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Article(id=1241036328306012486, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202411497, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732896000000, receivedDateStr=2024-11-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815718008, onlineDateStr=2026-03-18, pubDate=1757433600000, pubDateStr=2025-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815718008, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815718008, creator=13701087609, updateTime=1773815718008, updator=13701087609, issue=Issue{id=1241036327177744706, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='17', pageStart='3073', pageEnd='3264', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815717738, creator=13701087609, updateTime=1773840080282, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241138511152206262, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241138511152206263, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241036327177744706, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3142, endPage=3148, ext={EN=ArticleExt(id=1241036330080203083, articleId=1241036328306012486, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023, columnId=1228016570660745413, journalTitle=Modern Preventive Medicine, columnName=Environmental and Occupational Health, runingTitle=null, highlight=null, articleAbstract=
Objective

To clarify the spatial and temporal distribution pattern of the incidence of non-occupational carbon monoxide poisoning in Shandong Province, to reveal the meteorological driving factors behind it, and to provide reference for its scientific prevention and control.

Methods

Non-occupational carbon monoxide poisoning incident reports were collected through the China Disease Control and Prevention Information System Public Health Emergency Reporting Management Information System. Descriptive analysis was used to study the prevalence characteristics of non-occupational carbon monoxide poisoning. Spatial autocorrelation analysis and spatio-temporal scanning analysis were performed to explore spatio-temporal aggregation using ArcGIS 10.7 and SaTScan 10.1.2 software, respectively. The influence of meteorological factors on the incidence of non-occupational carbon monoxide poisoning was assessed using an optimal parameter geographical detector.

Results

A total of 12 088 cases of non-occupational carbon monoxide poisoning were reported in Shandong Province from 2019 to 2023, with peak incidence from November to March of the next year each year, and middle-aged and older populations people as the main high-risk groups. Global spatial autocorrelation analysis showed spatial clustering of non-occupational carbon monoxide poisoning incidence rates in both 2020—2022(P<0.05). The southern part of Shandong Province was gradually developing into a new hot spot. Spatio-temporal scanning analysis detected five aggregation zones, of which the primary aggregation zone was mainly concentrated in the northern part of Shandong Province (Nov 2019—Mar 2021, LLR=2 003.71, P<0.001). The results of the optimal parameter geographical detector showed that relative humidity and barometric pressure were important meteorological factors affecting morbidity, with the largest interaction between relative humidity and wind speed.

Conclusion

Non-occupational carbon monoxide poisoning in Shandong Province showed seasonal spatiotemporal clustering. It is recommended to strengthen the winter and spring health education work for middle-aged and older populations and other high-risk groups. At the same time, disease prevention and control organizations at all levels should establish good information communication with meteorological and propaganda departments. Early warning of non-occupational carbon monoxide poisoning accidents in key endemic areas such as northern Shandong Province and southern Shandong Province should be strengthened through these initiatives.

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目的

明确山东省非职业性一氧化碳中毒发病率的时空分布格局，揭示其背后的气象驱动因子，为其科学防控提供参考。

方法

通过中国疾病预防控制信息系统突发公共卫生事件报告管理信息系统收集非职业性一氧化碳中毒事件报告。采用描述性分析研究非职业性一氧化碳中毒的流行特征。采用ArcGIS 10.7和SaTScan 10.1.2软件分别进行空间自相关分析和时空扫描分析以探讨时空聚集性。采用参数最优地理探测器揭示气象因子对非职业性一氧化碳中毒发病率的影响程度。

结果

2019—2023年山东省累积报告非职业性一氧化碳中毒病例12 088例，每年11月—次年3月为发病高峰，中老年人为主要高危人群。全局空间自相关分析显示，2020—2022年非职业性一氧化碳中毒发病率均存在空间聚集性（P<0.05）。鲁南地区逐渐发展为新的热点区域。时空扫描分析探测出五个聚集区，其中一级聚集区主要集中在鲁北地区（2019年11月—2021年03月，LLR=2 003.71，P<0.001）。参数最优地理探测器结果显示，相对湿度和气压是影响发病率的重要气象因子，相对湿度和风速的交互作用最大。

结论

山东省非职业性一氧化碳中毒存在季节性时空聚集性。建议加强中老年群体等高危人群冬春季健康教育工作，同时各级疾病预防控制机构应与气象、宣传等部门建立良好的信息沟通，加强对鲁北及鲁南等重点流行区非职业性一氧化碳中毒事故的预警。

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孙成玺，E-mail:；
姜晓林，E-mail:。
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=JmedrR4pRPrTok8T6Ixlfg==, magXml=wgpOMi376KmtHZJbj1hdKg==, pdfUrl=null, pdf=oR1b9c9Kr5XgLuRkqFkNvg==, pdfFileSize=1140550, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=hUczGqaCjn7EJuZDfH8vbg==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=1aw6HsV+In9uLYm6BoGJ7g==, mapNumber=null, authorCompany=null, fund=null, authors=

孙成玺与姜晓林为共同通信作者

温子萱（1998—），女，硕士，研究方向：非职业性一氧化碳中毒

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温子萱（1998—），女，硕士，研究方向：非职业性一氧化碳中毒

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注：标准地图审图号GS（2024）0650号。

, figureFileSmall=R659tSYoOhx8eFAqT0dBUQ==, figureFileBig=MSJLWDp7R2+zx0N1LMNfWg==, tableContent=null), ArticleFig(id=1241139383013789810, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Figure 4, caption=Hot spot distribution of non-occupational CO poisoning cases in Shandong Province from 2019 to 2023, figureFileSmall=cXmMo5zlzjkyNFR21gMkEA==, figureFileBig=BKaNpQQ8FUf2YG21Bb5nfg==, tableContent=null), ArticleFig(id=1241139383089287289, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=图4, caption=2019—2023年山东省非职业性一氧化碳中毒病例热点分布

注：标准地图审图号GS（2024）0650号。

, figureFileSmall=cXmMo5zlzjkyNFR21gMkEA==, figureFileBig=BKaNpQQ8FUf2YG21Bb5nfg==, tableContent=null), ArticleFig(id=1241139383160590466, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Figure 5, caption=Spatiotemporal clustering of non-occupational CO poisoning cases in Shandong Province from 2019 to 2023, figureFileSmall=zEN/HQBCs5I3/8jzUS8qAg==, figureFileBig=2T3FrMlxtvOcI8DojfFbDg==, tableContent=null), ArticleFig(id=1241139383265448069, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=图5, caption=2019—2023年山东省非职业性一氧化碳中毒病例时空聚集性情况

注：标准地图审图号GS（2024）0650号。

, figureFileSmall=zEN/HQBCs5I3/8jzUS8qAg==, figureFileBig=2T3FrMlxtvOcI8DojfFbDg==, tableContent=null), ArticleFig(id=1241139383395471497, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Table 1, caption=

Meteorological factor index

, figureFileSmall=null, figureFileBig=null, tableContent=
指标解释单位
X1温度，2019—2023年各县/区逐年平均气温之和/5℃
X2降水，2019—2023年各县/区逐年累积降水之和/5mm
X3相对湿度，2019—2023年各县/区逐年平均相对湿度之和/5%
X4风速，2019—2023年各县/区逐年平均风速之和/5m/s
X5气压，2019—2023年各县/区逐年平均气压之和/5hPa
), ArticleFig(id=1241139383542272144, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=表1, caption=

气象因子指标

, figureFileSmall=null, figureFileBig=null, tableContent=
指标解释单位
X1温度，2019—2023年各县/区逐年平均气温之和/5℃
X2降水，2019—2023年各县/区逐年累积降水之和/5mm
X3相对湿度，2019—2023年各县/区逐年平均相对湿度之和/5%
X4风速，2019—2023年各县/区逐年平均风速之和/5m/s
X5气压，2019—2023年各县/区逐年平均气压之和/5hPa
), ArticleFig(id=1241139383655518361, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Table 2, caption=

Population distribution of non-occupational CO poisoning cases from 2019 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
人口学特征报告发病数构成比（%）
性别*
男4 54344.18
女5 74155.82
年龄（岁）*
0～3920.92
4～61621.62
7～187727.71
19～451 84918.46
46～592 33723.33
≥604 80347.96
人群分类*
农民1 95256.53
工人2206.37
幼小儿童2527.30
学生72921.11
餐饮食品工作361.04
家务及待业1384.00
离退人员391.13
其他872.52
), ArticleFig(id=1241139383760375970, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=表2, caption=

2019—2023年非职业性一氧化碳中毒病例的人群分布特征

, figureFileSmall=null, figureFileBig=null, tableContent=
人口学特征报告发病数构成比（%）
性别*
男4 54344.18
女5 74155.82
年龄（岁）*
0～3920.92
4～61621.62
7～187727.71
19～451 84918.46
46～592 33723.33
≥604 80347.96
人群分类*
农民1 95256.53
工人2206.37
幼小儿童2527.30
学生72921.11
餐饮食品工作361.04
家务及待业1384.00
离退人员391.13
其他872.52
), ArticleFig(id=1241139383865233576, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Table 3, caption=

Global spatial autocorrelation analysis of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）Moran IZ值P值是否聚集
20190.050.960.339否
20200.203.180.002是
20210.325.04<0.001是
20220.172.760.006是
20230.091.590.112否
), ArticleFig(id=1241139383982674092, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=表3, caption=

2019—2023年山东省非职业性一氧化碳中毒全局空间自相关分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）Moran IZ值P值是否聚集
20190.050.960.339否
20200.203.180.002是
20210.325.04<0.001是
20220.172.760.006是
20230.091.590.112否
), ArticleFig(id=1241139384074948786, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Table 4, caption=

Detection results of meteorological factors for incidence of non-occupational CO poisoning in Shandong Province from 2019 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
气象因子*q值P值
X10.100.204
X20.120.258
X30.300.018
X40.100.226
X50.150.016
), ArticleFig(id=1241139384188194999, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=表4, caption=

2019—2023年山东省非职业性一氧化碳中毒发病率气象因子探测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
气象因子*q值P值
X10.100.204
X20.120.258
X30.300.018
X40.100.226
X50.150.016
), ArticleFig(id=1241139384322412734, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=EN, label=Table 5, caption=

Interaction detection results of meteorological factors for non-occupational carbon monoxide poisoning incidence in Shandong Province from 2019 to 2023

, figureFileSmall=null, figureFileBig=null, tableContent=
气象因子*X1X2X3X4X5
X10.10
X20.340.12
X30.490.160.30
X40.360.320.520.10
X50.450.340.480.480.15
), ArticleFig(id=1241139384477601987, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241036328306012486, language=CN, label=表5, caption=

2019—2023年山东省非职业性一氧化碳中毒发病率气象因子交互探测结果

, figureFileSmall=null, figureFileBig=null, tableContent=
气象因子*X1X2X3X4X5
X10.10
X20.340.12
X30.490.160.30
X40.360.320.520.10
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2019—2023年山东省非职业性一氧化碳中毒时空聚集性及气象驱动因素分析
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温子萱 1 , 段青 2 , 张艺馨 3 , 马睿卿 1 , 张玉薇 2 , 郑文贵 1 , 孙成玺 3 , 姜晓林 1, 4
现代预防医学 | 环境与职业卫生 2025,52(17): 3142-3148
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现代预防医学 | 环境与职业卫生 2025, 52(17): 3142-3148
2019—2023年山东省非职业性一氧化碳中毒时空聚集性及气象驱动因素分析
全屏
温子萱1, 段青2, 张艺馨3, 马睿卿1, 张玉薇2, 郑文贵1, 孙成玺3 , 姜晓林1, 4
作者信息
  • 1.山东第二医科大学公共卫生学院，山东 潍坊 261053
  • 2.山东省疾病预防控制中心传染病防制所
  • 3.山东省疾病预防控制中心业务管理部，山东 济南 250014
  • 4.山东省疾病预防控制中心传染病预防控制重点实验室，山东 济南250014

    • 温子萱（1998—），女，硕士，研究方向：非职业性一氧化碳中毒

通讯作者:

孙成玺，E-mail:；
姜晓林，E-mail:。
Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023
Zi-xuan WEN1, Qing DUAN2, Yi-xin ZHANG3, Rui-qing MA1, Yu-wei ZHANG2, Wen-gui ZHENG1, Cheng-xi SUN3 , Xiao-lin JIANG1, 4
Affiliations
  • School of Public Health, Shandong Second Medical University, Weifang, Shandong 261053, China
出版时间: 2025-09-10 doi: 10.20043/j.cnki.MPM.202411497
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摘要
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目的

明确山东省非职业性一氧化碳中毒发病率的时空分布格局，揭示其背后的气象驱动因子，为其科学防控提供参考。

方法

通过中国疾病预防控制信息系统突发公共卫生事件报告管理信息系统收集非职业性一氧化碳中毒事件报告。采用描述性分析研究非职业性一氧化碳中毒的流行特征。采用ArcGIS 10.7和SaTScan 10.1.2软件分别进行空间自相关分析和时空扫描分析以探讨时空聚集性。采用参数最优地理探测器揭示气象因子对非职业性一氧化碳中毒发病率的影响程度。

结果

2019—2023年山东省累积报告非职业性一氧化碳中毒病例12 088例，每年11月—次年3月为发病高峰，中老年人为主要高危人群。全局空间自相关分析显示，2020—2022年非职业性一氧化碳中毒发病率均存在空间聚集性（P<0.05）。鲁南地区逐渐发展为新的热点区域。时空扫描分析探测出五个聚集区，其中一级聚集区主要集中在鲁北地区（2019年11月—2021年03月，LLR=2 003.71，P<0.001）。参数最优地理探测器结果显示，相对湿度和气压是影响发病率的重要气象因子，相对湿度和风速的交互作用最大。

结论

山东省非职业性一氧化碳中毒存在季节性时空聚集性。建议加强中老年群体等高危人群冬春季健康教育工作，同时各级疾病预防控制机构应与气象、宣传等部门建立良好的信息沟通，加强对鲁北及鲁南等重点流行区非职业性一氧化碳中毒事故的预警。

非职业性一氧化碳中毒  /  时空聚集性  /  参数最优地理探测器  /  气象因素
Objective

To clarify the spatial and temporal distribution pattern of the incidence of non-occupational carbon monoxide poisoning in Shandong Province, to reveal the meteorological driving factors behind it, and to provide reference for its scientific prevention and control.

Methods

Non-occupational carbon monoxide poisoning incident reports were collected through the China Disease Control and Prevention Information System Public Health Emergency Reporting Management Information System. Descriptive analysis was used to study the prevalence characteristics of non-occupational carbon monoxide poisoning. Spatial autocorrelation analysis and spatio-temporal scanning analysis were performed to explore spatio-temporal aggregation using ArcGIS 10.7 and SaTScan 10.1.2 software, respectively. The influence of meteorological factors on the incidence of non-occupational carbon monoxide poisoning was assessed using an optimal parameter geographical detector.

Results

A total of 12 088 cases of non-occupational carbon monoxide poisoning were reported in Shandong Province from 2019 to 2023, with peak incidence from November to March of the next year each year, and middle-aged and older populations people as the main high-risk groups. Global spatial autocorrelation analysis showed spatial clustering of non-occupational carbon monoxide poisoning incidence rates in both 2020—2022(P<0.05). The southern part of Shandong Province was gradually developing into a new hot spot. Spatio-temporal scanning analysis detected five aggregation zones, of which the primary aggregation zone was mainly concentrated in the northern part of Shandong Province (Nov 2019—Mar 2021, LLR=2 003.71, P<0.001). The results of the optimal parameter geographical detector showed that relative humidity and barometric pressure were important meteorological factors affecting morbidity, with the largest interaction between relative humidity and wind speed.

Conclusion

Non-occupational carbon monoxide poisoning in Shandong Province showed seasonal spatiotemporal clustering. It is recommended to strengthen the winter and spring health education work for middle-aged and older populations and other high-risk groups. At the same time, disease prevention and control organizations at all levels should establish good information communication with meteorological and propaganda departments. Early warning of non-occupational carbon monoxide poisoning accidents in key endemic areas such as northern Shandong Province and southern Shandong Province should be strengthened through these initiatives.

Non-occupational carbon monoxide poisoning  /  Spatiotemporal clustering  /  Optimal parameter geographical detector  /  Meteorological factors
温子萱, 段青, 张艺馨, 马睿卿, 张玉薇, 郑文贵, 孙成玺, 姜晓林. 2019—2023年山东省非职业性一氧化碳中毒时空聚集性及气象驱动因素分析. 现代预防医学, 2025 , 52 (17) : 3142 -3148 . DOI: 10.20043/j.cnki.MPM.202411497
Zi-xuan WEN, Qing DUAN, Yi-xin ZHANG, Rui-qing MA, Yu-wei ZHANG, Wen-gui ZHENG, Cheng-xi SUN, Xiao-lin JIANG. Spatiotemporal clustering and meteorological drivers of non-occupational carbon monoxide poisoning in Shandong Province from 2019 to 2023[J]. Modern Preventive Medicine, 2025 , 52 (17) : 3142 -3148 . DOI: 10.20043/j.cnki.MPM.202411497
正文
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非职业性一氧化碳中毒事件特指在日常生活环境中发生的一氧化碳中毒情况[1]。据统计，全世界每年仍有大约3万人死于各类一氧化碳中毒[2]，2023年，中国23个省（自治区、直辖市）报告非职业性一氧化碳中毒7 264例，病死率为1.20%。病例主要集中在中国的东北、华北和中西部地区，其中山东省作为北方采暖大省（39 15例）居于全国首位[3]。最常见的一氧化碳来源是供暖设备、烤架、炉灶等通风不足的燃气、煤气加热器具。本研究通过对山东省2019—2023年非职业性一氧化碳中毒的流行特点和时空聚集性进行分析，并运用最优参数地理探测器揭示其背后的气象驱动因子，为非职业性一氧化碳中毒事件的精准防控提供科学依据。
1　材料与方法
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1.1　数据来源
非职业性一氧化碳中毒事件报告来自中国疾病预防控制信息系统突发公共卫生事件报告管理信息系统，包括：事件发生时间、地点、原因、中毒人数和死亡人数等，报告事件均经疾病预防控制部门调查核实，并确认结案。人口数据基于中国疾病预防控制信息系统死因登记报告系统提供的常住人口。气象数据来源于国家气象科学数据中心（https://data.cma.cn/,HFMD），包括气温、降水、气压、风速和相对湿度指标，由X1～X5表示。见表1。
1.2　统计学分析
1.2.1　描述性分析
利用SPSS 26.0和R 4.3.3软件处理数据，采用描述性流行病学方法分析不同时间、人群及地区的非职业性一氧化碳中毒病例的分布情况。计算2019—2023年山东省非职业性一氧化碳中毒全人群及分性别、年龄、人群、县/区、月份的发病例数及发病率，并以月为时间单位绘制趋势图，描述总体发病水平。
1.2.2　全局空间自相关分析
采用ArcGIS 10.7软件的空间自相关性分析工具，探索非职业性一氧化碳中毒病例的空间分布特征，并以地图形式展现。莫兰指数（Moran I）作为衡量指标，能够有效揭示相邻地域间疾病聚集现象的存在与否及其强度。计算公式为：
N为观察值的数量；位置i和位置j的观察值分别为和；全部研究对象观察值的均值为；i和j之间的空间权重为（当i和j相邻时，为1，否则为0）[4]Moran I值域界定于-1～1。Moran I=0表示空间分布是随机的。 Moran I<0表示空间分布为离散型Moran I>0表示空间分布为聚类型[5-6]。
1.2.3　热点分析
使用ArcGis 10.7软件进行Getis-Ord Gi*指数分析。该指数用于衡量不同地理区域之间观测值的高值簇和低值簇，即热点区和冷点区空间分布情况。计算公式为：
xi为地区的观测值，wij为其空间权重，空间毗邻为1，不相邻为0。通过Z得分、P值和置信区间(Gi Bin)可识别高值或低值要素的空间聚集位置。置信区间Gi Bin可反映置信度为99%、95%、90%统计显著性的要素；Gi Bin为0的要素不显示出空间聚类，即其在空间分布上未形成显著的聚集模式[7]。
1.2.4　时空扫描分析
SaTScan 10.1.2软件用于时空扫描分析，可以用于描述非随机空间事件集群的统计特征，包括对其时间、持续时间和精确位置[8]。本研究选择回顾性时空分析，概率模型选择Poisson分布[9-10]，计算公式为：
μ(p)为在原假设下的扫描窗口p的理论病例数，np为扫描窗口p中的人口数，mq为研究区域内的总病例数，nq为研究区域内的总人口数。时间扫描窗口设定为默认值30%，空间扫描窗口比例设置在30%。根据预期与理论发病数计算得出的对数似然比（log likelihood ratio，LLR）进行聚集区排列。
1.2.5　驱动因素分析
地理探测器是由王劲峰等人提出的一种用于探索和分析空间异质性以及揭示其背后驱动力的一组统计学方法。传统地理探测器在参数选择上需要依赖研究者的经验和判断，可能导致结果主观性及计算精度差的问题[11]。参数最优地理探测器[12]（optimalparameters geographicaldetector，OPGD）通过算法以多种统计方式（等间距、自然间距、分位数间距、几何间距和标准偏差间距）自动选择最佳的参数组合，减少了人为干预的误差，获得最佳q值。因此，本研究采用参数最优地理探测器，借助R语言完成因子探测及交互作用探测。因子探测主要通过因子解释力的大小来衡量单个自变量对因变量空间分异的影响程度，交互作用探测用于分析两个自变量作用力叠加后对因变量的作用力是否增强[13]。q值计算公式为：
式中，q为因子解释力，范围为[0,1]，h值越大解释力越强；表示对自变量和因变量的分区数目，N和表示整个研究区和分区后区内的样本数和、表示整个研究区和分区后h区内的Y值方差[14]。
2　结果
收起
2.1　非职业性一氧化碳中毒流行概述
2019—2023年中国山东省累积报告非职业性一氧化碳中毒事件8 619起，各年分别报告2 358、2 008、2 269、1 561和423起。累积报告发病人数12 088例，各年分别为报告3 172、2 783、3 193、2 189和751例。年均发病率为2.4/10万。累积报告死亡234例，病死率为1.94%。见图1。
非职业性一氧化碳中毒发病率从2019年的3.17/10万下降到2023年的0.74/10万。发病呈现明显的冬季发病高峰，11月—次年3月的发病数占全年发病数的90.80%（10 976/12 088）。见图2。
2019—2023年115个县（区）报告非职业性一氧化碳中毒病例，报告县区数由2019年92个减少至2023年51个。报告发病数居前五位的地市为德州市（2 467例）、济南市（1 754例）、潍坊市（1 098例）、淄博市（1 055例）、威海市（1 051例）；前五位的县（区）为乐陵市（551例）、李沧区（533例）、临淄区（527例）、郓城县（460例）、高密市（448例）。见图3。
非职业性一氧化碳中毒报告病例中，男性病例为4 543例，女性病例为5 741例，男女性别比为0.79:1.00。男女病死率分别为33.89%、33.33%（χ2=6.516，P=0.011）。病例年龄以老年为主，≥60岁年龄组占47.96%（4 803/10 015）。人群职业以农民为主，占56.53%（1 952/3 453），工人占6.37%（220/3 453）；幼小儿童占7.30%（252/3 453），学生占21.11%（729/3 453）；餐饮食品工作占1.04%（36/3 453），家务及待业占4%（138/3 453），离退人员占1.13%（39/3 453），其他占2.52%（87/3 453）。见表2。
2.2　全局空间自相关分析
2019—2023年各年度非职业性一氧化碳中毒发病率率Moran I指数分别为0.05、0.20、0.32、0.17、0.09。除2019和2023年外，各年度均存在空间聚集性（P<0.05）。见表3。
2.3　热点分析
对2019—2023年山东省县/区非职业性一氧化碳中毒发病率进行热点分析，结果显示，2019—2023年热点覆盖县（区）数量均≥15个，2023年最少。发病热点主要集中鲁西北地区，主要包括德州市北部、济南市西部、滨州市西部，鲁西南地区的菏泽市东北部和济宁市西北部逐渐发展为新热点区域。见图4。
2.4　时空聚集性分析
时空扫描探测到五个聚集区。一级聚集区主要集中于鲁北地区，涉及宁津县、乐陵市、天桥区等县（区）（2019年11月—2021年03月，LLR=2003.71，P<0.001）；二级聚集区主要集中于鲁东地区，涉及荣成市、环翠区、龙口市等县（区）（2020年11月—2021年3月，LLR=821.31，P<0.001）；三级聚集区集中于郓城县（2021年12月—2022年2月，LLR=351.14，P<0.001），四级聚集区集中于郯城县（2020年12月—2021年01月，LLR=821.31，P<0.001）；五级聚集区主要集中于鲁南和鲁中地区，涉及平邑县、费县、兰山区等县（区）（2019年12月—2020年1月，LLR=108.35，P<0.001）。见图5。
2.5　气象驱动因素分析
因子探测呈现的q值大小如下：相对湿度（X3=0.30）>风速（X5=0.15）>降水（X2=0.12）>气压（X4=0.10）=温度（X1=0.10）。相对湿度（X3）和气压（X5）对非职业性一氧化碳的中毒发病率的影响具有统计学意义（P<0.05）。见表4。
交互探测结果表明，当相对X3（相对湿度因子）叠加了X2（降水因子）后，对山东省非职业性一氧化碳中毒发病率的解释力呈单因子非线性减弱。其余各因子两两叠加后，解释力均呈双因子增强，即任意两个驱动因子交互作用的效应都要超过他们各自独立作用。当X4（风速因子）叠加了X3（相对湿度因子）时，解释力最高为0.52。见表5。
3　讨论
收起
研究结果表明，2021—2023年山东省非职业性一氧化碳中毒事件起数、发病人数逐年下降。近年来，山东省人民政府出台了《山东省人民代表大会常务委员会关于修改<山东省燃气管理条例>的决定》《山东省城镇燃气安全专项整治实施方案》等文件以及开展对非集中供暖户开展登门入户的全覆盖式摸排行动，多方面防范了该类中毒事件的发生。2023年非职业性一氧化碳中毒发病例数显著下降可能与新型冠状病毒（COVID-19）感染疫情的长期影响有关。疫情在潜移默化中促使居民居家环境通风习惯改善；且2022年底解除对COVID-19疫情的管控直接促进了居民外出活动的增加，降低了家庭环境中非职业性一氧化碳中毒事件的发生率。
本研究发现，女性非职业性一氧化碳中毒的发病率高于男性，这可能与男性和女性在生活习惯、工作环境以及生理差异等方面的不同有关。女性多在家烹饪[15]且使用燃气热水器洗浴时时间较长，这增加了女性非职业性一氧化碳中毒风险。女性死亡风险低于男性，可能由于男性和女性在激活适应性免疫系统和先天免疫系统方面存在差异[16]，女性身体更够更快地消除一氧化碳，预后更好[17-18]。老年人群由于身体机能下降，对一氧化碳的敏感性和耐受力降低，即便在低浓度一氧化碳环境下，也更容易出现较严重的健康问题，因而成为发病的主要人群。
冬春季节是山东省非职业性一氧化碳中毒的高发期，这可能与紧闭门窗以保持室内温度，室内一氧化碳的积聚以及该时段内相对湿度和气压的变化密切相关。当相对湿度较高时，空气中的水蒸气含量增加，含碳物质如煤炭、木炭等的在不完全燃烧时产生的一氧化碳更难于扩散。同时，处于低压环境下的人类，身体血液循环更慢，新陈代谢速度减慢[19]，导致人体对一氧化碳的清除能力降低。山东省北部城市和沿海地区作为非职业性一氧化碳中毒发病的热点区，沿海地区由于海洋的影响，湿度和风速的变化可能更为复杂，二者的交互作用可能也是该地区非职业性一氧化碳中毒发生率较高的原因之一。
尽管人类难以直接干预气象条件，但仍有能力采取一系列措施来预防和减少此类事件的发生。鲁北和鲁南地区要重点防控非职业性一氧化碳中毒事件的发生，建议各级疾病预防控制机构与气象、宣传等部门建立良好的信息沟通[20]，加强对冬春季节非职业性一氧化碳中毒事故的预警。同时相关部门应对加强对高危人群的健康教育工作，全面普及一氧化碳报警器。对于农村地区而言，集体供暖和转向清洁燃料可能不是一个立即可行的解决方案，建议在短期内通过增加窗户面积等其他方式改变住房特点。可将预防非职业性一氧化碳中毒知识加入学校安全教育工作中，通过提高学生预防非职业性一氧化碳中毒意识进一步减少同居老人中毒事件的发生。
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2025年第52卷第17期
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doi: 10.20043/j.cnki.MPM.202411497
  • 接收时间：2024-11-30
  • 首发时间：2026-03-18
  • 出版时间：2025-09-10
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  • 收稿日期：2024-11-30
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    1.山东第二医科大学公共卫生学院，山东 潍坊 261053
    2.山东省疾病预防控制中心传染病防制所
    3.山东省疾病预防控制中心业务管理部，山东 济南 250014
    4.山东省疾病预防控制中心传染病预防控制重点实验室，山东 济南250014

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2种不同金属材料的力学参数
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Family		 属数
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
species (%)
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
栓菌属 Trametes 5 2.39
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