is the most common curable sexually transmitted infection (STI) in many developed countries [1
]. It is most prevalent among sexually active young adults, with the majority of infections being asymptomatic and remaining undiagnosed [2
]. In women, infection can ascend to the upper genital tract, causing pelvic inflammatory disease, which can then lead to tubal factor infertility, ectopic pregnancy and chronic pelvic pain [3
]. It is therefore essential to understand the transmission dynamics of C. trachomatis
in order to develop appropriate interventions aimed at reducing the incidence and prevalence of infections.
Mathematical and computational models of infectious disease dynamics are widely used to study transmission and predict the impact of public health interventions [4
]. With respect to C. trachomatis
, such models can help us to understand the potential impact of screening, a population-level intervention that aims to reducing chlamydia prevalence and associated complications through the regular detection and treatment of asymptomatically infected cases. There are no empirical studies that show what levels of coverage and frequency of screening are necessary to achieve a specified reduction in C. trachomatis
prevalence. The predictions from modelling studies can therefore be influential in guiding health policy decisions about chlamydia screening [7
The models that have been developed to investigate the impact of screening have used either deterministic, population-based [8
] or stochastic, individual-based approaches [11
]. The latter approach is sometimes favoured, because it allows the detailed description of sexual partnership dynamics. Taking sexual partnerships explicitly into account has been suggested as an important feature of models of chlamydia transmission to capture re-infection within partnerships and investigate the effect of partner notification [10
]. In spite of these advantages, it is challenging to appropriately parameterize individual-based models and analysis and interpretation of the results is generally difficult [15
]. As a matter of fact, the three individual-based models of C. trachomatis
transmission, which were designed to evaluate different screening interventions in the UK and The Netherlands, can show marked differences in the predictions which complicates their use in informing public health policy-making. The study by Kretzschmar et al.
] described key features of each model and compared the effect of implementing the same screening scenario in the three models. The predicted reductions in chlamydia prevalence in women after 10 years of the intervention were 85 per cent [12
], 25 per cent [11
] and 5 per cent [13
The contrasting predictions between the three individual-based models are a result of different assumptions about the sexual partnership dynamics and the infection parameters of C. trachomatis
. These are key determinants that are believed to affect the spread of STIs. Chlamydia trachomatis
, like many other STIs, is primarily found among sexually active young adults who change sexual partners frequently. This is a prerequisite for sustaining transmission of bacterial STIs with infectious periods between several months to years. Further key determinants are the duration of sexual partnerships, the gap interval between subsequent partnerships, concurrent partnerships and heterogeneity in sexual behaviour [18
]. In order to model the spread of C. trachomatis
within the population, and to obtain reasonable predictions of screening interventions, a solid comprehension of these behavioural characteristics is necessary.
Novel insights into the dynamics of sexual contacts have been gained during the last decade [21
] but no clear formalism to describe the individual's sexual behaviour has been established so far. Also, the necessary parameters that define duration of partnerships, concurrency or age-specific behaviour are exceedingly difficult to obtain and implement in mathematical and computational models. As an additional complication, many of these behavioural characteristics are generated by a multitude of parameters. In order to assess the ‘realism’ of such models, it is therefore necessary to evaluate them in light of empirical data. On the one hand, this allows decisions about which models and predictions should be favoured over others. On the other hand, the features and structure of the most ‘realistic’ models can also give insights into the underlying mechanisms of sexual partnership formation. One of the most comprehensive data sets available to date is Natsal 2000, a population-based probability sample survey of sexual attitudes and lifestyles in Britain [24
]. The large sample size of the survey allows the derivation of the characteristic distributions of measures of sexual behaviour in a population. In addition, C. trachomatis
prevalence was measured in a subset of sexually active respondents [19
]. Natsal 2000 therefore provides a unique opportunity to study certain characteristics of sexual behaviour and link them to the prevalence of C. trachomatis
In this paper, we aim to explain the key differences of the three individual-based models that resulted in contrasting predictions of the impact of screening interventions against C. trachomatis
]. By comparing the sexual partnership dynamics of the models to data from Natsal 2000, we find several properties that can affect the transmission of C. trachomatis
. We illustrate the various assumptions made when describing sexual partnerships and discuss their implications for the spread of C. trachomatis
and the impact of a standardized screening intervention. This allows us to report the most reasonable prediction from a specific screening intervention. As a useful summary measure to evaluate models of STI transmission, we propose that the distribution of infections among individuals with different levels of sexual activity should be described using the Gini coefficient.