Article(id=1321170138615894752, tenantId=1146029695717560320, journalId=1319373219989389464, issueId=1321170080579301694, articleNumber=null, orderNo=null, doi=10.11847/zgggws1145580, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720800000000, receivedDateStr=2024-07-13, revisedDate=1725206400000, revisedDateStr=2024-09-02, acceptedDate=1735488000000, acceptedDateStr=2024-12-30, onlineDate=1792921107199, onlineDateStr=2026-10-25, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921107199, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921107199, creator=13701087609, updateTime=1792921107199, updator=13701087609, issue=Issue{id=1321170080579301694, tenantId=1146029695717560320, journalId=1319373219989389464, year='2025', volume='41', issue='1', pageStart='1', pageEnd='128', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792921093363, creator='13701087609', updateTime=1792921093363, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=6, ext={EN=ArticleExt(id=1321170138808832737, articleId=1321170138615894752, tenantId=1146029695717560320, journalId=1319373219989389464, language=EN, title=Reliability and validity of a national intelligent infectious disease surveillance and early warning maturity questionnaire, columnId=1321169992624747130, journalTitle=Chinese Journal of Public Health, columnName=Special Report – Public Health Informatization Development and Artificial Intelligence, runingTitle=null, highlight=null, articleAbstract=
Objective

To evaluate the reliability and validity of a national questionnaire on the intelligent maturity of infectious disease surveillance and early warning.

Methods

A questionnaire was developed based on the intelligent maturity index system for infectious disease surveillance and early warning. Expert consultation was used to assess the content, items, and content validity of the questionnaire. A random sampling method was taken to select 700 county (district) CDCs in the Chinese mainland for a one-month questionnaire survey in mid-October 2023. Item analysis was performed with the critical ratio method. Reliability was evaluated using Cronbach′s α coefficient and Spearman-Brown coefficient. Validity was analyzed using confirmatory factor analysis and content validity index.

Results

In the item analysis, six items did not meet the standard: IT infrastructure construction, coverage of security measures such as cloud desktop and cloud terminal, adoption of national information standards for the informatization construction of infectious disease surveillance and early warning, ability to call national application programming interfaces (APIs) and use software development kits (SDKs) to achieve application integration, missed reporting rate of infectious diseases, and proportion of confirmed cases in the syndromic surveillance of infectious diseases. After comprehensive consideration, these items were retained. The reliability analysis showed that the total Cronbach′s α coefficient was 0.872. The Cronbach′s α coefficients for the three dimensions were 0.755 for intelligent strategy and foundation, 0.857 for intelligent business process and management, and 0.607 for intelligent effectiveness. The Spearman-Brown coefficient was 0.756. The validity analysis showed that the overall content validity index of the questionnaire was 0.985, and the content validity index of each item ranged from 0.820 to 1.000. The structural equation model constructed by confirmatory factor analysis fitted well after modification.

Conclusion

The national questionnaire on the intelligent maturity of infectious disease surveillance and early warning has good reliability and validity, and is suitable for evaluating the intelligent maturity of infectious disease surveillance and early warning on integrated regional platforms across provinces nationwide.

, authors=Qingliang LIU1, Qing GUO1, Gang LI2, Zixiong ZHAO1, Xuejie DU1, Jiaqi MA1, authorsList=Qingliang LIU, Qing GUO, Gang LI, Zixiong ZHAO, Xuejie DU, Jiaqi MA, authorCompany=null, correspAuthors=Jiaqi MA, authorNote=null, correspAuthorsNote=
MA Jiaqi, E-mail:
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目的

评价全国传染病监测预警智慧化成熟度调查问卷的信度与效度。

方法

根据传染病监测预警智慧化成熟度指标体系编制调查问卷,采用专家咨询的方式对问卷进行内容、条目以及内容效度等方面的评定。采用随机抽样的方法,于2023年10月中旬,抽取中国内地700家县(区)疾控中心开展为期1个月的问卷调查。利用临界比值法分析项目,以Cronbach′s α系数、Spearman-Brown系数评价信度,采用验证性因子分析和内容效度指数法分析效度。

结果

在项目分析中,IT基础设施建设情况、云桌面云终端等安全措施覆盖的用户范围、传染病监测预警的信息化建设采用国家信息标准情况、调用国家应用服务接口(API)使用开发工具包(SKD)实现应用集成的能力、传染病漏报率、传染病临床综合征监测中确诊病例比例6个条目未达标,综合考虑后予以保留。信度分析结果显示,总Cronbach′s α系数为0.872,3个维度的Cronbach′s α系数中智慧化战略与基础为0.755、业务流程与管理智慧化为0.857、智慧化成效为0.607,Spearman-Brown系数为0.756。分析效度显示,调查问卷总体内容效度指数为0.985,各条目内容效度指数0.820~1.000,验证性因子分析所构建的结构方程模型经修正后拟合良好。

结论

全国传染病监测预警智慧化成熟度调查问卷具有良好的信效度,适用于全国省统筹区域平台传染病监测预警智慧化成熟度评价的研究。

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刘庆亮(2000—),硕士在读,研究方向:传染病监测预警、疾控信息化建设

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马家奇,E-mail:
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(2022-01-21)[2024-07-15]. https://news.southcn.com/node_54a44f01a2/bd53d49099.shtml., articleTitle=null, refAbstract=null), Reference(id=1321170144357896989, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=5, rfOrder=4, authorNames=null, journalName=null, refType=null, unstructuredReference=重庆市人民政府办公厅. 重庆将建设传染病智慧化多点触发预警监测系统[EB/OL]. (2022-03-16)[2024-07-15]. https://wap.cq.gov.cn/zwgk/zfxxgkml/zdlyxxgk/shgysy/jbylws/202203/t20220316_10515606.html., articleTitle=null, refAbstract=null), Reference(id=1321170144429200158, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=6, rfOrder=5, authorNames=null, journalName=null, refType=null, unstructuredReference=樊锐, 赵自雄, 王松旺, 等. 传染病监测预警智慧化成熟度评估指标体系框架设计研究[J]. 中国数字医学, 2023, 18(5): 100-103,109., articleTitle=null, refAbstract=null), Reference(id=1321170144487920415, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=7, rfOrder=6, authorNames=null, journalName=null, refType=null, unstructuredReference=史静琤, 莫显昆, 孙振球. 量表编制中内容效度指数的应用[J]. 中南大学学报(医学版), 2012, 37(2): 152-155., articleTitle=null, refAbstract=null), Reference(id=1321170144567612192, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=8, rfOrder=7, authorNames=null, journalName=null, refType=null, unstructuredReference=王志燕, 高欢玲, 张红梅, 等. Kihon老年衰弱筛查量表的汉化及信效度检验[J]. 护理学杂志, 2021, 36(3): 10-13., articleTitle=null, refAbstract=null), Reference(id=1321170144697635617, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=9, rfOrder=8, authorNames=null, journalName=null, refType=null, unstructuredReference=刘芳, 谭春林, 柯雄, 等. 医学新技术应用中患者参与决策调查问卷的信度效度评价研究[J]. 中国医院, 2018, 22(10): 4-7., articleTitle=null, refAbstract=null), Reference(id=1321170145872040738, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=10, rfOrder=9, authorNames=null, journalName=null, refType=null, unstructuredReference=樊锐, 王松旺, 赵自雄, 等. 传染病监测预警智慧化成熟度分级评估模型构建与应用[J]. 中国数字医学, 2023, 18(10): 16-22., articleTitle=null, refAbstract=null), Reference(id=1321170145930760995, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=11, rfOrder=10, authorNames=null, journalName=null, refType=null, unstructuredReference=蒋小花, 沈卓之, 张楠楠, 等. 问卷的信度和效度分析[J]. 现代预防医学, 2010, 37(3): 429-431., articleTitle=null, refAbstract=null), Reference(id=1321170145993675556, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=12, rfOrder=11, authorNames=null, journalName=null, refType=null, unstructuredReference=马千惠, 郭爱敏, 赵小丹, 等. 慢性病患者社区护理需求调查表的研制及信度效度评价[J]. 中国护理管理, 2019, 19(7): 1029-1035., articleTitle=null, refAbstract=null), Reference(id=1321170146064978725, tenantId=1146029695717560320, 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Basic information of the experts by professional titles, work domain and years of service (years)

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项目类别人数构成比/%
职称正高级436.36
副高级654.55
中级19.09
工作领域传染病监测预警654.55
疾控信息化436.36
健康医疗大数据应用19.09
工作年限/年>20872.73
10~<20327.27
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专家基本信息

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项目类别人数构成比/%
职称正高级436.36
副高级654.55
中级19.09
工作领域传染病监测预警654.55
疾控信息化436.36
健康医疗大数据应用19.09
工作年限/年>20872.73
10~<20327.27
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Number and composition of county-level CDCs surveyed in each province and city

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省(自治区、直辖市)数量/家构成比/%
北京市10.1
天津市20.3
河北省375.3
山西省213.0
内蒙古自治区304.3
辽宁省152.1
吉林省142.0
黑龙江省334.7
上海市30.4
江苏省253.6
浙江省243.4
安徽省273.9
福建省253.6
江西省213.0
山东省395.6
河南省324.6
湖北省253.6
湖南省324.6
广东省304.3
广西壮族自治区243.4
海南省71.0
重庆市71.0
四川省355.0
贵州省223.1
云南省334.7
西藏自治区223.1
陕西省253.6
甘肃省213.0
青海省81.1
宁夏回族自治区60.9
新疆维吾尔自治区162.3
新疆生产建设兵团385.4
), ArticleFig(id=1321170143418372882, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, language=CN, label=表2, caption=

各省(自治区、直辖市)被调查县区级疾控中心数量及构成

, figureFileSmall=null, figureFileBig=null, tableContent=
省(自治区、直辖市)数量/家构成比/%
北京市10.1
天津市20.3
河北省375.3
山西省213.0
内蒙古自治区304.3
辽宁省152.1
吉林省142.0
黑龙江省334.7
上海市30.4
江苏省253.6
浙江省243.4
安徽省273.9
福建省253.6
江西省213.0
山东省395.6
河南省324.6
湖北省253.6
湖南省324.6
广东省304.3
广西壮族自治区243.4
海南省71.0
重庆市71.0
四川省355.0
贵州省223.1
云南省334.7
西藏自治区223.1
陕西省253.6
甘肃省213.0
青海省81.1
宁夏回族自治区60.9
新疆维吾尔自治区162.3
新疆生产建设兵团385.4
), ArticleFig(id=1321170143481287443, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, language=EN, label=Tab.3, caption=

Analysis of the questionnaire items

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维度条目临界比值法相关系数
t值df
智慧化战略与基础制定信息化规划7.021 a224.1270.348 a
制定信息化绩效管理方案8.924 a203.5040.385 a
IT基础设施建设情况2.589 b356.8240.133 a
主要负责人对信息化建设的重视程度8.884 a357.1120.361 a
办公自动化程度7.978 a259.2760.288 a
监测预警的数字化能力10.503 a249.0920.494 a
承担监测预警、信息化职能的机构设置7.183 a293.5610.330 a
跨机构间监测预警信息协同机制和运行9.983 a245.8270.462 a
信息化部署环境的集约化程度5.754 a351.0010.262 a
数据中心建设7.786 a192.2060.358 a
本地建设的疾控云是否能与国家级平台实现一体化集成3.457 a238.5670.223 a
是否实现与国家级平台无缝数据集成13.229 a288.9680.455 a
疾控虚拟专网、卫生专网、政务外网的多网融合应用8.617 a228.7480.419 a
云桌面、云终端等安全措施覆盖的用户范围2.212 b361.6590.091 b
传染病监测预警的信息化建设采用国家信息标准情况2.246 b349.4590.100 a
是否制定完善的数据治理操作规程8.950 a208.4730.436 a
监测预警业务管理类规范执行情况10.973 a357.6640.359 a
监测预警信息管理类规范执行情况11.074 a357.6700.360 a
调用国家应用服务接口(API),使用开发工具包(SKD)实现应用集成的能力2.778 a236.8440.195 a
是否制定数据交换接口的操作和技术规范9.934 a202.8710.467 a
网络安全等级保护测评情况11.234 a284.8910.408 a
数据安全措施落实情况11.713 a324.5300.413 a
个人隐私保护情况10.269 a328.9750.364 a
业务流程与管理智慧化临床综合征监测、传染病监测、流行病学调查、病原监测的数据关联融合情况11.853 a220.6470.471 a
传染病监测预警规则、模型、方法平台化的数据融合12.490 a218.5270.514 a
临床综合征信息采集的自动化12.378 a231.1370.517 a
跨机构间病原变异信息的互联互通9.990 a266.2230.442 a
医疗机构实时自动交换传染病个案信息至国家级平台的情况9.880 a200.5220.473 a
舆情识别情况4.939 a184.4730.343 a
基于平台分析模型的传染病实时分析研判11.032 a199.2340.498 a
多源数据监测预警防控效果的评估和自动生成报告14.177 a236.8860.539 a
预警模型应用情况11.146 a269.6670.462 a
基于平台自动配置预警信号分级分类并触发预警信号16.173 a297.1040.503 a
建立预警分级标准,平台根据预警阈值自动分类分级14.305 a258.5440.544 a
自动预警的准确性5.417 a316.1300.204 a
传染病监测预警信息反馈的对象范围16.839 a330.0320.525 a
实现不同对象不同流程的自动监控管理8.660 a189.3020.514 a
平台对不同对象灵活配置反馈内容并定向发送和接收7.362 a183.5590.486 a
传染病临床信息、流行病学调查、病原检测结果、病例随访信息间的实时交互12.402 a217.4950.584 a
省统筹区域平台与国家级平台监测信息实时同步14.508 a240.2550.546 a
基于平台实现各部门常态化信息相互通报13.056 a242.0220.520 a
智慧化成效传染病漏报率4.035 a288.7570.182 a
传染病临床综合征监测中确诊病例比例3.625 a331.4980.163 a
平台监测预警的及时性6.278 a336.6380.266 a
传染病监测信息分析频率6.786 a342.6350.251 a
传染病疫情分析结果反馈业务人员的及时性8.048 a340.1570.278 a
传染病监测预警平台获取多渠道病原检测时间<24 h的比例9.698 a325.7260.362 a
规定内实际采集或交换的数据占应采集或交换总数的比例13.553 a329.6260.427 a
信息平台功能使用的简单性5.600 a268.8030.260 a
平台编码、功能、流程、接口、统计图表定制的灵活性8.273 a265.6330.365 a
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调查问卷项目分析情况

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维度条目临界比值法相关系数
t值df
智慧化战略与基础制定信息化规划7.021 a224.1270.348 a
制定信息化绩效管理方案8.924 a203.5040.385 a
IT基础设施建设情况2.589 b356.8240.133 a
主要负责人对信息化建设的重视程度8.884 a357.1120.361 a
办公自动化程度7.978 a259.2760.288 a
监测预警的数字化能力10.503 a249.0920.494 a
承担监测预警、信息化职能的机构设置7.183 a293.5610.330 a
跨机构间监测预警信息协同机制和运行9.983 a245.8270.462 a
信息化部署环境的集约化程度5.754 a351.0010.262 a
数据中心建设7.786 a192.2060.358 a
本地建设的疾控云是否能与国家级平台实现一体化集成3.457 a238.5670.223 a
是否实现与国家级平台无缝数据集成13.229 a288.9680.455 a
疾控虚拟专网、卫生专网、政务外网的多网融合应用8.617 a228.7480.419 a
云桌面、云终端等安全措施覆盖的用户范围2.212 b361.6590.091 b
传染病监测预警的信息化建设采用国家信息标准情况2.246 b349.4590.100 a
是否制定完善的数据治理操作规程8.950 a208.4730.436 a
监测预警业务管理类规范执行情况10.973 a357.6640.359 a
监测预警信息管理类规范执行情况11.074 a357.6700.360 a
调用国家应用服务接口(API),使用开发工具包(SKD)实现应用集成的能力2.778 a236.8440.195 a
是否制定数据交换接口的操作和技术规范9.934 a202.8710.467 a
网络安全等级保护测评情况11.234 a284.8910.408 a
数据安全措施落实情况11.713 a324.5300.413 a
个人隐私保护情况10.269 a328.9750.364 a
业务流程与管理智慧化临床综合征监测、传染病监测、流行病学调查、病原监测的数据关联融合情况11.853 a220.6470.471 a
传染病监测预警规则、模型、方法平台化的数据融合12.490 a218.5270.514 a
临床综合征信息采集的自动化12.378 a231.1370.517 a
跨机构间病原变异信息的互联互通9.990 a266.2230.442 a
医疗机构实时自动交换传染病个案信息至国家级平台的情况9.880 a200.5220.473 a
舆情识别情况4.939 a184.4730.343 a
基于平台分析模型的传染病实时分析研判11.032 a199.2340.498 a
多源数据监测预警防控效果的评估和自动生成报告14.177 a236.8860.539 a
预警模型应用情况11.146 a269.6670.462 a
基于平台自动配置预警信号分级分类并触发预警信号16.173 a297.1040.503 a
建立预警分级标准,平台根据预警阈值自动分类分级14.305 a258.5440.544 a
自动预警的准确性5.417 a316.1300.204 a
传染病监测预警信息反馈的对象范围16.839 a330.0320.525 a
实现不同对象不同流程的自动监控管理8.660 a189.3020.514 a
平台对不同对象灵活配置反馈内容并定向发送和接收7.362 a183.5590.486 a
传染病临床信息、流行病学调查、病原检测结果、病例随访信息间的实时交互12.402 a217.4950.584 a
省统筹区域平台与国家级平台监测信息实时同步14.508 a240.2550.546 a
基于平台实现各部门常态化信息相互通报13.056 a242.0220.520 a
智慧化成效传染病漏报率4.035 a288.7570.182 a
传染病临床综合征监测中确诊病例比例3.625 a331.4980.163 a
平台监测预警的及时性6.278 a336.6380.266 a
传染病监测信息分析频率6.786 a342.6350.251 a
传染病疫情分析结果反馈业务人员的及时性8.048 a340.1570.278 a
传染病监测预警平台获取多渠道病原检测时间<24 h的比例9.698 a325.7260.362 a
规定内实际采集或交换的数据占应采集或交换总数的比例13.553 a329.6260.427 a
信息平台功能使用的简单性5.600 a268.8030.260 a
平台编码、功能、流程、接口、统计图表定制的灵活性8.273 a265.6330.365 a
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Structural equation model fitting indices

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项目RMAEACMIN/dfAGFIIFITLICFI
本模型0.0381.9870.8670.8500.8410.848
理想值<0.080<3.000>0.800>0.900>0.900>0.900
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结构方程拟合指标

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项目RMAEACMIN/dfAGFIIFITLICFI
本模型0.0381.9870.8670.8500.8410.848
理想值<0.080<3.000>0.800>0.900>0.900>0.900
), ArticleFig(id=1321170143795860247, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, language=EN, label=Tab.5, caption=

Cronbach′s α coefficients and correlation coefficients of the questionnaire

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维度Cronbach′s α系数维度间相关性
智慧化战略与基础业务流程与管理智慧化智慧化成效
智慧化战略与基础0.7551.000
业务流程与管理智慧化0.8570.611 a1.000
智慧化成效0.6070.286 a0.318 a1.000
), ArticleFig(id=1321170143858774808, tenantId=1146029695717560320, journalId=1319373219989389464, articleId=1321170138615894752, language=CN, label=表5, caption=

问卷的Cronbach′s α系数和相关系数

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维度Cronbach′s α系数维度间相关性
智慧化战略与基础业务流程与管理智慧化智慧化成效
智慧化战略与基础0.7551.000
业务流程与管理智慧化0.8570.611 a1.000
智慧化成效0.6070.286 a0.318 a1.000
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全国传染病监测预警智慧化成熟度调查问卷信效度分析
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刘庆亮 1 , 郭青 1 , 李刚 2 , 赵自雄 1 , 杜雪杰 1 , 马家奇 1, *, *
中国公共卫生 | 专题报道——公共卫生信息化建设与人工智能 2025,41(1): 1-6
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中国公共卫生 |专题报道——公共卫生信息化建设与人工智能 2025 , 41 (1) : 1 -6
全国传染病监测预警智慧化成熟度调查问卷信效度分析
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刘庆亮1, 郭青1, 李刚2, 赵自雄1, 杜雪杰1, 马家奇1, *, *
作者信息
  • 1中国疾病预防控制中心信息中心,传染病溯源预警与智能决策全国重点实验室 北京 102206
  • 2北京市疾病预防控制中心信息中心,北京 100013
通讯作者:
马家奇,E-mail:
作者简介:

刘庆亮(2000—),硕士在读,研究方向:传染病监测预警、疾控信息化建设

Reliability and validity of a national intelligent infectious disease surveillance and early warning maturity questionnaire
Qingliang LIU1, Qing GUO1, Gang LI2, Zixiong ZHAO1, Xuejie DU1, Jiaqi MA1
Affiliations
  • 1National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing 102206, China
  • 2Beijing Center for Disease Control and Prevention, Beijing 100013, China
  • Corresponding Author:
    MA Jiaqi, E-mail:
出版时间: 2025-01-10 doi: 10.11847/zgggws1145580
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目的

评价全国传染病监测预警智慧化成熟度调查问卷的信度与效度。

方法

根据传染病监测预警智慧化成熟度指标体系编制调查问卷,采用专家咨询的方式对问卷进行内容、条目以及内容效度等方面的评定。采用随机抽样的方法,于2023年10月中旬,抽取中国内地700家县(区)疾控中心开展为期1个月的问卷调查。利用临界比值法分析项目,以Cronbach′s α系数、Spearman-Brown系数评价信度,采用验证性因子分析和内容效度指数法分析效度。

结果

在项目分析中,IT基础设施建设情况、云桌面云终端等安全措施覆盖的用户范围、传染病监测预警的信息化建设采用国家信息标准情况、调用国家应用服务接口(API)使用开发工具包(SKD)实现应用集成的能力、传染病漏报率、传染病临床综合征监测中确诊病例比例6个条目未达标,综合考虑后予以保留。信度分析结果显示,总Cronbach′s α系数为0.872,3个维度的Cronbach′s α系数中智慧化战略与基础为0.755、业务流程与管理智慧化为0.857、智慧化成效为0.607,Spearman-Brown系数为0.756。分析效度显示,调查问卷总体内容效度指数为0.985,各条目内容效度指数0.820~1.000,验证性因子分析所构建的结构方程模型经修正后拟合良好。

结论

全国传染病监测预警智慧化成熟度调查问卷具有良好的信效度,适用于全国省统筹区域平台传染病监测预警智慧化成熟度评价的研究。

传染病监测预警  /  信度  /  效度  /  验证性因子分析
Objective

To evaluate the reliability and validity of a national questionnaire on the intelligent maturity of infectious disease surveillance and early warning.

Methods

A questionnaire was developed based on the intelligent maturity index system for infectious disease surveillance and early warning. Expert consultation was used to assess the content, items, and content validity of the questionnaire. A random sampling method was taken to select 700 county (district) CDCs in the Chinese mainland for a one-month questionnaire survey in mid-October 2023. Item analysis was performed with the critical ratio method. Reliability was evaluated using Cronbach′s α coefficient and Spearman-Brown coefficient. Validity was analyzed using confirmatory factor analysis and content validity index.

Results

In the item analysis, six items did not meet the standard: IT infrastructure construction, coverage of security measures such as cloud desktop and cloud terminal, adoption of national information standards for the informatization construction of infectious disease surveillance and early warning, ability to call national application programming interfaces (APIs) and use software development kits (SDKs) to achieve application integration, missed reporting rate of infectious diseases, and proportion of confirmed cases in the syndromic surveillance of infectious diseases. After comprehensive consideration, these items were retained. The reliability analysis showed that the total Cronbach′s α coefficient was 0.872. The Cronbach′s α coefficients for the three dimensions were 0.755 for intelligent strategy and foundation, 0.857 for intelligent business process and management, and 0.607 for intelligent effectiveness. The Spearman-Brown coefficient was 0.756. The validity analysis showed that the overall content validity index of the questionnaire was 0.985, and the content validity index of each item ranged from 0.820 to 1.000. The structural equation model constructed by confirmatory factor analysis fitted well after modification.

Conclusion

The national questionnaire on the intelligent maturity of infectious disease surveillance and early warning has good reliability and validity, and is suitable for evaluating the intelligent maturity of infectious disease surveillance and early warning on integrated regional platforms across provinces nationwide.

infectious disease surveillance and early warning  /  reliability  /  validity  /  confirmatory factor analysis
刘庆亮, 郭青, 李刚, 赵自雄, 杜雪杰, 马家奇. 全国传染病监测预警智慧化成熟度调查问卷信效度分析. 中国公共卫生, 2025 , 41 (1) : 1 -6 . DOI: 10.11847/zgggws1145580
Qingliang LIU, Qing GUO, Gang LI, Zixiong ZHAO, Xuejie DU, Jiaqi MA. Reliability and validity of a national intelligent infectious disease surveillance and early warning maturity questionnaire[J]. Chinese Journal of Public Health, 2025 , 41 (1) : 1 -6 . DOI: 10.11847/zgggws1145580
传染病监测预警是指通过长期、连续、系统地收集传染病在人群中的发生、发展、动态分布及其影响因素的数据资料,经过分析和解释之后,将相关信息呈递给上级机构,为其制定、调整评价传染病防治政策和措施提供依据,同时在传染病发生暴发和流行之前发出预警信号,提示暴发或流行的发生或范围扩大的可能性[1]。监测预警是有效防范和化解重大传染病风险的第一关口,强大的监测预警能力可以在源头上减少传染病的发生。2020年我国提出要增强早期监测预警能力,建立智慧化多点触发机制,健全多渠道监测预警机制[2]。新冠病毒感染疫情之后,省统筹传染病监测预警平台建设探索了传染病多渠道监测、多点触发智慧化预警工作,上海市依托其公共卫生综合监测预警信息平台进行了基于多元数据的多点触发智慧化预警[3];广东省建立了智慧化多点触发疾病防控监测预警系统,在监测预警的广度、智慧化程度、实战应用程度3个方面提升了监测预警的能力[4];重庆市提出建设传染病智慧化多点触发预警监测系统,融合多源数据线索,实现预警监测等等[5]。为科学合理地评估区域传染病智慧化监测预警平台的建设应用,有研究建立了传染病监测预警智慧化成熟度分级评估指标体系[6],该指标体系分为智慧化战略与基础、业务流程与管理智慧化、智慧化成效3个维度。本研究在已建立的指标体系基础上,于2023年10月中旬,采用问卷的形式对县(区)级疾控中心进行为期1个月的调查。本文分析了全国传染病监测预警智慧化成熟度调查问卷的信度与效度,判断是否适用于省统筹区域平台传染病监测智慧化成熟度的评价研究,以提高分析结果的准确性和可靠性。
本研究在问卷编制和内容效度分析阶段选取11名专家,包括省级疾控中心专家7位、地市级疾控中心专家2位、医院专家1位、医院管理中心专家1位。专家工作领域涉及传染病监测预警、疾控信息化和健康医疗大数据应用以及公共卫生管理专业。
采用随机抽样的方法,根据我国各省县(区)级疾控中心的数量,抽取700家县(区)级疾控中心进行调查,每家疾控中心填写一份调查问卷。调查问卷共发放700份,有效问卷700份、有效回收率为100%。
本研究严格遵循传染病监测预警智慧化成熟度指标体系[6],深入细致分析三级指标的含义,产生相关条目。采取专家咨询的方式评定调查问卷的内容、条目以及内容效度等。共进行了2轮专家咨询,第1轮咨询过程中,经讨论,调整了1个条目,将“云桌面、云终端建设和使用情况”调整为“云桌面、云终端等安全措施覆盖的用户范围”。第2轮咨询中,专家未提出调整的意见和建议,形成最终调查问卷。调查问卷分为3部分,分别为智慧化战略与基础维度,包括发展战略、资源保障和基础设施等;业务流程与管理智慧化维度,包括多源数据融合、主动疫情感知和动态风险评估等;智慧化成效维度,包括完整性、准确性、实效性和可用性等。
通过深入研究传染病监测预警智慧化成熟度指标体系,查阅相关文献形成,主要内容包括(1)专家基本信息,包括职称、工作领域、工作年限等。(2)传染病监测预警智慧化成熟度问卷和指标体系的相关性,采用4等级评分对关联程度进行评分,1=不相关,2=弱相关,3=较强相关,4=非常相关[7]。
第1轮专家咨询前,由研究小组以线上会议的形式向专家解读背景、目的、各指标和相关条目之间的关系,并现场进行了讨论和答疑。通过线上方式分别向专家发放调查问卷,并于3 d内完成调查内容的填写;咨询问卷回收后,审核问卷,确保准确无误。第2轮专家咨询前,针对第1轮专家评议结果较差的条目与专家一对一沟通,深入讨论交换意见,确保专家在准确理解指标体系的基础上,修改了问卷条目,第2轮咨询问卷均以网络问卷的形式分别发放给专家,要求专家在1周时间内填写完成。
以视频会议的形式,培训全国省、市、县(区)疾控中心调查问卷填写负责人,培训内容包括调查的目的、内容、意义、指标和条目的对应关系,问卷表述的含义等。由县(区)疾控中心信息部门或办公室牵头,组织涉及的信息部门、传染病监测业务部门的专家完成相关调研后填写上报。调查问卷填写后由本单位负责人审核,在问卷调查过程中,逐一核对已经确认提交的问卷,发现有漏填、错填,以及存在逻辑问题的情况,及时电话回访调查问卷填报人,确保问卷填写的正确性。
以Excel 2019软件录入核查,使用SPSS 25.0软件统计分析。以百分比表示计量资料。(1)项目分析。通过题总相关法分析各条目得分和问卷总分之间的相关性,相关系数小于0.2以及未达到显著水平的条目应予以删除[8]。采用极端值比较法将问卷总得分由高到低排序,以得分排序前27%为高分组,得分后27%为低分组进行独立样本t检验,保留差异有统计学意义的条目[9],检验水准α为0.05。(2)信度评价。采用Cronbach′s α系数评价问卷的一致性,以Spearman-Brown系数评价问卷的分半信度。(3)效度评价。内容效度:采用内容效度指数法,通过专家咨询的方式评价问卷的内容效度,评价指标采用条目水平I-CVI和总体水平S-CVI/Ave;结构效度:应用Amos 25软件建立结构方程模型,开展验证性因子分析,运用最大似然法评估,利用评估模型适配参数,评价问卷结构效度[8]。
按照国家卫生健康委员会发布的《2022中国卫生健康统计年鉴》中东、中、西部地区的划分标准,被调查的县(区)级疾控中心中,东部地区疾控中心208家(29.7%)、中部地区疾控中心为205家(29.3%)、西部地区疾控中心为287家(41.0%)。
按照问卷总分的排序分为高分组(得分前27%,189家)和低分组(得分后27%,189家),实行独立样本t检验。其中,IT基础设施建设情况、云桌面云终端等安全措施覆盖的用户范围、传染病监测预警的信息化建设采用国家信息标准情况、调用国家应用服务接口(API)使用开发工具包(SKD)实现应用集成的能力、传染病漏报率、传染病临床综合征监测中确诊病例比例6个条目相关系数小于0.2, IT基础设施建设情况、云桌面云终端等安全措施覆盖的用户范围、传染病监测预警的信息化建设采用国家信息标准情况、调用国家应用服务接口(API)使用开发工具包(SKD)实现应用集成的能力4个条目临界值(即t值)小于3,不满足要求。经过课题组讨论,决定保留这6个条目。
本研究各条目间KMO球形检验结果为0.859,差异具有统计学意义(χ2=8 583.596,P<0.01)。以智慧化战略与基础、业务流程与管理智慧化和智慧化成效为潜变量绘制路径图,初始拟合指数不是很理想,模型修正时在3个维度下分别增加2、5和1条残差相关路径后,模型拟合较为良好,拟合指标见表4。
采用专家咨询的方式评价问卷各条目与所测量内容的相关程度,问卷各条目内容效度(I-CVI)为0.820~1.000,3个维度的内容效度(CVI)智慧化战略与基础为0.980、业务流程与管理智慧化为0.980、智慧化成效为0.987;总体内容效度(S-CVI/Ave)为0.985。
在内部一致性检验中,3个维度Cronbach′s α系数:智慧化战略与基础为0.755、业务流程与管理智慧化为0.857、智慧化成效为0.607。整体问卷的Cronbach′s α系数为0.872。本研究将问卷分为奇数和偶数条目分别计算分半信度,结果显示Spearman-Brown系数为0.756。
习近平总书记在2020年6月2日专家学者座谈会上明确指出要“增强早期监测预警能力”“建立智慧化预警多点触发机制,健全多渠道监测预警机制”,为我国传染病监测预警工作提出了明确要求。目前,各省市都在加快推进省统筹区域传染病监测预警与应急指挥能力提升项目,项目涉及智慧化预警、舆情信息监测、辅助决策、大数据协同检测、智能流调等系统。本研究问卷能够评估各地省统筹区域平台的建设情况,以指导各地更好地开展省统筹区域平台建设,意义重大。调查问卷内容涵盖了区域传染病监测预警关键业务流程,包括支持与保障、数据采集与治理、疫情早期探测、风险评估、预警、信息反馈等[10],覆盖了疾控中心、医院等有关传染病监测部门,具有较高的综合性。此外,涉及较多的传染病监测预警和信息技术的融合应用,通过调查问卷,可以使基层机构明确传染病监测预警智慧化建设的发展方向。分析收集的调查问卷,能够对我国县(区)级疾控中心传染病监测预警的发展形成基础的认识,揭示其薄弱环节和需要改进的地方,指导资源的合理分配。此前,很少有研究对我国传染病监测预警智慧化程度进行深入了解,因此,本研究可为传染病监测预警智慧化的发展提供参考。
信度主要评价问卷的稳定性和一致性,常用的指标有内部一致性系数和分半信度等;效度主要评价问卷的准确度、有效性和正确性,常用的指标包括内容效度和结构效度等[11]。信度分析方面,一般认为Cronbach′s系数大于0.60即为良好[12],分半信度中Spearman-Brown系数大于0.70即为良好[13]。本问卷各维度的Cronbach′s系数在0.607~0.857,总体系数为0.872,Spearman-Brown系数为0.756。同时,取样数据来自全国范围,具有很好的代表性,可以认为问卷的内部一致性较为稳定。结构效度分析方面,因子分析中的KMO值越接近于1,说明变量的相关性越强、因子分析的效果越好[9]。本研究各条目间KMO球形检验结果为0.859,适合做因子分析。CMIN/df<3, 近似误差均方根(root-mean-square error of approximation,RMSEA)<0.080, 调整拟合优度指数(adjust goodness-of-fit index, AGFI)>0.800,增值适配指数(incremental fit index,IFI)、非规准适配指数(tucker-lewis index,TLI)、比较适配指数(comparative fit index,CFI)>0.900为模型拟合较好[14]。本研究中的IFI,TLI和CFI虽未达到标准,但已经接近最优适配标准,因而可以认为结构方程模型比较符合实际数据的现况。内容效度方面,通常认为各条目内容效度(I-CVI)≥0.78,总体内容效度指数(S-CVI)≥0.90,即为内容效度较好[15]。本研究各条目内容效度(I-CVI)为0.820~1.000,各维度内容效度为0.980~0.987,总体内容效度(S-CVI/Ave)为0.985。评议专家来自多家机构,如疾控中心、医院、医院管理部门,且有多位专家涉及多个专家领域,专家权威程度很高,因而内容效度符合要求。综上,本研究调查问卷具有良好的信度和效度。
本研究所依据的指标体系于2022年在河北、浙江、甘肃3个省份的12个试点县(区)开展指标体系的应用验证,结果显示,浙江省的传染病监测预警智慧化成熟度显著优于河北省和甘肃省,河北省和甘肃省的结果无明显差异。浙江省宁波市鄞州区的成熟度等级为4级,经分析临床综合征信息的自动化采集、智慧化的舆情监测、智慧化监测预警感知存在不足,与文献提到的鄞州区未来的工作重点是运用人工智能技术和大数据挖掘技术,探索用药数据、电子病历中特异性症状描述等更多类型数据的可用性一致[10]。该指标体系可以区分地区差异,用于评估传染病监测预警智慧化成熟度。同时本研究所编制的问卷在试点的基础上,经过多轮传染病监测、信息化领域专家的意见和建议进行修改和完善,选项梯度设置更为合理,能够评估我国传染病监测预警智慧化成熟度。
调查中IT基础设施建设,云桌面、云终端等安全措施覆盖的用户范围,传染病监测预警的信息化建设采用国家信息标准情况,调用国家应用服务接口(API),使用开发工具包(SKD)的应用集成4个条目的临界值不符合要求,因此问卷的高分组和低分组在这4个条目上差异无统计学意义。分析原因,主要为本研究在这4个条目的选项设置上,只设置了两个梯度,另一方面如采用云桌面和云终端等技术保障不同机构用户终端的安全性,省统筹区域平台通过调用接口的方式,使用国家级平台统一开发的工具包(SKD),实现国家和省两级平台间的信息协同等,从技术实现上有一定的前瞻性,全面实施需要具有一定的基础条件,导致填写问卷的机构选择的选项趋于一致。决断值较小,导致相关系数偏低,虽然在统计学方法上不满足要求,但这些条目与指标体系概念的对应明确,因而予以保留。
调查问卷中包含多道多选题,本单位传染病监测信息分析使用工具软件的能力(Excel,SPSS,Python,R,ArcGIS,SAS等)、疾控信息系统覆盖的传染病监测业务范围。指标类数据,如,传染病监测预警系统覆盖的医疗机构、疾控机构、病原监测机构的比例;医疗机构传染病报告率、病原检测率、流行病学调查覆盖率、随访到位率;专网覆盖区域医疗机构、疾控机构、病原检测机构数量等。然而,信效度分析一般只针对量表题,多选题和填空题无法进行信效度分析。因此,本研究在去除多选题和填空题之后,对剩余问题检验信效度。但是这些多选题和填空题对于指标的综合测算是不可或缺的,因而在实际调查中仍然包含这些问题。
当前社会信息技术日新月异,传染病监测预警的信息化技术在不断发展,预警数据多元化和集成化,预警模型逐渐引入人工智能、机器学习等智能化技术[16]。传染病监测预警的业务在不断变化,新发传染病和再发传染病频发,公众对传染病的关注度更高,要求政府和相关机构提供更全面的传染病监测预警服务等。因而本研究的问卷内容也应随着技术和业务的变化做出相应的动态调整,定期更新问卷内容,保持时效性和准确性,以便未来能更好地促进传染病监测预警智慧化建设。
综上所述,传染病监测预警智慧化成熟度调查问卷具有良好的信度和效度,该问卷从智慧化战略与基础、业务流程与管理智慧化、智慧化成效3个维度对区域传染病监测预警智慧化成熟度进行调查,适用于传染病监测预警智慧化成熟度的调查研究。

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doi: 10.11847/zgggws1145580
  • 接收时间:2024-07-13
  • 首发时间:2026-10-25
  • 出版时间:2025-01-10
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  • 收稿日期:2024-07-13
  • 修回日期:2024-09-02
  • 录用日期:2024-12-30
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    1中国疾病预防控制中心信息中心,传染病溯源预警与智能决策全国重点实验室 北京 102206
    2北京市疾病预防控制中心信息中心,北京 100013

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马家奇,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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