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目的:对成都市龙泉驿区2009—2018年风疹流行病学特征进行分析，为制定针对性的防控措施提供依据。方法:风疹病例资料来源于中国疾病预防控制信息系统的疾病监测信息报告管理系统，相关疫苗接种率数据来源于四川免疫规划信息管理系统。利用SaTScan 9.1.1软件，采用基于离散Poisson模型的时空扫描统计量Kulldorff方法，对龙泉驿区2009—2018年风疹病例资料进行描述性统计分析和时空扫描统计分析。结果:2009—2018年龙泉驿区共报告风疹病例229例，年平均发病率3.48 /10万；4至6月份发病数最多，占总病例数的80.35%（184/229）；发病年龄构成以15岁以下居多，占69.87%（160/229）；职业构成以学生居多，占51.09%（117/229）；龙泉街道平均发病率最高为5.81/10万。20例8~17月龄病例中，有免疫史的9例；8例18~23月龄病例中，0剂次7例、2剂次1例。结论:龙泉驿区应加强风疹防控力度，并继续做好适龄儿童含风疹类疫苗（rubella cotaining vaccine，RCV）免疫，适时开展重点人群RCV免疫工作。“,”Objective:To anlyze the epidemiological characteristics of rubella in Longquanyi district of Chengdu city from 2009 to 2018, so as to provide basis for the formulation of specific prevention and control measures.Methods:The data of rubella cases were collected from the Disease Prevention and Control Information Management System of the China Disease Prevention and Control Information System. Vaccination related information was from the Sichuan Provincial Immunization Planning Information System. The descriptive statistics and time-space scanning statistics were used to analyze the data of rubella cases, using Kulldorff method based on discretion Poisson model in SaTScan 9.1.1 software.Results:From 2009 to 2018, 229 cases of rubella were reported in Longquanyi district, with an average annual incidence of 3.48/100 000. From April to June, the number of cases was the highest, accounting for 80.35% (184/229) of all cases. In the constitution of onset age, most cases were under 15 years old, accounting for 69.87% (160/229). In the occupation composition, cases were mostly students, accounting for 51.09% (117/229). The highest average incidence was 5.81 /100 000 in Longquan street. Nine of twenty cases in 8-17 months old group had a history of vaccination. In the 8 cases aged 18 to 23 months, 7 cases received no vacination and 1 case had 2 doses of vaccine.Conclusions:Longquanyi district should strengthen the prevention and control from May to July, and provide routine RCV immunization for certain children as well as timely RCV immunization for key population.