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Article(id=1241319154301588166, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202502172, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739635200000, receivedDateStr=2025-02-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883148980, onlineDateStr=2026-03-19, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883148980, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883148980, creator=13701087609, updateTime=1773883148980, updator=13701087609, issue=Issue{id=1241319148798669160, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='12', pageStart='2113', pageEnd='2304', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883147667, creator=13701087609, updateTime=1773885555254, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241329247004971040, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241329247004971041, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241319148798669160, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2140, endPage=2145, ext={EN=ArticleExt(id=1241319154624549596, articleId=1241319154301588166, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of epidemic characteristics and influencing factors of mumps, Qinghai Province, 2014 - 2023, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

Toanalyze epidemiological characteristics and influencing factors of mumps in Qinghai Province from 2014 to 2023 and provide reference for the prevention and control of mumps.

Methods

The incidence data of Mumps in Qinghai Province from 2014 to 2023 were collected, and the descriptive method was used to analyze the change changing pattern of Mumps incidence. Geographically and temporally weighted regression (GTWR) was established, the mean regression coefficients were calculated, and different factors (Social factors: GDP per capita, number of students in schools; Natural factors: annual average temperature, annual average wind speed, annual total precipitation, annual total sunshine hours) on the incidence of Mumps.

Results

From 2014 to 2023, a total of 14 278 cases of Mumps were reported in Qinghai Province, with an average annual incidence rate of 24.06/100 000, of which the highest in 2019 (65.62/100 000) and the lowest in 2016 (8.28/100 000). The seasonal index showed that Mumps showed a bimodal epidemic trend in winter and summer. The male-to-female sex ratio was 1.2:1, the highest number of cases was found in 5-<15 years old (40.66%) and 15-<25 years old (35.94%); and the occupation was dominated by students (69.15%). The incidence rate was the highest in Xining City (29.29/100 000 people) and the lowest in Yushu Prefecture (8.49/100 000 people). The GTWR model results showed that there were positive associations between annual average temperature, per capita GDP, annual total precipitation and the incidence of Mumps; and negative associations between the number of students in schools, annual average wind speed, and annual total sunshine hours with the incidence rate of Mumps.

Conclusion

The reported incidence of Mumps in Qinghai showed an overall downward trend. Focus on children and students aged 5 to 25 in Xining; social and natural factors have spatial heterogeneity on the incidence of Mumps, and regional prevention and control measures can be formulated in each region according to the actual situation.

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目的

分析2014—2023年青海省流行性腮腺炎(流腮)流行特征及其影响因素，为流腮防控提供参考。

方法

收集2014—2023年青海省流腮发病数据，运用描述性方法分析流腮发病的变化规律；建立时空地理加权回归 (geographically and temporally weighted regression，GTWR)模型，计算回归系数均值，分析不同因素（社会因素：人均GDP、在校学生数；自然因素：年均气温、年均风速、年总降水量、年总日照时数）对流腮发病的影响。

结果

2014—2023年青海省共报告流腮病例数14 278，年平均发病率24.06/10万，其中2019年最高(65.62/10万)，2016年最低（8.28/10万）。季节指数显示：流腮呈现冬季、夏季双峰流行态势。男女性别比为1.2：1；5~14岁（40.66%）、15~24岁（35.94%）发病数最多；职业以学生(69.15%)为主。西宁市发病率（29.29/10万）最高，玉树州发病率 (8.49/10万）最低。GTWR模型结果显示：年均气温、人均GDP、年总降水量与流腮发病率存在正向关联；在校学生数、年均风速、年总日照时数与流腮发病率存在负向关联。

结论

青海省流腮报告发病率总体呈现下降趋势；重点关注西宁市地区5至25岁儿童、学生群体；社会、自然等因素对流腮发病存在空间异质性，各地区可根据实际情况，制定区域性防控措施。

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龙江和赵金华为共同通信作者。龙江，Email：
赵金华，
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邓萍（1998—），女，硕士在读，研究方向：新发突发急性传染病的时空动态研究

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邓萍（1998—），女，硕士在读，研究方向：新发突发急性传染病的时空动态研究

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邓萍（1998—），女，硕士在读，研究方向：新发突发急性传染病的时空动态研究

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Western Pacific, 2024, 52: 101217., articleTitle=Effect of heatwaves on mortality of Alzheimer’s disease and other dementias among elderly aged 60 years and above in China, 2013-2020: a population-based study, refAbstract=null)], funds=[Fund(id=1241319164334362852, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, awardId=青卫健办[2021]104号, language=CN, fundingSource=“昆仑英才·高原名医”项目(青卫健办[2021]104号), fundOrder=null, country=null), Fund(id=1241319164422443243, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, awardId=null, language=CN, fundingSource=2024年度公共卫生人才培养支持项目, fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241319155966726961, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, xref=1., ext=[AuthorCompanyExt(id=1241319155979309874, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, companyId=1241319155966726961, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Public Health, Medical College of Qinghai University, Xining, Qinghai 810016, China), AuthorCompanyExt(id=1241319155987698483, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, companyId=1241319155966726961, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.青海大学医学院公共卫生系，青海 西宁 810016)]), AuthorCompany(id=1241319156126110524, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, xref=2., ext=[AuthorCompanyExt(id=1241319156130304829, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, companyId=1241319156126110524, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.青海省疾病预防控制中心业务管理部门)]), AuthorCompany(id=1241319157623477065, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, xref=3., ext=[AuthorCompanyExt(id=1241319157631865674, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, companyId=1241319157623477065, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.重庆市疾病预防控制中心传染病预防控制所)])], figs=[ArticleFig(id=1241319162623086716, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Fig.1, caption=Seasonal index of mumps in Qinghai Province from 2014 to 2023, figureFileSmall=gVg3h/KPqKHy0KhUg51HCQ==, figureFileBig=puOhR7O0Uczdfq54Jd719A==, tableContent=null), ArticleFig(id=1241319162765693064, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=CN, label=图1, caption=2014—2023年青海省流行性腮腺炎季节指数, figureFileSmall=gVg3h/KPqKHy0KhUg51HCQ==, figureFileBig=puOhR7O0Uczdfq54Jd719A==, tableContent=null), ArticleFig(id=1241319163042517148, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Fig.2, caption=Regression coefficients of mumps incidence and influencing factors in cities and prefectures of Qinghai Province from 2014 to 2023, figureFileSmall=tWdn6b1tHO5cjKFrCuOiAQ==, figureFileBig=wKUxFk91Mn16N6t/eDzUGw==, tableContent=null), ArticleFig(id=1241319163176734886, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=CN, label=图2, caption=2014—2023年青海省各市州流行性腮腺炎发病与影响因素的回归系数图

注：审图号：青S（2025）030号。

, figureFileSmall=tWdn6b1tHO5cjKFrCuOiAQ==, figureFileBig=wKUxFk91Mn16N6t/eDzUGw==, tableContent=null), ArticleFig(id=1241319163315146922, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Table 1, caption=

Descriptive analysis of Mumps in Qinghai Province from 2014 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
描述性特征病例数（发病率/构成比%）
性别
男7 834（25.98）
女6 444（22.09）
年龄组（岁）
0~41 994（52.01）
5~145 805（72.19）
15~245 132（58.44）
25~591 185（3.71）
≥60162（2.40）
职业
散居儿童1 765（13.64）
托幼儿童2 227（17.21）
学生8 948（69.15）
其他1 338（10.34）
地区
西宁市6 956（29.29）
海东市3 547（24.87）
海北藏族自治州（海北州）351（12.52）
黄南藏族自治州（黄南州）709（26.03）
海南藏族自治州（海南州）731（15.91）
果洛藏族自治州（果洛州）300（14.89）
玉树藏族自治州（玉树州）348（8.49）
海西蒙古藏族自治州（海西州）1 336（26.30）
), ArticleFig(id=1241319163445170353, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=CN, label=表1, caption=

2014—2023年青海省流行性腮腺炎发病率描述性分析

, figureFileSmall=null, figureFileBig=null, tableContent=
描述性特征病例数（发病率/构成比%）
性别
男7 834（25.98）
女6 444（22.09）
年龄组（岁）
0~41 994（52.01）
5~145 805（72.19）
15~245 132（58.44）
25~591 185（3.71）
≥60162（2.40）
职业
散居儿童1 765（13.64）
托幼儿童2 227（17.21）
学生8 948（69.15）
其他1 338（10.34）
地区
西宁市6 956（29.29）
海东市3 547（24.87）
海北藏族自治州（海北州）351（12.52）
黄南藏族自治州（黄南州）709（26.03）
海南藏族自治州（海南州）731（15.91）
果洛藏族自治州（果洛州）300（14.89）
玉树藏族自治州（玉树州）348（8.49）
海西蒙古藏族自治州（海西州）1 336（26.30）
), ArticleFig(id=1241319163566805179, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Table 2, caption=

Global Moran I analysis of mumps incidence in Qinghai Province from 2014 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
年份Moran IZP空间分布
20140.1522.2260.017聚集
20150.1431.9200.009聚集
2016-0.258-0.6870.266随机
2017-0.438-1.7180.011离散
2018-0.325-1.0690.142随机
2019-0.184-0.2190.463随机
2020-0.0051.0120.184随机
2021-0.1070.1940.380随机
2022-0.2420.1940.295随机
2023-0.485-1.8050.004离散
), ArticleFig(id=1241319163705217213, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=CN, label=表2, caption=

2014—2023年青海省流行性腮腺炎发病率全局Moran I分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
年份Moran IZP空间分布
20140.1522.2260.017聚集
20150.1431.9200.009聚集
2016-0.258-0.6870.266随机
2017-0.438-1.7180.011离散
2018-0.325-1.0690.142随机
2019-0.184-0.2190.463随机
2020-0.0051.0120.184随机
2021-0.1070.1940.380随机
2022-0.2420.1940.295随机
2023-0.485-1.8050.004离散
), ArticleFig(id=1241319163831046344, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Table 3, caption=

OLS model test results

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量回归系数tP95% CIVIF
人均GDP1.8022.5870.012a-51.901~124.2011.393
在校学生数0.1030.3920.696-0.412~0.6183.147
年均气温2.6890.9600.340-2.794~8.1533.614
年均风速-0.147-0.0660.947-4.471~4.1781.280
年总降水量0.0272.4200.018a0.005~0.0491.581
年总日照时数-0.020-1.4300.157-0.048~0.0071.976
), ArticleFig(id=1241319163948486861, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=CN, label=表3, caption=

OLS模型检验结果

, figureFileSmall=null, figureFileBig=null, tableContent=
自变量回归系数tP95% CIVIF
人均GDP1.8022.5870.012a-51.901~124.2011.393
在校学生数0.1030.3920.696-0.412~0.6183.147
年均气温2.6890.9600.340-2.794~8.1533.614
年均风速-0.147-0.0660.947-4.471~4.1781.280
年总降水量0.0272.4200.018a0.005~0.0491.581
年总日照时数-0.020-1.4300.157-0.048~0.0071.976
), ArticleFig(id=1241319164036567250, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241319154301588166, language=EN, label=Table 4, caption=

The mean regression coefficients of GWR and GTWR models

, figureFileSmall=null, figureFileBig=null, tableContent=
模型GWR（Neighbor=30）GTWR（Neighbor=26）
最小值下四分位数平均值上四分位数最大值最小值下四分位数平均值上四分位数最大值
人均GDP-11.918-9.188-3.4902.791-1.799-11.918-0.1411.4992.7616.881
在校学生数-0.7720.6491.0901.7151.921-1.377-0.332-0.0940.0151.267
年均气温-26.696-14.474-10.369-1.9472.379-5.1693.6975.8508.09816.774
年均风速0.5801.5608.1927.71333.036-14.225-8.969-3.4700.54713.917
年总降水量-0.0070.0050.0290.0430.104-0.860-0.0380.0020.0310.109
年总日照时数-0.071-0.056-0.036-0.017-0.010-0.090-0.038-0.032-0.0120.018
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GWR与GTWR模型的回归系数

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模型GWR（Neighbor=30）GTWR（Neighbor=26）
最小值下四分位数平均值上四分位数最大值最小值下四分位数平均值上四分位数最大值
人均GDP-11.918-9.188-3.4902.791-1.799-11.918-0.1411.4992.7616.881
在校学生数-0.7720.6491.0901.7151.921-1.377-0.332-0.0940.0151.267
年均气温-26.696-14.474-10.369-1.9472.379-5.1693.6975.8508.09816.774
年均风速0.5801.5608.1927.71333.036-14.225-8.969-3.4700.54713.917
年总降水量-0.0070.0050.0290.0430.104-0.860-0.0380.0020.0310.109
年总日照时数-0.071-0.056-0.036-0.017-0.010-0.090-0.038-0.032-0.0120.018
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2014—2023年青海省流行性腮腺炎流行特征及影响因素分析
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邓萍 1, 2 , 龙江 3 , 赵金华 1, 2 , 覃胜林 1 , 姜雨淇 1 , 张阳 1
现代预防医学 | 流行病与统计方法 2025,52(12): 2140-2145
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现代预防医学 | 流行病与统计方法 2025, 52(12): 2140-2145
2014—2023年青海省流行性腮腺炎流行特征及影响因素分析
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邓萍1, 2, 龙江3 , 赵金华1, 2 , 覃胜林1, 姜雨淇1, 张阳1
作者信息
  • 1.青海大学医学院公共卫生系，青海 西宁 810016
  • 2.青海省疾病预防控制中心业务管理部门
  • 3.重庆市疾病预防控制中心传染病预防控制所

    • 邓萍（1998—），女，硕士在读，研究方向：新发突发急性传染病的时空动态研究

通讯作者:

龙江和赵金华为共同通信作者。龙江，Email：
赵金华，
Analysis of epidemic characteristics and influencing factors of mumps, Qinghai Province, 2014 - 2023
Ping DENG1, 2, Jiang LONG3 , Jin-hua ZHAO1, 2 , Sheng-lin QIN1, Yu-qi JIANG1, Yang ZHANG1
Affiliations
  • Department of Public Health, Medical College of Qinghai University, Xining, Qinghai 810016, China
出版时间: 2025-06-25 doi: 10.20043/j.cnki.MPM.202502172
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摘要
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目的

分析2014—2023年青海省流行性腮腺炎(流腮)流行特征及其影响因素，为流腮防控提供参考。

方法

收集2014—2023年青海省流腮发病数据，运用描述性方法分析流腮发病的变化规律；建立时空地理加权回归 (geographically and temporally weighted regression，GTWR)模型，计算回归系数均值，分析不同因素（社会因素：人均GDP、在校学生数；自然因素：年均气温、年均风速、年总降水量、年总日照时数）对流腮发病的影响。

结果

2014—2023年青海省共报告流腮病例数14 278，年平均发病率24.06/10万，其中2019年最高(65.62/10万)，2016年最低（8.28/10万）。季节指数显示：流腮呈现冬季、夏季双峰流行态势。男女性别比为1.2：1；5~14岁（40.66%）、15~24岁（35.94%）发病数最多；职业以学生(69.15%)为主。西宁市发病率（29.29/10万）最高，玉树州发病率 (8.49/10万）最低。GTWR模型结果显示：年均气温、人均GDP、年总降水量与流腮发病率存在正向关联；在校学生数、年均风速、年总日照时数与流腮发病率存在负向关联。

结论

青海省流腮报告发病率总体呈现下降趋势；重点关注西宁市地区5至25岁儿童、学生群体；社会、自然等因素对流腮发病存在空间异质性，各地区可根据实际情况，制定区域性防控措施。

气象因素  /  流行性腮腺炎  /  GTWR模型  /  社会因素  /  时空分布
Objective

Toanalyze epidemiological characteristics and influencing factors of mumps in Qinghai Province from 2014 to 2023 and provide reference for the prevention and control of mumps.

Methods

The incidence data of Mumps in Qinghai Province from 2014 to 2023 were collected, and the descriptive method was used to analyze the change changing pattern of Mumps incidence. Geographically and temporally weighted regression (GTWR) was established, the mean regression coefficients were calculated, and different factors (Social factors: GDP per capita, number of students in schools; Natural factors: annual average temperature, annual average wind speed, annual total precipitation, annual total sunshine hours) on the incidence of Mumps.

Results

From 2014 to 2023, a total of 14 278 cases of Mumps were reported in Qinghai Province, with an average annual incidence rate of 24.06/100 000, of which the highest in 2019 (65.62/100 000) and the lowest in 2016 (8.28/100 000). The seasonal index showed that Mumps showed a bimodal epidemic trend in winter and summer. The male-to-female sex ratio was 1.2:1, the highest number of cases was found in 5-<15 years old (40.66%) and 15-<25 years old (35.94%); and the occupation was dominated by students (69.15%). The incidence rate was the highest in Xining City (29.29/100 000 people) and the lowest in Yushu Prefecture (8.49/100 000 people). The GTWR model results showed that there were positive associations between annual average temperature, per capita GDP, annual total precipitation and the incidence of Mumps; and negative associations between the number of students in schools, annual average wind speed, and annual total sunshine hours with the incidence rate of Mumps.

Conclusion

The reported incidence of Mumps in Qinghai showed an overall downward trend. Focus on children and students aged 5 to 25 in Xining; social and natural factors have spatial heterogeneity on the incidence of Mumps, and regional prevention and control measures can be formulated in each region according to the actual situation.

Meteorological factors  /  Mumps  /  GTWR model  /  Social factors  /  Spatial and temporal distribution
邓萍, 龙江, 赵金华, 覃胜林, 姜雨淇, 张阳. 2014—2023年青海省流行性腮腺炎流行特征及影响因素分析. 现代预防医学, 2025 , 52 (12) : 2140 -2145 . DOI: 10.20043/j.cnki.MPM.202502172
Ping DENG, Jiang LONG, Jin-hua ZHAO, Sheng-lin QIN, Yu-qi JIANG, Yang ZHANG. Analysis of epidemic characteristics and influencing factors of mumps, Qinghai Province, 2014 - 2023[J]. Modern Preventive Medicine, 2025 , 52 (12) : 2140 -2145 . DOI: 10.20043/j.cnki.MPM.202502172
正文
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流行性腮腺炎（简称“流腮”）是一种由腮腺炎病毒引起的急性呼吸道传染病，主要表现为腮腺肿大、发热等症状，常见于儿童和青少年，严重者可并发脑炎或胰腺炎[1]。近年来流腮发病率有所下降，但部分地区，其流行风险仍较高[2]。青海为西部省份、地域辽阔，海拔高、空气稀薄、缺氧，其患病病程较长，加之比邻西藏、甘肃、新疆，其发病趋势及影响因素的探讨颇为重要。鉴于前期均为描述性研究，本文将时空地理加权回归（geographically and temporally weighted regression，GTWR）模型应用于探讨影响其流行的多维因素，为高海拔地区流腮防控提供依据。
1　资料与方法
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1.1　资料来源
数据来源于中国疾病预防控制系统的《传染病监测系统》，以年发病率、月发病率来体现，人口资料来源于《青海省统计年鉴》，社会因素：人均GDP和在校学生数来源于青海省各市州统计公报。自然因素：年均气温、年均风速和年总降水量来源于美国国家海洋和大气管理局（NOAA）下设的国家环境信息中心(NCEI)，缺失值用反距离权重法插值求得；年总日照时数来源于青海省统计年鉴，由于2024年统计年鉴未发行，进行求和取平均得到2023年各月的日照时数数据。因数据来源受限，避免结果可信度较低，故只采用以上6种影响因素进行分析。本文不涉及伦理审查和知情同意。
1.2　方法
1.2.1　季节指数
季节指数（Seasonal indices，SI）是衡量时间序列数据在不同季节或月份之间相对波动性的指标。首先计算历年每个月份平均发病率；其次计算所有月份发病率的平均值；最后每月平均发病率除以总平均发病率。SI>1：该月份的活动水平高于基准水平（整体平均水平），SI<1：低于基准水平，近似或等于1则无季节性[3]。
1.2.2　全局空间自相关
使用空间计量模型时，需要检验数据之间是否具有空间异质性，因此需对自变量进行全局空间自相关分析。选用全局莫兰指数(Moran I)来衡量，Moran I位于-1~1之间，大于0 ，P值≤0.05，为空间正相关；小于0，P值≤0.05，为空间负相关；接近于0或小于0，P值大于0.05表示随机分布，不存在空间相关性[4]。
1.2.3　多重共线性诊断
在建立GTWR模型之前，为避免自变量之间存在高度相关性，导致模型估计不稳定，因此需要对6个影响因素，使用方差膨胀因子（Variance Inflation Factor，VIF）进行多重共线性检验[5]，并采取已有研究中的判断条件(VIF小于5)来判断6个影响因素是否能满足建立模型的条件[6]。
1.2.4　时空地理加权回归模型
由于GTWR模型同时考虑了空间和时间的非平稳效应，可以使用解释变量的回归系数来描述空间和时间的影响[7]。GTWR模型表达式为：
yi表示第i个观测点的发病率；(ui,vi,ti)为第i个观测点的经度、维度、时间；β0(ui,vi,ti)表示第i个观测点的截距项；k表示自变量的数量；βk(ui,vi,ti)表示第i个观测点中第k个自变量的回归系数；xik是第k个自变量在第i个观测点的值；εi误差项，是第i个观测点的随机误差；P为变量的总数。在文中GTWR模型的回归系数用平均值、最小值、最大值、下四分位数、上四分位数进行呈现，将模型回归系数均值的绝对值作为影响程度的量化，回归系数均值为正时，表示自变量对因变量有正向关联，为负时，表示自变量对因变量有负向关联，值越大，影响程度越大。
1.2.5　模型评估
采用校正决定系数（Adjusted R-Square，adjR2）、校正赤池信息量准则（Akaike information criterion corrected，AICc）、残差平方和（Residual sum of squares，RSS）评估模型的拟合效果，adjR2 越高，AICc、RSS越低，模型的拟合效果越好[8]。
1.2.6　统计学处理
采用Excel 2021软件整理数据；使用SPSS 28.0软件进行描述性分析；采用GeoDa 19.0软件进行全局空间自相关分析；Arcgis 10.8软件进行时空地理加权回归模型构建，并绘制回归系数图，检验水准α=0.05。
2　结果
收起
2.1　时间特征
2.1.1　年度变化
2014—2023年青海省流腮累计报告病例数14 278，年平均发病率24.06/10万，在2019年发病率最高(65.62/10万)，2016年发病率最低（8.28/10万）。
2.1.2　季节性变化
季节指数结果显示，青海省流腮发病呈冬夏季流行，主季节高峰在5—6月，次季节高峰在11—12月。见图1。
2.2　人群特征
男性发病率（25.98/10万）高于女性（22.09/10万），性别比为1.2:1。
年龄组以15~24岁(40.66%)、15~24岁(35.94%)为主；5~14岁（72.19/10万）发病率最高，其次是15~24岁（58.44/10万）、0~4岁（52.01/10万）。
职业主要以学生(69.15%)为主，其次是托幼儿童(17.21%)、散居儿童(13.64%)、其他(10.34%)。见表1。
2.3　地区特征
青海省各市州流腮发病率由高到低依次为西宁市(29.29/10万)>海西州(26.30/10万)>黄南州(26.03/10万)>海东市(24.87/10万)>海南州(15.91/10万)>果洛州(14.89/10万)>海北州(12.52/10万)>玉树州(8.49/10万)，西宁市发病率最高，玉树州发病率最低。
2.4　影响因素分析
2.4.1　全局空间自相关
2014—2015年呈现空间正相关，2017年和2023年呈现空间负相关，其余年份不存在空间相关性，提示青海省流腮发病率在2014—2023年期间存在空间相关性，但空间聚集性较弱。见表2。
2.4.2　多重共线性诊断
多重共线性诊断结果显示各影响因素VIF值均小于5，且有一定的空间自相关性，具备建立共GTWR模型的条件，因此6个变量均可纳入模型之中。见表3。
2.4.3　时空地理加权回归模型
建立普通最小二乘法（Ordinary Least Squares，OLS）模型、地理加权回归（Geographically Weighted Regression，GWR）模型，并对回归结果进行了比较分析，以验证 GTWR 模型的有效性。其中用软件建立GWR、GTWR 模型均选择带宽方法为AICc，核类型为ADAPTIVE。OLS模型结果可知，除人均GDP、年总降水量，其余影响因素的t统计量没有通过显著性检验，但在 95%的置信水平上模型的P值为0.013，说明流腮发病率与各个因素之间在整体上具有显著的线性关系。见表3。与OLS模型（adjR2=0.127、AICc=704.433、RSS=26 227.420）、GWR模型（adjR2=0.406、AICc= 735.734、RSS=18 093.300）相比，GTWR 模型具有最高的adjR2（0.664）和较低的AICc（707.751）、RSS（10 215.100），表明 GTWR 模型在分析流腮发病率与6个因素之间的关系方面优于 OLS 和 GWR。
GTWR模型中人均GDP、年均气温、年总降水量回归系数平均值均大于0，说明这几个变量与青海省流腮发病率存在正向关联。在校学生数、年均风速、年总日照时数回归系数平均值均小于0，说明这些变量与青海省流腮发病率存在负向关联。见表4。其中人均GDP、年均气温、年均风速的影响程度较大，人均GDP的影响程度呈现西部、中南部、东南部高于中部、北部、东北部；年均风速的影响程度呈现西南部、西部、中南部高于东部和北部；年均气温则呈现北部、中部、西南部高于东部和西部。见图2。
3　讨论
收起
2014—2023年青海省流腮在2019年报告发病率最高(65.62/10万)，2016年报告发病率最低（8.28/10万），年平均发病率24.06/10万，高于安徽省[9]、福建省[10] ，低于重庆市[11]、甘肃省[12]等地区，这可能与各省的疫苗接种率、人口密度、流行病学特征等因素有关。
季节指数显示青海省流腮好发于冬夏季，高峰期为每年的5—6月、11—12月，与云南省[13]、贵州省[14]等地研究结果一致，建议青海省在流腮高峰期前后强化学校风险隐患排查，对学校传染病的“两案九制”落实进行督导检查，同时对疫苗接种进行查漏补缺。
人群分布可知，男性发病率高于女性，与安徽省研究一致[15]，可能与人群社交活动、自身免疫力以及个人习惯有关。5~14岁（40.66%）、15~24岁（35.94%）年龄段病例数最多，与以往以5~14岁（79.52%）为主的研究不同[16]，揭示青海省流腮发病年龄段发生后移，考虑与2剂次疫苗接种率较低有关，1剂次接种后儿童产生一定的免疫保护，但随着时间的推移，免疫力减弱，从而使得青少年和年轻成年人更容易感染。职业分布以学生为主，与内蒙古研究一致[17]，学生通常在学校中集中学习和活动，这种密集的人群环境更容易导致疾病的传播。
地区上，与其他市州相比，西宁市发病率较高，可能是因为西宁市人口密度较高，医疗设施和资源相对丰富，早期发现和治疗疾病的机会较多。
采用GTWR模型分析了多种影响因素对青海省流腮发病率的作用。结果表明，不同因素对流腮发病率的影响存在空间异质性，且影响强度在不同地区表现出明显差异。在校学生数对流腮发病率存在负向关联，这一结果可能与学校健康教育普及和疫苗接种率提高有关。大规模学校更容易组织集中的疫苗接种活动，从而提高群体免疫水平[18]。此外，学校作为健康教育的重要场所，能够有效提高学生对传染病的认知和预防意识，从而降低疾病传播风险。人均GDP对流腮发病率存在正向关联，就区域而言，玉树州、海西州、黄南州，受人均GDP的影响最大，可能是这些地区面临更大的经济结构问题和发展不平衡，以及基础医疗设施、医疗服务可及性等方面较弱。年均风速对流腮发病率有显著负向关联，较高的风速可能加速空气流通，降低病毒在空气中的浓度，从而减少传播风险。年均气温对流腮发病率有显著正向关联，流腮病毒属于副黏液病毒科，其传播效率与温度和湿度密切相关。高温环境可能增加病毒在人群中的传播风险[19]。年总降水量对流腮发病率有正向关联，降水量增加会导致环境湿度上升，这可能为病毒的传播和生存提供了更有利的条件[20]。年总日照时数对流腮发病率有负向关联，考虑与青海省地貌多样复杂、气候差异大有关，日照较强的地区，可能通过紫外线杀菌作用降低病毒在环境中的存活率[21]。
本文的局限性：流腮报告病例数可能存在漏报或误报；GTWR模型未能充分考虑不同变量之间的交互效应，这种交互作用可能会影响流腮发病的真实情况。而且纳入的影响因素较少，在后续研究中应完善对相关环境和社会经济数据的收集，使其涵盖更多受影响的区域和时间段，从而增强研究的代表性和可靠性。
基金
收起
  • “昆仑英才·高原名医”项目(青卫健办[2021]104号)
  • 2024年度公共卫生人才培养支持项目
文献
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2025年第52卷第12期
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doi: 10.20043/j.cnki.MPM.202502172
  • 接收时间：2025-02-16
  • 首发时间：2026-03-19
  • 出版时间：2025-06-25
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  • 收稿日期：2025-02-16
基金
“昆仑英才·高原名医”项目(青卫健办[2021]104号)
2024年度公共卫生人才培养支持项目
作者信息
    1.青海大学医学院公共卫生系，青海 西宁 810016
    2.青海省疾病预防控制中心业务管理部门
    3.重庆市疾病预防控制中心传染病预防控制所

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Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
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