 10.1080/21645515.2018.1462639
    1. CDC . Types of influenza viruses. . Accessed 10/20/2019.
    1. Bjørnstada ON, Viboudc C. Timing and periodicity of influenza epidemics. Proc Natl Acad Sci U S A. 2016. Nov 15;113(46):12899‐12901. 10.1073/pnas.1616052113

Source: PubMed

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