Abstract:
AIM:The effects of social isolation measures used to control the spread of COVID-19 are negatively impacting the mental health of many. One of the consequences of exposure to disasters/pandemics is an increase in alcohol use. The current study aimed to examine what predisposing (distal) and pandemic-related (proximal) factors were associated with increased drinking in the wake of the COVID-19 pandemic. METHODS:On 1 April 2020, 5158 Australians completed a survey from the COvid-19 and you: mentaL heaLth in AusTralia now survEy (COLLATE) project, a nationwide study aimed at tracking key mental health concerns. Using logistic regression, distal (demographics and previous drinking behaviors) and proximal (employment, lifestyle factors, and mood) factors were assessed for their association with increased drinking since the onset of the COVID-19 pandemic. RESULTS:Distal factors, including heavier drinking pre-pandemic, middle age, and average or higher income, and proximal factors, including job loss, eating more, changes to sleep as well as stress and depression, were all associated with increased drinking in the COVID-19 pandemic environment. Female sex and self-reported history of mental illness became nonsignificant after proximal measures were added to the model. Living alone, exercise, anxiety, and status as an essential or health-care worker were not associated with increased drinking. CONCLUSION:These results provide guidance as to who might be targeted to receive support based on predisposing demographic factors and pre-pandemic drinking behavior. Second, they indicate what behaviors/factors accompany increased alcohol use and provide targets for psychosocial and psychoeducational supports to address these proximal factors.
journal_name
Psychiatry Clin Neuroscijournal_title
Psychiatry and clinical neurosciencesauthors
Neill E,Meyer D,Toh WL,van Rheenen TE,Phillipou A,Tan EJ,Rossell SLdoi
10.1111/pcn.13099subject
Has Abstractpub_date
2020-10-01 00:00:00pages
542-549issue
10eissn
1323-1316issn
1440-1819journal_volume
74pub_type
杂志文章abstract:AIM:Neuroimaging-based multivariate pattern-recognition methods have been successfully used to develop diagnostic algorithms to distinguish patients with major depressive disorder (MDD) from healthy controls (HC). We developed and evaluated the accuracy of a multivariate classification method for the differentiation of...
journal_title:Psychiatry and clinical neurosciences
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journal_title:Psychiatry and clinical neurosciences
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journal_title:Psychiatry and clinical neurosciences
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journal_title:Psychiatry and clinical neurosciences
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