Biomedical
Exploring factors in fear of COVID-19 and its GIS-based nationwide distribution: the case of Bangladesh
rnfinity
Mohammed A. Mamun
Mohammed A. Mamun
Department of Public Health and Informatics, Jahangirnagar University, .
Abstract
"BACKGROUND: The COVID-19 pandemic is a public health threat of international concern, intensifying peoples' psychological risk and vulnerability by strengthening mental health stressors such as fear, panic and uncertainty. The unexpected fear of COVID-19 has been reported to be associated with suicide occurrences, similar to prior pandemics. AIMS: Identifying the factors associated with fear of COVID-19 could help us to develop better mental health strategy and practice to improve the situation here in Bangladesh. This was the first attempt to present a Geographic Information System (GIS)-based distribution of fear of COVID-19 across the country's administrative districts in a nationwide sample. METHOD: Data for a total of 10 067 individuals were collected by an online survey during the first wave of the pandemic (1 to 10 April 2020); data for 10 052 participants were finally analysed after excluding 15 transgender individuals. The survey questionnaire included items concerning sociodemographic, behavioural and health-related variables, COVID-19-related issues, and the Bangla Fear of COVID-19 Scale. RESULTS: The mean fear of COVID-19 scores was 21.30 ± 6.01 (out of a possible 35) in the present sample. Female gender, highly educated, non-smoker, non-alcohol consumer, having chronic diseases, using social media, and using social media and not using newspapers as COVID-19 information sources were associated with a higher level of fear of COVID-19. Higher levels of fear of COVID-19 were found in districts of Magura, Panchagarh, Tangail, Sunamganj and Munshiganj; by contrast, Kushtia, Pirojpur, Chapainawabganj, Jhalokathi and Naogaon districts had lower fear of COVID-19. Based on the GIS-distribution, fear of COVID-19 was significantly associated with the district as well as in respect to its gender-based and education-level-based associations. However, fear of COVID-19 and COVID-19 cases were heterogeneously distributed across the districts; that is, no consistent association of higher COVID-19 cases with higher fear of COVID-19 was found. CONCLUSIONS: This study being exploratory in nature may help to facilitate further studies, as well as directing governmental initiatives for reducing fear of COVID-19 in at-risk individuals. Providing adequate resources and mental health services in the administrative regions identified as highly vulnerable to fear of COVID-19 is recommended."
Key Questions
What factors contribute to fear of COVID-19 in Bangladesh?
The study explores various factors contributing to fear of COVID-19 in Bangladesh, including demographic variables, socio-economic status, access to healthcare, and exposure to misinformation. These factors are analyzed to understand their impact on public fear and anxiety during the pandemic.
How is Geographic Information Systems (GIS) used in the study?
GIS is used to map and analyze the nationwide distribution of fear of COVID-19 in Bangladesh. The study leverages spatial data to identify hotspots of fear, correlate them with infection rates, and visualize patterns across different regions.
What are the key findings of the study regarding fear of COVID-19?
The study identifies key factors such as age, gender, education level, and urban vs. rural residence as significant predictors of fear. It also highlights regional disparities in fear levels, with higher fear observed in areas with greater COVID-19 exposure and limited healthcare access.
How does fear of COVID-19 vary across different regions of Bangladesh?
Fear of COVID-19 varies significantly across regions, with urban areas and regions with higher infection rates reporting greater fear. The study uses GIS to visualize these variations and identify clusters of high and low fear.
What role does misinformation play in fear of COVID-19?
Misinformation, particularly through social media and informal networks, is identified as a major contributor to fear. The study examines how false information about the virus, treatments, and vaccines exacerbates anxiety and fear among the population.
How does socio-economic status influence fear of COVID-19?
Socio-economic status plays a significant role, with lower-income groups reporting higher levels of fear due to limited access to healthcare, financial instability, and greater vulnerability to the economic impacts of the pandemic.
What are the implications of fear of COVID-19 for public health?
High levels of fear can lead to negative public health outcomes, such as vaccine hesitancy, overburdening of healthcare systems, and mental health issues. The study emphasizes the need for targeted interventions to address fear and its consequences.
How can GIS-based analysis inform public health strategies?
GIS-based analysis provides valuable insights into the spatial distribution of fear and its correlates. This information can guide public health strategies by identifying high-risk areas, tailoring communication campaigns, and allocating resources more effectively.
What demographic groups are most affected by fear of COVID-19?
The study finds that women, older adults, and individuals with lower education levels are more likely to experience higher levels of fear. These groups may require targeted support to address their specific concerns and vulnerabilities.
How does the study address mental health concerns related to COVID-19?
The study highlights the mental health implications of fear, including anxiety, depression, and stress. It calls for integrated mental health services as part of the pandemic response to address these issues.
What are the limitations of the study?
Limitations include reliance on self-reported data, potential biases in survey responses, and challenges in capturing real-time changes in fear levels. The study acknowledges these limitations and suggests areas for future research.
How can the findings of this study be applied to other countries?
The findings can inform similar studies in other low- and middle-income countries, particularly those with comparable socio-economic and healthcare challenges. The GIS-based approach can also be adapted to other contexts to analyze fear and its distribution.
What recommendations does the study provide for policymakers?
The study recommends targeted public health campaigns to combat misinformation, improve healthcare access, and address the mental health needs of vulnerable populations. It also emphasizes the importance of data-driven decision-making using GIS tools.
How does the study contribute to the global understanding of pandemic-related fear?
The study adds to the global understanding of pandemic-related fear by providing a detailed case study of Bangladesh. It highlights the role of socio-economic, demographic, and geographic factors in shaping fear and offers insights applicable to other regions.
