The COVID-19 pandemic has generated fear, panic, distress, anxiety, and depression among many people in Bangladesh. In this cross-sectional study, we examined factors associated with different levels of psychological impact as a result of COVID-19 in Bangladesh. From April 1 to 30, 2020, we used a self-administered online questionnaire to collect data from 10,609 respondents. Using the Impact of Event Scale-Revised to assess the psychological impact of the COVID-19 pandemic on respondents, we categorized the levels of impact as normal, mild, moderate, or severe. Ordinal logistic regression was used to examine the associated factors. The prevalence of mild, moderate, and severe psychological impact was 10.2%, 4.8%, and 45.5%, respectively. Multivariate analysis revealed that the odds of reporting normal vs mild, moderate, or severe psychological impact were 5.9 times higher for people living in the Chittagong Division, 1.7 times higher for women with lower education levels, 3.0 times higher among those who were divorced or separated, 1.8 times higher for those working full time, and 2.4 times higher for those living in shared apartments. The odds of reporting a psychological impact were also higher among people who did not enforce protective measures inside the home, those in self-quarantine, those who did not wear face masks, and those who did not comply with World Health Organization precautionary measures. Increased psychological health risks due to COVID-19 were significantly higher among people who experienced chills, headache, cough, breathing difficulties, dizziness, and sore throat before data collection. Our results showed that 1 in 2 respondents experienced a significant psychological impact as a result of the COVID-19 pandemic. Public health researchers should consider these factors when targeting interventions that would have a protective effect on the individual's psychological health during a pandemic or future disease outbreak.
Over the past 3 decades, the diversity of ethnic, religious, and political backgrounds worldwide, particularly in countries of the Middle East and North Africa (MENA), has led to an increase in the number of intercountry conflicts and terrorist attacks, sometimes involving chemical and biological agents. This warrants moving toward a collaborative approach to strengthening preparedness in the region. In disaster medicine, artificial intelligence techniques have been increasingly utilized to allow a thorough analysis by revealing unseen patterns. In this study, the authors used text mining and machine learning techniques to analyze open-ended feedback from multidisciplinary experts in disaster medicine regarding the MENA region's preparedness for chemical, biological, radiological, and nuclear (CBRN) risks. Open-ended feedback from 29 international experts in disaster medicine, selected based on their organizational roles and contributions to the academic field, was collected using a modified interview method between October and December 2022. Machine learning clustering algorithms, natural language processing, and sentiment analysis were used to analyze the data gathered using R language accessed through the RStudio environment. Findings revealed negative and fearful sentiments about a lack of accessibility to preparedness information, as well as positive sentiments toward CBRN preparedness concepts raised by the modified interview method. The artificial intelligence analysis techniques revealed a common consensus among experts about the importance of having accessible and effective plans and improved health sector preparedness in MENA, especially for potential chemical and biological incidents. Findings from this study can inform policymakers in the region to converge their efforts to build collaborative initiatives to strengthen CBRN preparedness capabilities in the healthcare sector.