Journal of Epidemiology
Online ISSN : 1349-9092
Print ISSN : 0917-5040
ISSN-L : 0917-5040

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The Association of Work-related Stress According to the Demand–Control Model With Aggravation of Pre-existing Disease During the First State of COVID-19 Emergency in Japan
Yupeng HeHiroshi YatsuyaChifa ChiangAtsuhiko OtaRyo OkuboTomohiro IshimaruTakahiro Tabuchi
著者情報
ジャーナル オープンアクセス 早期公開
電子付録

論文ID: JE20210146

この記事には本公開記事があります。
2版: 2021/10/19
1版: 2021/09/18
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Background: The job environment has changed a lot during the period of the coronavirus disease 2019 (COVID-19) pandemic. This study aimed to investigate the association between work-related stress and aggravation of pre-existing disease in workers during the first state of COVID-19 emergency in Japan.

Methods: Data were obtained from a large internet survey conducted between August 25 and September 30, 2020 in Japan. Participants who reported that they had a job as well as current history of disease(s) (ie, pre-existing conditions) were included (n = 3,090). Aggravation of pre-existing disease during the state of emergency was self-reported. Work-related stress from April 2020 (since the state of COVID-19 emergency) was assessed according to a job demand–control model. Multivariable logistic regression models were used to analyze the association.

Results: Aggravation of pre-existing diseases was reported by 334 participants (11%). The numbers of participants with high demand and low control were 112 (18%) and 100 (14%), respectively. Compared to medium demand, high demand was significantly associated with aggravation of pre-existing diseases (odds ratio 1.77; 95% confidence interval, 1.30–2.42). Low control compared to medium control was also significantly associated with aggravation of pre-existing diseases (odds ratio 1.39; 95% confidence interval, 1.02–1.92).

Conclusion: Work-related stress during the first state of COVID-19 emergency was associated with aggravation of pre-existing disease during that period.

著者関連情報
© 2021 Yupeng He et al. This is an open access article distributed under the terms of Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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