Epidemiological characteristics and spatial-temporal aggregation analysis of epidemic mumps in Yunnan Province from 2010 to 2021
SHEN Xiulian and WANG Junying contributed equally to this article
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摘要:
目的 通过对2010-2021年云南省流行性腮腺炎(流腮)流行病学特征和发病率时空分布特征进行分析，探讨其发病时空聚集区域及影响因素，以期为云南省流腮有效预防和控制措施的制定提供理论依据。 方法 采用GeoDa 1.8.12、SaTScan 9.5、ArcGis 10.8等软件，以云南省2010-2021年流腮上报的发病资料为研究对象，在县区级的基础上，进行全局-局部的空间自相关分析及空间异质性分析，并进行时空扫描。 结果 云南省2010-2021年共报告流腮病例124 786例，年平均发病率为22.14/10万; 发病率男性高于女性; 其中5~＜10岁儿童发病率最高(129.86/10万); 发病季节趋势显著，主要集中于5-7月和11-12月; 年平均发病率排名前3位的地区分别为德宏傣族景颇族自治州(德宏州)、迪庆藏族自治州(迪庆州)和西双版纳傣族自治州(西双版纳州); 流腮发病存在空间聚集性; “高-高”聚集区主要分布在昆明市、德宏州、保山市和临沧市; “低-低”聚集区从滇西地区逐步向滇南地区扩散; 影响流腮发病的因素主要为人均国内生产总值(gross domestic product, GDP)、海拔和年平均气温; 时空扫描结果显示最大可能聚集区分布在滇西北及滇中地区，聚集时间为2010年5月18日-2012年1月23日。 结论 2010-2021年云南省流腮的发病率在空间及时间上均呈聚集区域分布，对流腮防控措施的制定提供了科学的参考依据。 Abstract:Objective This study aimed to analyze the epidemiological characteristics of epidemic mumps and the spatial-temporal distribution of its incidence in Yunnan Province from 2010 to 2021. It also aimed to explore the spatial-temporal aggregation area and the influencing factors of the disease, providing a theoretical basis for effective prevention and control measures of mumps in Yunnan Province. Methods County-level global/local spatial autocorrelation analysis and spatial heterogeneity analysis which based on GeoDa 1.8.12, SaTScan 9.5, and ArcGis 10.8 software were performed on the reported cases of epidemic mumps in Yunnan Province from 2010 to 2021. Results A total of 124 786 epidemic mumps cases were reported in Yunnan Province from 2010 to 2021. The average annual incidence rate was 22.14 per hundred thousand. The incidence was higher in men than in women. And the highest incidence rate was in children aged 5- < 10 (129.86 per hundred thousand). The incidence also had a distinct seasonal trend. A bimodal distribution was presented annually, one of which was concentrated from May to July, and the other arose from November to December. Dehong Prefecture had the highest average annual incidence rate, followed by Diqing Prefecture and Xishuangbanna Prefecture. There was also a spatial aggregation in the incidence of the disease. The "high-high" cluster areas were mainly distributed in Kunming, Dehong, Baoshan and Lincang cities. The "low-low" clusters gradually spread from western Yunnan to southern Yunnan. The main factors affecting the incidence of epidemic mumps were per capita gross domestic product (GDP), altitude, and annual average temperature. The spatial-temporal scanning from 2010 to 2021 showed that the maximum possible clusters were distributed in northwest and central Yunnan, and the aggregation period was from May 18, 2010 to January 23, 2012. Conclusions The incidence of epidemic mumps in Yunnan Province (2010-2021) is distrbuted in spatial and temporal clustering areas, which can provide scientific reference for formulating epidemic mumps prevention and control measures. -
图 1 2010-2021年云南省流腮发病情况
Figure 1. Incidence of epidemic mumps in Yunnan Province from 2010 to 2021
图 2 2010-2021年云南省流腮发病的时间分布
Figure 2. Time distribution of epidemic mumps incidence in Yunnan Province from 2010 to 2021
图 3 2010-2021年云南省流腮发病率地区分布图
Figure 3. Regional distribution of epidemic mumps incidence in Yunnan Province from 2010 to 2021
图 4 2010-2021年云南省流腮发病率LISA聚类地图分布
Figure 4. LISA clustered map distribution of epidemic mumps incidence in Yunnan Province from 2010 to 2021
图 5 GWR模型局部回归系数分布图
1. GWR：地理加权回归模型; 2. GDP：国内生产总值。
Figure 5. Distribution of local regression coefficient of regional geographical of GWR Model
1. GWR: geographical weighted regression; 2. GDP: gross domestic product.
图 6 2010—2021年云南省流腮发病时空扫描分布图(以周为时间段)
Figure 6. Scanned spatial and temporal distribution of epidemic mumps incidence in Yunnan Province from 2010 to 2021 (time period in weeks)
表 1 2010-2021年云南省流腮的空间全局自相关分析
Table 1. Spatial global autocorrelation analysis of mumps in Yunnan Province from 2010 to 2021
年份Year Moran′s I 均数Mean 标准差Std Error Z值Z value P值P value 2010 0.122 3 -0.008 1 0.050 6 2.579 9 0.014 2011 0.037 8 -0.011 3 0.053 9 0.909 2 0.161 2012 0.055 7 -0.005 8 0.052 8 1.162 2 0.122 2013 -0.002 9 -0.008 9 0.037 6 0.157 6 0.362 2014 0.218 1 -0.009 6 0.054 6 4.167 8 0.001 2015 0.343 2 -0.006 7 0.049 4 7.079 0 0.001 2016 0.145 3 -0.007 0 0.051 3 2.967 1 0.004 2017 0.178 5 -0.011 3 0.048 9 3.883 2 0.003 2018 0.016 8 -0.008 6 0.045 3 0.559 9 0.247 2019 0.227 3 -0.008 3 0.052 9 4.456 4 0.001 2020 0.025 5 -0.008 9 0.042 0 0.816 8 0.140 2021 0.028 2 -0.009 2 0.054 1 0.690 8 0.239 
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表 2 OLS建模结果
Table 2. Modeling results of OLS
变量Variable 系数Coefficient 标准差Std Error t值t value P值P value VIF 截距Intercept -7.514 503 32.854 174 -0.228 723 0.819 478 — 人口密度/(人·km2) Population density /(people·km-2) -0.001 871 0.003 157 -0.592 500 0.554 642 1.313 879 人均GDP/元Per capita GDP /yuan 0.000 145 0.000 059 2.469 384 0.014 946 ① 1.447 262 海拔/m Altitude /m -0.011 727 0.004 043 -2.900 370 0.004 442 ① 2.915 315 年平均气温/℃ Annual average temperature /℃ -1.282 807 0.579 540 -2.213 494 0.028 763 ① 2.736 558 年平均降水量/mm Annual average precipitation /mm -0.003 881 0.004 680 -0.829 245 0.408 618 1.602 374 年平均相对湿度/% Annual average relative humidity /% 0.189 297 0.271 879 0.696 256 0.487 623 3.286 417 年平均风速/(m·s-1) Annual average wind speed /(m·s-1) 0.132 140 2.466 261 0.053 579 0.957 353 1.798 270 注：OLS, 普通最小二乘法; GDP, 国内生产总值; VIF, 方差膨胀系数。
①代表通过显著性检验。
Note: OLS, ordinary least squares; GDP, gross domestic product; VIF, Variance inflation factor.
① means passing the significance test.
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