a markov model of the cost-effectiveness of pharmacist ......ence in the baseline clinical inputs to...

102 Journal of Managed Care Pharmacy JMCP March 2013 Vol. 19, No. 2 www.amcp.org

A Markov Model of the Cost-Effectiveness of Pharmacist Care for Diabetes in Prevention of Cardiovascular Diseases: Evidence from Kaiser Permanente Northern California

Junhua Yu, PhD, MS; Bijal M. Shah, PhD; Eric J. Ip, PharmD, BCPS, CSCS, CDE; and James Chan, PharmD, PhD

ABSTRACT

BACKGROUND: It has been demonstrated in previous studies that pharma-cist management of patients with type 2 diabetes mellitus (T2DM) in the outpatient setting not only improves diabetes-related clinical outcomes such as hemoglobin A1c but also blood pressure (BP), total cholesterol (TC), and quality of life. Improved control of BP and TC has been shown to reduce the risks of cardiovascular disease (CVD), which has placed a heavy economic burden on the health care system. However, no study has evaluated the cost-effectiveness of pharmacist intervention programs with respect to the long-term preventive effects on CVD outcomes among T2DM patients.

OBJECTIVES: To (a) quantify the long-term preventive effects of pharma-cist intervention on CVD outcomes among T2DM patients using evidence from a matched cohort study in the outpatient primary care setting and (b) assess the relative cost-effectiveness of adding a clinical pharmacist to the primary care team for the management of patients with T2DM based on improvement in CVD risks with the aid of an economic model.

METHODS: Clinical data between the periods of June 2007 to February 2010 were collected from electronic medical records at 2 separate clinics at Kaiser Permanente (KP) Northern California, 1 with primary care physi-cians only (control group) and the other with the addition of a pharmacist (enhanced care group). Patients in the enhanced care group were matched 1:1 with patients in the control group according to baseline characteris-tics that included age, gender, A1c, and Charlson comorbidity score. The estimated 10-year CVD risk for both groups was calculated by the United Kingdom Prospective Diabetes Study (UKPDS) Risk Engine (version 2) based on age, sex, race, smoking status, atrial fibrillation, duration of dia-betes, levels of A1c, systolic BP (SBP) and TC, and high-density lipoprotein cholesterol (HDL-C)observed at 12 months. There was no statistical differ-ence in the baseline clinical inputs to the Risk Engine (A1c [P = 0.115], SBP [P = 0.184], TC [P = 0.055], and HDL-C [P = 0.475]) between the 2 groups. A Markov model was developed to simulate the estimated CVD outcomes over 10 years and to estimate cost-effectiveness. The final outcomes examined included incremental cost and effectiveness measured by life years and per quality-adjusted life year gained. Both deterministic sensitivity analysis (SA) and probabilistic SA were conducted to examine the robustness of the results.

RESULTS: The estimated risks for coronary heart disease (CHD) and stroke (both nonfatal and fatal) at the end of the follow-up were consistently lower in the enhanced care group compared with the control group, even though baseline risks in both groups were similar. The absolute risk reduction (ARR) between the enhanced care and control groups increased over time. For example, the ARR for nonfatal CHD risk in year 1 was 0.5% (1.2% vs. 0.7%), whereas the ARR increased to 5.5% in year 10 (14.8% vs. 9.3%). Similarly, the ARR between the enhanced care and the control groups was

RESEARCH

calculated as 0.3% for fatal CHD in year 1 and increased to 4.6% in year 10. Results from the Markov model suggest that the enhanced care group was shown to be a dominant strategy (less expensive and more effective) compared with the control group in the 10-year evaluation period in the base-case (average or mean results) scenario. Sensitivity analysis that took into account the uncertainty in all important variables, such as wage of pharmacists, utility weight (the degree of preference individuals have for a particular health state or condition), response rate to pharmacists’ care, and uncertainty associated with the estimated 10 years of CVD risk, revealed that the relative value of enhanced care was robust to most of the variations in these parameters. Notably, the level of cost-effectiveness measured by net monetary value depends on the time horizon adopted by the payers and the magnitude of CVD risk reduction. The enhanced care group has a higher chance of being considered as a cost-effective strategy when a longer time horizon such as a minimum of 4 to 5 years is adopted.

CONCLUSIONS: Adding pharmacists to the health care management team for diabetic patients improves the long-term CVD risks. The longer-term CVD risk reductions were shown to be more dramatic than the short-term reduction. A longer time horizon adopted by health plans in managing T2DM patients has a higher probability of making the intervention cost-effective.

J Manag Care Pharm. 2013;19(2):102-14

Copyright © 2013, Academy of Managed Care Pharmacy. All rights reserved.

•The economic burden associated with cardiovascular disease(CVD) complications among type 2 diabetes mellitus (T2DM)patients issubstantial.Adultswithdiabetesare2-4timesmorelikelytohaveheartdiseaseorstroke,andthetotaldirectmedicalcarecosts for treating1typicalestablishedCVDpatientcanbe$18,953peryear.

•PharmacistmanagementofpatientswithT2DMintheoutpatientsetting not only improves diabetes-related clinical outcomessuchashemoglobinA1cbutalso the secondaryoutcomes suchas bloodpressure (BP) and cholesterol levels,which are highlypredictiveofCVDrisks.

•Pharmacist disease management programs have been shownto be cost-effectivewhen considering labor andprogram costs.However, no studies have evaluated the cost-effectiveness ofpharmacistinterventionprogramswithrespecttothelong-termCVDoutcomesamongT2DMpatientsusingarigorousmodelingapproach.

What is already known about this subject

www.amcp.org Vol. 19, No. 2 March 2013 JMCP Journal of Managed Care Pharmacy 103


wasconductedinKaiserPermanente(KP)NorthernCalifornia.A teamof 16 primary care physicians (PCPs) in the InternalMedicineDepartmentreferredtheirdiabeticpatientswithpoorglycemiccontrol(i.e.,A1c>7%)totheclinicalpharmacistformorestringentcontrolandmedicalfollow-up.Thepharmacistmanaging patients in this study was clinically trained, hav-ing completed aDoctor of Pharmacy (PharmD)degree and a1-year post-doctoral pharmacy residency and having earnedaCertifiedDiabetesEducatorcredential.Thepharmacistpre-scribed and adjusted medications, ordered laboratory work,ordered and administered immunizations, provided diabetesself-management education, and worked to optimize overallglycemicandcardiovascularcareofpatients.ThestudyfoundthatintheenhancedcaregroupthemeanA1cdecreasedfrom9.5%to6.9%comparedwith9.3%to8.4%inthecontrolgroup(P <0.001)after12months.8Comparedwiththecontrolgroup,the enhanced care group increased theprobability of achiev-ing anA1c <7% (odds ratio (OR) 3.9,P <0.001), low-densitylipoproteincholesterol (LDL-C)<100milligramsperdeciliter(mg/dL;OR2.0,P =0.0152),BP<130/80millimeterofmercury(mmHg; OR 2.0, P =0.0016), and all 3 goals simultaneously(OR3.2,P =0.0004).The10-yearcoronaryheartdisease(CHD)riskdecreasedfrom16.4%to9.3%intheenhancedcaregroupcomparedwith17.4%to14.8%inthecontrolgroup(P < 0.001).8

Giventheclinicalbenefitsofpharmacist interventionpro-grams for diabetic patients, it is important to quantify theireconomicvalueincontrollinglong-termCVDriskfactorsfromthepayerperspective.Twostudieshaveexploredtheeconomicimpact of pharmacist interventions in managing diabeticpatients.However, furtherdataareneeded toexpand the lit-eraturetodocumentthepreventiveoutcomesofCVDrisksandaddressmethodologicalissuesposedbytheexistingliterature.

TheAshevilleProjectwas1of the first large-scale studiestodocumentthepositiveimpactofapharmacist-rundiabetesmanagement programon clinical and economic outcomes inthecommunitypharmacysetting.9Morerecently,theDiabetesTenCityChallenge reported similar results.10 Although bothstudiesattemptedtoassesstheeconomicoutcomesofpharma-cistintervention,theyreliedonevidencecollectedfromcom-munitypharmacysettingsingeographicallydistinctlocations.Furthermore,bothstudiesutilizedoutcomesmeasuredusingapre-/post-comparisonstudydesignwithoutusingaconcurrentcontrol group. This study design has 2 main disadvantages:inabilitytocontrolforconfoundingfactorschangingoverthefollow-upperiodandregressionto themean—bothofwhichmaybias thestudyoutcomes. Inaddition,of the2economicstudiesavailable(withacontrolgroup),only1evaluatedphar-macistcareintheoutpatientsetting;however,itwaslimitedbydocumentingonlyprogramcharacteristicsandlaborcosts.11,12 In summary, previous economic evaluations have the draw-backsofeitherrelyingonevidencelackinggeneralizabilityor

Type2diabetesmellitus(T2DM)isanindependentriskfactor for cardiovasculardisease (CVD)and is increas-ingly being recognized as a controllable risk factor.

Adultswithdiabetesare2-4timesmorelikelytohaveaheartdiseaseorastroke,whichisthecauseofdeathinatleast65%ofthesepatients.1Thetotaldirectmedicalcarecostsfortreat-ing an establishedCVDpatient canbe$18,953per year.2 Inparticular, the costs forpatientswhoexperience a secondaryCVDhospitalizationare4.5timeshighercomparedwiththosewhoavoidsubsequentinpatientstays.2Thus,alargeproportionoftheeconomicburdenofT2DMcanbeattributedtocardio-vascular complications, and substantial cost savings can beachievedthrougheffectivepreventionofCVD.

PharmacistcareforT2DMpatientscanbeinstrumental inthepreventionofCVD.Ithasbeendemonstratedinnumerousstudies that pharmacistmanagement of patients with T2DMin theoutpatient setting can improveglycemic levels suchashemoglobin A1c3 alongwith blood pressure (BP), cholesterollevels,4,5 and overall quality of life (QOL).3,4,6 Since BP andcholesterol levels are significant predictors of long-termmac-rovasculardiseasesuchascoronaryarterydisease,stroke,andperipheral vascular disease,7 better control of these clinicalmarkerswouldpresumablydecreaseCVDriskamongdiabeticpatients.Theinterventionweattemptedtoevaluateinthisstudy

•Theestimatedlong-termrisksforcoronaryheartdisease(CHD)andstroke(bothnonfatalandfatal)attheendoffollow-upwereconsistently lower in the enhanced care group comparedwiththe control group, even though baseline risks in both groupsweresimilar.

• The absolute risk reduction (ARR) between the enhanced careand control groups increasedover time.For example, theARRin the nonfatalCHD risk in year 1was 0.5% (1.2% vs. 0.7%),whereastheARRincreasedto5.5%inyear10(14.8%vs.9.3%).Similarly, theARRbetween the enhanced care and the controlgroups was calculated as 0.3% for fatal CHD in year 1 andincreasedto4.6%inyear10.

•TheMarkovmodelconstructedusingdatafromamatchedcohortstudydemonstratedthattheenhancedcaregroupdominatedthecontrol groupwith lower treatment cost ($35,740 vs. $44,528)per patient andmore life years (8.9 vs. 8.1 years) and quality-adjusted life-years (QALY;5.51vs. 5.02years)over the10-yearperiod.

•The resultswere robust to almost all possible variationsof therelevant parameters in the model except for the time horizonof thehealthplan.Basedonour research, ifhealthplanswerewillingtopay$50,000/QALY,itwouldtakeatleast4-5yearsfortheadditionofclinicalpharmaciststothehealthcareteamtobecost-effectivewithoutanyuncertainty.

What this study adds



omittingeconomiccostsrelevanttopayers.Mostimportantly,noneofthesestudiesevaluatedthecost-effectivenessofphar-macist intervention programs with respect to the long-termCVDoutcomesamongT2DMpatientsusinga rigorousmod-eling approach.Theneed for data on long-term effectivenessof treatmentstrategiesamongdiabeticpatientshas longbeenrecognized.13

Therefore,theoverallgoalsofthisstudyareto(a)quantifythelong-termpreventiveeffectsofpharmacistinterventiononCVDoutcomesamongT2DMpatientsusingevidence fromamatched cohort study in the outpatient primary care settingand(b)assesstherelativecost-effectivenessofaddingaclinicalpharmacist to the primary care team for themanagement ofpatientswithT2DMbasedonimprovementinestimatedCVDriskswiththeaidofaneconomicmodel.

■■  MethodsModel OverviewAMarkovmodel with 1-year cycles was developed to simu-late CVD events and death risk for 2 hypothetical cohortsofpatients: (1) theenhancedcaregroupwherepatientsweremanagedbyaclinicalpharmacistwhowasintegratedaspartoftheprimarycareteamasaproviderand(2)thecontrolgroupwhere patients received only the usual care of primary carephysicians(PCPs).Figure1depictsasimplifiedpresentationofthemodel.14Patientsinbotharmsenteredthemodelthroughthe“well”state.Aspatientsagedduringthe10-yearsimulation,theycouldremainfreeofevents,developafirstCHDorstroke,develop recurrent CHD or stroke (either fatal or nonfatal),or die from the events or other natural causes in anymodelcycle.Weassumed thatpatientswhohadexperienceda firstCHDeventorstrokewouldcontinuetobeexposedtotheriskforsubsequentstrokesorCHDevents,withamaximumof3nonfatalevents(1strokeand2CHD,2strokesand1CHD,3

CHDor3strokes).14ThismodelwasaMarkovmodelwith11mutually exclusive health states to take into account variouscombinationsofamaximumtotalof3strokesandCHDevents(well, freeofhistoryof events, survival fromprimary stroke,survival from second stroke, survival from third stroke, sur-vivalfromprimaryCHD,survivalfromsecondCHD,survivalfrom third CHD, survival from CHD once and stroke once,survivalfromCHDtwice[once]andstrokeonce[twice],eventdeath,andotherdeath).Basedonthe total timespent in thedifferenthealthstates, themodelestimatedtheexpectedsur-vivalforpatientsineachtreatmentarm,anditwascombinedwithcostandqualityoflife(QOL)datatoestimatetherelativecost-effectiveness.ItisworthnotingthatstrokeandCHDhaddifferingimpactsontheutilityandcostsaccumulatedoverthetimehorizonofthemodel.Theutilityreductionforstrokewasdifferent than that forCHD; in addition, the treatment costsforCHDandstrokealsovaried.Therefore,itwasimportanttohavedifferenthealthstatestoidentifytheexacttypeofeventsandthenumberofeventsfromthepointofviewofcalculation.Forexample,oncepatientsenteredthehealthstateofsurvivalofprimarynonfatalCHD,therewere6possibilitiesoftransi-tionsinthenextcycle:(1)transittothestateofsurvivalfromCHDonceandstrokeonceifanonfatalstrokehappened;(2)transittoeventdeathifafatalstrokehappened;(3)transittosurvival from secondCHD if a nonfatal CHD happened; (4)transittosurvivalofeventdeathifafatalCHDhappened;(5)remaininthestateofsurvivalfromprimaryCHDifnoeventhappened; and (6) other nonevent death.CVD risks used inthe Markov model were estimated using United KingdomProspectiveDiabetesStudy(UKPDS)RiskEngine(version2)8,15 withclinicaloutcomesmeasuredinthematched-cohortstudyastheinputs.8Specifically,itwasdevelopedbasedonlongitu-dinaldataandtookintoaccountfactorssuchasage,sex,race,smokingstatus,atrialfibrillation,durationofdiabetes,levelsofA1c,systolicBP(SBP),totalcholesterol(TC)andhigh-densitylipoprotein cholesterol (HDL-C) after diagnosis of T2DM.15,16 TheinputsfortheRiskEnginearediscussedinthefollowingsections(Note:theclinicalvaluesusedasmodelinputsintheUKPDSRiskEnginearepartofthefindingsofanotherstudybyIpetal.8).

Model InputsClinical inputs for CVD risk prediction.ClinicalinputsusedfortheestimationofCVDriskswerecollectedfromaretrospec-tivematched-cohortstudythattookplacein2clinicsbasedoutofKPNorthernCalifornia,alargehealthmaintenanceorgani-zation(HMO)withanintegratedhealthcaremodel.KPmain-tainscomprehensivemedicalutilizationdatathatincludesanelectronicmedicalrecordforallpatientencounters,laboratoryresults,andprescriptionsusingstandardizedmethods.

Thesampleselectionmethods for thisstudyaredescribedelsewhere.8Briefly,dataforindividualpatientswerecollectedconcurrently from 2 medical facilities for the purpose of

No history of CVD

History of CHD

History of both

History of stroke

Death due to CHD

Death due to other causes

Death due to stroke

FIGURE 1 Markov Model for CVD Events for Patients with Type 2 Diabetes

CHD = coronary heart disease; CVD = cardiovascular disease.



comparison: the enhancedcaregroup (204patients) and thecontrol group (407 patients). The 2-site design was utilizedto address concerns that chronic disease state managementstudiesmay be susceptible to contamination, since pharma-cistscouldhavebeeninvolvedincaringforthecontrolgrouppatients.3 Also, the 2-site design eliminated the potentialfor a “learning effect” at the same sitewherephysiciansmay“learn” from the recommendations made by the pharmacistand topass these recommendationson topatientsnotbeingseen by the pharmacist.8 To furtherminimize selection biasandensurepatientsbetweenthe2groupswerecomparableinbaselineCVDrisks,patientsfromthe2groupswerematchedbasedonthefollowingcharacteristicsatbaseline:age(within2 years difference), gender, A1c (within 0.8 difference), andCharlsonComorbidityIndex(CCI;within4-pointdifference).WecalculatedCCIbasedonthefollowingbaselinecharacter-istics inbothgroups:CVDwithmildorno residualorministroke, CVD/stroke/myocardial infarction, dementia, chronicpulmonary disease, ulcer, mild liver disease, hemiplegia,tumor, metastatic solid tumor, leukemia, lymphoma, humanimmunodeficiency virus but no acquired immunodeficiencysyndrome (AIDS), and AIDS. This resulted in 147 matchedpatientsineachgroup,respectively,inthefinalanalysis.8TheprimaryclinicalinputsintheRiskEngine,includingA1c,SBP,TC,andHDL-Cobtainedattheendofthe12-monthfollow-upforbothgroups,wereusedtoestimatethe10-yearCVDrisk.AsshowninPartAofTable1,therewerenosignificantdiffer-ences inthebaselinevaluesof the followingvariables,whichweretheprimaryclinical inputs fortheUKPDSRiskEngine:A1c (P =0.115), SBP (P =0.184), TC (P =0.055), and HDL-C(P =0.475).8However,patientsintheenhancedcaregrouphadsignificant improvements relative to patients in the controlgrouponA1c(P <0.001),SBP(P <0.001),andTC(P <0.001)attheendof the12-month follow-up.NodifferenceswereseenforHDL-C(P = 0.2835).8

Transition probabilities.Eacharrow inFigure1 indicates atransition fromonehealthstate toanother,whichoccuredatyearlyintervals.WeusedtheUKPDSRiskEnginetoestimatethe transition probabilities of various CVD events: absoluterisk of fatal and nonfatal CHD and stroke.With the predic-tionalgorithminthisRiskEngine,theobservedeffectsofA1c,SBP, TC, andHDL-C, alongwith other characteristics at theindividualpatientlevel(PartAinTable1),weretranslatedtotheexpected10-yearrisksforCHDeventsandstroke(PartBinTable1).PartBinTable1reportsthemeanrisksforthe147patientsinbothgroups.AsshowninPartATable1,therewasnosignificantdifferencebetweenthe2groupsinthe4majorclinical inputsatbaseline.Therefore, it isnot surprising thatthere were no significant differences between the 2 groupsin the predicted 10-year CVD risk at baseline as a result ofmatching(P =0.18forCHDandfatalstroke;P =0.176forCHD;

P =0.243 for stroke).8 Other transition probabilities includeage-dependentnoncardiovascularmortalityrates,whichwerederivedfromtheU.S.VitalStatistics.17ItisalsoassumedthatanonfatalstrokeorCHDeventwouldincreasemortality,withtheincreasedriskmeasuredbytherelativerisk(RR)ofdeathafter the event. According to the literature, the RR for deathafterstrokeandCHDisestimatedtobe2.318and3.719(Table2),respectively.

Part B in Table 1 displays the CVD risk estimated bythe UKPDS Risk Engine based on the data collected at the12-month follow-up.These estimateswereused as transitionprobabilitiesforthe2hypotheticalcohortsof55-year-olddia-beticpatientsintheMarkovmodel.After12monthsoffollow-up, the estimated risks for CHD and stroke (both nonfatalandfatal)wereconsistentlylowerintheenhancedcaregroupcompared with the control group. The yearly risk increasedas the patients aged over the 10-year period in each group.However,itisworthnotingthatabsoluteriskreduction(ARR)betweentheenhancedcaregroupandcontrolgroupincreasedovertime.Forexample,theARRinthenonfatalCHDriskinyear1was0.5%(1.2%vs.0.7%),whereastheARRincreasedto5.5%inyear10(14.8%vs.9.3%)infavoroftheenhancedcaregroup.Similarly,theARRbetweentheenhancedcontrolgroupandcaregroupwascalculatedas0.3%forfatalCHDinyear1andincreasedto4.6%inyear10.

Cost inputs and utilization of medical resources.Wecon-ductedtheanalysisfromtheperspectiveofathird-partypayer.Assumptionsabout thecostofCHDandstrokewerederivedfromtheliteratureasshowninTable3.WeusedtheMedicalConsumer Price Index to generate an inflation factor, whichwasappliedtothepastcostfigurestoconvertitto2011U.S.dollars. A 3% discount rate was applied to all costs in bothbranchesatbase-caseanalysis.

The total monthly average diabetes-related drug cost perpatientwascalculated taking intoaccount thatpatientsuseddifferent types ofmedications. Information used in this cal-culation included the percentage of patients taking 1, 2, or3 types ofmedications in each group, the averagewholesaleprice, and the medication adherence rate. For example, theaveragemonthlycostoforaldiabeticagents=%ofpatientson1agent×priceof30-daysupplyofmetformin+%ofpatientson2agentsofmetforminandglipizide×priceof30-daysupplyofthe2agents+%ofpatientsonthe3agentsofmetformin,glipizide, andpioglitazone×price of 30-day supplyof the3agents. For other medications, including insulin, antihyper-tensivemedications, andantihyperlipidemicmedications, themonthlycostperpatientwasapproximatedbythepercentageof patients on a type ofmedicationmultiplied by the corre-spondingdrugcosts(Table3).

Totalwagepaidtothepharmacistperpatientperyearwascalculatedbasedonthehourlywagerateandthetotalnumber



ofhoursspentoninitialconsultationandfollow-upphonecallspermonth.Theaverage lengthofconsultationwasestimatedtobe45minutesfortheinitialconsultation(facetoface)and15 minutes for each follow-up visit (typically via telephone)as recorded from the clinical study. The average number ofinitialconsultationandfollow-upvisitswereidentifiedintheenhancedcaregroupasshowninTable3.Thetotalphysicianvisitfeeperyearwascalculatedbythefeepervisitmultipliedbytheaveragenumberofvisitsperyearineachgroup.

Utility Measures TheutilitymeasuresforeachofthehealthstatesintheMarkovmodelwereobtainedeither fromthe literatureorwerebased

onassumptionsdue to lackofevidence.Utilityweight is thedegree of preference individuals have for a particular healthstateorcondition.Thisweightcanvarybetween0and1.Bydefinition,avalueof1representsperfecthealthandavalueof0representsdeath.Adiscountrateof5%wasappliedtobothbranches.

Allassumptionsweresubjectedtosensitivityanalysis(SA).Themodel assumed thatpatientshave lowerutility after thefirsteventofCHDorstrokethanpatientswhoremainfreeofCVDevents. Inaddition,patientswillexperienceadisutilityforeachadditionalCHDorstrokeafterthefirstevent.Theutil-ityvaluesusedinthestudyareshowninTable2.

A. Major Clinical Inputs to UKPDS Risk Engine Version 2

Major Clinical InputsSBP

(mmHg)

Baseline 12-Month Follow-Up

HbA1c (%)

TC (mg/dL)

HDL-C (mg/dL)

SBP (mmHg)

HbA1c (%)

TC (mg/dL)

HDL-C (mg/dL)

CG(n=147) Mean 131 9.3 189.6 43.4 131 8.4 179.2 44.4 95%CI (128.6,133.4) (9.0, 9.5) (182.2,196.9) (41.9,45.0) (128.9, 133.1) (8.1,8.7) (172.0,186.4) (42.6,46.1)

ECG(n=147) Mean 128.9 9.5 179.4 44.6 126.2 6.9 154.4 44.9 95%CI (126.3,131.6) (9.2,9.7) (172.6,186.2) (42.9,46.2) (123.5, 128.9) (6.8,7.1) (149.1,159.7) (43.3,46.6)

PValuea 0.184 0.115 0.055 0.475 0.001 < 0.001 < 0.001 0.2835

B. CVD Risk Estimation Based on the 12-Month Clinical Inputs by UKPDS Risk Engine Version 2

Type of CVD Risk

Coronary Heart Disease Stroke

Nonfatal Fatal Nonfatal Fatal

Forecast Year Group Mean (%) 95% CIb(%) Mean (%) 95% CIb(%) Mean (%) 95% CIb(%) Mean (%) 95% CIb(%)

1 CG 1.20 (0.8, 1.8) 0.70 (0.5, 1.1) 0.50 (0.3, 0.8) 0.10 (0, 0.1) ECG 0.70 (0.5, 1.0) 0.40 (0.3, 0.5) 0.40 (0.2,0.6) 0.00 (0, 0.1)2 CG 2.50 (1.7,3.6) 1.50 (1.0, 2.3) 1.10 (0.7,1.7) 0.10 (0.1, 0.3) ECG 1.50 (1.1, 2.0) 0.80 (0.6,1.1) 0.80 (0.5, 1.3) 0.10 (0.1, 0.2)3 CG 3.80 (2.7,5.4) 2.40 (1.6,3.6) 1.70 (1.1,2.6) 0.20 (0.1,0.4) ECG 2.30 (1.7,3.1) 1.20 (0.9,1.7) 1.30 (0.8, 2.1) 0.20 (0.1, 0.3)4 CG 5.20 (3.7,7.4) 3.30 (2.3,4.9) 2.40 (1.5,3.6) 0.30 (0.2,0.6) ECG 3.10 (2.3,4.2) 1.70 (1.2,2.4) 1.90 (1.2, 3.0) 0.20 (0.1, 0.5)5 CG 6.70 (4.7,9.3) 4.30 (2.9,6.3) 3.10 (2.0,4.7) 0.40 (0.2, 0.8) ECG 4.00 (3.0,5.4) 2.20 (1.6,3.1) 2.50 (1.5, 3.9) 0.30 (0.2,0.6)6 CG 8.20 (5.8, 11.3) 5.40 (3.7,7.7) 4.00 (2.5, 5.9) 0.50 (0.3, 0.9) ECG 5.00 (3.7,6.6) 2.80 (2.1, 3.9) 3.20 (2.0, 5.0) 0.40 (0.2, 0.8)7 CG 9.70 (7.0,13.4) 6.50 (4.5,9.3) 4.90 (3.1,7.2) 0.70 (0.4,1.2) ECG 6.00 (4.5,8.0) 3.50 (2.5,4.8) 3.90 (2.4,6.2) 0.50 (0.3, 0.9)8 CG 11.40 (8.1, 15.5) 7.70 (5.3, 10.9) 5.90 (3.8,8.7) 0.80 (0.5,1.4) ECG 7.00 (5.3,9.4) 4.20 (3.0, 5.8) 4.80 (3.0,7.5) 0.60 (0.3, 1.1)9 CG 13.10 (9.4,17.7) 8.90 (6.2,12.5) 7.00 (4.5,10.2) 1.00 (0.5,1.7) ECG 8.10 (6.1,10.8) 4.90 (3.5,6.8) 5.80 (3.6,8.9) 0.70 (0.4,1.4)10 CG 14.80 (10.6,20.0) 10.30 (7.2,14.3) 8.30 (5.3, 12.0) 1.10 (0.6,1.9) ECG 9.30 (6.9,12.4) 5.70 (4.1,7.9) 6.80 (4.2,10.5) 0.90 (0.5,1.6)

aP values were obtained from nonparametric Wilcoxon signed-ranks tests.bUKPDS Risk Engine (version 2) provided the 95% CI for each patient for each type of CVD risk and the means of those risks for the 147 patients in each group were reported.CG = control group; CI = confidence interval; CVD = cardiovascular disease; ECG = enhanced care group; HbA1c = hemoglobin A1c; HDL-C = high-density lipoprotein cholesterol; SBP = systolic blood pressure; TC = total cholesterol; UKPDS = United Kingdom Prospective Diabetes Study.

TABLE 1 Major Clinical Inputs to UKPDS Risk Engine Version 2 and Estimated CVD Risks



parameter indicating the proportion of patients in theenhancedcaregroupwhomightnot respond to the inter-vention and revert to their baseline A1c, SBP, and lipidlevels.Themean1-to10-yearriskswerethenrecalculatedusing thebaselinevaluesof thoseclinicalmarkers insteadof the12-monthvalueas theprediction input.Hence, thetransition probabilities of CVD risks for the enhanced

Model OutputsThekey outcomes of interest in themodelwere incrementalcostperquality-adjustedlife-year(QALY)gainedandlife-yearsgained. The total costs per patient calculated for each armincluded costs for medications, treatment costs for nonfatalCVDevents, laborcostsforpharmacists,andphysiciansvisitfee.Life-yearswerecalculatedwithdifferenttimehorizonsofthemodel.

Major model assumptions.1. Thebase-casescenarioofthisMarkovmodelassumedthat

the cohort of simulatingpatients in both groups achievedthe mean values of clinical outcomes (A1c, SBP, TC, andHDL-C) observed at the end of the 12-month follow-upperiod.

2. For each set of clinical inputs, UKPDS generated a 95%confidence interval (CI) around the estimatedmean risks.Meanrisksbasedonclinicalinputsatthe12-monthfollow-up were used in the base-case analysis, while the upperboundsof the95%CIof riskswasevaluated in theworstcase scenario and the lowerboundsof the95%CI of therisksinthebestcasescenario.

3. Itwasassumedinthebase-caseanalysisthattheprobabilitiesofthesecondandthirdeventwouldbethesameasthatofthefirst-timeeventpredictedbytheUKPDS.InrecognitionofthefactthattheUKPDSequationisforafirsteventonly,20 theassumptioninthebase-caseanalysiswasrelaxedinthescenarioanalysiswherethemeanoddsratioofthesecondorthirdeventwas2.33andrangedfrom1.67to2.79.

4. To account for the possibility that not all patients willachieve the observed average outcome, we varied the

CostValue for Base-Case Analysis

Range for Sensitivity Analysis Source

CVD eventsCHD $37,462 ±50%base Strakaetal.37

Stroke $32,916 ±50%base Strakaetal.37

Labor costHourlywageofpharmacist $56.20 $40-$80 BureauofLabor

Statistics38

Physicianvisitfee $60 $50-$100 KaiserPermanente39

Utilization of medicationsPercentageusinginsulin(ECG/CG)a

0.6/0.1 ±50%base

Ipetal.8PercentageusingBPdrugs(ECG/CG)

76.20/75.50 ±50%base

Percentageusingantihyperlipidemicmedications(ECG/CG)

89.80/81.60 ±50%base

Average cost of medication per montha

Insulinb $80.50 $50-100Priceswere

obtainedfromwww.drugs.com

LipidandBPmedicationsc $36.50 $25-50

Oraldiabeticagents (ECG/CG)d

$52.30/$44.50 $30-60

Utilization of other medical resources Numberofpharmacist face-to-facevisitse

1.2 1.1-1.3

Ipetal.8Numberoffollow-up phonecallse

9.5 8.4-10.6

Numberofphysicianvisitse 2.3 2.1-2.5

Baselineadherence rateofdrugs

65% 60%-70% Murrayetal.22

Improvementin adherencerate

15% 10%-30% Murrayetal.22

aThis is the price for a 30-day supply adjusted by the proportion of patients using the medications.bThe average whole sale price of Novolin N/R was used because this is the drug typically used according to the pharmacists in charge of this study. cThe sum of whole sale price of most prevalent statin and lisinopril was used in the calculation of the average costs adjusted by the percentage of patients on those medications.dThe price was the average price of 3 types of oral agents (metformin, glipizide, and Actos) weighted by the percentage of patients using 1, 2, and 3 different types of agents in the enhanced care group and control group.eData were obtained from the study sample as described in the Ip et al. study (2012).8

BP = blood pressure; CG = control group; ECG = enhanced care group.

TABLE 3 Parameters of Cost and Utilization of Medical Services Used in the Model

Base Case

Range for Sensitivity Analysis Sources

Utilities for each health states in the Markov modelPost-CHD 0.77 ±30%base Gageetal.40

Post-STKa 0.675 (0.6-0.9) Lampeetal.18

AdditionalCHD -0.055 ±30%base Clarkeetal.41

AdditionalSTK -0.164 ±30%base Clarkeetal.41

Well 0.782 ±30%base Clarkeetal.41

Death 0Relative risk of mortality: increase in risk of mortality relative to all causes mortalitySTK 2.315 (1-4.6) Lampeetal.18

CHD 3.716 (3-4.7) Dennisetal.19

aTo estimate the stroke utility in the main analysis, it was assumed in the study that 70% of initial strokes were nondisabling, 15% partially disabling, and 15% dis-abling, based on data from the literature. CHD = coronary heart disease; CVD = cardiovascular disease; STK = stroke.

TABLE 2 Utility Weights for Health States and Increased Risk of Death Due to CVD Events



This study was approved by both Touro University-California Institutional Review Board (IRB Application no.P-0909) and KP Northern California Institutional ReviewBoard(IRBApplicationno.CN-09EIp-01-H).

■■  Results Base-Case and Scenario Analyses Table4presentstheresultsoftheeconomicevaluationoftheenhancedcaregrouprelativetothecontrolgroup.Themodelsuggests that the enhanced control group dominated the controlgroupwithlowertreatmentcost($35,740vs.$44,528)perpatientandmorelifeyears(8.9vs.8.1)andQALY(5.51vs.5.02)overthe10-yearperiod.Threetypesofscenarioanalysiswere conducted. The dominant results of the enhanced caregroupremainedevenafterevaluatingtheworstcasescenario,whentheupperboundofthe95%CIoftheriskswasused,andthebest-casescenario,whenthelowerboundsofthe95%CIoftheriskswereapplied.Inthethirdscenario,thetransitionprobabilities for the first-timeCVDevent remained the sameas thebasecase,but therisks for thesecondandthirdCVDeventswereset to2.33timeshigher thantheestimated first-timeprobabilitybyUKPDS.20Again, thedominant resultsoftheenhancedcaregroupwererepeated.

Sensitivity Analysis Multiple one-way SA. Appendix A shows a selection of theone-waySA(tornadodiagram)forthenetmonetarybenefitofusingenhancedcare.Inthisdiagram,eachbarrepresentstheimpactofuncertaintyinanindividualvariableontheresults.The horizontal bar was generated for each selected variablewhen the baseline estimate of the variable was varied overplausibleranges(tables2and3),withawiderbarindicatingagreaterpotentialeffectonthemonetarybenefit.Allparameterswerevariedaroundthebase-casevaluewithinacertainrangeasspecified.Thevariableidentifiedinthetornadodiagramashavingthelargestimpactonthenetmonetarybenefitgivenathreshold willingness to pay at $50,000/QALY was the timehorizonof this analysis, followedby theutility of thehealthstatus fordiabeticpatients freeofCVDevents (AppendixA).However,allvariations innetmonetarybenefitsdueto thesevariableswerewithin the positive range, demonstrating thattheenhancedcaregroupremainedthepreferredstrategy.Alltheremainingvariableshadnearlynoimpactonthenetmon-etarybenefits.

One-way SA. One-way SA was conducted to examine theindividualimpactoftheadoptionofdifferenttimehorizonsonthestudyoutcomes.Itisworthnotingthatasthetimehorizonwasextendedfortheanalysis,thenetmonetarybenefitsoftheenhancedcaregroupversus thecontrolgroup increased (seeAppendix B). It seems that aminimumof 4 years is neededfor the interventionprogram to achieve higher netmonetary

caregroupwerethencalculatedasaweightedaverage:thepercentageofpatientswhowillmaintaintheimprovedout-comes×CVDrisksestimatedusingthe12-monthclinicalinputs + (1- the percentage of patientswhowillmaintainthe improved outcomes) × CVD risk estimated using thebaselineclinicalinputs.

5. It was assumed that the control group had continued tomaintainthebaselineadherencerateof65%,whereastheenhancedcaregrouphadachievedahigheradherencerateand,hence,hadhighermedicationcosts (basedonexpertopinionandotherpublishedevidence).22

Base-Case Analysis and Scenario AnalysisIn the base-case analysis, the Markov model was estimatedusing themeanCVD risks for the 10-year follow-up period.Theincrementalcost-effectivenessratio(ICER)wascalculated,andtheanalysiswasthenrepeatedfor2otherscenarioswherethe lowerboundandtheupperboundof95%CIwereused.TreeAgePro23wasusedtoperformdecisionanalyses.AthirdscenarioanalysiswasconductedassumingthesecondorthirdCVDeventwas2.33timesmorelikelytooccurthanestimatedbytheUKPDSRiskEngine.

Sensitivity Analysis SAwasconductedtotesttherobustnessofthebaselineresults.ThreetypesofSAwereperformed:one-waySA,multipleone-waySA,andprobabilisticSA.Allthevariableswereexaminedusing the plausible range specified in tables 1 and 2 for thedeterministic SA. Finally, Monte Carlo simulation was con-ductedboth for thebase-case and third scenario to allowallvariablestovarysimultaneouslyinafurtherefforttoassesstherobustnessofourfindings.Forthisanalysis,variablesrelatedto costs were assigned log-normal distributions; number ofvisitsandfollow-upphonecallswereassignedPoissondistri-bution;whileallothersweregivenuniformdistributionwithintheassumedplausiblerange.

Scenario Strategy Cost ($)Life

Years QALY

BasecaseECG 35,740 8.90 5.518

DominatedCG 44,528 8.10 5.020

Scenario1: Lowrisk

ECG 29,580 9.40 5.783DominatedCG 36,462 8.80 5.413

Scenario2: Highrisk

ECG 42,792 8.32 5.166DominatedCG 51,628 7.28 4.568

Scenario3: 2ndand3rdeventwithhigherrisk

ECG 30,503 8.87 5.420DominatedCG 50,679 7.71 4.810

CG = control group; ECG = enhanced care group; QALY = quality-adjusted life-year; T2DM = type 2 diabetes melletus.

TABLE 4 Cost-Effectiveness of Pharmacist Intervention in T2DM Patients: 10-Year Horizon



benefitsthanthecontrolgroupwhileholdingallotherparam-eters at the base-case scenario. However, this threshold wasshortenedto3.3yearswhenthescenariowaschangedtothesituation that the second and third event had higher odds(OR=2.33)thanthefirstevent.

Probabilistic sensitivity analysis results.Acost-effectivenessacceptability curve (Figure 2) showed the likelihood of theintervention being considered cost-effective compared withthe control care at various levels of willingness to pay bythepayers.Enhanced carewas shown tohave a consistentlyhigher chance of being the favored strategy regardless of thelevelofwillingnesstopay(WTP)ifalongertimehorizonwasadopted:10yearsforbasecasescenario(Figure2)and7yearsforthethirdscenario.Inbothsituations,theshorterthetimehorizon adopted, the lesser the chance for enhanced care tobepreferred.Ifa1-to3-yeartimehorizonwasadopted,add-ingpharmacists into thehealth care teambecame less likelytobepreferredthanusingaPCPonlyinbothcases.ItseemsthatatthelevelofWTPequalto$50,000/QALY,a5-yeartimehorizoninthebasecaseistheminimumlengthoftimefortheprogramtobecomethefavoredstrategygiventheuncertaintyofotherfactors.However,theminimumlengthwasshortenedto4yearsinthethirdscenarioanalysis,whensecondorthirdeventswerepresumedtooccuratahigherratewithORof2.33.

Therefore,probabilisticSAresultsreinforcedtheimportanceofcorrelationbetweenthelengthofinterventionandthemagni-tudeofpreventiveeffectsinCVDriskreductionindeterminingthelikelihoodofcost-effectivenessofenhancedcare.

■■  DiscussionOur study, a comprehensive cost-effectiveness analysis of apharmacist-led diabetes management intervention in a pri-mary care setting, shows that pharmacist intervention canhelp reduce the long-term CVD risk among patients withT2DM while reducing costs and increasing QALYs. Takingintoaccountthelong-termcostsavingsassociatedwithfewerCVD events, enhanced care (which involves the pharmacistasamemberoftheprimarycareteam)isadominantstrategy(cost and life saving) comparedwith the control group (PCPonly).Thefindingsofthestudywerequiterobust.Theresultssuggested that itwouldbe cost-effective to incorporatephar-macists into the health care management team for diabeticpatients.

It is worth mentioning that previous literature has alsodocumented the effectiveness of a variety of interventionsby other health care professionals aswell.Diabetesmanage-ment interventions delivered individually or as a team byphysicians,nurses,pharmacists,andotherdiabeteseducators

FIGURE 2 Cost-Effectiveness Acceptability by Time Horizon of the Model

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Prop

ortio

n of

Mon

te C

arlo

Sim

ulat

ions

Favo

ring

Phar

mac

ist I

nter

vent

ion

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000

Decision Makers’ WTP ($/QALY)

1 year 4 years 5 years 10 years

CE = cost-effectiveness; QALY = quality adjusted life years; WTP = willing to pay.



have been shown to improve patient outcomes in numerous studies.24 However, our study differs from previous stud-ies in the intervention strategy, sample populations, andthe outcomes and time horizons examined. In order fordecision makers to gauge the potential of cost-effective-ness of our pharmacist-led intervention relative to othertypes of intervention, it is necessary to compare the stud-ies that evaluated other types of intervention with ours. Arecent cost-effectiveness analysis study found that nurse specialists gave diabetes care according to a pre-set protocolthat was similar to care provided by physicians in terms ofqualityof life andeconomicvaluebutwithpotential savingsduetolowerlaborcosts.25Inarecentmeta-analysisof11pre-definedqualityimprovement(QI)strategiesfordiabetes,itwasshown that themost effectiveQI strategywas team changes(i.e.,thepharmacistornursehasanactiveroleinpatientmoni-toringandadjustingdrugregimens)withfurtherA1creductionof 0.33% (95%CI 0.28-0.45; 120 trials), LDL cholesterol by0.10millimolesperliter(mmol/L;0.05-0.14;47trials),SBPby3.13(2.19-4.06;65trials),anddiastolicbloodpressure(DBP)by1.55mmHg(0.95-2.15;61mmHgtrials)versususualcare.26 Ofparticular importance,QIstrategies thatallowedpharma-cists and specialist nurses to make independent changes todrugtherapieswerefoundtobemosteffective.Thisisexactlythemajorfeatureofourpharmacist-ledinterventionprogram.OurinterventionprogramhasachievedameanA1creductionfrom 9.5% to 6.9% comparedwith the reduction from 9.3%to 8.4% in the control group after 12months, resulting in a1.7% improvement (P <0.001). The patients under enhancedcare also experienced amean reduction of LDLby 10.1mg/dL,areductionofSBPby2.6mmHg,andareductionofDBPby2.3mmHg.AccordingtotheUKPDS,a1%decreaseinA1cisassociatedwitha37%reductioninmicrovascularcomplica-tionsanda21%reduction in the riskofanydiabetes-relatedcomplication or death.19 The degree of A1c reduction due toenhancedcarewouldpotentiallytranslateintoa96%decreasein microvascular complications and a 55% reduction in anydiabetes-related complication or death.26 Using the UKPDSRiskEngine,wefoundthata2.6%reductioninA1camongtheenhancedcaregroupreducedtheir10-yearnonfatalCHDriskfrom16.4%to9.3%(a43%reduction)andthefatalCHDriskfrom11.3%to5.7%(a50%reduction).8

In terms of the findings about costs, available data fromthe Asheville Project, a longitudinal pre/post cohort study,showedthatdiabetespatientsreceivingcarefromacommunitypharmacist reduced theirmean total directmedical costs by$1,200whilemaintainingclinicallymeaningfulimprovementsintheirA1covera5-yearfollow-upperiod.9TheDiabetesTenCityChallenge,amultisitecommunitypharmacyhealthman-agementprogram fordiabetespatients, also showeda$1,079inaveragetotalhealthcarecostsperpatientperyear.10Inour

study,wefoundthattheaverageannualcostsoverthe10-yearperiodwas$3,574intheenhancedcaregroupversus$4,453in the care group at base case (Table 4),which translates to$879savingsperyear.Anothereconomicstudyevaluatingthecost-effectivenessofstrategiesformanagingpeopleathighriskfordiabetes foundthat theannualdiabetes-relatedcostofanaveragepatientwithdiabeteswas$4,121.27Therefore,thecostsincurredbyourinterventionandcostsavingsduetotheinter-ventionarecomparabletootherinterventionsaswell.IntermsofQALY, the study focusingon life-style intervention amongdiabetic patients found that theQALY in the 30-year periodwascalculatedas11.47827withanaverageof0.38QALYperyear.AnotherstudyconductedinEurope28foundthatintensivetreatmentamongdiabeticpatientswith18.6yearsofsurvivalhadaQALYof10.2,withanaverageof0.5QALYperyear,anditofferedaQALYgainof0.094peryearcomparedwithcon-ventionaltherapy.Incontrast,ourstudyofthe10-yearoutcomefoundthattheQALYperyearaverages0.45-0.57QALY(Table4), offering a QALY gain of 0.04-0.07 per year. The higherQALY per year compared with the life-style study might beduetothedifferenceinthecohortsofpatientsinthe2studiesandthemodelhorizon.Thepreviousstudyincludedpatientswithhigherriskfordiabetesinthemodel,andtheytendedtobeolderandthushavelowerqualityoflife.However,thelowerQALYper year comparedwith theEuropean studymightbebecausetheutilityweightassignedtodiabeticpatientsis0.814asopposedto0.782inourstudy.

Notably,theSAsuggestedthat1ofthedrivingfactorsofthecost-effectivenessofthisprogramisthetimehorizonadoptedby decision makers. This is partly because the longer-termCVD risk reduction wasmore dramatic than the short-termreductionasestimatedby theUKPDSRiskEngine.Basedonour research, if health plans were willing to pay $50,000/QALY,itwouldtakeatleast4-5yearsfortheadditionofaclini-calpharmacisttothecareteamtoensurecost-effectivenesstocoveralluncertaintiesfactoredintothemodel.

One of the strengths of our study is that the model wasprimarily based on real-world data, which included clinicaleffectivenessandhealthcareutilizationdatacollectedfrom2comparableclinicswithmatchingbaselinedatatoreducebias.This isan improvementcomparedwithpreviousstudies thatlacked comparison groups and had short follow-up periods(lessthan1year),smallersamplesizes,unequalbaselinechar-acteristics across groups, and lack of cardiovascularmarkerssuchasBPandlipidlevels.3Also,aMarkovmodelwasusedtosimulate2matchedhypotheticalcohortsofpatientstoestimatethelong-termCVDriskreduction.

Another unique contribution of our modeling study wasthe use of the UKPDS Risk Engine in the projection of car-diovascularoutcomesoverthelongtermbasedonshort-termclinical surrogates for T2DM patients. The Risk Engine was



addition, this study also brings attention to the finding thatthe lengthof theenrollmentperioddeterminestheeconomicbenefits of the pharmacists’ preventive efforts. It emphasizesthenotion thatpayersmightnotbeable to reap thebenefitsofpreventive interventionprograms targetedatpatientswithchronic diseases unless they can keep the patients in theirplansforacertainperiodoftime.

Limitations There are limitations to this study worthy of discussion.First, this study relies heavily on evidence generated from aretrospectivecohortanalysisandwasperformedat2medicalfacilitiesusingaquasi-experimentalstudydesign.Arandom-izedcontrolled study includingagreaternumberofpatients,involvingmorepharmacistproviders and incorporatingmul-tiplemedicalfacilitieswouldfurtherenhancethequalityoftheclinicalandeconomicevidence.

Second, there were different aspects to the pharmacistintervention in this study. However, the quasi-experimentalstudydesigndidnotallowustoestimatetheindividualeffectsof those actions on the costs and benefits. Specifically, theimprovedoutcomesinthepharmacistinterventiongroupmayhavebeenbecauseofchangesindrugdosing,changesofdrugregimen,theface-to-faceclinicalconsultation,frequentfollow-up phone visits, or improved medication adherence. Thoseactionswerenotcomparedbetweengroupsdirectly;therefore,it is impossible to disentangle the possiblemultidimensionaleffectsofvariousinterventionstrategies.Consequently,itmaybedifficult fordecisionmakers todecideon the focusof theintervention.Ideally,ifitcouldbeestablishedthatthefrequentpharmacistfollow-upphonevisitshelpedimprovemedicationoptimizationandadherencerates,thentheprovidermightbeinterestedininvestinginaninterventionprogramwiththesefeatures.34,35 If it were the face-to-face contact with pharma-ciststhatwasmoreeffective,thentheprovidermightneedtomakesureadequatephysicalspace(i.e.,examroomorprivateoffice) is available in the clinical setting.However, the studywasnotabletoidentifywhichmanagementschemewouldhelpcontributemosttotheimprovedoutcomes.Futurestudiesarenecessary to examine the direct impact of specific manage-mentstrategiesonthecost-effectivenessofvariouspharmacistinterventionprograms.

Arelatedconcern is that thepatients in thecontrolgroupintheretrospectivestudyalsoexperiencedvariousdegreesofimprovementinoutcomesattheendof12months.8Similarly,there is noway to identify the specific causes in the controlgroup responsible for the improvement. If actions taken bypharmacistsintheenhancedcaregroupalsooccurredinthecontrolgroup,suchasmonitoringandcorrectionof inappro-priatemedication use, this could lead to biased estimates ofthe impactof the intervention.However, thiswouldbias the

derived from UKPDS data and was designed to reflect riskin the generalT2DMpopulation; therefore, it providedmorerelevantpredictionsthantheFraminghamscoreadoptedbyarecentpublication.29The reliabilityofusing theFraminghamequation tocalculateCHDrisk indiabetespatientshasbeenquestionedbecauseofthesmallproportionofdiabetespatientsintheFraminghamstudy.30Forexample,Ladhanietal.(2012)utilizedtheUKPDSRiskEngine,whichisconsideredamorereliable method of measuring cardiovascular risk in T2DMpatients.31 One of the concerns for those adopting the inter-vention program in the long termmight be the difficulty inmaintaining the improved outcomes observed at the 1-yearfollow-up.Thisconcernwasaddressedinourstudybyvaryingtherangesofthekeyvariables—thepercentageofpatientswhowouldbeabletomaintaintheachievedoutcomesfrom50%to100%intheSA.Notably,thisvariablehadminimumimpactonthenetmonetarybenefit,suggestingthattheapproachwouldstillbecost-effectiveevenifaproportionofpatientswouldnotbe able tomaintain the identified improvement as shown inthisstudy.

As the prevalence of diabetes continues to increase andchanges in the health care system because of the PatientProtection Affordable Care Act take effect, millions of newpatients are expected to need care. The ongoing shortage ofprimarycarepractitioners,coupledwiththe increasingnum-bers of patients with T2DM, pose significant challenges tothe health care system.32 In the face of this shortage, severalhealth care professionals have stepped in to fulfill this role.Arecentmeta-analysis showed that team-based interventions(i.e.,pharmacistornurse involved inpatientmonitoringandadjustingdrugregimens)wasthemosteffectivediabetesman-agement strategy comparedwith several others.26 In a recentreporttotheU.S.SurgeonGeneral,pharmacistsweresingledoutashealthcareprofessionalswhomanagedisease throughmedications and other patient care services but are not rec-ognizedashealthcareprovidersbynationalhealthpolicy inspite of evidence to the contrary.32Given their expertise andexperience, coupled with their accessibility, pharmacists areuniquely positioned to play amuch larger role in the healthcaredeliverysystem.

Currently,theaccountablecareorganization(ACO)conceptis being promoted as an integrated approach to managingchronic conditions such as diabetes and is partly driven bythe Affordable Care Act.33 Although the act was not explicitaboutincludingpharmacistsaspartofthehealthcareteam,33 theU.S.SurgeonGeneralrecentlyrecognizedthat“pharmacypracticemodels(implementedincollaborationwithphysiciansoraspartofahealthteam)improvepatientandhealthsystemoutcomes and optimize primary care access and delivery.”32

ThisstudyprovidesfurtherevidenceofthebeneficialclinicalandeconomicpotentialofincludingpharmacistsinACOs.In



results of the study against the intervention group. Anotherrelatedconcernisthatourimplicationforhealthplansastothelengthofinterventionrequiredforthesamecohortofenrolleesin theirplans isnot clear-cut.Although4-5years are identi-fiedtobethepreferredtimehorizon,thechoicealsodependsonthemagnitudeofCVDriskreduction.FouryearsisfoundtobetheminimumlengthwhensecondorthirdCVDeventshavehigheroddstooccur,whilealongerperiodof5yearsisneededwhenassumingallCVDeventsoccurattheprobabilityofafirst-timeevent.

Finally,pharmacistsalarieshavebeenasourceofconcernforproviderswhenconsideringtheimplementationofthissortofprogram.11This study suggested that thepharmacist costshave no significant impact on the cost efficiency of imple-mentingtheprogramintheKPHMOsetting.Thereareafewreasonswhyweshouldbecarefulingeneralizingtheresultstootherclinicalsettings.Forexample,otherhealthcaredeliverysystemsmight differ fromKP inmajor areas such as facilityinfrastructure, logistical planning, upfront training costs ofpharmacists qualified for managing medication therapy, thenatureoftheintervention,andthepatientpopulation—allofwhichmightinfluencethecostcalculationalgorithm.Inaddi-tion, the KP diabetes population is drawn from an insuredpopulationinnorthernCaliforniaandmaynotberepresenta-tive of other geographic regions, the United States, or otherhealthplans.36

■■  ConclusionsIn this analysis, we estimated that adding pharmacists tothe health care team for the direct management of diabeticpatientssignificantlyimprovedlong-termCVDrisks.Theesti-matedlonger-termCVDriskreductionappearsmoredramaticthan the short-term reduction. The results of the economicmodelsuggestthatwhetherpharmacist interventionprovidesa cost-effective management tool crucially depends on thelengthoftheeffectiveperiodofintervention,whichislargelydeterminedby the lengthof time that thepatient is enrolledin the health plan.Considering all sources of uncertainty, itseemsthataminimumof4-5yearsofconsistentenrollmentisrequiredfortheinterventiontobecost-effectivewithoutmuchuncertainty.Thestudyprovidesinsightsthatwillbebeneficialforpayers todeterminewhether it is feasible toaddpharma-ciststothehealthcareteamforthedirectmanagementofdia-betespatients.Futureresearchisneededtoimproveknowledgeabout the relative cost-effectiveness of the specific interven-tionsperformedbypharmacistsindifferentclinicalsettings.

JUNHUA YU, PhD, MS, is Assistant Professor, Department of Social, Behavioral, and Administrative Science; BIJAL M. SHAH, PhD, is Associate Professor, Department of Social, Behavioral, and Administrative Science; and ERIC J. IP, PharmD, BCPS, CSCS, CDE, is Associate Professor, Department of Pharmacy Practice, Touro University College of Pharmacy, Vallejo, California. ERIC J. IP, PharmD, BCPS, CSCS, CDE, is also Diabetes Clinical Pharmacist, Department of Internal Medicine, Kaiser Permanente Mountain View Clinics, Mountain View, California. JAMES CHAN, PharmD, PhD, is Pharmacy Quality and Outcomes Coordinator, Kaiser Permanente Medical Care Program, Pharmacy Outcomes Research Group, Oakland, California.

AUTHOR CORRESPONDENCE: Junhua Yu, PhD, MS, Assistant Professor, Department of Social, Behavorial, and Administrative Science, Touro University College of Pharmacy, 1310 Club Dr., Mare Island, Vallejo, CA 94592. Tel.: 707.638.5913; E-mail: [email protected].

Authors

DISCLOSURES

This research was funded by the American Association of Colleges ofPharmacy(AACP)NewPharmacyFacultyResearchAwardsprogram.

ConceptanddesignwereperformedbyYu,Shah,Ip,andChan.DatawerecollectedbyYu,Ip,andShahandinterpretedbyIp,Chan,Yu,andShah.Yu,Shah,Ip,andChanwroteandrevisedthemanuscript.

REFERENCES

1.UniversityofPittsburghDiabetesPreventionSupportCenter.Diabetes&relatedconditions—cardiovasculardisease.Availableat:http://www.diabetesprevention.pitt.edu/cardiovasculardisease.aspx.AccessedFebruary26,2013.

2.PascoJA,JackaFN,WilliamsLJ,etal.Clinicalimplicationsofthecytokinehypothesisofdepression:theassociationbetweenuseofstatinsandaspirinandtheriskofmajordepression.Psychother Psychosom. 2010;79(5):323-25.

3.WubbenDP,VivianEM.Effectsofpharmacistoutpatientinterventionsonadultswithdiabetesmellitus:asystematicreview.Pharmacotherapy. 2008;28(4):421-36.

4.KoenigsfeldCF,HorningKK,LogemannCD,SchmidtGA.Medicationtherapymanagementintheprimarycaresetting:apharmacist-basedpay-for-performanceproject.J Pharm Pract. 2012;25(1):89-95.

5.PadiyaraRS,D’SouzaJJ,RihaniRS.Clinicalpharmacistinterventionandtheproportionofdiabetespatientsattainingpreventionobjectivesinamul-tispecialtymedicalgroup.J Manag Care Pharm.2011;17(6):456-62.

6.KraemerDF,KradjanWA,BiancoTM,LowJA.Arandomizedstudytoassesstheimpactofpharmacistcounselingofemployer-basedhealthplanbeneficiarieswithdiabetes:theEMPOWERstudy.J Pharm Pract. 2012;25(2):169-79.

7.ClarkePM,GrayAM,BriggsA,etal.Amodeltoestimatethelifetimehealthoutcomesofpatientswithtype2diabetes:theUnitedKingdomProspectiveDiabetesStudy(UKPDS)OutcomesModel(UKPDSno.68).Diabetologia.2004;47(10):1747-59.

http://www.diabetesprevention.pitt.edu/cardiovasculardisease.aspx

http://www.diabetesprevention.pitt.edu/cardiovasculardisease.aspx



8.IpEJ,ShahBM,YuJ,ChanJ,NguyenLT,BhattDC.Enhancingdiabetescarebyaddingapharmacisttotheprimarycareteam:ananalysisofshort-termclinicalmarkersandlong-termcardiovascularoutcomes. Am J Health-Syst Pharm.[inpress].

9.CranorCW,BuntingBA,ChristensenDB.TheAshevilleProject:long-termclinicalandeconomicoutcomesofacommunitypharmacydiabetescareprogram.J Am Pharm Assoc (Wash).2003;43(2):173-84.

10.FeraT,BlumlBM,EllisWM.DiabetesTenCityChallenge:finaleconom-icandclinicalresults.J Am Pharm Assoc (2003).2009;49(3):383-91.

11.RothmanRL,SoSA,ShinJ,etal.Laborcharacteristicsandprogramcostsofasuccessfuldiabetesdiseasemanagementprogram.Am J Manag Care.2006;12(5):277-83.

12.TaylorSJ,MilanovaT,HourihanF,etal.Acost-effectivenessanalysisofacommunitypharmacist-initiateddiseasestatemanagementservicefortype2diabetesmellitus.Int J Pharm Pract.2005;13(1):33-40.

13.Jacobs-vanderBruggenMA,vanBaalPH,HoogenveenRT,etal.Cost-effectivenessoflifestylemodificationindiabeticpatients.Diabetes Care. 2009;32(8):1453-58.

14.RamseySD,ClarkeLD,RobertsCS,SullivanSD,JohnsonSJ,LiuLZ.Aneconomicevaluationofatorvastatinforprimarypreventionofcardiovasculareventsintype2diabetes.Pharmacoeconomics.2008;26(4):329-39.

15.StevensRJ,KothariV,AdlerAI,StrattonIM.TheUKPDSriskengine:amodelfortheriskofcoronaryheartdiseaseinTypeIIdiabetes(UKPDS56).Clin Sci (Lond).2001;101(6):671-79.

16.KothariV,StevensRJ,AdlerAI,etal.UKPDS60:riskofstrokeintype2diabetesestimatedbytheUKProspectiveDiabetesStudyriskengine.Stroke. 2002;33(7):1776-81.

17.AriasE.UnitedStateslifetables,2006.Natl Vital Stat Rep.2010;58(21):1-40.

18.LampeFC,WhincupPH,WannametheeSG,ShaperAG,WalkerM,EbrahimS.Thenaturalhistoryofprevalentischaemicheartdiseaseinmiddle-agedmen.Eur Heart J.2000;21(13):1052-62.

19.DennisMS,BurnJP,SandercockPA,BamfordJM,WadeDT,WarlowCP.Long-termsurvivalafterfirst-everstroke:theOxfordshireCommunityStrokeProject.Stroke.1993;24(6):796-800.

20.PrescottA,BaileyJE,KellyKJ,MunyombweT,GrayA,SummersLK.Theeffectivenessandcostofsingleandmulti-factorialcardiovascularriskfactormodificationtoguidelinetargetsintype2diabetes.Prim Care Diabetes.2012;6(1):67-73.

21.HaffnerSM,LehtoS,RonnemaaT,PyoralaK,LaaksoM.Mortalityfromcoronaryheartdiseaseinsubjectswithtype2diabetesandinnondia-beticsubjectswithandwithoutpriormyocardialinfarction. N Engl J Med. 1998;339(4):229-34.

22.MurrayMD,YoungJ,HokeS,etal.Pharmacistinterventiontoimprovemedicationadherenceinheartfailure:arandomizedtrial.Ann Intern Med. 2007;146(10):714-25.

23.TreeAgePro.TreeAgeSoftware,Inc.Williamstown,MA.

24.RendersCM,ValkGD,GriffinSJ,WagnerEH,EijkVanJT,AssendelftWJ.Interventionstoimprovethemanagementofdiabetesinprimarycare,outpatient,andcommunitysettings:asystematicreview.Diabetes Care. 2001;24(10):1821-33.

25.ArtsEE,Landewe-CleurenSA,SchaperNC,VrijhoefHJ.Thecost-effec-tivenessofsubstitutingphysicianswithdiabetesnursespecialists:arandom-izedcontrolledtrialwith2-yearfollow-up.J Adv Nurs.2012;68(6):1224-34.

26.TriccoAC,IversNM,GrimshawJM,etal.Effectivenessofqualityimprovementstrategiesonthemanagementofdiabetes:asystematicreviewandmeta-analysis.Lancet.2012;379(9833):2252-61.

27.EddyDM,SchlessingerL,KahnR.Clinicaloutcomesandcost-effective-nessofstrategiesformanagingpeopleathighriskfordiabetes.Ann Intern Med.2005;143(4):251-64.

28.GaedeP,ValentineWJ,PalmerAJ,etal.Cost-effectivenessofintensifiedversusconventionalmultifactorialinterventionintype2diabetes:resultsandprojectionsfromtheSteno-2study.Diabetes Care.2008;31(8):1510-15.

29.AlMazrouiNR,KamalMM,GhabashNM,YacoutTA,KolePL,McElnayJC.Influenceofpharmaceuticalcareonhealthoutcomesinpatientswithtype2diabetesmellitus.Br J Clin Pharmacol.2009;67(5):547-57.

30.ColemanRL,StevensRJ,RetnakaranR,HolmanRR.Framingham,SCORE,andDECODEriskequationsdonotprovidereliablecardiovascularriskestimatesintype2diabetes.Diabetes Care.2007;30(5):1292-93.

31.LadhaniNN,MajumdarSR,JohnsonJA,etal.Addingpharmaciststoprimarycareteamsreducespredictedlong-termriskofcardiovasculareventsintype2diabeticpatientswithoutestablishedcardiovascular disease:resultsfromarandomizedtrial.Diabet Med. 2012;29(11):1433-39.

32.GibersonS,YoderS,LeeMP.Improvingpatientandhealthsystemout-comesthroughadvancedpharmacypractice:areporttotheU.S.SurgeonGeneral2011.OfficeoftheChiefPharmacist.U.S.PublicHealthService.December2011.Availableat:http://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf.AccessedFebruary19, 2013.

33.LiptonHL.Pharmacistsandhealthreform:goforit!Pharmacotherapy. 2010;30(10):967-72.

34.PietteJD,WeinbergerM,McPheeSJ,MahCA,KraemerFB,CrapoLM.Doautomatedcallswithnursefollow-upimproveself-careandglycemiccon-trolamongvulnerablepatientswithdiabetes? Am J Med. 2000;108(1):20-27.

35.PietteJD,McPheeSJ,WeinbergerM,MahCA,KraemerFB.Useofauto-matedtelephonediseasemanagementcallsinanethnicallydiversesampleoflow-incomepatientswithdiabetes.Diabetes Care.1999;22(8):1302-09.

36.KarterAJ,SchillingerD,AdamsAS,etal.ElevatedratesofdiabetesinPacificIslandersandAsiansubgroups:TheDiabetesStudyofNorthernCalifornia(DISTANCE).Diabetes Care.2012.Oct24.[Epubaheadofprint]

37.StrakaRJ,MamdaniM,DamenJ,etal.Economicimpactsattributabletotheearlyclinicalbenefitofatorvastatintherapy—aUSmanagedcareper-spective.Curr Med Res Opin.2007;23(7):1517-29.

38.U.S.DepartmentofLabor.BureauofLaborStatistics.Occupationalemploymentstatistics.AlphabeticallistofSOCoccupations.Availableat:http://www.bls.gov/oes/2002/oes_alph.htm#M.AccessedFebruary14,2013.

39.KaiserPermanente.2011samplefeelistforNorthernCalifornia.January2011.Availableat:http://www.personnel.saccounty.net/Benefits/Documents/2011SampleFeeList-Ncal.pdf.AccessedFebruary26,2013.

40.GageBF,CardinalliAB,AlbersGW,OwensDK.Cost-effectivenessofwarfarinandaspirinforprophylaxisofstrokeinpatientswithnonvalvularatrialfibrillation.JAMA.1995;274(23):1839-45.

41.ClarkeP,GrayA,HolmanR.Estimatingutilityvaluesforhealthstatesoftype2diabeticpatientsusingtheEQ-5D(UKPDS62).Med Decis Making. 2002;22(4):340-49.

http://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf

http://www.accp.com/docs/positions/misc/Improving_Patient_and_Health_System_Outcomes.pdf

http://www.bls.gov/oes/2002/oes_alph.htm#M

http://www.personnel.saccounty.net/Benefits/Documents/2011SampleFeeList-Ncal.pdf

http://www.personnel.saccounty.net/Benefits/Documents/2011SampleFeeList-Ncal.pdf



APPEnDIx A Tornado Diagram for Multiple One-Way Sensitivity Analysis

Years of time horizon: 1 to 10Utility of health status free of CVD event: 0.6 to 1Discount rate of utility: 0.0 to 0.1Cost of treatment of CHD ($): 18,731 to 56,193Discount rate of cost: 0.01 to 0.05Percentage of patients in EG maintaining the improved outcome: 0.5 to 1Cost of treatment for stroke ($): 16,458 to 49,374Utility of post 1 CHD: 0.7 to 0.84Medication compliance rate at baseline: 0.6 to 0.8Monthly cost of insulin ($): 50 to 100Monthly cost of lipid and pressure medications ($): 25 to 50Mean number of types of insulin in pharm’s group: 0.5 to 0.8Percentage of increase in adherence in EG: 0.1 to 0.3Average hourly wage for pharmacists ($): 40 to 100Utility of post-stroke: 0.6 to 0.74Utility loss for an additional stroke: 0.0117 to 0.213Relative risk of mortality post 1 CHD: 1 to 4.6Relative risk of mortality post 1 stroke: 1 to 4.6Number of follow-up consultations by pharmacist per year: 8.4 to 10.6Utility loss for an additional CHD: 0.0385 to 0.0715Relative risk of mortality after more than 2 events: 3 to 7Number of initial consultations with pharmacist: 1.1 to 1.3Number of visits to physician per year: 2.1 to 2.5Physician visit fee: ($) 50 to 200Mean number of types of insulin in CG: 0.1 to 0.2Percentage of patients in CG maintaining the improved outcome: 0.5 to 1

60K 160K 260K 360KNet Monetary Benefit

CG = control group; CHD = coronary heart disease; CVD = cardiovascular disease; EG = enhanced care group; K = thousands.

Effects on Net Monetary Benefit at Willingness to Pay=$50,000/QALY

Enhanced Care Group

70K

90K

110K

130K

150K

170K

190K

210K

230K

250K

1.0 4.0 7.0 10.0

Net

Mon

etar

y B

enef

it (W

TP =

50,0

00)

Number of Years Evaluated in Markov Model

Control Group

APPEnDIx B One-Way Sensitivity Analysis

WTP = willingness to pay.