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Article(id=1241035544738722480, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202408123, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1723132800000, receivedDateStr=2024-08-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815531191, onlineDateStr=2026-03-18, pubDate=1735056000000, pubDateStr=2024-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815531191, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815531191, creator=13701087609, updateTime=1773815531191, updator=13701087609, issue=Issue{id=1241035543589483182, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='24', pageStart='4417', pageEnd='4608', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815530917, creator=13701087609, updateTime=1773815686426, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036195896029478, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036195896029479, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4423, endPage=4428, ext={EN=ArticleExt(id=1241035545191707316, articleId=1241035544738722480, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the epidemiological characteristics and influencing factors of Brucellosis in Inner Mongolia Autonomous region, 2021-2023, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the epidemiological characteristics of human brucellosis in Inner Mongolia from 2021 to 2023 and the spatial variation of meteorological factors affecting its incidence, providing a scientific basis for prevention, control, and public health resource allocation.

Methods

The de-identified human brucellosis case data from January 1st, 2021 to December 31st, 2023, were collected from the Inner Mongolia Center for Disease Control and Prevention. Descriptive analysis was used to study three distributions of human brucellosis in Inner Mongolia. A mixed local autoregressive geographical weighted regression model analyzed the monthly spatial autocorrelation and heterogeneity of brucellosis. The model’s fitting effect was compared with spatial autoregressive, geographical weighted regression, and local autoregressive geographical weighted regression models.

Results

From 2021 to 2023, 56,483 cases of brucellosis in Inner Mongolia, with a peak incidence from March to July. The incidence in males was 53.67 per 100,000, significantly higher than in females (χ2 = 25.803，P<0.05). Farmers comprised the largest proportion of cases. The highest incidence occurred in people aged 41 to 60 (43.60 per 100,000). Brucellosis incidence in Inner Mongolia showed a positive spatial correlation, with high-incidence areas mainly in the east and west. The Mixed GWR-LSAR model had the best fit. The effects of wind speed, temperature, pressure, and evaporation varied spatially.

Conclusion

Brucellosis in Inner Mongolia exhibited seasonal, demographic, and spatial patterns, with varying meteorological impacts across months and regions. Therefore, prevention and control strategies should be targeted and regional to effectively reduce the risk.

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目的

探讨2021—2023年内蒙古自治区人间布鲁氏菌病(布病)的流行特征及气象因素对布病发病率影响的空间变化特征，为其防控和公共卫生资源分配提供科学依据。

方法

从内蒙古自治区疾病预防控制中心收集了经脱敏处理的2021年1月1日至2023年12月31日的人间布病病例数据，通过描述性分析探究内蒙古自治区布病的三间分布。利用局部自回归混合地理加权回归(Mixed GWR-LSAR)模型逐月分析布病发生的空间自相关性和异质性，并与空间自回归模型、地理加权回归模型和局部自回归地理加权回归模型的拟合效果相比较。

结果

2021—2023年内蒙古布病病例56 483例，3—7月为高发期；男性发病率为53.67/10万，显著高于女性(χ2=25.803，P<0.05)；病例职业构成中农民占比最大；41~60岁人群发病率最高(43.60/10万)；内蒙古布病发病率呈空间正相关，高发地多集中于东部和西部；Mixed GWR-LSAR模型拟合效果最优；月均风速、气温、气压和蒸发量在各月对布病发病率的影响存在空间差异。

结论

内蒙古布病流行的季节性、人群和空间分布呈流行病学特征，气象因素在不同月份和地区对布病发病率的影响不同。因此，布病防控策略需具有针对性和区域性，以更有效地降低布病发生风险。

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洪志敏，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=wpHmRRbz/zvX0AA9nD+U4w==, magXml=Uf2lrPiylkfcdFfcmNE89A==, pdfUrl=null, pdf=9AgoTvbV38eVIeuJtAifWg==, pdfFileSize=1126733, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=SEtoQUBCMgx1GSdMQZfznQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=d+LawbK/S8FPYmkzHsESYw==, mapNumber=null, authorCompany=null, fund=null, authors=

朱新文(1999—)，女，硕士在读，研究方向：空间统计推断理论及研究

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朱新文(1999—)，女，硕士在读，研究方向：空间统计推断理论及研究

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Results of spatial autocorrelation of human brucellosis incidence in Inner Mongolia from 2021 to 2023

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年份I值Z值P值“高-高”聚集地区所属盟市（个数）
20210.2153.2810.001巴彦淖尔市(5)、通辽市(5)、赤峰市(2)、兴安盟(2)、锡林郭勒盟(2)
20220.1782.8050.005巴彦淖尔市(6)、鄂尔多斯市(2)、通辽市(1)、锡林郭勒盟(1)、赤峰市(1)
20230.1662.6220.008巴彦淖尔市(6)、鄂尔多斯市(2)、乌兰察布市(1)、赤峰市(1)
), ArticleFig(id=1241069991563424449, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035544738722480, language=CN, label=表1, caption=

2021—2023年内蒙古人间布病发病率的空间自相关分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
年份I值Z值P值“高-高”聚集地区所属盟市（个数）
20210.2153.2810.001巴彦淖尔市(5)、通辽市(5)、赤峰市(2)、兴安盟(2)、锡林郭勒盟(2)
20220.1782.8050.005巴彦淖尔市(6)、鄂尔多斯市(2)、通辽市(1)、锡林郭勒盟(1)、赤峰市(1)
20230.1662.6220.008巴彦淖尔市(6)、鄂尔多斯市(2)、乌兰察布市(1)、赤峰市(1)
), ArticleFig(id=1241069991693447874, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035544738722480, language=EN, label=Table 2, caption=

Diagnostic information of four models

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模型SARGWRGWR-LSARMixed GWR-LSAR
RSSAICcRSSAICcRSSAICcRSSAICc
1月206.8831.6199.9813.3162.1792.893.6746.3
2月206.1837.5175.5796145.6784.2101.8774.8
3月188.3815.36.7×1053.3×1031.2×1052.8×10375.4675.5
4月196.2825.82.4×1084.0×1037.6×1032.0×10397.3729.9
5月210.4847.78.6×1053.4×1032.7×1042.4×10381.6781.8
6月188.4815.5158.4775.2144.3782.786.6721.6
7月181.3817.4141798.7104.6778.4101.1756.3
8月153.6761.5109.3726.770.1702.877.9688.5
9月191.3822.34.0×1042.5×1031.3×1042.2×10391.9734.8
10月249.7883.26.2×1042.6×1031.1×1031.4×103124.7810.4
11月247.5867.85.2×1063.5×1035.1×1052.2×103119.5794.3
12月332.2959.9163.6870.8136.1870.1129.2872.4
), ArticleFig(id=1241069991861220041, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035544738722480, language=CN, label=表2, caption=

四种模型的诊断信息

, figureFileSmall=null, figureFileBig=null, tableContent=
模型SARGWRGWR-LSARMixed GWR-LSAR
RSSAICcRSSAICcRSSAICcRSSAICc
1月206.8831.6199.9813.3162.1792.893.6746.3
2月206.1837.5175.5796145.6784.2101.8774.8
3月188.3815.36.7×1053.3×1031.2×1052.8×10375.4675.5
4月196.2825.82.4×1084.0×1037.6×1032.0×10397.3729.9
5月210.4847.78.6×1053.4×1032.7×1042.4×10381.6781.8
6月188.4815.5158.4775.2144.3782.786.6721.6
7月181.3817.4141798.7104.6778.4101.1756.3
8月153.6761.5109.3726.770.1702.877.9688.5
9月191.3822.34.0×1042.5×1031.3×1042.2×10391.9734.8
10月249.7883.26.2×1042.6×1031.1×1031.4×103124.7810.4
11月247.5867.85.2×1063.5×1035.1×1052.2×103119.5794.3
12月332.2959.9163.6870.8136.1870.1129.2872.4
), ArticleFig(id=1241069991970271951, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035544738722480, language=EN, label=Table 3, caption=

Estimated range of coefficients of Mixed GWR-LSAR model

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系数最小值P25中位数P75最大值
空间自回归系数-0.9900.2560.6990.890 0.990
月均风速-1.8490.0110.4340.8683.235
月均气温-1.780-0.1360.0170.2381.579
月均气压-1.248 -0.1330.0530.2341.563
月均蒸发量-14.733 -0.422 0.3851.35331.575
), ArticleFig(id=1241069992079323863, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035544738722480, language=CN, label=表3, caption=

Mixed GWR-LSAR模型系数估计范围

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系数最小值P25中位数P75最大值
空间自回归系数-0.9900.2560.6990.890 0.990
月均风速-1.8490.0110.4340.8683.235
月均气温-1.780-0.1360.0170.2381.579
月均气压-1.248 -0.1330.0530.2341.563
月均蒸发量-14.733 -0.422 0.3851.35331.575
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2021—2023内蒙古自治区布鲁氏菌病流行特征与影响因素分析
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朱新文 1 , 洪志敏 1 , 高永明 2 , 王虎虎 2
现代预防医学 | 流行病与统计方法 2024,51(24): 4423-4428
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现代预防医学 | 流行病与统计方法 2024, 51(24): 4423-4428
2021—2023内蒙古自治区布鲁氏菌病流行特征与影响因素分析
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朱新文1, 洪志敏1 , 高永明2, 王虎虎2
作者信息
  • 1.内蒙古工业大学理学院，内蒙古 呼和浩特 010051
  • 2.内蒙古自治区疾病预防控制中心

    • 朱新文(1999—)，女，硕士在读，研究方向：空间统计推断理论及研究

通讯作者:

洪志敏，E-mail:
Analysis of the epidemiological characteristics and influencing factors of Brucellosis in Inner Mongolia Autonomous region, 2021-2023
Xin-wen ZHU1, Zhi-min HONG1 , Yong-ming GAO2, Hu-hu WANG2
Affiliations
  • College of Science, Inner Mongolia University of Technology, Hohhot, Inner Mongolia 010051, China
出版时间: 2024-12-25 doi: 10.20043/j.cnki.MPM.202408123
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摘要
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目的

探讨2021—2023年内蒙古自治区人间布鲁氏菌病(布病)的流行特征及气象因素对布病发病率影响的空间变化特征，为其防控和公共卫生资源分配提供科学依据。

方法

从内蒙古自治区疾病预防控制中心收集了经脱敏处理的2021年1月1日至2023年12月31日的人间布病病例数据，通过描述性分析探究内蒙古自治区布病的三间分布。利用局部自回归混合地理加权回归(Mixed GWR-LSAR)模型逐月分析布病发生的空间自相关性和异质性，并与空间自回归模型、地理加权回归模型和局部自回归地理加权回归模型的拟合效果相比较。

结果

2021—2023年内蒙古布病病例56 483例，3—7月为高发期；男性发病率为53.67/10万，显著高于女性(χ2=25.803，P<0.05)；病例职业构成中农民占比最大；41~60岁人群发病率最高(43.60/10万)；内蒙古布病发病率呈空间正相关，高发地多集中于东部和西部；Mixed GWR-LSAR模型拟合效果最优；月均风速、气温、气压和蒸发量在各月对布病发病率的影响存在空间差异。

结论

内蒙古布病流行的季节性、人群和空间分布呈流行病学特征，气象因素在不同月份和地区对布病发病率的影响不同。因此，布病防控策略需具有针对性和区域性，以更有效地降低布病发生风险。

人间布鲁氏菌病  /  气象因素  /  空间变化特征  /  局部自回归混合地理加权回归模型
Objective

To explore the epidemiological characteristics of human brucellosis in Inner Mongolia from 2021 to 2023 and the spatial variation of meteorological factors affecting its incidence, providing a scientific basis for prevention, control, and public health resource allocation.

Methods

The de-identified human brucellosis case data from January 1st, 2021 to December 31st, 2023, were collected from the Inner Mongolia Center for Disease Control and Prevention. Descriptive analysis was used to study three distributions of human brucellosis in Inner Mongolia. A mixed local autoregressive geographical weighted regression model analyzed the monthly spatial autocorrelation and heterogeneity of brucellosis. The model’s fitting effect was compared with spatial autoregressive, geographical weighted regression, and local autoregressive geographical weighted regression models.

Results

From 2021 to 2023, 56,483 cases of brucellosis in Inner Mongolia, with a peak incidence from March to July. The incidence in males was 53.67 per 100,000, significantly higher than in females (χ2 = 25.803，P<0.05). Farmers comprised the largest proportion of cases. The highest incidence occurred in people aged 41 to 60 (43.60 per 100,000). Brucellosis incidence in Inner Mongolia showed a positive spatial correlation, with high-incidence areas mainly in the east and west. The Mixed GWR-LSAR model had the best fit. The effects of wind speed, temperature, pressure, and evaporation varied spatially.

Conclusion

Brucellosis in Inner Mongolia exhibited seasonal, demographic, and spatial patterns, with varying meteorological impacts across months and regions. Therefore, prevention and control strategies should be targeted and regional to effectively reduce the risk.

Human brucellosis  /  Meteorological factors  /  Spatial change characteristics  /  Local autoregressive mixed geographical weighted regression model
朱新文, 洪志敏, 高永明, 王虎虎. 2021—2023内蒙古自治区布鲁氏菌病流行特征与影响因素分析. 现代预防医学, 2024 , 51 (24) : 4423 -4428 . DOI: 10.20043/j.cnki.MPM.202408123
Xin-wen ZHU, Zhi-min HONG, Yong-ming GAO, Hu-hu WANG. Analysis of the epidemiological characteristics and influencing factors of Brucellosis in Inner Mongolia Autonomous region, 2021-2023[J]. Modern Preventive Medicine, 2024 , 51 (24) : 4423 -4428 . DOI: 10.20043/j.cnki.MPM.202408123
正文
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布鲁氏菌病(布病)是种由布鲁氏杆菌侵入机体而引起的人畜共患传染病，人类通常因接触患病动物及其分泌物或食用未消毒的乳制品感染，从而出现发热、疲劳、关节炎等症状[1]，威胁人类的健康。布病在全球广泛分布，每年确诊病例超50万，实际感染人数可能是报告病例的10至25倍[2]。因此，探究布病影响因素并加强防控措施是公共卫生领域的重要工作。
气象因素通过影响农牧业活动、布鲁氏菌和病媒生物的存活等[3]，进而影响布病的传播。研究表明气象因素在布病流行中起重要作用。例如，Cao等[4]通过时间序列模型分析发现风速与布病发病率呈正相关，与大气压、平均温度和相对湿度呈负相关。Zhang等[5]通过逐步回归法确定降水量和相对湿度与布病发病率呈负相关。然而，上述方法往往假定研究区域内的回归效应均匀一致，忽略了布病数据的空间自相关性和异质性。已有研究表明布病的发生存在明显的空间分布特征。Sun等[6]证明了布病的发生具有空间自相关性，并通过空间自回归(SAR)模型证明了气温和相对湿度与布病发病率显著相关。Zhang等[7]运用多尺度地理加权回归模型以解决布病发生的空间异质性，并得出气温抑制布病发生的结论。然而，上述研究仅考虑了空间异质性，布病作为传染型疾病，其发生的自相关性是不容忽视的。局部自回归地理加权回归(GWR-LSAR)模型[8]结合了SAR模型[9]与地理加权回归(GWR)模型[10]的优点，兼顾了数据的空间自相关性和异质性特征。在GWR-LSAR模型中假设所有自变量对因变量的影响均为局部异质的，这导致模型的计算复杂度增加和对全局相关关系解释能力的不足。考虑到布病为人畜共患传染病，且多由动物传染给人类，其传播受一些混杂因素的影响，包括(但不限于)牲畜繁育和畜产品交易高峰等。这些混杂因素对布病发生的影响可视为是全局平稳的。因此，为了消除混杂因素在分析布病与气象因素关系时的不利影响，本文使用更灵活的局部自回归混合地理加权回归(Mixed GWR-LSAR)模型，以定量分析布病发生的空间局部特征。
内蒙古自治区拥有我国最大的草原牧区和重要的畜产品产区，家畜群体密集且人畜接触频繁，布病发病率持续处于较高的水平。据统计，2011年全区人间布病病例数超2万例，2017和2018连续两年布病疫情排全国首位，且仍有上升趋势[11-12]。本文旨在分析内蒙古人间布病的流行特征，应用Mixed GWR-LSAR模型分析布病发病率受气象因素影响的空间变化模式，并与SAR模型、GWR模型和GWR-LSAR模型拟合效果比较，分析结果可作为布病防控和公共卫生资源分配的参考依据。
1 资料与方法
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1.1 资料来源
布病数据来自内蒙古自治区疾病预防控制中心，通过脱敏处理，所获取的数据包括患者的常住地、年龄、性别和职业信息。气象数据来源于NASA(https://daac.gsfc.nasa.gov/)，包括月均比湿度(g/kg)、风速(m/s)、降水量(mm)、气温(℃)、气压(kPa)和蒸发量(mm)。布病数据和气象数据的空间维度为内蒙古的103个旗县区，时间维度为2021年1月1日至2023年12月31日。人口数据来自对应时间窗口期的《内蒙古统计年鉴》。内蒙古地图底图来源于自然资源部官网(http://bzdt.ch.mnr.gov.cn/)，审图号为GS(2019)1822号。
1.2 数据预处理
为避免因区域人口密度不同造成的布病发病率的计算偏差，文中使用每10万人的累积发病率(CI/10万)来反映布病的发生风险。在计算内蒙古各旗县区布病累积发病率时，由于数据收集偏差，某些区域CI值可能为0，但不代表实际布病发生风险为0。因此，使用层次贝叶斯模型[13]对各旗县区的布病累积发病率进行校正，并通过R 4.2.0软件中“R2WinBUGS”包实现此过程。
1.3 研究方法
利用ArcGIS 10.7软件，以内蒙古旗县区为单位计算全局和局部莫兰指数，分析布病发病率的空间自相关性和聚集模式。建模前，通过方差膨胀因子(VIF)和皮尔逊相关系数检测各气象变量的多重共线性，剔除不符合标准(VIF>10或相关系数绝对值>0.7)的变量。鉴于布病发生的季节特征，为更好地捕捉布病发病率与气象因素的关系，本文以月为单位建立模型，并引入哑变量以排除混杂因素的干扰。混杂因素包括3—5月的牲畜繁育高峰、9—11月的畜产品交易高峰和新型冠状病毒感染疫情。将混杂因素作为全局平稳的哑变量纳入模型，筛选后的气象因素作为空间变化的自变量，校正后的累积发病率的对数为因变量，构建Mixed GWR-LSAR模型[14]量化分析内蒙古布病发生的空间自相关性和异质性。模型表达式如下：
其中为空间滞后项，ρ(ui,vi)为空间自回归系数，(ui,vi)为观测点的位置坐标；W=(wij)n×n是空间邻接矩阵，wij同1.2节所述；βv(ui,vi)=(β1(ui,vi),β2(ui,vi),…,βq(ui,vi))T为第i个位置的空间变系数，βc=(βq+1,βq+2,…,βp)T为常系数；和分别为第i个位置的空间变系数和常系数的自变量观测值，yi是因变量在指定位置的观测值；εi为误差项独立同分布于N(0,σ2)。采用两阶段最小二乘法[8]估计模型(1)中的自回归系数和空间变系数。
模型拟合效果通过校正的赤池信息准则(AICc)和残差平方和(RSS)进行评价，较小的AICc和RSS值表明模型拟合效果较好。系数估计值的统计特征采用最小值、上四分位数(P25)、中位数、下四分位数(P75)和最大值进行描述。
2 结果
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2.1 布病的三间分布
时间分布：2021—2023年内蒙古人间布病报告病例总数56 483例，病例数呈下降趋势，但不显著(P=0.296)。布病的发生呈现季节特征，每年的3—7月为高发期，尤其是5、6月，见图1。人群分布：男性布病发病率为53.67/10万，女性为24.39/10万，差异显著(χ2=25.803，P<0.05)。41~60岁的中年群体发病率最高为43.60/10万，6岁以下最低为0.39/10万。农民是占比最大的群体，见图2。空间分布：内蒙古布病发病率存在明显的空间差异，高发地集中于东部和西部地区，见图3。
2.2 空间自相关分析
全局莫兰指数值(I值)为正数，表明内蒙古布病发病率呈空间正相关，并具有统计学意义(P<0.05)。局部莫兰指数显示，布病发病率具有明显的空间聚集特征，主要为西部和东部的“高-高”聚集模式。见表1。
2.3 多重共线性诊断
月均比湿度的VIF值为13.508，月均降水量与月均气压和蒸发量的相关系数介于0.707至0.849。因此，文中剔除月均比湿度和降水量，最终选择月均气温、气压、风速和蒸发量作为影响布病发病率的气象因素进行建模。
2.4 模型拟合结果对比
SAR模型、GWR模型和GWR-LSAR模型的RSS和AICc值均高于Mixed GWR-LSAR模型，见表2。这表明Mixed GWR-LSAR模型的拟合效果最佳，故本文将基于该模型系数估计结果进行具体分析。
2.5 Mixed GWR-LSAR模型系数估计及可视化分析
各系数估计值的范围涵盖了正数和负数，呈现了较明显的波动，见表3。内蒙古布病发病率大多受邻近区域的正向影响，与空间自相关分析结果一致。风速对中部和西部地区的布病发病率呈较强的促进作用，而抑制作用主要集中于东部。气温大多抑制布病发病率，但在初冬(12月)，全区气温起促进作用。气压对西部的布病发病率有较强的正面影响，负面影响出现在东部部分地区。在4月和深秋，蒸发量与全区布病发病率呈正相关。其他时间，蒸发量的积极影响集中于西部，消极影响集中于东部。见图4~8。
3 讨论
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2021—2023年，内蒙古布病病例数逐年下降，可见内蒙古采取的布病防控措施取得了一定成效。尽管病例数有所减少，但从整体看下降趋势并不显著。因此防疫工作仍需持续加强，防控措施仍需不断完善。内蒙古布病高发期在3—7月，此时正值动物繁殖高峰期，同时是畜牧活动频繁的时期，如剪羊毛、接生畜禽等[15-16]。布病患者多为男性、农民和41至61岁人群。此年龄段的人们在农牧工作中作用关键，且男性更多地从事屠宰、肉类加工和放牧等工作，进而增加其感染风险[11,17]。建议根据季节和人群特点，加强高风险群体的布病预防教育和防护举措宣传。
内蒙古布病发病率存在空间正相关性，即周边地区发病率较高时，当地发病率也倾向较高。这种空间溢出效应可能与贸易往来有关。若运输过程中未能有效管理，染病牲畜及其产品可能将病原体带入其它地区，导致布病扩散。因此，各地应加强检疫措施，严格控制牲畜及其产品流通，并加强区域间的信息共享和协作，以便及时发现和控制疾病的传播。
在逐月分析布病发病率与气象因素关系时，Mixed GWR-LSAR模型的拟合效果优于SAR模型、GWR模型和GWR-LSAR模型。该模型不仅能同时处理空间自相关性和异质性，还能综合考虑全局和局部的影响，从而更准确地捕捉布病发病率与气象因素关系的空间分布特征。基于该模型结果，发现风速在内蒙古中部和西部与布病发生呈正向影响，与先前研究一致[5]。而在东部则起抑制作用。这可能与内蒙古西高东低的风速分布形式有关，在东部低风速地区，风速增加有助于分散局部悬浮积累的布病气溶胶，降低其浓度[18-19]，进而降低人与牲畜的感染概率。因此，建议定期监测空气中布鲁氏菌气溶胶浓度，确保良好的空气流通，以降低布病传播风险。有研究表明气温抑制布病的发生[20]。本研究发现，初冬期间，全区气温对布病发病率呈正面影响。可能是由于内蒙古初冬气温波动较大，这不仅会改变牲畜的繁育时间，增加人与牲畜的接触机会，还会影响病原体、中间媒介和宿主复制的速度[16,21]。特别是气温升高时，一些在低温环境下不活跃的病原体变得活跃[22]，从而增加感染风险。气压对发病率的影响因地而异，其中西部地区的气压变化对发病率的影响效应较强。这或许与地理位置相关，内蒙古西部位于青藏高原东侧，青藏高原作为气象变化的敏感区，通过大气环流影响周边的气压变化[23]。目前气压对布病发生的作用机制尚未明确，但其会通过影响空气传播条件改变病原体的释放和运输，进而影响大气中布鲁氏菌的浓度[24]。蒸发量的促进作用在空间上主要集中于西部，时间上集中于四月和秋季。内蒙古西部拥有大片沙漠，水分含量低，相比东部更为干燥。病原体在干燥的空气中会因蒸发而缩小，从而增加它们在空气中的稳定性和存活时间[25]。在四月和秋季内蒙古的气候较为干燥，其中四月是沙尘天气频发时期。温度适宜、干燥且高蒸发量为布鲁氏菌的生存和传播创造了适宜环境[26]。此外，布病可通过黏膜和呼吸道传播。干燥的环境会损害鼻腔黏膜和呼吸道[27-28]，进而增加布鲁氏菌侵入机体的机率。因此，建议居民采取措施保持适宜的空气湿度，如使用加湿器，以降低布病的感染风险。
综上，2021—2023年内蒙古布病病例数逐年减少且呈季节性规律，男性、农民和中年人应为布病预防的重点对象。月均风速、气温、气压和蒸发量对布病发病率的影响存在空间差异，且差异随月份而变化。因此，应采取有针对性且分区域实施的防控策略，以降低内蒙古的布病发生风险。据了解，本文是首次采用局部自回归混合地理加权回归模型研究布病发病率和气象因素的关系，相较于传统的空间自回归模型和地理加权回归模型展现出更优的效果，为布病及其他对气候敏感性传染病的研究提供了新思路。
基金
收起
  • 国家自然科学基金项目(81860605)
  • 内蒙古自然科学基金项目(2023MS01001)
  • 内蒙古自治区直属高校基金科研业务费项目(JY 20220087)
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2024年第51卷第24期
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doi: 10.20043/j.cnki.MPM.202408123
  • 接收时间：2024-08-09
  • 首发时间：2026-03-18
  • 出版时间：2024-12-25
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  • 收稿日期：2024-08-09
基金
国家自然科学基金项目(81860605)
内蒙古自然科学基金项目(2023MS01001)
内蒙古自治区直属高校基金科研业务费项目(JY 20220087)
作者信息
    1.内蒙古工业大学理学院，内蒙古 呼和浩特 010051
    2.内蒙古自治区疾病预防控制中心

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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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