Evidence map›Paper›PMID 30578142›Full record

ArticleJournal of infection and public health

Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronavirus.

Oyelola Adegboye, Timor Saffary, Majeed Adegboye, Faiz Elfaki

Abstract read
In one paragraph

Article in Journal of infection and public health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Oyelola AdegboyeAustralian Institute of Tropical Health & Medicine, James Cook University, Townsville, QLD 4811, Australia. Electronic address: oyelola.adegboye@jcu.edu.au.
Timor SaffaryIndependent Researcher, MD 20876, USA.
Majeed AdegboyeAmerican University of Nigeria, 640001 Yola, Nigeria.
Faiz ElfakiDepartment of Mathematics, Statistics and Physics, Qatar University, 2713 Doha, Qatar.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring outbreaks of infectious diseases, transmission of the pathogen can form networks of infected individuals connected either directly or indirectly.

methodsNetwork 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.

resultsAbout 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.

conclusionOur 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.

Indexed as

Disease OutbreaksAdultCommunity NetworksCoronavirus InfectionsCross InfectionFemaleHealth PersonnelHumansLength of StayLogistic ModelsMaleMiddle AgedMiddle East Respiratory Syndrome CoronavirusRisk FactorsSaudi ArabiaHealthcare workersHospital-acquired infectionsMERSNetwork analysis

Identifiers

PMID30578142
PMCPMC7102844

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.