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Nuria Perez-Alvarez 1,2 Dr. Jose A. Muñoz-Moreno 2 Prof. Guadalupe Gómez 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

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Nuria Perez-Alvarez 1,2 Dr. Jose A. Muñoz-Moreno 2 Prof. Guadalupe Gómez 1

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  1. COST EFFECTIVENESS EVALUATION FOR PROMOTING HIVTREATMENT ADHERENCE: COHORT SIMULATION USING APILOT 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.

  2. OUTLINE • Introduction • Aim and Motivation • Material and Methods • Results • Discussion

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

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

  5. ProADH study Time (weeks) Time (weeks) PsS = Psychoeducationalsession promoting adherence V = Medical visit and blood test

  6. 2. MOTIVATION Obtain information about the program performance • CD4 cells/mm3 using interim data: • 1 month of follow-up • of 20 patients

  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/mm3 evolution

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

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

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

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

  14. Transition probabilities Increased or maintained Decreased Dead W0 to W8 W8 to W48 Matrix probabilities for the Experimental Group

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

  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

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

  19. Probability Sensitivity Analysis Simulated Data: Mean incrementcost=413 € PPY Mean incrementutilities=0.60 ProADHData: Incrementcost=1243 € PPY Incrementutilities=0.44 PPY= Per Patient Year

  20. 5. Discussion • 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…

  22. Thanksforyourattention

  23. References • Death rate in spanish HIV infected patients under ART: “death rate of 2.80/100 person-years” Pérez-Hoyos et. al 2003 • 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 25 Increased or maintained Decreased Dead W0 to W8 W8 to W48 Matrix probabilities for the Control Group

  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 • fs is the fraction of the cohort in state s • Us is the utility of state s • Cs is the cost of state s

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

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