Predictors of somatic symptoms during the COVID-19 pandemic: a national longitudinal survey in Japan Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1136/bmjopen-2023-082439
Objectives The COVID-19 pandemic has highlighted the long-term consequences of SARS-CoV-2 infection, termed long COVID. However, in the absence of comparative groups, the differentiation of disease progression remains difficult, as COVID-19 symptoms become indistinguishable from symptoms originating from alternative etiologies. This study aimed to longitudinally investigate the association between COVID-19 exposure and the somatic symptoms in the Japanese general population. Design This was a longitudinal cohort study with 1-year follow-up. Setting and participants Longitudinal data from 19 545 individuals who participated in the Japan Society and New Tobacco Internet Survey (JASTIS) 2022 and 2023 were included. In this study, we used data from the 2022 JASTIS as baseline data and the 2023 JASTIS as follow-up data. Based on questionnaire responses, respondents were classified into three categories of exposure to COVID-19. Outcome measures The somatic symptoms were assessed by the Somatic Symptom Scale-8 (SSS-8). Using generalised linear models adjusted for baseline covariates, we calculated the ORs of having very high somatic symptoms assessed by SSS-8, attributable to COVID-19 exposure (no COVID-19 cases as the reference group). Results Follow-up completers were divided into three groups according to COVID-19 exposure (no COVID-19, n=16 012; COVID-19 without O2 therapy, n=3201; COVID-19 with O 2 therapy, n=332). After adjusting for all covariates, COVID-19 cases with O 2 therapy had a significant positive association (OR 7.60, 95% CI 5.47 to 10.58) with a very high somatic symptoms burden while other COVID-19 exposure groups did not. Pre-existing physical and psychological conditions were also associated with increased risk of somatic symptoms. Conclusion The findings of our study suggest that the severity of COVID-19 symptoms requiring O 2 therapy in the acute phase led to high somatic symptoms. Pre-existing conditions were also associated with a subsequent risk of somatic symptoms.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjopen-2023-082439
- https://bmjopen.bmj.com/content/bmjopen/14/5/e082439.full.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396750155
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4396750155Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1136/bmjopen-2023-082439Digital Object Identifier
- Title
-
Predictors of somatic symptoms during the COVID-19 pandemic: a national longitudinal survey in JapanWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-01Full publication date if available
- Authors
-
Norio Sugawara, Takahiro Tabuchi, Keita Tokumitsu, Norio Yasui‐FurukoriList of authors in order
- Landing page
-
https://doi.org/10.1136/bmjopen-2023-082439Publisher landing page
- PDF URL
-
https://bmjopen.bmj.com/content/bmjopen/14/5/e082439.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://bmjopen.bmj.com/content/bmjopen/14/5/e082439.full.pdfDirect OA link when available
- Concepts
-
Medicine, Coronavirus disease 2019 (COVID-19), Pandemic, Cohort study, Longitudinal study, Epidemiology, Demography, Cohort, Population, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Internal medicine, Disease, Environmental health, Pathology, Infectious disease (medical specialty), SociologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
30Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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