A Bayesian micro simulation to evaluate the cost effectiveness of interventions for mastitis control during the dry period in UK dairy herds

ContentslistsavailableatScienceDirect

Preventive

Veterinary

Medicine

j ou rn a l h om ep a ge :w w w . e l s e v i e r . c o m / l o c a t e / p r e v e t m e d

A

Bayesian

micro-simulation

to

evaluate

the

cost-effectiveness

of

interventions

for

mastitis

control

during

the

dry

period

in

UK

dairy

herds

P.M.

Down

a,∗

_,

_A.J.

_Bradley

a,b

_,

_J.E.

_Breen

a,b

_,

_W.J.

_Browne

c

_,

_T.

_Kypraios

d

_,

_M.J.

_Green

a

a_{UniversityofNottingham,SchoolofVeterinaryMedicineandScience,SuttonBoningtonCampus,LoughboroughLE125RD,UnitedKingdom} b_{QualityMilkManagementServicesLtd,CedarBarn,EastonHill,Easton,WellsBA51DU,UnitedKingdom}

c_{GraduateSchoolofEducationandCentreforMultilevelmodelling,UniversityofBristol,35BerkeleySquare,BristolBS81JA,UnitedKingdom} d_{UniversityofNottingham,SchoolofMathematicalSciences,UniversityPark,NottinghamNG72RD,UnitedKingdom}

a

r

t

i

c

l

e

i

n

f

o

Articlehistory:

Received8February2016 Receivedinrevisedform 10September2016 Accepted13September2016

Keywords:

Dairycow Mastitiscontrol Bayesian Cost-effectiveness Decisionmaking Dry-period

a

b

s

t

r

a

c

t

Importanceofthedryperiodwithrespecttomastitiscontrolisnowwellestablishedalthoughthe pre-ciseinterventionsthatreducetheriskofacquiringintramammaryinfectionsduringthistimearenot clearlyunderstood.Thereareveryfewinterventionstudiesthathavemeasuredtheclinicalefficacyof specificmastitisinterventionswithinacost-effectivenessframeworksothereremainsalargedegreeof uncertaintyabouttheimpactofaspecificinterventionanditscosteffectiveness.Theaimofthisstudy wastouseaBayesianframeworktoinvestigatethecost-effectivenessofmastitiscontrolsduringthe dryperiod.Datawereassimilatedfrom77UKdairyfarmsthatparticipatedinaBritishnationalmastitis controlprogrammeduring2009–2012inwhichthemajorityofintramammaryinfectionswereacquired duringthedryperiod.Thedataconsistedofclinicalmastitis(CM)andsomaticcellcount(SCC)records, herdmanagementpracticesanddetailsofinterventionsthatwereimplementedbythefarmeraspartof thecontrolplan.Theoutcomesusedtomeasuretheeffectivenessoftheinterventionswerei)changes intheincidencerateofclinicalmastitisduringthefirst30daysaftercalvingandii)therateatwhich cowsgainednewinfectionsduringthedryperiod(measuredbySCCchangesacrossthedryperiodfrom <200,000cells/mlto>200,000cells/ml).ABayesianone-stepmicrosimulationmodelwasconstructed suchthatposteriorpredictionsfromthemodelincorporateduncertaintyinallparameters.The incre-mentalnetbenefitwascalculatedacross10,000MarkovchainMonteCarloiterations,toestimatethe cost-benefit(andassociateduncertainty)ofeachmastitisintervention.Interventionsidentifiedasbeing cost-effectiveinmostcircumstancesincludedselectingdry-cowtherapyatthecowlevel,dry-cowrations formulatedbyaqualifiednutritionist,useofindividualcalvingpens,firstmilkingcowswithin24hof calvingandspreadingbeddingevenlyindry-cowyards.Theresultsofthisstudyhighlightedtheefficacy ofspecificmastitisinterventionsinUKconditionswhich,whenincorporatedintoacosteffectiveness framework,canbeusedtooptimizedecisionmakinginmastitiscontrol.Thisinterventionstudy pro-videsanexampleofhowanintuitiveandclinicallyusefulBayesianapproachcanbeusedtoformthe basisofanon-farmdecisionsupporttool.

©2016PublishedbyElsevierB.V.

1. Introduction

Mastitisremainsoneofthemostcostlyendemicdiseasestothe dairyindustryworldwideintermsofproduction,animalwelfare andpotentialriskstopublichealth(Bradley,2002;

Hagnestam-NielsenandOstergaard,2009;Halasa,2012).Theimportanceofthe

∗Correspondingauthor.

E-mailaddress:[email protected](P.M.Down).

dryperiodwithrespecttomastitiscontrolisnowwellestablished

(BradleyandGreen,2000,2004;Greenetal.,2002;Bradleyetal.,

2015)howeverthepreciseinterventionsthatreducetheriskof acquiringintramammaryinfections(IMI)duringthistimearenot clearlyunderstood.

Thereexists a vast body of literature reporting associations betweenvariousmanagementpracticesanddifferentmeasuresof udderhealthe.g.Dufouretal.(2011).Apotentiallimitationwith riskfactorstudiesisthattheycannotalwaysprovideevidenceof causationandsothereremains alargedegreeofuncertaintyas

http://dx.doi.org/10.1016/j.prevetmed.2016.09.012

tothelikelyimpactthataspecificinterventionhasandtherefore itsoverallcost-effectiveness.Interventionstudiescanprovide evi-denceofcausation(Rubin,2007;Martin,2013),buttherearevery fewinterventionstudiesthathavesoughttomeasuretheefficacyof specificmastitiscontrolinterventionswithinacost-effectiveness framework (Green etal.,2010).Furthermore,uncertaintyabout theclinicalandfinancialbenefitofanintervention,willaffectthe decisiontoimplementit(Greenetal.,2010;Huijpsetal.,2010). Ifpotentialinterventionsare tobeprioritisedin a rationaland evidence-basedway,cost-benefitanalysesarerequiredthat cap-turetheuncertaintyoftheefficacyofinterventions.

Withlimitedresourcesavailabletoa commercialdairyfarm, itisimportantthatpotentialmastitisinterventionsareprioritised notonlyaccordingtotheirefficacy,butalsoonthelikelyreturn oninvestment.The efficientuseofavailable resourcesrequires anunderstandingoftheopportunitycostswherebyresourcesare allocatedtofundoneinterventionattheexpenseofthepotential ‘benefits’affordedbyanalternativeintervention. Itisnecessary whendecidingwhethertoemployresourcesinoneareatobeable tocomparetheprobabilityofanetbenefitinthat areawithall otherpotentialareaswhere thoseresourcescouldbeemployed

(BriggsandGray,1999).Thisisthedilemmafacedbyveterinary

decisionmakers,and withmany possiblemastitisinterventions makingclaimsonfarmresources,thisdecisionisrarelyintuitive.

Bayesianmethodsarenowwidelyadoptedbythehuman medi-calfieldfortheanalysisofcost-effectiveness(Briggs,2001;Claxton

etal.,2002;O’HaganandStevens,2002;SpiegelhalterandBest,

2003; Claxton, 2008). A key feature of such cost-effectiveness

studiesisthatBayesiananalysisofdecisionmakingunder uncer-taintyrequires modelparameters tobespecified asprobability distributions(FelliandHazen,1999).Theprobabilitydistributions representthedegreeofuncertaintysurroundingthetruevaluesof modelparameters,whichisthenpropagatedthroughthemodel

(O’Hagan,2003).Thisapproachmakesitpossibletomakegenuine

probabilitystatementsaboutthemagnitudeofsuchparameters andattachaprobabilitytoaspecifichypothesisofinterest(Gurrin etal.,2000).Onemethodtermedcomprehensivedecision mod-elling, integrates evidence synthesis and parameter estimation withprobabilisticdecisionanalysisinasingleunifiedframework

(Cooperetal.,2004;Spiegelhalteretal.,2004;Adesetal.,2006).

Theresultingposteriorprobabilitydistributionprovidesa realis-ticprobabilisticinterpretationfromwhichstatementsaboutthe probabilityofahypothesiscanbemade.

Theaimofthisstudywastoinvestigatethecost-effectiveness ofmastitiscontrolinterventionstoreduceIMIcausedby environ-mentalpathogens(opportunisticinvadersfromanenvironmental reservoir) during the dry period. An integrated Bayesian cost-effectiveness framework was used to construct a probabilistic decisionmodelthatcouldbeusedtoinformclinicaldecision mak-ing.

2. Materialsandmethods

2.1. BackgroundtotheAHDBDairyMastitisControlPlan(DMCP)

TheDMCP(Greenetal.,2007a)wasdeliveredbylocalveterinary

practitionersanddairyfarmconsultantsthroughouttheUKwho receivedtrainingbytheplansupportteamconsistingofspecialist dairyveterinarians.Wheneachdairyfarmwasenrolledontothe DMCP,acopyoftheirherdhealthandperformancedata, includ-ingclinicalmastitis(CM)incidenceandsomaticcellcount(SCC) records,wassubmittedtotheDMCPsupportteamforanalysis.The farmwassubsequentlyvisitedbytheplandelivererwhocompleted aquestionnaireconsistingof377questionscoveringallaspectsof managementrelevanttomastitiscontrolandalsoincluded

obser-vationsandmeasurementstobecollectedduringthevisit.Each of the377questions/observationswasassociated witha corre-spondinginterventione.g.Question:Howoftenarestrawyards completelycleanedout?Intervention:Strawyardsmustbe com-pletelymuckedout,atleast,every4weeks.Theanswerstothis questionnairewererecordedin abespoke softwareprogramme calledthe‘ePlan’(‘SUM-ITComputerSystemsLtd,’2009).TheDMCP supportteamanalysed theclinicaland subclinicalmastitisdata submittedbytheplan delivererwiththeaimofidentifyingthe primaryherdinfectionpatterni.e.predominantlyenvironmental orcontagious,andwhetherIMI’swerelikelytobepredominantly acquiredduringthedryperiodorlactatingperiod,asdescribed pre-viously(Bradleyetal.,2008).Eachherdwasassignedoneoffour ‘diagnoses’;environmentaldryperiod(EDP),environmental lacta-tion(EL),contagiousdryperiod(CDP)orcontagiouslactation(CL) andthiswasrecordedintheePlansoftware.OncealloftheePlan questionsandherd‘diagnosis’hadbeencompleted,theePlan soft-warewasusedtohighlightareasofsuboptimalmanagementmost relevanttothe‘diagnosis’(Greenetal.,2007a).Theplandeliverer ‘prioritised’5–10ofthesehighlightedinterventionsfordiscussion withthefarmerandsoughtagreementonwhichofthemto imple-ment.Themastitisperformancewasmonitoredonaregularbasis thereafterbyevaluatingbothclinicalandsubclinicalmastitisdata andafullanalysiscompletedaftera12monthperiod.

2.2. Datacollection

Therewere265plandeliverersatthetimeofthestudyand each wasasked tosubmit theirePlan data, which consistedof theanswerstothequestionnaire,theinterventionsprioritisedand theherd‘diagnosis’foreachofthefarmstheyhadvisited.They werealsoaskedtosubmittheherdhealthandperformancedata recorded on-farm,consisting of CM and SCCrecords,herdsize and milkproduction data,covering the12 monthsprior tothe DMCPstartdateandthefirst12monthsfromaftertheplanwas implemented.Outofthe265plandeliverers,87 plandeliverers responded.Fromthe87plandeliverersthatresponded,ePlandata werereceivedforatotalof452herdsthathadparticipatedinthe DMCPduring2009–2012.Completeherdhealthandperformance datawereavailablefor290ofthe452herdssubmitted.The87plan deliverersthathadrespondedwereaskedtospecifythe interven-tionsthatwereactuallyimplementedon-farmoverthe12months aftertheinitialherdvisit.Theplandelivererssubmittedthis infor-mationfor212outofthe290herdsforwhichcompletedatawere available.Fromthe212herdswithcompletedata,77herdswere assignedan‘EDP’diagnosisandthereforeusedinthisstudy.All of thisinformation wascollated ina MicrosoftAccessdatabase (MicrosoftCorp.,Redmond,WA).

2.3. Dataanalysis

Fig.1. Overviewofthe1-stepmicro-simulationprocedureusingtheclinical mas-titismicro-simulationmodelasanexample.IRCM30=incidencerateofclinical mastitisinthefirst30daysaftercalving.

monthlypercentageofcowsthathadaSCC<200,000cells/mlat themilkrecordingpriortodryingoff,thatwere>200,000cells/ml atthefirstmilkrecordingafterparturition(HighSCC),whichhas alsobeenshowntobeindicativeofnewdryperiodintramammary infections(Bradleyetal.,2002;Cooketal.,2002;BradleyandGreen,

2005).

Interventions that had been implemented on at least two farmswere identified and for each farm, categorisedas 0 (not already implemented at the time of the initial farm visit and notimplementedfollowingtheinterventionvisit),1(notalready implementedatthetimeoftheinitialfarmvisitbutimplemented followingtheDMCP)or2(alreadyimplementedatthetimeofthe initialfarmvisitornotapplicable).Interventionswereclassifiedas notapplicablewhentheyconcernedanareaofmanagementnot relevanttoaparticularfarm(e.g.managementofdrycowcubicles onafarmthatusedstrawyardstohousethedrycows).Collinearity betweencovariateswasassessedusingPearsonproduct-moment correlationcoefficients,andnosubstantialcollinearitywasfound (allcorrelationcoefficientswere<0.7).

ABayesianone-stepmicro-simulationmodelwasconstructed inOpenBUGSversion3.2.2(Lunnetal.,2009)separatelyforeachof thetwooutcomes,incorporatingamultipleregressionmodeland anonwardscost-effectivenessmicro-simulation,basedon meth-odsdescribedpreviously(Spiegelhalteretal.,2004).Therefore,the posteriordistributionsfromtheone-stepmicro-simulationmodel incorporateduncertaintyinallmodelparameters(Fig.1.).

Theregressionmodelsthatwereincorporatedinthefirststage ofthemicro-simulationmodelstooktheform;

Yi=ˇ0+ˇ1x1i+ˇ2x2i+ˇ3x3i+...+ˇpxpi+εii=1,...,nεi∼N(0,2ε) (1)

where Yi=the ith observation of the outcome variable, ˇ0=intercept value, xpi=the pth predictor variable for the ith herd, ˇp=the pth regression coefficient, εi=the residual error,

p=numberofpredictorvariablesandn=thenumberofherds. Theoutcomevariable(Yi)usedfortheclinicalmastitis regres-sionmodelwasthepercentagechangeintheIRCM30duringthe12 monthperiodfromimplementationoftherecommended interven-tionsandtheoutcomevariable(Yi)usedinthesomaticcellcount regressionmodelwasthepercentagechangeintheHighSCCrate duringthis12monthperiod.Bothofthesevariableswere approx-imatelynormallydistributedasdeterminedusingQ–Qplots.The influenceofanyoutlyingresidualswasassessedusingtheCook’sD value.

Clinicalmastitisregressionmodeloutcome

=IRCM30(12months)−IRCM30(initial)

IRCM30(initial) ×100

where IRCM30(12 months)=the mean IRCM30 during the

first 12 months after the mastitis control plan started and IRCM30(initial)=themeanIRCM30duringthe12monthsbefore themastitiscontrolplanstarted.

Somaticcellcountregressionmodeloutcome

=HighSCC(12_HighSCCmonths)_(initial)−HighSCC(initial)×100

where HighSCC(12 months)=the mean HighSCC during the first12monthsafterthemastitiscontrolplanstartedand High-SCC(initial)=themeanHighSCCduringthe12monthsbeforethe mastitiscontrolplanstarted.

Vague prior distributions were used for model parameters asfollows;␴2

∼Gamma(0.001,0.001),and␤∼Normal(0,106).The

predictedvaluesfromthemodelfortheoutcomevariablesforeach herdwerecomparedwiththeobserveddataanddisplayed graphi-callytoillustratemodelperformance.Fullprobabilitydistributions oftheinterventionefficacyestimatesfromtheregressionmodels werecarriedforwardintothenextstagesofthemicro-simulation model.

Thepurposeofthemicro-simulationwastosimulatethe cost-effectivenessofeachinterventionintheoreticalherdswithaherd sizeof120cows,withdifferentinitialratesofIRCM30andHighSCC anddifferentcostsassociatedwithimplementingeach interven-tion(Fig.1).ThevaluesforIRCM30andHighSCConthesimulated farmspriortointerventionsbeingimplementedweretakenfrom actualdatafrom125herdsthathadpreviouslyparticipatedinthe DMCPsothatarangeofplausiblescenarioswereused.The micro-simulationcomprisedthestepsdescribed below;eachstep was undertakenateachmodeliteration.

Step1.Theregressionmodel(1)wasusedtoobtainanestimate ofthepercentagechangeintheIRCM30aftera12monthperiod foreachinterventionforagivenherd.TheinitialIRCM30increased ordecreasedaccordingtotheestimatedpercentagechangeand thisresultedinapredictednewIRCM30foreachfarmonceithad implementedtheintervention(IRCM30PRED).

Step2.Theresultingincrease/decreaseinthenumberofcases duringa12monthperiod(CASESCMPREV)ina120-cowherdwas thensimulatedforeachinterventionindividuallybymultiplying thedifferencebetweentheinitialIRCM30(IRCM30INITIAL)andthe predictedIRCM30(IRCM30PRED)by10toconvertthedenominator fromthenumberofcasesper12cows,tothenumberofcasesper 120cows:

CASESCMPREV=(IRCM30INITIAL−IRCM30PRED)×10

multiply-Fig.2. Probabilisticcost-effectivenesscurvesforspecificinterventions.IRCM30=theincidencerateofclinicalmastitisinthefirst30daysaftercalving.HighSCC=monthly percentageofcowsthathadasomaticcellcount<200,000cells/mlatthemilkrecordingpriortodryingoff,thatwere>200,000cells/mlatthefirstmilkrecordingafter parturition.

ingthenumberofcasesprevented(CASESCMPREV)bythecostofa caseofclinicalmastitis(COSTCM):

SAVINGCM=CASESCM×COSTCM

Acost per caseof clinicalmastitiswithin 30days ofcalving wasspecifiedasafullprobabilitydistribution,COSTCM∼Normal (mean=313,sd=101),basedonastochasticsimulationstudyin

theUK(Greenetal.,2009).Acostwasselectedatrandomfromthis

distributionateachiterationandmultipliedbythenumberofcases preventedtogiveanoverallsavinginpoundssterlingassociated withtheimplementationofeachintervention.

Step4.Theincrementalnetbenefit(INB)wascalculatedateach iterationtorepresenttheoverallnetbenefitafterall‘savings’and ‘costs’hadbeenconsideredoverthe12monthperiod:

INBCM=SAVINGCM−COSTINT

Thecostofimplementingeachintervention(COSTINT)was spec-ifiedasoneoffourdifferentvaluestakenfromacrossaplausible spectrumrangingfroma‘lowcost’scenario(

£

250/12months)to a‘highcost’scenario(

£

1000/12months).Duetothehuge inter-farmvariationinthecostofimplementingmastitisinterventions, anyspecifiedrangecouldbeconsideredtobearbitrary.Therefore, ratherthantryingtopredicttheactualcostofimplementing spe-cificinterventions,arangeofvalueswasspecifiedtoprovidean indicationastohowmuch ‘roomfor investment’therewasfor eachspecificintervention.Theactualcostofimplementationcanbe enteredinthedecisionsupporttoolinordertomakefarm-specific predictions.

Parametersthroughoutthemodelwereestimatedfrom10,000 MarkovchainMonteCarlo(MCMC)iterations,followinga

burn-in of 1000simulations. Threechainsstarting at ‘overdispersed’ initialvalues weresimulatedand convergencewasassessedby comparing intra- and inter-chain variability using the Brooks-Gelman-Rubindiagnostic(BrooksandGelman,1998;Gelmanand

Rubin,1992).

Anindicatorvariablewassetto1ateachinterventionwhenthe micro-simulationmodelpredictedanINBof

£

1000orgreaterand otherwiseto0.Themeanvalueofthisindicatoroverthe10,000 iter-ationsprovidedanestimateoftheprobabilityofexceedingareturn of

£

1000.PredictionsofINBwereplottedforeachofthefour dif-ferentvaluesofCOSTINTtoproduceprobabilisticcost-effectiveness curvesthatdisplaytheprobabilityofsaving,atleast,

£

1000over12 monthsatdifferentlevelsofmastitisforeachintervention(Fig.2). Acutpointprobabilityof≥60%forasaving≥

£

1000ina12month periodwasusedtolabelinterventionsaspotentiallycost-effective; theseinterventionsarereported.Asavingof

£

1000ina12month periodwasconsideredbytheauthorstobeaworthwhilesaving fordemonstrationpurposesbutfarmerswillbeabletostipulate theirowndesiredlevelofsavinginthedecisionsupporttool.An alternativeapproachwouldhavebeentosimplyprovidethe result-ingposteriordistributionfortheINB,however,byselectingthe thresholdof

£

1000,wewereabletodemonstratehowthe poste-riordistributioncanbeusedtoprovideintuitivepredictionsfor clinicaldecisionmakers.

2.4. Somaticcellcountmicro-simulationmodel

Fig.3. Scatterplotofobservedandpredictedvaluesofthepercentagechangeintheincidencerateofclinicalmastitisinthefirst30daysaftercalving(IRCM30)inthe12 monthssincethemastitiscontrolplanwasinstigated.

HighSCCwasdefinedbythenormaldistribution;COSTSCC∼Normal (mean=290,sd=112)(Greenetal.,2009).

3. Results

3.1. Herdparameters

The median size of the 77 herds selected for analysis was

187 cows (range 51–553) and the median 305d milk yield

was8611kg(range4297–10590). Themedianincidencerateof CM in the 12 months prior to mastitis interventions was59.5 cases/100cows/year (range18–164)and themedian12-month average BMSCC was 206,000 cells/ml (range 74,000–398,000). ThemedianIRCM30atthetimeof theinitialherdvisit was13 cases/100cows/month(12-monthaverage,range0.25–36.25)and themedianHighSCCratewas18.35%/month(12-monthaverage, range1.9–43.8).

3.2. Interventions

A total of 112interventions were evaluated in the analysis and the number of farms implementing each of the interven-tionsrangedfrom2 to15. Interventionsthat werefoundtobe cost-effectiveinmostscenarioswerereportedresultingin13 inter-ventionsfortheCMmodeland9interventionsfortheSCCmodel. Thereportedinterventionscouldbebroadlygroupedintothree cat-egories;managementofthedrycowenvironment,managementof thecalvingcowenvironmentandtheselectionandapplicationof drycowtherapy(Tables1and2).

3.3. Micro-simulationmodels

3.3.1. Regressionmodelfit

Bothregressionmodelsdemonstratedagoodabilitytopredict theincidencerateofIRCM30andHighSCCforagivenfarm,with themodelpredictionsexplainingover84%ofthevariabilityinthe observeddataintheclinicalmastitisregressionmodel(Fig.3)and 78%inthesomaticcellcountregressionmodel(Fig.4).

3.3.2. Cost-effectivenessoutcome

Theprobabilityofanincrementalnetbenefitofatleast

£

1000for differentinterventionsisprovidedinTables1and2andFig.2. Inter-ventionsintheclinicalmastitismicro-simulationmodelthatwere cost-effectivefor mostfarms(>75%probabilityof saving

£

1000 withinitialIRCM30of2cases/12cowsandaCOSTINTof

£

500)were drycowrationsbeingformulatedbyasuitablyqualified nutrition-istasopposedtoanunqualifiedperson,selectingdrycowtherapy (DCT)atcowlevel(selective)ratherthanatherdlevel(blanket), balancingcalciumandmagnesiuminthedrycowrations,designing cubicles insucha way that 90%of dry cows liedin them cor-rectlyandnotdrying-offcowsduringfoottrimmingprocedures. Theinterventionsinthesomaticcellcountmicro-simulationmodel thatwerecost-effectiveformostfarms(>75%probabilityofsaving

£

1000withinitialHighSCCrateof20%andaCOSTINTof

£

500)were spreadingbeddingevenlyindrycowyardsasopposedtopoor bed-dingspreading,abruptdryingoffasopposedtooncedailymilking andcalvinginindividualpensasopposedtocommunalyards.

Interventionsintheclinicalmastitismicro-simulationmodel thatweresensitivetothecostoftheinterventionandtheinitial IRCM30and thereforeonly likelytobecost-effective incertain scenariosincludedcleaningdrycowcubiclesatleasttwicedaily, calvinginindividualcalvingpensasopposedtocommunalyards, milkingcowsforthefirsttimewithin24hofcalvingand consider-ingbothantibioticandnon-antibioticdrycowtherapyapproaches forlowsomaticcellcountcows.Interventionsinthesomaticcell countmicro-simulationmodelthatweresensitivetothecostofthe interventionandtheinitialHighSCCrateincludedmilkingcowsfor thefirsttimewithin24hofcalving,removingcalvesfromthecow within24hofbirthanddifferentiatinginfectedfromuninfected cowsatdryingoffusingSCCrecordsfromthecurrentlactation.

4. Discussion

Fig.4. Scatterplotoftheobservedandpredictedvaluesofthepercentagechangeinthemonthlypercentageofcowsthathadasomaticcellcount<200,000cells/mlatthe milkrecordingpriortodryingoff,thatwere>200,000cells/mlatthefirstmilkrecordingafterparturition(HighSCC)inthe12monthssincethemastitiscontrolplanwas instigated.

Table1

Probabilityofsavingatleast£1000after12monthsatdifferentincidenceratesofclinicalmastitisinthefirst30daysaftercalving(IRCM30)andfordifferentcostsof implementingtheintervention.Interventionsrankedfrommostcost-effectivetoleast.

CostofIntervention(£)

Intervention IRCM30(cases/12cows) 250 500 750 1000

Drycowrationsshouldbeformulatedbya suitablyqualifiednutritionaladvisor

1.5 0.92 0.89 0.85 0.80

2.0 0.95 0.94 0.92 0.89

3.0 0.98 0.97 0.96 0.95

Drycowtherapy(DCT)shouldbeselectedat thecowlevel(asuitableproductforeachcow) ratherthanherdlevel

1.5 0.84 0.76 0.67 0.57

2.0 0.92 0.88 0.83 0.77

3.0 0.97 0.96 0.93 0.91

Calciumandmagnesiumshouldbebalancedto preventmilkfever

1.5 0.77 0.71 0.65 0.58

2.0 0.85 0.81 0.77 0.71

3.0 0.90 0.88 0.86 0.84

Cowsmustnotbedriedoffduring foot-trimming

1.5 0.76 0.70 0.63 0.56

2.0 0.83 0.79 0.75 0.70

3.0 0.89 0.87 0.85 0.82

Cubiclesshouldbedesignedsuchthatatleast 90%ofdrycowswilllieinthemcorrectlyatall times

1.5 0.74 0.67 0.60 0.54

2.0 0.82 0.78 0.73 0.68

3.0 0.88 0.86 0.84 0.81

Dung,soilingandwetbeddingshouldbe removedatleasttwicedailyfromdrycow cubicles

1.5 0.70 0.59 0.48 0.38

2.0 0.83 0.76 0.68 0.60

3.0 0.92 0.89 0.85 0.80

Cowsshouldbemilkedforthefirsttimewithin 24hofcalving

1.5 0.70 0.60 0.50 0.40

2.0 0.81 0.75 0.68 0.60

3.0 0.90 0.87 0.83 0.79

Cowsshouldcalveinindividualpensrather thancommunalyards

1.5 0.65 0.56 0.47 0.39

2.0 0.76 0.70 0.63 0.56

3.0 0.86 0.82 0.78 0.74

Bothantibioticandnon-antibioticDCT approachesshouldbeconsideredforlow somaticcellcountcows

1.5 0.50 0.39 0.29 0.21

2.0 0.65 0.56 0.47 0.39

3.0 0.80 0.74 0.68 0.62

Cleanbeddingmaterialshouldbeappliedto drycowcubiclesatleastoncedailyifusing organicbedding

1.5 0.46 0.36 0.27 0.20

2.0 0.61 0.52 0.44 0.36

3.0 0.76 0.70 0.64 0.58

Pasturemustnotbegrazedformorethan2 consecutiveweeksandmustberestedforat least4weeksbeforecowsarereturnedtograze

1.5 0.41 0.33 0.26 0.20

2.0 0.52 0.45 0.39 0.33

3.0 0.63 0.59 0.54 0.49

Strawyardsforcalvingcowsshouldbecleaned outcompletelyatleastoncepermonth

1.5 0.40 0.28 0.20 0.13

2.0 0.58 0.47 0.37 0.29

3.0 0.74 0.67 0.60 0.53

Calvesmustonlybeallowedtosuckletheir owndamtopreventthepossibletransferof pathogensinmilkbetweencows

1.5 0.28 0.19 0.12 0.07

2.0 0.44 0.35 0.26 0.19

Table2

Probabilityofsavingatleast£1000after12monthsatdifferentmonthlypercentagesofcowsthathadaSCC<200,000cells/mlatthemilkrecordingpriortodryingoff, thatwere>200,000cells/mlatthefirstmilkrecordingafterparturition(HighSCC)anddifferentcostsofimplementingtheintervention.Interventionsrankedfrommost cost-effectivetoleast.

CostofIntervention(£)

Intervention HighSCC% 250 500 750 1000

Cowsshouldcalveinindividualpensrather thanyardsratherthancommunalyards

15 0.84 0.78 0.72 0.65

20 0.90 0.86 0.83 0.78

30 0.95 0.93 0.91 0.89

Dryingoffmustbeabrupt;thatis,cowsshould notbemilkedoncedailyinthedayspriorto drying-off

15 0.84 0.78 0.72 0.65

20 0.90 0.86 0.83 0.78

30 0.95 0.93 0.91 0.89

Beddingshouldbespreadevenlyratherthan unevenlyinstrawyardsfordrycows

15 0.80 0.76 0.71 0.66

20 0.86 0.83 0.79 0.76

30 0.90 0.89 0.87 0.85

Theremustbegoodventilationbutwithout draughtsinallcalvingcowhousing

15 0.64 0.56 0.48 0.41

20 0.73 0.67 0.61 0.55

30 0.82 0.79 0.75 0.71

Thecalfshouldberemovedfromthecow within24hofbirthafterensuringcolostrum hasbeenfed

15 0.62 0.53 0.44 0.36

20 0.74 0.66 0.60 0.52

30 0.84 0.80 0.76 0.72

Cowsshouldbemilkedforthefirsttimewithin 24hofcalving

15 0.59 0.49 0.40 0.32

20 0.72 0.64 0.56 0.48

30 0.83 0.79 0.74 0.69

Drycowtherapymustbeadministeredhygienically,asdetailedinthe standardoperatingprocedureprovidedwiththetrainingmaterials

15 0.52 0.43 0.34 0.26

20 0.65 0.57 0.49 0.42

30 0.78 0.73 0.68 0.63

Youshoulddifferentiateinfectedfromuninfectedcows usingsomaticcellcountrecordsfromthecurrentlactation

15 0.42 0.34 0.27 0.21

20 0.54 0.47 0.40 0.34

30 0.66 0.61 0.56 0.51

Eachquartershouldbestrippedwithin4hofcalvingto checkformastitis

15 0.38 0.29 0.22 0.16

20 0.52 0.44 0.36 0.29

30 0.68 0.62 0.55 0.49

madeavailabletoveterinarians/advisorsinvolvedwith implement-ingtheAHDBDairyMastitisControlPlanintheUnitedKingdom.

Interventionsrelatingtothedesignandcomfortofdrycow cubi-clessuchasdesigningcubiclesinsuchawaythatcowslieinthem correctlyandremovingdung andwetbeddingfromcubiclesat leasttwicedailywerepotentiallycost-effectiveinterventionsin theclinicalmastitismicro-simulation,andaspectsofmanagement relatedtothesehavebeenhighlightedpreviously(Barkemaetal., 1999).Thisearlierstudyidentifiedthetypeofcubicledividerand thicknessofcubiclebeddingtobeassociatedwiththeincidence rateofclinicalmastitis.Thehygieneofdrycowcubicleshasbeen associatedwithchangesinbulkmilksomaticcellcount(Barkema etal.,1998)andclinicalmastitisincidencerate(Schukkenetal.,

1991;Greenetal.,2007b),andthishighlightstheneedtoprovide

comfortable,cleancubiclesfordrycowsaswellaslactatingcows. Anotheraspectofthedry cowenvironmentwhich is impor-tantbut commonlyoverlooked isthegrazing management and specificallytherotationofpaddocks.Inthisstudya‘graze2,rest 4’policywasused(paddocksaregrazedfornomorethan2 con-secutiveweeks and thenrested fornoless than 4weeks), and whilsttheeffectofthisinterventionwasrelativelysmallineach ofthemicro-simulationmodels,thecombinedpredicted reduc-tioninclinicalmastitisandsomaticcellcountwouldmakethisan interventionlikelytobecost-effective,providingthecostto imple-mentitwasmodest.Thisisinagreementwithtwopreviousstudies thatfoundthisinterventiontobeassociatedwithreducedsomatic cellcountsandclinicalmastitisincidenceinUKdairyherds(Green

etal.,2007b,2008),andemphasisestheneedtoconsiderpasture

contaminationandwaystomitigatetheserisks.

Theuseof individualcalvingpens,asopposed tocommunal yards,hadahighprobabilityofcost-effectiveness,andthishasbeen identifiedasanimportantriskfactorbypreviousstudies(Hutton

etal.,1991;Bartlettetal.,1992;Barkemaetal.,1998;Barnouin

et al.,2004; O’Reilly et al., 2006).This effect maybe due to a

reductioninpathogenexposurebutmayalsoreflectindirectly,the

negativeimpactofcross-sucklingcalveswhichhasbeenassociated withclinicalmastitisincidenceinthecurrentstudyandpreviously

(Green etal.,2007a).Cross-sucklingwouldalsobelesslikelyto

occurwhencalves areremovedwithin24hof calvingand this interventionwasassociatedwithamoderateprobabilityofa

£

1000 returninthesomaticcellcountmicro-simulationmodel.

Selectingdrycowtherapyatcowlevelwasassociatedwitha reduced IRCM30,ashasbeenreportedpreviously(Green etal.,

2007b).Sinceneithertheproductsusednorthecriteriaappliedto

selectbetweencowswasspecifiedinthisstudy,itremainsunclear whethersomeapproachestoselectivedrycowtherapyaresuperior toothers.

Having a policy of using both antibiotic and non-antibiotic approacheswhendrying-offlowsomaticcellcountcowswas pre-dictedtoreduceIRCM30andwasverylikelytobeacost-effective intervention.Importantly,suchapolicywillalsoreducethe quan-tityofantimicrobialusageonfarm.Withtheincreasingconcerns aboutantibioticresistancecomesanincreasingpressureondairy farmerstoreduceantibioticusage(Calletal.,2008;Oliveretal., 2011).Inherdssuchasthesewithalowprevalenceofcontagious mastitisandarelativelylowbulkmilksomaticcellcount,the tar-getingof antibioticdry cowtherapy atcows infected atdrying offanduseofnon-antibioticteat-sealantsinuninfectedcowsis arationalandeffectiveapproachtodrycowtherapy(Huxleyetal.,

2002;Greenetal.,2008;Bradleyetal.,2010;Cameronetal.,2015).

Irrespectiveofwhichdrycowtherapyproductsareusedat drying-off,interventionsaffectingthehygieneoftheprocedureitself,and thecleanlinessoftheenvironmentinwhichitisperformed,were showntobepotentiallycost-effectiveinbothmodelsandconfirms previousstudyfindings(Peeleretal.,2000;Barnouinetal.,2004,

2005;Greenetal.,2008).

magnesiumtopreventmilkfever.Thisisalsoinagreementwith otherstudiesinvestigatingtheroleofnutritioninmastitiscontrol

(Kremeretal.,1993;OltenacuandEkesbo,1994;O’Rourke,2009).

Thesestudiesreportedthatmastitiswasmorelikelytooccurin cowsdiagnosedwithclinicalketosisandcowsdeficientin vita-minsandtraceelementssuchasselenium,vitaminE,copper,zinc, vitaminAand␤-carotene.

Theuncertaintyinclinicalandfinancialoutcomeforan indi-vidualfarmisimportant,andillustratestheusefulnessofusinga probabilitydistributionforanticipatedfinancialreturns.The inte-gratedBayesianmodelusedinthisanalysissimultaneouslyderived thejointposterior distributionfor allunknownparameters and propagatedtheeffects throughthepredictivecost-effectiveness model. In this example, uncertainty in the cost of mastitis for eachherdisincludedaswellastheuncertaintyoftheeffectsof theinterventions.Thereareseveraladvantagesofthisapproach, whichhavebeenoutlinedpreviously(SpiegelhalterandBest,2003;

Spiegelhalteretal.,2004).Themaindisadvantagesoftheunified

BayesianapproachincludetheneedforfullMCMCsoftwareinorder toobtainasolutionalthoughthisiscurrentlyfreelyavailable(Lunn etal.,2000)anditcanalsobedifficulttoevaluateorcheckmodel accuracy(Greenet al.,2010).Such unifiedBayesianmodelsare usedwidelyinhumanmedicine(Parmigiani,2002;Spiegelhalter etal.,2004),buttherearefewexamplesintheveterinary

litera-ture(Greenetal.,2010;Archeretal.,2014).Theyprovideauseful

methodtoimprovetheunderstandingoftheuncertaintiesinvolved inclinicaldecisionmakingandthereforehavemuchtoofferthe decisionanalystanddecision-maker(Cooperetal.,2004).

The results of this research were incorporated into a spreadsheet-baseddecisionsupporttooltoenablevetsand farm-erstoexploredifferentscenariosapplicabletothem.Farm-specific parameterscanbeenteredandrequiredsavingsspecified,resulting inpredictionsthatarerelevanttoeachindividualfarm. Informa-tionregardingtheherdsize,currentclinicalandsubclinicalmastitis performanceandcostscanbeinputtedinadditiontothecostof implementingeachintervention.Thelevelofsavingrequiredafter 12monthsisthenspecifiedaccordingtothefarmers needsand thedecisionsupporttoolcalculatestheprobabilityofmakingthe specifiedlevelofreturnanddisplaysthisasaprobability distribu-tionsotheuncertaintycanbevisualised.Thedecisionsupporttool alsoallowsdifferentcombinationsofinterventionstobeevaluated simultaneouslysothatmanydifferentscenarioscanbeexplored.

Thisresearchmeasuredthecost-effectivenessofmastitis inter-ventionsinherdsspecificallywithan‘EDP’diagnosisandassuch itisdifficulttoknowhowthesefindingswouldtranslatetoherds moregenerally.Theresultsmayhavebeeninfluencedby participa-tionbiasduetocharacteristicscommontotheplandeliverersthat submitteddatacomparedwiththosethatdidn’t.Itisalsolikelythat theresultsarebiasedtowardsherdsseekingveterinaryinputwith respecttomastitiscontrolratherthanbeingrepresentativeofthe nationalherdasawhole.However,thedatamostlikelyprovidesa truereflectionofdairyherdsseekingveterinaryinputwithrespect tomastitiscontrolandis,therefore,ofvaluetothoseinvolvedin thedeliveryoftheseservices.

5. Conclusions

Inthisstudy,datafrom77UKdairyherdswereusedtoexplore thecost-effectivenessofspecificmastitiscontrolinterventionsin herdswithaparticularproblemwithIMIacquiredduringthedry period.ResultsfromtheBayesianmicro-simulationmodels iden-tified thata variety of interventionswould be costeffective in differentfarmcircumstances.Thecost-effectiveness ofdifferent interventionshasbeenincorporatedinadecisionsupporttoolto

assistoptimaldecisionmakingbyveterinarypractitionersinthe field.

Conflictofintereststatement

Theauthorshavenoconflictsofinterest

Acknowledgements

PeterDown wasfundedby aBBSRCcase studentship; grant numberBB/I015493/1,andreceivedfurtherfundingfromAHDB Dairy. The study sponsors were not directly involved in study design, analysis or interpretationof data,in the writing of the manuscript,orinthedecisiontosubmitthemanuscriptfor pub-lication.Wewouldliketothankthevets,consultantsandfarmers involvedinthestudy.

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