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.
目的: 探索应用轨迹分析模型拟合HIV阴性男男性行为（MSM）人群肛周人乳头瘤病毒（HPV）感染状态变化趋势的可行性。
方法: 2016年9月1日至2017年9月30日于乌鲁木齐市采用滚雪球法招募HIV阴性MSM者，以调查对象入组时间为基准，每6个月随访一次，采集肛管内脱落细胞并进行HPV DNA分型鉴定。纳入完成基线、6个月、12个月随访的研究对象，以感染不同型别HPV的累加数量为因变量，随访次数为自变量构建轨迹分析模型，分别探索将受试者分为一个、二个、三个及四个亚组时的HPV感染状态变化轨迹，并运用贝叶斯信息标准值（BIC）、贝叶斯因子对数值和平均验后分组概率（AvePP）评价模型拟合效果。
结果: 共招募400名HIV阴性MSM者，其中187名MSM者纳入模型分析。结果发现，将HPV感染状态变化趋势按两组轨迹模型拟合效果最优。该模型中，第一亚组占54.5%（102/187），HPV感染状态变化曲线呈下降趋势；第二亚组占45.5%（85/187），HPV感染状态变化曲线呈上升趋势。
结论: 应用轨迹分析模型能有效区分HIV阴性MSM人群HPV感染状态的变化趋势，有助于探寻HPV感染的高危人群。