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Epidemiology poster session 5: Transmission dynamic: coinfection
P1-S5.26 Better-understanding of the dynamics of gonorrhoea and chlamydia through analysis of coinfection
  1. K Turner1,
  2. P White2,
  3. G Garnett2,
  4. N Gay3,
  5. N Low4,
  6. C Althaus4,
  7. Partner Notification Group5
  1. 1University of Bristol, Bristol, UK
  2. 2Imperial College London & HPA, London, UK
  3. 3UK
  4. 4University of Bern, Bern, Switzerland
  5. 5UK

Abstract

Background Coinfection with gonorrhoea and chlamydia occurs frequently. It is well known that the proportion of gonorrhoea-infected individuals who also have chlamydia is higher than the proportion of chlamydia-infected people who also have gonorrhoea. This difference has implications for the detection and management of infections and might simply reflect the higher prevalence of chlamydia than gonorrhoea in the general population. The objective of this study was to explore the characteristics of chlamydia–gonorrhoea coinfection in a transmission dynamic model and determine whether levels of coinfection might give insights into hard-to-measure behavioural parameters such as mixing patterns.

Methods We designed a simple transmission dynamic model to capture gonorrhoea and chlamydia dynamics within a heterosexual population. We fitted the model to empirical surveillance data (Gonococcal Resistance Antimicrobials Surveillance Programme, GRASP) about levels of infection and the NATSAL 2000 sexual behaviour survey. The baseline prevalence of chlamydia was 2.6%. We tested whether the model replicated the observed prevalence of coinfection. We then extended the model to improve its realism and capture potential interactions including cotreatment, cotransmission, short term acquired immunity or changes in symptom severity or susceptibility.

Results The best fitting model predicts a gonorrhoea prevalence of between 0.4 and 0.7% and of those with gonorrhoea 15% (men) and 17% (women) also have chlamydia, compared with empirical estimates of 28% (men) and 38% (women). Of those with chlamydia, 3.0% and 2.6% of men and women are coinfected with gonorrhoea. The model also predicts an increasing prevalence of coinfection with increasing sexual activity.

Conclusions A trend of increasing coinfection with increasing sexual activity is observed in the empirical data for men, but not women: the highest risk women with gonorrhoea have lower levels of chlamydia then the moderate activity women. The best fitting models underestimate the level of coinfection observed. Cotreatment and temporary immunity to chlamydia do not appear sufficient to explain the differences between the model and observations. Differences in coinfection levels are a complex phenomenon that do not just reflect differences in population prevalence and are not captured by the simplest models.

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