Demand for Health Information on COVID-19 among Vietnamese.
Identifieur interne : 002302 ( Main/Corpus ); précédent : 002301; suivant : 002303Demand for Health Information on COVID-19 among Vietnamese.
Auteurs : Huong Thi Le ; Diep Ngoc Nguyen ; Ahmed Sam Beydoun ; Xuan Thi Thanh Le ; Thao Thanh Nguyen ; Quan Thi Pham ; Nhung Thi Kim Ta ; Quynh Thi Nguyen ; Anh Ngoc Nguyen ; Men Thi Hoang ; Linh Gia Vu ; Bach Xuan Tran ; Carl A. Latkin ; Cyrus S H. Ho ; Roger C M. HoSource :
- International journal of environmental research and public health [ 1660-4601 ] ; 2020.
English descriptors
- KwdEn :
- Adult (MeSH), Betacoronavirus (pathogenicity), COVID-19 (MeSH), Consumer Health Informatics (statistics & numerical data), Coronavirus Infections (epidemiology), Coronavirus Infections (virology), Cross-Sectional Studies (MeSH), Female (MeSH), Humans (MeSH), Information Seeking Behavior (MeSH), Male (MeSH), Middle Aged (MeSH), Pandemics (MeSH), Pneumonia, Viral (epidemiology), Pneumonia, Viral (virology), SARS-CoV-2 (MeSH), Vietnam (epidemiology).
- MESH :
- geographic , epidemiology : Vietnam.
- epidemiology : Coronavirus Infections, Pneumonia, Viral.
- pathogenicity : Betacoronavirus.
- statistics & numerical data : Consumer Health Informatics.
- virology : Coronavirus Infections, Pneumonia, Viral.
- Adult, COVID-19, Cross-Sectional Studies, Female, Humans, Information Seeking Behavior, Male, Middle Aged, Pandemics, SARS-CoV-2.
Abstract
Due to the rapid spread of coronavirus, Vietnam introduced its first national partial lockdown on April 1st, 2020. The public relied on online sources, whether through official websites or phone-based applications, to acquire up-to-date health information, provide accurate instructions, and limit misinformation. This study aims to provide insight regarding the current level of awareness of the pandemic, and to identify associated factors in Vietnamese participants to recommend necessary interventions. A cross-sectional study was conducted using a web-based survey during the first week of the lockdown period. There were 341 observations collected using a snowball sampling technique. A Tobit multivariable regression model was used to identify factors associated with the demand for each category of health information. The most requested information was the latest updated news on the epidemic, followed by information about disease symptoms and updated news on the outbreak. The prevalence of diverse socioeconomic, demographic, and ethnic factors in Vietnam requires consideration of the specific health information needs of unique groups. Identifying group-specific demands would be helpful to provide proper information to fulfill each population group's needs.
DOI: 10.3390/ijerph17124377
PubMed: 32570819
PubMed Central: PMC7344690
Links to Exploration step
pubmed:32570819Le document en format XML
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<series><title level="j">International journal of environmental research and public health</title>
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<term>Betacoronavirus (pathogenicity)</term>
<term>COVID-19 (MeSH)</term>
<term>Consumer Health Informatics (statistics & numerical data)</term>
<term>Coronavirus Infections (epidemiology)</term>
<term>Coronavirus Infections (virology)</term>
<term>Cross-Sectional Studies (MeSH)</term>
<term>Female (MeSH)</term>
<term>Humans (MeSH)</term>
<term>Information Seeking Behavior (MeSH)</term>
<term>Male (MeSH)</term>
<term>Middle Aged (MeSH)</term>
<term>Pandemics (MeSH)</term>
<term>Pneumonia, Viral (epidemiology)</term>
<term>Pneumonia, Viral (virology)</term>
<term>SARS-CoV-2 (MeSH)</term>
<term>Vietnam (epidemiology)</term>
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<keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Vietnam</term>
</keywords>
<keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term>
<term>Pneumonia, Viral</term>
</keywords>
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<term>Pneumonia, Viral</term>
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<term>Humans</term>
<term>Information Seeking Behavior</term>
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<front><div type="abstract" xml:lang="en">Due to the rapid spread of coronavirus, Vietnam introduced its first national partial lockdown on April 1st, 2020. The public relied on online sources, whether through official websites or phone-based applications, to acquire up-to-date health information, provide accurate instructions, and limit misinformation. This study aims to provide insight regarding the current level of awareness of the pandemic, and to identify associated factors in Vietnamese participants to recommend necessary interventions. A cross-sectional study was conducted using a web-based survey during the first week of the lockdown period. There were 341 observations collected using a snowball sampling technique. A Tobit multivariable regression model was used to identify factors associated with the demand for each category of health information. The most requested information was the latest updated news on the epidemic, followed by information about disease symptoms and updated news on the outbreak. The prevalence of diverse socioeconomic, demographic, and ethnic factors in Vietnam requires consideration of the specific health information needs of unique groups. Identifying group-specific demands would be helpful to provide proper information to fulfill each population group's needs.</div>
</front>
</TEI>
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<DateCompleted><Year>2020</Year>
<Month>07</Month>
<Day>23</Day>
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<Day>18</Day>
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<Title>International journal of environmental research and public health</Title>
<ISOAbbreviation>Int J Environ Res Public Health</ISOAbbreviation>
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<ArticleTitle>Demand for Health Information on COVID-19 among Vietnamese.</ArticleTitle>
<ELocationID EIdType="pii" ValidYN="Y">E4377</ELocationID>
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<Abstract><AbstractText>Due to the rapid spread of coronavirus, Vietnam introduced its first national partial lockdown on April 1st, 2020. The public relied on online sources, whether through official websites or phone-based applications, to acquire up-to-date health information, provide accurate instructions, and limit misinformation. This study aims to provide insight regarding the current level of awareness of the pandemic, and to identify associated factors in Vietnamese participants to recommend necessary interventions. A cross-sectional study was conducted using a web-based survey during the first week of the lockdown period. There were 341 observations collected using a snowball sampling technique. A Tobit multivariable regression model was used to identify factors associated with the demand for each category of health information. The most requested information was the latest updated news on the epidemic, followed by information about disease symptoms and updated news on the outbreak. The prevalence of diverse socioeconomic, demographic, and ethnic factors in Vietnam requires consideration of the specific health information needs of unique groups. Identifying group-specific demands would be helpful to provide proper information to fulfill each population group's needs.</AbstractText>
</Abstract>
<AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Le</LastName>
<ForeName>Huong Thi</ForeName>
<Initials>HT</Initials>
<AffiliationInfo><Affiliation>Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Nguyen</LastName>
<ForeName>Diep Ngoc</ForeName>
<Initials>DN</Initials>
<AffiliationInfo><Affiliation>Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Faculty of Pharmacy, Duy Tan University, Da Nang 550000, Vietnam.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Beydoun</LastName>
<ForeName>Ahmed Sam</ForeName>
<Initials>AS</Initials>
<AffiliationInfo><Affiliation>College of Medicine, University of Cincinnati, Cincinnati, OH 45221, USA.</Affiliation>
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<Author ValidYN="Y"><LastName>Le</LastName>
<ForeName>Xuan Thi Thanh</ForeName>
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</AffiliationInfo>
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<ForeName>Thao Thanh</ForeName>
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<AffiliationInfo><Affiliation>Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.</Affiliation>
</AffiliationInfo>
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<ForeName>Quan Thi</ForeName>
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<AffiliationInfo><Affiliation>Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Ta</LastName>
<ForeName>Nhung Thi Kim</ForeName>
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</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Hoang</LastName>
<ForeName>Men Thi</ForeName>
<Initials>MT</Initials>
<Identifier Source="ORCID">0000-0001-8187-8277</Identifier>
<AffiliationInfo><Affiliation>Institute for Global Health Innovations, Duy Tan University, Da Nang 550000, Vietnam.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Faculty of Pharmacy, Duy Tan University, Da Nang 550000, Vietnam.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Vu</LastName>
<ForeName>Linh Gia</ForeName>
<Initials>LG</Initials>
<AffiliationInfo><Affiliation>Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Tran</LastName>
<ForeName>Bach Xuan</ForeName>
<Initials>BX</Initials>
<AffiliationInfo><Affiliation>Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi 100000, Vietnam.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.</Affiliation>
</AffiliationInfo>
</Author>
<Author ValidYN="Y"><LastName>Latkin</LastName>
<ForeName>Carl A</ForeName>
<Initials>CA</Initials>
<Identifier Source="ORCID">0000-0002-7931-2116</Identifier>
<AffiliationInfo><Affiliation>Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Ho</LastName>
<ForeName>Cyrus S H</ForeName>
<Initials>CSH</Initials>
<AffiliationInfo><Affiliation>Department of Psychological Medicine, National University Hospital, Singapore 119074, Singapore.</Affiliation>
</AffiliationInfo>
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<Author ValidYN="Y"><LastName>Ho</LastName>
<ForeName>Roger C M</ForeName>
<Initials>RCM</Initials>
<Identifier Source="ORCID">0000-0001-9629-4493</Identifier>
<AffiliationInfo><Affiliation>Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore.</Affiliation>
</AffiliationInfo>
<AffiliationInfo><Affiliation>Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore 119077, Singapore.</Affiliation>
</AffiliationInfo>
</Author>
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<Language>eng</Language>
<GrantList CompleteYN="Y"><Grant><GrantID>VINIF.2020.COVID-19.DA03</GrantID>
<Agency>Vingroup Innovation Foundation (VINIF)</Agency>
<Country>International</Country>
</Grant>
</GrantList>
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<PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType>
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<ArticleDate DateType="Electronic"><Year>2020</Year>
<Month>06</Month>
<Day>18</Day>
</ArticleDate>
</Article>
<MedlineJournalInfo><Country>Switzerland</Country>
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<NlmUniqueID>101238455</NlmUniqueID>
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<CitationSubset>IM</CitationSubset>
<MeshHeadingList><MeshHeading><DescriptorName UI="D000328" MajorTopicYN="N">Adult</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000073640" MajorTopicYN="N">Betacoronavirus</DescriptorName>
<QualifierName UI="Q000472" MajorTopicYN="N">pathogenicity</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000086382" MajorTopicYN="N">COVID-19</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D000074522" MajorTopicYN="N">Consumer Health Informatics</DescriptorName>
<QualifierName UI="Q000706" MajorTopicYN="Y">statistics & numerical data</QualifierName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D018352" MajorTopicYN="N">Coronavirus Infections</DescriptorName>
<QualifierName UI="Q000453" MajorTopicYN="Y">epidemiology</QualifierName>
<QualifierName UI="Q000821" MajorTopicYN="N">virology</QualifierName>
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<MeshHeading><DescriptorName UI="D003430" MajorTopicYN="N">Cross-Sectional Studies</DescriptorName>
</MeshHeading>
<MeshHeading><DescriptorName UI="D005260" MajorTopicYN="N">Female</DescriptorName>
</MeshHeading>
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