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, Spain2 Lluita contra la SIDA Foundation, Badalona, Spain

EMR-IBS Conference. Tel-Aviv, 25 April 2013.

OUTLINE2

1. Introduction2. Aim and Motivation3. Material and Methods4. Results5. Discussion

1. INTRODUCTION

Prospective clinical trials expensive time consuming

Simulation can help model building input parameters

3

Clinical background

HIV infection Longer survival times

Treatment Adherence Treatment success Virus without developing resistances

Resources allocation - educational program

ProADH study

4

ProADH study

Time (weeks)

Time (weeks)

Experimental Group

0 2 4     12 2436 48

PsS PsS PsS

V V V V V V

Control Group

0 2 4     12 2436 48

V V V V V VPsS = Psychoeducational session promoting adherenceV = Medical visit and blood test

2. MOTIVATION

Obtain information about the program performance CD4 cells/mm3

using interim data: 1 month of follow-up of 20 patients

6

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

3. MATERIAL AND METHODS

Cohort simulation Model specifications Transition probabilities Health measurement Costs Indicators to summarize C-E Probability sensitivity analysis

8

Cohort simulation9

Death(Da)Decreased (D)

Increased or maintained

Death (Da)

Increasedor maintained

Death (Da)Decreased(D)

Death (Da)Decreased(D)IDII Increasedor maintained

Decreased (D)

COHORT Increased or maintained

IIDDa

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

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

0-8 week 8-… weeks

11

Weeks

0-8 week 8-48 weeks

12

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

Weeks

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

Transition probabilities

  I or M D DaI or M 0.6364 0.3626 0.0010

D 0.6364 0.3626 0.0010Da 0 0 1

14

  I or M D DaI or M 0.3182 0.6808 0.0010

D 0.3182 0.6808 0.0010Da 0 0 1

W0 to W8 W8 to W48

Matrix probabilities for the Experimental Group

DeadDecreased

Increased or maintained

Health measurement15

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)

Costs16

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

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

EECC

EC

ICER

4. RESULTS18

ICER = (13,674-13,227)/(4.75-4.15) = 745 €/utility

ExperimentalTtm

Control Ttm

Probability Sensitivity Analysis

19

-20000 -10000 0 10000 20000

-4-2

02

46

8

Cost effectiveness plane (A vs B)

Incremental utility gain

Incr

em

en

tal c

ost

(€)

Experimental ttm is more costly

Experimental ttm is less costly

Simulated Data:Mean increment cost=413 € PPYMean increment utilities=0.60

ProADH Data:Increment cost=1243 € PPYIncrement utilities=0.44Experim. has less utilities Experim. has more utilities

PPY= Per Patient Year

5. Discussion20

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

Thanks to…21

THANKS FOR YOUR ATTENTION

References23

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]

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.

Transition probabilities

  I or M D DaI or M 0.5556 0.4434 0.0010

D 0.5556 0.4434 0.0010Da 0 0 1

25

  I or M D DaI or M 0.2778 0.7212 0.0010

D 0.2778 0.7212 0.0010Da 0 0 1

W0 to W8 W8 to W48

Matrix probabilities for the Control Group

DeadDecreased

Increased or maintained

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).

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.

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.

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

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

S

sss Uf

1

*

S

sss Cf

1

*

30

4. RESULTS31

Simulated dataICER = (13674-13227)/(4.75-4.15) = 745 €/utilityReal data ICER = (13772-12529)/(4.44-4) = 2825 €/utility

Control TmtExperimental Ttm

32

0 20 40 60 80 100

0.0

0.2

0.4

0.6

0.8

1.0

Value of ceiling ratio (K €)

Pro

ba

bili

ty c

ost

-effe

ctiv

eA-ExperimentalB-Control