Investigating the effect of climatic parameters on mental disorder admissions
The main objective of this study was to evaluate the role of climatic parameters and phenomena including the monthly number of dusty/rainy/snowy/foggy days, cloudiness (Okta), horizontal visibility, and barometric pressure (millibar) on major depressive disorder, bipolar, schizophrenia, and schizoaffective admissions. The monthly data related to the number of admissions in Farshchian hospital and climatic parameters from March 2005 to March 2017 were extracted. Random forest regression and dynamic negative binomial regression were used to examine the relationship between variables; the statistical significance was considered as 0.05. The number of dusty/rainy/snowy/foggy days, cloudiness, and the number of days with vision less than 2 km had a significant positive relationship with admissions due to schizophrenia (p < 0.05). Barometric pressure had a negative effect on schizophrenia admissions (p < 0.001). The number of dusty/rainy/snowy/foggy days and cloudiness had a significant effect on schizoaffective admissions (p < 0.05). Bipolar admissions were negatively associated with rainy days and positively associated with dusty days and cloudiness (p < 0.05). The number of rainy/dusty/snowy days and cloudiness had a positive significant effect on major depressive disorder admissions. The results of the present study confirmed the importance of climatic parameter variability for major depressive disorder, bipolar, schizophrenia, and schizoaffective admissions.
KeywordsClimate Major depressive disorder Bipolar, schizophrenia Schizoaffective
We would like to thank the Vice-Chancellor of Research and Technology, Hamadan University of Medical Sciences, for the approval and support of the study.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Anderson CA (2012) Climate change and violence. The encyclopedia of peace psychology. Wiley-Blackwell, HobokenGoogle Scholar
- Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2:18–22Google Scholar
- Liboschik T, Fokianos K, Fried R (2015) tscount: an R package for analysis of count time series following generalized linear models, Universitätsbibliothek DortmundGoogle Scholar
- QUALITY, I. O. M. C. O. T. E. O. C. C. O. I. A. & HEALTH P (2011) Climate change, the indoor environment, and health. National Academies PressGoogle Scholar
- WHO (2018) Available: http://www.who.int/whr/2001/media_centre/press_release/en/ [Accessed]
- Yang W, Mu L, Shen Y (2015) How we can use twitter data to better understand weather-related depression. USApp–American politics and policy blogGoogle Scholar