Evidence map›Paper›PMID 40245400›Full record

ArticleJournal of medical Internet research2025

Search Volume of Insomnia and Suicide as Digital Footprints of Global Mental Health During the COVID-19 Pandemic: 3-Year Infodemiology Study.

Sheng-Hsuan Lin, Kuan-Pin Su, Hsiao-Hui Tsou, Pei-Hsuan Hsia, Yu-Hsuan Lin

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Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Sheng-Hsuan LinInstitute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.ORCID https://orcid.org/0000-0003-0714-2354
Kuan-Pin SuAn-Nan Hospital, China Medical University, Tainan, Taiwan.ORCID https://orcid.org/0000-0002-4501-2502
Hsiao-Hui TsouInstitute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.ORCID https://orcid.org/0000-0001-6773-4111
Pei-Hsuan HsiaInstitute of Statistics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.ORCID https://orcid.org/0009-0003-5440-4211
Yu-Hsuan LinInstitute of Population Health Sciences, National Health Research Institutes, Miaoli County, Taiwan.ORCID https://orcid.org/0000-0001-9393-3886

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe global COVID-19 pandemic's mental health impact was primarily studied in the initial year of lockdowns but remained underexplored in subsequent years despite evolving conditions. This study aimed to address this gap by investigating how COVID-19-related factors, including nationwide COVID-19 deaths and incidence rates, influenced mental health indicators over time.

objectiveThis study aimed to examine the interplay among national COVID-19 pandemic deaths, incidence rates, stay-at-home behaviors, and mental health indicators across different income-level countries. Specifically, we assessed the mediating role of stay-at-home behaviors in the relationship between the COVID-19 pandemic deaths and mental health indicators.

methodsWe analyzed data from 45 countries spanning March 2020 to October 2022. COVID-19-related factors included national COVID-19 pandemic deaths and incidence rates, obtained from publicly available datasets. Stay-at-home behaviors were assessed using Google Location History data, which captured residence-based cell phone activity as a proxy for mobility patterns. Mental health indicators were evaluated through Google Trends data, measuring changes in search volumes for "insomnia" and "suicide." The interplay among these variables was assessed using mediation analysis to quantify the proportion mediated by stay-at-home behaviors in the association between COVID-19 deaths and mental health indicators.

resultsIn high-income countries, during the first pandemic year (March 2020 to February 2021), a higher monthly COVID-19 death count was associated with increased searches for "insomnia," with a total effect estimate of 2.1×10

conclusionsOur findings highlight how the mental health impact of the pandemic varied across income groups and evolved over time. The mediating effect of stay-at-home behaviors was significant in the early phases but diminished in later stages, particularly in high-income countries. Meanwhile, middle-income countries exhibited unique patterns that suggest alternative protective factors. These insights can inform tailored mental health interventions and policy strategies in future public health crises.

Indexed as

COVID-19Global HealthMental HealthSleep Initiation and Maintenance DisordersSuicideHumansIncidencePandemicsSARS-CoV-2COVID-19insomniainternet searchesmediation analysisstay-at-home measuressuicide

Identifiers

PMID40245400
PMCPMC12046254

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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.