Discrepancies between observed data and predictions from mathematical modelling of the impact of screening interventions on Chlamydia trachomatis prevalence

Abstract

Mathematical modelling studies of C. trachomatis transmission 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 heterosexual C. trachomatis transmission, parameterized using data from England about sexual behaviour, C. trachomatis testing, diagnosis and prevalence, and Markov Chain Monte Carlo methods for statistical inference. In our baseline scenario, 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 68% (95% Bayesian credible interval 46-87%). The estimated screening rate was 4.3 (2.2-6.6) times higher for infected than for uninfected women in 2000, decreasing to 2.1 (1.4-2.9) in 2011. Despite incorporation of these factors, the model still predicted a marked decline in C. trachomatis prevalence. To reduce the gap between modelling and data, advances are needed in knowledge about factors influencing the coverage of chlamydia screening, the immunology of C. trachomatis and changes in C. trachomatis prevalence at the population level.

Figure 1

Schematic illustration of the C. trachomatis transmission model. S susceptible; I^A asymptomatically infected; I^S symptomatically infected; R partially immune; Y^A asymptomatic reinfection; Y^S symptomatic reinfection. Parameters are described in Table 1.

Figure 2

Fit of model 3 (full model) to age-specific chlamydia prevalence for men and women in 2000 and 2011. Grey boxes and horizontal lines: posterior mean and 95% Bayesian credible intervals. Black dots and vertical bars: Estimated prevalence from Natsal-2 (2000) and Natsal-3 (2011) (mean and 95% confidence intervals).

Figure 3

Fit of model 4 (full model) to age-specific per capita number of diagnoses for men and women between 2000 and 2011. Coloured lines and shaded areas: posterior mean and 95% Bayesian credible intervals. Vertical bars and dots: Minimum and maximum estimates for number of diagnoses from Chandra and colleagues, and midpoints of these estimates (used for fitting).

Figure 4

Differential screening coverage: Model-estimated change in the ratio of the screening rates in infected vs. susceptible individuals for men and women of 16–24 years old between 2000 and 2011. Coloured lines and shaded areas: Posterior mean and 95% Bayesian credible intervals.

Figure 5

Model-estimated change in chlamydia prevalence in men and women between 2000 and 2011. Best-fit: Full model (model 4) including changes in the proportion of tests done in infected individuals and partial immunity (posterior mean and 95% Bayesian credible intervals). Hypothetical: Scenario in which the proportion of screening tests in infected individuals is kept at the same level as was estimated for 2000. Error bars for 2000: chlamydia prevalence in men/women aged 18–24 from Natsal-2; error bars for 2011: chlamydia prevalence in men/women aged 16–24 from Natsal-3 (mean and 95% confidence intervals).