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COST EFFECTIVENESS EVALUATION FOR PROMOTING HIV TREATMENT ADHERENCE: COHORT SIMULATION USING A PILOT STUDY DATA Nuria Perez-Alvarez1,2 Dr. Jose A. Muñoz-Moreno2 Prof. Guadalupe Gómez1 1Technical University of Catalonia, Barcelona, Spain 2 Lluita contra la SIDA Foundation, Badalona, Spain EMR-IBS Conference. Tel-Aviv, 25 April 2013.

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**OUTLINE Introduction Aim and Motivation Material and Methods Results**

Discussion

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**1. INTRODUCTION Prospective clinical trials Simulation can help**

expensive time consuming Simulation can help model building input parameters

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**Clinical background HIV infection Treatment Adherence**

Longer survival times Treatment Adherence Treatment success Virus without developing resistances Resources allocation - educational program ProADH study ProADH study (pilot controlled randomized prospective trial)

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ProADH study Experimental Group 2 4 12 24 36 48 PsS V Control Group Time (weeks) Time (weeks) !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!EXPLICAR AQUI QUE USO LOS CD4 COMO MEDIDA DE SALUD [Poster IAS-JOSE] Treatment adherence for HIV is a major requirement for achieving therapy success. Recently there has been growing efforts to apply interventions promoting adherence to combined antiretroviral therapy (cART), although little is known about their benefits on economizing resources. We aimed to analyze the economical impact of a psychoeducational adherence-based intervention program through a cost-effectiveness analysis. De la tesis: Figure Chronogram of the study procedures by branch of health care intervention. Time expressed in weeks (w) of follow-up (48w, considered equivalent to 1 year). PSABI is the psychoeducational adherence-based intervention. BBT is the Baseline blood test, CV is the Clinical visit, MA is the Monitoring analysis and QoLA is Quality of life questionnaire assessment. Del abstract enviado a EMR-IBS Israel: A pilot controlled randomized prospective trial was designed to evaluate the effect of a psychoeducational adherence-based program on the CD4+ recovery and its associated economical cost, including two branches: educational group (n=11) and standard of care group (n=9). PsS = Psychoeducational session promoting adherence V = Medical visit and blood test

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**2. MOTIVATION Obtain information about the program performance**

CD4 cells/mm3 using interim data: 1 month of follow-up of 20 patients 1 month data and 20 patients. 11 patients in the experimental group and 9 patients in the control group. The interim data we have are the CD4 cell counts at w0 and w4 and the cost for patient/month.

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GOAL Build cost-effectiveness model to assess the program performance for 1 year of follow-up using real data from interim analysis published papers and theoretical knowledge about the CD4 cells/mm3 evolution

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**3. MATERIAL AND METHODS Cohort simulation Model specifications**

Transition probabilities Health measurement Costs Indicators to summarize C-E Probability sensitivity analysis

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**Cohort simulation COHORT IDII IIDDa Increased or maintained**

Decreased (D) Death(Da) COHORT Increased or maintained Decreased (D) Death (Da) Decreased(D) Death (Da) Increased or maintained Markov models are a specific type of discrete state-transiton simulation models. The simulated cohort of pacients is divided into a finite number of states based on the current healt status of the patient. The states are mutually exclusive and collectively exhaustive. The crucial assumption of the Markow cohort model is that future events only depend on the current health state of the patient, and not on prior events. Time is handled as a discrete periods of the same length which are called cycles. Decreased(D) Death (Da) Increased or maintained IDII IIDDa

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**Model Specifications (I)**

Health states: Increased or maintained the CD4 cells level Decreased the CD4 cells levels Death The time horizon: 1 year Cycles length: 1 month individuals in the cohort A Markov model was developed to represent the transition of a cohort of naive HIV infected patients through different health states. A time horizon of 1 years was chosen and 1-months treatment cycles were estimated. The perspective of the Spanish National Healthcare System was chosen. Cost and effectiveness discount rates not applied. We have data only for 1 year.

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**Model Specifications (II) Two phases in the CD4+ recovery**

Robins et al. PIE: Figure 8. Change in CD4+ cell count from baseline in percentiles (10th, 25th, 50th, 75th, and 90th) for all AIDS Clinical Trials Group (ACTG) protocol 384 patients (n =978). This plot is based on HIV-positive antiretroviral therapy–naive patients after initiating HAART in ACTG protocol 384 with a median (interquartile range) baseline CD4+ cell count of 279 (98–444) cells/mm3. *At weeks 16 and 24, the ΔCD4+ cell count was positively associated with the baseline CD4+ cell count. The median ΔCD4+ cell count was ~47 and 39 cells greater for patients with a baseline CD4+ cell count of >500 cells/mm3 versus ≤50 cells/mm3 at weeks 16 and 24, respectively. **There might be a lack of precision after week 96 because of dropouts and limited follow-up in ACTG protocol 384. Explicacion: Because increases in CD4+ cell counts were similar across all strata and ART assignments, we plotted percentiles for ΔCD4+ cell counts through week 144 (figure 8). With use of a CD4+ cell count increase of ≥100 cells/mm3 as a criterion for immunologic success, a greater proportion of patients in the lower strata had immunologic success at weeks 96 and 144 (P = . 005 and P < .001, respectively; table 3). Study definitions: Baseline CD4+ cell count strata 1–5 were defined as ≤50, 51–200, 201–350, 351–500, and >500 cells/mm3, respectively. Weeks 0-8 week 8-… weeks

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**Model Specifications (III) Two phases in the CD4+ recovery**

Robins et al. PIE: Figure 8. Change in CD4+ cell count from baseline in percentiles (10th, 25th, 50th, 75th, and 90th) for all AIDS Clinical Trials Group (ACTG) protocol 384 patients (n =978). This plot is based on HIV-positive antiretroviral therapy–naive patients after initiating HAART in ACTG protocol 384 with a median (interquartile range) baseline CD4+ cell count of 279 (98–444) cells/mm3. *At weeks 16 and 24, the ΔCD4+ cell count was positively associated with the baseline CD4+ cell count. The median ΔCD4+ cell count was ~47 and 39 cells greater for patients with a baseline CD4+ cell count of >500 cells/mm3 versus ≤50 cells/mm3 at weeks 16 and 24, respectively. **There might be a lack of precision after week 96 because of dropouts and limited follow-up in ACTG protocol 384. Explicacion: Because increases in CD4+ cell counts were similar across all strata and ART assignments, we plotted percentiles for ΔCD4+ cell counts through week 144 (figure 8). With use of a CD4+ cell count increase of ≥100 cells/mm3 as a criterion for immunologic success, a greater proportion of patients in the lower strata had immunologic success at weeks 96 and 144 (P = . 005 and P < .001, respectively; table 3). Study definitions: Baseline CD4+ cell count strata 1–5 were defined as ≤50, 51–200, 201–350, 351–500, and >500 cells/mm3, respectively. Weeks 0-8 week 8-48 weeks

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**Input parameters Transition probabilities between health states**

CD4 cells evolution described in specialized literature [Gandhi et al. 2006, Robins et al. 2009] Interim data from real study Death ratio Health measurement Drugs and program development prices: Spanish medicine database, referred to 2010.

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**Transition probabilities**

Dead Decreased Increased or maintained W0 to W8 W8 to W48 I or M D Da 0.6364 0.3626 0.0010 1 I or M D Da 0.3182 0.6808 0.0010 1 -Matrix probabilities for the Experimental Group Probabilidades de transicion estimadas para 9 (GC) y 11(GE) pacientes que llegaron a 1 mes de seguimiento. Abstract Jose Muñoz IAS. Matrix probabilities for the Experimental Group

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**Increased or Maintained**

Health measurement Health Status Health Score Increased or Maintained 1 Decreased Dead Score per health considered 0 and 1 to compute the number of times the CD4+ counts increase or decrease The same health score for both groups Good response if CD4 (t+1) ≥ CD4 (t) REVISAR si imponer los valores 0 y 1 son correctos!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! REVISAR LA CONSTRUCCION DEL MARCADOR DE MEJORA EN TERMINOS DE SALUD A W 48 O NO!

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**Costs The perspective of the Spanish National Healthcare System**

Resources use and costs (per month/patient) Cost in € Antiretroviral treatment 931 Concomitant treatment 24 Human resources 75 Psychoeducational session 37 Monitoring analyses 79 Mean cost (€) per patient per month Total cost (€ ) per 100 patients per year Experimental Group 1146 Control Group 1109 adjusting monetary unit (e.g., Dollars) amounts to reflect the time value of money Future dollars are worth less than current dollars Most also believe we should discount health effects (e.g., years of life) Typically these are both discounted at 3% per year (e.g., $1 today = $0.97 next year = $0.94 in 2 years, etc.), though recommendations vary The perspective of the Spanish National Healthcare System was chosen. Experimental Group 1,375,200 Control Group 1,330,800 ∆=37 ∆=44,400 The perspective of the Spanish National Healthcare System

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**Indicators to summarize C-E**

Cost-effectiveness analysis assesses both treatment costs and outcomes. The Incremental Cost Effectiveness Ratio (ICER) is obtained by Probability sensitivity analysis Cost-effectiveness analysis assesses both treatment costs and outcomes. Effects (treatment outcomes) are measured in one-dimensional units, such as life years gained. The Incremental Cost Effectiveness Ratio (ICER) is obtained by taking the ratio of the incremental difference in total cost (C) to the incremental difference in benefits (E) between programs [10] (e.g. program A and program B). (eq. 1) The ICER can be interpreted as the incremental cost of producing effect by a treatment alternative compared to the next most effective alternative, and can be expressed, for example, as the cost per life year gained. This incremental ratio, as opposed to the average cost-effectiveness ratio, is the relevant variable to consider when deciding on the allocation of resources which maximizes the health effects for a given amount of resources [11]. If program A both has lower costs and greater effect compared to program B, it is dominating program B, and program A should consequently be implemented. Program A could in this case also be termed cost-saving. Probability Sensitivity Analysis Input parameters extracted from a theoretical distribution Results presented in a cost-effectiveness plane Del abstract: “The stability of the results is assessed with a probabilistic study by drawing each model parameter value from a specific probability distribution reflecting either patient’s individual characteristics or parameter uncertainty.” Assess how robust the results are to uncertain assumptions out the mechanism of HIV disease progression and the costs of health care used in the model The exploration is going to be done one-way by varying the Probabilities of becoming undetectable The accuracy of the test The cost of both therapeutic strategies ********************. Sensitivity analysis: varying assumptions and inputs to examine how much variation there is in the cost-effectiveness ratio One-way: varying only one input at a time Two-way: varying two inputs simultaneously Multivariable: all inputs varied simultaneously

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**4. RESULTS ICER = (13,674-13,227)/(4.75-4.15) = 745 €/utility**

ExperimentalTtm Control Ttm Real data (circulo y triangulo); simulated data (cuadrado y rombo) Gran diferencia en coste entre real y simulado “ Real data (circulo y triangulo) The mean (SD) cost per patient month (PPM) in the EG was 1252 (460) Euros and 1139 (275) in the CG “. Analytical results on cost-effectiveness for the studied scenarios. Time horizon: 3 years. Total cost per 3 years . Conclusions about the more cost-effective strategy cannot be drawn from these numerical results, since the more expensive strategies of treatment and testing lead to the higher Utility. The willingness to pay (WTP) per unit of response will be the cutting point to choose between these therapeutic strategies ICER = (13,674-13,227)/( ) = 745 €/utility

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**Probability Sensitivity Analysis**

Experimental ttm is more costly Experimental ttm is less costly Simulated Data: Mean increment cost=413 € PPY Mean increment utilities=0.60 ProADH Data: Increment cost=1243 € PPY Increment utilities=0.44 > mean(CEA_PSA$Incr_costAB) [1] > mean(CEA_PSA$Incr_utAB) [1] Real increment cost 1243= Real increment utilities 0,44 Experim. has less utilities Experim. has more utilities PPY= Per Patient Year

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**5. Discussion The model Limitations Advantages**

infra-estimated the cost over estimated the health outcome Limitations The structure of the model can be seen as a simplification of the real problem Depends on the quality of the input parameters Few information about the “real patients” Advantages It may help to allocate resources most efficiently without running an experiment The structure of the model can be seen as not scientifically Requires reasonable knowledge about the natural history of disease and its long-term outcomes Simplifying the history of the disease and using input parameters may help allocate resources most efficiently without running an experiment.

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Thanks to…

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**Thanks for your attention**

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References Death rate in spanish HIV infected patients under ART: “death rate of 2.80/100 person-years” Pérez-Hoyos et. al 2003 _highly_active_antiretroviral.9.aspx Biphasic Behaviour of CD4+ “As reported elsewhere, there was a biphasic reconstitution of CD4+ cell counts: a rapid increase during the first 8 weeks followed by a more gradual increase” From Gandhi RT, Spritzler J, Chan E, et al. Effect of baseline- and treatment-related factors on immunologic recovery after initiation of antiretroviral therapy in HIV-1–positive subjects: results from ACTG 384. J Acquir Immune Defic Syndr 2006;42:426–34. [PubMed: ]

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ProADH The participants were all men, middle-aged with a median (Interquartile Range) of 35 (30-45) years old, who were infected mainly via sex with other men (90%). The median number of cART changes during the study was 2, with a minimum of 0 and a maximum of 4 changes. Initially, 20 patients were allocated in each treatment group but 5 and 2 were loss of follow up in the control and experimental group, respectively.

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**Transition probabilities**

25 Dead Decreased Increased or maintained W0 to W8 W8 to W48 I or M D Da 0.5556 0.4434 0.0010 1 I or M D Da 0.2778 0.7212 0.0010 1 -Matrix probabilities for the Experimental Group Probabilidades de transicion estimadas para 9 (GC) y 11(GE) pacientes que llegaron a 1 mes de seguimiento. Abstract Jose Muñoz IAS. Matrix probabilities for the Control Group 25

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Abstract For nearly 25 years, CD4+ cell counts have been used as the primary indicator of HIV-1 disease progression. Patient’s adherence to the treatment may result in higher total CD4+ cell counts and more durable virological suppression. A pilot controlled randomized prospective trial was designed to evaluate the effect of a psychoeducational adherence- based program on the CD4+ recovery and its associated economical cost, including two branches: educational group (n=11) and standard of care group (n=9).

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**A transition probabilities Markov model is used to perform an economic evaluation.**

A patient’s cohort travelling through defined health status until the time horizon is reached is simulated. The transition probabilities between health status are determined taking into account the efficacy of the therapeutic strategies chosen and the biphasic reconstitution of CD4+ cell counts: a rapid increase during the first 8 weeks followed by a more gradual increase. Real data from an interim analysis of 1 month of follow up combined with CD4 dynamics information from the literature is used to simulate a cohort for a cost-effectiveness analysis at 1 year follow-up. Economic costs were assessed from the National Health System payer perspective. The stability of the results is assessed with a probabilistic study by drawing each model parameter value from a specific probability distribution reflecting either patient’s individual characteristics or parameter uncertainty.

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A cohort of simulated patients travelled in sequences of 1 month transitions between the following health status: CD4 Increased, Maintenance and Decreased. Model results included the costs of performing the educational program and incremental cost- effectiveness ratios (ICER). This simulated cohort results can guide the discussions on the convenience of extending the educational program into the medical practice.

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Total time 15 minutes Talk 12 minutes; 3-minutes questions

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**Indicators to summarize C-E (II)**

Utility cycle sum was calculated by: Cost cycle sum: Where: S is the total number of states fs is the fraction of the cohort in state s Us is the utility of state s Cs is the cost of state s

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**4. RESULTS Simulated data**

Control Tmt Experimental Ttm Real data (circulo y triangulo); simulated data (cuadrado y rombo) Gran diferencia en coste entre real y simulado “ Real data (circulo y triangulo) The mean (SD) cost per patient month (PPM) in the EG was 1252 (460) Euros and 1139 (275) in the CG “. Analytical results on cost-effectiveness for the studied scenarios. Time horizon: 3 years. Total cost per 3 years . Conclusions about the more cost-effective strategy cannot be drawn from these numerical results, since the more expensive strategies of treatment and testing lead to the higher Utility. The willingness to pay (WTP) per unit of response will be the cutting point to choose between these therapeutic strategies Simulated data ICER = ( )/( ) = 745 €/utility Real data ICER = ( )/(4.44-4) = 2825 €/utility

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