1. © Imperial College LondonPage 1 Understanding the current spread of HIV Geoff Garnett

2. © Imperial College LondonPage 2 Contents Exploring past declines in prevalence Modelling isolated versus combined interventions Using statistical analysis to understand the relationship between drivers and risk

3. © Imperial College LondonPage 3 1998 Estimated number of adults and children living with HIV, by region, 1990–2007 Num ber HIV+ (Millions) Oceania Middle East & North Africa Eastern Europe & Central Asia Latin America and Caribbean North America and W & C Europe Asia Sub-Saharan Africa 0 10 20 30 40 19901992199419962000200220042006 UNAIDS Report 2008

4. © Imperial College LondonPage 4 The natural course of incidence and prevalence of a local HIV epidemic over time Time (years) 0 5 10 15 20 25 01020304050 Percent Incidence /Prevalence Prevalence HIV Incidence HIV infection Incidence AIDS deaths R t =R 0 >1 R t <1 R t =1 Interested in current incidence – but even if avalidated test available would require an order of magnitude increase in sample sizes.

5. © Imperial College LondonPage 5 HIV will spread from those with high numbers of contacts to those with low numbers of contacts – Incidence declines – As mortality increases we expect prevalence to decline. Questions: 1)Is decline greater than or qualitatively different to expectation representing a changing pattern of risk over the population. 2)Do those newly sexually active maintain the risk patterns of their predecessors? Can simulate expected course of the epidemic and compare with observed to identify past significant declines in incidence and risk.

6. © Imperial College LondonPage 6 Best fit 50% decline The Sharpest HIV Decline in Southern Africa Today 18:30-20:30 Room 10 0.01.02.03.04.05 Chance that change occurred 0204060 % reduction in partner change rate Comparing modelled HIV trends with different levels of behaviour change to urban antenatal clinic data from Zimbabwe – most likely a 50% decline in risk around 2001. Urban ANC sites HIV prevalence (%) 60 40 20 0 1980 1990 2000 2010 Hallett et al

7. © Imperial College LondonPage 7 Tipping point R 0 =1 Increasing contacts, transmission likelihood, duration Stable HIV Prevalence 30% reduction transmission effect of circumcision in men 50% reduction in partner numbers Increased heterogeneity Impact of interventions depends epidemiological context Combining interventions can have synergies

8. © Imperial College LondonPage 8 A framework is required to understand both the individual and the populations risk of HIV Proximate determinants framework (Gregson/Boerma & Weir) Underlying Proximate Biological HIV Education Number of sex partners Exposure to infection Social epidemiology framework (Poundstone et al) Social Individual Transmission dynamics HIV Social capital Education c.β.D Structure Discrimination

9. © Imperial College LondonPage 9 Can test statistically for individual acquisition whether framework applies and how well risks at different levels have been indentified Expect a proximate determinant to be a significant risk factor for HIV. Expect a underlying determinant to be a significant risk factor for HIV. Expect underlying determinants to be significant risks for proximate determinants. Expect proximate determinants to control for underlying determinants in a multivariate logistic regression. - If not we have failed to identify pathway through which underlying determinants act.

10. © Imperial College LondonPage 10 Proximate determinants framework explored in rural Zimbabwe: Lewis et al 2007 STI Prevalent Infections – 845 HIV+ of 4331 men; 1334 HIV+ 5149 women Lopman et al 2008 IJE Incident infections – 98 of 2242 men and 113 of 3265 women seroconverted Examples in men for prevalent infections: Significant Proximate Determinants Multiple partners, genital sores, unwell partner Significant Underlying determinants Men – risk: skilled labourer*, attended bar, believes beer drinking essential * remained significant adjusting for proximate determinants

11. © Imperial College LondonPage 11 Conclusions: A decline in prevalence does not necessarily indicate successful risk reduction. Can detect significant declines by comparing with null models. With prevalence measures we are restricted to looking back a number of years, but have no good tools to measure incidence Combining interventions is important rather than relying on a single approach Can start to understand drivers of risk by exploring hypothesis in multivariate logistic regressions.