Evidence map›Paper›PMID 39541118›Full record

ArticleJAMA network open2024

COVID-19 Incidence and Age Eligibility for Elementary School.

Eve Lin, Alyssa Bilinski, Philip A Collender, Vivian Lee, Sohil R Sud, Tomás M León, Lauren A White, Justin V Remais, Jennifer R Head

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Eve LinCollege of Letters and Sciences, University of California, Berkeley.
Alyssa BilinskiSchool of Public Health, Brown University, Providence, Rhode Island.
Philip A CollenderDivision of Environmental Health Sciences, University of California, Berkeley.
Vivian LeeCollege of Letters and Sciences, University of California, Berkeley.
Sohil R SudCalifornia Department of Public Health, Richmond.
Tomás M LeónCalifornia Department of Public Health, Richmond.
Lauren A WhiteCalifornia Department of Public Health, Richmond.
Justin V RemaisDivision of Environmental Health Sciences, University of California, Berkeley.
Jennifer R HeadDivision of Environmental Health Sciences, University of California, Berkeley.

Funding

Modelled estimation of population immunity for coccidioidomycosis and the role of immunologically naïve populations in the shifting epidemiology of coccidioidomycosis in CaliforniaK01AI173529 · NIAID · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jennifer Head · 2023 to 2026
$478k
NIAID NIH HHS K01 AI173529
6 · The paper itself

Abstract

Importance: Understanding the role of school attendance on transmission of SARS-CoV-2 among children is of importance for responding to future epidemics. Estimating discontinuities in outcomes by age of eligibility for school attendance has been used to examine associations between school attendance and a variety of outcomes, but has yet to be applied to describe associations between school attendance and communicable disease transmission. Objective: To estimate the association between eligibility for elementary school and COVID-19 incidence. Design, Setting, and Participants: This case series used data on all pediatric COVID-19 cases reported to California's disease surveillance system between May 16, 2020, and December 15, 2022, among children within 24 months of the age threshold for school eligibility. Exposure: Birthdate before or after the age threshold for elementary school eligibility during periods when school was remote vs in person. Main Outcomes and Measures: COVID-19 cases and hospitalizations. Results: Between May 16, 2020, and December 15, 2022, there were 688 278 cases of COVID-19 (348 957 cases [50.7%] among boys) and 1423 hospitalizations among children who turned 5 years within 24 months of September 1 of the school year when their infection occurred. The mean (SD) age of the study sample was 5.0 (1.3) years. After adjusting for higher rates of testing in schooled populations, the estimated pooled incidence rate ratio among kindergarten-eligible individuals (eg, those born just before the age threshold for school eligibility) compared with those born just after the eligibility threshold for in-person fall 2021 semester was 1.52 (95% CI, 1.36-1.68), for in-person spring 2022 semester was 1.26 (95% CI, 1.15-1.39), and for in-person fall 2022 semester was 1.19 (95% CI, 1.03-1.38). Reported incidence rates among school-eligible children remained higher during the month-long winter 2021-2022 school break but were lower during the longer summer break that followed. The findings were unable to establish whether associations between school eligibility and COVID-19 incidence were based on in-school vs out-of-school routes (eg, classrooms vs school buses). The study lacked power to detect associations between school attendance and hospitalization. Results were robust to functional form. A simulation study was conducted to demonstrate bias associated with nonadjustment for differential case acquisition by exposure status. Conclusions and Relevance: In this case series of children in California, the magnitude of the association between school eligibility and COVID-19 incidence decreased over time and was generally lower than other published associations between out-of-school child social interactions and COVID-19 incidence. This regression discontinuity design approach could be adapted to other geographies and/or disease systems to assess associations between schooling and disease transmission.

Indexed as

COVID-19SARS-CoV-2SchoolsAdolescentAge FactorsCaliforniaChildChild, PreschoolFemaleHospitalizationHumansIncidenceMale

Identifiers

PMID39541118
PMCPMC12316186

What OpenQuestion holds

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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.