Determinants and prediction of Chlamydia trachomatis re-testing and re-infection within 1 year among heterosexuals with chlamydia attending a sexual health clinic

Front Public Health. 2023 Jan 13:10:1031372. doi: 10.3389/fpubh.2022.1031372. eCollection 2022.

Abstract

Background: Chlamydia trachomatis (chlamydia) is one of the most common sexually transmitted infections (STI) globally, and re-infections are common. Current Australian guidelines recommend re-testing for chlamydia 3 months after treatment to identify possible re-infection. Patient-delivered partner therapy (PDPT) has been proposed to control chlamydia re-infection among heterosexuals. We aimed to identify determinants and the prediction of chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia to identify potential PDPT candidates.

Methods: Our baseline data included 5,806 heterosexuals with chlamydia aged ≥18 years and 2,070 re-tested for chlamydia within 1 year of their chlamydia diagnosis at the Melbourne Sexual Health Center from January 2, 2015, to May 15, 2020. We used routinely collected electronic health record (EHR) variables and machine-learning models to predict chlamydia re-testing and re-infection events. We also used logistic regression to investigate factors associated with chlamydia re-testing and re-infection.

Results: About 2,070 (36%) of 5,806 heterosexuals with chlamydia were re-tested for chlamydia within 1 year. Among those retested, 307 (15%) were re-infected. Multivariable logistic regression analysis showed that older age (≥35 years old), female, living with HIV, being a current sex worker, patient-delivered partner therapy users, and higher numbers of sex partners were associated with an increased chlamydia re-testing within 1 year. Multivariable logistic regression analysis also showed that younger age (18-24 years), male gender, and living with HIV were associated with an increased chlamydia re-infection within 1 year. The XGBoost model was the best model for predicting chlamydia re-testing and re-infection within 1 year among heterosexuals with chlamydia; however, machine learning approaches and these self-reported answers from clients did not provide a good predictive value (AUC < 60.0%).

Conclusion: The low rate of chlamydia re-testing and high rate of chlamydia re-infection among heterosexuals with chlamydia highlights the need for further interventions. Better targeting of individuals more likely to be re-infected is needed to optimize the provision of PDPT and encourage the test of re-infection at 3 months.

Keywords: Chlamydia trachomatis; heterosexual; machine learning; predictive model; re-infection; re-testing; risk factors; variable importance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Australia
  • Chlamydia Infections* / diagnosis
  • Chlamydia Infections* / epidemiology
  • Chlamydia trachomatis
  • Female
  • HIV Infections*
  • Heterosexuality
  • Humans
  • Male
  • Reinfection
  • Sexual Health*
  • Young Adult

Grants and funding

This work was supported by Australian National Health and Medical Research Council Emerging Leadership Investigator Grant (Grant GNT1172873 to EC; GNT1193955 to JO), the Australian National Health and Medical Research Council Leadership Investigator Grant (Grant GNT1172900 to CF), National Natural Science Foundation of China (Grant 81950410639 to LZ), Outstanding Young Scholars Funding (Grant 3111500001 to LZ), Xi'an Jiaotong University Basic Research and Profession Grant (Grant xtr022019003 to LZ, Grant xzy032020032 to LZ), Epidemiology modeling and risk assessment (Grant 20200344 to LZ), and Xi'an Jiaotong University Young Talent Support Grant (Grant YX6J004 to LZ).