Understanding dynamics and overlapping epidemiologies of HIV, HSV-2, chlamydia, gonorrhea, and syphilis in sexual networks of men who have sex with men

Front Public Health. 2024 Apr 2:12:1335693. doi: 10.3389/fpubh.2024.1335693. eCollection 2024.

Abstract

Introduction: We aimed to investigate the overlapping epidemiologies of human immunodeficiency virus (HIV), herpes simplex virus type 2 (HSV-2), chlamydia, gonorrhea, and syphilis in sexual networks of men who have sex with men (MSM), and to explore to what extent the epidemiology of one sexually transmitted infection (STI) relates to or differs from that of another STI.

Methods: An individual-based Monte Carlo simulation model was employed to simulate the concurrent transmission of STIs within diverse sexual networks of MSM. The model simulated sexual partnering, birth, death, and STI transmission within each specific sexual network. The model parameters were chosen based on the current knowledge and understanding of the natural history, transmission, and epidemiology of each considered STI. Associations were measured using the Spearman's rank correlation coefficient (SRCC) and maximal information coefficient (MIC).

Results: A total of 500 sexual networks were simulated by varying the mean and variance of the number of partners for both short-term and all partnerships, degree correlation, and clustering coefficient. HSV-2 had the highest current infection prevalence across the simulations, followed by HIV, chlamydia, syphilis, and gonorrhea. Threshold and saturation effects emerged in the relationship between STIs across the simulated networks, and all STIs demonstrated moderate to strong associations. The strongest current infection prevalence association was between HIV and gonorrhea, with an SRCC of 0.84 (95% CI: 0.80-0.87) and an MIC of 0.81 (95% CI: 0.74-0.88). The weakest association was between HSV-2 and syphilis, with an SRCC of 0.54 (95% CI: 0.48-0.59) and an MIC of 0.57 (95% CI, 0.49-0.65). Gonorrhea exhibited the strongest associations with the other STIs while syphilis had the weakest associations. Across the simulated networks, proportions of the population with zero, one, two, three, four, and five concurrent STI infections were 48.6, 37.7, 11.1, 2.4, 0.3, and < 0.1%, respectively. For lifetime exposure to these infections, these proportions were 13.6, 21.0, 22.9, 24.3, 13.4, and 4.8%, respectively.

Conclusion: STI epidemiologies demonstrate substantial overlap and associations, alongside nuanced differences that shape a unique pattern for each STI. Gonorrhea exhibits an "intermediate STI epidemiology," reflected by the highest average correlation coefficient with other STIs.

Keywords: HIV epidemiology; men who have sex with men; modeling; public health; sexually transmitted infections/diseases.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Chlamydia*
  • Gonorrhea* / complications
  • Gonorrhea* / epidemiology
  • HIV
  • HIV Infections* / complications
  • HIV Infections* / epidemiology
  • Herpesvirus 2, Human
  • Homosexuality, Male
  • Humans
  • Male
  • Sexual and Gender Minorities*
  • Sexually Transmitted Diseases* / epidemiology
  • Syphilis* / epidemiology

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. RO acknowledges the support of Precursory Research for Embryonic Science and Technology (PRESTO) grant number JPMJPR15E1 from Japan Science and Technology Agency (JST), Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Young Scientists (B) 19 K20393, and Japan Agency for Medical Research and Development (AMED) under Grant Number JP23fk0108676. This publication was made possible by ARG01-0522-230273 from the Qatar Research, Development and Innovation Council. The findings achieved herein are solely the responsibility of the authors. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors are also grateful for infrastructure support provided by the Biostatistics, Epidemiology, and Biomathematics Research Core at Weill Cornell Medicine-Qatar.