ClinicoEconomics and Outcomes Research
`
`Dove
`
`8
`
`ORig inal REsEaRCh
`
`| on g -term cost-effectiven ess of a typica|
`an tipsych otics in the trea tmen t of a dults
`with schizoph ren ia in the Us
`
`This article was published in the following Dove Press journal:
`ClinicoEconomics and Outcomes Research
`12 September 2013
`
`Ken O’Day
`Krithika Ra jag opalan
`Kel | ie Meyer
`an drei Pikal dv
`an ton y oebel?
`'Xcen da, Palm harbor, Fl,
`2sun ovion Pharmaceuticals,
`Ma rl boroug h, Ma, Usa
`
`Correspon den ce: Ken O’Day
`global health Econ omicsand
`Outcomes Resea rch, Xcen da,
`4114 Wood! an ds Pkwy, suite 500,
`Pal mhaprbor, Fl 34685, Usa
`Tel +1 727 771 4156
`Fax +1 727 771 4144
`Ema il ken .oda y@xcen dauco
`
`Background:The purpose of this study was to evaluate the long-term cost-effectiveness
`(including hospitalizations and cardiometabolic consequences) ofatypical antipsychotics among
`adults with schizophrenia.
`Methods: A 5-year Markov cohort cost-effectiveness model, from a US payer perspective,
`was developed to compare lurasidone, generic risperidone, generic olanzapine, generic zip-
`rasidone, aripiprazole, and quetiapine extended-release. Health states included in the model
`were patients: on an initial atypical antipsychotic; switched to a second atypical antipsychotic;
`and on clozapine after failing a second atypical antipsychotic. Incremental cost-effectiveness
`ratios (ICERs) assessed incremental cost/hospitalization avoided. Effectiveness inputs included
`discontinuations, hospitalizations, weight change, and cholesterol change from comparative
`clinical trials for lurasidone and for aripiprazole, and the Clinical Antipsychotic Trials of
`Intervention Effectiveness for other comparators. Atypical antipsychotic-specific relative risk of
`diabetes obtained from a retrospective analysis was used to predict cardiometabolic events per
`Framingham body mass index risk equation. Mental health costs (relapsing versus nonrelaps-
`ing patients) and medical costs associated with cardiometabolic consequences (cardiovascular
`events and diabetes management) were obtained from published sources. Atypical antipsychotic
`costs were estimated from Red Book®prices at dose(s) reported in clinical data sources used in
`the model (weighted average dose of lurasidone and average dose for all other comparators).
`Costs and outcomes were discounted at 3%, and model robustness was tested using one-way
`and probabilistic sensitivity analyses.
`Results: Ziprasidone, olanzapine, quetiapine extended-release, and aripiprazole were dominated
`by other comparators and removed from the comparative analysis. ICER for lurasidone versus
`risperidone was $25,884/relapse-related hospitalization avoided. At a $50,000 willingness-to-
`pay threshold, lurasidone has an 86.5% probability ofbeing cost-effective, followed by a 7.2%
`probability for olanzapine, and 6.3% for risperidone. One-way sensitivity analysis showed the
`modelis sensitive to lurasidone and generic risperidone hospitalization rates.
`Conclusion: Generic risperidone is the least costly atypical antipsychotic. Lurasidone is more
`costly and more effective than risperidone and is cost-effective at willingness-to-pay thresholds
`of greater than $25,844 per hospitalization avoided. The favorable cost-effectiveness of lur-
`asidone1s driven by its clinical benefits (eg, efficacy in preventing hospitalizations in patients
`with schizophrenia) and its minimal cardiometabolic adverse effect profile.
`Keywords: cost-effectiveness, economic, model, schizophrenia, atypical antipsychotic
`
`Introduction
`The diagnosed prevalence of schizophrenia in the USis only 0.51%,! yet the disease
`imposes a significant burden on patients, caregivers, and society, resulting in an
`
`submit your manuscript
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`Cl in icoEcon omics an d Outcomes Research 2013:5 459-470
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`Ec)DE| © 2018 O’DayaitThis work is published by Dove Medical Press Ltd, and licensed under Creative Commons Aitriijution — Non Commercial (ung
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`O Da y et a l
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` estimated total annual excess cost of $62.7 billion in 2002 in
`the US.2 Schizophrenia is also one of the most challenging
`diseases to treat due to its variable presentation, the hetero-
`geneity of clinical response to treatment, poor adherence,
`and low rates of persistence with treatment.3,4
`Poor adherence to antipsychotic treatment has been
`shown to increase the risk of relapse and subsequent
` hospitalization and to increase related resource utilization
`and costs.3,5,6 According to 2008 data from the Healthcare
`Cost and Utilization Project, there were 356,000 hospital
`stays for schizophrenia and other psychotic disorders in
`the US, comprising 19% of all mental health and substance
`abuse-related hospitalizations. Patients admitted to hospital
`for schizophrenia have the highest average total cost per stay
`($7,500), with an average duration of 11.1 days.7 Patients
`who had experienced a relapse of psychotic symptoms
`within the previous 6 months incurred four times higher
`costs than schizophrenia patients without a recent relapse
`(P ,
` 0.01).8
`While atypical antipsychotics (AAPs) are relatively
`well tolerated, they are often associated with metabolic
`side effects. These adverse effects may include weight gain,
`hyperglycemia, insulin resistance, and lipid abnormalities.
`The American Diabetes Association Consensus on Antip-
`sychotic Drugs and Obesity and Diabetes recognizes that
`certain atypical antipsychotic agents are also associated with
`increased risk of developing metabolic syndrome, new-onset
`diabetes, and cardiovascular disease.9 It has been reported
`that patients taking AAPs have approximately two times the
`risk of metabolic syndrome and diabetes compared with the
`general population.10,11 In addition, patients on AAPs have
`been found to be 9% more likely to develop diabetes than
`those taking conventional antipsychotics.12,13 Metabolic side
`effects of atypical antipsychotics, especially weight gain, may
`contribute to premature treatment discontinuation and poor
`adherence,4,14 which can lead to symptom worsening, relapse,
`and greater health care resource utilization.15,16
`There has been continuing unmet clinical and economic
`need for new AAPs that not only effectively reduce the
`occurrence of acute relapses but also have a neutral or
`minimal impact on metabolic parameters. Such agents may
`have the potential to reduce the costs of care by reducing the
`incidence of new-onset diabetes or cardiovascular disease
`and/or improving treatment compliance and reducing acute
`exacerbations and subsequent hospitalizations. In clinical
`studies, lurasidone (Latuda®, Sunovion Pharmaceuticals,
`Marlborough, MA, USA), an AAP approved by the US Food
`and Drug Administration in October 2010, has demonstrated
`
`lower annual rates of relapses and relapse-related hospitaliza-
`tions compared with quetiapine extended-release. In addition,
`lurasidone also has been reported to have a more favorable
`cardiometabolic profile compared with other major AAPs in
`both clinical trials and in the real-world practice setting, thus
`potentially offering a cost-effective alternative therapy for
`patients with schizophrenia.17 Therefore, the objective of this
`health economic model was to assess the cost-effectiveness
`of lurasidone compared with other available generic and
`branded atypical antipsychotics in the treatment of schizo-
`phrenia from a US payer perspective, including direct medical
`costs; direct nonmedical costs and indirect costs, such as lost
`productivity, were not included in the model.
`Materials and methods
`Model desig n
`A Microsoft Excel®-based Markov cohort model was devel-
`oped to assess the cost-effectiveness of lurasidone compared
`with other AAPs available for treating adult patients with
`schizophrenia. Treatment comparators evaluated in the
`model included aripiprazole (Abilify ®, Bristol-Myers Squibb
` Company, Princeton, NJ, USA), lurasidone, olanzapine
`(generic), quetiapine extended-release (Seroquel XR®,
`AstraZeneca Pharmaceuticals LP, Wilmington, DE, USA),
`risperidone (generic), and ziprasidone (generic). The cost-
`effectiveness analysis was conducted over a 5-year time
`horizon from a third-party payer perspective in the US.
`Costs and outcomes associated with AAPs and incorpo -
`rating treatment switching were modeled using this Markov
`cohort analysis. Patients in the model start on lurasidone
`or another AAP (aripiprazole, olanzapine, quetiapine
` extended-release, risperidone, and ziprasidone). When
`patients discontinue the first AAP for any cause (adverse
`event or lack of efficacy), they transition to a composite
`AAP. The composite AAP therapy was incorporated into the
`model to account for treatment switching within a single
`state, as patients remain on the composite therapy until they
`fail due to lack of efficacy and transition to clozapine.18
`Patients failing the composite due to adverse events were
`assumed to remain on the composite and continue to incur
`the associated costs and outcomes, thereby simulating treat-
`ment switching (Figure 1). The composite therapy was opera-
`tionalized by averaging the discontinuation rates (transition
`probabilities), costs, and outcomes of the other comparator
`AAPs among which the patient might possibly switch (eg, if
`a patient initiates treatment with lurasidone, then the com -
`posite would reflect the average of aripiprazole, olanzapine,
`quetiapine extended-release, risperidone, and ziprasidone).
`
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`Cost-effectiven ess of a typical an tipsych otics
`
`Clozapine Discontinue
`
`Comparator
`
`Composite atypical
`antipsychotic
`
`Figure | Cost-effectiven ess model structure.
`
`any cause
`
`Patients discontinuing the composite health state due to
`lack of efficacy were considered to be refractory and were
`switched to clozapine. Patients switched to clozapine were
`projected to remain on clozapine for the remainder of the
`5-year analysis.
`Modeled costs in the analysis include pharmacy, mental
`health, diabetes management, and cardiovascular event-
`related costs inflated to 2012 USdollars using the Medical
`Care Componentofthe Bureau ofLaborStatisticsConsumer
`Price Index.!° The model outcome was relapse-related hos-
`pitalizations avoided, and the per patient mean value was
`estimated over the 5-year time horizon. Costs in the model
`include the cost of hospitalizations; hence, to avoid double-
`counting, the outcome represents the clinical benefit to
`patients associated with avoiding relapse and a subsequent
`hospitalization, and not cost savings” A standard discount
`tate of 3% was used for both costs and outcomes. The model
`
`comparators were ranked from least to most costly and then
`incremental cost-effectiveness ratios (ICERs), representing
`the difference in cost divided by the difference in outcome
`between the comparator agents, were calculated after exclud
`ing dominated and extendedly dominated options. One-way
`and probabilistic sensitivity analyses were also conducted to
`test model robustness.
`
`in puts
`Model
`Pa tien t cha ra cteristics
`
`The population for the model included adult patients diag
`nosed with schizophrenia. Patient characteristics for the base
`case scenario were specified to reflect the average schizo-
`phrenia patient enrolled in the lurasidoneclinicaltrials: male
`(73% of patients were male), age 38 years, weight 77.3 kg,
`body mass index (BMI) 26.3, with a mean total cholesterol
`of 192 mg/dL, high-density lipoprotein of 48 mg/dL, and
`systolic blood pressure (BP) of 120 mmHg.”! In addition,
`5.5% of patients were assumed to have diabetes and 67%
`to be smokers. In the model, patient gender, high-density
`lipoprotein, systolic BP, and smoking were static and did not
`change over time. Patient age, total cholesterol, and diabetes
`were a function oftime (age), therapy (diabetes), and time on
`therapy (total cholesterol). Patient characteristics were used
`
`Discontinue
`lack of efficacy
`
`to estimate the risk ofcardiovascular events and mortality in
`the model based on Framingham risk equations.#
`
`Effectiven ess pa ra meters
`Effectiveness parameters were obtained from adjusted
`indirect comparisons of various outcome measures from
`different clinical sources. The annual transition probabilities
`between the different Markov states were based on rates for
`
`total discontinuation, discontinuation due to lack ofefficacy
`(used to determine relapses), and hospitalizations (used to
`determine relapse-related hospitalizations) from four major
`data sources (Table 1).
`Discontinuation due to lack of efficacy and hospitat
`ization rates for olanzapine, risperidone, quetiapine, and
`ziprasidone were obtained directly from the first phase of
`
`Table | Discon tin ua tion an dh ospita| ization rates
`Input
`Base case
`SE
`Reference
`| ura sidon e
`
`0.534
`Total discon tin uation (%)
`OEfficacyDdiscontinuation(%) 0.143
`hospital ization (%)
`0.057
`Olanzapine
`0.491
`Total discon tin uation (%)
`OEfficacyDdiscontinuation(%) 0.099
`hospital ization (%)
`0.078
`Risperidon e
`0.588
`Total discon tin uation (%)
`OEfficacyOdiscontinuationO(%) 0.192
`hospital ization (%)
`0.105
`Quetia pin e
`0.678
`Total discon tin uation (%)
`OEfficacyDdiscontinuation(%) 0.196
`hospital ization (%)
`0.140
`Zipra sidon e
`0.649
`Total discon tin uation (%)
`OEfficacyDdiscontinuation(%) 0.168
`hospital ization (%)
`0.122
`a ripipra zol e
`0.662
`Total discon tin uation (%)
`OEfficacydiscontinuation(%) 0.183
`hospital ization (%)
`0.144
`Cl oza pine
`0.379
`Total discon tin uation (%)
`OEfficacyDdiscontinuation(%) 0.069
`hospital iza tion (%)
`0.052
`Abbreviation:sE, stan dard error.
`
`24
`
`4
`
`4
`
`4
`
`4
`
`25
`
`27
`
`0.041
`0.044
`0.026
`
`0.025
`0.014
`0.012
`
`0.025
`0.014
`0.014
`
`0.025
`0.018
`0.016
`
`0.034
`0.023
`0.020
`
`0.025
`0.014
`0.012
`
`0.061
`0.030
`0.010
`
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`Clinical Antipsychotic Trials of Intervention Effectiveness
`(CATIE), a prospective double-blind study of 1,493 patients
`with schizophrenia.4 Given that the first phase of the CATIE
`trial was an 18-month study, the discontinuation rates over
`18 months were adjusted to 12 months, assuming that the rate
`at which patients discontinued treatment was constant.
`Discontinuation and hospitalization rates for aripiprazole
`and lurasidone were based on an indirect comparison with the
`drugs included in the CATIE trial. Aripiprazole was directly
`compared with olanzapine in a long-term, open-label study,25
`in which the relative risk for all-cause discontinuation for
`aripiprazole was 1.349, and the relative risk of discontinu-
`ation due to lack of efficacy was 1.851. Therefore, the rates
`of total discontinuation and discontinuation due to lack of
`efficacy of olanzapine from the CATIE trial4 were multiplied
`by these relative risks to obtain estimates of the discontinu-
`ation rates for aripiprazole. Because no hospitalization rate
`was available for aripiprazole, estimation of this rate was
`based on the relative risk of lack of efficacy for aripiprazole
`in Chrzanowski et al25 and on the hospitalization rate for
`olanzapine from the CATIE trial.4
`In a similar manner, lurasidone was indirectly compared
`with the other model comparators using a direct comparison
`with quetiapine extended-release in a double-blind, parallel-
`group, 12-month comparative study.24 In that study, lurasi-
`done was found to have a relative risk of discontinuation for
`any cause of 0.787, a relative risk of discontinuation due to
`lack of efficacy of 0.728, and a relative risk of hospitaliza-
`tion of 0.404 versus quetiapine extended-release. These
`respective rates for lurasidone were then multiplied by the
`rates of discontinuation for any cause, rates of discontinua-
`tion due to lack of efficacy, and rates of hospitalization for
`quetiapine immediate-release from the CATIE trial to derive
`the respective rates for lurasidone. Based on the results of
`randomized clinical trials comparing quetiapine extended-
`release 400 mg/day and quetiapine immediate-release
`400 mg/day, it was assumed that quetiapine extended-release
`and quetiapine immediate-release have similar efficacy and
`safety profiles.26
`Finally, the rates for clozapine were based on an 18-month
`study by McEvoy et al27 and were adjusted to 12-month
`rates. Standard errors for the transition probabilities were
`calculated based on the proportions and sample sizes from
`the published studies.
`
`Ca rdiometa bol ic pa ra meters
`The model structure also incorporates the costs and out-
`comes of cardiometabolic consequences of treatment with
`
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`
`each AAP. Many AAPs have been associated with increased
`cardiovascular risk by causing weight gain, an increase in
`lipid levels, and a higher risk of diabetes.28 As such, cardio-
`metabolic parameters in the model included annual weight
`change (kg/year), annual cholesterol change (mg/dL/year),
`and diabetes relative risk (Table 2).
`The model incorporates these risk factors by utilizing
`data from comparative clinical trials4,24,25 of the rate of
`weight gain and lipid increase of AAPs and data from a
`large retrospective analysis of the risk of diabetes. 29 In order
`to track time on therapy to estimate the amount of weight
`gain and lipid increase, time-dependent substates (tunnel
`states) based on the time at which a segment of the cohort
`initiates a new therapy, were incorporated into the cohort
`analysis.18 For each annual cycle in the model, the age of
`the patient cohort increases, baseline weight is adjusted by
`time on AAP therapy, total cholesterol is adjusted by time
`on AAP therapy, and diabetes is adjusted by the relative
`risk based on the AAP. These adjusted risk factors were
`then applied to the Framingham 10-year cardiovascular
`risk profile using the Framingham BMI risk equation. 23
`
`Table 2 Ca rdiometa bol ic pa ra meters
`
`Input
`
`Base
`case
`l ura sidon e, weig h t ch a n g e (kg /yea r)0.70
`
`l ura sidon e, ch ol esterol ch a n g e
`0.0
`(mg /dl /yea r)
`1.00
`l ura sidon e, dia betes rel a tive risk
`Ol a n za pin e, weig h t ch a n g e (kg /yea r)10.91
`
`Ol a n za pin e, ch ol esterol ch a n g e
`6.3
`(mg /dl /yea r)
`1.15
`Ol a n za pin e, dia betes rel a tive risk
`Risperidon e, weig h t ch a n g e (kg /yea r)2.18
`-0.9
`
`Risperidon e, ch ol esterol ch a n g e
`(mg /dl /yea r)
`1.01
`Risperidon e, dia betes rel a tive risk
`Quetia pin e, weig h t ch a n g e (kg /yea r)2.73
`
`Quetia pin e, ch ol esterol ch a n g e
`4.4
`(mg /dl /yea r)
`1.20
`Quetia pin e, dia betes rel a tive risk
`Zipra sidon e, weig h t ch a n g e (kg /yea r)-1.64
`-5.5
`
`Zipra sidon e, ch ol esterol ch a n g e
`(mg /dl /yea r)
`1.00
`Zipra sidon e, dia betes rel a tive risk
`a ripipra zol e, weig h t ch a n g e (kg /yea r)2.06
`-1.00
`
`a ripipra zol e, ch ol esterol ch a n g e
`(mg /dl /yea r)
`1.00
`a ripipra zol e, dia betes rel a tive risk
`Cl oza pin e, weig h t ch a n g e (kg /yea r)2.73
`
`Cl oza pin e, ch ol esterol ch a n g e
`3.9
`(mg /dl /yea r)
`Cl oza pin e, dia betes rel a tive risk
`Abbreviation: sE, sta n da rd error.
`
`1.57
`
`SE
`
`Reference
`
`24
`
`a ssumption
`4
`
`29
`4
`
`29
`4
`
`29
`4
`
`a ssumption
`25
`
`a ssumption
`27
`
`1.64
`1.6
`
`0.04
`1.64
`1.6
`
`0.04
`1.64
`1.6
`
`0.04
`1.09
`1.6
`
`0.11
`1.64
`2.1
`
`0.04
`1.64
`1.6
`
`0.04
`2.73
`3.1
`
`0.15
`
`29
`
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`Cost-effectiven ess of a typica l a n tipsych otics
`
`Table 3 a n n ua l trea tmen t costs a n d resource util iza tion
`
`According to the BMI equation, cardiovascular risk is a
`function of age, BMI, untreated systolic BP, treated sys-
`tolic BP, smoking, and diabetes. Finally, the 10-year risks
`were adjusted to one-year risks to calculate the expected
`number of annual cardiovascular disease events based on
`the cohort risk factors.
`
`Morta l ity ra tes
`Published age-specific and gender-specific mortality tables
`were used to determine patient mortality over the 5-year
`time horizon.30 The mortality risk due to cardiovascular
`events and suicide was estimated separately in the model;
` therefore, the population mortality rates were adjusted
`to exclude the increased mortality risks associated with
`suicide and cardiovascular disease among patients with
`schizophrenia. Patient suicides were calculated based on
`the rate per 100,000 patient-years (mean 579, standard error
`52) from a published systematic review of suicide rates31
`and were assumed to be identical for all AAPs. Cardiovas-
`cular disease mortality was estimated by multiplying the
`number of patients experiencing a cardiovascular event by
`the fatal cardiovascular event rate (mean 9.5%, standard
`error 2.0%).32
`
`Drug costs a n d resource util iza tion
`Annual drug costs were estimated based on the wholesale
`acquisition costs of each AAP (generic olanzapine, generic
`risperidone, generic ziprasidone, aripiprazole, quetiapine
`extended-release, and clozapine) as reported in the Red
`Book® as of October 9, 201233 using the weighted average
`cost calculated based on the average daily dose from the
`CATIE trial (Table 3). Annual costs for lurasidone were
`based on patient utilization from a 12-month, multicenter,
`double-blind, parallel-group study of flexibly dosed lurasi-
`done (40 160 mg/day),24 in which 15% of patients received
`a dose of 40 mg or 80 mg and 85% of patients received a
`dose of 120 mg or 160 mg. Because lurasidone 160 mg was
`not an approved dose and was as effective as the 120 mg
`dose, for the purposes of the model, it was assumed that all
`patients receiving lurasidone 160 mg received lurasidone
`120 mg.
`Resource utilization costs were obtained from the pub-
`lished literature (Table 3). The annual costs of psychiatric
`care were obtained from a published prospective, observa -
`tional, noninterventional study of schizophrenia in the US
`comparing 310 patients with and 1,247 patients without a
`relapse.34 Relapse was defined as any of the following: psy-
`chiatric hospitalization, use of emergency services, use of a
`
`Base case
`
`$9,116
`$7,862
`$2,239
`$9,853
`$3,514
`$9,748
`$1,986
`
`Input
`a n n ua l trea tmen t costs
`
`l ura sidon e
` Ol a n za pin e
` Risperidon e
` Quetia pin e
` Zipra sidon e
` a ripipra zol e
` Cl oza pin e
`Resource util iza tion
` n o rel a pse cost
`
` Rel a pse (n o
`h ospita l iza tion ) cost
` Rel a pse (with
`h ospita l iza tion ) cost
` Dia betes ma n a g emen t$7,900
` Ca rdiova scul a r
`$40,637
`even t cost
`Note: a l l a moun ts sh own in Us dol l a rs.
`Abbreviation: sE, sta n da rd error.
`
`$11,535
`$13,358
`
`$44,223
`
`
`
`
`
`
`SE
`
`$456
`$393
`$112
`$493
`$176
`$487
`$99
`
`$481
`$962
`
`$4,326
`
`$790
`$4,064
`
`Reference
`
`24, 33
`4, 33
`4, 33
`4, 33
`4, 33
`25, 33
`27, 33
`
`34, 19
`34, 19
`
`34, 19
`
`35, 19
`32, 19
`
`crisis bed, or a suicide attempt, and was determined by sys -
`tematic abstraction of data from patients medical records.
`Drug costs were estimated using average wholesale price
`minus 15%, and psychiatric hospitalizations were based
`on per diem costs adjusted across sites using their relative
`value units. Cost components included costs of medica-
`tions (antipsychotics; other psychotropics, such as mood
`stabilizers, anticholinergics, antidepressants, antianxiety
`drugs; and sleep agents), psychiatric hospitalizations, day
`treatment, emergency services, psychosocial group therapy,
`medication management, individual therapy, and assertive
`community treatment/case management. Costs for patients
`with a relapse and a psychiatric hospitalization versus those
`with a relapse and no psychiatric hospitalization were dif -
`ferentiated by subtracting the hospitalization costs from the
`former group.
`The costs of diabetes management were obtained from a
`study35 published by the American Diabetes Association that
`estimated the annual attributable costs of diabetes based on
`data from multiple sources, including the Medical Expendi-
`ture Panel Survey. This estimate included costs associated
`with hospital inpatient care, outpatient and physician office
`visits, emergency visits, nursing facility stays, home health
`visits, visits with other health professionals, and prescrip-
`tion drug and medical supply use. The rate of diabetes was
`multiplied by the annual diabetes cost to estimate the total
`costs of diabetes.
`The annual costs of a cardiovascular event were esti -
`mated based on the one-month attributable costs from a
`
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`large administrative claims analysis in the US 32 using an
`incidence-based cost of illness that included costs for myo-
`cardial infarction, cardiac arrest, congestive heart failure,
`angina pectoris, transient ischemic attack, hemorrhagic
`stroke, ischemic stroke, peripheral vascular disease, coronary
`artery bypass graft surgery, and coronary angioplasty. The
`number of cardiovascular events was multiplied by the cost
`per event to estimate the total cost of cardiovascular disease
`events for the cohort.
`
`sen sitivity a n a l yses
`The robustness of the model results were tested using a one-
`way deterministic sensitivity analysis and a probabilistic
`sensitivity analysis. The one-way deterministic sensitivity
`analysis was conducted to quantify the impact of uncertainty
`around the mean value of individual model parameters. For
`the low and high values, the one-way sensitivity analysis used
`the 95% confidence interval based on the mean and standard
`error for all model parameters.
`In addition, sensitivity analyses using other scenarios
`listed below were conducted to evaluate the potential impact
`on the cost-effectiveness results:
` Using the Framingham lipid risk equation in place of the
`Framingham BMI risk equation
` Changing the discount rate from 3% to a range of
`0% 5%
` Running the analysis with pharmacy costs only and
`removing cardiometabolic costs.
`A probabilistic sensitivity analysis was conducted to
`quantify the impact of uncertainty of all model parameters by
`simultaneously sampling from the 95% confidence interval
`for each parameter distribution. Beta distributions were used
`for probabilities and percentages, log normal distributions
`were used for relative risks, and normal distributions were
`used for the remainder of parameters.
`
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`
`Results
`Over the 5-year time horizon of the model, generic ris-
`peridone patients had the lowest total discounted health
`care costs, followed by generic ziprasidone, lurasidone,
`generic olanzapine, and other branded products, quetiapine
`extended-release and aripiprazole (Table 4). Lurasidone
`was associated with the lowest number of relapse-related
`hospitalizations (0.40), followed by olanzapine (0.42).
`Aripiprazole had the highest number of relapse-related
`hospitalizations (0.49). Full disaggregated model results
`are shown in Table 4.
`In the incremental cost-effectiveness analysis, aripip-
`razole, quetiapine extended-release, and ziprasidone were
`dominated by risperidone (ie, more costly and less effec-
`tive) whereas aripiprazole, quetiapine extended-release, and
`olanzapine were dominated by lurasidone and were therefore
`removed from the incremental analysis (Figure 2). The ICER
`for lurasidone versus risperidone was $25,884 per relapse-
`related hospitalization avoided.
`sen sitivity a n a l yses
`Figure 3 shows the results of the one-way sensitivity analysis
`comparing lurasidone and risperidone for the most impactful
`parameters at an assumed willingness-to-pay threshold of
`$50,000 per hospitalization avoided. At this threshold, lurasi-
`done is the preferred therapeutic option, with an incremental
`net monetary benefit of $1,480 compared with risperidone.
`As shown in the tornado diagram, the model is sensitive to
`the following two parameters: lurasidone hospitalization rate
`(incremental net monetary benefit range, $4,530 to $7,489)
`and risperidone hospitalization rate ( $1,469 to $4,429). The
`model results were insensitive to the other model parameters
`over the tested ranges.
`When costs of relapses and hospitalization are included
`and cardiometabolic costs are excluded from the analysis, the
`
`Table 4 Discoun ted cl in ica l outcomes a n d costs for a typica l a n tipsych otics
`
`Lurasidone
`
`Olanzapine
`
`Risperidone
`
`Quetiapine XR
`
`Ziprasidone
`
`Aripiprazole
`
`Outcomes (per pa tien t)
`0.3953
` h ospita l iza tion s
`0.0591
` Dia betes
` Ca rdiova scul a r disea se even ts 0.0373
`Costs (per pa tien t)
`$29,947
` Ph a rma cy
`$71,142
` Men ta l h ea l th
`$2,236
` Dia betes
`$1,514
` Ca rdiova scul a r
`$104,840
` Tota l costs
`Note: a l l a moun ts sh own in Us dol l a rs.
`Abbreviation: XR, exten ded-rel ea se.
`
`464
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`
`0.4182
`0.0586
`0.0397
`
`$29,159
`$72,037
`$2,289
`$1,611
`$105,096
`
`0.4567
`0.0591
`0.0376
`
`$25,519
`$73,960
`$2,244
`$1,528
`$103,251
`
`0.4857
`0.0586
`0.0377
`
`$29,058
`$75,232
`$2,292
`$1,532
`$108,115
`
`0.4705
`0.0598
`0.0372
`
`$26,483
`$74,513
`$2,260
`$1,512
`$104,768
`
`0.4907
`0.0597
`0.0376
`
`$29,121
`$75,431
`$2,260
`$1,529
`$108,341
`
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`

`Dovepress
`
`Lesseffective and
`morecostly
`$110,000
`
`Cost-effectiven ess of a typical an tipsych otics
`
`Moreeffective and
`morecostly
`
`
`
`Cost(USdollars)
`
`$109,000
`
`$108,000
`
`$107,000
`
`$106,000
`
`$105,000
`
`$104,000
`
`$103,000
`
`$102,000
`
`@
`
`@ Risperidone
`
`@ Ziprasidone
`
`@ Lurasidone
`
`© Olanzapine
`
`@ Quetiapine XR
`
`® Aripiprazole
`
`$101,000
`-0.70
`
`Less effective and
`
`less costly
`
`-0.60
`
`-0 50
`
`-0.40
`
`-0.30
`
`- 0.20
`
`-0.10
`
`0.00
`
`Hospitalizations
`
`More effective and
`
`less costly
`
`Figure 2 iCER per rel a pse-rel a ted h ospita | ization a voided.
`Abbreviations: iCER, in cremen tal cost-effectiven ess ra tio; XR, exten ded-rel ea se.
`
`model results in an ICER of $26,109 for lurasidone versus
`risperidone.
`Results of the probabilistic sensitivity analysis at a
`willingness-to-pay threshold of $50,000 per hospitalization
`avoided indicated that lurasidone is the most cost-effective
`
`AAP. followed by olanzapine, risperidone, ziprasidone,
`
`quetiapine extended-release, and aripiprazole {able 5).
`At a willingness-to-pay threshold of $50,000, lurasidone
`has an 86.5% probability of being cost-effective, fol-
`lowed by a 7.2% probability for olanzapine, and 6.3%
`for risperidone (Figure 4). At willingness-to-pay thresh-
`olds below approximately $26,000 per relapse avoided,
`
`Lurasidone — hospitalization (%)
`
`Risperidone — hospitalization (%)
`
`Lurasidone — efficacy discontinuation (%)
`
`Lurasidone — treatment cost ($/year)
`
`Relapse (with hospitalization) cost (S/year)
`
`Risperidone — total discontinuation (%)
`Risperidone — efficacy discontinuation (%)
`
`Risperidone — treatment cost ($/year)
`
`Relapse (no hospitalization) cost (S/year)
`
`$2,000
`
` $0
`
`$4,000
`
`$6,000
`
`$8,000
`
`Figure 3 On e-way sen sitivity anal ysis resul ts (torn a do diag ram).
`Note: a moun tssh own are in Us dollars.
`
`Incremental net monetary beneCt
`
`Low value
`High value
`
`Cl in icoEcon omics an d Outcomes Research 2013:5
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`

`O'Day etal
`
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`
`Table 5 Proba bil istic sen sitivity an al ysis resul ts
`Net monetary bene * t
`Rank
`Drug
`Cost
`(95% Cl)
`(95% Cl)
`$104,877
`-$124,653
`($99,656, $110,021)
`(-$130,282, -$118,965)
`-$126,031
`$105,116
`($99,731, $110,306)
`(-$131,498, -$120,405)
`-$126,107
`$103,267
`($97,854, $108,789)
`($131,915, -$120,352)
`-$128,372
`$104,814
`($99,423, $110,471)
`(-$134,243, -$122,405)
`(0.4488, 0.4959)
`-$132,442
`0.4857
`$108,56
`($102,197, $113,781)
`(-$138,469, -$126,166)
`(0.4662, 0.5077)
`-$132,913
`0.4908
`$108,371
`($102,537, $114,002)
`(-$138,886, -$126,659)
`(0.4720, 0.5114)
`Note: *NetmonetaryDbenefitDat0aDwillingness-to-payDthresholdJof(1$50,000DOperhospitalizationavoided.DAmounts(]shownDareDinOUSDollars.
`Abbreviations:10Cl,confidenceDinterval;0XR,extended-release.
`
`1
`
`2
`
`3
`
`4
`
`5
`
`6
`
`| ura sidon e
`
`Olanzapine
`
`Risperidon e
`
`Zipra sidon e
`
`Quetia pin e XR
`
`aripipra zol e
`
`Hospitalizations
`(95% Cl)
`0.3955
`(0.3706, 0.4225)
`0.4183
`(0.4017, 0.4368)
`0.4568
`(0.4378, 0.4761)
`0.4712
`
`risperidone has the highest probability of being cost-
`effective
`
`Discussion
`
`A similar study comparing lurasidone with aripiprazole for
`second-line use in patients with schizophrenia found lurasi
`don