What do models estimate to be the impacts on HIV incidence of various percentages of people with HIV on ART ? National AIDS Trust Treatment as Prevention.

What do models estimate to be the impacts on HIV incidence of various percentages of people with HIV on ART ?

National AIDS TrustTreatment as Prevention Seminar25th November 2010Southwark Cathedral, London

Death rate ~ 5.2 per 1000 person years

If early ART reduces risk by 50% => risk reduction 2.6 / 1000 person years

1 death averted per 385 person years of ART

- Will initiation of ART in people with CD4 count > 350 / 500 be funded ?

- Assessment of cost-effectiveness requires a model that takes account of reductions in incidence

Policy of initiation of treatment at CD4 counts > 350 may require cost justification

Study group on Death Rates at High CD4 count in ART naïve people. Lancet 2010

Montaner et al, Lancet 2010

Association between number on HAART and new HIV diagnoses

Association between mean “community” viral load and new HIV infections

Das et al, PLOS One 2010

~ 90% of gay men have been tested for HIV

High rates of HIV testing but rising incidence ingay men in Australia

Wand et al, 2010, Prestage et al, 2008

Granich et al, Lancet 2008

1980 2000 2020 2040

Models of the impact of ART on transmission

ART can function as an effective prevention tool, even with high levels of drug resistance and risky sex

Velasco-Hernandez JX, Gershengorn HB, Blower SM. Lancet Inf Dis 2002

The use of treatment as prevention has the potential to reduce HIV epidemics only if consistent condom use is maintained.

Wilson et al, Lancet 2008

ART is predicted to have individual and public health benefits ...but the benefit can be lost by residual infectivity or …… sexual disinhibition...

Abbas UL, Anderson RM, Mellors JW. JAIDS 2006

ART cannot be seen as a direct transmission prevention measure, regardless of the degree of coverage

Baggaley RF, Garnett GP, Ferguson NM. PLoS Med 2006

Expansion of HAART (amongst those with CD4 < 200 / < 350) led to substantial reductions in the growth of the HIV epidemic and related costs Lima et al JID 2008

Predicted effects on HIV incidence depend onassumptions on:

- Testing coverage and frequency

- Effect on individual health of early ART

- Feasibility of identifying people in primary infection

- Durability of adherence / viral load suppression on ART

- Development and transmission of drug resistant virus

- Change in unprotected sex due to HIV diagnosis

- Change in unprotected sex due to viral suppression

- Extent of reduction in infectivity with ART

Fixed variables Variables updatedat infection over time

Years from infection

0 0.25 0.5 0.75 1.00 1.25

Calendar dateAge at infectionGenderPrimary resistance

Calendar dateAgeViral loadCD4 countRisk of AIDS / death

Use of specific ARVsResistance mutations

HIV synthesis modelCreates a ‘dataset’ of the course of infection and therapy for individual simulated patients.

HIV progression in absence of ART

PCP prophylaxis

Gender

Age

Viral load

CD4 count AIDS

Death from HIV

Death from other cause

Phillips et al, HIV Medicine 2007; Lancet 2008

Assumed 1.5-fold increased

Effect of ART

CD4 count

Phillips et al, HIV Medicine 2007, Lancet 2008

Death from HIV*

Acquisition of new resistance

mutations

Time on current regimen

Viral load

CD4 counts

AIDS*

*influenced by age and PCP prophylaxis also

Current adherence

# Active drugsin regimen

Switch tonext line of ART

Failure of current line

of ART

Effect of stopping ART

CD4 count

Phillips et al, HIV Medicine 2007, Lancet 2008

Death From HIV*

Viral load

CD4 counts

AIDS*

Probability of resuming ART

Time off ART

Loss from majority virus of

acquired resistance mutations

*influenced by age and PCP prophylaxis alsoOther processes include: - Loss to follow-up - Substitution of drugs due to toxicity

Fit to observed data

Phillips et al, HIV Medicine 2007

Observed Modelled

Natural history

AIDS by 10 years 46% 48%Median CD4 count at diagnosis of AIDS ~ 40 35 % dead by 1 year from initial AIDS 40% 45%

Effect of ART

Virologic failure by 7 years 27% 29%>1 resistance mutation by 7 yrs 19% 25%

Rate of viral rebound in those with < 50 cps/mL 3-6% per yr 6% per yr>1 resistance mutation to 3 classes by 6 yrs 4% 6%Mean CD4 count increase at 3 years 273 270

Additional variables updated over time

Years from 1985

1985 1985.25 1985.5 1985.75 1986.00 1986.25

e.g.Calendar dateInfection with HIVSexual risk behaviour:- Long term partnership status- Number of new partners

HIV transmission synthesis model: Heterosexual epidemic in southern Africa Creates a ‘dataset’ of the lifetime experiences of ~50,000 people in apopulation, aged over 15.

Number of new partners

Gender

Risk of HIV infection in uninfected subject

Probability ofHIV infection

Long term partner HIV+

Number of newpartners who are

HIV+

Number of new partnerships formed

by HIV+ people Current viralload of infectedpartner

Subject

ConcurrentHIV+ population

Incidence and prevalenceof HIV in people with

long term partnerships

Longterm partnership

status

Age

Risk of infection also depends on current STI

Comments

- In southern African heterosexual epidemic setting:

Assuming that unprotected sex with long term partners will reduce upon HIV diagnosis, a policy of frequent testing is likely to be beneficial for incidence, regardless of whether ART initiation threshold is CD4 200, CD4 350 or higher.

- Plans to adapt this model for MSM in UK

Conclusion

Models so far have demonstrated that intensive HIV testing with early ART initiation can, in principle, lead to substantial reductions in incidence if certain conditions hold.

Models required now are ones that will give as realistic and detailed assessment as possible of the predicted impact of frequent testing and early ART on HIV incidence, and thus enable estimation of cost-effectiveness of the approach.

Acknowledgements

Valentina Cambiano

Geoff GarnettDeenan PillayMarco VitoriaDiane BennettDeenan PillayJens Lundgren

Current funding from National Institute for Health Research Programme Grant