Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus
Identifieur interne : 000937 ( Main/Exploration ); précédent : 000936; suivant : 000938Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus
Auteurs : Oyelola Adegboye [Australie] ; Timor Saffary [États-Unis] ; Majeed Adegboye [Nigeria] ; Faiz Elfaki [Qatar]Source :
- Journal of Infection and Public Health [ 1876-0341 ] ; 2018.
Descripteurs français
- KwdFr :
- Adulte, Adulte d'âge moyen, Arabie saoudite (épidémiologie), Coronavirus du syndrome respiratoire du Moyen-Orient (isolement et purification), Durée du séjour, Facteurs de risque, Femelle, Flambées de maladies, Humains, Infection croisée (), Infection croisée (épidémiologie), Infections à coronavirus (), Infections à coronavirus (épidémiologie), Modèles logistiques, Mâle, Personnel de santé, Réseaux communautaires.
- MESH :
- isolement et purification : Coronavirus du syndrome respiratoire du Moyen-Orient.
- épidémiologie : Arabie saoudite, Infection croisée, Infections à coronavirus.
- Adulte, Adulte d'âge moyen, Durée du séjour, Facteurs de risque, Femelle, Flambées de maladies, Humains, Infection croisée, Infections à coronavirus, Modèles logistiques, Mâle, Personnel de santé, Réseaux communautaires.
- Wicri :
- geographic : Arabie saoudite.
English descriptors
- KwdEn :
- Adult, Community Networks, Coronavirus Infections (epidemiology), Coronavirus Infections (prevention & control), Cross Infection (epidemiology), Cross Infection (prevention & control), Disease Outbreaks, Female, Health Personnel, Humans, Length of Stay, Logistic Models, Male, Middle Aged, Middle East Respiratory Syndrome Coronavirus (isolation & purification), Risk Factors, Saudi Arabia (epidemiology).
- MESH :
- geographic , epidemiology : Saudi Arabia.
- epidemiology : Coronavirus Infections, Cross Infection.
- isolation & purification : Middle East Respiratory Syndrome Coronavirus.
- prevention & control : Coronavirus Infections, Cross Infection.
- Adult, Community Networks, Disease Outbreaks, Female, Health Personnel, Humans, Length of Stay, Logistic Models, Male, Middle Aged, Risk Factors.
Abstract
During outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly.
Network centrality metrics were used to characterize hospital-acquired Middle East Respiratory Syndrome Coronavirus (HA-MERS) outbreaks in the Kingdom of Saudi Arabia between 2012 and 2016. Covariate-adjusted multivariable logistic regression models were applied to assess the effect of individual level risk factors and network level metrics associated with increase in length of hospital stay and risk of deaths from MERS.
About 27% of MERS cases were hospital acquired during the study period. The median age of healthcare workers and hospitalized patients were 35 years and 63 years, respectively, Although HA-MERS were more connected, we found no significant difference in degree centrality metrics between HA-MERS and non-HA-MERS cases. Pre-existing medical conditions (adjusted Odds ratio (aOR) = 2.43, 95% confidence interval: (CI) [1.11–5.33]) and hospitalized patients (aOR = 29.99, 95% CI [1.80–48.65]) were the strongest risk predictors of death from MERS. The risk of death associated with 1-day increased length of stay was significantly higher for patients with comorbidities.
Our investigation also revealed that patients with an HA-MERS infection experienced a significantly longer hospital stay and were more likely to die from the disease. Healthcare worker should be reminded of their potential role as hubs for pathogens because of their proximity to and regular interaction with infected patients. On the other hand, this study has shown that while healthcare workers acted as epidemic attenuators, hospitalized patients played the role of an epidemic amplifier.
Url:
DOI: 10.1016/j.jiph.2018.12.002
PubMed: 30578142
PubMed Central: 7102844
Affiliations:
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coronavirus</title><author><name sortKey="Adegboye, Oyelola" sort="Adegboye, Oyelola" uniqKey="Adegboye O" first="Oyelola" last="Adegboye">Oyelola Adegboye</name><affiliation wicri:level="1"><nlm:aff id="aff0005">Australian Institute of Tropical Health & Medicine, James Cook University, Townsville, QLD 4811, Australia</nlm:aff><country xml:lang="fr">Australie</country><wicri:regionArea>Australian Institute of Tropical Health & Medicine, James Cook University, Townsville, QLD 4811</wicri:regionArea><wicri:noRegion>QLD 4811</wicri:noRegion></affiliation></author><author><name sortKey="Saffary, Timor" sort="Saffary, Timor" uniqKey="Saffary T" first="Timor" last="Saffary">Timor Saffary</name><affiliation wicri:level="2"><nlm:aff id="aff0010">Independent Researcher, MD 20876, USA</nlm:aff><country xml:lang="fr">États-Unis</country><wicri:regionArea>Independent Researcher, MD 20876</wicri:regionArea><placeName><region type="state">Maryland</region></placeName></affiliation></author><author><name sortKey="Adegboye, Majeed" sort="Adegboye, Majeed" uniqKey="Adegboye M" first="Majeed" last="Adegboye">Majeed Adegboye</name><affiliation wicri:level="1"><nlm:aff id="aff0015">American University of Nigeria, 640001 Yola, Nigeria</nlm:aff><country xml:lang="fr">Nigeria</country><wicri:regionArea>American University of Nigeria, 640001 Yola</wicri:regionArea><wicri:noRegion>640001 Yola</wicri:noRegion></affiliation></author><author><name sortKey="Elfaki, Faiz" sort="Elfaki, Faiz" uniqKey="Elfaki F" first="Faiz" last="Elfaki">Faiz Elfaki</name><affiliation wicri:level="1"><nlm:aff id="aff0020">Department of Mathematics, Statistics and Physics, Qatar University, 2713 Doha, Qatar</nlm:aff><country xml:lang="fr">Qatar</country><wicri:regionArea>Department of Mathematics, Statistics and Physics, Qatar University, 2713 Doha</wicri:regionArea><wicri:noRegion>2713 Doha</wicri:noRegion></affiliation></author></analytic><series><title level="j">Journal of Infection and Public Health</title><idno type="ISSN">1876-0341</idno><idno type="eISSN">1876-035X</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adult</term><term>Community Networks</term><term>Coronavirus Infections (epidemiology)</term><term>Coronavirus Infections (prevention & control)</term><term>Cross Infection (epidemiology)</term><term>Cross Infection (prevention & control)</term><term>Disease Outbreaks</term><term>Female</term><term>Health Personnel</term><term>Humans</term><term>Length of Stay</term><term>Logistic Models</term><term>Male</term><term>Middle Aged</term><term>Middle East Respiratory Syndrome Coronavirus (isolation & purification)</term><term>Risk Factors</term><term>Saudi Arabia (epidemiology)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Arabie saoudite (épidémiologie)</term><term>Coronavirus du syndrome respiratoire du Moyen-Orient (isolement et purification)</term><term>Durée du séjour</term><term>Facteurs de risque</term><term>Femelle</term><term>Flambées de maladies</term><term>Humains</term><term>Infection croisée ()</term><term>Infection croisée (épidémiologie)</term><term>Infections à coronavirus ()</term><term>Infections à coronavirus (épidémiologie)</term><term>Modèles logistiques</term><term>Mâle</term><term>Personnel de santé</term><term>Réseaux communautaires</term></keywords><keywords scheme="MESH" type="geographic" qualifier="epidemiology" xml:lang="en"><term>Saudi Arabia</term></keywords><keywords scheme="MESH" qualifier="epidemiology" xml:lang="en"><term>Coronavirus Infections</term><term>Cross Infection</term></keywords><keywords scheme="MESH" qualifier="isolation & purification" xml:lang="en"><term>Middle East Respiratory Syndrome Coronavirus</term></keywords><keywords scheme="MESH" qualifier="isolement et purification" xml:lang="fr"><term>Coronavirus du syndrome respiratoire du Moyen-Orient</term></keywords><keywords scheme="MESH" qualifier="prevention & control" xml:lang="en"><term>Coronavirus Infections</term><term>Cross Infection</term></keywords><keywords scheme="MESH" qualifier="épidémiologie" xml:lang="fr"><term>Arabie saoudite</term><term>Infection croisée</term><term>Infections à coronavirus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adult</term><term>Community Networks</term><term>Disease Outbreaks</term><term>Female</term><term>Health Personnel</term><term>Humans</term><term>Length of Stay</term><term>Logistic Models</term><term>Male</term><term>Middle Aged</term><term>Risk Factors</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adulte</term><term>Adulte d'âge moyen</term><term>Durée du séjour</term><term>Facteurs de risque</term><term>Femelle</term><term>Flambées de maladies</term><term>Humains</term><term>Infection croisée</term><term>Infections à coronavirus</term><term>Modèles logistiques</term><term>Mâle</term><term>Personnel de santé</term><term>Réseaux communautaires</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Arabie saoudite</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>During outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly.</p></sec><sec><title>Methods</title><p>Network centrality metrics were used to characterize hospital-acquired Middle East Respiratory Syndrome Coronavirus (HA-MERS) outbreaks in the Kingdom of Saudi Arabia between 2012 and 2016. Covariate-adjusted multivariable logistic regression models were applied to assess the effect of individual level risk factors and network level metrics associated with increase in length of hospital stay and risk of deaths from MERS.</p></sec><sec><title>Results</title><p>About 27% of MERS cases were hospital acquired during the study period. The median age of healthcare workers and hospitalized patients were 35 years and 63 years, respectively, Although HA-MERS were more connected, we found no significant difference in degree centrality metrics between HA-MERS and non-HA-MERS cases. Pre-existing medical conditions (adjusted Odds ratio (aOR) = 2.43, 95% confidence interval: (CI) [1.11–5.33]) and hospitalized patients (aOR = 29.99, 95% CI [1.80–48.65]) were the strongest risk predictors of death from MERS. The risk of death associated with 1-day increased length of stay was significantly higher for patients with comorbidities.</p></sec><sec><title>Conclusion</title><p>Our investigation also revealed that patients with an HA-MERS infection experienced a significantly longer hospital stay and were more likely to die from the disease. Healthcare worker should be reminded of their potential role as hubs for pathogens because of their proximity to and regular interaction with infected patients. On the other hand, this study has shown that while healthcare workers acted as epidemic attenuators, hospitalized patients played the role of an epidemic amplifier.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct><analytic><author><name sortKey="Chowell, G" uniqKey="Chowell G">G. Chowell</name></author><author><name sortKey="Abdirizak, F" uniqKey="Abdirizak F">F. Abdirizak</name></author><author><name sortKey="Lee, S" uniqKey="Lee S">S. 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Dick</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Christley, R M" uniqKey="Christley R">R.M. Christley</name></author><author><name sortKey="Pinchbeck, G" uniqKey="Pinchbeck G">G. Pinchbeck</name></author><author><name sortKey="Bowers, R" uniqKey="Bowers R">R. Bowers</name></author><author><name sortKey="Clancy, D" uniqKey="Clancy D">D. Clancy</name></author><author><name sortKey="French, N" uniqKey="French N">N. French</name></author><author><name sortKey="Bennett, R" uniqKey="Bennett R">R. Bennett</name></author></analytic></biblStruct></listBibl></div1></back></TEI><affiliations><list><country><li>Australie</li><li>Nigeria</li><li>Qatar</li><li>États-Unis</li></country><region><li>Maryland</li></region></list><tree><country name="Australie"><noRegion><name sortKey="Adegboye, Oyelola" sort="Adegboye, Oyelola" uniqKey="Adegboye O" first="Oyelola" last="Adegboye">Oyelola Adegboye</name></noRegion></country><country name="États-Unis"><region name="Maryland"><name sortKey="Saffary, Timor" sort="Saffary, Timor" uniqKey="Saffary T" first="Timor" last="Saffary">Timor Saffary</name></region></country><country name="Nigeria"><noRegion><name sortKey="Adegboye, Majeed" sort="Adegboye, Majeed" uniqKey="Adegboye M" first="Majeed" last="Adegboye">Majeed Adegboye</name></noRegion></country><country name="Qatar"><noRegion><name sortKey="Elfaki, Faiz" sort="Elfaki, Faiz" uniqKey="Elfaki F" first="Faiz" last="Elfaki">Faiz Elfaki</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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