Evidence map›Paper›PMID 35067255›Full record

ArticleEuropean psychiatry : the journal of the Association of European Psychiatrists2022

Biopsychosocial exposure to the COVID-19 pandemic and the relative risk of schizophrenia: Interrupted time-series analysis of a nationally representative sample.

Yael Travis-Lumer, Arad Kodesh, Yair Goldberg, Abraham Reichenberg, Sophia Frangou, Stephen Z Levine

Abstract read
In one paragraph

Article in European psychiatry : the journal of the Association of European Psychiatrists, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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0cells of the map it votes in
4citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Yael Travis-LumerThe Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel.ORCID 0000-0002-2897-4451
Arad KodeshDepartment of Community Mental Health, University of Haifa, Haifa, Israel.
Yair GoldbergThe Faculty of Industrial Engineering and Management, Technion - Israel Institute of Technology, Haifa, Israel.ORCID 0000-0002-4544-0910
Abraham ReichenbergDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0003-2729-8575
Sophia FrangouDepartment of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York, USA.ORCID 0000-0002-3210-6470
Stephen Z LevineDepartment of Community Mental Health, University of Haifa, Haifa, Israel.ORCID 0000-0002-5544-0420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundStudies of COVID-19 pandemic biopsychosocial exposure and schizophrenia risk showed contradictory results, were undertaken early in the pandemic, and did not consider lockdowns or COVID-19 infection. Hence, we examined the association between COVID-19 biopsychosocial exposure and incident schizophrenia.

methodsAn interrupted time-series study design was implemented based on Israeli electronic health records from 2013 to 2021 with national coverage. The period coinciding with the COVID-19 pandemic biopsychosocial exposures from March 2020 to February 2021 was classified as exposed, otherwise unexposed. The effect of the COVID-19 pandemic on incident schizophrenia was quantified by fitting a Poisson regression and modeling the relative risk (RR) and corresponding 95% confidence intervals (CI). Three scenarios were projected from the third lockdown to 10 months to forecast incident schizophrenia rates and their associated 95% prediction intervals (PI).

resultsThe total population (N = 736,356) yielded 4,310 cases of incident schizophrenia over time. The primary analysis showed that the period exposed to the COVID-19 pandemic was associated with a reduced RR (RR = 0.81, 95% CI = 0.73, 0.91, p < 0.001). This conclusion was supported in 12 sensitivity analyses, including scrutinizing lockdowns and COVID-19 infection status. Two of three forecast scenarios projected an incident increase (6.74, 95% PI = 5.80, 7.84; 7.40, 95% PI = 6.36, 8.60).

conclusionsThe reduced risk of schizophrenia during the pandemic suggests no immediate triggering of new onsets either by the virus or the pandemic-induced psychosocial adversities. Once restrictions are lifted, the increased projected presentations have implications for clinicians and healthcare policy.

Indexed as

COVID-19SchizophreniaCommunicable Disease ControlHumansPandemicsRiskSARS-CoV-2COVID-19epidemiologypublic healthschizophrenia

Identifiers

PMID35067255
PMCPMC8853851

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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.