王怡珺, 沈福杰, 舒敏, 毛宇明, 韩颖俊, 杨怀霞, 赵根明. 上海市黄浦区流感发病的人群及时空分布特征[J]. 环境与职业医学, 2021, 38(4): 408-413. DOI: 10.13213/j.cnki.jeom.2021.20387
引用本文: 王怡珺, 沈福杰, 舒敏, 毛宇明, 韩颖俊, 杨怀霞, 赵根明. 上海市黄浦区流感发病的人群及时空分布特征[J]. 环境与职业医学, 2021, 38(4): 408-413. DOI: 10.13213/j.cnki.jeom.2021.20387
WANG Yijun, SHEN Fujie, SHU Min, MAO Yuming, HAN Yingjun, YANG Huaixia, ZHAO Genming. Demographic and spatial-temporal distributions of influenza cases in Huangpu District, Shanghai[J]. Journal of Environmental and Occupational Medicine, 2021, 38(4): 408-413. DOI: 10.13213/j.cnki.jeom.2021.20387
Citation: WANG Yijun, SHEN Fujie, SHU Min, MAO Yuming, HAN Yingjun, YANG Huaixia, ZHAO Genming. Demographic and spatial-temporal distributions of influenza cases in Huangpu District, Shanghai[J]. Journal of Environmental and Occupational Medicine, 2021, 38(4): 408-413. DOI: 10.13213/j.cnki.jeom.2021.20387

上海市黄浦区流感发病的人群及时空分布特征

Demographic and spatial-temporal distributions of influenza cases in Huangpu District, Shanghai

  • 摘要: 背景

    各地季节性流感的流行特征不尽相同。目前研究多基于流感样病例监测数据,而利用流感发病数据可以较为准确地描述流感的空间分布特征。

    目的

    通过流感发病数据,探索上海市黄浦区流感发病的人群及时空分布特征。

    方法

    流感确诊病例来源于中国疾病预防控制信息系统,利用描述性流行病学方法对黄浦区2 500例流感确诊病例开展分析。病例发病时间为2013年4月1日—2019年3月31日,以每年4月1日—次年3月31日为一个流感周期,共包括6个流感周期。流行期指12月—次年2月及7—8月。分析不同流感周期、不同人群特征、不同街道的甲、乙型流感病例数的构成特征。以居委为单位计算流感发病率,收集黄浦区23家医疗机构的地理要素,利用ArcGIS 10.1建立流感空间数据库,对流感发病率进行空间自相关分析和热点分析,并进行可视化展示。

    结果

    上海市黄浦区流感周期平均发病率为64.14/10万。所有流感周期均观察到冬季峰,在2014—2015年、2015—2016年和2017—2018年观察到夏季峰。流感病例中甲、乙型各占71.52%和28.48%,其中甲型以季H3型为主,乙型以Yamagata系为主,不同流感周期甲、乙型流感的构成比差异有统计学意义(P=0.003)。在2018—2019年的流感周期中观察到流行季节甲型流感构成比高于非流行季节(P=0.002)。流感病例的男女性别比为1:1.25,中位年龄47岁。不同年龄段的甲、乙型流感病例构成比差异有统计学意义(P < 0.001):甲型流感在≥65岁年龄组占比最高,乙型流感在5~17岁年龄组占比最高。不同的职业人群其甲、乙型流感病例的构成比明显不同(P=0.031),工人中甲型流感病例的占比最高(75.47%),学生中乙型流感病例的占比最高(37.15%)。全局空间自相关分析显示流感病例的分布呈空间正相关(Moran's I=0.42,P < 0.001)。局部热点分析显示,甲型流感发病率的Gi指数范围为-2.13~7.65,热点区域主要分布在黄浦区东部的29个居委和西部的6个居委,乙型流感发病率的Gi指数范围为-2.19~7.68,热点区域主要分布在黄浦区东部的24个居委。结合黄浦区医疗机构分布图层观察到流感发病热点区域靠近黄浦区2家国家级流感监测点医院。

    结论

    上海市黄浦区以甲型流感流行为主,学生对乙型流感易感,应重点关注学校学生聚集性疫情的发生,流感发病呈空间正相关,主要热点区域分布在东、西部两个区域。

     

    Abstract: Background

    The epidemiological characteristics of influenza vary among districts and regions. Many studies exploit influenza-like illness surveillance data; however, more accurate demographic and spatial-temporal distributions of influenza can be achieved by using local influenza incidence data.

    Objective

    This study depicts demographic and spatial-temporal distributions of influenza cases in Huangpu District of Shanghai.

    Methods

    Descriptive epidemiological method was utilized to describe the epidemiological characteristics of 2 500 influenza cases in Huangpu District of Shanghai sourced from the information platform of Chinese Center for Disease Control and Prevention. The onset time of all cases was between 1 April, 2013 and 31 March, 2019. An influenza cycle was defined as from 1 April to 31 March next calendar year, and 6 influenza cycles were included in this study. Flu activity peaked between December and February next year, and between July and August. The proportions of influenza A and B cases in different influenza cycles, populations, and subdistricts were calculated. ArcGIS 10.1 was used to establish a database of influenza incidences in each residential committee and geographic information about 23 hospitals in Huangpu District. Spatial autocorrelation and hot spot analysis were performed to visualize the geographic hot spot distribution of influenza cases.

    Results

    The average influenza incidence was 64.14 per 105. Winter peaks were seen during each influenza season and summer peaks were observed in three seasons of 2014-2015, 2015-2016, and 2017-2018. Of all the cases, influenza A, mainly subtype H3, accounted for 71.52%, whereas influenza B, mainly Yamagata linage virus, did 28.48%. There were significant differences between the proportions of influenza A and B in each influenza cycle (P=0.003). During the influenza cycle of 2018-2019, the proportion of influenza A was significantly higher in epidemic season than that in non-epidemic season (P=0.002). The overall male to female ratio of the influenza cases was 1:1.25 and the median age was 47 years. Significant differences between the proportions of influenza A and B cases were shown among age groups (P < 0.001) and occupation groups (P=0.031). Influenza A infection cases were more in residents aged ≥ 65 years, while influenza B infection cases were more in those aged 5-17 years. Workers topped in influenza A cases (75.47%), while students did in influenza B cases (37.15%). Significant positive spatial autocorrelations of influenza cases were found within Huangpu District (Moran's I=0.42, P < 0.001). Hot spots for influenza A were detected in 29 residential committees in eastern area and 6 in western area of the district, where Gi ranged from -2.13 to 7.65. Hot spots for influenza B were seen in 24 residential committees in eastern area, where Gi ranged from -2.19 to 7.68. Along with the distribution of hospitals, those hot spots were close to two national influenza sentinel hospitals.

    Conclusion

    Influenza A is prevalent in Huangpu District of Shanghai, and students are more sensitive to influenza B. Influenza control strategies, therefore, should focus on influenza clusters among students. There are spatial autocorrelations between the influenza cases, and influenza hot spots are distributed in eastern and western areas of the study area.

     

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