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Dear Editor,
The study, "Self-Reported PrEP Use and Risk of Bacterial STIs Among Ontarian Men Who Are Gay or Bisexual," provides valuable insights into the relationship between PrEP uptake and STI incidence among GBM in Ontario. The use of advanced statistical models, such as marginal structural models (MSMs), inverse probability weighting (IPW), and generalized estimating equation (GEE) Poisson models, reflects a rigorous approach to addressing time-varying confounding and selection bias. This careful analysis strengthens the study’s validity and contributes meaningfully to sexual health research.
While described as a mixed-methods study, the article focuses heavily on quantitative data. Incorporating qualitative insights—such as participant experiences with PrEP or STI testing—could have added valuable context to interpret behavior shifts, including reasons for condomless sex or decisions to seek PrEP. This triangulation would also help explain patterns, like increased gonorrhea rates without similar rises in other STIs.
The exclusion of HIV-positive participants narrows the scope of the study. Although HIV-positive individuals do not take PrEP, they engage in overlapping sexual networks with HIV-negative individuals and influence STI transmission dynamics. Including HIV-positive participants in a parallel analysis could offer insights into sexual behaviors and public health strategies for STI prevention.
While the study provides demographic distribution data, it would further enhance credibility to report subgroup-specific participation, as purposive sampling aimed to capture diverse experiences (e.g., transgender individuals, rural residents). Reporting these details in future studies would increase transparency and ensure the findings reflect the lived experiences of all intended subgroups.
The assumption of nondifferential misclassification could introduce detection bias, as PrEP users undergo more frequent STI screening, leading to higher detection of asymptomatic infections. Stratifying results by screening frequency would provide more accurate incidence estimates. Additionally, while multiple imputation was used to address missing data, assuming data were missing at random (MAR) could bias the results if participants at higher risk were more likely to drop out. Sensitivity analyses under a missing-not-at-random (MNAR) framework would strengthen findings.
Finally, while weekly electronic diaries minimize recall bias, the study does not specify whether participants were prompted to anchor their reporting to significant dates or events. Exploring such techniques in future research could improve data reliability.
We appreciate the authors' valuable contributions to PrEP and STI research and hope these suggestions will inspire further studies in this field.
Sincerely,
Gayathri Kothawar
Department of Epidemiology
University of Florida
gayathrikothawar@ufl.edu