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Article(id=1322608013010129139, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202505262, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747670400000, receivedDateStr=2025-05-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263923169, onlineDateStr=2026-10-29, pubDate=1761321600000, pubDateStr=2025-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263923169, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263923169, creator=13701087609, updateTime=1793263923169, updator=13701087609, issue=Issue{id=1322607837742747790, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='20', pageStart='3649', pageEnd='3840', issueExtLink='null', onlineDate='null', pubDate='1761321600000', pubDateStr='2025-10-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263881383, creator='13701087609', updateTime=1793264238791, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609336933794638, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609336933794639, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322607837742747790, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3824, endPage=3829, ext={EN=ArticleExt(id=1322608013337284852, articleId=1322608013010129139, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Quality of notifiable infectious diseases reporting, Sichuan, 2023-2024, columnId=1228016571713520579, journalTitle=Modern Preventive Medicine, columnName=Health Supervison, runingTitle=null, highlight=null, articleAbstract=
Objective

To understand the quality of notifiable infectious diseases reporting in Sichuan Province from 2023 to 2024 and its influencing factors, and to provide reference basis for improving the reporting quality.

Methods

A total of 263 medical institutions were randomly selected by stratified sampling. Through on-site data review, case screening and spot checks, and verification of the reporting system, an investigation was conducted on the implementation of the management of notifiable infectious disease reporting in medical institutions, as well as the reporting rate of notifiable infectious diseases, the timely reporting rate, the completeness rate of filling, the accuracy rate of filling, and the consistency rate of online reporting information. Statistical analysis was conducted using R software 4.4.1. Logistic regression was used to analyze the influencing factors of report quality, and the TOPSIS method was adopted to evaluate the comprehensive filling quality of infectious disease reporting cards.

Results

From 2023 to 2024, a total of 263 medical institutions were investigated, and the reporting rate of notifiable infectious diseases was 98.69%. The reporting rate in public medical institutions was higher than that in private ones (χ2=4.49, P< 0.05). The reporting rates of public medical institutions at or above the prefectural-level, county-level, and township-level showed a decreasing trend (Z=-5.68, P<0.01). The reporting rate of admission and discharge registration was the highest, followed by the outpatient department, and the reporting rate of radiological imaging registration was the lowest. The central region had the highest reporting rate (99.73%), while the three-state region had the lowest (94.00%). The timely reporting rate of the infectious disease report card was 95.40%, the complete filling rate was 97.15%, the filling accuracy rate was 86.78%, and the consistency rate of online report information was 94.49%. The comprehensive filling quality of infectious disease report cards in public township-level medical institutions is the best, followed by public county-level, public prefectural-level and public provincial-level medical institutions in sequence. The comprehensive filling quality of private medical institutions is the worst. Setting and filling norms for outpatient logs and admission and discharge logs, as well as conducting self-inspection notifications and rewards and punishments in a standardized manner, could improve the quality of infectious disease reporting.

Conclusion

The quality of notifiable infectious diseases reporting in medical institutions in Sichuan Provincewas generally good, but there are still weak areas and links in the reporting management work. Attention should be paid to the underreporting of public low-level medical institutions and the quality problems of the reporting cards of private and public high-level medical institutions. The quality of filling in basic information such as outpatient and admission and discharge registration, self-examination and feedback of abnormal information in radiological imaging registration should be strengthened.

, authors=Ling LI, Fei DU, Yun-na ZHANG, Wei LI, Yao MA, Yan LIU, Zhi-jun YING, Qiang LV, authorsList=Ling LI, Fei DU, Yun-na ZHANG, Wei LI, Yao MA, Yan LIU, Zhi-jun YING, Qiang LV, authorCompany=null, correspAuthors=Qiang LV, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322608016478818553, articleId=1322608013010129139, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=四川省2023—2024年法定传染病报告质量调查分析, columnId=1228016571923235795, journalTitle=现代预防医学, columnName=卫生监督, runingTitle=null, highlight=null, articleAbstract=
目的

了解四川省2023—2024年法定传染病报告质量及其影响因素，为提高报告质量提供参考依据。

方法

采用分层随机抽样共抽取263家医疗机构，通过现场资料查看、病例筛选及抽查、报告系统核实等，对调查对象法定传染病报告管理工作开展情况和法定传染病报告率、及时报告率、填写完整率、填写准确率、网络报告信息一致率进行调查。利用R 4.4.1软件进行统计分析，logistic回归用于报告质量影响因素分析，采用TOPSIS法进行传染病报告卡综合填报质量评价。

结果

2023—2024年共调查263家医疗机构，法定传染病报告率98.69 %。公立医疗机构报告率高于民营（χ2=4.49，P<0.05）。公立地市级及以上、县区级、乡镇级医疗机构报告率呈逐级降低趋势（Z=-5.68，P<0.01）。出入院登记报告率最高，其次是门诊，放射影像登记报告率最低。中部地区报告率最高（99.73 %），三州地区最低（94.00 %）。传染病报告卡报告及时率为95.40 %，填写完整率为97.15 %，填写准确率为86.78 %，网络报告信息一致率为94.49 %。公立乡镇级医疗机构传染病报告卡综合填报质量最好，后续依次为公立县区级、公立地市级、公立省级医疗机构，民营医疗机构综合填报质量最差。门诊日志、出入院日志设置填写规范、规范开展自查通报与奖惩，可以提高传染病报告质量。

结论

四川省医疗机构法定传染病报告质量总体较好，但报告管理工作仍存在薄弱地区和环节。应关注公立低级别医疗机构漏报、民营和公立高级别医疗机构传染病报告卡报告质量问题，加强门诊、出入院登记等基础资料填写质量、自查和放射影像登记异常信息反馈。

, authors=李羚, 杜飞, 张云娜, 李蔚, 马瑶, 刘燕, 应智峻, 吕强, authorsList=李羚, 杜飞, 张云娜, 李蔚, 马瑶, 刘燕, 应智峻, 吕强, authorCompany=null, correspAuthors=吕强, authorNote=

李羚（1988—），女，硕士，副主任医师，研究方向：传染病疾病控制

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吕强，E-mail:
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李羚（1988—），女，硕士，副主任医师，研究方向：传染病疾病控制

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李羚（1988—），女，硕士，副主任医师，研究方向：传染病疾病控制

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(In Chinese), articleTitle=Analysis of the quality and influencing factors of statutory infectious disease reports based on automatic collection mode of regional information platform in Wenzhou City, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322608017183461626, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, xref=null, ext=[AuthorCompanyExt(id=1322608017191850235, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, companyId=1322608017183461626, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1322608017204433148, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, companyId=1322608017183461626, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=四川省疾病预防控制中心，四川　成都　610041)])], figs=[ArticleFig(id=1322608028503888180, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=EN, label=Table 1, caption=

Reporting Rates of notifiable infectious diseases from different sources in different types and levels of medical institutions

, figureFileSmall=null, figureFileBig=null, tableContent=
类型/级别合计门诊登记
实查登记数（例）网络报告数（例）报告率（%）实查登记数（例）网络报告数（例）报告率（%）
公立18 82418 58898.759 7359 60998.71
省级1 4931 47999.0667767098.97
地市级6 4156 35899.113 1643 13198.96
县区级9 7519 62498.705 0775 01598.78
乡镇级1 1651 12796.7481779397.06
民营2 1302 09198.171 2251 20698.45
合计20 95420 67998.6910 96010 81598.68
类型/级别住院登记放射影像登记
实查登记数（例）网络报告数（例）报告率（%）实查登记数（例）网络报告数（例）报告率（%）
公立7 3897 33299.231 7001 64796.88
省级65565299.5416115797.52
地市级2 5892 57899.5866264998.04
县区级3 8723 83398.9980277696.76
乡镇级27326998.53756586.67
民营74573498.5216015194.38
合计8 1348 06699.161 8601 79896.67
), ArticleFig(id=1322608028583579957, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=CN, label=表1, caption=

不同类型、级别医疗机构不同来源法定传染病报告率

, figureFileSmall=null, figureFileBig=null, tableContent=
类型/级别合计门诊登记
实查登记数（例）网络报告数（例）报告率（%）实查登记数（例）网络报告数（例）报告率（%）
公立18 82418 58898.759 7359 60998.71
省级1 4931 47999.0667767098.97
地市级6 4156 35899.113 1643 13198.96
县区级9 7519 62498.705 0775 01598.78
乡镇级1 1651 12796.7481779397.06
民营2 1302 09198.171 2251 20698.45
合计20 95420 67998.6910 96010 81598.68
类型/级别住院登记放射影像登记
实查登记数（例）网络报告数（例）报告率（%）实查登记数（例）网络报告数（例）报告率（%）
公立7 3897 33299.231 7001 64796.88
省级65565299.5416115797.52
地市级2 5892 57899.5866264998.04
县区级3 8723 83398.9980277696.76
乡镇级27326998.53756586.67
民营74573498.5216015194.38
合计8 1348 06699.161 8601 79896.67
), ArticleFig(id=1322608028642300214, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=EN, label=Table 2, caption=

Reporting rates of notifiable infectious diseases in different regions

, figureFileSmall=null, figureFileBig=null, tableContent=
地区类别实查登记数（例）网络报告数（例）报告率（%）
北部5 9055 84799.02
中部7 1137 09499.73
南部5 6865 62398.89
三州2 2502 11594.00
合计20 95420 67998.69
), ArticleFig(id=1322608028696826167, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=CN, label=表2, caption=

不同地区法定传染病报告率

, figureFileSmall=null, figureFileBig=null, tableContent=
地区类别实查登记数（例）网络报告数（例）报告率（%）
北部5 9055 84799.02
中部7 1137 09499.73
南部5 6865 62398.89
三州2 2502 11594.00
合计20 95420 67998.69
), ArticleFig(id=1322608028906541368, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=EN, label=Table 3, caption=

Ranking of the comprehensive filling quality of infectious disease report cards in various types and levels of medical institutions

, figureFileSmall=null, figureFileBig=null, tableContent=
类型/级别报告及时率（%）填写完整率（%）填写准确率（%）网络报告信息一致率（%）Ci排序
公立95.6397.1486.6695.46--
省级93.1895.9186.7393.640.314
地市级94.4497.2886.6095.800.433
县区级96.8397.4085.8095.750.442
乡镇级94.7796.4290.5794.490.781
民营94.2297.2087.3989.320.295
合计95.4097.1586.7894.49--
), ArticleFig(id=1322608028977844537, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=CN, label=表3, caption=

各类型、级别医疗机构传染病报告卡综合填报质量排序

, figureFileSmall=null, figureFileBig=null, tableContent=
类型/级别报告及时率（%）填写完整率（%）填写准确率（%）网络报告信息一致率（%）Ci排序
公立95.6397.1486.6695.46--
省级93.1895.9186.7393.640.314
地市级94.4497.2886.6095.800.433
县区级96.8397.4085.8095.750.442
乡镇级94.7796.4290.5794.490.781
民营94.2297.2087.3989.320.295
合计95.4097.1586.7894.49--
), ArticleFig(id=1322608029057536314, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=EN, label=Table 4, caption=

Comparison of relevant factors for grouping according to the reporting quality of notifiable infectious diseases

, figureFileSmall=null, figureFileBig=null, tableContent=
因素报告质量无问题（41家）报告质量有问题（195家）χ2值P值
门诊日志设置填写是否规范9.85<0.01
是1838
否23157
出入院日志设置填写是否规范11.35<0.01
是2770
否14125
检验填写反馈是否规范0.190.66
是36163
否532
影像填写反馈是否规范0.830.36
是37162
否433
制度是否健全规范2.060.15
是28156
否1339
是否规范开展自查通报与奖惩3.710.05
是34129
否766
培训是否规范开展0.950.33
是33171
否824
), ArticleFig(id=1322608029116256571, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=CN, label=表4, caption=

按法定传染病报告质量分组相关因素比较

, figureFileSmall=null, figureFileBig=null, tableContent=
因素报告质量无问题（41家）报告质量有问题（195家）χ2值P值
门诊日志设置填写是否规范9.85<0.01
是1838
否23157
出入院日志设置填写是否规范11.35<0.01
是2770
否14125
检验填写反馈是否规范0.190.66
是36163
否532
影像填写反馈是否规范0.830.36
是37162
否433
制度是否健全规范2.060.15
是28156
否1339
是否规范开展自查通报与奖惩3.710.05
是34129
否766
培训是否规范开展0.950.33
是33171
否824
), ArticleFig(id=1322608029187559740, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=EN, label=Table 5, caption=

Logistic regression analysis of influencing factors of reporting quality of notifiable infectious diseases

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因素β WaldP值OR值（95% CI）
门诊日志设置填写规范-0.8650.4124.4160.0360.421（0.188~0.943）
出入院日志设置填写规范-1.0390.4096.4520.0110.354（0.159~0.789）
检验填写反馈规范-0.2850.5630.2570.6120.752（0.249~2.265）
影像填写反馈规范-0.3850.5670.4300.5120.680（0.215~2.150）
制度健全规范0.8170.4263.6810.5502.263（0.983~5.212）
规范开展自查通报与奖惩-0.9690.4664.3290.0370.380（0.152~0.945）
规范开展培训0.5450.5031.1730.2791.725（0.643~4.626）
), ArticleFig(id=1322608029279834429, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608013010129139, language=CN, label=表5, caption=

法定传染病报告质量影响因素logistic回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β WaldP值OR值（95% CI）
门诊日志设置填写规范-0.8650.4124.4160.0360.421（0.188~0.943）
出入院日志设置填写规范-1.0390.4096.4520.0110.354（0.159~0.789）
检验填写反馈规范-0.2850.5630.2570.6120.752（0.249~2.265）
影像填写反馈规范-0.3850.5670.4300.5120.680（0.215~2.150）
制度健全规范0.8170.4263.6810.5502.263（0.983~5.212）
规范开展自查通报与奖惩-0.9690.4664.3290.0370.380（0.152~0.945）
规范开展培训0.5450.5031.1730.2791.725（0.643~4.626）
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四川省2023—2024年法定传染病报告质量调查分析
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李羚 , 杜飞 , 张云娜 , 李蔚 , 马瑶 , 刘燕 , 应智峻 , 吕强
现代预防医学 | 卫生监督 2025,52(20): 3824-3829
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现代预防医学 |卫生监督 2025 , 52 (20) : 3824 -3829
四川省2023—2024年法定传染病报告质量调查分析
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李羚, 杜飞, 张云娜, 李蔚, 马瑶, 刘燕, 应智峻, 吕强
作者信息
  • 四川省疾病预防控制中心，四川　成都　610041
通讯作者:
吕强，E-mail:
作者简介:

李羚（1988—），女，硕士，副主任医师，研究方向：传染病疾病控制

Quality of notifiable infectious diseases reporting, Sichuan, 2023-2024
Ling LI, Fei DU, Yun-na ZHANG, Wei LI, Yao MA, Yan LIU, Zhi-jun YING, Qiang LV
Affiliations
  • Sichuan Center for Disease Control and Prevention, Chengdu, Sichuan 610041, China
出版时间: 2025-10-25 doi: 10.20043/j.cnki.MPM.202505262
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摘要
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目的

了解四川省2023—2024年法定传染病报告质量及其影响因素，为提高报告质量提供参考依据。

方法

采用分层随机抽样共抽取263家医疗机构，通过现场资料查看、病例筛选及抽查、报告系统核实等，对调查对象法定传染病报告管理工作开展情况和法定传染病报告率、及时报告率、填写完整率、填写准确率、网络报告信息一致率进行调查。利用R 4.4.1软件进行统计分析，logistic回归用于报告质量影响因素分析，采用TOPSIS法进行传染病报告卡综合填报质量评价。

结果

2023—2024年共调查263家医疗机构，法定传染病报告率98.69 %。公立医疗机构报告率高于民营（χ2=4.49，P<0.05）。公立地市级及以上、县区级、乡镇级医疗机构报告率呈逐级降低趋势（Z=-5.68，P<0.01）。出入院登记报告率最高，其次是门诊，放射影像登记报告率最低。中部地区报告率最高（99.73 %），三州地区最低（94.00 %）。传染病报告卡报告及时率为95.40 %，填写完整率为97.15 %，填写准确率为86.78 %，网络报告信息一致率为94.49 %。公立乡镇级医疗机构传染病报告卡综合填报质量最好，后续依次为公立县区级、公立地市级、公立省级医疗机构，民营医疗机构综合填报质量最差。门诊日志、出入院日志设置填写规范、规范开展自查通报与奖惩，可以提高传染病报告质量。

结论

四川省医疗机构法定传染病报告质量总体较好，但报告管理工作仍存在薄弱地区和环节。应关注公立低级别医疗机构漏报、民营和公立高级别医疗机构传染病报告卡报告质量问题，加强门诊、出入院登记等基础资料填写质量、自查和放射影像登记异常信息反馈。

医疗机构  /  法定传染病  /  报告质量  /  影响因素
Objective

To understand the quality of notifiable infectious diseases reporting in Sichuan Province from 2023 to 2024 and its influencing factors, and to provide reference basis for improving the reporting quality.

Methods

A total of 263 medical institutions were randomly selected by stratified sampling. Through on-site data review, case screening and spot checks, and verification of the reporting system, an investigation was conducted on the implementation of the management of notifiable infectious disease reporting in medical institutions, as well as the reporting rate of notifiable infectious diseases, the timely reporting rate, the completeness rate of filling, the accuracy rate of filling, and the consistency rate of online reporting information. Statistical analysis was conducted using R software 4.4.1. Logistic regression was used to analyze the influencing factors of report quality, and the TOPSIS method was adopted to evaluate the comprehensive filling quality of infectious disease reporting cards.

Results

From 2023 to 2024, a total of 263 medical institutions were investigated, and the reporting rate of notifiable infectious diseases was 98.69%. The reporting rate in public medical institutions was higher than that in private ones (χ2=4.49, P< 0.05). The reporting rates of public medical institutions at or above the prefectural-level, county-level, and township-level showed a decreasing trend (Z=-5.68, P<0.01). The reporting rate of admission and discharge registration was the highest, followed by the outpatient department, and the reporting rate of radiological imaging registration was the lowest. The central region had the highest reporting rate (99.73%), while the three-state region had the lowest (94.00%). The timely reporting rate of the infectious disease report card was 95.40%, the complete filling rate was 97.15%, the filling accuracy rate was 86.78%, and the consistency rate of online report information was 94.49%. The comprehensive filling quality of infectious disease report cards in public township-level medical institutions is the best, followed by public county-level, public prefectural-level and public provincial-level medical institutions in sequence. The comprehensive filling quality of private medical institutions is the worst. Setting and filling norms for outpatient logs and admission and discharge logs, as well as conducting self-inspection notifications and rewards and punishments in a standardized manner, could improve the quality of infectious disease reporting.

Conclusion

The quality of notifiable infectious diseases reporting in medical institutions in Sichuan Provincewas generally good, but there are still weak areas and links in the reporting management work. Attention should be paid to the underreporting of public low-level medical institutions and the quality problems of the reporting cards of private and public high-level medical institutions. The quality of filling in basic information such as outpatient and admission and discharge registration, self-examination and feedback of abnormal information in radiological imaging registration should be strengthened.

Medicalinstitution  /  Notifiable infectious diseases  /  Report quality  /  Influencing factors
李羚, 杜飞, 张云娜, 李蔚, 马瑶, 刘燕, 应智峻, 吕强. 四川省2023—2024年法定传染病报告质量调查分析. 现代预防医学, 2025 , 52 (20) : 3824 -3829 . DOI: 10.20043/j.cnki.MPM.202505262
Ling LI, Fei DU, Yun-na ZHANG, Wei LI, Yao MA, Yan LIU, Zhi-jun YING, Qiang LV. Quality of notifiable infectious diseases reporting, Sichuan, 2023-2024[J]. Modern Preventive Medicine, 2025 , 52 (20) : 3824 -3829 . DOI: 10.20043/j.cnki.MPM.202505262
正文
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传染病监测报告是传染病预防和控制的重要基础。高质量的传染病信息报告对防控策略及措施的制定起着至关重要的作用[1]。医疗机构作为传染病主要的法定报告机构，承担着传染病的发现和报告职责[2]。我国传染病监测系统的建立，实现了传染病疫情信息实时动态监测[3]。对法定传染病报告质量进行评价分析可以及时掌握传染病报告中存在的问题，为优化监测工作提供有力抓手。本研究对2023—2024年四川省各级各类医疗机构法定传染病报告管理情况进行调查，报告质量及其影响因素进行综合分析和评价，发现存在的问题，为进一步规范管理工作、提高法定传染病报告质量提供参考依据。
1　材料与方法
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1.1　调查对象
利用分层随机抽样，全省21个市（州）每个市（州）医疗机构按公立和民营分别进行抽样，公立医疗机构抽取省级1家（无省级医疗机构则不抽取）、地市级1家、县区级、乡镇级医疗机构各1~2家，民营医疗机构抽取1~2家，作为调查对象。共抽取263家医疗机构。
1.2　调查内容与方法
调查方法主要为现场资料查看、病例筛选及抽查、报告系统核实等，对调查对象法定传染病报告管理工作开展情况和报告质量进行调查。包括门诊、出入院日志设置与填写、检验检测及影像部门登记与反馈、传染病报告相关制度及落实和培训情况。同时查阅前一年门诊和出入院日志初步诊断为法定传染病的病例信息和放射影像登记中记录的肺结核。1个季度抽取1个月，每年共抽查4个月的门诊和出入院日志，每月抽查30例，如果抽查月份病例不足30例，则该月病例全查，必要时扩大抽查月份。公立县级及以上医疗机构抽查不少于60例，乡级和民营医疗机构抽查不少于10例。抽查2个月的放射影像登记中为肺结核的病例，每月抽查10例，不足10例全查。通过《中国疾病预防控制信息系统》查看抽查病例报告情况并计算报告率。从抽查的病例中再随机抽取20例（公立县级及以上）/10例（公立乡级和民营）病例的卡片，进行报告及时率、填写完整率、填写准确率及网络报告信息一致率的评价。
为比较不同地区医疗机构报告率，将市（州）按地域划分为北、中、南和三州4个地区，其中巴中、达州、广安、南充、遂宁、广元6个市（州）为北部地区，成都、德阳、绵阳、眉山、雅安、资阳6个市（州）为中部地区，自贡、泸州、内江、宜宾、乐山、攀枝花6个市（州）为南部地区，三州为甘孜州、阿坝州、凉山州[4]。
1.3　统计分析
采用TOPSIS法对法定传染病报告卡综合填报质量进行评价。TOPSIS法是一种应用广泛的多属性最优方案选择方法[5]。通过此方法可以科学评价各对象的差异,更好的对数据进行分析利用。首先对原始数据矩阵进行归一化处理，得到最优和最劣方案，通过比较评价对象与最优值和最劣值距离，来获得其与最优值和最劣值的相对接近度，从而得到Ci值，Ci值越接近1，则其越接近最优标准，传染病报告卡综合填报质量越好[6]。
R 4.4.1软件用于统计分析，检验水准α=0.05。利用非条件logistic回归进行报告质量影响因素分析[4]。
2　结果
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2.1　报告率
2.1.1　不同类型、级别医疗机构报告率
2023—2024年共调查263家医疗机构，其中公立200家，民营63家。公立中包括省级医疗机构11家、市级44家、县级84家、乡镇级61家。共抽查法定传染病20 954例，报告20 679例，报告率98.69 %。公立报告率高于民营，差异有统计学意义（χ2=4.49，P<0.05）。公立中不同级别调查单位报告率差异有统计学意义（χ2=45.25，P<0.01），报告率在地市级及以上、县区级、乡镇级医疗机构中呈逐级降低趋势（Z=-5.68，P<0.01）。住院报告率最高，其次是门诊，放射影像登记报告率最低，报告率依次呈下降趋势（Z=-3.40，P<0.01），见表1。
2.1.2　不同地区医疗机构报告率
北部、中部、南部和三州四个地区报告率进行比较，不同地区医疗机构法定传染病报告率差异有统计学意义（χ2=448.54，P<0.01），其中中部地区报告率最高（99.73 %），三州地区最低（94.00 %），见表2。
2.2　报告及时率、填写完整率、填写准确率、网络报告信息一致率
从抽查的已报告病例中，随机抽取3 612张报告卡，查看纸质报告卡信息填写情况并与系统中的卡片信息进行对比，除填写准确率为86.78 %，其余3个指标均大于90 %。为比较各类型、级别医疗机构传染病报告卡综合填报质量，利用TOPSIS法对报告及时率、填写完整率、填写准确率、网络报告信息一致率四个指标进行综合评价，四个指标权重分别为0.26、0.21、0.36、0.17。结果显示，公立乡镇级医疗机构传染病报告卡综合填报质量最好，后续依次为公立县区级、公立地市级、公立省级医疗机构，民营医疗机构综合填报质量最差。见表3。
2.3　报告质量影响因素分析
传染病报告率、报告及时率、填卡完整率、填写准确率、网络报告信息一致率均为100%定义为报告质量无问题，上述指标任一项未达到100%者定义为报告质量有问题，按报告质量有无问题将医疗机构分为两组，对两组传染病报告管理相关因素进行对比分析。除外无传染病报告、无门诊、住院或检验影像的医疗机构，共236家医疗机构纳入分析。结果显示，门诊日志、出入院日志设置填写规范机构数占比分别为23.73 %和41.10 %，检验、影像填写反馈规范机构数均为84.32 %，制度健全规范、规范开展自查通报与奖惩、规范开展培训机构数占比分别为77.97 %、69.07 %，86.44 %。其中门诊日志、出入院日志设置填写规范机构占比在两组比较中差异有统计学意义，见表4。
2023—2024年法定传染病报告质量影响因素分析结果显示，法定传染病报告质量与门诊、住院日志设置填写情况、自查通报与奖惩开展情况相关。门诊日志设置填写规范（OR=0.421, 95 % CI=0.188~0.943）、住院日志设置填写规范（OR=0.354, 95 % CI=0.159~0.789）、规范开展自查通报与奖惩（OR=0.380, 95 % CI=0.152~0.945）是传染病报告质量的保护因素，可以提高传染病报告质量，见表5。
3　讨论
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研究结果显示，四川省2023—2024年报告率达到98.69 %，高于四川省2016年的94.28 %[4]，提示四川省通过加强法定传染病报告管理工作，报告质量有所提升，同时随着医疗机构信息化建设的不断推进，具有医院信息系统的医疗机构，实现了资料登记和报告的自动化，提高了报告的效率和质量[7-8]。与全国和其他省份比较，高于云南、山东、吉林[5,9-10]，但低于全国平均[11]。住院报告率最高，与四川省2016年调查结果、云南省一致[4,12]，放射影像登记报告率最低，尤其是乡镇级和民营医疗机构，提示要关注放射影像登记肺结核病例的报告，加强医疗机构检验检查相关科室传染病异常信息反馈工作，已实现自动抓取和预警的医疗机构要以杜绝漏报为目标，优化相关信息系统，同时加强临床医生传染病诊断报告相关培训。分地区看，中部片区医疗机构法定传染病报告率最高，三州片区最低，与2016年研究结果一致[4]，可能与中部片区经济较为发达、信息化和业务水平较高等有关[5]。
分析不同类型、级别医疗机构报告率，结果显示公立医疗机构法定传染病报告率高于民营，与安徽省研究结果一致[13]，公立中地市级及以上、县区级、乡镇级报告率呈逐级降低趋势，民营和乡镇级医疗机构因报卡量少，对传染病报告工作重视不够，诊断报告意识不强，人员流动较大，医生传染病报告管理专业知识掌握不到位，报告流程不熟悉，提示要加强民营和乡镇级医疗机构的传染病报告管理工作，增强报告意识、稳定报告队伍、加强传染病报告管理相关培训。2023—2024年四川省医疗机构传染病报告卡报告及时率为95.40 %，填写完整率97.15 %，填写准确率86.78 %，网络报告信息一致率94.49 %，除填写准确率外，其余指标均较2016年有所升高[4]，但仍有较大上升空间。TOPSIS法是一种常用的综合评价方法[5]，本研究首次利用TOPSIS法评价四川省传染病报告卡填报质量，研究结果显示，2023—2024年公立乡镇级医疗机构传染病报告卡综合填报质量最好，后续依次为公立县区级、公立地市级、公立省级医疗机构，呈现级别越低填报质量越好的趋势，可能与高级别医疗机构就诊量和报卡量大、病例来源和种类复杂有关[5,10]，提示报卡量较大的医疗机构，要加强自查力度，保障配备足够专职报告人员，优化医院电子门诊、住院等信息系统，使其满足传染病报告要求[14]。民营医疗机构综合填报质量仍是最差，提示应对民营医疗机构进行全面培训和指导。
四川省医疗机构传染病报告管理工作逐步规范，但仍存在薄弱环节，门诊日志、出入院日志设置填写规范机构数占比不足50 %，检验、影像填写反馈、制度建设、自查和培训开展也还要进一步优化加强。报告质量影响因素分析结果显示，门诊日志、住院日志设置填写规范、规范开展自查通报与奖惩的医疗机构，传染病报告质量更好，与其他研究结果一致[15]。门诊和出入院日志作为传染病报告基础采集资料，是决定报告质量的关键因素[15]。通过定期自查，可以及时发现本院传染病报告中存在的问题，有效提高传染病报告质量[16]。值得注意的是，检验、影像填写反馈、制度建设和培训等因素在报告质量两组比较中无明显差异，这些因素是否能真正有效评价医疗机构报告质量，是我们需要关注的问题，一方面因素评价大多为纸质资料查阅，可能无法全面真实反映实际工作质量，另一方面，信息化时代传染病报告方式和手段不断变化，如何科学评价报告质量是当前面临的重要问题[17]。
传染病监测报告是传染病防控的基础，医疗机构的报告质量能否真实反映发病水平是传染病防控成败的关键。随着医疗机构传染病报告管理工作信息化建设的不断加深，尤其是传染病智能监测预警前置软件的应用，可以从源头上杜绝漏报、迟报，提高完整性、准确性[18]。要更大程度上发挥传染病自动上报和前置机的效用，尤其对于公立高级别医疗机构，还要关注各系统、软件、平台传染病报告相关功能的完善[9,19]，同时防止过度依赖信息系统，依然要加强定期自查[6,20]，做好报告方式改变的过渡，助力提高医疗机构法定传染病报告质量。

文献
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2025年第52卷第20期
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doi: 10.20043/j.cnki.MPM.202505262
  • 接收时间：2025-05-20
  • 首发时间：2026-10-29
  • 出版时间：2025-10-25
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  • 收稿日期：2025-05-20
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    四川省疾病预防控制中心，四川　成都　610041

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吕强，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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