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Oyane NM, Bjelland I, Pallesen S, Holsten F, Bjorvatn B.
Department of Public Health and Primary Health Care, University of Bergen, Norway; Department of Child and Adolescent Mental Health Services, Haukeland University Hospital, Norway; Department of Psychosocial Science, University of Bergen, Norway; Department of Psychiatry, University of Bergen, Norway eNorwegian Competence Center for Sleep Disorders, Haukeland University Hospital, Norway
Background: The purpose of this study was to assess how seasonal changes in mood and behaviour were associated with depression and anxiety symptoms in a sample from a general population, and to investigate how prevalence figures were affected by month of questionnaire completion.
Methods: The target population included all individuals in the Hordaland county (Norway) born 1953-57 (N = 29,400). In total, 8598 men (57% response rate) and 9983 women (70% response rate) attended the screening station. Half of the men (randomly chosen) and all of the women were offered a questionnaire to fill in with items on seasonality. This was measured using the Global Seasonality Score (GSS), a central component of the Seasonal Pattern Assessment Questionnaire (SPAQ). The Hospital Anxiety and Depression Scale (HADS) was used to measure anxiety and depression. Both questionnaires were completed by 2980 men (68.9%) and 8074 women (80.9%).
Results: Seasonality was positively associated with levels of both anxiety and depression regardless of the season the interview took place. In subjects with a low/moderate degree of seasonality there were modestly higher levels of depressive symptoms during November through March than the other months.
Limitations: We had a substantial number of non-responders.
Conclusions: Our results raise the possibility of seasonality being a separate dimensional trait associated with both anxiety and depression.
tags: anxiety disorders clinical depression seasonality
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Epidemiological studies describing population characteristics which collect data at one point in time and then consider relationships between observed characteristics.. Because they don't look at time trends they cannot establish causes.
