2018 Individual and network characteristic associated with hospital-acquired Middle East Respiratory Syndrome coronaviru

2018·1.15 MB·English

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ContentslistsavailableatScienceDirect
Journal of Infection and Public Health

journal homepage: http://www.elsevier.com/locate/jiph
Individual and network characteristic associated with

hospital-acquired Middle East Respiratory Syndrome coronavirus

OyelolaAdegboyea,∗,TimorSaffaryb,MajeedAdegboyec,FaizElfakid

aAustralianInstituteofTropicalHealth&Medicine,JamesCookUniversity,Townsville,QLD4811,Australia
bIndepende ntResear ch er,MD2 0876,U SA
cAmericanUn iversityofN iger ia,6400 01Yola,Nigeria
dDepartme ntofMath em atics,St atisticsa ndPh ysics,QatarUniversity,2713Doha,Qatar

a r t i c l e i n f o a b s t r a c t

Articlehistory: Background:Duringoutbreaksofinfectiousdiseases,transmissionofthepathogencanformnetworksof
Receiv ed16July2018 infectedindi viduals connected ei therdirec tlyorindi rectly.
AReccceepivteedd i5n Dr eevcies medb eforr2m0 1381 October 2018 Methods : Network ce ntrality me trics w ere used to characterize hospital-acquired Middle East Respiratory
SyndromeCoronavirus(HA-MERS)outbreaksintheKingdomofSaudiArabiabetween2012and2016.

Covariate-adjustedmultivariablelogisticregressionmodelswereappliedtoassesstheeffectofindividual
Keywords: levelriskfactorsan dnetworklev elmetri csassociat edwith incre aseinle ng thofh osp italsta y andriskof
Hospital-acquiredinfections
deathsfromMERS.
MERS
Results:About27%ofMERScaseswerehospitalacquiredduringthestudyperiod.Themedianageof
Healthcareworkers
Network an alysis hMeEaRltShcwaerere wmoorkreercso annnde chtoesdp,iwtaelizfoeud npdatnieonstisg nwifiercea n3t5d yifefaerrse nacned i6n3d yeegarrese, creesnptreacltiitvyelmy,e Atrlitchsobuegthw HeeAn-

HA-MERSandnon-HA-MERScases.Pre-existingmedicalconditions(adjustedOddsratio(aOR)=2.43,

95%confidenceinterval:(CI)[1.11–5.33])andhospitalizedpatients(aOR=29.99,95%CI[1.80–48.65])

werethestrongestriskpredictorsofdeathfromMERS.Theriskofdeathassociatedwith1-dayincreased

lengthofstaywassignificantlyhigherforpatientswithcomorbidities.

Conclusion:OurinvestigationalsorevealedthatpatientswithanHA-MERSinfectionexperiencedasig-

nificantlylongerhospitalstayandweremorelikelytodiefromthedisease.Healthcareworkershouldbe

remindedoftheirpotentialroleashubsforpathogensbecauseoftheirproximitytoandregularinterac-

tionwithinfectedpatients.Ontheotherhand,thisstudyhasshownthatwhilehealthcareworkersacted

asepidemicattenuators,hospitalizedpatientsplayedtheroleofanepidemicamplifier.

©2018 TheAuthors.PublishedbyElsevierLimitedonbehalfofKingSaudBinAbdulazizUniversity

forHealthSciences.ThisisanopenaccessarticleundertheCCBY-NC-NDlicense(http://

creativecommons.org/licenses/by-nc-nd/4.0/).
Introduction suchenvironmentstheformationoflargeclustersofinfectionsas

observedduringtheoutbreakintheKingdomofSaudiArabia(KSA)

MiddleEastRespiratorySyndromeCoronavirus(MERS)istrans- [4]andSouthKorea(SK)[5].Clustersizeofhuman-to-humantrans-

mittedviainteractionsamongindividuals.Thedangerofinfection missionsofMERShasbeenshowntovaryandahighvariabilityand

ishighestforgroupsofindividualslivingincloseproximity.From heterogeneityinthetransmissionpotentialhavebeenunderscored

theintermittenttransmissionthatoccurredinanimal-to-human, [6,7].

many human-to-human cases of MERS have also been docu- The first case of the Middle East Respiratory Syndrome Coro-

mentedwithinfamilyandhealthcarefacilities[1–3].Transmission navirus (MERS-CoV) was reported in 2012. By February 2018, a

ofMERSpathogencanformnetworksofinfectedindividualsthat totalof2182laboratory-confirmedMERS-CoVinfectionshadbeen

wereconnectedeitherdirectlyorindirectly.Oneshouldexpectin reported to the World Health Organization (WHO) [8]. The dis-

easehasnowspreadtoover27countrieswithmostindexpatients

eitherresidingorrecentlytravelingtoareasneighboringtheAra-

bianPeninsula[9,10].Similarly,thevastmajorityofthetotalcases

∗ Correspondingauthor. (82%) occurred in KSA [8]. The global mortality rate was highest

E-mailaddress:[email protected](O.Adegboye).

https://doi.org/10.1016/j.jiph.2018.12.002
1876-0341/©2018 TheAuthors.PublishedbyElsevierLimitedonbehalfofKingSaudBinAbdulazizUniversityforHealthSciences.Thisisanopenaccessarticleunderthe

CCBY-NC-NDlicense(http://creativecommons.org/licenses/by-nc-nd/4.0/).

Pleasecitethisarticleinpressas:AdegboyeO,etal.Individualandnetworkcharacteristicassociatedwithhospital-acquiredMiddle

EastRespiratorySyndromecoronavirus.JInfectPublicHealth(2018),https://doi.org/10.1016/j.jiph.2018.12.002
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(58%)atthebeginningoftheepidemics(September2012–February dateofonsetofdisease(ordatereportedwheneverdateofonset

2013)anditdroppedcontinuouslytoanabsolutelowof23%during wasnotavailable)anddateofdeath/discharged.

September2015–February2016.AsofFebruary2018,theseinfec-

tionshasledto779documenteddeaths(amortalityrateof36%)

[8].Peoplewithintheage-group50–59yearsareatthehighestrisk Studypopulationanddefinitions

ofbeinginfectedasprimarycasesandhavethehighestmortality

rate[8].Forty-fivedaysurvivalratewaslowestinpatientsolder The study population consisted of patients with confirmed

than65years(44.86%)[11].Also,healthcareworkers(HCW)are MERS infection. The cases were confirmed via real-time RNA-

regularlyexposedtoMERSduetotheirregularcontactswithMERS positive using Reverse transcription polymerase chain reaction

patientsandareatgreaterriskofbeinginfected;however,theyare (RT-PCR)showingpositivePCRonatleasttwospecificgenomictar-

lesslikelytodieofthedisease[10,12–14]. getsupstreamEprotein(upE)andORF1aorasinglepositivetarget

Strong links between healthcare facilities and the spread of (upE)withsequencingofasecondtarget(RdRpSeqassay)orNgene

the MERS disease have been found in KSA, where the major- (NSeqassay)[25].Overall,787patientswithknowncontacthistory

ity of patients were in contact with other patients at healthcare toidentifytheplaceofexposurewhichwasclassifiedasHA-MERS

facilities[15–18].Unfortunately,thisphenomenoniswidespread ornonHA-MERSwereincludedinthisstudy.AMERSinfectionis

andwell-knownasnosocomialinfections(hospital-acquiredinfec- describedashospitalacquired(HA-MERS)ifthepatienthascontact

tions)whichoccurfrequentlywithsurgical-siteinfections(SSIs), with confirmed patients (alive or deceased) or healthcare work-

pneumoniaandgastrointestinalinfectionsamongthetophospital- ers,orhealthcarefacilitieswhichhadMERS-CoVoutbreakwhile

acquiredinfections(HAIs)[19,20]. nonHA-MERSwerethoseacquiredelsewheresuchascommunity,

There has been a number of documented outbreaks of MERS household/family[26].

infection within clusters of healthcare facilities among hospital-

izedpatientsandhealthcareworker[15,17,18].In2015,casesof

MERSwerereportedinSKwhentheindexpatientreturnedfrom Statisticalanalysis

histriptotheArabianPeninsulawherehehadcontractedMERS

[18]. The disease spread out across various cities in SK within Thedatawasanalysedinthreestages.First,descriptivestatistics

twomonths,expandingfromoneto17hospitalsandinfectinga were presented as medians and interquartile range for contin-

total of 186 people. Similarly, a major MERS outbreak was reg- uous variables, and frequencies and percentages for categorical

isteredatatertiary-carehospitalinRiyadhin2015[4,17,21,22]. variables. Odds ratios (OR) together with their 95% confidence

The escalation of the Riyadh outbreak was linked to extended intervalwerealsousedforcategoricalvariables.Thechi-squaretest

healthcare-related human-to-human transmissions [4,17,21,22]. wasusedtocomparepatient’sattributes(categoricalvariables)for

Theseoutbreakswereattributedtofewindexcasesandthelevel thoseinfectionsacquiredinthehospitalandthoseacquiredelse-

oftheirspreadingdependedoninteractionsbetweenindividuals. whereinthecommunitywhiletheMann-WhitneyU-testwasused

Forexample,82outofthe186infectedpatientsinSKweretraced tocomparecontinuousattributes(continuousvariables).

backtooneindexpatientaloneduetotheovercrowdedemergency In the second stage, the unit of analysis for the networked

roomwithpatients,visitorsandhealthcareworker[23]. datawerethenodesrepresentingindividualsinfectedwithMERS.

This study focused on cases of hospital-acquired MERS (HA- In network analysis, the nodes (individual patients) have distin-

MERS)inSaudiArabia.Theobjectivesofthisstudyweretoexplore guishableattributessuchasage,gender,etc.,whileinteractionsor

the structure of transmission networks formed by these out- relationshipsbetweennodesarecallededgesorlinks[27].Anet-

breaksinordertodescribeitsroutesandtherelationshipbetween workcanbedefinedasacollectionofnodesconnectedbyedges

patients’characteristicsandthediseasenetworkmetrics.Specif- wherenodesand/oredgeshaveattributes[28].Eachpatient(node)

ically, we will investigate the effects of place of exposure in the wasassignedauniqueidentificationnumberandhis/hercontact

transmissionmechanismsofMERS,whetheroutbreaksinthehos- historywastrackedwithin14daysoftheonsetofthedisease.MERS

pital vs. outbreaks elsewhere in the community have significant patients who were in contact with other laboratory-confirmed

differencesinthelengthofhospitalstay(LOS).Similarly,weesti- MERS patients were identified and a list of each patient-contact

mate the risk of death associated with MERS diseases between pair(dyad)wasprepared.Adyadisalinkedpairofpatients(nodes)

HA-MERSandnonHA-MERS. inthenetworkthatisthefundamentalunitforderivingnetwork

metrics. The outbreak network visualization and network analy-

siswereconductedinUCINET6.0Version1.00[29].Thefollowing

Materialsandmethods centralitymetricswereusedtomeasurethestructuralimportance

ofpatients(nodes)inanetwork.“Degreecentrality”wasusedto

Datasource revealthemostactivenodesinthenetworkandhowwellanode

isconnectedwithitsneighbours—anodedegreeisthenumberof

Thedataforthisstudywasbasedonlaboratoryconfirmedand edgeincidentsonanode;the“betweennesscentrality”wasused

probable cases of MERS-CoV infection in the KSA between 2012 tomeasurehowmanypairsofnodesanodecanbeconnectedto

and 2016 from various sources such as WHO bulletins, media throughashortestpathwhile“eigenvectorcentrality”wasusedto

reportsandKingdomofSaudiArabiaMinistryofHealth(MoH),and measuretheimportanceofanodedependingontheimportanceof

obtainedfromthecase-by-caselistcompiledandmaintainedbyDr. itsneighbours[27,29].

AndrewRambaut[24].Thedatasetswerealsoassessedforaccuracy In the final analysis, a covariate-adjusted multivariable logis-

withthosereportedbyFluTrackers,KSAMoHandWHO.Thedata tic regression model was used to assess the effects of individual

contains information on patient demographics, clinical outcome, levelriskfactorsandnetworklevelmetrics(patientsnestedwithin

whetherthepatientwasahealthcareworker(HCW),comorbidity networks)onriskofdeathsfromMERSbetweenHA-MERSandnon-

statusofthepatient,andplaceofexposuretoknownriskfactors. HA-MERSpatients.Similarly,weusedageneralizedlinearmodel

Weusedthefollowingapproachestoestimatelengthofhospital toidentifydisease-riskfactorsassociatedwiththeincreaseinthe

stay(LOS):(1)werestrictedouranalysistothosepatientswhoare lengthofstay(LOS)betweenHA-MERSandnon-HA-MERSpatients.

stillaliveandthosethatdiedwithin60daysforshort-timeriskof Weusedstepwiseselectiontoselectthevariablesforinclusion

deathanalysis(2)LOSwascalculatedasthedifferencebetweenthe intheregressionmodels.Allstatisticalanalyseswereconductedin

Pleasecitethisarticleinpressas:AdegboyeO,etal.Individualandnetworkcharacteristicassociatedwithhospital-acquiredMiddle

EastRespiratorySyndromecoronavirus.JInfectPublicHealth(2018),https://doi.org/10.1016/j.jiph.2018.12.002
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Table1

CharacteristicsofHAI-MERSandnon-HAI-MERScasesinSaudiArabiabetweenJune2012andSeptember2016.Numberofcases(%)ormedian(IQR).

Variables Hospital-acquired Acquiredelsewhere p-value Odds-ratio(95%C.I.)

N=378 N=409

Age 47(33–64) 46(31–60) <0.0860 1.01(1.00,1.02)
Leng thofstaya 19 (13–29) 14 (10–19) <0.0001 1.02 (1.00, 1.03)

Gender
Male 200(52.9%) 287(70.2%) <0.0001 2.04(1.53,2.74)

Female 176(46.6%) 121(29.6%) Ref

Unknown 2(0.5%) 1(0.2%)

Comorbidity
Presence 225(69.5%) 204(48.8%) <0.0001 1.88(1.43,2.48)

Absence 94(24.9%) 77(18.8%) Ref

Unknown 59(15.6%) 128(31.3%)

Outcome
Fatal 125(33.1%) 100(24.4%) <0.0001 1.53(1.12–2.08)

Non-fatal 253(66.9%) 309(75.6%) Ref

Healthcareworker(HCW) 166(43.9%)

a Thelengthofstaywascalculatedasdifferencebetween:dateofonsetofsymptomsanddatedischargedordateofdeath.

Table2

Descriptivesummaries(andunadjustedoddsratio)forcasesofhospitalacquiredMERSinfectionamongdifferentgroups.Numberofcases(%)ormedian(IQR).

Riskfactors Placeofinfection Total(N=378)

Healthcareworkers Hospitalizedpatient Hospitalvisitor Fatal Non-fatal Odds-ratio(95%C.I.)

N=166(43.9%) N=194(51.3%) N=18(4.8%) N=125(33.1%) N=253(66.9%)

Age 35(28–44) 63(51–75) 44(38–60) 68(54–77) 39(30–54.5) 1.06(1.05,1.08)

Lengthofstay 16(12–25) 39(17.75–7.75) 12(9–16) 18(10.3–28) 17(12.5–26) 1.02(1.00–1.03)

Gender
Male 102(61.5%) 127(65.5%) 11(61.1%) 83(66.4%) 117(46.3%) 2.26(1.46,3.56)

Female 62(37.3%) 67(34.5%) 7(39.95%) 42(33.6%) 134(53%)

Unknown 2(1.2%) 0 0 2(0.8%)

Comorbidity
Presence 32(45.8%) 182(93.8%) 11(61.11) 117(93.6%) 108(42.7%) 19.28(8.30,56.29)

Absence 76(45.7%) 11(5,67%) 7(38.9) 5(4.0%) 89(35.1%)

Unknown 58 1(0.51) 0 23(2.4%) 56(22.1%)

MortalityN(%)

Fatal 5(3%) 119(61.3%) 1(5.6%)

Non-fatal 161(97%) 75(38.7%) 17(94.4%)

OR(95%CI) Ref 31.1(22.05,48.95) 1.89(0.09,12.68)

SAS9.3SoftwareVersion6oftheSASSystemforWindows[30]and MalepatientsweremorelikelytohaveHA-MERSinfectioncom-

inferencewasat5%levelofsignificance. paredtofemales(unadjustedoddsratio(OR)=2.04,95%confidence

interval(CI),[1.53–2.74]).Therewereslightlymorepatientswith

comorbiditiesamongHA-MERS(69.5%)thannon-HA-MERS(48.8%)

Results (P-value<0.0001).Patientswithcomorbiditiesweretwicelikelyto

haveHA-MERSthanpatientswithoutcomorbidities(OR=1.88,95%

Overall 787 cases were included in this study. There were CI[1.43–2.48]).Similarly,beingahealthcareworkerandofolder

378 (48%) cases of HA-MERS infection while 409 (52%) cases age significantly increased the odds of having a HA-MERS infec-

occurredelsewhereinthecommunity,forinstancewithinhouse- tion(Table1).Patientswithlongerhospitalstaysweresignificantly

holds (Table 1). Three different HA-MERS groups were defined morelikelytohaveanHA-MERSthannon-HA-MERS(OR=1.02,95%

basedontheirtypeofexposure:(1)Healthcareworker,(2)Hospital CI[1.00–1.03]).

visitors,(3)Hospitalizedpatients.Thedemographiccharacteristics Table 2 presents the descriptive summaries of the HA-MERS

of the infected patients are presented in Table 1. Patients with cases and unadjusted odds ratio for mortality due to MERS.

HA-MERS had significantly longer stays in the hospital (Median Although those patients who died of MERS disease were signif-

(Med)LOS=19days,Interquartilerange(IQR)=13–29)compared icantly less likely to have HA-MERS infection than those with

tonon-HA-MERS(Med.LOS=14days,IQR=10–19).Onehundred non-fatal health outcome (Table 1), place of infection signifi-

and twenty-five (33.1%) of the HA-MERS cases died during their cantlyinfluencedmortalityfromMERSdiseaseamongHA-MERS

hospital stay while 100 (22.4%) of the non-HA-MERS cases died patientswithgreaterriskforhospitalizedpatients(OR=31.1,95%

during their treatment in the hospital. There was no significant CI[22.05–48.95[).

differencebetweenthemedianageofHA-MERSandthatofnon- IntheunadjustedanalysisinTable2,thelikelihoodoffatality

HA-MERS, 47 years (33.0–64) vs. 46 years (31–60) in Table 1; from MERS disease increased proportionally with age by a fac-

however,ageamonghealthcareworkers,hospitalizedpatientsand tor of 6% for every unit increase, fatal cases in male HA-MERS

hospitalvisitorsdifferedsignificantly(Table2).Theoverallcrude patients were more likely than fatal cases in female HA-MERS

fatality rate (CFR) was 32%, with significantly higher CFR in HA- patients (OR=2.26, 95% CI [1.46–3.56]). Among the 378 HA-

MERScases(33.1%)thanamongnonHA-MERS(24.4%)(Table1). MERScases,comorbiditieswererecordedin225(69.5%)casesout

Similarly, 69.5% of HA-MERS patients had comorbidities against of which 117 cases were fatal. HA-MERS patients with comor-

48.8%ofnon-HA-MERSpatients(Fig.1). bidities were at a significantly higher risk of death from MERS

Pleasecitethisarticleinpressas:AdegboyeO,etal.Individualandnetworkcharacteristicassociatedwithhospital-acquiredMiddle

EastRespiratorySyndromecoronavirus.JInfectPublicHealth(2018),https://doi.org/10.1016/j.jiph.2018.12.002
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Fig.1. DistributionofweeklynumberofMERScasesbyweekofsymptomonsetandthenumberofHA-MERSinKSA.Whereverthedateofonsetisnotavailable,thedate

hospitalizedordatethediseaseisreportedwasused(whichevercomesfirst).

disease than patients with no comorbidity (OR=19.28, 95% CI [1.80–48.65])werethestrongestriskpredictorsofmortalityfrom

[8.30–56.29]). MERS.
BetweenHA-MERSinfectionsandnonHA-MERSinfections,the

effectofone-dayincreaseinLOSonriskofdeathafteradjustingfor
Transmissionnetwork
severalpredictorsisillustratedinTable5.Wehavefoundthatwhen

agealonewasinthemodel,therewasasignificantincreaseinrisk
The network structure of HA-MERS infection is presented in
ofdeathbetweenHA-MERSandnonHA-MERS.Aftercontrolling
Fig.2togetherwiththedegreecentralitymetrics.Becausethese
forageandcomorbidities,wefoundthatriskofMERSmortality
networkcentralitymetricswerehighlycorrelated,weshalllimit
weresignificantlyhigherinpatientswithHA-MERScomparedwith
our focus to degree centrality. The network density of HA-MERS
patientswithoutHA-MERS(OR=4.41,95%CI[1.29–14.98]).
was0.019(1.9%)withanaveragedegreeof1.8contacts.Greater

degreecentralitywasassociatedwithincreasedriskofdeathfrom

MERS.Ourresultssuggestthathealthcareworkershaveonaverage
Discussion
significantly lower degree centrality scores than non-healthcare

workers.AlthoughHA-MERSweremoreconnected,wehavefound
Preventing the spread of emerging infectious diseases within
no significant difference in degree centrality between HA-MERS
healthcare settings is of utmost importance [31]. Early warning
and non HA-MERS cases. Patient’s transmission degree central-
systems and infection control mechanisms were essential for an
ity was significantly negatively correlated with age. As depicted
efficient global public health response. In 2013, Assiri et al. [2]
in Fig. 2, the larger node size represents the prioritized patients
warned that human-to-human outbreaks of MERS can occur in
(1664, 124, 1025, 133, 897, 898) based on the degree centrality
healthcare settings which could be associated with considerable
metricsbecausetheyhavethemosttiestootherpatientswithin
morbidity.Recentstudieshavedocumentedandinvestigatedthe
thenetwork.
outbreaks of MERS in hospitals [4,17,18,22,32]. This study sets

out to estimate the risk of death associated with MERS diseases

Lengthofstayandriskofdeath betweenHA-MERSandnonHA-MERS,toexplorethestructuresof

transmissionnetworksformedbyMERSpatientsandtoinvestigate

On the basis of unadjusted analysis, HA-MERS, hospitalized theeffectsofplaceofexposureontheriskofdeathsfromMERS,

patients,olderpatientsandpatientswithcomorbiditieswerepos- whetherhospitaloutbreakssignificantlyincreaselengthofhospital

itively associated with length of hospital stay while being HWC stay(LOS).Similarly,wealsotestedifinfectedindividualsbecome

has a negative association. Results from further investigation of super-spreadersbecausetheywereexposedinaspecificareaor

theassociatedriskfactorsforincreasedLOSamongMERSpatients not.

aftercontrollingforotherriskfactorsrevealedthatonlypatients SeveralstudieshavereportedhospitaloutbreakofMERScases

with comorbidities significantly increased the length of hospital inKSA[2,4,17,32–34],UnitedArabEmirates[35]andSouthKorea

stay(Table3). [18,23,34]. In this study, we have identified that, about 48% of

Table4showstheestimatedriskofdeathassociatedwitheach MERSpatientswithknowncontacthistorycanbelinkedtohealth-

patient’scharacteristicsusedinthisstudy.Theadjustedanalysis care settings through person-to-person transmission and a large

indicatesthatcomorbidity,HCW,hospitalizedpatient,hospitalvis- numberofthoseinfectedwerehealthcareworkers.Theroleofthe

itor,ageandLOSweresignificantlyassociatedwithriskofmortality patient’scharacteristicswasexploredwithnetworkanalysis,since

fromMERS. thepropagationofthepathogenvariesamongpatients,visitorsand

In the model, patients with comorbidities (OR=2.43, 95% healthcareworkers[17].Somenodesmayamplifytheintensityof

CI [1.11–5.33[) and hospitalized patients (OR=29.93, 95% CI diseasetransmissionwhileothersmightattenuatethespread[36].

Pleasecitethisarticleinpressas:AdegboyeO,etal.Individualandnetworkcharacteristicassociatedwithhospital-acquiredMiddle

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Fig.2. VisualizationofMERS-CoVcasesduringtheoutbreak.Isolatedcasedwerenotincludedinthefigure.Thesizeofthenoderepresentsthedegreecentrality,increasing

nodesizesimpliesthemoreimportantthepatientis.

Table3 Althoughmostofthepatientsinthisstudyhadcomorbidity,they
Factor s associated (95% confidence limits) with length of hospital stay after the onset didnotsi gnifica nt lya mplifyth es prea dofth edi sease.Onthe con-
ofMERS(N=787).
trary,hospitalizedpatientswithcomorbidityhadahigherriskof

Riskfactors Effect 95%confidencelimits P-value spreadingthedisease.

Older patients were more likely to have a hospital-acquired
Lower Upper MERSinf ectiontha nnon -hosp ital-acq ui redM ER Sinfections.Older
Agea −0.045 −0.135 0.061 0.4241
GHeAn-MdeErR (Sm(aylees)) 6−.11.38301 −−05..612168 114.6.7922 4 00..20782758 peaesoeplaet sheeeamlt htcoa rheavfaec bileiteiens stthaatinstaictaolltyh emroprlea ceexspowsheidc htom tihgeh tdibse-
Comorbid ity(tr ue) 4.976 b 0.294 9.247 0.0306 ther esu ltofacom bination ofsen io rpeop lebeen admit tedtot he
HCW(yes) −3.322 −8.22 2 2.499 0.1844 hos pitalm or e frequentlydu et otheir advance dage andhavin g less
Hospi talize dpatients −0.258 −7.336 6.819 0.943
Hospitalvisit ors −6.933 2 −8.475 9.065 0.9474 active social interactions than younger people. This is consistent
Degreec entrality c −1.033 −3.010 1.008 0.3061 withpreviousfindingsthatthechancesofdyingfromtheMERS
Betwee nnesscentralityc 0.899 −2.833 1.904 0.6368 grew with inc reasing a ge b eyon d 25 ye ars [10]. It also con firms
Eigenvectorc entralityc 9.548 −21.116 15.64 5 0.5421 theco mmo nassumpt iont hatthed ang erof infect ion isg reaterfor

a A1-yearincreaseinage. seniorpatientsand,therefore,specialattentionneedstobepaidto
b St atistica lsignific an tat5%level. them.
c Aunitincr easeincen tra lity metrics.

Table4

Oddsratioandthe95%confidencelimitsofriskofdeathassociatedwithMERSdisease(includingpatientswhodiedduringhospitalstay)(N=787).

Riskfactors Oddsratio 95%confidencelimits P-value

Lower Upper

Gender:malevs.female 1.413 0.832 2.401 0.2317
Comorbi dityt rue vs.fals e 2.432 f 1.110 5.332 0.0068
Healthcarew orke rtr uevs .false 0.085f 0.018 0.395 <0.0001
Hospitalize dpatien tyes vs .no 29.93f 1.804 48.65 3 0.0177
Hospitalvisit orsyes vs.n o 0.095f 0.005 1.787 0.0357

HA-MERS(yesvs.no) 2.392 0.3 19.059 0.168
Agea 1.028f 1.0 13 1.044 0.0002
Lengthofstay1-dayb 0.981f 0.971 0.991 0.0002
Length of stay 7-dayc 0.873f 0.814 0.937 0.0002
Length of stay 14-dayd 0.763f 0.662 0.878 0.0002
Degree ce ntral itye 0.882 0.639 1.22 0.4488

a 1-yearincreaseinage.
b 1-dayi ncreasei nl engthofhospitalstay.
c 7-day increase in length of hospital stay.
d 14-da yincreas ein length o fhospita lstay.
e Unitinc reasein de greece nt ralityme tric.
f Stati sticalsign ifi cantat 5%level.

Pleasecitethisarticleinpressas:AdegboyeO,etal.Individualandnetworkcharacteristicassociatedwithhospital-acquiredMiddle

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Table5

Oddsratioandthe95%confidencelimitsofriskofdeathassociatedwithMERSdiseasefora1-dayincreaseinlengthofhospitalstay(N=787).

Model Riskfactors Oddsratio 95%confidencelimits

HA-MERSvs.nonHA-MERS Lower Upper

1 Adjustedforage 1.0239 1.0010 1.0475

2 Adjustedforage+comorbidity 4.4106 1.2986 14.9797

3 Model2+Healthcareworker 0.0704 0.0235 0.2114

Theriskofdeathassociatedwithincreasedlengthofstaywas tionofsecondarycasescausedbyeachprimarycase.Lastly,lackof

significantlyhigherforpatientswithcomorbiditiesandhospital- informationonhospitalspreventedusfromexploringthespread

acquiredMERSinfections.TheimpactofMERSinfectiontogether ofMERSbetweenhospitals.

withanotherdiseaseorconditionwasinvestigatedearlier.Sucha

combinationwasmuchmorelikelytobefatal[10,37].Thisresult Conclusions

isinsofarimportantasitappliestoalargeportionofthepopula-

tiongiventhefactthatmanywereaffectedbynon-communicable Duringinfectiousdiseaseoutbreaks,networksofinfectedindi-

diseasesofaffluencesuchasdiabetes,obesity,heartdiseases,etc. vidualsmaybeformeddependingonthenatureofthepathogen’s

Forinstance,morethanhalfofthepopulationofSaudiArabiawith transmission.Themechanismsofthetransmissionandthestruc-

theageofatleast50yearshasdiabetes[38]. ture of the networks need to be well-understood in order to

Ouranalysishasrevealedthatpatientswithahospital-acquired optimize preventive measures, and have reliable early warning

MERSinfectionexperiencedasignificantlylongerhospitalstayand systems as well as effective treatment methods. The outcomes

wereassociatedwithahigherriskofdeathfromthedisease.This of our research emphasize the importance of putting patients

mightbecloselylinkedtothesecondoutcome,becausethegroup withcommunicablediseases,especiallylife-threateningdiseases,

ofhospital-acquiredinfectionsincludedpatientswhohadalready immediately under quarantine and minimizing the access of

been hospitalized for other health issues. The length of hospital healthcareworkerstosuchpatients.Suchprecautionarymeasures

stayhasbeeninvestigatedfromvariousperspectivesbothmedical could be lifesaving, in particular for patients with comorbidi-

andeconomical[39–42].OurresultisinaccordancewithGlance ties and/or of senior age who need to be observed closer during

et al. [43], who showed that the length of hospital stay, associ- theirentirehospitalstay.Moreover,healthcareworkersshouldbe

atedcostsandmortalityrateofhospital-acquiredinfectionswere advised on their potential role as hubs for pathogens due to the

significantly higher for trauma patients. We tested the correla- natureoftheiroccupation.Looseadherencestopreventiveandpro-

tionofcentralitymetricswitheachotherandotherpatient’slevel tectivemeasuresbythehealthcareworkersshouldbeidentified

characteristics.Allbuttheeigenvectorandbetweennessshowed andimmediatelycorrectedinordertoavoidthenegativerolethey

significantassociation,apropertywhichmightbelessevidentfor mayplayintransmittingtheagent.

complicatednetworks[44].

Many studies indicate that healthcare workers are at greater Funding

riskofMERSinfection[10,12–14,33].However,wefoundhealth-

careworkerswhowereatthereceivingendofMERSinfectionsto Nofundingsources.
acta sepidem icat tenua tor .He althcare work er sareo ftenincom -

pliancewithriskmanagementapproachestoreduceandcontrol
Competinginterests

transmissionofMERSbywearingprotectivegearsandareaware

ofotherhygienicmeasures,toreducethedoseofinfectiousagents
Nonedeclared.

preventingfurtherspreadofthedisease.Inthesamevein,hospi-

talizedpatientsplayedtheroleofanepidemicamplifier,i.e.they
Ethicalapproval
playedmoretheroleoftransmitters.Thiscomplementsanearlier

publicationshowingthatthevastmajorityofdocumentedMERS
Notrequired.
patients had contacts with other patients in healthcare facilities

and that nosocomial infections occurred more often in outbreak
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