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Article(id=1320438091190481728, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, articleNumber=null, orderNo=null, doi=10.11847/zgggws1148645, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1755014400000, receivedDateStr=2025-08-13, revisedDate=1760630400000, revisedDateStr=2025-10-17, acceptedDate=1769356800000, acceptedDateStr=2026-01-26, onlineDate=1792746573493, onlineDateStr=2026-10-23, pubDate=1775750400000, pubDateStr=2026-04-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792746573493, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792746573493, creator=13701087609, updateTime=1792746573493, updator=13701087609, issue=Issue{id=1320438090615862080, tenantId=1146029695717560320, journalId=1319373219989389464, year='2026', volume='42', issue='4', pageStart='385', pageEnd='512', issueExtLink='null', onlineDate='null', pubDate='1775750400000', pubDateStr='2026-04-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792746573356, creator='13701087609', updateTime=1792748076043, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320444393430040620, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320444393430040621, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1320438090615862080, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=418, endPage=423, ext={EN=ArticleExt(id=1320438091450528578, articleId=1320438091190481728, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy, columnId=1320438091370836801, journalTitle=Chinese Journal of Public Health, columnName=Special Report—Development of Public Health Evaluation Indicator Frameworks, runingTitle=null, highlight=null, articleAbstract=
Objective

To establish comprehensive indicator systems for early warning of acute respiratory infection (ARI) based on multi-source data.

Methods

The Delphi method was adopted for expert consultation. Twenty experts were invited to score the necessity, availability, and sensitivity of different early warning indicators, and the weights were determined based on the scoring results.

Results

The two rounds of expert consultation showed the expert positive coefficients being both 100%, the coefficients of expert authority being both 0.79, and the coefficients of concordance being 0.280 and 0.487 (P < 0.05), respectively. According to the characteristics of ARI in the stages of occurrence, transmission, and epidemics, two indicator systems–the early warning indicator system for risk of ARI and the early warning indicator system for potential clustering or outbreak of ARI–were established. The former included 22 indicators in 5 categories, and the latter included 29 indicators in 6 categories. In the early warning indicator system for risk of ARI, the top 3 indicators with the highest weights were immunization coverage rate (0.052), variations of known pathogens (0.051), and variations of dominant prevalent strains (0.051). In the early warning indicator system for potential clustering or outbreak of ARI, the top 3 indicators with the highest weights were surveillance data of respiratory syndrome (0.040), positive rate of specific pathogen (0.040), and number of cases reported in online direct report system (0.040).

Conclusions

The comprehensive indicator systems for early warning of ARI based on multi-source data and Delphi method established in this study have certain scientificity and practicability, which can provide support for improving the multi-point triggered early warning of infectious diseases.

, authors=Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU, authorsList=Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU, authorCompany=null, correspAuthors=Xihong LYU, authorNote=null, correspAuthorsNote=
LYU Xihong, 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=1320438091798655812, articleId=1320438091190481728, tenantId=1146029695717560320, journalId=1319373219989389464, language=CN, title=急性呼吸道传染病预警综合指标体系构建：基于德尔菲专家咨询法, columnId=1320438091530220355, journalTitle=中国公共卫生, columnName=专题报道——公共卫生评价指标体系构建, runingTitle=null, highlight=null, articleAbstract=
目的

构建多源数据急性呼吸道传染病预警综合指标体系。

方法

使用德尔菲法进行专家咨询，邀请20名专家对不同预警指标的必要性、可获得性、敏感性进行评分，并依据评分结果确定权重。

结果

2轮专家咨询专家积极系数均为100%、专家权威系数为0.79，专家意见协调程度分别为0.280与0.487（P<0.05）。依据急性呼吸道传染病的发生、传播与流行各阶段特点，建立了急性呼吸道传染病风险预警指标体系和急性呼吸道传染病征兆预警指标体系，其中前者包含五类22项预警指标，后者包含六大类29项预警指标。急性呼吸道传染病风险预警指标体系中，权重最高的前3项指标依次为“人群疫苗接种率”（0.052）、“已知病原体的变异”（0.051）和“优势流行株改变”（0.051）；急性呼吸道传染病征兆预警指标体系中，权重最高的3项指标依次为“呼吸道症候群监测数据”（0.040）、“病原阳性率”（0.040）和“网络直报病例数”（0.040）。

结论

本研究依据德尔菲法建立的多源数据急性呼吸道传染病预警综合指标体系具有一定的科学性与实用性，为完善多点触发的传染病预警提供支撑。

, authors=李萌, 王超, 孔园园, 韩宁, 冯杰, 吕锡宏*, *, authorsList=李萌, 王超, 孔园园, 韩宁, 冯杰, 吕锡宏, authorCompany=null, correspAuthors=吕锡宏, authorNote=

李萌（1989—），主管医师，硕士，研究方向：急性传染病防制

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吕锡宏，E-mail：
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Early warning indicators and their weights for risk of acute respiratory infection

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一级指标二级指标必要性得分可获得性得分敏感性得分综合得分权重排序
气象与环境气温、湿度、风速、雨量、气压、辐射等3.7503.9303.1303.5910.04120
空气质量指数（PM2.5、PM10、CO、NO2、O3、SO2）3.1803.9902.6503.2090.03721
极端天气（如热浪/极端高温日、寒潮/极端低温日、
强降水与水浸、干旱、强风暴、强沙尘暴与空气污染等）		3.2303.6302.6003.1230.03622
病原及变异检出新病原（未知/已知的新亚型）4.9503.4804.5304.4420.0515
已知病原体的变异（传染力与传播途径、致病力、毒力、
免疫逃逸、耐药性改变等）		4.9003.5804.6404.4820.0512
优势流行株改变4.7304.0504.4804.4770.0513
实验室生物安全事故（高致病性病原微生物菌/毒株
或者样本被盗、被抢、丢失、泄漏时间等）		4.6303.2503.5003.9110.04512
是否有投入使用的特效药物3.8503.7503.4003.6770.04218
人群免疫与人口学特征健康人群隐性感染或病原携带水平4.5302.1804.1503.8140.04415
健康人群抗体水平4.5802.7504.2003.9950.04611
人群疫苗接种率4.7404.4504.4204.5620.0521
人口年龄分布、性别分布4.2004.2003.2003.8710.04414
人口密度4.3403.6803.5003.8980.04513
流动人口占比3.8503.3303.5303.6140.04119
国内外疫情形势国际传染病疫情趋势4.5804.1403.4804.1070.0479
检疫传染病/关注传染病输入情况4.6303.8004.1504.2630.0497
周边省市/地区传染病疫情趋势4.7403.6504.2004.2880.0496
WHO发布的传染病流行/暴发的通告等4.4804.7503.4504.2090.0488
其他因素大型集会/活动相关传染病疫情数据4.4003.1003.5803.8030.04316
大规模人口流动4.3002.9503.4503.6810.04217
污水监测阳性率及新变异株情况4.2703.7503.8804.0110.04610
疾病流行时间段（月份、年份）的到来4.6304.4204.3004.4690.0514
), ArticleFig(id=1320438096282366831, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1320438091190481728, language=CN, label=表1, caption=

急性呼吸道传染病风险预警指标体系及权重

, figureFileSmall=null, figureFileBig=null, tableContent=
一级指标二级指标必要性得分可获得性得分敏感性得分综合得分权重排序
气象与环境气温、湿度、风速、雨量、气压、辐射等3.7503.9303.1303.5910.04120
空气质量指数（PM2.5、PM10、CO、NO2、O3、SO2）3.1803.9902.6503.2090.03721
极端天气（如热浪/极端高温日、寒潮/极端低温日、
强降水与水浸、干旱、强风暴、强沙尘暴与空气污染等）		3.2303.6302.6003.1230.03622
病原及变异检出新病原（未知/已知的新亚型）4.9503.4804.5304.4420.0515
已知病原体的变异（传染力与传播途径、致病力、毒力、
免疫逃逸、耐药性改变等）		4.9003.5804.6404.4820.0512
优势流行株改变4.7304.0504.4804.4770.0513
实验室生物安全事故（高致病性病原微生物菌/毒株
或者样本被盗、被抢、丢失、泄漏时间等）		4.6303.2503.5003.9110.04512
是否有投入使用的特效药物3.8503.7503.4003.6770.04218
人群免疫与人口学特征健康人群隐性感染或病原携带水平4.5302.1804.1503.8140.04415
健康人群抗体水平4.5802.7504.2003.9950.04611
人群疫苗接种率4.7404.4504.4204.5620.0521
人口年龄分布、性别分布4.2004.2003.2003.8710.04414
人口密度4.3403.6803.5003.8980.04513
流动人口占比3.8503.3303.5303.6140.04119
国内外疫情形势国际传染病疫情趋势4.5804.1403.4804.1070.0479
检疫传染病/关注传染病输入情况4.6303.8004.1504.2630.0497
周边省市/地区传染病疫情趋势4.7403.6504.2004.2880.0496
WHO发布的传染病流行/暴发的通告等4.4804.7503.4504.2090.0488
其他因素大型集会/活动相关传染病疫情数据4.4003.1003.5803.8030.04316
大规模人口流动4.3002.9503.4503.6810.04217
污水监测阳性率及新变异株情况4.2703.7503.8804.0110.04610
疾病流行时间段（月份、年份）的到来4.6304.4204.3004.4690.0514
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Early warning indicators and their weights for potential clustering or outbreak of acute respiratory infection

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一级指标二级指标必要性得分可获得性得分敏感性得分综合得分权重排序
就医前自我处理特定药物销售（抗生素、止咳药、退热药、镇痛药、特效药等）4.4002.9803.8003.8450.03221
快速检测试剂销量3.7802.7803.3803.3970.02829
集体单位因病缺勤缺课情况4.4803.4803.9804.0640.03416
临床就诊与病原学监测发热门诊/急诊/儿科/呼吸内科就诊人数4.6804.8404.5504.6770.0396
发热门诊/急诊/儿科/呼吸内科患者特定症状人次数4.4503.9004.0604.1830.03511
ILI/SARI病例数与医疗机构就诊病例百分比4.8304.6304.5304.6810.0395
医疗机构临床诊断人数4.4303.8003.8804.0910.03415
临床检验结果4.2303.8503.9304.0360.03418
特定药物使用（抗生素、止咳药、退热药、镇痛药、特效药等）4.3203.6803.9804.0470.03417
实验室病原检出4.7004.0504.6004.5040.0388
住院监测与用药重点科室（呼吸内科、ICU、急诊、儿科）因呼吸道症状住院人数4.6904.1503.8804.2880.03610
呼吸道症状住院病例死亡情况4.3904.1303.7804.1240.03513
特定药物使用（抗生素、止咳药、退热药、镇痛药、特效药等）4.1803.5803.6303.8480.03220
传染病监测动态网络直报病例数4.8304.8804.5004.7340.0403
不明原因肺炎监测数据4.5804.4803.8004.2980.0369
呼吸道症候群监测数据4.8304.8804.6004.7670.0401
病原阳性率4.7804.8804.6004.7460.0402
苗子事件与突发公共卫生事件4.7304.7404.3004.5910.0387
聚集性/暴发疫情、群体性发热事件4.8504.6704.6304.7320.0404
重点单位/重点人群症状监测数据4.6803.3503.9504.1050.03414
基本再生数（R0）上升4.2803.2004.1003.9490.03319
舆情监测动态百度热搜（网络搜索）3.7604.0203.4803.7330.03123
官方新闻3.4304.3203.2003.5780.03026
自媒体、社交平台等3.7003.7003.6003.6670.03125
非药物干预措施实施暂停城市内公共交通3.0004.0303.6503.4730.02928
娱乐场所关闭与公共集会禁止3.2503.9203.6503.5500.03027
学校、托幼机构关闭3.5504.0803.7003.7330.03124
疫情信息快速公布3.7004.1503.6503.7970.03222
（疑似）病例严格管控与隔离治疗4.1404.2004.1704.1650.03512
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急性呼吸道传染病征兆预警指标体系及权重

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一级指标二级指标必要性得分可获得性得分敏感性得分综合得分权重排序
就医前自我处理特定药物销售（抗生素、止咳药、退热药、镇痛药、特效药等）4.4002.9803.8003.8450.03221
快速检测试剂销量3.7802.7803.3803.3970.02829
集体单位因病缺勤缺课情况4.4803.4803.9804.0640.03416
临床就诊与病原学监测发热门诊/急诊/儿科/呼吸内科就诊人数4.6804.8404.5504.6770.0396
发热门诊/急诊/儿科/呼吸内科患者特定症状人次数4.4503.9004.0604.1830.03511
ILI/SARI病例数与医疗机构就诊病例百分比4.8304.6304.5304.6810.0395
医疗机构临床诊断人数4.4303.8003.8804.0910.03415
临床检验结果4.2303.8503.9304.0360.03418
特定药物使用（抗生素、止咳药、退热药、镇痛药、特效药等）4.3203.6803.9804.0470.03417
实验室病原检出4.7004.0504.6004.5040.0388
住院监测与用药重点科室（呼吸内科、ICU、急诊、儿科）因呼吸道症状住院人数4.6904.1503.8804.2880.03610
呼吸道症状住院病例死亡情况4.3904.1303.7804.1240.03513
特定药物使用（抗生素、止咳药、退热药、镇痛药、特效药等）4.1803.5803.6303.8480.03220
传染病监测动态网络直报病例数4.8304.8804.5004.7340.0403
不明原因肺炎监测数据4.5804.4803.8004.2980.0369
呼吸道症候群监测数据4.8304.8804.6004.7670.0401
病原阳性率4.7804.8804.6004.7460.0402
苗子事件与突发公共卫生事件4.7304.7404.3004.5910.0387
聚集性/暴发疫情、群体性发热事件4.8504.6704.6304.7320.0404
重点单位/重点人群症状监测数据4.6803.3503.9504.1050.03414
基本再生数（R0）上升4.2803.2004.1003.9490.03319
舆情监测动态百度热搜（网络搜索）3.7604.0203.4803.7330.03123
官方新闻3.4304.3203.2003.5780.03026
自媒体、社交平台等3.7003.7003.6003.6670.03125
非药物干预措施实施暂停城市内公共交通3.0004.0303.6503.4730.02928
娱乐场所关闭与公共集会禁止3.2503.9203.6503.5500.03027
学校、托幼机构关闭3.5504.0803.7003.7330.03124
疫情信息快速公布3.7004.1503.6503.7970.03222
（疑似）病例严格管控与隔离治疗4.1404.2004.1704.1650.03512
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急性呼吸道传染病预警综合指标体系构建：基于德尔菲专家咨询法
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李萌 , 王超 , 孔园园 , 韩宁 , 冯杰 , 吕锡宏 *, *
中国公共卫生 | 专题报道——公共卫生评价指标体系构建 2026,42(4): 418-423
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中国公共卫生 |专题报道——公共卫生评价指标体系构建 2026 , 42 (4) : 418 -423
急性呼吸道传染病预警综合指标体系构建：基于德尔菲专家咨询法
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李萌, 王超, 孔园园, 韩宁, 冯杰, 吕锡宏*, *
作者信息
  • 上海市松江区疾病预防控制中心（上海市松江区卫生健康监督所）传染病防制科，上海 201620
通讯作者:
吕锡宏，E-mail：
作者简介:

李萌（1989—），主管医师，硕士，研究方向：急性传染病防制

Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy
Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU
Affiliations
  • Department of Infectious Disease Control and Prevention, Shanghai Songjiang District Center for Disease Control and Prevention (Shanghai Songjiang District Health Supervision Institute), Shanghai 201620, China
    • Corresponding Author:
    LYU Xihong, E-mail:
出版时间: 2026-04-10 doi: 10.11847/zgggws1148645
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摘要
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目的

构建多源数据急性呼吸道传染病预警综合指标体系。

方法

使用德尔菲法进行专家咨询，邀请20名专家对不同预警指标的必要性、可获得性、敏感性进行评分，并依据评分结果确定权重。

结果

2轮专家咨询专家积极系数均为100%、专家权威系数为0.79，专家意见协调程度分别为0.280与0.487（P<0.05）。依据急性呼吸道传染病的发生、传播与流行各阶段特点，建立了急性呼吸道传染病风险预警指标体系和急性呼吸道传染病征兆预警指标体系，其中前者包含五类22项预警指标，后者包含六大类29项预警指标。急性呼吸道传染病风险预警指标体系中，权重最高的前3项指标依次为“人群疫苗接种率”（0.052）、“已知病原体的变异”（0.051）和“优势流行株改变”（0.051）；急性呼吸道传染病征兆预警指标体系中，权重最高的3项指标依次为“呼吸道症候群监测数据”（0.040）、“病原阳性率”（0.040）和“网络直报病例数”（0.040）。

结论

本研究依据德尔菲法建立的多源数据急性呼吸道传染病预警综合指标体系具有一定的科学性与实用性，为完善多点触发的传染病预警提供支撑。

急性呼吸道传染病  /  预警  /  德尔菲法  /  指标体系
Objective

To establish comprehensive indicator systems for early warning of acute respiratory infection (ARI) based on multi-source data.

Methods

The Delphi method was adopted for expert consultation. Twenty experts were invited to score the necessity, availability, and sensitivity of different early warning indicators, and the weights were determined based on the scoring results.

Results

The two rounds of expert consultation showed the expert positive coefficients being both 100%, the coefficients of expert authority being both 0.79, and the coefficients of concordance being 0.280 and 0.487 (P < 0.05), respectively. According to the characteristics of ARI in the stages of occurrence, transmission, and epidemics, two indicator systems–the early warning indicator system for risk of ARI and the early warning indicator system for potential clustering or outbreak of ARI–were established. The former included 22 indicators in 5 categories, and the latter included 29 indicators in 6 categories. In the early warning indicator system for risk of ARI, the top 3 indicators with the highest weights were immunization coverage rate (0.052), variations of known pathogens (0.051), and variations of dominant prevalent strains (0.051). In the early warning indicator system for potential clustering or outbreak of ARI, the top 3 indicators with the highest weights were surveillance data of respiratory syndrome (0.040), positive rate of specific pathogen (0.040), and number of cases reported in online direct report system (0.040).

Conclusions

The comprehensive indicator systems for early warning of ARI based on multi-source data and Delphi method established in this study have certain scientificity and practicability, which can provide support for improving the multi-point triggered early warning of infectious diseases.

acute respiratory infection  /  early warning  /  Delphi method  /  indicator system
李萌, 王超, 孔园园, 韩宁, 冯杰, 吕锡宏. 急性呼吸道传染病预警综合指标体系构建：基于德尔菲专家咨询法. 中国公共卫生, 2026 , 42 (4) : 418 -423 . DOI: 10.11847/zgggws1148645
Meng LI, Chao WANG, Yuanyuan KONG, Ning HAN, Jie FENG, Xihong LYU. Development of indicator systems for early warning of acute respiratory infection: a Delphi stduy[J]. Chinese Journal of Public Health, 2026 , 42 (4) : 418 -423 . DOI: 10.11847/zgggws1148645
正文
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21世纪以来，新发传染病与再发传染病疫情不断出现，尤其新亚型流感病毒感染[1]、新型冠状病毒感染[2]等急性呼吸道传染病（acute respiratory infection, ARI）严重危害人类健康，阻碍社会发展与经济建设，乃至对国家安全产生严重影响。目前，我国传染病预警系统仍存在一定的局限性，尤以预警关口滞后、数据来源单一与预警技术薄弱等[3]为主要表现；另外，当前信息技术的发展与非医疗卫生机构监测数据来源的不断拓宽（如网络热搜、重点场所与人群监测、自发购药等[3–4]）也为传染病早期预警提供了新的可能性，但目前仍缺少兼备操作性、实用性的预警指标体系，严重制约了当前传染病防控工作的有效开展。为尽快建立一套急性呼吸道传染病预警指标体系，本研究于2025年1—6月，结合上海市松江区急性呼吸道传染病防控工作实际，从急性呼吸道传染病个体间传播与聚集性发病的不同时间节点入手，遴选20名专家，应用德尔菲法初步探索并筛选兼具科学性、可操作性、自适应性的预警指标，构建基于多源数据的急性呼吸道传染病预警综合指标体系，以期为探索、完善多点触发的传染病预警机制提供助力。
1　对象与方法
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1.1　专家遴选
从上海市/区级疾病预防控制中心、各级医疗机构、卫生行政、高校等传染病监测预警相关单位或部门进行专家遴选，工作/研究专业覆盖急性呼吸道传染病监测及防制、卫生应急管理、卫生事业管理、理论流行病学等多个领域。结合专家专业职称、研究方向、近年成果、工作经历与岗位等信息进行资格审核，向符合要求的专家发出咨询邀请并明确研究目的、咨询流程、时间节点等关键内容，最终遴选20名专家，遴选标准包括：（1）从事研究相关专业领域≥10年，并已取得一定的研究成果；（2）具有中级及以上技术职称；（3）对本研究领域有深入思考和研究的兴趣，能够完成本次咨询。
1.2　调查方法
1.2.1　指标体系初拟与指标条目池形成
2025年1月起，在传染病流行病学、公共卫生监测预警理论与传染病监测预警专家共识等基础上，拟构建包含“急性呼吸道传染病风险预警指标”与“急性呼吸道传染病征兆预警指标”两大分类的指标体系，并依据进行分级设置不同级别的指标。项目组成员自国家卫生健康委员会、国家疾病预防控制局、中国疾病预防控制中心等官方网站检索、收集传染病预防控制相关法律法规与急性呼吸道传染病监测工作方案、防控指南、专家共识等，并以“急性呼吸道传染病”“预警”“多源数据”“德尔菲法”“指标体系”等为关键词，在中国知网、万方医学网、Pubmed、Web of Science等国内外数据库检索急性呼吸道传染病监测及预警相关文献，提取、归纳备选预警指标；召开项目组专题会议研讨与专家咨询结果，初步形成预警指标条目池。
1.2.2　德尔菲法专家咨询
项目组于2025年4—6月分别进行了2轮德尔菲法专家咨询。通过电子邮件向专家发放、回收专家咨询表，第1轮咨询表全部回收后，计算各指标不同属性相应得分，整合专家意见。第2轮专家咨询在第1轮咨询结果的基础上，将第1轮各指标平均得分和建议反馈至专家，要求专家对调整后的指标进行第2轮评分与比较，并再次整合全部评分结果与建议。第1轮专家咨询表内容主要包括急性呼吸道传染病背景介绍、咨询表填写说明（德尔菲法主要特征与注意事项、咨询表回收方式等）、专家一般情况（性别、年龄、学历、职称、专业方向、工作单位、从事专业年限等）、指标属性评价（各属性内容说明及评分依据）等；第2轮专家咨询表内容主要包括第1轮专家函询调查结果摘要与意见归纳、填表说明、指标属性的再次评价等。结果判定：（1）专家积极系数（Cj）：即为专家应答率，代表专家对本研究的参与及配合程度，积极系数越高，则专家参与及配合程度越高，一般认为应答率≥ 70% 时，认为专家积极程度较高[5]。（2）专家权威系数（Cr）：为专家判断依据系数（Ca）与熟悉程度系数（Cs）的均值，其中专家判断依据系数（Ca）由指标评价中判断依据及其影响程度决定，熟悉程度系数（Cs）由专家对指标内容、定义与获得方法的熟悉程度决定[6]；当专家权威系数（Cr）≥0.7时，认为结果可接受[7]。（3）专家意见协调程度：即为专家意见的一致性，用Kendall′s W协调系数表示，其取值范围为（0，1），值越大代表意见趋于协调；当P<0.05时，认为协调系数有统计学意义[8]。
1.2.3　指标权重与指标筛选
依据指标各属性（必要性、可获得性、敏感性）之间的两两重要性对比情况，采用Satty标度法构建判断矩阵[9–10]，依据专家给出的重要性对比评分计算预警指标各属性的权重。本研究采用Likert 5点评分法对指标各属性进行评价。各轮专家咨询表回收后，计算各属性评分情况并使用“界值法”对指标进行筛选[11]，其中各属性评分均数、满分比的界值为“界值=均数–标准差”，均数、满分频率的得分>界值则指标入选；变异系数的界值为“界值=均值+标准差”，变异系数得分<界值，则指标入选。综合分值为各指标不同属性得分乘以对应属性权重之和，计算公式为：综合分值=必要性得分×必要性权重+可获得性评分×可获得性权重+敏感性得分×敏感性权重；以全部指标的综合分值合计为分母，以各指标的综合分值为分子，计算各指标得分在合计得分中所占比例即得出各指标权重，一类指标下全部二级指标的权重之和即为该一类指标的合计权重。
1.3　统计分析
使用Microsoft Excel 2019进行问卷录入与数据整理，并计算专家积极系数、判断依据系数、熟悉程度系数、专家权威系数与各指标属性评分均数、标准差、变异系数、满分比等；使用Yahhp 10.1进行层次分析，使用IBM SPSS Stastistics 25.0进行Kendall′s W协调指数的计算，检验水准α=0.05。
2　结　果
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2.1　专家基本情况
20名专家中男性11人，女性9人。平均年龄为（42±6）岁；专家职称以高级、副高级为主（共13名）。7名专家专业为急性传染病防制，6名专家专业为公共卫生，其余7名专家专业包括卫生管理、传染病临床诊疗、理论流行病学与实验室检验检测；平均工作年限为（17±7）年。
2.2　专家积极程度
2轮德尔菲专家咨询中，每轮函询调查的专家积极系数均为100%，专家对项目关注性、参与程度总体较高；第1轮专家咨询中共有9名专家提出31条建设性意见，第2轮专家咨询中共有7名专家提出17条建设性意见，合计收集意见48条。
2.3　专家权威程度及意见协调程度
依据第1轮专家咨询中专家对各预警指标的判断依据与熟悉程度自评打分，得出判断依据系数为0.77、熟悉程度系数为0.80，专家权威系数为0.79，表明本研究专家权威程度较高。第1、2轮专家咨询Kendall′s W协调系数依次为0.280（χ2=1 018.264，P<0.05）与0.487（χ2=1 518.042，P<0.05），均有统计学意义；2轮的协调程度均达到有效共识标准，且第2轮协调程度较第1轮有所提高。
2.4　指标筛选、增删与修改
所有预警指标的9个衡量尺度中，≥5个不符合要求的指标予以剔除；任意>3个不符合要求的指标，结合小组讨论成果予以保留或剔除。第1轮专家咨询后，结合专家评分与小组讨论结果，自“急性呼吸道传染病风险预警指标”原有的27项指标中剔除“自然灾害事件”“蚊蝇鼠蟑等病媒生物监测情况”“重点动物宿主病原携带率监测情况”“禽畜养殖、屠宰、加工、销售场所污染病原体检测情况”与“禽畜群体性发病/死亡”等5项指标，并将“极端天气”修改为“极端天气（如热浪/极端高温日、寒潮/极端低温日、强降水与水浸、干旱、强风暴、强沙尘暴与空气污染等）”，共保留22项指标；自“急性呼吸道传染病征兆预警指标”中原有的34项指标中剔除“个人防护耗材销量”“重要医疗器械使用情况”“12320热线等咨询、投诉”“出行禁令”等4项指标，对3项指标进行了修改，其中“药物销售”修改为“特定药物销售（抗生素、止咳药、退热药、镇痛药、特效药等）”，将“发热门诊/急诊/儿科/呼吸内科患者症状”修改为“发热门诊/急诊/儿科/呼吸内科患者特定症状人次数”，将“初步诊断数”修改为“医疗机构临床诊断数”，共保留30项指标。第2轮专家咨询后，“急性呼吸道传染病风险预警指标”22项指标无修改，“急性呼吸道传染病征兆预警指标”30项指标中剔除1项（“120急救记录”），共保留29项指标。
2.5　预警指标体系建立及其权重
2.5.1　各指标属性权重计算结果
依据第1轮专家咨询中预警指标各属性的两两对比评分，计算出各预警指标的必要性、可获得性、敏感性权重依次为0.44、0.22与0.34，其中必要性权重最高，可获得性权重最低。第2轮专家咨询中使用专家评分法再次确定不同属性权重，必要性、可获得性、敏感性权重依次为0.41、0.25与0.34。
2.5.2　急性呼吸道传染病风险预警指标体系及其权重（表1）
经过2轮专家咨询，构建了急性呼吸道传染病风险预警指标体系，共包含五类22项指标，权重最高的前3项指标依次为“人群疫苗接种率”（0.052）、“已知病原体的变异”（0.051）与“优势流行株改变”（0.051），均超过0.05；合计权重最高的种类指标为“人群免疫与人口学特征类”（0.271）与“病原及变异类”（0.240）。
2.5.3　急性呼吸道传染病征兆预警指标体系及其权重（表2）
经过2轮专家咨询，构建了急性呼吸道传染病征兆预警指标体系，共包含六类29项指标，权重最高的前3项指标依次为“呼吸道症候群监测数据”（0.040）、“病原阳性率”（0.040）与“网络直报病例数”（0.040）；合计权重最高的种类指标为“传染病监测动态类”（0.301）与“住院监测与用药类”（0.253）。
3　讨　论
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德尔菲专家咨询法是一种主观评价法。该方法基于专家在专业方面的经验与学识，收集、汇总相关领域专家的评价与意见、建议，并进行数次交流与反馈，据此对评价对象做出定性、定量相结合的评价方法[12]，专家数量以15～20人为宜[13]。本研究遴选了20名来自传染病防控一线工作的专家进行德尔菲法专家咨询，专家平均工作年限为17年，专业方向涉及传染病监测、临床诊疗、卫生管理、应急处置与理论研究等传染病防控全链条的各环节，为本次指标体系的构建奠定了坚实的基础。本研究中专家权威程度系数为0.79，且2轮调查中专家积极系数均为100%，表明专家积极性高、权威性好，咨询结果具有一定的代表性。
近年来，国内已陆续开展关于急性呼吸道传染病预警指标体系的相关研究，王瑶瑶等[14]立足于监测信息的4个主要来源（机构管理/业务、公众平台、医疗机构、监测/检测），结合重点人群及行为特征进行进一步的细化，建立了“急性呼吸道传染病监测数据源指标体系”；张颖、程潇等[15–16]依据病原流行特征及传染病发生发展过程，先后建立了“新型冠状病毒Omicron变异株预警指标体系”和“我国口岸城市呼吸道传染病多源数据预警综合指标体系”。上述指标体系数据来源于海关、公安、卫生、市场监管等部门，并为开展传染病预警工作提供了新思路。国外预警指标应用起步较早，欧盟的早期预警响应系统（EWRS）已实现了成员国/其他国家主管当局与国际性组织（如WHO）之间的信息交换，已成功用于发出预警信号，与协调应对传染病跨境疫情的处置[17]；美国疾病预防控制中心已将废水监测广泛用于提供疫情防控措施的指导（如增加检测及疫苗接种等）[18]，有效提升了传染病疫情高峰的早期识别。本研究侧重于探讨多源数据前提下的预警体系指标建立，覆盖了“传染病发生—传播—流行”的全链条；建立了“急性呼吸道传染病风险预警指标体系”和“急性呼吸道传染病征兆预警指标体系”，并依据预警信息来源不同进一步细分，分别对应“传播源头控制”和“疫情早期响应”两大实际防控工作需求。“急性呼吸道传染病风险预警指标”聚焦导致呼吸道传染病发生大规模传播的各种源头因素，共包含五类22项预警指标，涉及气象、未知的新病原检出或已知病原体变异、人群疫苗接种与免疫水平、人口基本属性与构成、人口流动与国内外传染病形势等多种因素；“急性呼吸道传染病征兆预警指标”聚焦呼吸道传染病传播的发生征兆与发生后的影响，共包含六大类29项预警指标，涉及就医前自我处理、就诊与住院、监测动态、舆情监测以及非药物干预措施推行等多种因素，指标涉及内容、信息来源与指标数量较以往研究得到了一定的拓展，实现了多源数据对预警的补充价值。
值得注意的是，本指标体系中出现部分重要指标存在必要性高而获得性较低的情况，如风险预警指标体系中的“健康人群隐性感染或病原携带水平”和“健康人群抗体水平”；究其原因，在于当前传染病监测工作主要围绕出现明显症状的病例开展，而针对隐性感染与健康人群的监测开展难度较大，对监测规模、目标人群选取与监测对象依从性等[19–20]有较高的要求。此外，征兆预警指标体系中的“特定药物销售（抗生素、止咳药、退热药、镇痛药、特效药等）”“重点单位/重点人群症状监测数据”等指标也存在上述情况，最主要的原因可能为获取与统计难度较大且多部门间定期协商体制与数据交换机制有待健全[16]。此外，征兆预警体系中“舆情监测”分类下相关指标权重较低，推测可能与舆情信息分析利用难度较大、舆情监测能力仍需提升[21]等因素有关。因此在未来的舆情监测工作中，仍需探索适宜本地区的舆情监测方法，提升舆情监测类指标在预警工作中的重要性。
本研究依据德尔菲专家咨询法，初步建立了基于多源数据的急性呼吸道传染病预警指标体系，并依据传染病发生、传播与流行的不同阶段与细分出两大类指标，为完善本地区传染病监测预警机制提供助力。本研究尚有不足之处，首先，目前常见急性呼吸道传染病的监测主要聚焦在流行性感冒与新冠病毒感染，而对非常规监测的病原体则了解甚少，本指标体系对于其他少见呼吸道传染病的敏感性、准确性有待确认；其次，本研究主要依据上海市松江区急性呼吸道传染病防控工作实际，如需应用于其他地区，需依据地区实际考虑各指标不同属性评分及权重的调整。在后续工作中，需要进一步拓展常规监测覆盖面，尤其可优先将本指标体系中同时具有高必要性与高可获得性的指标纳入监测实践；健全跨部门协作机制、加强信息交换与分析利用。依据拟监测传染病的流行特征，可自本指标体系中选取部分指标开展预警工作，进一步探讨不同预警指标组合下的灵敏度与准确度，为完善指标体系提供数据支持与决策依据。

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doi: 10.11847/zgggws1148645
  • 接收时间：2025-08-13
  • 首发时间：2026-10-23
  • 出版时间：2026-04-10
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  • 收稿日期：2025-08-13
  • 修回日期：2025-10-17
  • 录用日期：2026-01-26
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    上海市松江区疾病预防控制中心（上海市松江区卫生健康监督所）传染病防制科，上海 201620

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吕锡宏，E-mail：
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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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