Wiwat Peerapatanapokin The Thai HIV Epidemic: Can it be Controlled with Current Approaches? Insights from Modeling and Epidemiological Analysis Wiwat Peerapatanapokin National Consultation on the Strategic Use of ARVs Bangkok, Thailand 9 August 2012

2 Asian Epidemic Model AEM – a behavioral process model that simulate transmission dynamic in low level and concentrated epidemic Subpopulation in the model include Male IDU, Female IDU, MSM, MSW, FSW, Injecting FSW, male client, low risk male and general Female Built in turn over for each subpopulation Transmission route include homosexual, heterosexual, IDU, intra-marital, extra-marital (casual sex), and mother to child transmission.

The Asian Epidemic Model (AEM) Sizes & behavioral trends in Clients Sex workers IDUs MSM Population at large AEM Calculation Engine Observed HIV trends (white lines) Injecting drug users Female sex workers Adult males Adult females Probabilities of transmission and start years

Baseline Scenario for HIV in Thailand

Number of New Infection in baseline scenarios

Number of New Infection in baseline scenarios

Reduce of New HIV by 2/3 scenario

Behavior change in Reduction of new infection by 2/3   2010 2011 2012 2013 2014 2015 2016 Condom Use among MSM 70% 73% 77% 80% 83% 87% 90% Condom Use among FSW 82% 84% 86% 88% 93% 95% Condom Use among Regular Partner 2% 8% 14% 20% 46% 57% 45% Condom Use among Casual Sex 36% 41% 50% 63% Injection Sharing among IDU 32% 29% 25% 23% 18%

BSS: Percent Ever Had Sex เคยมีเพศสัมพันธ์

Percent visiting FSW

Percent had casual sex

Percent Had sex with men in past year

Percent condom use with FSW ตัดสองจุดที่แปลกแยกออกไป (ม.๕)

Last sex condom used with Casual sex

Last sex condom used with among MSM from BSS

ART Component of AEM model Population – sexual freq, condom use, STD Uninfected Clients Visit CSW Naive Infected Clients naive Death PHD_0 ART Component of AEM model Progress to Death Population – sexual freq, condom use, STD Symptomatic illness tSiD_0 D1_pu1_1 Policy filter for 1st eligibility criterion (Asymptomatic) Public1 1st line ART N1_pu1 Death Ppu1HD_1 behavioral or toxicity Ppu1HD_2 D1_pr1_2 Papu1HD_2 Sapu1_12 D1_apu1_2 Pu1 2nd line ART Death D1_pu1_2 Spu1_12 R1_pu1_2 APu1 2nd line ART Pr1 2nd line ART Spr1_12 Ppr1HD_2 Move to-from non-client, IDU, MSM, MSW with Resistant virus Move to-from non-client, IDU, MSM, MSW with naive virus R1_apu1_2 R1_pr1_2 Visit CSW Resistant Infected Clients resistant Symptomatic illness Progress to Death PHD_r tSiD_r N1_apu1 Augmented Pu 1 1st line ART Death Papu1HD_1 D1_apu1_1 N1_pr1 Private 1 1st line ART Death D1_pr1_1 Ppr1HD_1 N1_pu2 N1_apu2 N1_pr2 Policy filter for 2nd eligibility criterion (Symptomatic) Public 2 ART Augmented Public 2 ART Private 2 ART 18

Demand and Supply of ART by Asymptomatic HIV+ Demand for VCT of VCT Price Distance Quality Information Demand and Supply of ART by Symptomatic HIV+ Demand for ART by Mode Supply of ART by Mode ART Utilization = min {Dem, Sply} of ART & OI Price Distance Quality Information Growth rate of ART slots, % ART slots reserved for symptomatic VCT HIV- Prevalence rate HIV+ Informed Asymptomatic Supply of ART by Mode Residual supply is available for Eligible Informed Asymptomatic No ART CD4 >200 Eligible Informed Asymptomatic CD4 <200 Demand for ART by Mode of ART & OI Price Distance Quality Information Say a few words about modeling policy: Reality is that Government cannot will outcomes. Even with the best of intentions, govts cannot go out there and identify who needs ART and make sure they get it, and make sure they adhere. Outcomes have to be achieved by manipulating a limited set of policy instruments or levers, which government has at its disposal. These may include the availability and price of ART; its quality; the information that PHAs have. They may also include the rate of growth in supply of ART, and decisions about who gets priority in accessing ART. This very complicated graph tries to summarize this policy process. What you see in yellow are the policy instruments – some of the levers that govt can manipulate The interaction of these policy levers with people’s behavior is what determines how many persons finally get ART. What is unique about this work is that we have created a set of policy sheets which allow one to manipulate these policy levers, and then integrate them into the AEM model to simulate their impact on the epidemic. This is what we’ve tried to do for the NAPHA scenario; and for the alternative policy options. ART utilization = min {Dem, Sply} Total HIV + Persons on ART

ART inputs and assumptions (1) ART covered under universal coverage ARV reduces infectivity 96% Behaviors in the public do not change because of ART Eligibility criteria to receive ART Symptomatic AIDS Asymptomatic with CD4 < 200 or CD4 < 350 CD4 All VCT Baseline level of VCT 10% in low risk and 30% in high risk Expand level of VCT 10% in low risk and 90% in high risk

ART inputs and assumptions (2) Progressions No ART HIV to AIDS ~ 10 years AIDS to Death ~ 0.9 years First line ART Median time of retention ~ 7 years

New HIV

Cumulative Death

PLHA

Number on ART

Number of Non-treat

Mode of transmission in Expand ART and VCT scenario

Number of New Infection in baseline scenarios

Thank you