Evidence map›Paper›PMID 35272626›Full record

ArticleBMC infectious diseases2022

Longitudinal social contacts among school-aged children during the COVID-19 pandemic: the Bay Area Contacts among Kids (BACK) study.

Kristin L Andrejko, Jennifer R Head, Joseph A Lewnard, Justin V Remais

Abstract read
In one paragraph

Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 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

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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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

4 authors.

Kristin L AndrejkoDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
Jennifer R HeadDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
Joseph A LewnardDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, USA.
Justin V RemaisDivision of Environmental Health Sciences, University of California, Berkeley, CA, USA. jvr@berkeley.edu.

Funding

Integrating Epidemiologic and Environmental Approaches to Understand and Predict Coccidioides Exposure and Coccidioidomycosis EmergenceR01AI148336 · NIAID · UNIVERSITY OF CALIFORNIA BERKELEY · PI REMAIS, JUSTIN V · 2020 to 2024
$4.5M
Dev-Core-001P2CHD073964 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI EDWARD ANDREW MIGUEL · 2019 to 2026
$2.9M
berkeley population center P2CHD073964foundation for the national institutes of health R01AI125842foundation for the national institutes of health R01AI148336midas coordination center MIDASSUP2020national science foundation 2032210NIAID NIH HHS R01 AI148336NICHD NIH HHS P2C HD073964university of california multicampus research programs and initiatives 17-446315
6 · The paper itself

Abstract

backgroundThe San Francisco Bay Area was the first region in the United States to enact school closures to mitigate SARS-CoV-2 transmission. The effects of closures on contact patterns for schoolchildren and their household members remain poorly understood.

methodsWe conducted serial cross-sectional surveys (May 2020, September 2020, February 2021) of Bay Area households with children to estimate age-structured daily contact rates for children and their adult household members. We examined changes in contact rates over the course of the COVID-19 pandemic, including after vaccination of household members, and compared contact patterns by household demographics using generalized estimating equations clustered by household.

resultsWe captured contact histories for 1,967 households on behalf of 2,674 children, comprising 15,087 non-household contacts over the three waves of data collection. Shortly after the start of shelter-in-place orders in May 2020, daily contact rates were higher among children from Hispanic families (1.52 more contacts per child per day; [95% CI: 1.14-2.04]), households whose parents were unable to work from home (1.82; [1.40-2.40]), and households with income < $150,000 (1.75; [1.33-2.33]), after adjusting for other demographic characteristics and household clustering. Between May and August 2020, non-household contacts of children increased by 145% (ages 5-12) and 172% (ages 13-17), despite few children returning to in-person instruction. Non-household contact rates among children were higher-by 1.75 [1.28-2.40] and 1.42 [0.89-2.24] contacts per child per day in 5-12 and 13-17 age groups, respectively, in households where at least one adult was vaccinated against COVID-19, compared to children's contact rates in unvaccinated households.

conclusionsChild contact rates rebounded despite schools remaining closed, as parents obtained childcare, children engaged in contact in non-school settings, and family members were vaccinated. The waning reductions observed in non-household contact rates of schoolchildren and their family members during a prolonged school closure suggests the strategy may be ineffective for long-term SARS-CoV-2 transmission mitigation. Reductions in age-assortative contacts were not as apparent amongst children from lower income households or households where adults could not work from home. Heterogeneous reductions in contact patterns raise concerning racial, ethnic and income-based inequities associated with long-term school closures as a COVID-19 mitigation strategy.

Indexed as

COVID-19Influenza, HumanAdolescentAdultChildChild, PreschoolCross-Sectional StudiesHumansPandemicsSARS-CoV-2United StatesChildren social networksContact rateContact surveyCOVID-19Physical distancingReproduction numberSARS-COV-2School closures

Identifiers

PMID35272626
PMCPMC8907906

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.