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2023 Wildfires in Canada: Living in Wildfire Regions in Alberta and Nova Scotia Doubled the Odds for Residents to Experience Likely Generalized Anxiety Disorder Symptoms.

Gloria Obuobi-DonkorReham A Hameed ShalabyBelinda AgyapongRaquel da Luz DiasVincent Israel Opoku Agyapong
Published in: Journal of clinical medicine (2024)
Background: Wildfires have become increasingly prevalent in various regions, resulting in substantial environmental and psychological consequences that have garnered increasing attention. Objective: This study aims to examine the prevalence of likely Generalized Anxiety Disorder (GAD) and explore the determinants of likely GAD during the wildfires in Alberta and Nova Scotia. Methods: Data were collected online through a cross-sectional survey from 14 May-23 June 2023. Alberta and Nova Scotia participants self-subscribed to the program by texting 'HopeAB' or 'HopeNS' to a short code, respectively. The GAD-7-validated tool was used to collect information on likely GAD. Results: This study included 298 respondents while one hundred and twelve respondents lived in a region of Alberta/Nova Scotia affected by the wildfires (37.7%). The prevalence of likely GAD among the respondents was 41.9%. Respondents who lived in a region of Alberta/Nova Scotia recently impacted by the wildfires were twice as likely to experience GAD symptoms (OR = 2.4; 95% C.I. 1.3-4.3). Conclusions: The study's identification of a statistically significant relationship between residing in a wildfire-impacted region and likely GAD shows the association between environmental and psychological well-being. However, the relatively small sample size and self-reported assessment of GAD symptoms may limit the generalizability of the findings. Further research involving a larger sample size delving into potential predictors could facilitate strategies for mitigating the mental health consequences of natural disasters.
Keyphrases
  • mental health
  • risk factors
  • sleep quality
  • social media
  • working memory
  • quality improvement
  • machine learning
  • life cycle