Estimating age-dependent per-encounter chlamydia trachomatis acquisition risk via a Markov-based state-transition model

Yu Teng, Nan Kong, Wanzhu Tu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Background: Chlamydial infection is a common bacterial sexually transmitted infection worldwide, caused by C. trachomatis. The screening for C. trachomatis has been proven to be successful. However, such success is not fully realized through tailoring the recommended screening strategies for different age groups. This is partly due to the knowledge gap in understanding how the infection is correlated with age. In this paper, we estimate age-dependent risks of acquiring C. trachomatis by adolescent women via unprotected heterosexual acts. Methods: We develop a time-varying Markov state-transition model and compute the incidences of chlamydial infection at discrete age points by simulating the state-transition model with candidate per-encounter acquisition risks and sampled numbers of unit-time unprotected coital events at different age points. We solve an optimization problem to identify the age-dependent estimates that offer the closest matches to the observed infection incidences. We also investigate the impact of antimicrobial treatment effectiveness on the parameter estimates and the differences between the acquisition risks for the first-time infections and repeated infections. Results: Our case study supports the beliefs that age is an inverse predictor of C. trachomatis transmission and that protective immunity developed after initial infection is only partial. Conclusions: Our modeling method offers a flexible and expandable platform for investigating STI transmission.

Original languageEnglish (US)
Article number7
JournalJournal of Clinical Bioinformatics
Volume4
Issue number1
DOIs
StatePublished - 2014

Keywords

  • Acquisition risk
  • Chlamydial infection
  • Parameter estimation
  • State transition model
  • Transmission probability

ASJC Scopus subject areas

  • Health Informatics

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