Evidence map›Paper›PMID 35157056›Full record

ArticleJAMA network open2022

Model-Estimated Association Between Simulated US Elementary School-Related SARS-CoV-2 Transmission, Mitigation Interventions, and Vaccine Coverage Across Local Incidence Levels.

John Giardina, Alyssa Bilinski, Meagan C Fitzpatrick, Emily A Kendall, Benjamin P Linas, Joshua Salomon, Andrea L Ciaranello

Erratum issuedAbstract read
In one paragraph

Article in JAMA network open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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  5. Review
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  8. Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.Medical decision making : an international journal of the Society for Medical Decision Making · 2024
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  9. Observational
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  17. Error in Figure 3.JAMA network open · 2022
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

John GiardinaCenter for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Alyssa BilinskiDepartment of Health Services, Policy, and Practice, Department of Biostatistics, Brown School of Public Health, Providence, Rhode Island.
Meagan C FitzpatrickCenter for Vaccine Development and Global Health, University of Maryland School of Medicine, Baltimore.
Emily A KendallDivision of Infectious Diseases, Johns Hopkins University School of Medicine, Baltimore, Maryland.
Benjamin P LinasBoston University Schools of Medicine and Public Health, Boston Medical Center, Boston, Massachusetts.
Joshua SalomonCenter for Health Policy and Center for Primary Care and Outcomes Research, Stanford University School of Medicine, Stanford, California.
Andrea L CiaranelloDivision of Infectious Disease and Medical Practice Evaluation Center, Massachusetts General Hospital, Boston.

Funding

Optimizing HIV Care in Less Developed CountriesR01AI058736 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI Kenneth Alan Freedberg, Emily Parker Hyle · 2004 to 2026
$21.5M
Optimizing HIV Care in Less Developed CountriesR37AI058736 · NIAID · MASSACHUSETTS GENERAL HOSPITAL · PI FREEDBERG, KENNETH ALAN · 2018 to 2024
$6.6M
Making Better Decisions: Policy Modeling for AIDS and Drug AbuseR37DA015612 · NIDA · STANFORD UNIVERSITY · PI DOUGLAS K OWENS · 2019 to 2026
$6.3M
Translational modeling of individual- and population-level outcomes of novel tuberculosis drug regimensK08AI127908 · NIAID · JOHNS HOPKINS UNIVERSITY · PI KENDALL, EMILY A · 2017 to 2021
$879k
Mathematical modeling to optimize rotavirus vaccination in sub-Saharan AfricaK01AI141576 · NIAID · UNIVERSITY OF MARYLAND BALTIMORE · PI FITZPATRICK, MEAGAN COLLEEN · 2019 to 2023
$649k
NIAID NIH HHS K01 AI141576NIAID NIH HHS K08 AI127908NIAID NIH HHS R01 AI058736NIDA NIH HHS R37 DA015612
6 · The paper itself

Abstract

Importance: With recent surges in COVID-19 incidence and vaccine authorization for children aged 5 to 11 years, elementary schools face decisions about requirements for masking and other mitigation measures. These decisions require explicit determination of community objectives (eg, acceptable risk level for in-school SARS-CoV-2 transmission) and quantitative estimates of the consequences of changing mitigation measures. Objective: To estimate the association between adding or removing in-school mitigation measures (eg, masks) and COVID-19 outcomes within an elementary school community at varying student vaccination and local incidence rates. Design, Setting, and Participants: This decision analytic model used an agent-based model to simulate SARS-CoV-2 transmission within a school community, with a simulated population of students, teachers and staff, and their household members (ie, immediate school community). Transmission was evaluated for a range of observed local COVID-19 incidence (0-50 cases per 100 000 residents per day, assuming 33% of all infections detected). The population used in the model reflected the mean size of a US elementary school, including 638 students and 60 educators and staff members in 6 grades with 5 classes per grade. Exposures: Variant infectiousness (representing wild-type virus, Alpha variant, and Delta variant), mitigation effectiveness (0%-100% reduction in the in-school secondary attack rate, representing increasingly intensive combinations of mitigations including masking and ventilation), and student vaccination levels were varied. Main Outcomes and Measures: The main outcomes were (1) probability of at least 1 in-school transmission per month and (2) mean increase in total infections per month among the immediate school community associated with a reduction in mitigation; multiple decision thresholds were estimated for objectives associated with each outcome. Sensitivity analyses on adult vaccination uptake, vaccination effectiveness, and testing approaches (for selected scenarios) were conducted. Results: With student vaccination coverage of 70% or less and moderate assumptions about mitigation effectiveness (eg, masking), mitigation could only be reduced when local case incidence was 14 or fewer cases per 100 000 residents per day to keep the mean additional cases associated with reducing mitigation to 5 or fewer cases per month. To keep the probability of any in-school transmission to less than 50% per month, the local case incidence would have to be 4 or fewer cases per 100 000 residents per day. Conclusions and Relevance: In this study, in-school mitigation measures (eg, masks) and student vaccinations were associated with substantial reductions in transmissions and infections, but the level of reduction varied across local incidence. These findings underscore the potential role for responsive plans that deploy mitigation strategies based on local COVID-19 incidence, vaccine uptake, and explicit consideration of community objectives.

Indexed as

AdolescentChildChild, PreschoolCommunicable Disease ControlCOVID-19COVID-19 VaccinesFemaleHumansIncidenceMaleModels, StatisticalRisk AssessmentSARS-CoV-2SchoolsStudentsVaccination CoverageCOVID-19 Vaccines

Identifiers

PMID35157056
PMCPMC8845023

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.