Return on investment: How do whole societies benefit from improved services and coverage for key populations? Bradley Mathers Kirby Institute UNSW Australia

Return on investment

What are the potential returns? COMPREHENSIVE PACKAGE OF INTERVENTIONS CRITICAL ENABLERS HEALTH SECTOR INTERVENTIONS HIV prevented HIV managed ▼HIV related mortality ▼HIV related morbidity Other conditions prevented Other conditions managed ▼ related mortality ▼ related morbidity ▼ Health expenditure ($) ▲ Productivity ($) ▼Stigma and discrimination ▲Social inclusion + participation ▲Welfare + safety ▲ Happiness ▲ Wellbeing ▲Confidence + positive living

Benefit to key populations and to society as a whole

Prevalence of men who have sex with men 1, people who inject drugs 2 and female sex workers 3 Eastern Europe MSM: % PWID:0.8 – 2.1% FSW:0.4 – 1.5% East and South Asia MSM: % PWID:0.1 – 0.2% FSW:0.3 – 2.6% Oceania MSM:2.5 – 5.7% PWID:0.5 – 0.6% FSW: – Western Europe MSM:– PWID:0.2 – 0.4% FSW: % Sub Saharan Africa MSM:1.0 – 60.0% PWID:0.1 – 1.1% FSW: % Latin America & The Caribbean MSM: % PWID:0.2 – 0.5% FSW: % North America MSM:– PWID:0.6 – 0.8% FSW:– Middle East & N Africa MSM:– PWID: % FSW:– Central Asia MSM:– PWID:0.7 – 0.9% FSW: % 1. Caceres et al. (2008) Epidemiology of male same-sex behaviour and associated sexual health indicators in low- and middle-income countries. 2. UNODC (2014) World Drug Report. 3. Vandepitte et al. (2006) Estimates of the number of female sex workers in different regions of the world

Distribution of new HIV infections across risk groups Estimated using UNAIDS Mode of Transmission model; * uses Asian Epidemiological Model (AEM) 2008?

Distribution of new HIV infections across risk groups 2008? Estimated using UNAIDS Mode of Transmission model; * uses Asian Epidemiological Model (AEM)

Distribution of new HIV infections across risk groups 2008? Estimated using UNAIDS Mode of Transmission model; * uses Asian Epidemiological Model (AEM)

Allocative efficiency What is the impact of investing in strategies for key populations?

The Optima Model Mathematical model of HIV transmission and disease progression Integrated with costing and financial analysis framework Provides outputs on how best to allocate available resources for a range of outcomes including how to: o Minimize incident HIV infections o Save disability adjusted life years (DALYs) o Minimize spending commitments o Meet other defined targets DP Wilson, R Gray, C Kerr, A Shattock, R Stuart. The Kirby Institute.

Allocative efficiency example #1: An African country with a low level HIV epidemic

HIV prevalence-General population:0.54% -Female sex workers:1.60 – 10.0% -Men who have sex with men: 1.9 – 6.3% Estimated 6,400 new infections in 2013 (Gen. popln incidence 0.05%) Allocative efficiency example #1: An African country with a low level epidemic Low income country HIV response largely reliant on donor funding (over 65% in 2013) Decreasing amount of funding available Budget of USD 6.4 million available for HIV response in 2013 Projected incidence if 2013 spending levels remain constant New infections per year (1000s) Personal communication: R Stuart, A Shattock, R Gray & DP Wilson, The Kirby Institute in collaboration with the World Bank (2014) 2013

Actual 2013 budget allocation‘Optimised’ budget allocation General population HTC Gen.pop. condom pgm. Cumulative incidence ,000 infections averted Lower risk males Personal communication: R Stuart, A Shattock, R Gray & DP Wilson, The Kirby Institute in collaboration with the World Bank (2014)

Impact of increasing/decreasing total expenditure

Allocative efficiency example #2: A concentrated epidemic in an Eastern European country

New infections per year (1000s) Projected incidence Upper-middle-income economy HIV response reliant on international donor funding (~50% in 2011) HIV prevalence-General population: 0.2% (2008)  0.4% (2012) -People who inject drugs:15% -Female sex workers:5.8% -Men who have sex with men: 4.0% Incidence is increasing Primary mode of infection shifting from injecting to sexual transmission Allocative efficiency example #2:Belarus – concentrated epidemic Female PWID Male PWID MSM Client of FSW FSW Low risk male Low risk female DP Wilson et al. HIV resource needs, efficient allocation and resource mobilization for the Republic of Belarus. Report for UNAIDS, 2013

Current budget allocation‘Optimised’ budget allocation New infections per year (1000s) Female PWID Male PWID MSM Client of FSW FSW Low risk male Low risk female DP Wilson et al. HIV resource needs, efficient allocation and resource mobilization for the Republic of Belarus. Report for UNAIDS, 2013

Personal communication: R Stuart, A Shattock, R Gray & DP Wilson, The Kirby Institute in collaboration with the World Bank (2014) Expected future healthcare savings 2015 – 2020 Incident HIV cases prevented  HIV-related care and treatment expenditure not required DP Wilson et al. HIV resource needs, efficient allocation and resource mobilization for the Republic of Belarus. Report for UNAIDS, 2013

In summary Key populations disproportionally affected Proven effective strategies exist Substantial health, social and economic benefits to be gained both by people from key populations and society as a whole Limited resources are potentially wasted by not investing in key populations