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      Association of In-person vs Virtual Education With Community COVID-19 Case Incidence Following School Reopenings in the First Year of the COVID-19 Pandemic

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          Key Points

          Question

          Was resuming in-person vs virtual instruction for middle and high school students in the fall of 2020 associated with differences in COVID-19 community incidence?

          Findings

          In a cohort study of 51 matched pairs of counties that reopened with in-person vs virtual instruction, those that reopened with in-person schooling experienced slightly higher county-level COVID-19 incidence at 6 and 8 weeks after in-person reopening compared with those that reopened virtually.

          Meaning

          These findings of this cohort study should be interpreted in the context of competing concerns of reduced school engagement and social well-being of children in prolonged virtual learning environments.

          Abstract

          This cohort study estimates the association of in-person vs virtual instruction for students at the sixth grade or above with county-level COVID-19 incidence in the first year of the COVID-19 pandemic.

          Abstract

          Importance

          The variability in timing of middle and secondary school reopenings during the 2020 to 2021 school year in the US presents an opportunity to examine the associations of different approaches to in-person education with changes in community COVID-19 incidence. Early studies on this topic have reached mixed conclusions and may be biased by unmeasured confounders.

          Objective

          To estimate the association of in-person vs virtual instruction for students at the sixth grade level or above with county-level COVID-19 incidence in the first year of the COVID-19 pandemic.

          Design, Setting, and Participants

          This cohort study included matched pairs of counties resuming school programs with in-person vs virtual instruction, drawn from 229 US counties that contained a single public school district and with county populations exceeding 100 000 residents. Counties that contained 1 single public school district and reopened in-person schooling for students at the sixth grade level or above during the fall of 2020 were matched 1-to-1 with counties whose school district reopened with only virtual instruction, based on geographic proximity, population-level demographic factors, the resumption of school district–level fall sports activity, and baseline county COVID-19 incidence rates. Data were analyzed from November 2021 to November 2022.

          Exposures

          In-person instruction for students at the sixth grade level or above resuming between August 1 and October 31, 2020.

          Main Outcomes and Measures

          County-level daily COVID-19 incidence per 100 000 residents.

          Results

          The inclusion criteria and subsequent matching algorithm led to the identification of 51 pairs of matched counties among 79 total unique counties. Exposed counties had a median (IQR) of 141 840 (81 441-241 910) residents each, and unexposed counties had a median (IQR) of 131 412 (89 011-278 666) residents each. County schools with in-person vs virtual instruction had similar daily COVID-19 case incidence within the first 4 weeks after in-person reopening, but counties with in-person instruction had higher daily incidence beyond 4 weeks. Daily case incidence per 100 000 residents among counties with in-person instruction, compared with counties with virtual instruction, was higher at 6 weeks (adjusted incidence rate ratio, 1.24 [95% CI, 1.00-1.55]) and at 8 weeks after (adjusted incidence rate ratio, 1.31 [95% CI, 1.06-1.62]). This outcome was also concentrated in counties where schools provided full rather than hybrid instructional models.

          Conclusions and Relevance

          In a cohort study of matched pairs of counties that reopened with in-person vs virtual instruction at the secondary school level in the 2020 to 2021 academic year, counties with in-person school instructional models early in the COVID-19 pandemic experienced increases in county-level COVID-19 incidence at 6 and 8 weeks after in-person reopening, compared with counties with virtual instructional models.

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          Most cited references19

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          Presenting Characteristics, Comorbidities, and Outcomes Among 5700 Patients Hospitalized With COVID-19 in the New York City Area

          There is limited information describing the presenting characteristics and outcomes of US patients requiring hospitalization for coronavirus disease 2019 (COVID-19).
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            A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics

            Abstract The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
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              Learning loss due to school closures during the COVID-19 pandemic

              School closures have been a common tool in the battle against COVID-19. Yet, their costs and benefits remain insufficiently known. We use a natural experiment that occurred as national examinations in The Netherlands took place before and after lockdown to evaluate the impact of school closures on students’ learning. The Netherlands is interesting as a “best-case” scenario, with a short lockdown, equitable school funding, and world-leading rates of broadband access. Despite favorable conditions, we find that students made little or no progress while learning from home. Learning loss was most pronounced among students from disadvantaged homes. Suspension of face-to-face instruction in schools during the COVID-19 pandemic has led to concerns about consequences for students’ learning. So far, data to study this question have been limited. Here we evaluate the effect of school closures on primary school performance using exceptionally rich data from The Netherlands ( n ≈ 350,000). We use the fact that national examinations took place before and after lockdown and compare progress during this period to the same period in the 3 previous years. The Netherlands underwent only a relatively short lockdown (8 wk) and features an equitable system of school funding and the world’s highest rate of broadband access. Still, our results reveal a learning loss of about 3 percentile points or 0.08 standard deviations. The effect is equivalent to one-fifth of a school year, the same period that schools remained closed. Losses are up to 60% larger among students from less-educated homes, confirming worries about the uneven toll of the pandemic on children and families. Investigating mechanisms, we find that most of the effect reflects the cumulative impact of knowledge learned rather than transitory influences on the day of testing. Results remain robust when balancing on the estimated propensity of treatment and using maximum-entropy weights or with fixed-effects specifications that compare students within the same school and family. The findings imply that students made little or no progress while learning from home and suggest losses even larger in countries with weaker infrastructure or longer school closures.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                14 April 2023
                April 2023
                14 April 2023
                : 6
                : 4
                : e238300
                Affiliations
                [1 ]PolicyLab, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [2 ]Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
                [3 ]Leonard Davis Institute of Health Economics at University of Pennsylvania, Philadelphia
                [4 ]Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
                [5 ]Clinical Futures, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [6 ]Formerly PolicyLab, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
                [7 ]Merck and Company, Rahway, New Jersey
                Author notes
                Article Information
                Accepted for Publication: March 1, 2023.
                Published: April 14, 2023. doi:10.1001/jamanetworkopen.2023.8300
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2023 Matone M et al. JAMA Network Open.
                Corresponding Author: Meredith Matone, DrPH, PolicyLab, Children’s Hospital of Philadelphia, 2716 South St, Roberts Building, Philadelphia, PA ( Matonem@ 123456chop.edu ).
                Author Contributions: Drs Matone and Wang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Matone, Wang, Marshall, Huang, Rubin.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Matone, Wang, Marshall, Huang, Rubin.
                Critical revision of the manuscript for important intellectual content: Wang, Marshall, Worsley, Filograna, Rubin.
                Statistical analysis: Wang, Marshall, Huang, Rubin.
                Administrative, technical, or material support: Matone, Marshall, Worsley, Rubin.
                Supervision: Matone, Huang, Rubin.
                Conflict of Interest Disclosures: None reported.
                Data Sharing Statement: See Supplement 2.
                Article
                zoi230264
                10.1001/jamanetworkopen.2023.8300
                10105309
                37058303
                eea278c3-9740-43d3-ac9d-264e1706f831
                Copyright 2023 Matone M et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 2 January 2023
                : 1 March 2023
                Categories
                Research
                Original Investigation
                Online Only
                Public Health

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