1. Chlamydia screening programs: are epidemiology and mathematical modelling looking at the same thing? Nicola Low Institute of Social and Preventive Medicine University of Bern Bern, Switzerland 17th IUSTI World Congress 2016, 9-12 May 2016, Marrakesh Symposium 2, Mathematical modelling for STI: basic science and epidemiology applications

2. Outline > How does chlamydia screening work in models and real life? > Chlamydia screening programme effects in models > Chlamydia prevalence surveys in the era of chlamydia screening > Factors affecting chlamydia screening impact > Discussion 2

3. How should chlamydia screening work? 3 Chlamydia infection No infection Disability Infertility Early disease PID Testing Model

4. Mathematical modelling studies 1 4 Kretzschmar M et al, Am J Epidemiol 2001 Althaus CL et al. Epidemics 2010

5. Mathematical modelling studies 2 5 Andersen B et al. Sex Transm Dis 2006

6. Model predictions and observed data 6 2000-2001National Chlamydia Screening Programme 2010-2011 Model: UK National Audit Office 2009; Observed data: Sonnenberg P et al. Lancet 2013

7. Population-based estimates of chlamydia prevalence in women 7 NHANES, Datta SD et al. Sex Transm Dis 2012; Natsal, Sonnenberg P et al. Lancet 2013; CSI, van den Broek et al. BMJ 2012 NHANES, National Health and Nutrition Examination Surveys; Natsal, National Surveys of Sexual Attitudes and Lifestyles; CSI, Chlamydia Screening Implementation

8. Summary > Mathematical models of Chlamydia trachomatis transmission with different structures and making different assumptions about infection transmission show that screening and treatment reduce the prevalence of chlamydia infection > Chlamydia prevalence surveys repeated in the same population show modest or no reduction in chlamydia prevalence over time > What factors could help explain the discrepancy? 8

9. Factors affecting observed effect of chlamydia screening Survey factors > Imprecision ↕ > Test sensitivity increase ↓ > Transmissibility high ↓ > Duration of infection short ↓ > Re-infection not in model ↑ > Long period of immunity ↓ (Ryosuke Omori) Intervention factors > Sudden introduction ↑ > Screening at random ↓ > Partner notification not in model ↓ > High sexual partner change ↑ > Sexual mixing by age/activity ↕ 9 Infection factors Behavioural factors

10. Re-infection reduces the impact of screening > Most compartmental models do not take into account re- infections within partnerships > Impact of screening overestimated > Screening still reduces prevalence in the model 10

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12. Chlamydia screening rate predicted from model > US CDC recommendation —Annual chlamydia test for sexually active women ≤25 yrs > US National Health and Nutrition Surveys —≈600 sexually active women 15 to 25 years (total 5000) > Slope -0.24 per 2 years, 95% CI -2.23, 1.75, p 0.73 > Mathematical model predicts chlamydia test rate 10% per year 12 Heijne JCM, et al. Presented at 19th ISSTDR, Quebec City, July 2011.

13. Patterns of screening uptake and coverage 13 Scenario 2 Heijne JCM et al. Epidemics 4 Conference. Amsterdam, Nov 2013

14. Staged increase in screening introduction > Earlier introduction of screening might not be captured > Additional efforts might have a lower yield 14 Althaus CL. Presented at Swedish Medical Assocation conference 2011

15. Discussion > Chlamydia infection remains prevalent in many countries > Opportunistic testing and treatment can reach substantial proportions of sexually active young adults > Mathematical models of hypothetical screening interventions overestimate impact on chlamydia prevalence > Assumptions about implementation of screening, re-infection, infection parameters can all affect strength of impact > Combined effects of these factors needs to be investigated Symposium 8: Chlamydia: the good, the bad and the possible (Thurs am) 15

16. Acknowledgements > Christian Althuas, Joost Smid > Janneke Heijne, Sereina Herzog > Charlotte Kent, Guoyu Tao (CDC) > Sarah Woodhall (Public Health England) > Cath Mercer, Pam Sonnenberg (Natsal) 16