Home > Publications database > Mental health of individuals with pre-existing mental illnesses at the beginning of the COVID-19 pandemic: results of the German National Cohort (NAKO). > print

001     293954
005     20241031115614.0
024 7 _ |a 10.3389/fpubh.2024.1451631
|2 doi
024 7 _ |a pmid:39377001
|2 pmid
024 7 _ |a pmc:PMC11456423
|2 pmc
037 _ _ |a DKFZ-2024-02019
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a Stein, Janine
|b 0
245 _ _ |a Mental health of individuals with pre-existing mental illnesses at the beginning of the COVID-19 pandemic: results of the German National Cohort (NAKO).
260 _ _ |a Lausanne
|c 2024
|b Frontiers Media
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1728466367_6353
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a The COVID-19 pandemic prompted a range of studies on mental health, with mixed results. While numerous studies reported worsened conditions in individuals with pre-existing mental disorders, others showed resilience and stability in mental health. However, longitudinal data focusing on the German population are sparse, especially regarding effects of age and pre-existing mental disorders during the early stages of the pandemic.To assess the interplay between psychiatric history, age, and the timing of the pandemic, with a focus on understanding how these factors relate to the severity of depression and anxiety symptoms.Exploratory analyses were based on 135,445 individuals aged 20-72 years from the German National Cohort (NAKO). Depressive and anxiety symptoms were assessed before and after the first wave of the pandemic. Inferential statistical analyses and negative binomial regression models were calculated.Persons with a self-reported psychiatric history exhibited comparable levels of depression and anxiety symptom severity after the first wave of the pandemic compared to the time before. In contrast, individuals without a psychiatric history, particularly those in their 20s to 40s, experienced an increase in mental health symptom severity during the first wave of the pandemic.Analyses focuses on the first wave of the pandemic, leaving the long-term mental health effects unexplored.Future research should consider age-specific and mental-health-related factors when addressing global health crises. Additionally, it is important to explore factors influencing resilience and adaptation, aiming to develop targeted interventions and informed policies for effective mental health management during pandemics.
536 _ _ |a 313 - Krebsrisikofaktoren und Prävention (POF4-313)
|0 G:(DE-HGF)POF4-313
|c POF4-313
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de
650 _ 7 |a COVID-19 pandemic
|2 Other
650 _ 7 |a German National Cohort (NAKO)
|2 Other
650 _ 7 |a anxiety
|2 Other
650 _ 7 |a depression
|2 Other
650 _ 7 |a longitudinal cohort study
|2 Other
650 _ 7 |a mental health
|2 Other
650 _ 2 |a Humans
|2 MeSH
650 _ 2 |a COVID-19: epidemiology
|2 MeSH
650 _ 2 |a COVID-19: psychology
|2 MeSH
650 _ 2 |a Middle Aged
|2 MeSH
650 _ 2 |a Germany: epidemiology
|2 MeSH
650 _ 2 |a Adult
|2 MeSH
650 _ 2 |a Male
|2 MeSH
650 _ 2 |a Female
|2 MeSH
650 _ 2 |a Aged
|2 MeSH
650 _ 2 |a Anxiety: epidemiology
|2 MeSH
650 _ 2 |a Depression: epidemiology
|2 MeSH
650 _ 2 |a Depression: psychology
|2 MeSH
650 _ 2 |a Mental Health: statistics & numerical data
|2 MeSH
650 _ 2 |a Mental Disorders: epidemiology
|2 MeSH
650 _ 2 |a Mental Disorders: psychology
|2 MeSH
650 _ 2 |a Cohort Studies
|2 MeSH
650 _ 2 |a Young Adult
|2 MeSH
650 _ 2 |a Pandemics
|2 MeSH
650 _ 2 |a Age Factors
|2 MeSH
650 _ 2 |a SARS-CoV-2
|2 MeSH
650 _ 2 |a Severity of Illness Index
|2 MeSH
700 1 _ |a Pabst, Alexander
|b 1
700 1 _ |a Berger, Klaus
|b 2
700 1 _ |a Karch, André
|b 3
700 1 _ |a Teismann, Henning
|b 4
700 1 _ |a Streit, Fabian
|b 5
700 1 _ |a Grabe, Hans J
|b 6
700 1 _ |a Mikolajczyk, Rafael
|b 7
700 1 _ |a Massag, Janka
|b 8
700 1 _ |a Lieb, Wolfgang
|b 9
700 1 _ |a Castell, Stefanie
|b 10
700 1 _ |a Heise, Jana-Kristin
|b 11
700 1 _ |a Schulze, Matthias B
|b 12
700 1 _ |a Gastell, Sylvia
|b 13
700 1 _ |a Harth, Volker
|b 14
700 1 _ |a Obi, Nadia
|b 15
700 1 _ |a Peters, Annette
|b 16
700 1 _ |a Huemer, Marie-Theres
|b 17
700 1 _ |a Bohmann, Patricia
|b 18
700 1 _ |a Leitzmann, Michael
|b 19
700 1 _ |a Schipf, Sabine
|b 20
700 1 _ |a Meinke-Franze, Claudia
|b 21
700 1 _ |a Hebestreit, Antje
|b 22
700 1 _ |a Fuhr, Daniela C
|b 23
700 1 _ |a Michels, Karin B
|b 24
700 1 _ |a Jaskulski, Stefanie
|b 25
700 1 _ |a Stocker, Hannah
|0 P:(DE-He78)104fae0755c89365b7ae32238b3f1f52
|b 26
|u dkfz
700 1 _ |a Koch-Gallenkamp, Lena
|0 P:(DE-He78)13aa5fe9d9961c9fd67193befb0dcf88
|b 27
|u dkfz
700 1 _ |a Willich, Stefan N
|b 28
700 1 _ |a Keil, Thomas
|b 29
700 1 _ |a Löffler, Markus
|b 30
700 1 _ |a Wirkner, Kerstin
|b 31
700 1 _ |a Riedel-Heller, Steffi G
|b 32
700 1 _ |a Cohort, for German National
|b 33
|e Collaboration Author
773 _ _ |a 10.3389/fpubh.2024.1451631
|g Vol. 12, p. 1451631
|0 PERI:(DE-600)2711781-9
|p 1451631
|t Frontiers in Public Health
|v 12
|y 2024
|x 2296-2565
856 4 _ |u https://inrepo02.dkfz.de/record/293954/files/fpubh-12-1451631.pdf
856 4 _ |u https://inrepo02.dkfz.de/record/293954/files/fpubh-12-1451631.pdf?subformat=pdfa
|x pdfa
909 C O |o oai:inrepo02.dkfz.de:293954
|p VDB
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 26
|6 P:(DE-He78)104fae0755c89365b7ae32238b3f1f52
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 27
|6 P:(DE-He78)13aa5fe9d9961c9fd67193befb0dcf88
913 1 _ |a DE-HGF
|b Gesundheit
|l Krebsforschung
|1 G:(DE-HGF)POF4-310
|0 G:(DE-HGF)POF4-313
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-300
|4 G:(DE-HGF)POF
|v Krebsrisikofaktoren und Prävention
|x 0
914 1 _ |y 2024
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b FRONT PUBLIC HEALTH : 2022
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2023-01-12T14:15:14Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2023-01-12T14:15:14Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2023-01-12T14:15:14Z
915 _ _ |a Creative Commons Attribution CC BY (No Version)
|0 LIC:(DE-HGF)CCBYNV
|2 V:(DE-HGF)
|b DOAJ
|d 2023-01-12T14:15:14Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-26
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1180
|2 StatID
|b Current Contents - Social and Behavioral Sciences
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0130
|2 StatID
|b Social Sciences Citation Index
|d 2023-10-26
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-10-26
915 _ _ |a IF >= 5
|0 StatID:(DE-HGF)9905
|2 StatID
|b FRONT PUBLIC HEALTH : 2022
|d 2023-10-26
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-10-26
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-10-26
920 1 _ |0 I:(DE-He78)C070-20160331
|k C070
|l C070 Klinische Epidemiologie und Alternf.
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)C070-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21