Trajectory modeling for estimating the trend of human papillomavirus infection status among men who have sex with men

Abstract

To investigate whether trajectory model can be used to explore the trend of anal human papillomavirus (HPV) infection status among HIV-negative men who have sex with men (MSM). HIV-negative MSM were recruited by using the "snowball" method from 1st September 2016 to 30th September 2017 in Urumqi. The subjects were followed-up every six months since enrollment. The cell samples in anal canal were collected and the 37-type HPV test kits were used for identification and classification of HPV infection at both baseline and follow-up visits. Taking the cumulative number of different types of HPV as the dependent variable and follow-up visits as the independent variable, the trajectory model was established for the study subjects who completed baseline, 6 months and 12 months follow-up. The model was used to simulate the trend of HPV infection status when the subjects were divided into 1, 2, 3 and 4 subgroups. Bayesian information criterion (BIC), log Bayes factor and average posterior probability (AvePP) were used to evaluate the fitting effect. A total of 400 HIV-negative MSM were recruited at baseline and 187 subjects completed baseline and two follow-ups. The fitting effect attained best when the variation trend was divided into two subgroups. The first subgroup accounted for 54.5%(102/187) of the total, and the curve of change in HPV infection was decreasing; the second subgroup accounted for 45.5%(85/187) of the total, and the curve of change in HPV infection was increasing. Trajectory model can effectively distinguish the trend of HPV infection status in HIV-negative MSM to identify the high-risk group of HPV infection.