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Article(id=1240722569947574398, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202502415, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773740912185, onlineDateStr=2026-03-17, pubDate=1760025600000, pubDateStr=2025-10-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773740912185, onlineIssueDateStr=2026-03-17, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773740912185, creator=13701087609, updateTime=1773740912185, updator=13701087609, issue=Issue{id=1240722566957027366, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='19', pageStart='3457', pageEnd='3648', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=0, createTime=1773740911472, creator=13701087609, updateTime=1773740981732, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240722861736906836, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240722861736906837, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240722566957027366, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3521, endPage=3525, ext={EN=ArticleExt(id=1240722570274730128, articleId=1240722569947574398, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Spatiotemporal characteristics and influencing factors of pulmonary tuberculosis in China: 2011—2020, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=

Objective To explore the spatiotemporal characteristics of pulmonary tuberculosis (TB) in China and its macro influencing factors, and to provide a theoretical basis for the formulation of TB prevention and control measures. Methods The data of tuberculosis incidence in China from 2011 to 2020 were collected. A two-stage distributed lag nonlinear model (DLNM) was constructed to assess the lag effect and nonlinear relationship of meteorological factors on TB incidence, and a hierarchical Bayesian spatiotemporal model was used to analyze the relationship between population, economic factors, health services, and TB incidence. Results TB incidence in China was on a downward trend, with spatial clustering, and there were intensifying hotspots (Sichuan Province) and persistent hotspots(Liaoning Province, Shaanxi Province). The attributable fractions of low temperature(<6.7 ℃), low relative humidity(<55.59%), high wind speed(>3.00 m/s), and short sunshine duration(<2.7 hours) were 14.44%, 5.75%, 3.30%, and 7.88%, respectively. The urbanization rate(RR=1.009, 95% CI: 1.007-1.012) and the night light index (RR=1.009, 95% CI: 1.007-1.013) were positively correlated with TB incidence, while the number of hospital beds(RR=0.996, 95% CI: 0.994-0.998) and the proportion of elderly population (RR=0.973, 95% CI: 0.969-0.979) were negatively correlated with it. Conclusion The TB incidence in China has generally shown a downward trend. Attention should be paid to the prevention and control of hotspots, and the monitoring of macro influencing factors should be strengthened to take intervention measures as early as possible.

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目的 探讨中国肺结核的时空特征及其宏观影响因素，为制定肺结核疫情防控措施提供理论依据。方法 收集2011—2020年全国肺结核数据，构建两阶段分布滞后非线性模型，评估气象因素对结核病发病的滞后效应和非线性关系，并采用层次贝叶斯时空模型，评估肺结核发病风险与人口、经济、卫生服务等因素的关系。结果 中国肺结核发病率呈下降趋势,具有空间聚集性，存在加强热点（四川省）和持续热点（辽宁省、陕西省）。低温（<6.70℃）、低相对湿度（<55.59%）、高风速（>3.00m/s）、短日照时间（<2.7 h）归因分值分别为14.44%、5.75%、3.30%和7.88%。城镇化率（RR=1.009,95%CI: 1.007～1.012）、夜间灯光指数（RR=1.009,95%CI: 1.007～1.013）与肺结核发病正相关,而医疗机构床位数（RR=0.996,95%CI: 0.994～0.998）、老年人口比例（RR=0.973,95%CI: 0.969～0.979）与其负相关。结论 我国肺结核发病率总体呈下降趋势，应关注热点地区的防控,加强宏观影响因素的监测，尽早采取干预措施。

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毕建萍，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=xpTZObNTk5rOzW+WJSw2UA==, magXml=PJAkmdRvGLwrFx7Ntgg9OQ==, pdfUrl=null, pdf=4/31FDJ2Yr5nU1N/BTHI2w==, pdfFileSize=3410205, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=itmTZX536kAHzWgHgDp+Ow==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Rp4s0mWauZ0OIB1dcYGDAA==, mapNumber=null, authorCompany=null, fund=null, authors=

毕建萍（1981—），女，硕士，副主任医师，研究方向：环境卫生学

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毕建萍（1981—），女，硕士，副主任医师，研究方向：环境卫生学

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毕建萍（1981—），女，硕士，副主任医师，研究方向：环境卫生学

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Spatio-temporal distribution of pulmonary tuberculosis and influencing factors in Beijing, 2008-2018[J].Chinese Journal of Epidemiology, 2021, 42(7): 1240-1245.(In Chinese), articleTitle=Spatio-temporal distribution of pulmonary tuberculosis and influencing factors in Beijing, 2008-2018, refAbstract=null), Reference(id=1240933505211880393, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, doi=null, pmid=null, pmcid=null, year=2023, volume=30, issue=12, pageStart=34229, pageEnd=34242, url=null, language=null, rfNumber=[21], rfOrder=24, authorNames=Wang XQ, Zhang KD, Yu WJ, journalName=Environmental Science and Pollution Research International, refType=null, unstructuredReference=Wang XQ, Zhang KD, Yu WJ, et al. Associations of exposures to air pollution and greenness with mortality in a newly treated tuberculosis cohort[J]. Environmental Science and Pollution Research International, 2023, 30(12): 34229-34242., articleTitle=Associations of exposures to air pollution and greenness with mortality in a newly treated tuberculosis cohort, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1240933499658621526, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, xref=null, ext=[AuthorCompanyExt(id=1240933499667010135, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, companyId=1240933499658621526, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Hangzhou Xiaoshan District Centre for Disease Control and Prevention (Hangzhou Xiaoshan District Health Supervision Institute), Hangzhou, Zhejiang 311203, China), AuthorCompanyExt(id=1240933499671204441, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, companyId=1240933499658621526, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=杭州市萧山区疾病预防控制中心（杭州市萧山区卫生监督所），浙江 杭州 311203)])], figs=[ArticleFig(id=1240933501730607822, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=EN, label=Figure 1, caption=Spatiotemporal cluster distribution of TB epidemic in China from 2011 to 2020, figureFileSmall=lIv6oFhEdjXq/cKjsSnaaw==, figureFileBig=9w4nNsKUwLAu+1WdtgV1ug==, tableContent=null), ArticleFig(id=1240933501814493907, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=CN, label=图1, caption=2011—2020年全国肺结核疫情时空聚集性分布, figureFileSmall=lIv6oFhEdjXq/cKjsSnaaw==, figureFileBig=9w4nNsKUwLAu+1WdtgV1ug==, tableContent=null), ArticleFig(id=1240933501923545821, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=EN, label=Figure 2, caption=The cumulative effect and the lag effect for P75, P25 of meteorological factor on TB incidence, figureFileSmall=C0kStHuMAtzkSOYjm2AJtg==, figureFileBig=5KQf4rwm5JOQVLy0XbIltg==, tableContent=null), ArticleFig(id=1240933502015820515, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=CN, label=图2, caption=气象因素对肺结核发病的累积效应及P75、P25时的滞后效应

注：图A平均气温；图B平均相对湿度；图C平均风速；图D日照时间。

, figureFileSmall=C0kStHuMAtzkSOYjm2AJtg==, figureFileBig=5KQf4rwm5JOQVLy0XbIltg==, tableContent=null), ArticleFig(id=1240933502129066729, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=EN, label=Table 1, caption=

Incidence and global spatial autocorrelation analyses of TB from 2011 to 2020

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）发病率（1/10万）Moran I值Z值P 值
201170.750.353.233 4<0.001
201270.270.393.636 4<0.001
201366.470.403.773 5<0.001
201465.020.383.597 4<0.001
201562.850.393.717 5<0.001
201660.480.434.083 2<0.001
201759.650.444.287 1<0.001
201858.580.364.226 9<0.001
201955.020.484.565 2<0.001
202047.480.484.491 8<0.001
合计61.550.434.074 8<0.001
), ArticleFig(id=1240933502238118640, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=CN, label=表1, caption=

2011—2020年肺结核发病率及全局空间自相关分析

, figureFileSmall=null, figureFileBig=null, tableContent=
年份（年）发病率（1/10万）Moran I值Z值P 值
201170.750.353.233 4<0.001
201270.270.393.636 4<0.001
201366.470.403.773 5<0.001
201465.020.383.597 4<0.001
201562.850.393.717 5<0.001
201660.480.434.083 2<0.001
201759.650.444.287 1<0.001
201858.580.364.226 9<0.001
201955.020.484.565 2<0.001
202047.480.484.491 8<0.001
合计61.550.434.074 8<0.001
), ArticleFig(id=1240933502351364854, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=EN, label=Table 2, caption=

Posterior mean and RR of influencing factors for TB incidence

, figureFileSmall=null, figureFileBig=null, tableContent=
变量后验均值及95%CIRR及95%CI
城镇化率0.009(0.007，0.012)1.009(1.007，1.012)
医疗机构床位数-0.004(-0.006，-0.002)0.996(0.994，0.998)
老年人口比例-0.027(-0.032，-0.021)0.973(0.969，0.979)
夜间灯光指数0.009(0.006，-0.013)1.009(1.007，1.013)
), ArticleFig(id=1240933502439445245, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240722569947574398, language=CN, label=表2, caption=

肺结核发病影响因素后验均值及RR

, figureFileSmall=null, figureFileBig=null, tableContent=
变量后验均值及95%CIRR及95%CI
城镇化率0.009(0.007，0.012)1.009(1.007，1.012)
医疗机构床位数-0.004(-0.006，-0.002)0.996(0.994，0.998)
老年人口比例-0.027(-0.032，-0.021)0.973(0.969，0.979)
夜间灯光指数0.009(0.006，-0.013)1.009(1.007，1.013)
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2011—2020年中国肺结核时空特征及影响因素分析
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毕建萍 , 徐兴福
现代预防医学 | 流行病与统计方法 2025,52(19): 3521-3525
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现代预防医学 | 流行病与统计方法 2025, 52(19): 3521-3525
2011—2020年中国肺结核时空特征及影响因素分析
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毕建萍 , 徐兴福
作者信息
  • 杭州市萧山区疾病预防控制中心（杭州市萧山区卫生监督所），浙江 杭州 311203

    • 毕建萍（1981—），女，硕士，副主任医师，研究方向：环境卫生学

通讯作者:

毕建萍，E-mail:
Spatiotemporal characteristics and influencing factors of pulmonary tuberculosis in China: 2011—2020
Jian-ping BI , Xing-fu XU
Affiliations
  • Hangzhou Xiaoshan District Centre for Disease Control and Prevention (Hangzhou Xiaoshan District Health Supervision Institute), Hangzhou, Zhejiang 311203, China
出版时间: 2025-10-10 doi: 10.20043/j.cnki.MPM.202502415
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摘要
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目的 探讨中国肺结核的时空特征及其宏观影响因素，为制定肺结核疫情防控措施提供理论依据。方法 收集2011—2020年全国肺结核数据，构建两阶段分布滞后非线性模型，评估气象因素对结核病发病的滞后效应和非线性关系，并采用层次贝叶斯时空模型，评估肺结核发病风险与人口、经济、卫生服务等因素的关系。结果 中国肺结核发病率呈下降趋势,具有空间聚集性，存在加强热点（四川省）和持续热点（辽宁省、陕西省）。低温（<6.70℃）、低相对湿度（<55.59%）、高风速（>3.00m/s）、短日照时间（<2.7 h）归因分值分别为14.44%、5.75%、3.30%和7.88%。城镇化率（RR=1.009,95%CI: 1.007～1.012）、夜间灯光指数（RR=1.009,95%CI: 1.007～1.013）与肺结核发病正相关,而医疗机构床位数（RR=0.996,95%CI: 0.994～0.998）、老年人口比例（RR=0.973,95%CI: 0.969～0.979）与其负相关。结论 我国肺结核发病率总体呈下降趋势，应关注热点地区的防控,加强宏观影响因素的监测，尽早采取干预措施。

肺结核  /  分布式滞后非线性模型  /  层次贝叶斯模型  /  时空特征  /  影响因素

Objective To explore the spatiotemporal characteristics of pulmonary tuberculosis (TB) in China and its macro influencing factors, and to provide a theoretical basis for the formulation of TB prevention and control measures. Methods The data of tuberculosis incidence in China from 2011 to 2020 were collected. A two-stage distributed lag nonlinear model (DLNM) was constructed to assess the lag effect and nonlinear relationship of meteorological factors on TB incidence, and a hierarchical Bayesian spatiotemporal model was used to analyze the relationship between population, economic factors, health services, and TB incidence. Results TB incidence in China was on a downward trend, with spatial clustering, and there were intensifying hotspots (Sichuan Province) and persistent hotspots(Liaoning Province, Shaanxi Province). The attributable fractions of low temperature(<6.7 ℃), low relative humidity(<55.59%), high wind speed(>3.00 m/s), and short sunshine duration(<2.7 hours) were 14.44%, 5.75%, 3.30%, and 7.88%, respectively. The urbanization rate(RR=1.009, 95% CI: 1.007-1.012) and the night light index (RR=1.009, 95% CI: 1.007-1.013) were positively correlated with TB incidence, while the number of hospital beds(RR=0.996, 95% CI: 0.994-0.998) and the proportion of elderly population (RR=0.973, 95% CI: 0.969-0.979) were negatively correlated with it. Conclusion The TB incidence in China has generally shown a downward trend. Attention should be paid to the prevention and control of hotspots, and the monitoring of macro influencing factors should be strengthened to take intervention measures as early as possible.

Tuberculosis  /  Distributed lag non-linear model  /  Hierarchical Bayesian model  /  Spatial-temporal characteristics  /  Influencing factors
毕建萍, 徐兴福. 2011—2020年中国肺结核时空特征及影响因素分析. 现代预防医学, 2025 , 52 (19) : 3521 -3525 . DOI: 10.20043/j.cnki.MPM.202502415
Jian-ping BI, Xing-fu XU. Spatiotemporal characteristics and influencing factors of pulmonary tuberculosis in China: 2011—2020[J]. Modern Preventive Medicine, 2025 , 52 (19) : 3521 -3525 . DOI: 10.20043/j.cnki.MPM.202502415
正文
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结核病长期以来是威胁到全球人类健康的主要公共卫生问题，2023年全球报告新发病例约1 080万例[1]，因结核病死亡人数为125万，结核病重返全球单一传染病死因首位。据世界卫生组织(WHO)统计，30个结核病高负担国家占全球估算发病数87%，我国是全球结核病第三大负担国（6.8%）。
国内外肺结核流行的生态学研究主要涉及人口学特征、自然环境因素、社会经济因素等方面。气象流行病学研究中，数学建模考虑的影响因素具备多样性，成为重要的研究发向之一，其中，分布滞后非线性模型(distributed lag nonlinear model，DLNM)除了有数学建模的一般优点外，还能同时评估出气象因素的滞后效应和非线性效应[2-3]。在第二阶段采用多元meta分析，合并各地估计的参数，获取气象因素对肺结核发病的总效应，能克服单一地区研究可能产生的偏倚。在宏观因素关联性分析中，引入人口、经济、卫生服务、夜间灯光指数和归一化植被指数等指标，采用层次贝叶斯时空模型，利用其能避免参数过多导致的过拟合问题的优点，使模型计算得到的结果更加准确[4]。由此评估宏观影响因素对肺结核发病风险，为肺结核科学防治提供理论依据。
1　资料与方法
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1.1　数据来源
2011—2020年全国31个省份（直辖市、自治区，不包括港澳台）肺结核数据来自公共卫生科学数据中心。2015—2019年肺结核日监测数据来自“中国疾病预防控制信息系统”。省级电子地图数据来自国家地理信息公共服务平台。2015—2019年气象监测数据包括各省每天的平均气温、平均相对湿度、平均风速、日照时间，来源于国家气象科学数据中心。通过《中国统计年鉴》收集2011—2020年各省份每年人口数、人口密度、人均地区生产总值、城镇化率、卫生总费用、每万人医疗机构床位数、每万人卫生技术人员数、老年人口比例。收集2011—2020年31个省份逐月归一化植被指数，其来源于美国NASA定期发布MOD13A3数据集，反映土地覆盖植被状况的一种遥感指标，数值越高代表植被越茂盛。收集2011—2020年31个省份逐年夜间灯光指数，其来自美国国家海洋和大气管理局国家环境信息中心，在一定程度上反映城市经济水平和人口集中分布情况。
1.2　Joinpoint回归分析
使用Joinpoint回归软件(版本4.9.0.0)分析2011—2020年全国肺结核发病时间趋势,依据搜索法建模，蒙特卡洛置换检验和贝叶斯信息准则作为优化策略，根据均方差选择最佳模型，计算年度变化百分比（annual percent change，APC）、平均年度变化百分比（average annual percent change，AAPC）和95%置信区间（confidence interval，CI）。
1.3　空间自相关、新兴时空热点分析
使用Moran I进行全局空间自相关分析，判断2011—2020年各省肺结核发病率是否存在空间聚集性。新兴时空热点分析聚合每个省的2011—2020年各年度肺结核发病数据，创建时空立方体，结合Getis-Ord Gi*统计量和Mann-Kendall趋势检验，探测出测八种特定热点或冷点趋势：新增、连续、加强、持续、逐渐减少、分散、振荡和历史[5]。
1.4　两阶段DLNM分析
基于每日数据采用两阶段DLNM分析2015—2019年气象因素与肺结核发病的关系。第一阶段：利用交叉基函数，建立基于准泊松分布的DLNM模型，检测各省气象因素非线性暴露反应关系和延迟效应。第二阶段，合并各省参数，构建多变量meta回归模型，以气象因素中位数为参考值，计算每个自变量对结核病发病的累积效应。气象因素P75和P25作为高、低两水平，计算>P75和<P25的归因分值（attributable fraction）及95%CI。气象因素样条函数的自由度(v)设置为3。参考以往结核病的平均潜伏期研究文献[6]，将最长滞后期设定为60 d。调整模型中长期趋势的自由度(7～9)和自然立方样条函数的自由度(3～5)来检验模型的稳定性。利用R 4.2.2 “dlnm”包实现DLNM模型和“mvmeta”包实现多元meta分析。
1.5　贝叶斯时空模型
贝叶斯时空模型是基于贝叶斯统计思想,为分析时空数据而建立的模型，模型分解为影响因素协变量、空间和时间的分量。本研究的影响因素协变量包括2011—2020年各省份人口数、人口密度、人均地区生产总值、城镇化率、卫生总费用、每万人医疗机构床位数、每万人卫生技术人员数、老年人口比例、归一化植被指数和夜间灯光指数，进行无量纲化处理，剔除方差膨胀系数>10的变量或无统计学意义的变量，建立最佳贝叶斯时空模型，采用R4.2.2“INLA”包中的集成嵌套拉普拉斯近似算法(integrated nested laplace approximation, INLA)进行拟合，获得影响因素协变量的后验均值[7]。
2　结果
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2.1　肺结核时空分布特征
2011—2020年全国累积报告肺结核病例1 381 498例，发病率从2011年70.75/10万下降到2020年47.48/10万。Joinpoint回归分析显示，AAPC=-4.15%（95%CI:-4.59%～-3.61%）,2011—2018年APC =-2.74%(95%CI:-3.26%～-1.83 %)，2018年出现转折点，2018—2020年下降增速(APC=-8.95%,95%CI:-10.99%～-5.77%) 。历年全局Moran I值从2011年的0.35上升至2020年的0.48，Z值>3.23(P<0.001），表明肺结核发病呈显著的空间正自相关，空间聚集随时间逐渐增强。见表1。
新兴时空热点分析探测出三类时空热点，分别为：加强热点，即成为热点时间较早且聚集性渐强的地区，为四川省；持续热点，即长期为热点但聚集强度平衡的地区，包括辽宁省和陕西省；分散热点，即时间上不连续的热点地区，包括福建省、黑龙江省和云南省。同时探测出四类时空冷点，分别为：新增冷点，即近两年开始成为冷点的地区，包括湖北省、重庆市；持续冷点，即长期为冷点且强度平衡的地区，为浙江省；连续冷点，即成为冷点地区时间相对较晚但有连续性的地区，包括海南省、江苏省、北京市；分散冷点，即时间不连续的冷点地区，包括河南省、甘肃省、广西壮族自治区、山东省。其余为无特征模式，见图1。
2.2　气象因素与结核病发病的关系
基于方差膨胀因子分析，平均气温、平均相对湿度、平均风速、日照时间之间不存在多重共线性，纳入两阶段DLNM分析。图2揭示了气象因子与肺结核发病的总体累积暴露-反应关系及高、低水平的滞后效应。以平均气温中位数15.80℃为参考，发病风险先上升后下降，-10.08℃时RR达到峰值(RR=3.87,95%CI:2.30～6.50)。>15.80℃，呈现保护效应。热效应：平均气温P75（22.97℃）时有保护效应，>22.97℃的归因分值为-18.97%(95%CI:-22.15%～-15.93%)。冷效应：平均气温P25（6.70℃）时次日及20 d后危出现危害效应并持续至第54 d，<6.70℃的归因分值为14.44%(95%CI:13.10%～15.77%)。
以相对湿度中位数70.17%为参考,27.32%～70%和87.11%～94.89%是肺结核发病的危险因素，94.89%时RR达到峰值(RR=1.48 ,95% CI:1.00～2.20)。高湿度(80.78%）时当天有危害效应，>80.78%的归因分值为-0.89%(95%CI: -2.60%～0.92%)。低湿度（55.59%）时滞后54 d后有保护效应，<55.59%的归因分值为5.75%(95%CI: 3.80%～7.52%)。
以平均风速中位数2.18 m/s为参考，1.48～2.18 m/s为保护效应，<0.45 m/s呈现危害效应，风速越小，RR越大，无风天气达峰值（RR=3.66，95%CI:1.37～9.78）；>2.18 m/s呈现危害效应。风速P75（3.00m/s）和P25（1.69m/s）时滞后效应均无统计学意义。高风速>3.00 m/s的归因分值为3.30%(95%CI:1.41%～5.12%)，低风速（<1.69 m/s）的归因分值为0.60%(95%CI:-1.29%～2.29%)。
以日照时间中位数6.15 h为参考，<2.45 h和>11.78 h，有危害风险，13.6 h RR达到峰值（RR= 3.41，95% CI: 1.42～8.23)。日照时间P75（8.44 h）、P25（2.70 h）时滞后效应均无统计学意义。长日照时间（>8.44 h）的归因分值为1.92%(95%CI:-0.05%～3.77%)。短日照时间（<2.70 h）的归因分值为7.88%(95%CI: 5.61%～10.00%)。
2.3　肺结核发病的可能影响因素
层次贝叶斯模型分析显示，城镇化率（RR=1.009）、夜间灯光指数（RR=1.009）与肺结核发病呈正相关，而医疗机构床位数（RR=0.996）、老年人口比例（RR=0.973）对肺结核发病具有显著的负向作用。见表2。
3　讨论
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通过对中国肺结核时空特征分析发现，中国肺结核发病率呈下降趋势，空间聚集性呈增强趋势，与已有研究结果相符[8]。新兴时空热点分析探测出四川省为加强热点，辽宁省、陕西省为持续热点，提示疫情在该地区持续发展，是重点防控地区。持续冷点、连续冷点、新增冷点的出现，提示疫情在这些地区可能得到了有效控制，应结合历年防控策略进行疫情趋势研判和经验总结，为其他地区优化防治策略提供参考[9]。
目前全国性的气象因素与肺结核发病的关系研究较少，本文两阶段DLNM分析结果显示，低气温、低相对湿度增加结核病发病风险，高风速呈现危害效应。低温、干燥环境，导致人体呼吸道纤毛运动减弱，黏液清除能力下降，导致肺结核发病风险增大，与香港、安徽等地研究一致[10-11]。以往风速与肺结核病发病关系的研究结果并不一致[12-13]，可能与高风速的双相作用相关，更高的风速加速了结核分枝杆菌的播散；同时，也加速了空气流通,降低空气污染物的浓度[13]，而空气污染物与肺结核发病密切相关。日照时间<2.45 h或>11.78 h，增加肺结核发病风险。较短的日照时间可以减少紫外线照射，温度较低有助于结核分枝杆菌生存。日照可以增加人体对维生素D的吸收，提高人体素质，从而减少人群结核病感染率[14]。极端长日照时间，气温高，人们多在室内聚集，感染结核病机会增加。
层次贝叶斯模型用随机效应表示发病风险在时空上的变异，考虑了先验效应和样本信息，同时修正相关影响因素的效应，提高了模型结果的准确性，结果显示，医疗机构床位数是肺结核发病保护因素，与以往研究结果一致[15-16]，医疗机构床位数相对较多的地区，医疗卫生资源更充足，更有助于控制肺结核的传播。老年人口比例与肺结核发病负相关，不同于四川、上海研究[17-18]，前者认为老年人口比例高是危险因素，后者认为老年人口比例与肺结核发病无关，认为老年人口比例高时，经济水平也较高、卫生资源也更充足，有助于控制肺结核的传播[17]。城镇化率高，肺结核发病风险也高，城镇化带来教育、医疗和卫生服务的改善，有利于一些传染病预防与控制，同时也带来了生态环境破坏、人口密度上升和人类流动性增加，这些因素可能会加剧一些传染病的传播[19-20]。研究发现，夜间灯光指数是肺结核发病的危险因素，可能原因为高夜间灯光指数区域通常人口密度较高，部分人群（如流动人口）医疗卫生服务可及性较差；常伴随较高的工业活动和交通流量，空气污染（如PM2.5、NO2）可能增加肺结核的发病风险[21]。
本研究为肺结核宏观因素生态学研究，可能存在生态学谬误。此外，一些重要的因素如空气质量、HIV感染率、肺结核治愈率未收集，可能引起偏倚。综上所述，我国肺结核发病率总体呈下降趋势，个别地区防控压力较大，应加强风险因素的监测预警与应急响应，实施区域差异化防控策略，降低肺结核发病风险。
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2025年第52卷第19期
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doi: 10.20043/j.cnki.MPM.202502415
  • 接收时间：2025-02-25
  • 首发时间：2026-03-17
  • 出版时间：2025-10-10
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  • 收稿日期：2025-02-25
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    杭州市萧山区疾病预防控制中心（杭州市萧山区卫生监督所），浙江 杭州 311203

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2种不同金属材料的力学参数
科
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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