Our model tracks the population of people who inject drugs and it was formulated to describe the change in the number of people in different disease states over time

(1)

Appendix: Description of the mathematical model, parameter values, cost data, and sensitivity analyses

Description of mathematical model ... 1

Equations ... 3

Model parameters ... 14

Healthcare costs and health utilities ... 25

Model outcomes versus available data ... 26

Summary of economic results ... 28

Results from sensitivity analyses ... 29

References ... 31

Description of mathematical model

A mathematical model was developed to estimate HIV and HCV incidence and other disease outcomes. Our model tracks the population of people who inject drugs and it was formulated to describe the change in the number of people in different disease states over time. The model tracks the entry of new injectors into the uninfected population and those who die due, by health state, over time. All parameter values were

estimated based on published literature and available data from Australian reports and databases.

A schematic diagram of compartments in the HIV and HCV transmission model for inject drug users (IDUs) is presented in Figure 2 of the main manuscript. The change in the number of people in each compartment was tracked mathematically by formulating a system of ordinary differential equations. Twenty-one

compartments represent IDUs who are infected with HIV: CD4+ T cell levels (>500 cells per l, 350-500 cells per l, 200-350 cells per l, and <200 cells per l) for both diagnosed and undiagnosed; then HIV diagnosed individuals may initiate antiretroviral therapy for first-line treatment; those who failed treatment may receive second-line treatment. The description of health states are shown in Table A.1. Twenty-two compartments represent IDUs who are infected with HCV: in acute stage, fibrosis stages F0, F1, F2, F3, and F4, whether they are diagnosed, undiagnosed or receiving treatment. People infected with HCV who have advanced fibrosis can progress to clinical outcomes of liver failure or hepatocellular carcinoma, or can receive a liver transplant.

It is assumed that individuals who progress to these three clinical outcomes no longer receive HCV treatment due to the severity of their health status. Coinfection is not considered in this model.

Table A.1: Number of compartments in HIV/HCV.

Appendix

(2)

HIV HCV

1. Uninfected HIV 1. Uninfected HCV

2-5. Infected, Undiagnosed (CD4>500, CD4 350-500, CD4 200-350, CD4<200)

2-7. Infected, Undiagnosed (Acute, F0-F4) 6-9. Infected, Diagnosed (CD4>500, CD4 350-500, CD4

200-350, CD4<200)

8-13. Infected, Diagnosed (Acute, F0-F4) 10-13. Infected, 1stline ART (CD4>500, CD4 350-500,

CD4 200-350, CD4<200)

14-19. Infected, Treatment (Acute, F0-F4) 14-17. Infected, Failure of ART (CD4>500, CD4 350-500,

CD4 200-350, CD4<200)

20-22. Liver failure, hepatocellular carcinoma, liver transplant

18-21. Infected, 2ndline ART (CD4>500, CD4 350-500, CD4 200-350, CD4<200)

An ordinary differential equation (ODE) was developed to describe the change in the number of people in each of these compartmental health states over time; since there is one ODE for each compartment, there were 43 ODEs in total. The rate of change in the numbers of people in each compartment depends on the net rates of people entering and leaving the health state. Each ODE was mathematically described based on standard translation from the schematic diagram of the model presented in Figure 2 of the main manuscript [1] (with the addition of rates of initiation of injecting and leaving the population (background

death/migration/cessation of injecting, drug-related death, health state-specific death). For example, the ODE representing the rate of change in the number of people uninfected with HIV can be written as following:

where is the number of uninfected active IDUs, is the annual number of people who commence injecting drugs, is the mortality rate among general population, is the drug-related death rate, and is the ‘force of infection’ or per-capita rate at which susceptible IDUs acquire infection.

The force of infection is the only rate between health states to be dependent on other health states (namely, numbers of people in the infected health states). To calculate the force of infection, we assume that each IDU injects an average of times per year and denote the receptive syringe sharing rate (RSS) as and the

prevalence in the population as P(t). The probability of infection from a contaminated syringe per use is denoted by . We assume that syringe cleaning has effectiveness and cleaning occurs in proportion of shared injections. Given these definitions, the force of infections is given mathematically by:

.

C h a n g e in

u n in fected s E n try in to F o rce o f B a ckg ro u n d D ru g -rela t ed p o p u la tio n H IV in fectio n d ea th d ea th

D

d S S

d t    

 

 

     

 

 

(3)

Equations

HIV-infected individuals Susceptible

Undiagnosed

Diagnosed

Change in

uninfecteds Entry into Force of Background Drug-related population HIV infection death death

HIV D

dS S

dt

   

 

 

    

 

 

Change in

uninfecteds Entry into Force of Background Drug-related

population HIV infection death death

HIV D

dS S

dt    

 

 

     

 

 

350500

500 500 350500 350500

Change in infecteds

(350<CD4<500, HIV-related

Undiagnosed) Progress from Background Drug-related death Progress t

CD4>500 death death (350<CD4<500)

U

D

dI I

dt     

 

      

 

(

350500 350500

o Testingrate 200<CD4<350 350<CD4<500)

I

U



 

 

 

200350

350500 350500 200350 200

Change in infecteds

(200<CD4<350, HIV-related

Undiagnosed) Progress from Background Drug-related death 350<CD4<500 death death (200<CD4<350) U

U

D

dI I

dt     

 

     

 

(

350 200350 200350

Progress to Testing rate CD4<200 200<CD4<350)

I

U



 

 

 

( 200

200350 200350 200 200

Change in infecteds (CD4<200,

Progress from

Undiagnosed) Background Drug-related HIV-related Testing rate

200<CD4<350 death death death (CD4<200) CD4<200) U

U

D

dI I

dt     

 

      

  

200

I

U

 



(4)

First-line treatment

( 500

500 500 500 500 500

Change in infecteds

(CD4>500, Commence 1stl

Diagnosed

diagnosed) Background Drug-related HIV-related Progress to

CD4>500) death death death (CD4>500) 350<CD4<500 D

U

D

dI I

dt            

(

500 ine treatment CD4>500)

I

D

 

 

 

( )

350500

500 500 350500 350500

350500 Change in infecteds

(350<CD4<500,

diagnosed) Progress from Diagnosed CD4>500 350<CD4<500 D

D U

HIV-related Background Drug-related

death death de

D

dI I I

dt  

  

 

  

) (350

350500 350500 350500

Commence 1stline treatment Progress to

CD4<500) 200<CD4<350

ath (350<CD4<500

I

D

 



 

 

   

 

 

(20 )

200350

350500 350500 200350 200350

200350 Change in infecteds

(200<CD4<350,

diagnosed) Progress from Diagnosed 350<CD4<500 0<CD4<350 D

D U

Background Drug-related

death death

D

dI I I

dt  

  

 

  

) (200

200350 200350 200350

Commence 1stline treatment Progress to

HIV-related

CD4<350) CD4<200

death (200<CD4<350

I

D

 







 

   

 

 

( ) )

200

200350 200350 200 200 200

Change in infecteds (CD4<200,

Progress from Diagnosed

diagnosed) Background Drug-related HIV-related

200<CD4<350 CD4<200 death death death (CD4<200 D

D U

D

dI I I

dt          

(

200 200

Commence 1stline treatment CD4<200)

I

D



 

 

  

 

 

(5)

Treatment failure

1st

Change in infecteds (CD4>500)

during 1st treatment Viral supression

Commenced 1st line during 1st line therapy Backgrou therapy (CD4>500) (350<CD4<500)

500 D

500 500 350500 350500

dI I I

dt      

1st

HIV-related

nd Drug-related death Viral rebound

death death (on ART) ( CD4>500)

D T 500

I

500

  

 

 

  

 

 

1st

Change in infecteds (350<CD4<500)

during 1st treatment Commenced 1st line Viral supression therapy during 1st line therapy (350<CD4<500) (200<CD4<3

350500 D

350500 350500 200350 200350

dI = I + ω I

dt 

1

50)

HIV-related

Viral supression Background Drug-related death Viral rebound

(350<CD4<500 ) death death (on ART) (350<CD4<500 )

D T 350500 350500 350500

- μ + μ + μ +   I

 

 

 

 

 

st

1st

during 1st treatment Viral supression

Commenced 1st line during 1st line therapy therapy (200<CD4<350) (CD4<200)

200350 D

200350 200350 200 200

dI = I + ω I

dt

- μ



1st

HIV-related

Viral supression Background Drug-related death Viral rebound

(200<CD4<350 ) death death (on ART) (200<CD4<350 )

D T 200350 200350 200350

+ μ + μ +   I

 

 

 

 

 

1st

C hange in infecteds (C D4<200)

during 1st treatment

C ommenced 1st line therapy (C D4<200)

200 D

200 200

HIV-related Background Drug-related de

death death

D T

dI = I

dt

- μ + μ + μ



 

 



1st

Viral supression ath Viral rebound

(C D4<200 ) (on ART) (C D4<200 )

200 200 200

+   I

 

 

 

(6)

Fail

1st 2 nd

Change in treatment failure infecteds

(CD4>500) Viral rebound Viral rebound during 1st line therapy during 2nd line therapy

(CD4>500) (CD4>500)

500 S

500 500 500 500

Background

dI I I

dt  



 



Fail

HIV-related

Pr ogress to

Drug-related death Commence 2nd line

( 350 CD4<500)

death death (on ART) therapy (CD4>500)

F

D T 500 500

I

500

   





 

     

 

  

Fail

1 st 2 nd

(350<CD4<500) Viral rebound Viral rebound during 1st line therapy during 2nd line therapy

(350<CD4<500) (35

350500 S

350500 350500 350500 350500

dI I I

dt    

Fail

Pr ogress from CD4>500 after 0<CD4<500) 1st line treatment failure

F 500 500

Pr ogress to Background Drug-related HIV-related

( 200 CD4<350)

death death death (on ART)

F

D T 350500 35

 I

    





 

     

 

Fail

Commence 2nd line therapy (350<CD4<500)

0500

I

350500

 

 



Fail

1st 2 nd

(200<CD4<350) Viral rebound Viral rebound during 1st line therapy during 2nd line therapy

(200<CD4<350) (20

200350 S

200350 200350 200350 200350

dI I I

dt    

Fail

Pr ogress from 350<CD4<500 after 0<CD4<350) treatment failure

F

350500 350500

Pr ogress to Background Drug-related HIV-related

CD4<200 death death death (on ART)

F

D T 200350 2003

 I

    



 

     

 

Fail

Commence 2nd line therapy (200<CD4<350)

50

I

200350

 

 



Fail

1 st 2 nd

Change in treatment failure

infecteds (CD4<200) Viral rebound Viral rebound during 1st line therapy during 2nd line therapy

(CD4<200) (CD4<200)

200 S F

200 200 200 200 200350 2003

dI I I I

dt      

Fail

Pr ogress from 200<CD4<350 after

treatment failure

50

Background Drug-related HIV-related Commence 2nd line death death death (on ART) therapy (CD4<200)

D T 200

I

200

   

 

 

     

 

 

(7)

Second-line treatment

2 nd

Fail 2 nd

Change in infecteds (CD4>500)

on 2nd line treatment Viral supression

Commence 2nd line during 2nd line therapy therapy (CD4>500) from 350<CD4<500 500

500 500 350500 35000

Ba

dI I I

dt  



 



500 2 nd

Viral rebound during 2nd line therapy ckground Drug-related HIV-related

(CD4>500)

death death death (on ART)

S

D T

I

500

  

  

    

 

 



2 nd

Fail 2 nd

on 2nd line treatment Commence 2nd line Viral supression therapy during 2nd line therapy (350<CD4<500) from

350500

350500 350500 200350 200350

dI I I

dt    

200<CD4<350

Viral rebound during HIV-related

Viral supression 2nd line therapy

Background Drug-related death

(350<CD4<500) (350<CD4<500)

death death (on ART)

S

D T 350500 350500

I

35

    

 

 

      

  

 

05002 nd

(8)

2 nd

Fail 2 nd

on 2nd line treatment Commence 2nd line Viral supression therapy during 2nd line therapy (200<CD4<350 ) from CD4<200 200350

200350 200350 200 200

dI I I

dt    

2 nd

Viral rebound during HIV-related

Viral supression 2nd line therapy

Background Drug-related death

(200<CD4<350) (200<CD4<350 )

death death (on ART)

S

D T 200350 200350

I

200350

    

 

 

      

  

 

2 nd

Fail

Change in infecteds (CD4<200)

on 2nd treatment Commence 2nd line HIV-related Viral re

therapy Background Drug-related death

(CD4<200) death death (on ART)

200 S

200 200 D T 200

dI I

dt

    



    



2 nd

bound during

2nd line therapy Viral supression (CD4<200) (CD4<200)

200 I200





 



(9)

HCV-infected individuals Susceptible

Undiagnosed

Change in

uninfecteds Entry into Force of Background Drug-related population HCV infection death death

HCV D

dS S

dt

   

 

 

    

 

 

tan 0 (

Change in

acute infecteds Background Drug-related Spon eoous Progress to Testing rate F acute )

death death clearance of HCV

New infections U

A U

HCV D A A A

dI S I

dt      

 

 

       

 

 

( 0

0 0 0

Change in

F0 infecteds Progress from Background Drug-related Testingrate Progress to

acute death death F 0) F 1

U

U U

F

A A D F F F

dI I I

dt     

 

 

      

 

 

( 1

0 0 1 1 1

Change in

Progress from

F1 infecteds Background Drug-related Testing rate Progress to

F0 death death F 1) F 2

U

U U

F

F F D F F F

dI I I

dt     

 

 

      

 

 



( 2

1 1 2 2 2

Change in

F2 infecteds Progress from Background Drug-related Testingrate Progress to

F1 death death F 2) F 3

U

U U

F

F F D F F F

dI I I

dt     

 

 

      

 

 



(10)

Diagnosed

( 4 3

2 2 3 3 3

Change in

Progress from

F3 infecteds Background Drug-related Testingrate Progress to

F2 death death F 3) F

U U

F

F F D F F F

dI I I

dt     

 

 

      

 

 

( 4

3 3 4 4 4 4

Change in

Progress from Progress to

F4 infecteds Background Drug-related Testingrate Progress to

F3 death death F 4) liver failure HCC

U

U U

F

F F D F F LF F HCC F

dI I I

dt      

 

 

       

  

 

 

(

tan 0

1

Change in

Cease treatment acute infecteds Diagnosed

(acute) acute )

D

U T

A

A A A A A

Progress to Commence Background Drug-related Spon eoous

F trea

death death clearance of HCV

D A A

dI I I

dt   

    

  

 

     

 

( tment acute )

D

I

A

 

 



 

0 1 ( 0

0

0 0

1

0 0 0 0

Change in

Cease treatment Diagnosed

acute infecteds (F0) (F ) Background death Drug-related death Progress to F treatment F )Commence D

U T D

F

F F F F F D F F F

dI I I I

dt       

 

 

        

 

 

(11)

Receiving HCV treatment

1

1 1 0 0 1

(

1 1

(1 )

Change in

Diagnosed Progress from Cease treatment F1 infecteds

(F0) F0 (F1)

D

U D T

F

F F F F F F F

Background Drug-related Commence Pro death death treatment F 1)

D F F

dI I I I

dt    

   

   

 

    

 

1 gress to

F 2

D

I

F

 

 



2

2 2 1 1 2

(

2 2

(1 )

Change in

Diagnosed Cease treatment

F2 infecteds Progress from

(F2) F1 (F2)

D

U D T

F

F F F F F F F

Progre Background Drug-related Commence

death death treatment F 2)

D F F

dI I I I

dt    

   

   

 

    

 

2 ss to F 3

D

I

F

 

 



3

3 3 2 2 3

(

3 3

(1 )

Change in

(F3) F2 (F3)

D

U D T

F

F F F F F F F

Background Drug-related Commence Pro death death treatment F 3)

D F F

dI I I I

dt    

   

   

 

    

 

4

3 gress to

F

D

I

F

 

 



4

4 4 3 3 4

(

4 4

(1 )

Change in

(F4) F3 (F4)

D

U D T

F

F F F F F F F

Background Drug-related Commence P death death treatment F 3)

D F F LF

dI I I I

dt    

   

   

 

    

 

4 4

Progress to rogress to

liver failure HCC

D

F HCC

I

F



 

 

 

(12)

(

(1 )

Change in acute infecteds on

Cease treatment

treatment Commenced Background Drug-related Viral clearance

treatment (acute) death death F 4) on treatment (acute)

T A D

A A D A A A A A

dI I

dt        

 

       

 

0 Progress to F during treatment

T T

I

A

 

 



0

0 0

(

(1

0

)

Change in F0 infecteds on

Commenced treatment Progress from acute

treatment (F0) during treatment

T

T T D

F

A A F F

Cease treatment Background Drug-related

death death F 0)

D F F

dI I I

dt  

   

 

 

     

 

1

0 0 0

Viral clearance Progress to F on treatment (F0) during treatment

T T

F F F

I

F

  

 

 

 

1

0 0 1 1

(

(1 )

Progress from F0

treatment Commenced

during treatment treatment (F1) T

T T D

F

F F F F

M

D F F

dI I I

dt  

    

 

 

     

 

2

1 1

M M T T

F



F



F

I

F

 

 

 

2

1 1 2 2

(

(1 )

Commenced treatment Progress from F1

treatment (F2) during treatment

T

T T D

F

F F F F

D F F F

dI I I

dt  

    

 

 

     

 

3

2 2

T T

F F

I

F

 

 

 

 

(13)

3

2 2 3 3

(

(1 )

Progress from F2 Commenced treatment

T T D

F

F F F F

D F F

dI I I

dt  

   

 

 

    

 

4

3 3

T T

F F F

I

F

  

 

  

 

4

3 3 4 4

(

(1 )

Progress from F3 Commenced treatment

T T D

F

F F F F

D F F

dI I I

dt  

   

 

 

    

 

4 Viral clearance on treatment (F4)

T

F F

I

F

 

 

 

 

4 4 4 4

Change in liver

Progress from F4 Progress from F4

failure infecteds Background Liver failure Progress

(undiagnosed) (diagnosed) death related death

U D

LF

F LF F F LF F LF LFHCC

dI I I

dt     

 

     

 

to Progress to

HCC LT

LFLT

I

LF



 

 

 

4 4 4 4

Change in

Progress from F4 Progress from F4

HCC infecteds Background HCC

(undiagnosed) (diagnosed) Progress from LF death related death

U D

HCC

F HCC F F HCC F LFHCC LF HCC

dI I I I

dt     

 

    



Progress to LT

HCCLT

I

HCC



 

 

 

 

Change in

Progress from

Liver transplants Background Liver transplant

liver failure Progress from HCC death related death LT

LFLT LF HCCLT HCC LT LT

dI I I I

dt    

 

 

     

 

 

(14)

Model parameters

Table A.1: Model parameters related to HIV

Symbol Description Values References

Transmission

Transmission probability of HIV per injection with a contaminated syringe

0.6-0.8% [2, 3],

Effectiveness of ART 50-80% [4-10]

Testing rate

Proportion of individuals that received HIV test every year

48-66% [11]

Disease progression of individuals without treatment

Average time for HIV-infected individuals to progress from CD4 count >500 to CD4 count 350- 500

4.09 (3.79-4.42) years [12],

Average time for HIV-infected individuals to progress from CD4 count 350-500 to CD4 count 200-350

1.96 (1.81-2.13) years

Average time for HIV-infected individuals to progress from CD4 count 200-350 to CD4 count

<200

1.96 (1.81-2.13) years

Disease progression on treatment (viral suppression)

Average time for HIV infected individuals on ART to progress from CD4 count <200 to CD4 count 200-350

2.80 (2.33-3.58) years [13] ,

Average time for HIV infected individuals on ART to progress from CD4 count 200-350 to CD4 count 350-500

1.42 (0.90-3.42) years

Average time for HIV infected individuals on ART to progress from CD4 count 350-500 to CD4 count

>500

2.20 (1.07-7.28) years

Commencement of treatment

Proportion of individuals with CD4 count >500 that commence treatment for HIV each year

0.2 Experiment

al variable Proportion of individuals with CD4 count 350-500

that commence treatment for HIV each year

0.5



HIV ^a

r



4 500

1_CD _ b

350 4 500

1 __CD _

200 4 350

1 __CD _

4 200

1 ^U_CD _ c

200 4 350

1^U __CD _

350 4 500

1^U __CD _

4 5 0 0

C D _

350 CD4 500

 _ _

(15)

Proportion of individuals with CD4 count 200-350 that commence treatment for HIV each year

0.75-0.85

Proportion of individuals with CD4 count <200 that commence treatment for HIV each year

0.85-0.95 Stopping treatment

Percentage of individuals on ART who cease therapy each year

1-5%

Response to treatment

Percentage of individuals on ART to experience viral rebound per year

3-6% [14]

Response to treatment

Average time after treatment failure for individuals with CD4 count > 200 to go on second line ART

6-18 months Experiment

al variable Average time for individuals on ART with CD4

count <200 to go on second-line ART

2-3 months Mortality Rates

HIV-related death rate for patients with CD4 count

>500 cells per μL

0.051% (0.035-0.068%) [15]

HIV-related death rate for patients with CD4 count 350-500 cells per μL

0.128% (0.092-0.164%) [15]

HIV-related death rate per 100 person-years for patients with CD4 count 200-350 cells per μL

1.0% (0.2-2.0)% [15, 16]

HIV-related death rate per 100 person-years for patients with CD4 count <200 cells per μL

4.08 (0.30-7.86)%

Numerous studies have estimated the transmission risk of HIV in an occupational setting due to needlestick injury [17-23].

A model-based analysis evaluating population-level data in New Haven estimated the risk as ~0.7% [24]. Few studies have directly estimated the probability of HIV transmission per injection by IDUs using a contaminated syringe. In a long-term cohort study among injecting drug users in Bangkok, Thailand, a probability of transmission per exposure with a contaminated syringe was estimated to be 0.6% (0.4-0.9%) [3]. A review and meta-analysis suggested that the probability of transmission following a needlestick exposure is 0.23% (0-0.46%) and the infectivity per intravenous drug injection had a median of 0.8% (ranging 0.63%-2.4%) [2]. Estimates from studies based on occupational exposure tend to have lower transmission risk than estimates of risk by intravenous drug injection. Based on the injecting drug studies, we assume that the probability of transmission per drug-injection with a contaminated syringe ranges 0.6-0.8%.

A summary of the relation between HIV-1 RNA concentration and decline in CD4⁺ count from the prospective study by Mellors et al. [12] is given below:

Plasma HIV-1 RNA

concentration (copies/mL)

Mean decrease in CD4⁺ T cell count per year (cells/μL)

≤ 500 -36.3 (-30 ,-42.3)

501-3,000 -44.8 (-39.1,-50.5)

3,001-10,000 -55.2 (-50.7,-59. )

10,001-30,000 -64.8 (-59.6,-70 0)

200 CD4 350

 _ _

4 200

CD _



S ^d



200 4 350

1 __CD _

4 200

1 _CD _

4 500

CD _

350 CD4 500

 _ _

200 CD4 350

 _ _

4 200

CD _

a

b

(16)

> 30,000 -76.5 (-70.5,-82.9)

With this data, and assuming that the average viral load is ~10^4.87 copies per mL for people without treatment, the CD4⁺ T cell count decreases by an average of 76.5 (70.5, 82.9) every year.

To progress through the >500 CD4 cell category, we assume that the average CD4 count is 800 cells/μL after the 2-month acute phase of HIV infection and then declines at the constant rate of 76.5 (70.5, 82.9) cells/μL each year. Then the average time to progress through this compartment is 2/12 + 300/(76.5 (70.5, 82.9)) years; that is 4.09 (3.79, 4.42) years.

To progress through the 350-500 and 200-350 CD4 cell categories, we assume an average loss of 150 CD4 cells. Then the average time to progress through this compartment is 150/(76.5 (70.5, 82.9)) years; that is 1.96 (1.81, 2.13) years.

Below is a summary of data from [25] for changes in CD4 count over time among people who are on effective cART.

CD4 count at initiation of

cART (cells per μL) Time since starting cART (years)

Current CD4 (cells per μL) means (95% CI)

≤200 <1 76 (53-99)

1-3 69 (63-76)

3-5 50 (36-69)

>5 2 (18-46)

201-350 <1 129 (91-166)

1-3 50 (25-74)

3-5 47 (24-69)

> 23 (2-44)

>350 <1 90 (37-144)

1-3 50 (18-82)

3-5 17 (-17-51)

>5 21 (-12-54)

We use this data to estimate the average time to progress through our CD4 categories whilst on effective cART. For people with undetectable viral load:

 For CD4 count increases from 0 to 200 cells per μL, average increases of 76 (53-99) cells per μL can be expected during the first year and then 69 (63-76) cells per μL during the second and third years. Therefore, it can be expected to take 2.80 (2.33-3.58) years to progress through this category.

 For CD4 count increases from 200 to 350 cells per μL, we have a 150 CD4 count increase. In this interval, the CD4 count increases by 129 (91-166) cells per μL during the first year and then 50 (25-74) CD4 count during the second year. Therefore, it can be expected to take 1.42 (0.9-3.42) years to progress through this category.

 For CD4 count increases from 350 to 500 cells per μL, then we have a 150 CD4 count increase. In this interval, the CD4 count increases by 90 (37-144) cells per μL during the first year and then 50 (18-82) cells per μL during the second year. Therefore, it can be expected to take 2.20 (1.07-7.28) years to progress through this category.

EuroSIDA study [26] investigated that the HCV serostatus does not influence CD4 recovery among patients on ART. It was found that there was no difference in CD4 gain among HIV/HCV coinfected and HIV mono-infected patients after starting ART. Therefore we assume the same recovery rate for HIV/HCV coinfected patient as HIV mono-infected patient.

15.4/100 person years is the average rate of stopping one regime due to toxicity but the vast majority usually start another regime [27]. Very few people who commence ART stop altogether (expert opinion). Therefore, we take the absolute rate of completely stopping therapy to range from 1-5% per year as an experimental variable.

c

d