Article(id=1320428890867220760, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320428830876086873, articleNumber=null, orderNo=null, doi=10.11847/zgggws1149262, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1762358400000, receivedDateStr=2025-11-06, revisedDate=1768752000000, revisedDateStr=2026-01-19, acceptedDate=1772812800000, acceptedDateStr=2026-03-07, onlineDate=1792744379964, onlineDateStr=2026-10-23, pubDate=1788969600000, pubDateStr=2026-09-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792744379964, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792744379964, creator=13701087609, updateTime=1792744379964, updator=13701087609, issue=Issue{id=1320428830876086873, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='9', pageStart='1025', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='1788969600000', pubDateStr='2026-09-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792744365662, creator='13701087609', updateTime=1792746244140, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320436709859381360, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320428830876086873, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320436709859381361, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320428830876086873, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1135, endPage=1139, ext={EN=ArticleExt(id=1320428891097907481, articleId=1320428890867220760, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Relationship between medical visit intervals and the risk of clustered tuberculosis outbreaks in schools: a retrospective survey based on first-episode cases, columnId=1320427686514770171, journalTitle=Chinese Journal of Public Health, columnName=Survey and Report, runingTitle=null, highlight=null, articleAbstract=
Objective

To analyze the relationship between the visit intervals of indicator cases and the risk of clustered tuberculosis outbreaks in schools.

Methods

A total of 623 indicator cases of pulmonary tuberculosis involved in the pulmonary tuberculosis epidemic in Guangzhou city, Guangdong province from September 2023 to September 2024 were included for analysis. Restricted cubic splines (RCS), unconditional logistic regression analysis, and receiver operating characteristic curve (ROC) were used to evaluate the relationship between the visit intervals of indicator cases and the risk of clustered tuberculosis outbreaks in schools.

Results

Among 623 tuberculosis indicator cases included in this study, 66 cases were clustered with high-risk outbreaks, and 557 cases were clustered with low-risk outbreaks or had no correlation. The differences in visit interval [41.00(12.00, 104.00)d vs 7.00(0.00, 17.00)d] and positive rate of pathogenic examination (69.70% VS 46.14%) were statistically significant between two groups (Z/χ2 = –37.401, 13.108, P < 0.05). When the visit intervals of positive indicator cases in etiological examination exceeded 14 days or that of negative cases exceeded 8 days, the risk of pulmonary tuberculosis outbreaks in schools showed a linear increasing trend. For every 1 day increase in the visit interval, the possibility of high risk outbreaks in schools increased by 15.50% (OR = 1.155, 95%CI: 1.050–1.680, P < 0.05). When the visit intervals were analyzed by ternary, it was found that with the extension of visit interval, the risk of clustered pulmonary tuberculosis outbreaks in schools increased (Z = 12.520, P < 0.05). The ROC curve analysis showed that the threshold for predicting the high concentration risk of pulmonary tuberculosis in schools based on the visit interval was 23 days, AUC was 0.918, 95%CI = 0.829 to 0.964.

Conclusions

The extended visit intervals of indicator cases may be a key early warning indicator for high-risk clustered outbreaks of pulmonary tuberculosis in schools.

, authors=Xiaoshan LIN, Weiyun HE, Guangfeng YUAN, Guobiao LIU, Yajuan FENG, Jieying YANG, Jingwen LAI, authorsList=Xiaoshan LIN, Weiyun HE, Guangfeng YUAN, Guobiao LIU, Yajuan FENG, Jieying YANG, Jingwen LAI, authorCompany=null, correspAuthors=Weiyun HE, authorNote=null, correspAuthorsNote=
HE Weiyun, E-mail:
, copyrightStatement=Copyright © 2026 Chinese Journal of Public Health. All rights reserved., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320428891961934112, articleId=1320428890867220760, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=就诊间隔与学校肺结核疫情聚集风险关系:基于首发病例的回顾性调查, columnId=1320427686770622717, journalTitle=中国公共卫生, columnName=调查报告与分析, runingTitle=null, highlight=null, articleAbstract=
目的

分析指示病例就诊间隔与学校肺结核疫情聚集风险的关系。

方法

纳入2023年9月—2024年9月广东省广州市肺结核疫情涉及的623例肺结核指示病例为研究对象。采用限制性立方样条(RCS)、非条件logistic回归分析法、受试者工作特征曲线(ROC)评估指示病例就诊间隔与学校肺结核疫情聚集风险的关系。

结果

本次纳入的623例肺结核指示病例中66例为高风险疫情聚集、557例为低风险疫情聚集或无关联性,2组病例的就诊间[41.00(12.00,104.00)d vs 7.00(0.00,17.00)d]、病原学检查阳性率(69.70% vs 46.14%)差异均有统计学意义(Z/χ2= –37.401、13.108,P<0.05)。当病原学检查阳性指示病例就诊间隔超过14 d或阴性病例超过8 d,学校肺结核疫情风险呈线性增加趋势;就诊间隔每增加1 d,学校肺结核疫情高聚集风险的可能性增加15.50%(OR=1.155,95%CI=1.050~1.680,P<0.05)。将就诊间隔按三分位分析显示,就诊间隔延长,学校肺结核疫情聚集风险上升(Z=12.520,P<0.05)。ROC曲线结果显示,就诊间隔预测学校肺结核疫情高聚集风险的阈值为23 d,AUC为0.918,95%CI=0.829~0.964。

结论

指示病例就诊间隔延长可能是学校肺结核疫情发展为高聚集风险的关键预警指标。

, authors=林晓珊, 何蔚云*, *, 袁光峰, 刘国标, 冯亚娟, 杨洁莹, 赖静文, authorsList=林晓珊, 何蔚云, 袁光峰, 刘国标, 冯亚娟, 杨洁莹, 赖静文, authorCompany=null, correspAuthors=何蔚云, authorNote=

林晓珊(1987—),本科,副主任医师,研究方向:结核病防控

, correspAuthorsNote=
何蔚云,E-mail:
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Comparison of categorical variables of indicator cases with different characteristics in high- and low-risk clustered outbreaks

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特征分类高风险疫情聚集(n=66)低风险疫情聚集或无关联性(n=557)χ2值P值
例数构成比/%例数构成比/%
性别男生4060.6130053.86 1.0830.298
女生2639.3925746.14
学校类别小学 0 0.00 50 8.98 4.5280.642
初中1827.2710017.95
高中/职业院校3959.0932257.81
大学 913.64 8515.26
学校角色学生6090.9152293.72 0.7560.384
教职工 6 9.09 35 6.28
肺结核发现方式常规体检 4 6.06 8314.90 0.6580.592
因症就诊5481.8244579.89
密切接触者筛查 812.12 29 5.21
病原学检查阴性2030.3030053.8613.108<0.001
阳性4669.7025746.14
), ArticleFig(id=1320428900522508634, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320428890867220760, language=CN, label=表1, caption=

不同特征指示病例在高、低风险疫情聚集中分类变量的比较

, figureFileSmall=null, figureFileBig=null, tableContent=
特征分类高风险疫情聚集(n=66)低风险疫情聚集或无关联性(n=557)χ2值P值
例数构成比/%例数构成比/%
性别男生4060.6130053.86 1.0830.298
女生2639.3925746.14
学校类别小学 0 0.00 50 8.98 4.5280.642
初中1827.2710017.95
高中/职业院校3959.0932257.81
大学 913.64 8515.26
学校角色学生6090.9152293.72 0.7560.384
教职工 6 9.09 35 6.28
肺结核发现方式常规体检 4 6.06 8314.90 0.6580.592
因症就诊5481.8244579.89
密切接触者筛查 812.12 29 5.21
病原学检查阴性2030.3030053.8613.108<0.001
阳性4669.7025746.14
), ArticleFig(id=1320428900614783323, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320428890867220760, language=EN, label=Tab.2, caption=

Comparison of continuous variables of indicator cases with different characteristics in high- and low-risk clustered outbreaks

, figureFileSmall=null, figureFileBig=null, tableContent=
特征高风险疫情聚集(n=66)低风险疫情聚集或无关联性(n=557)t值P值
年龄/岁12.00±3.0011.50±3.001.2800.201
班级密度/(人/平方米) 1.45±0.20 1.50±0.251.5660.118
宿舍密度/(人/平方米) 4.00±0.80 4.20±0.851.8180.069
就诊间隔时间/d41.00(12.00,104.00)7.00(0.00,17.00) –37.401<0.001
诊断间隔时间/d13.00(4.00,33.00)12.00(3.00,30.00)–0.6200.120
), ArticleFig(id=1320428900698669404, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320428890867220760, language=CN, label=表2, caption=

不同特征指示病例在高、低风险疫情聚集中连续变量的比较

, figureFileSmall=null, figureFileBig=null, tableContent=
特征高风险疫情聚集(n=66)低风险疫情聚集或无关联性(n=557)t值P值
年龄/岁12.00±3.0011.50±3.001.2800.201
班级密度/(人/平方米) 1.45±0.20 1.50±0.251.5660.118
宿舍密度/(人/平方米) 4.00±0.80 4.20±0.851.8180.069
就诊间隔时间/d41.00(12.00,104.00)7.00(0.00,17.00) –37.401<0.001
诊断间隔时间/d13.00(4.00,33.00)12.00(3.00,30.00)–0.6200.120
), ArticleFig(id=1320428900786749789, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320428890867220760, language=EN, label=Tab.3, caption=

Accuracy of visit intervals in predicting the high-risk clustered outbreaks of pulmonary tuberculosis in schools

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指标阈值/d敏感度/%特异度/%阳性预测值/%阴性预测值/%AUC95%CI
就诊间隔23.0085.2080.0096.5044.500.9180.829~0.964
病原学阴性就诊间隔15.0089.5071.5098.0041.200.9120.837~0.948
病原学阳性就诊间隔30.0081.0084.5093.5060.200.9320.823~0.969
), ArticleFig(id=1320428900874830174, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320428890867220760, language=CN, label=表3, caption=

就诊间隔预测学校肺结核高风险疫情聚集风险的准确性

, figureFileSmall=null, figureFileBig=null, tableContent=
指标阈值/d敏感度/%特异度/%阳性预测值/%阴性预测值/%AUC95%CI
就诊间隔23.0085.2080.0096.5044.500.9180.829~0.964
病原学阴性就诊间隔15.0089.5071.5098.0041.200.9120.837~0.948
病原学阳性就诊间隔30.0081.0084.5093.5060.200.9320.823~0.969
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就诊间隔与学校肺结核疫情聚集风险关系:基于首发病例的回顾性调查
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林晓珊 , 何蔚云 *, * , 袁光峰 , 刘国标 , 冯亚娟 , 杨洁莹 , 赖静文
中国公共卫生 | 调查报告与分析 2026,42(9): 1135-1139
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中国公共卫生 |调查报告与分析 2026 , 42 (9) : 1135 -1139
就诊间隔与学校肺结核疫情聚集风险关系:基于首发病例的回顾性调查
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林晓珊, 何蔚云*, * , 袁光峰, 刘国标, 冯亚娟, 杨洁莹, 赖静文
作者信息
  • 广州市胸科医院 广州 510095
通讯作者:
何蔚云,E-mail:
作者简介:

林晓珊(1987—),本科,副主任医师,研究方向:结核病防控

Relationship between medical visit intervals and the risk of clustered tuberculosis outbreaks in schools: a retrospective survey based on first-episode cases
Xiaoshan LIN, Weiyun HE , Guangfeng YUAN, Guobiao LIU, Yajuan FENG, Jieying YANG, Jingwen LAI
Affiliations
  • Guangzhou Chest Hospital, Guangzhou 510095, China
  • Corresponding Author:
    HE Weiyun, E-mail:
出版时间: 2026-09-10 doi: 10.11847/zgggws1149262
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目的

分析指示病例就诊间隔与学校肺结核疫情聚集风险的关系。

方法

纳入2023年9月—2024年9月广东省广州市肺结核疫情涉及的623例肺结核指示病例为研究对象。采用限制性立方样条(RCS)、非条件logistic回归分析法、受试者工作特征曲线(ROC)评估指示病例就诊间隔与学校肺结核疫情聚集风险的关系。

结果

本次纳入的623例肺结核指示病例中66例为高风险疫情聚集、557例为低风险疫情聚集或无关联性,2组病例的就诊间[41.00(12.00,104.00)d vs 7.00(0.00,17.00)d]、病原学检查阳性率(69.70% vs 46.14%)差异均有统计学意义(Z/χ2= –37.401、13.108,P<0.05)。当病原学检查阳性指示病例就诊间隔超过14 d或阴性病例超过8 d,学校肺结核疫情风险呈线性增加趋势;就诊间隔每增加1 d,学校肺结核疫情高聚集风险的可能性增加15.50%(OR=1.155,95%CI=1.050~1.680,P<0.05)。将就诊间隔按三分位分析显示,就诊间隔延长,学校肺结核疫情聚集风险上升(Z=12.520,P<0.05)。ROC曲线结果显示,就诊间隔预测学校肺结核疫情高聚集风险的阈值为23 d,AUC为0.918,95%CI=0.829~0.964。

结论

指示病例就诊间隔延长可能是学校肺结核疫情发展为高聚集风险的关键预警指标。

学校肺结核  /  暴发流行  /  学生  /  指示病例  /  疫情聚集风险  /  就诊间隔  /  广州市
Objective

To analyze the relationship between the visit intervals of indicator cases and the risk of clustered tuberculosis outbreaks in schools.

Methods

A total of 623 indicator cases of pulmonary tuberculosis involved in the pulmonary tuberculosis epidemic in Guangzhou city, Guangdong province from September 2023 to September 2024 were included for analysis. Restricted cubic splines (RCS), unconditional logistic regression analysis, and receiver operating characteristic curve (ROC) were used to evaluate the relationship between the visit intervals of indicator cases and the risk of clustered tuberculosis outbreaks in schools.

Results

Among 623 tuberculosis indicator cases included in this study, 66 cases were clustered with high-risk outbreaks, and 557 cases were clustered with low-risk outbreaks or had no correlation. The differences in visit interval [41.00(12.00, 104.00)d vs 7.00(0.00, 17.00)d] and positive rate of pathogenic examination (69.70% VS 46.14%) were statistically significant between two groups (Z/χ2 = –37.401, 13.108, P < 0.05). When the visit intervals of positive indicator cases in etiological examination exceeded 14 days or that of negative cases exceeded 8 days, the risk of pulmonary tuberculosis outbreaks in schools showed a linear increasing trend. For every 1 day increase in the visit interval, the possibility of high risk outbreaks in schools increased by 15.50% (OR = 1.155, 95%CI: 1.050–1.680, P < 0.05). When the visit intervals were analyzed by ternary, it was found that with the extension of visit interval, the risk of clustered pulmonary tuberculosis outbreaks in schools increased (Z = 12.520, P < 0.05). The ROC curve analysis showed that the threshold for predicting the high concentration risk of pulmonary tuberculosis in schools based on the visit interval was 23 days, AUC was 0.918, 95%CI = 0.829 to 0.964.

Conclusions

The extended visit intervals of indicator cases may be a key early warning indicator for high-risk clustered outbreaks of pulmonary tuberculosis in schools.

tuberculosis  /  outbreaks of disease  /  student  /  case report  /  risk of clustered outbreaksin  /  medical visit intervals  /  Guangzhou city
林晓珊, 何蔚云, 袁光峰, 刘国标, 冯亚娟, 杨洁莹, 赖静文. 就诊间隔与学校肺结核疫情聚集风险关系:基于首发病例的回顾性调查. 中国公共卫生, 2026 , 42 (9) : 1135 -1139 . DOI: 10.11847/zgggws1149262
Xiaoshan LIN, Weiyun HE, Guangfeng YUAN, Guobiao LIU, Yajuan FENG, Jieying YANG, Jingwen LAI. Relationship between medical visit intervals and the risk of clustered tuberculosis outbreaks in schools: a retrospective survey based on first-episode cases[J]. Chinese Journal of Public Health, 2026 , 42 (9) : 1135 -1139 . DOI: 10.11847/zgggws1149262
肺结核病程隐匿,指示病例即学校内首次报告的肺结核病例,作为学校肺结核疫情发现和控制的起点,其就诊间隔是影响学校肺结核疫情传播和聚集风险的关键因素之一[1–3]。相关研究[4–5]表明,指示病例就诊间隔增加,可能加速肺结核在校园内隐匿传播,进一步增加学校肺结核疫情聚集风险。本研究纳入2023年9月—2024年9月广州市肺结核疫情涉及的623例肺结核指示病例,分析指示病例就诊间隔与学校肺结核疫情聚集风险的关系,以期为学校肺结核疫情的防控提供科学依据及实践指导。
2023年9月—2024年9月广东省广州市肺结核疫情涉及的623例指示病例。排除标准:存在视听障碍、言语功能障碍等可能影响结果准确性的疾病。本研究获经广州市胸科医院医学伦理委员会审批(批号:KY–2025–039)。
(1)指示病例[6]:2023年9月—2024年9月,广东省广州市各级各类学校内首次报告的肺结核病例。本研究中的指示病例均为在读学生。(2)就诊间隔[7]:患者出现可疑症状至首次就医的时间。该数据通过患者访谈结合门诊病历复核获取,以病历记录时间为准。(3)学校肺结核疫情聚集风险:在学校范围内,因密切接触或共同暴露导致肺结核病例聚集的可能性。(4)肺结核疫情聚集高低风险[8]:同一所学校的同一办学点,一个学期内出现≥2例无明确传播或暴露关联的病例定义为低风险疫情聚集,≥2例且有明确传播关系或暴露关联的病例定义为高风险疫情聚集。(5)无关联性病例:与其他病例不存在密切接触、共同暴露等可能导致传播因素的病例。
当学校首次发现肺结核病例时,通过国家结核病登记系统、学校内部登记或教师反馈等方式获取病例信息。采用半结构化访谈、现场调查相结合的方法收集病例数据,并填写肺结核患者个案调查表,对就诊间隔等关键时间变量,优先采用电子病历系统中的时间戳。确定病例的密切接触范围后,进行筛查并填写密切接触者筛查表。随访6个月,依据在密切接触者中筛查出的肺结核病例数量,判定疫情聚集的风险等级。
采用SPSS 26.0软件分析数据,对于正态分布的计量资料($ \overline{x}\pm s $),采用t检验;非正态分布的计量资料M(P25,P75),采用Mann-Whitney U检验;计数资料(n,%)组间比较采用χ2检验,多组间比较采用秩和检验。RCS分析就诊间隔与学校肺结核疫情聚集风险的潜在剂量–反应关系。节点数据依据Harrell推荐设置为3个,位置分别置于就诊间隔分布的第10、50、90百分位数(3个节点)。通过AIC最小化原则选择最优模型。非条件logistic回归分析法评估就诊间隔与学校肺结核高风险疫情聚集风险之间的联系强度、方向。ROC曲线评估就诊间隔预测学校肺结核高风险疫情聚集的准确性。双侧检验水准α=0.05。
本次研究共纳入某市区学校发生肺结核疫情涉及的623例指示病例,其中66例为高风险疫情聚集指示病例,就诊间隔为41.00(12.00,104.00)d,557例为低风险疫情聚集或无关联性指示病例就诊间隔为7.00(0.00,17.00) d,差异均有统计学意义(Z= –37.401,P<0.05)。高、低风险疫情聚集或无关联性指示病例的病原学检查阳性率(69.70% vs 46.14%)差异也有统计学意义(χ2=13.108,P<0.05)。
RCS多因素分析结果显示,指示病例就诊间隔与学校肺结核疫情风险之间不存在显著的非线性关系(χ2=0.22,P>0.05);当病原学检查阳性指示病例就诊间隔超过14 d或阴性病例超过8 d,学校肺结核疫情风险呈线性增加趋势。
以学校肺结核疫情聚集风险(高风险疫情聚集=1、低风险疫情聚集/无关联性=0)为结局变量,指示病例就诊间隔(连续变量)为核心暴露变量纳入非条件logistic回归分析。结果显示,就诊间隔每增加1 d,学校肺结核疫情高聚集风险的可能性增加15.50%(OR=1.155,95%CI=1.050~1.680,P<0.05)。将就诊间隔按三分位分析显示,就诊间隔延长,学校肺结核疫情聚集风险上升(Z=12.520,P<0.05)。
ROC曲线分析显示,就诊间隔预测学校肺结核疫情高聚集风险的AUC为0.918,95%CI=0.829~0.964。根据Youden指数最大法则,确定阈值为23.00 d,对应的敏感度、特异度分别为85.20%、80.00%。
本次研究共纳入广州市区学校发生肺结核疫情涉及的623例指示病例,其中66例高风险疫情聚集指示病例就诊间隔为41(12,104)d,明显长于557例低风险疫情聚集或无关联性指示病例就诊间隔7(0,17)d,且就诊间隔每延长1 d,学校肺结核疫情高聚集风险的概率增加15.00%(OR=1.155,95%CI=1.050~1.680,P<0.05)。提示就诊间隔过长,可能增加学校肺结核传播、扩散速度,进一步增加疫情聚集风险。肺结核早期症状多表现为咳嗽、咳痰或低热,非医学专业人群易将上述症状与呼吸道感染症状混淆,导致就诊延迟。部分地区基层医疗机构对肺结核病的诊疗能力可能存在不足,肺结核感染患者就诊后,也可能出现就诊间隔延长的现象。有研究[9–11]表明,就诊延迟是导致学校肺结核疫情持续存在的重要原因之一,不仅会加重肺结核患者病情,还会加速结核杆菌传播。本研究发现,当指示病例就诊间隔超过23 d时,肺结核可能在校园内隐匿传播,学校应高度重视肺结核疫情聚集事件的发生。进一步评估就诊间隔预测学校肺结核疫情聚集风险的准确性,发现指示病例就诊间隔的预测效能AUC为0.918,95%CI=0.829~0.964,可见其能较好地识别疫情高聚集风险,可作为评估学校肺结核疫情聚集风险的有效预警指标。此外,本研究发现,病原学阳性就诊间隔阈值(30 d)高于病原学阴性阈值(15 d),可能归因于病原学阳性指示病例的临床症状更显著,较高的病原体载量可能导致其传染性更强,从而促使阳性指示病例的就诊时间更早,就诊前感染的密切接触者更多。学校应加强学生健康监测,尽早识别、隔离肺结核患者,尤其是病原学检查阳性的肺结核患者,降低学校肺结核疫情聚集风险[12]。本研究虽在整体上达到了进行RCS分析和非条件logistic回归分析法所需的基本样本量要求,但高风险事件的数量相对有限(66例,占总样本的10.6%),可能导致RCS分析中样条函数在极端值区域的估计方差增加,一定程度上影响就诊间隔与高风险聚集之间复杂非线性关联的能力。此外,病例来源于医疗机构报告和学校主动监测系统,可能未覆盖未就诊的轻症病例,从而高估就诊间隔与聚集风险之间的关联强度。未来研究可通过扩大样本量,进一步验证就诊间隔与聚集风险之间的确切剂量–反应模式。
为了有效预防和控制学校肺结核疫情聚集,学校可从多个角度制定干预措施。如有研究通过横断面调查,发现改善学校环境卫生条件可有效降低学校肺结核疫情聚集的发生概率[13]。在肺结核疫情初期控制传染源是降低密切接触者感染风险的有效策略,加强健康教育、提高防控意识是帮助学生识别肺结核症状的必要条件[14]。学校可通过加强学生健康监测及肺结核健康教育,以阻断肺结核传播链,避免疫情扩散;同时提高学生防控意识、培养健康行为,共同降低学校肺结核疫情聚集风险。病例发现和报告效率直接影响指示病例的就诊间隔[15],快速发现和报告病例能有效缩短传染期,减少肺结核传播机会,从而降低学校肺结核疫情聚集风险。肺结核经规范治疗后,传染性会迅速降低,通过隔离治疗可减少肺结核患者在传染期内的活动范围、时间及复发导致的二次传播,一定程度上降低学校肺结核疫情聚集风险[16]。在实际工作中,学校可与疾控部门、医疗机构密切合作,通过综合防控,保障学生、教职工的健康安全。密切接触者是肺结核传播的高风险人群[17],早期筛查可以发现肺结核感染者或潜伏感染者,及时采取干预措施可避免其成为新的传染源,降低肺结核传播、疫情扩散的可能性,避免肺结核疫情在学校内聚集性发生。
综上所述,指示病例就诊间隔延长可能是学校肺结核疫情发展为高聚集风险的关键预警指标。学校可通过改善学校环境卫生条件、加强学生健康监测及肺结核健康教育、提高病例发现和报告效率,规范肺结核学生治疗管理,加强密切接触者筛查等干预措施可以降低肺结核疫情高聚集风险。

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2026年第42卷第9期
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doi: 10.11847/zgggws1149262
  • 接收时间:2025-11-06
  • 首发时间:2026-10-23
  • 出版时间:2026-09-10
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  • 收稿日期:2025-11-06
  • 修回日期:2026-01-19
  • 录用日期:2026-03-07
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    广州市胸科医院 广州 510095

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2种不同金属材料的力学参数
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genus
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species
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属
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种数
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species
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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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