SOA 长期护理保险死亡率和失效研究

objective.ndependent.EfectiveTM

Long-TermCareInsuranceMortalityandLapseStudy

November2021

DevelopedbytheLong-TermCareValuationWorkGroupoftheAmericanAcademyofActuaries

andtheSocietyofActuariesResearchInstitute

WarrenJones,MAAA,FSA,FCA

Chairperson

LoLindaChow,MAAA,FSA

SivakumarDesai,MAAA,FSA

NoelleDestrampe,MAAA,FSA

RobertHanes,MAAA,FSA

PeggyHauser,MAAA,FSA

LaurelKastrup,MAAA,FSA

MatthewKlaus,MAAA,FSA

PerryKupferman,MAAA,FSA

DianeMui,MAAA,ASA

LisaParker,MAAA,ASA

MariannePurushotham,MAAA,FSA

StevenSchoonveld,MAAA,FSA

BruceStahl,MAAA,ASA

JamesStoltzfus,MAAA,FSA

RobertYee,MAAA,FSA

2

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Contents

I.INTRODUCTION 3

II.BACKGROUND 4

III.MORTALITY 6

IV.LAPSE 24

V.TOTALTERMINATION 38

VI.APPENDICES 42

Appendix1–LTCAWGRequest 42

Appendix2–ActiveLivesActualLapsestoExpectedComparisons 43

Appendix3–RecommendedLapseTables 46

Appendix4–RecommendedMortalityTables 50

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I.INTRODUCTION

OnMay5,2016,theLong-TermCareValuation(B)WorkGroupofNationalAssociationofInsuranceCommissioners’(NAIC’s)HealthActuarial(B)TaskForce’sLong-TermCareActuarialWorkingGroup

(LTCAWG)requestedrecommendationsfromtheAmericanAcademyofActuaries(Academy)

1

andtheSocietyofActuariesResearchInstitute(SOA)

2

toreplacethemortalityandlapsebasesforstatutory

minimumreserves.AcopyoftherequestisincludedinAppendix1tothisreport.

TheAcademyandSOAcreatedaLong-TermCareValuationWorkGroup(WorkGroup)toaddresstherequest.TheWorkGroupischairedbyWarrenJones,theMortalitySubgroupisledbyBruceStahl,andtheLapseSubgroupisledbyBobYee.TheWorkGrouphasprovidedregularupdatestotheLTCAWGatnationalmeetingsandprovidedopportunitiesfortheLTCAWGmemberstoaskquestionsregardingtheworkinprogress.

ThisreportpresentstherecommendedlapsetablesinAppendix3,andrecommendedmortalitytablesinAppendix4,anddescribesthemethodologyandprocessindevelopingthesetables.

1TheAmericanAcademyofActuariesisa19,500-memberprofessionalassociationwhosemissionistoservethepublicandtheU.S.actuarialprofession.Formorethan50years,theAcademyhasassistedpublicpolicymakersonalllevelsbyproviding

leadership,objectiveexpertise,andactuarialadviceonriskandfinancialsecurityissues.TheAcademyalsosetsqualification,practice,andprofessionalismstandardsforactuariesintheUnitedStates.

2ServingastheresearcharmoftheSocietyofActuaries,theSOAResearchInstituteprovidesobjective,data-drivenresearchbringingtogethertriedandtruepracticesandfuture-focusedapproachestoaddresssocietalchallengesandbusinessneeds.TheInstituteprovidestrustedknowledge,extensiveexperienceandnewtechnologiestoactuaries,employers,regulators,

researchfundersandthepublic,tohelpthemeffectivelyidentify,predictandmanagerisks.

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II.BACKGROUND

Theminimumstatutoryreservebasisforlong-termcare(LTC)insuranceisdocumentedintheNAIC

ValuationManualVM-25:HealthInsuranceReservesMinimumReserveRequirements.Themortality

tablespecifiedforcurrentlynewissuesisthe1994GroupAnnuityMortality(GAM)Table.Thelapseratespecifiedisthelesserofx%ofthevoluntarylapserateusedinthecalculationofgrosspremiumsandy%,wherexandyvarybypolicyyear:

Forpolicyyearone(1),xis80%andyis6%.

Forpolicyyearstwo(2)throughfour(4),xis80%andyis4%.

Forpolicyyearsfive(5)andlater,xis100%andyis2%,exceptforgroupinsurance,forwhichyis3%.

Boththemortalityandlapsebasesfortheminimumreserverequirementshavebeenrevisedovertheyearswiththechangestoapplytonewissuesonly.Themortalitybasishasbeenthatforwholelife

insuranceorpayoutannuities.

GeneralApproach

Ourchargeistodeveloprecommendedmortalityandlapsetablesforvaluationonbothatotal-livesbasisandanactive-livesbasis.Thischargedictatestheapproachwehavechosentodevelopsuchtables.

Valuationtablesareconservativeinnature.Alogicalmethodistodevelopbasictablesbasedon

experiencefirstandthenconsiderthemarginstobeadded.Becausethemortalityandlapsetableswouldbeusedincombination,itisdesirableforbothtablestobeasconsistentaspossiblewithrespecttothedatasourceandthefactorsthatthetablesvaryby.TheWorkGrouprecognizesthelikelihoodofthe

under-reportingofdeathforhealthypolicyholders.Thus,thedelineationbetweendeathandlapseisnotalwaysclear.Consistencyinbothdatasourceandfactorscanfacilitatetheassessmentofcombined

deathsandlapsesforreasonablenesschecks.

SourceofData

TheWorkGroupdefinedthebasemortalitytabletobethe2012IndividualAnnuityMortalityTable.

Further,developmentofmortalitymarginsandthelapseassumptionistobebasedontherecent

SOA/LIMRALTCVoluntaryLapseandMortalityExperienceStudy(theStudy).Itiscomprisedofexperiencedatafrom2000through2011for22companies.Fromtheaggregatedata,weobservedtrendsduringthestudyperiod,especiallyforlapse.Accordingly,weselectedtheobservationperiod2008-2011toreflectmorerecenttrends.Furthermore,theStudyidentifiedcertainparticipatingcompanieswithrelatively

moreaccuratedatasubmitted.Datafrom10companies(Definition2[DEFN2]companies)satisfiedthefollowingconditions:

1.Deathsareseparatelyidentifiedfromlapses,

2.Unknownterminationsarelessthan25%oftotalterminations,and

3.PerformedmatchingwithSocialSecuritydeathrecordswithinthepreviousthreeyearsfromthedateofsubmission.

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ApartfromsomepreliminarycomparisonsbetweenDEFN2dataandthefulldataset,wefurther

restrictedourdatatotheseDEFN2companies.WecomparedourtabulatedlapsecountsandexposureswiththepublishedsummarydatafromtheSOA.WeconcludedthatourdataisreasonablyrepresentativeofcorrespondingsummarydatafromtheStudy.TheDEFN2companiesrepresentapproximately70%oftheindustryexperiencefortheexposureperiodused.

ThefollowingtableshowsthesummarystatisticsoftheDEFN2subsetofdata:

Table1.DEFN2Exposures

TotalLives

3

ActiveLives

Counts

Exposure

Years

Counts

Exposure

Years

Mortality—Individual

142,647

9.4MM

95,474

9.0MM

Lapse—Individual

4

197,000

9.4MM

197,000

9.0MM

Lapse—Group

5

302,000

4.9MM

302,000

4.8MM

3Themortalitywasderivedusingindividualmortalityandtestedusingbothindividualandgroupdata.Also,thefiguresinthislineinclude19,599deathsfrom“Substandard”and“Unknown”riskclassesthatdonotappearinthe“DeathCountTotals”tablelaterinthisreport.

4ThelapsecountisthesameforTotalLivesandActiveLivesastheimmaterialnumberofdisabledlifelapsesthatwereignored.5Ibid.

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III.MORTALITY

PurposeandScope

TheMortalitySubgroupworkedtoidentifyreasonablemortalitytablestobeusedinsettingstatutory

reservesforindividualLTCIpolicies,eitherasaparticularsetoftablesorasguidancethattheNAICcouldexpectfrominsurers.

TheWorkGroupfurtherrecognizedthattheNAICallowsprinciple-basedreservingforlifeinsuranceandthat,therefore,theNAICmaybeinterestedinguidanceforasimilarapproachwithLTCImortality.

Guidanceforprinciple-basedreservingmaybeofparticularinterestwhenconsideringmaritalstatusandunderwritingriskclasses.

Whensettingmortalityassumptionsinaccordancewithactuarialstandardsofpractice(ASOPs)forLTC(e.g.,ASOP18

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),itisappropriatetoconsidertheeffectsofbothselectionandclassofapplicantson

expectedmortalityexperience(section3.2.2).Consequently,inadditiontootherpotentialmortalityratedifferentiators,theanalysisconsideredriskclassandmaritalstatus,representingbothselectionandclassofapplicants.

DataQuality

AddressingdataqualityunderASOP23,DataQuality,

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theWorkGroupreliedupontheSocietyof

Actuaries’IntercompanyStudyfrom2015tomakesurethedatawas“clean”andasuniformaspossibleforcomplexanddiversedatathatcamefrom10insurancecompanies.

TheWorkGroupsoughttofollowtheASOPoncredibilityprocedures(ASOP25

8

)initswork.Industrydataismorerelevantthangeneralpopulationdatafortworeasons.First,theselectionprocesswhenissuingLTCI,fromboththeapplicants’andtheinsurers’perspectives,mayresultintheinsuredpopulationbeingalowmortalitysubsetofthegeneralpopulation.Second,amortalitystudyofthisnaturerequires

considerationofnumerouscellsofdata.Thenumberofdeathsinagivencellmayormaynotbecredible.Lookingatdatafrommultipleinsurersincreasesthenumberofcellsthatarecredible.

6ActuarialStandardsBoard;ActuarialStandardofPracticeNo.18

,Long-TermCareInsurance;

May2011.7

ActuarialStandardsBoard;ActuarialStandardofPracticeNo.23,DataQuality;

December2016.

8

ActuarialStandardsBoard;ActuarialStandardofPracticeNo.25,CredibilityProcedures;

December2013.

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Toassessdatacredibilitywithrespecttoratedevelopment,itisacommonpracticetoconsider1,082ormorecountsinaparticularcellinTables2and3belowtobefullycredible.

9

Itisalsotypicaltoassigna

lowercountnumbertoannotatepartialcredibilityforacell.Forexample,aminimumcountof271canbeconsideredaspartiallycredible.

10

Using271deathsasameasure,themajorityofthecellsinTable2

belowcanbeconsideredaspartiallycredible.

Table2.DeathCounts(TotalLives)BySex,RiskClass,AttainedAge,andMaritalStatus

Female:PreferredRiskFemale:StandardRiskMale:PreferredRiskMale:StandardRisk

AttainedAge

Married

Single

Total

Married

Single

Total

Married

Single

Total

Married

Single

Total

Under60

217

113

330

373

184

557

180

69

249

384

113

497

60-64

393

194

587

708

357

1,065

407

106

513

887

192

1,079

65-69

670

346

1,016

1,329

839

2,168

795

177

972

1,720

425

2,145

70-74

1,022

716

1,738

2,402

1,670

4,072

1,375

370

1,745

3,331

934

4,265

75

270

252

522

650

520

1,170

414

115

529

938

293

1,231

76

266

288

554

717

562

1,279

454

148

602

1,112

332

1,444

77

272

296

568

786

675

1,461

515

132

647

1,167

342

1,509

78

243

304

547

868

779

1,647

485

190

675

1,261

460

1,721

79

330

415

745

937

904

1,841

527

163

690

1,436

520

1,956

80

304

442

746

951

999

1,950

527

186

713

1,426

594

2,020

81

313

459

772

984

1,129

2,113

542

169

711

1,541

652

2,193

82

338

509

847

999

1,224

2,223

527

218

745

1,534

666

2,200

83

337

591

928

1,008

1,290

2,298

546

228

774

1,557

748

2,305

84

307

602

909

925

1,345

2,270

526

208

734

1,549

762

2,311

85

344

694

1,038

954

1,395

2,349

515

241

756

1,451

735

2,186

86

316

757

1,073

868

1,506

2,374

487

276

763

1,460

756

2,216

87

308

727

1,035

791

1,520

2,311

434

254

688

1,302

799

2,101

88

258

803

1,061

725

1,452

2,177

397

208

605

1,184

733

1,917

89

248

745

993

624

1,362

1,986

356

198

554

964

589

1,553

90-94

590

2,605

3,195

1,543

4,483

6,026

943

625

1,568

2,467

1,911

4,378

95and

over

112

914

1,026

266

1,542

1,808

199

171

370

360

463

823

Total

7,458

12,772

20,230

19,408

25,737

45,145

11,151

4,452

15,603

29,031

13,019

42,050

9Fullcredibilitymeansthatthereisa90%probabilitythattheobservedrateiswithin5%ofthetrueunderlyingresult.Somepractitionerswouldacceptaslowas200datapointsasminimallycredible(approximately40%partialcredibility).

10Correspondingto1,082asfullcredibility,271countsmeansthatthereisa90%probabilitythattheobservedrateiswithin10%ofthetrueunderlyingrate.

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Table3.ActiveLifeDeathCounts

DeathCounts(Active

Lives)

AttainedAge

Female

Male

Total

Under60

1,029

978

2,007

60-64

1,829

1,890

3,719

65-69

3,325

3,460

6,785

70-74

5,367

6,001

11,368

75

1,425

1,631

3,056

76

1,502

1,859

3,361

77

1,607

1,916

3,523

78

1,765

2,082

3,847

79

2,032

2,324

4,356

80

2,077

2,277

4,354

81

2,079

2,388

4,467

82

2,173

2,400

4,573

83

2,219

2,465

4,684

84

2,133

2,399

4,532

85

2,163

2,273

4,436

86

2,162

2,204

4,366

87

2,061

2,036

4,097

88

1,932

1,848

3,780

89

1,720

1,402

3,122

90-94

5,052

3,946

8,998

95andover

1,336

707

2,043

Total

46,988

48,486

95,474

Asimportantasusingmultiplecontributorsistoenhancecredibility,usingmultiplecontributorsalso

compromisestheuniformityofthedataand,inthatsense,reducesthecredibility.Forexample,thestudyidentifiedmaritalstatusbasedinpartonthepresenceofaspousediscount,

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andsomeinsurersapplyaspousediscountbasedonthelegalstatusofbeingmarriedalone,whileothersrequirebothspousesto

applyforcoverage,andstillothersrequirebothspousestobeissuedpolicies.Similarly,thedatarecordedwhetherpolicieswerepreferred,standard,orsubstandardrisks,

12

buteachinsurerdefinesthehealth

11Thestudyvariable“maritalstatus”isbasedonacombinationofthedatafields“maritalstatusatissue”and“marital

discount.”Inthecasewheremaritalstatusatissuewasprovided,thatfieldwasused.Forapproximately43%ofthepoliciessubmittedforthestudy,maritalstatusatissuewasnotprovided.For37%ofthepolicies,themartialstatusatissuewasnotprovidedbutthemaritaldiscountwasprovided(andwasusedtodefinethestudymaritalstatusvariableforthosepolicies).Fortheremainingpolicies(approximately20%),neithermaritalstatusatissuenormaritaldiscountwereprovided;these

caseswerecodedasmaritalstatus=“unknown.”

12TheSOAstudydefinedtheriskclassusing“PremiumClass”as“Theclassinwhichthepolicywasissuedrelativetothebase

policy.”Ifanunderwritingdiscountorloadwasgiven,thenpreferredorsubstandardwasprovidedbythecompany.Thedataprovideddidnotpermitaligningacrosscompanies.

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statusoftheriskindifferentwaysfollowingitsownparticularunderwritingguidelines.TheNAICmaywanttoaddressthislackofhomogeneitywhendeterminingwhatmortalitytablestandardstosetforstatutoryreserves.

TheWorkGroupalsochosetorelyonstatisticalmetricstoidentifythesignificanceofthevariables

associatedwitheachpolicy.Thestatisticalmetricshelptoidentifyinteractionsamongthevariables,

allowingtheWorkGrouptominimizethenumberofparametersneededforsettingmortalitytables.TheyalsohelptoidentifywhichvariablestheNAICmaywanteachinsurertoconsidermorecloselywhen

settingreservesforLTCI,usingtheinformationasguidance.

Whensettingtablesusingthestatisticallyidentifiedvariables,theWorkGroupemployedsmoothingtechniquesthatgeneratedreasonableoutcomes.Partoftheanalysisinvolvedaniterativesmoothingprocess.Atyoungerandolderageswheredatawassparse,weusedtheslopeofthe2012IndividualAnnuityMortalityBasictables(2012IAM)asaguide.

Beforeconvertingthesmoothedtablesasguidanceforderivingthevaluationtables,theWorkGroup

decidedtoapplymortalityimprovementtorecognizethatLTCinsuredmortalityhaslikelyimprovedsincethetimeoftheexperienceperiod2008–2011.Aseparatesectiononmortalityimprovementisprovidedbelow.

Avaluationtableisexpectedtobeconservative;forlongevityriskssuchaswithLTCI,“conservative”

meansusinglowermortalitythanexpected.Pastannuitytableshaveessentiallyusedmortalityratesthatare90%oftheexperience,withmodificationsforveryoldagesbeyond100.TheWorkGroup

recommendedusingthe90%factoronceagainandcappingthemortalityofthevaluationtableat0.400,whichgenerallylimitsthemortalityatveryoldages.Alargemajorityofinsurers’actualmortality

experienceexceededthisproposedmortalityfactor.

TheWorkGroupdoesnotthinktheNAICshouldignoremaritalstatusandriskclassbecause,despitethelackofhomogeneityinthedefinitions,thesevariablesstillprovedtobeinfluentialinthepredictionoftheactualmortality.

TheWorkGroupdoesnotnecessarilythinktheNAICshouldusemaritalstatusandriskclasswithoutanyconsiderationforspecificinsurerdefinitionsandpractices.Asstatedpreviously,insurerstreatthese

differently,and,whiletheseitemshadahighstatisticalsignificance,theeffectoftheseitemswilllikelyvaryaccordingtothedefinitionfollowedorpracticesusedbyeachinsurer.

TheWorkGroupdidnothavethedatanecessarytoidentifythespecificvaluesforeachmaritalstatus

definitionorriskclassidentification.Therefore,adjustingforthesevariablesmaynotbepracticalwithouttheNAICgrantingindividualcompaniestheabilitytojustifyhowtheirmortalitymightappropriatelyvary

fromthefindingsidentifiedinthisreport.IftheNAICallowedindividualinsurerstojustifysuch

differences,eachinsurercouldeasilyaddmarginbyapplyingasimilarconservativefactorof90%tothemortalityratesthatitidentifies.

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Methods

IssueAges

Thedatawasconsistentwithmeasuringageasagelastbirthday(ALB).Notablesareofferedtoconvertthefindingstoagenearestbirthday(ANB).

Exposures

Forpolicyholderswhodiedwithinaparticularperiod,theWorkGroupchosetousethefullexposure,

meaningthecountoflivesatthebeginningoftheperiodbeingmeasured;foreveryoneelse,theWork

Groupchosetousetheexact(daily)exposure(forexample,apolicythatlapsedthreemonthsintoits

policyyearwastreatedashavingone-fourthofanexposureyearforthatparticularyear).ThismethodofcalculatingexposureisconsistentwiththeBalducciHypothesis,whichessentiallyassumesmortalityratesdecreaseduringtheexposureperiod.Asareminder,theBalducciassumptionmayhavedistortionswhenthemortalityratesarerelativelyhighandcredibilityislow.Pleasesee“ExperienceStudyCalculations”

writtenbyDavidAtkinsonandJohnMcGarryonbehalfoftheSOA.Pleasealsonotethatthemannerusedtoderivemortalityratesinthisstudyreliedheavilyonthe2012IAMslopeatveryoldageswherethelackofcredibilityisofgreatestconcern(themethodisdescribedlater).The2012IAMstudyalsousedthe

Balduccimethodandsmoothing.

PredictiveVariables

LIMRAdevelopedastatisticalGeneralizedLinearModelandotherstatisticalmethodstoidentifyvariablesthathadthegreatestsignificanceinexplainingthemortalityexperienceandmightbemostappropriateforuseinprojectingfutureexperience.

Initially,theanalysisrecognizedninevariables:sex,attainedage,policyyear,coveragetype(whether

nursinghomeonly,homehealthcareonly,comprehensive,orother),anindicatorforthepresenceofanautomaticincreasingbenefit,anindicatorthatthepolicyhashadarateincrease,theunderwritingrisk

class(Preferred,Standard,orSubstandard),maritalstatus,andanindicatorforwhetherthepolicyhadanunlimitedmaximumbenefitperiod.

Somefactorswererepresentedbymultipleindicatorvariables.Forexample,PremiumRiskClasswasrepresentedbythreeindicators.(ForTable4,weidentifiedthehighestWaldChi-Squarevaluefromthegroupofindicators.)

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Table4showsameasureofthesignificanceofthevariablesunderaPoissondistributionandisgenerallyrepresentativeofothermeasures.Underotherdistributions,thefactor“Gender”hadgreaterstatisticalsignificanceyet,evenhere,wedeemedthesignificancetobestrong.

SignificantUsing

.

Table4CovariatesPoissonDistribution

SignificantCovariates

Factor

WaldChi-Square

Age

7,838

PremiumRiskClass

5,508

LifetimeMaximum(LimitedorUnlimited)

3,851

CoverageType

2,336

MaritalStatus(presenceofspousediscount)

1,605

PremiumRateIncrease

276

PolicyYear

110

Gender

6

AutomaticIncreasingBenefitMaximums

0

Forexample,usingthestandardChi-squaredtestinthePoissondistribution,mostofthefactorshadaprobabilityofbeingstatisticallysignificantatover0.9999.Genderhadaprobabilityofbeingstatisticallysignificantat0.9884,comparedtotheAutomaticIncreasingBenefitMaximumatonly0.2639.

Thefactors(variables)wereselectedbasedon(1)thosethathadahighrateofresponses(somevariableshadarelativelyhighnumberofmissingvalues),(2)thosethathadamaterialinfluenceonthemortality(sometimesavariableappearedtobestatisticallysignificantbutonlytriviallyalteredthemortalityrate),and(3)thosethatwerenotredundant(notstatisticallycorrelatedwithotherfactors).Suchanalysis

reducedtheinitialninetofive:sex,age,policyyear,underwritingriskclass,andmaritalstatus.(Agecanbeidentifiedeitheratissueageorattainedagebecausethepresenceofpolicyyearcorrelatesthetwoagemeasurements.).

Thefindings,therefore,providetablesthatincludethefivepredictivevariables.Fortwoofthe

variables—underwritingclassandmaritalstatus—theunderlyingdataisnothomogeneousby

contributingcompanybecausecontributinginsurersderiveordefinethesedifferently,andpresumablythedifferencesforsomeinsurerscouldbelargerforsomethanothers.Thefindingsprovideasetof

tablesthatoffertheoptiontoexcludeunderwritingriskclassandmaritalstatus.TheNAICmaywanttoconsidertheoptionalityofthesetwovariables.

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DevelopmentofTables

TheWorkGroupnoticedthattheselectionperiodinthedataappearedtobeabout20years,andthatthenumberofdeathsinpolicyyears21andoverwasnotverycredible.TheWorkGroup,consisting

primarilyofLTCIactuaries,thenreliedheavilyuponSusanWilleat,anactuaryfromRGAwithmanyyearsofexperienceinderivingmortalitytables.Sheinitiallyidentifiedasetofultimatetablespriorto

identifyingselectionfactors.Sheincreasedthenumberofdeathsfrompolicyyears15to20toestimatetheultimatemortality,andshedidsoiterativelyinordertofindagoodfit.Startingwithanincreaseofanywherebetween2%and4%,shenarrowedtheannualizedincreasestoanaverageof3%.Therefore,thedeathcountsfrompolicyyear15wereincreasedbyafactorof1.036inordertoidentifythenumber

ofdeathsexpectedwithoutanyvaluefrominitialunderwriting.Sheadjustedthedeathcountsforpolicyyears16through20inasimilarmanner.Forexample,sheincreaseddeathcountsfrompolicyyear19byafactorof1.032,andfrompolicyyear20byafactorof1.03.

Willeatusedtheultimateratesforquinquennialagebandstoimprovecredibility.Thisresultrequireda1.02overall“true-up”factortomatchtheactualexperience.TheWorkGroupthenidentifiedatrendlinebyapplyingGompertz’slaw(mortalityincreasesexponentially)throughtheExcelGROWTHfunction.Thisresultedinapatternforages60to89(infive-yearagebands)thattheWorkGroupconsideredtobe

materiallyinconsistentwiththeactualdata,sotheWorkGroupmodifiedtheapproachtofindtwosuitabletrendlines.Then,theWorkGroupfollowedthroughwithapplyingselectionfactorsand

calculatedfinalactualtoexpectedfactors.Thefemaleactualdatahadonlyasmalldeparturefromages75to79,whilethemaleactualdatahadamajordeparturethattheWorkGroupdeemedunusable.TheWorkGroupdecidedtoapplythefemaleratiostothemaledatainordertogetabetterfit.

Next,theWorkGroupgraduatedthecurvesforages60to89usingtheKarup-Kingsix-pointgraduationtechniqueandthenextendedthetabletoyoungerandolderagesusingtheslopeofthe2012IAMasa

guide.Initially,theWorkGrouptriedtosettheexpectedmortalityratesatyoungeragesusing90%ofthe2012IAM,butiterativelyfoundthat100%formalesand120%forfemalesappearedtobeabetterfittotheactualdata.Forolderages,theWorkGroupflooredtheKarup-Kingresultsat107%ofthe2012IAMformalesand101%forfemales.SeeFigures1and2below.

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Figure1.MaleUltimateMortalityRates,AgeLastBirthday

100

06065707580859095100105110115

600

500

oke