Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2018
[Trajectory modeling for estimating the trend of human papillomavirus infection status among men who have sex with men].
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Huang Bingxue, Sang Guoyao, Tuo Xiaoqing, Tian Tian, Abidan Ainiwaer, Dai Jianghong
Аннотация
OBJECTIVE: 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).
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Trajectory model can effectively distinguish the trend of HPV infection status in HIV-negative MSM to identify the high-risk group of HPV infection.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: Trajectory model can effectively distinguish the trend of HPV infection status in HIV-negative MSM to identify the high-risk group of HPV infection.