摘要目的:了解泸州市新报告HIV-1感染人群新发感染特征，以期得到高危人群特征，为当地制定精准防治措施提供依据。方法:对2018—2021年泸州市新报告的病例开展HIV-1限制性抗原亲和力酶联免疫方法检测。采用卡方检验进行单因素分析， Logistic回归模型进行多因素分析，应用空间自相关及热点分析方法，探索泸州市新发感染空间分布特征。 结果:选取每年奇数月网报的病例共计4 494例开展检测，新发感染病例673人，新发感染比例14.98%。新发感染病例以男性(472/673，70.13%)、已婚(335/673，49.78%)、异性传播(621/673，92.27%)、年龄>50岁(520/673，77.27%)、初中或以下教育程度(599/673，89.00%)为主。各年新发感染比例分别为11.74%(129/1 099)、17.11%(247/1 444)、13.57%(154/1 134)和17.50%(143/817)，差异具有统计学意义( χ2＝20.024， P<0.001)，呈上升趋势( χ2＝5.997， P＝0.014)。不同传播途径、性别、文化程度、职业、样本来源和现住址差异具有统计学意义(均 P<0.05)。 Logistic回归分析显示，性别、婚姻状况、传播途径、报告年份、职业、现住址、样本来源均是新发感染的影响因素。2018年和2021年新发病例发病存在空间自相关特征；热点地区由分散逐渐转为相对集中，数量随报告年份有所波动。 结论:泸州市HIV-1新发感染比例在全省范围内相对较低，但呈上升趋势。青年、学生、同性传播、离退人员等特征人群中新发比例较高。应重点关注热点地区，加强热点区域内常规监测及重点人群检测。

abstractsObjective:To understand the characteristics of new infections in the newly reported HIV-1 infected population in Luzhou, to find out the characteristics of the high-risk population, which may provide a basis for developing precise prevention and control measures locally.Methods:HIV-1 LAg Avidity EIA test was applied for newly reported cases in Luzhou from 2018 to 2021. The chi-square test was used for univariate analysis, logistic regression model for multivariate analysis, and spatial autocorrelation and hotspot analysis were applied to explore the spatial distribution characteristics of new infections in Luzhou city.Results:A total of 4 494 cases reported in the odd-numbered months were selected for testing, with 673 newly infected cases and the proportion of newly infected cases was 14.98%. Newly infected cases were predominantly male (472/673, 70.13%), married (335/673, 49.78%), heterosexual transmission (621/673, 92.27%), age>50 years (520/673, 77.27%), and junior high school or lower education (599/673, 89.00%). The proportion of new infections in each year were 11.74% (129/1 099), 17.11% (247/1 444), 13.57% (154/1 134) and 17.50% (143/817), respectively, with a statistically significant difference ( χ2=20.024, P<0.001) and an upward trend ( χ2=5.997, P=0.014). There were statistically significant differences in different transmission routes, gender, education level, occupation, sample source and residence (all P<0.05). Logistic regression analysis showed that gender, marital status, transmission route, current address, and sample source were all influencing factors for new infections. There are spatial autocorrelation characteristics of new case incidence in 2018 and 2021, hotspot areas gradually shifted from dispersed to relatively concentrated, with the number fluctuating with reported year. Conclusions:The proportion of new HIV-1 infections in Luzhou is relatively low throughout the province, but it is on the rise. The proportion of new infections is higher among youth, students, homosexual transmission, and retired persons. Attention should be focused on hotspot areas and routine surveillance and testing of key populations in hotspot areas should be strengthened.