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COST EFFECTIVENESS EVALUATION FOR PROMOTING HIV TREATMENT ADHERENCE: COHORT SIMULATION USING A PILOT STUDY DATA Nuria Perez-Alvarez 1,2 Dr. Jose A. Muñoz-Moreno.

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Presentation on theme: "COST EFFECTIVENESS EVALUATION FOR PROMOTING HIV TREATMENT ADHERENCE: COHORT SIMULATION USING A PILOT STUDY DATA Nuria Perez-Alvarez 1,2 Dr. Jose A. Muñoz-Moreno."— Presentation transcript:

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

2 OUTLINE 2 1. Introduction 2. Aim and Motivation 3. Material and Methods 4. Results 5. Discussion

3 1. INTRODUCTION  Prospective clinical trials  expensive  time consuming  Simulation can help  model building  input parameters 3

4 Clinical background  HIV infection  Longer survival times  Treatment Adherence  Treatment success  Virus without developing resistances  Resources allocation - educational program ProADH study 4

5 Time (weeks) Experimental Group 024 12243648 PsS VVVVVV Control Group 024 12243648 VVVVVV PsS = Psychoeducational session promoting adherence V = Medical visit and blood test

6 2. MOTIVATION Obtain information about the program performance CD4 cells/mm 3 using interim data: 1 month of follow-up of 20 patients 6

7 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/mm 3 evolution

8 3. MATERIAL AND METHODS  Cohort simulation  Model specifications  Transition probabilities  Health measurement  Costs  Indicators to summarize C-E  Probability sensitivity analysis 8

9 Cohort simulation 9 Death(Da)Decreased (D) Increased or maintained Death (Da) Increased or maintained Death (Da) Decreased(D) Death (Da)Decreased(D) IDII Increased or maintained Decreased (D) COHORT Increased or maintained IIDDa

10 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  10 000 individuals in the cohort 10

11 Model Specifications (II) Two phases in the CD4+ recovery  0-8 week  8-… weeks 11 Weeks

12  0-8 week  8-48 weeks 12 Model Specifications (III) Two phases in the CD4+ recovery Weeks

13 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. 13

14 Transition probabilities I or MDDa I or M 0.63640.36260.0010 D 0.63640.36260.0010 Da 001 14 I or MDDa I or M 0.31820.68080.0010 D 0.31820.68080.0010 Da 001 W0 to W8W8 to W48 Matrix probabilities for the Experimental Group DeadDecreased Increased or maintained

15 Health measurement 15  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)

16 Costs 16 Mean cost (€) per patient per month The perspective of the Spanish National Healthcare System Resources use and costs (per month/patient) ∆=37 Total cost (€ ) per 100 patients per year ∆=44,400

17 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 17

18 4. RESULTS 18 ICER = (13,674-13,227)/(4.75-4.15) = 745 €/utility ExperimentalTtm Control Ttm

19 Probability Sensitivity Analysis 19 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 Experim. has less utilities Experim. has more utilities PPY= Per Patient Year

20 5. Discussion 20  The model  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

21 Thanks to… 21

22 THANKS FOR YOUR ATTENTION

23 References 23  Death rate in spanish HIV infected patients under ART: “death rate of 2.80/100 person-years” Pérez-Hoyos et. al 2003 Death rate in spanish HIV infected patients under ART: “” Pérez-Hoyos et. al 2003  http://journals.lww.com/aidsonline/Fulltext/2003/02140/Effectiveness_of _highly_active_antiretroviral.9.aspx http://journals.lww.com/aidsonline/Fulltext/2003/02140/Effectiveness_of _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: 16810109]

24 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.

25 Transition probabilities I or MDDa I or M 0.55560.44340.0010 D 0.55560.44340.0010 Da 001 25 I or MDDa I or M 0.27780.72120.0010 D 0.27780.72120.0010 Da 001 W0 to W8W8 to W48 Matrix probabilities for the Control Group DeadDecreased Increased or maintained

26 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).

27 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.

28 A cohort of 10.000 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.

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

30 Indicators to summarize C-E (II)  Utility cycle sum was calculated by:  Cost cycle sum: Where:  S is the total number of states  f s is the fraction of the cohort in state s  U s is the utility of state s  C s is the cost of state s 30

31 4. RESULTS 31 Simulated data ICER = (13674-13227)/(4.75-4.15) = 745 €/utility Real data ICER = (13772-12529)/(4.44-4) = 2825 €/utility Control Tmt Experimental Ttm

32 32


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