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AnEconometricMethodforEstimatingPopulationParameters
fromNon-RandomSamples:AnApplicationtoClinicalCaseFinding
ZoëMcLaren,AssistantProfessorSchoolofPublicHealth,UniversityofMichigan
RulofBurger,AssociateProfessorDept.ofEcon,StellenboschUniversity,SouthAfrica
April2017Paperdownloadatzoemclaren.comForthcominginHealthEconomics
Motivation
HowareMDR-TBprevalenceratescurrentlydetermined?
• Surveillancestudy– Accuratebutinfrequent
• Notificationrates– Numberofreportedcaseslikelyunderestimate
• WHOcrudeadjustmenttonotificationrates– Basedonexpertopinion(Glaziouetal.2015)
Ourcontribution
• Developaneweconometricmethodforestimatingpopulationmeansfromaselectedsample– Identifiesunder-detection– Minimaldatarequirements:routinedata– Minimalassumptions– Lowcost,easyreal-worldimplementation
• Usefulformonitoringrareandemergingdiseases• Weestimatethat16to26%ofallmulti-drugresistantTBcasesinSouthAfricawereundiagnosed2004-2011
Foundationformethod
• Methodstoaddresssampleselectiononunobservedcharacteristicsineconomicsliterature– Instrumentalvariables(seeImbensandAngrist1994,Angristand
Imbens1995)– Bivariatenormalstyleselectionmodels(seeHeckman1976)
• HIVlit:adjustingsurveyestimatesforrepresentativeness– Interviewerrandomeffects(McGovernetal.2015)– Heckman-typeselectionmodels(Barnighausenetal.2011,Hoganet
al.2012,ClarkandHoule2014)– Adjustingforsurveynon-responseusingmortalityrates(Nyirendaet
al.2010)
Context:MDR-TB
Multi-drugresistantTB
• Resistanttothetwofirst-lineTBdrugs• Indistinguishablefromdrug-susceptible• Only12%ofnewTBcasestestedforMDRglobally• MDRpatientscomprise:– Lessthan5%ofTBcases– 13%ofTBdeaths– 20%ofTBspending
GuidelinesforMDRtesting• Cliniciansobserveriskfactors– TBhistory–Weakenedimmunesystem– Highriskofexposure:prisoners,miners,healthworkers
• RiskfactorsareimperfectpredictorsofMDR• Cannottesteveryoneforallpossibleformsofdrugresistance
• TesttheproportionofpatientsθwithhighestlikelihoodofMDRbasedonobservedsignalx
TheoreticalModel
Generalmodel
• Supposewehaveapopulationforwhichwewanttoknowthemeanofoutcomey
• Wehavearoutinesamplewithobservationsselectedtomaximizevalueofy
• Characteristicx isobserved• Mappingofx tounobservabley isnotfullyknown• Determiningvalueofy hasassociatedcost
Keyfeaturesofclinicaldecisionmaking
• PatientsmustbetestedbeforeMDRtreatment
• Toofewresourcestotesteverypatient• Clinicianobservesnoisysignalaboutpatient’slikelihoodofMDR-TB
• Testingresourcesdeterminedexogenously:– Testmaterials,labcapacity– Funding– Clinicianawarenessandtraining
Keyfeaturesofclinicaldecisionmaking
• Clinicianwilltestpatientsdeemedmostlikelytohavedrug-resistanceuntilresourcesareexhausted
• WeassumeclinicianbeliefsaboutmappingbetweenriskfactorsandMDR+isconsistentwithintimeperiods
• CliniciansdonotknowactualMDR-TBprevalence
Methods
Identificationstrategy
• Useplausiblyexogenousvariationinthresholdθtodrawinferencesabout– Distributionofyinthepopulation– Samplingmechanism• Clinician’sabilitytopredictMDR+basedonobservablesignalx
• Assumeconsistentbeliefsaboutmappingofxtoy
• Regressiondiscontinuityintuition– Relaxconstraintontestingresources
Identificationstrategy
• Samplemeansatobservedthresholdproportiontestedθ0 informativeaboutunobservablex0:
• Withabinaryy,conditionalexpectationsimplifiesto:
whereµ ispopulationprevalence• Rewritingrelationshipbetweenyandxinerrorform:
• Normalize b0 tozero
Estimation
• Rewriteconditionalexpectation:
• Assumeerrortermefollowsstandardnormaldistribution
• Noclosed-form(analytical)solutionsoweusenumericalapproximationtechniques
• Estimateparametervaluesusingmaximumlikelihoodandgeneralizedmethodofmoments
• Gridsearchestofindmostpromisingparameterspace
Policychangesasinstrumentalvariables
• Exogenousdiscontinuouschangesintestingresources(θ)– Timeperiod– XDRpaperpresentedatinternationalconference– Nationalstrategicplanintroduced
• Shouldnotaffectclinician’sunderstandingofriskfactorsortheunderlyingrateofMDR-TB
Data
Data• NationalHealthLaboratoryServicedatabase• Laboratorydatabaseoftestresultsfor~90%ofallsuspectedTBcases– Jan2004– Sept2010(PriortoXpert)– 2,190,780 TB+testresults– 8,647,12 patients– 4,764healthfacilities
AccessingNHLSTBdatabase
Data
• ExamineMDR-TBtestingforthosewhoaretestedforTB,andhaveaTB+result
• Minimaldatarequirements• Extractnumberofpatients:– TB+result– TestedforMDR–MDR+result
Results
FractionofTB+testedforMDR
0%
5%
10%
15%
20%
25%
2004 2005 2006 2007 2008 2009 2010 2011
Testshare
0%
2%
4%
6%
8%
10%
12%
14%
16%
5% 7% 9% 11% 13% 15% 17% 19% 21% 23% 25%
Percento
fsam
pledM
DR-TBpo
sitive
Theta0
MarginalsamplingefficiencyE(y|theta<theta0) E(y|theta0)
Samplingefficiency:percentofMDRtestedwhoareMDR+
PredictedMDR+matchesobservedMDR+overtime
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
2003 2004 2005 2006 2007 2008 2009 2010 2011
ShareofallTB+patientswhoareMDR-TB+
Observed Predicted
Mainresults:robustnesschecksMethod: ML GMM GMM GMM GMM
Instruments: Time Time Policies Policies
(1) (2) (3) (4) (5)Prevalence 0.0305*** 0.0298*** 0.0342*** 0.0305*** 0.0301***
(0.0002) (0.0013) (0.0004) (0.0016) (0.0004)
Signal-to-noise 1.0749*** 1.0891*** 0.9336*** 1.0716*** 1.0846***
(0.0003) (0.0489) (0.0117) (0.057) (0.0142)
Observations 262,845 262,845 262,853 262,850 262,842
Clustering No No Yes No Yes
Pseudo R2 #1 0.694 0.699 0.529 0.691 0.703
Pseudo R2 #2 0.695 0.695 0.694 0.695 0.696
Log likelihood -90646.845
GMM criterion 0.00131 0.01626 0.00044 0.0078
QuadraticMDR-TBtimetrendestimates
0%
1%
2%
3%
4%
5%
6%
7%
2004 2005 2006 2007 2008 2009 2010 2011
MDR
-TBprevalence
Year
ML(constant) GMM:TimeIV(constant) GMM:PolicyIV(constant)
ML(quadratic) GMM:TimeIV(quadratic) GMM:PolicyIV(quadratic)
Conclusions
• Evidencethat“official”MDRratesaretoolow– 16-26%ofMDRcaseswereundiagnosed– Moreresourcesareneeded
• Routinedatacanprovidereal-timetracking– Widelyavailableandunder-used– Valuablewherecannottesteveryoneorwherecompliancewithguidelines<100%
• ClearapplicationsbeyondTB
Thankyou!