Presentation on theme: "National Institute for Public Health and the Environment Comparison of network models for STI transmission and intervention: how useful are they for public."— Presentation transcript:
National Institute for Public Health and the Environment Comparison of network models for STI transmission and intervention: how useful are they for public health? Mirjam Kretzschmar Centre for Infectious Disease Control, RIVM, and Julius Center for Health Sciences & Primary Care University Medical Centre Utrecht, The Netherlands DIMACS Workshop Tübingen, October 2008
National Institute for Public Health and the Environment Acknowledgements Katy Turner, Imperial College London, UK Pelham Barton, Birmingham University, UK John Edmunds, London School of Hygiene and Tropical Medicine, UK Nicola Low, University of Bern, Switzerland
National Institute for Public Health and the Environment Aim of this study Compare three models for the transmission dynamics of chlamydia infection that have been used to assess the effectiveness of different screening strategies. All three models are built on the same principles and ideas.
Background of the models RIVM model: to evaluate effectiveness of opportunistic chlamydia screening program in the Netherlands (Kretzschmar et al 2001) ClaSS model: to evaluate proactive, register-based chlamydia screening using home sampling in the UK (Low et al 2007) HPA model: to evaluate opportunistic national chlamydia screening programme in England (Turner et al 2006)
Individuals Events: - Aging, death - Formation, dissolution of partnerships - Transmission of infection Outcomes: Epidemiological measures: - Incidence - Prevalence Measures of network structure: - connected components - paths between individuals - centrality B C Population D A Age Partners Sex Disease status
Sexual partnerships Only heterosexual partnerships Steady and casual partnerships -duration -frequency of sex acts Highly active core group -5% of age class 15-35, gradual movement out of core group with increasing age -at most one steady partnership, simultaneous casual partnerships singles Casual pairs Steady pairs separation formation Only core group
Common features of all models Individual based stochastic simulation models Models describe partnership formation and dissolution, sexual networks Sexual behaviour parameters validated using sexual behaviour surveys Transmission and course of infection was described in a similar way Baseline prevalence comparable ( ~ 3%)
Screening and partner notification Standardized hypothetical opportunistic screening and PN protocols -Age group screened, 16-24 -Women only or men and women -Screening uptake 35% every year -Treatment for 45% of partners Compare prevalence after 10 years of screening
Change in chlamydia prevalence in women (16-44) after 10 years of screening
Where are differences between models? R 0 the basic reproduction number Transmission probability Condom use Contact rate number sex partners Duration infectious period Treatment Partner notification Screening
Transmission probabilities RIVMClaSSHPA Male–Female0.110.1540.0375 Female–Male0.110.1220.0375 Frequency of sex acts 1/day casual 0.25/day steady 0.5/day1/day for 30 days then 0.25/day
Duration of infection RIVMClaSSHPA Duration asymptomatic infection (mean in days) Men Women 200 370 200 180 Duration symptomatic infection (active treatment-seeking, mean in days) Men Women 33 40 30 40 30 Proportion seeking treatment (symptomatic only) Men Women 0.5 0.3 0.64 0.25 0.0 0.045 Effective partner notification (%)45% 20%
Partnership formation RIVMClaSSHPA Concurrency5% 15-35 yr olds can have 2+ concurrent partners 9% men, 5% women 15-30 yrs can have 2+ concurrent partners 5% of people desire 2 partners until they are 35 Gaps between partnerships YesNoYes Data setNL survey 1989ClaSS survey 2001NATSAL 2000
Effective contact rate m is mean and v variance in number of partners in last year. Then for populations stratified by numbers of contacts: Anderson & May 1991
Summary measures sexual activity Effective contact rate is very high for youngest age group in HPA model. Effective contact rate is higher for females than for males in RIVM and ClaSS models.
Proportion ever treated for Chlamydia (no screening)
Comparison Prescreening prevalence similar in all 3 models but differences in underlying dynamics HPA model, UK -Low transmission, low treatment levels, high contact rates, low PN rate -Screening can have large impact, because treatment before screening was low (fitted to observed data). ClaSS model, UK -High transmission, high pre-screening treatment and partner notification -Screening has little added value. RIVM model, Netherlands -High transmission, medium pre-screening treatment, high partner notification -Medium impact of screening Large discrepancy in prevalence of young males between models – impact of male screening?
Conclusions We do not know which, if any, model accurately predicts transmission or impact of intervention Between model variations in sexual contact rate, per- contact transmission probability, and proportion treated before screening all influence differences in output. Further comparison work to examine effects of these differences needed Better empirical data needed for models External validation needed
Questions for discussion How do we best validate individual based models? When do we know that model results can be trusted? Meta-modelling studies? Are individual based models of this complexity useful for public health decision making?
National Institute for Public Health and the Environment References Kretzschmar M, Turner KME, Barton PM, Edmunds WJ, Low N. Modeling the population impact of Chlamydia screening programs: comparative study. Submitted. Turner KM et al. Developing a realistic sexual network model of chlamydia transmission in Britain. Theor.Biol Med Model. 2006;3:3. Kretzschmar M et al. Comparative model-based analysis of screening programs for Chlamydia trachomatis infections. Am.J.Epidemiol. 2001;153:90-101. Low N et al. Epidemiological, social, diagnostic and economic evaluation of population screening for genital chlamydial infection. Health Technol.Assess. 2007;11:1-184. Low N, Heijne JCM, Kretzschmar M. Use of mathematical modeling to inform chlamydia screening policy decisions. J Infect Dis; in press.