Perceived fear of COVID-19 and its associated factors among Nepalese older adults in eastern Nepal: A cross-sectional study

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Abstract

Background

Coronavirus disease 2019 (COVID-19) has affected all age groups worldwide, but older adults have been affected greatly with an increased risk of severe illness and mortality. Nepal is struggling with the COVID-19 pandemic. The normal life of older adults, one of the vulnerable populations to COVID-19 infection, has been primarily impacted. The current evidence shows that the COVID-19 virus strains are deadly, and non-compliance to standard protocols can have serious consequences, increasing fear among older adults. This study assessed the perceived fear of COVID-19 and associated factors among older adults in eastern Nepal.

Methods

A cross-sectional study was conducted between July and September 2020 among 847 older adults (≥60 years) residing in three districts of eastern Nepal. Perceived fear of COVID-19 was measured using the seven-item Fear of COVID-19 Scale (FCV-19S). Multivariate logistic regression identified the factors associated with COVID-19 fear.

Results

The mean score of the FCV-19S was 18.1 (SD = 5.2), and a sizeable proportion of older adults, ranging between 12%-34%, agreed with the seven items of the fear scale. Increasing age, Dalit ethnicity, remoteness to the health facility, and being concerned or overwhelmed with the COVID-19 were associated with greater fear of COVID-19. In contrast, preexisting health conditions were inversely associated with fear.

Conclusion

Greater fear of the COVID-19 among the older adults in eastern Nepal suggests that during unprecedented times such as the current pandemic, the psychological needs of older adults should be prioritized. Establishing and integrating community-level mental health support as a part of the COVID-19 preparedness and response plan might help to combat COVID-19 fear among them.

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An interactive web-based dashboard to track COVID-19 in real time

Ensheng Dong, Hongru Du, Lauren Gardner (2020)

In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.

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Using social and behavioural science to support COVID-19 pandemic response

Jay Bavel, Katherine Baicker, Paulo S Boggio … (2020)

The COVID-19 pandemic represents a massive global health crisis. Because the crisis requires large-scale behaviour change and places significant psychological burdens on individuals, insights from the social and behavioural sciences can be used to help align human behaviour with the recommendations of epidemiologists and public health experts. Here we discuss evidence from a selection of research topics relevant to pandemics, including work on navigating threats, social and cultural influences on behaviour, science communication, moral decision-making, leadership, and stress and coping. In each section, we note the nature and quality of prior research, including uncertainty and unsettled issues. We identify several insights for effective response to the COVID-19 pandemic and highlight important gaps researchers should move quickly to fill in the coming weeks and months.

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The Fear of COVID-19 Scale: Development and Initial Validation

Daniel Ahorsu, Chung-Ying Lin, Vida Imani … (2020)

Background The emergence of the COVID-19 and its consequences has led to fears, worries, and anxiety among individuals worldwide. The present study developed the Fear of COVID-19 Scale (FCV-19S) to complement the clinical efforts in preventing the spread and treating of COVID-19 cases. Methods The sample comprised 717 Iranian participants. The items of the FCV-19S were constructed based on extensive review of existing scales on fears, expert evaluations, and participant interviews. Several psychometric tests were conducted to ascertain its reliability and validity properties. Results After panel review and corrected item-total correlation testing, seven items with acceptable corrected item-total correlation (0.47 to 0.56) were retained and further confirmed by significant and strong factor loadings (0.66 to 0.74). Also, other properties evaluated using both classical test theory and Rasch model were satisfactory on the seven-item scale. More specifically, reliability values such as internal consistency (α = .82) and test–retest reliability (ICC = .72) were acceptable. Concurrent validity was supported by the Hospital Anxiety and Depression Scale (with depression, r = 0.425 and anxiety, r = 0.511) and the Perceived Vulnerability to Disease Scale (with perceived infectability, r = 0.483 and germ aversion, r = 0.459). Conclusion The Fear of COVID-19 Scale, a seven-item scale, has robust psychometric properties. It is reliable and valid in assessing fear of COVID-19 among the general population and will also be useful in allaying COVID-19 fears among individuals.

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Author and article information

Contributors

Uday Narayan Yadav:

ORCID: https://orcid.org/0000-0002-6626-1604

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Om Prakash Yadav: Role: ConceptualizationRole: Data curationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing

Devendra Raj Singh:

ORCID: https://orcid.org/0000-0003-1450-9476

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SoftwareRole: Writing – original draftRole: Writing – review & editing

Saruna Ghimire: Role: ConceptualizationRole: MethodologyRole: SupervisionRole: Writing – review & editing

Binod Rayamajhee: Role: ConceptualizationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing

Sabuj Kanti Mistry: Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Lal Bahadur Rawal: Role: ConceptualizationRole: Data curationRole: MethodologyRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – review & editing

ARM Mehrab Ali: Role: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Man Kumar Tamang: Role: ConceptualizationRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing

Suresh Mehta: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing

Masamine Jimba: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS One

Journal ID (publisher-id): plos

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 26 July 2021

Publication date Collection: 2021

Publication date PMC-release: 26 July 2021

Volume: 16

Issue: 7

Electronic Location Identifier: e0254825

Affiliations

[1 ] Centre for Primary Health Care and Equity, UNSW, Sydney, Australia

[2 ] School of Population Health, UNSW, Sydney, Australia

[3 ] Centre for Research, Policy and Implementation, Biratnagar, Nepal

[4 ] Torrens University, Sydney, Australia

[5 ] School of Health Medical and Social Sciences, Central Queensland University, Sydney, Australia

[6 ] Ministry of Health and Population, Kathmandu, Nepal

[7 ] Department of Public Health, Asian College for Advance Studies, Purbanchal University, Biratnagar, Nepal

[8 ] Department of Sociology and Gerontology and Scripps Gerontology Center, Miami University, Oxford, OH, United States of America

[9 ] School of Optometry and Vision Science, Faculty of Medicine and Health Sciences, UNSW, Sydney, Australia

[10 ] Department of Infection and Immunology, Kathmandu Research Institute for Biological Sciences, Lalitpur, Nepal

[11 ] BRAC University, Dhaka, Bangladesh

[12 ] Aureolin Research, Consultancy and Expertise Development Foundation, Dhaka, Bangladesh

[13 ] Queensland Brain Institute, The University of Queensland, Brisbane, Australia

University of Tokyo, JAPAN

Author notes

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: unyadav1@ 123456gmail.com , u.yadav@ 123456unsw.edu.au

Author information

Uday Narayan Yadav https://orcid.org/0000-0002-6626-1604

Devendra Raj Singh https://orcid.org/0000-0003-1450-9476

Article

Publisher ID: PONE-D-21-02030

DOI: 10.1371/journal.pone.0254825

PMC ID: 8312955

PubMed ID: 34310639

SO-VID: c5b29a49-32b1-4b67-a0e5-96e290c4dd65

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 19 January 2021

Date accepted : 4 July 2021

Page count

Figures: 2, Tables: 2, Pages: 15

Funding

The author(s) received no specific funding for this work.

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Data Availability All relevant data are within the manuscript and its Supporting Information files.

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Perceived fear of COVID-19 and its associated factors among Nepalese older adults in eastern Nepal: A cross-sectional study

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Abstract

Background

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Novel Coronavirus Disease COVID-19

Most cited references 37

An interactive web-based dashboard to track COVID-19 in real time

Using social and behavioural science to support COVID-19 pandemic response

The Fear of COVID-19 Scale: Development and Initial Validation

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