Discrepancies between observed data and predictions from mathematical modelling of the impact of screening interventions onChlamydia trachomatisprevalence

Abstract

Mathematical modelling studies ofC. trachomatistransmission predict that interventions to screen and treat chlamydia infection will reduce prevalence to a greater degree than that observed in empirical population-based studies. We investigated two factors that might explain this discrepancy: partial immunity after natural infection clearance and differential screening coverage according to infection risk. We used four variants of a compartmental model for heterosexualC. trachomatistransmission, parameterized using data from England about sexual behaviour andC. trachomatistesting, diagnosis and prevalence, and Markov Chain Monte Carlo methods for statistical inference. A model in which partial immunity follows natural infection clearance and the proportion of tests done in chlamydia-infected people decreases over time fitted the data best. The model predicts that partial immunity reduced susceptibility to reinfection by 72% (95% Bayesian credible interval 57-86%). The estimated screening rate was 4.6 (2.6-6.5) times higher for infected than for uninfected women in 2000; this decreased to 2.1 (1.4-2.9) in 2011. Other factors not included in the model could have further reduced the expected impact of screening. Future mathematical modelling studies investigating the effects of screening interventions onC. trachomatistransmission should incorporate host immunity and changes over time in the targeting of screening.