Prediction of Chlamydia trachomatis infection to facilitate selective screening on population and individual level: a cross-sectional study of a population-based screening programme
- PMID: 26843401
- DOI: 10.1136/sextrans-2015-052048
Prediction of Chlamydia trachomatis infection to facilitate selective screening on population and individual level: a cross-sectional study of a population-based screening programme
Abstract
Objectives: To develop prediction models for Chlamydia trachomatis (Ct) infection with different levels of detail in information, that is, from readily available data in registries and from additional questionnaires.
Methods: All inhabitants of Rotterdam and Amsterdam aged 16-29 were invited yearly from 2008 until 2011 for home-based testing. Their registry data included gender, age, ethnicity and neighbourhood-level socioeconomic status (SES). Participants were asked to fill in a questionnaire on education, sexually transmitted infection history, symptoms, partner information and sexual behaviour. We developed prediction models for Ct infection using first-time participant data-including registry variables only and with additional questionnaire variables-by multilevel logistic regression analysis to account for clustering within neighbourhoods. We assessed the discriminative ability by the area under the receiver operating characteristic curve (AUC).
Results: Four per cent (3540/80 385) of the participants was infected. The strongest registry predictors for Ct infection were young age (especially for women) and Surinamese, Antillean or sub-Saharan African ethnicity. Neighbourhood-level SES was of minor importance. Strong questionnaire predictors were low to intermediate education level, ethnicity of the partner (non-Dutch) and having sex with casual partners. When using a prediction model including questionnaire risk factors (AUC 0.74, 95% CI 0.736 to 0.752) for selective screening, 48% of the participating population needed to be screened to find 80% (95% CI 78.4% to 81.0%) of Ct infections. The model with registry risk factors only (AUC 0.67, 95% CI 0.656 to 0.675) required 60% to be screened to find 78% (95% CI 76.6% to 79.4%) of Ct infections.
Conclusions: A registry-based prediction model can facilitate selective Ct screening at population level, with further refinement at the individual level by including questionnaire risk factors.
Keywords: CHLAMYDIA TRACHOMATIS; INFECTION; PREVENTION; PUBLIC HEALTH; SCREENING.
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