1. National Institute for Public Health and the Environment HIV, HCV, and HBV in injecting drug users in Europe Mirjam Kretzschmar Centre for Infectious Disease Control, RIVM, and Julius Center for Health Sciences & Primary Care University Medical Centre Utrecht, The Netherlands May 2009 EMCDDA conference

2. HIV infections newly diagnosed in injecting drug users, by year of report, by country, cases per million, 1996–2006. Source: EMCDDA website

3. HIV prevalence among injecting drug users — studies with national and subnational coverage. All injecting drug users, 2005 and 2006Young injecting drug users (under age 25), 2005 to 2006 Source: EMCDDA website 100%75% Black dots: national Blue dots: subnational

4. Estimated HCV antibody prevalence among injecting drug users — studies with national and subnational coverage Young injecting drug users (under age 25), 2005 to 2006 All injecting drug users, 2005 and 2006 Source: EMCDDA website 100%

5. Prevalence of markers of HBV infection estimated among national and subnational samples of injecting drug users 2005 to 2006, where data are available Percentage positive for ever infected (antiHBc) Percentage positive for current infection (HBsAg) Source: EMCDDA website 100%16%

6. Questions How are HIV and HCV prevalence related? And HBV? How do these prevalences depend on risk behaviour, duration of injecting, intervention? What is the impact of harm reduction on incidence and prevalence? Use statistical methods and mathematical modelling to get some answers

7. Project First project: Sept. 2006 – Nov. 2007 tendered by EMCDDA and conducted as a collaboration between EMCDDA and School of Public Health, University of Bielefeld -Set up team of modellers to work on analysis of European data -Produce 5 draft papers for publication in international journals -Resulted in collaboration with epidemiologists (the ‘Study group‘) Second phase: Collaboration with WHO Europe project: ‘Review statistical methods for estimating HIV incidence in countries with concentrated epidemics’ -Discuss other modelling issues and approaches -Continue EMCDDA work, link with WHO interests Background: EMCDDA EU network on drug related infectious diseases (HIV, hepatitis B/C in IDUs: experts, national focal points in 30 countries

8. Relationships between HIV and HCV prevalence Vickerman et al. submitted HIV and HCV prevalence data for 310 regions from published studies Thresholds?

9. Force of infection links incidence and prevalence B λ μ susceptible Force of infection (FOI):  risk per time unit for a susceptible person to become infected  depends on exposure and therefore on prevalence  can be different for different groups of IDU  can change during drug use career

10. Link between FOI and heterogeneity Estimates of force of infections from seroprevalence studies in different populations Sutton et al. J Viral Hepatitis 2008 Frailty function indicates heterogeneity with respect to exposure

11. Hamata et al; in preparation Force of infection as a function of time since start of injecting: Exposure duration Caveats:  steady state assumption  impact of intervention?

12. Hurley et al Lancet 1997Amundsen et al Eur J Pub Health 2003 How effective have NEPs been? How can we interpret ecological studies?

13. Decline HIV and HCV incidence in ACS 1985-2005 Amsterdam Cohort Studies among drug users Prospective HIV testing Retrospectively tested for HCV antibodies 952 ever injecting DU 58 HCV infections 90 HIV infections HCV HIV 30 0 10 0 1985 - 2004 Van den Berg et al. Eur J Epidemiol 2007

14. Is it all really the effect of harm reduction? Possible other explanation: Demographic changes in IDU population (e.g., ageing) Disease related mortality in those groups at highest risk of infections in the first decade of the HIV epidemic might have led to a change in the composition of the IDU population with less risk behaviour and lower transmission rates at the population level over calendar time. Smit et al, JAIDS 2008

15. Conclusions Epidemiology of HIV and HCV is closely related, but need to understand better thresholds and transmission dynamics Force of infection links incidence and prevalence, can say something about heterogeneity if we have data about more than on infection The impact of calender time on these relationships is not yet clear, need cohort studies to analyse that We need to disentangle impact of harm reduction from other influences – demographic changes, behaviour changes

16. National Institute for Public Health and the Environment Acknowledgements Lucas Wiessing Peter Vickerman Ziv Shkedy Emma White Andrew Sutton Viktor Mravcik Cathy Matheï Maria Prins Fernando Vallejo Barbara Suligoi Lillebil Norden and all other members of the study group