Predicting the population impact of chlamydia screening programmes: comparative mathematical modelling study. Issue 5 (18th May 2009)

• Record Type: Journal Article
• Authors: Kretzschmar, M; Turner, K M E; Barton, P M; Edmunds, W J; Low, N
• Is Part Of: Sexually transmitted infections. Volume 85:Issue 5(2009)
• Page Start: 359
• Page End: 366
• Publication Date: 2009-05-18
• Subjects: Sexually transmitted diseases -- Periodicals; HIV infections -- Periodicals; 616.951005
• DOI: 10.1136/sti.2009.036251
• Languages: English
• ISSNs: 1368-4973

Abstract

Abstract : Background: Published individual-based, dynamic sexual network modelling studies reach different conclusions about the population impact of screening for Chlamydia trachomatis . The objective of this study was to conduct a direct comparison of the effect of organised chlamydia screening in different models. Methods: Three models simulating population-level sexual behaviour, chlamydia transmission, screening and partner notification were used. Parameters describing a hypothetical annual opportunistic screening program in 16–24 year olds were standardised, whereas other parameters from the three original studies were retained. Model predictions of the change in chlamydia prevalence were compared under a range of scenarios. Results: Initial overall chlamydia prevalence rates were similar in women but not men and there were age and sex-specific differences between models. The number of screening tests carried out was comparable in all models but there were large differences in the predicted impact of screening. After 10 years of screening, the predicted reduction in chlamydia prevalence in women aged 16–44 years ranged from 4% to 85%. Screening men and women had a greater impact than screening women alone in all models. There were marked differences between models in assumptions about treatment seeking and sexual behaviour before the start of the screening intervention. Conclusions: Future models of chlamydia transmission should be fitted to both incidence and prevalence data. This meta-modelling study provides essential information for explaining differences between published studies and increasing the utility of individual-based chlamydia transmission models for policy making.