Nov 30, 2007 - Based on earlier work by White & Comiskey (2007) on a model ..... and presented some results obtained by Andrew Sutton and results by.
Final Report EMCDDA Project
Coordination of a working group to develop mathematical and statistical models and analyses of protective factors for HIV infection among injecting drug users
November 2007
Mirjam Kretzschmar and Lucas Wiessing
Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
Table of contents Summary
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Background
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Aims of the project Set up: expert group and products Time schedule
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Results
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Centralised data repository Estimation of the force of infection from the individual level data sets Time trend analysis in the aggregate EU data set Correlation between HIV and HCV in the aggregate EU data set HIV and the treatment-relapse cycle Future research issues for modelling drug related infectious diseases Other ongoing work by study group members Interpretation and conclusions References Activities
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Meeting Lisbon October 2006 Topics identified for project work Abstracts draft papers Table available data Workshop Lisbon April 2007 Data analysis and draft papers Meeting Lisbon October 2007 Overall assessment of project activities and timing
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List of output
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Future activities
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Spin-off activities Future proposals
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Acknowledgements
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List of participants in the study group
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Appendix A Call for Tender & Project Proposal B Abstracts initially suggested projects C Table available data D Agenda Workshop E Minutes Workshop F List of Core Variables G Agreement data exchange H Abstracts of ongoing and future work I Draft papers
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Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
Summary There is large variation in HIV prevalence among populations of injecting drug users (IDU) across the European Union. The aim of this project was to investigate risk and protective factors (including interventions) that may explain this heterogeneity and that may be targeted by future policy measures. A study network of mathematical modellers and statisticians and epidemiologists carrying out studies among IDU was set up and available data were brought together for analysis in a central data repository. Three meetings were held and five specific analyses were initiated which are reported here. In addition multiple contributions in the form of a review paper, presentations and abstracts were brought into the project which are also reported. The five main analyses have concentrated on estimating the force of infection (FOI - using two different approaches), investigating the association between HIV and HCV prevalence, analysing time trends in HIV and HCV in the EU and studying the effects of drug treatment entry and relapse on HIV prevalence. The FOI analyses may form an innovative approach to estimating incidence from cross-sectional data (prevalence) which are relatively well available. These analyses also resulted in an indicator of the heterogeneity in risk of infection within a population, which may possibly in future work be linked to prevention efforts. Understanding the association between HIV and HCV prevalence is a prerequisite to understanding the potential of using HCV as an early warning indicator of injecting risk of HIV in populations where HIV is very low. An explorative analysis to better understand the inter-relations in the spread of HIV and HCV was undertaken using dynamic transmission models. The models were used to investigate the association between HIV and HCV prevalence, which resulted in the finding of a clear threshold phenomenon. While for HCV prevalence below 40% the HIV prevalence is consistently near zero, for higher HCV prevalence the maximum obtainable HIV prevalence is linearly related to the HCV prevalence. Model simulations were not only able to reproduce this correlation pattern, but were also able to shed some light on the role of population heterogeneity in the relationship between these prevalences. Using a statistical approach based on generalized linear models time trends in HIV and HCV prevalence in Europe were analysed suggesting that the prevalences of both infections are decreasing. It remains to be seen whether this may be an effect of interventions or of other factors. Moreover, a first analysis with a transmission model investigating the effects of drug treatment and relapse on HIV transmission showed that testing of drug users currently not in treatment can have a large effect on HIV prevalence provided that drug users who are aware of being HIV positive reduce their risk behaviour. The analysis of the data sets from the centralised data repository exploited the fact that information about more than one infection was present simultaneously on an individual level or on an aggregate population level. Furthermore, the information was available for a number of different countries which differed in their epidemiological characteristics. Those features of the data set enabled an analysis of the heterogeneity in various IDU populations with respect to the risk of becoming infected with HIV, HBV, or HCV, on the distribution of that risk, and on how that risk depends on behavioural factors related to injecting behaviour. Most important conclusions were that regardless of the virus and the country, beginning injectors face a much higher risk of becoming infected than experienced injectors, and more so when they start injecting at an older age. The known risk factors such as sharing of syringes and high frequencies of injecting were confirmed as risk factors in the analysis although interestingly results where slightly different between countries. Co-infections of HIV and HCV occurred more frequently than expected in a random distribution in all populations 3
Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
showing that both infections are correlated. Some populations however, displayed much more heterogeneity in the joint distribution of HIV and HCV than others. There seems to be a clear distinction between countries with a relatively homogeneous risk profile (Spain and Italy) and high HIV prevalence and countries with high levels of heterogeneity (England & Wales, Czech Republic) and low levels of HIV prevalence. Belgium takes an intermediate position both in terms of risk profile and in HIV prevalence. The Czech Republic seems to be in a different situation than other countries with respect to risk profile and FOI, maybe because the drug use epidemic there has only begun more recently. Whereas in Spain, Italy, and the UK the drug use epidemic has long been going on and many drug users have been injecting for many years, in the Czech Republic most drug users have only injected for a few years at most. Possibly a targetted intervention to high risk IDU could have a strong protective effect in the Czech Republic, if started before an HIV epidemic has taken off. More research needs to be done to understand how the phase of the drug use epidemic and heterogeneity in risk behaviour interact, and what the consequence is for the transmission dynamics of HIV and HCV. A start was made in that direction with an investigation of the transmission dynamics of HIV and HCV when assuming a linear relationship between the transmission probabilities for both infections. First analysis showed that HCV established itself at higher endemic prevalences for lower levels of risk behaviour than does HIV with a linear relationship between the prevalences above a certain threshold value of HCV prevalence. This result has several implications for intervention: one is that the prevalence of hepatitis C can give some indication of what level of HIV prevalence can be attained in a given IDU population, i.e. the risk of a major HIV epidemic in IDU might be interpreted from HCV data. Furthermore, the combination of HIV and HCV prevalence indirectly gives some information about the heterogeneity in risk behaviour of a population and possibly about the phases of the two epidemics. However, more research using dynamic transmission models is necessary to understand in more detail the relationships between the phase of the drug use epidemic, heterogeneity in risk behaviour, and the transient and endemic prevalences of HIV and HCV. Possibly, information obtained from the estimated distribution of risk behaviour in a population could be combined with an analysis using transmission models to obtain insight into the potential of future spread of HIV and HCV in a given IDU population. Concerning risk factors, the age at first injection is shown to be a risk factor for HCV and HIV infection in some populations with an increasing FOI for older ages at first injection. This means that injecting drug users who start injecting at an older age have a higher risk of becoming infected soon after the beginning of their injecting career. The reason might be that they mix with older age groups of injecting drug users who have a higher prevalence already, or that they proceed faster to high risk injecting behaviour. Decreasing trends were seen in the aggregate data set for both HIV and HCV prevalence in Europe. It remains to be seen whether these trends can be related to the intervention efforts in the various countries/locations or, firstly, if these trends might not be related to changes in data collection over time for example if progressively more low-prevalence (non-urban) areas would have been studied. We are planning to extend the statistical analysis of longitudinal trends to include intervention data as far as available. From preliminary results about the effects of HIV testing it seems that increasing HIV testing rates will be beneficial for the IDU community even without increased antiviral treatment options. Testing should not, however, be limited to drug treatment centers, but an effort should be made to offer HIV testing to IDU not currently under treatment.
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Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
Finally, from a comparison of the data sets from different European countries that were contributed to the central data repository, a number of factors emerged that seemed to drive the HIV and HCV epidemics in different populations. A very important factor was migration from high risk populations. For example in the Czech republic, where little migration from high risk Eastern European countries was observed up to now, the HIV prevalence is very low. New data presented at a recent workshop, however, indicates that this situation might be changing. In Estonia an extremely high HIV prevalence was observed in Russian speaking IDU communities. Also in other studies migration or factors related to social exclusion of cultural minorities was mentioned as an important factor in determining prevalence and incidence. In conclusion, a better understanding of the joint epidemiology of HIV and HCV is emerging from the research conducted in this project, but more work is needed to fully understand the dynamic interaction between drug use and the two infections, and to translate these insights into concrete conclusions about the impact of possible intervention strategies.
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Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
Background Following a call for tender (see appendix A) and in close collaboration with the EMCDDA the University of Bielefeld carried out the project “Coordination of a working group to develop mathematical and statistical models and analyses of protective factors for HIV infection among injecting drug users”. The project started in September 2006, with the aim of forming a network of mathematical modellers who would work with data on the prevalence of drug related infectious diseases (DRID) and risk behaviour in populations of injecting drug users (IDU) in Europe. The question was whether mathematical modelling could help to explain the large differences in prevalence of HIV across Europe and whether protective factors for the spread of HIV could be identified to support better intervention policies. An analysis of hepatitis B (HBV) and hepatitis C (HCV) prevalence in IDU was considered as part of the project. This enables studying the relationship between injecting and sexual risk behaviour and transmission of infection also in countries with very low HIV prevalence. Furthermore, we expected additional insight into intervention effects and protective factors from comparing the prevalence of HIV, HBV, and HCV in the same populations. The project was set up as a group of experts in mathematical and statistical modelling from 8 centers in 7 countries. Almost all of the participating scientists have extensive experience in mathematical and/or statistical modelling of infectious diseases, some of them have published specifically about modelling DRID (see appendix A for details about the expert network). After the contract was signed, Peter Vickerman (London School of Hygiene and Tropical Medicine) joined the project as an additional expert. After the annual EMCDDA meeting in Lisbon in October 2006 a number of epidemiologists, who had conducted studies and were willing to contribute their data and collaborate in a modelling project, also joined the expert group, and a wider study network was formed under joint coordination of University of Bielefeld and EMCDDA. The time schedule for the proposed work was as follows: 15 September 2006: start of contract (first payment) 25 September 2006: abstracts received by EMCDDA for circulation 10-11 October 2006: discuss proposed analyses with EMCDDA expert network / member states at EMCDDA expert meeting October 2006 – end of March 2007: production of draft scientific articles and conceptual framework / planning paper (these papers form the interim report – the second payment will depend on acceptance of the interim report by the EMCDDA) April-May 2007: circulation to EMCDDA expert network / member states for comments and receive back comments April-June 2007: peer review June 2007 – end of September 2007: incorporate comments, peer review and finalise papers September 2007: Submit final report to EMCDDA incorporating the conceptual framework / planning paper and five (or more) scientific articles as well as a short summary overview of all results. October 2007: presentations at EMCDDA expert meeting October – November 2007: acceptance of final report by EMCDDA and/or incorporation of final comments from EMCDDA and resubmission of final report. (final payment will depend on acceptance of the final report by the EMCDDA)
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Final Report CT.06.EPI.205.1.0 November 2007
Mirjam Kretzschmar & Lucas Wiessing
Results A number of subprojects were defined following email exchange and discussions during a meeting in April 2007. The proposed analyses included 1. Estimation of the FOI from the individual data (two different approaches) 2. Time trend analysis in the aggregate EU data set 3. Correlation between HIV and HCV in the aggregate EU data set 4. HIV and the treatment-relapse cycle Centralised data repository Following discussion about data needs for some of the modelling projects and available data sets in various countries, the study group decided on a list of key variables that should be present in all data sets used. We then established a centralised data repository containing data sets with individual records from six different countries and the aggregate EU data available from the EMCDDA. In addition, a data set from England and Wales was available on a bilateral basis even if not included in the centralised data repository. The conditions for using the centralised data repository were formulated in a general agreement (see Appendix G). Estimation of the force of infection from the individual level data sets An important focus of the work using the data from the centralised data repository was on estimating the force of infection (FOI) for HIV, HCV and HBV in different European countries. The force of infection is the risk per time unit for an uninfected IDU to become infected and depends on transmission probabilities, contact rates and the prevalence in the population. The relevant time scale here is the exposure time or time since first injection. Two different methods were employed for estimating the FOI. One is a method first developed by Farrington (1990) and modified by Sutton et al (2006) to accommodate for estimating the FOI for two or more infections simultaneously. The latter offers the advantage that the additional information on how two infections are distributed in the population can be used. Besides estimates for the FOI for both infections, a ‘frailty’ distribution can be found that describes the heterogeneity in risk in the population. In his ongoing analysis Sutton et al used the data sets from Belgium, Italy, Spain, the Czech Republic (2 data sets) and the UK (England & Wales). Where possible a distinction was made between beginning (injecting career length
