Evidence map›Paper›PMID 35700024›Full record

ArticleJournal of medical Internet research2022

Excess Google Searches for Child Abuse and Intimate Partner Violence During the COVID-19 Pandemic: Infoveillance Approach.

Corinne A Riddell, Krista Neumann, N Jeanie Santaularia, Kriszta Farkas, Jennifer Ahern, Susan M Mason

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
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

4 citing papers in PubMed, 15 citations in OpenAlex.

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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

6 authors at 2 institutions in 1 country.

Corinne A RiddellDivision of Biostatistics, School of Public Health, University of California, Berkeley, CA, United States.ORCID 0000-0001-9517-0739
Krista NeumannDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, United States.ORCID 0000-0002-7527-4482
N Jeanie SantaulariaDivision of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States.ORCID 0000-0002-2861-7606
Kriszta FarkasDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, United States.ORCID 0000-0003-1605-580X
Jennifer AhernDivision of Epidemiology, School of Public Health, University of California, Berkeley, CA, United States.ORCID 0000-0002-3853-9760
Susan M MasonDivision of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, United States.ORCID 0000-0002-7635-9832
University of California, Berkeley · USUniversity of Minnesota · US

Funding

Minnesota Population Center Science and Technical CoreP2CHD041023 · NICHD · UNIVERSITY OF MINNESOTA · PI THERESA LOUISE OSYPUK · 2016 to 2026
$4.3M
Dev-Core-001P2CHD073964 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI EDWARD ANDREW MIGUEL · 2019 to 2026
$2.9M
Interdisciplinary Population Health Science Training: Linking Multilevel Forces Across TimeT32HD095134 · NICHD · UNIVERSITY OF MINNESOTA · PI THERESA LOUISE OSYPUK, John Robert Warren · 2019 to 2026
$2.5M
Computational Social Science Training ProgramT32HD101364 · NICHD · UNIVERSITY OF CALIFORNIA BERKELEY · PI Jennifer Ahern, PATRICK T BRADSHAW · 2020 to 2026
$1.7M
NICHD NIH HHS P2C HD041023NICHD NIH HHS P2C HD073964NICHD NIH HHS T32 HD095134NICHD NIH HHS T32 HD101364
6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has created environments with increased risk factors for household violence, such as unemployment and financial uncertainty. At the same time, it led to the introduction of policies to mitigate financial uncertainty. Further, it hindered traditional measurements of household violence.

objectiveUsing an infoveillance approach, our goal was to determine if there were excess Google searches related to exposure to child abuse, intimate partner violence (IPV), and child-witnessed IPV during the COVID-19 pandemic and if any excesses are temporally related to shelter-in-place and economic policies.

methodsData on relative search volume for each violence measure was extracted using the Google Health Trends application programming interface for each week from 2017 to 2020 for the United States. Using linear regression with restricted cubic splines, we analyzed data from 2017 to 2019 to characterize the seasonal variation shared across prepandemic years. Parameters from prepandemic years were used to predict the expected number of Google searches and 95% prediction intervals (PI) for each week in 2020. Weeks with searches above the upper bound of the PI are in excess of the model's prediction.

resultsRelative search volume for exposure to child abuse was greater than expected in 2020, with 19% (10/52) of the weeks falling above the upper bound of the PI. These excesses in searches began a month after the Pandemic Unemployment Compensation program ended. Relative search volume was also heightened in 2020 for child-witnessed IPV, with 33% (17/52) of the weeks falling above the upper bound of the PI. This increase occurred after the introduction of shelter-in-place policies.

conclusionsSocial and financial disruptions, which are common consequences of major disasters such as the COVID-19 pandemic, may increase risks for child abuse and child-witnessed IPV.

Indexed as

Child AbuseCOVID-19Intimate Partner ViolenceChildHumansInfodemiologyPandemicsSearch EngineUnited Statesabusechild abuseCOVID-19domestic violenceGooglehousehold violenceinfoveillanceviolence

Identifiers

PMID35700024
PMCPMC9202515
OpenAlexW4282562286

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.