| 应用log-binomial回归和logistic回归分析法定传染病报告质量影响因素 |
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| 引用本文： | 付志智,邓革红,蔡富文,宫晨,韩姗珊,周健宇,许意清. 应用log-binomial回归和logistic回归分析法定传染病报告质量影响因素[J]. 疾病监测, 2017, 32(9): 768-773. DOI: 10.3784/j.issn.1003-9961.2017.09.015 |
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| 作者姓名： | 付志智 邓革红 蔡富文 宫晨 韩姗珊 周健宇 许意清 |
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| 作者单位： | 1.广西壮族自治区疾病预防控制中心信息管理科, 广西 南宁 530028 |
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| 摘 要： | 目的 探讨广西壮族自治区（广西）医疗机构法定传染病报告质量影响因素，并比较log-binomial回归与logistic回归模型估计关联强度的差异。方法 采用多阶段分层抽样方法抽取广西县级以上医疗机构为调查对象，开展现场调查收集信息，在R v3.3.3中拟合log-binomial回归和logistic回归模型。结果 共抽查法定报告传染病2 458例，平均报告率为95.08%，及时报告率为97.74%，报告卡填写完整率为77.60%，准确率为61.24%，网络直报录入信息一致率为95.27%，身份证填报完整率为75.59%。多变量log-binomial回归分析结果表明认真开展自查工作（PR=1.03，95%CI:1.01~1.05）和按要求开展培训（PR=1.08，95%CI:1.02~1.15）能有效促进法定传染病报告率的提高；设置项目齐全的门诊日志（PR=1.21，95%CI:1.07~1.37）、认真开展自查工作（PR=1.09，95%CI:1.03~1.14）、建立奖惩制度（PR=2.03，95%CI:1.49~2.78）和按要求开展培训（PR=1.18，95%CI:1.02~1.37）均能有效促进报告卡完整率的提高。在定性判别影响因素对结局事件发生概率影响时，logistic回归和log-binomial回归结果基本一致，但结局发生频率和其在比较组间差值每增加1.00%，logistic回归估计值OR相较于PR分别增加高0.65%（95%CI:0.34%~0.95%）和1.31%（95%CI:0.20%~2.41%）。结论 广西县级以上医疗机构法定传染病报告质量仍有待提高，进一步改进院内自查方法，加强培训工作，落实奖惩制度，规范设置诊疗日志，加强医务人员传染病报告法律意识，是提高报告质量的重中之重。此外，log-binomial回归应被推广应用于横断面或队列研究中定量估计暴露与结局变量的关联强度。
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| 关 键 词： | 传染病 网络直报 log-binomial回归 logistic回归 |
| 收稿时间： | 2017-03-10 |
Analysis on factors influencing performance of notifiable communicable diseases reporting with log-binomial regression and logistic regression models |
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| FU Zhi-zhi,DENG Ge-hong,CAI Fu-wen,GONG Chen,HAN Shan-shan,ZHOU Jian-yu,XU Yi-qing. Analysis on factors influencing performance of notifiable communicable diseases reporting with log-binomial regression and logistic regression models[J]. Disease Surveillance, 2017, 32(9): 768-773. DOI: 10.3784/j.issn.1003-9961.2017.09.015 |
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| Authors: | FU Zhi-zhi DENG Ge-hong CAI Fu-wen GONG Chen HAN Shan-shan ZHOU Jian-yu XU Yi-qing |
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| Affiliation: | 1.Information Management Section of Guangxi Zhuang Autonomous Region Center for Disease Control and Prevention, Nanning 530028, Guangxi, China |
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| Abstract: | Objective To identify the factors influencing the performance of notifiable communicable diseases reporting in medical institutions in Guangxi and compare the differences in correlation strength estimated by logistic regression model and log-binomial regression model.Methods The field survey was conducted in medical institutions at county levels and above selected through multistage stratified sampling in Guangxi.Multivariable logistic regression analysis and log-binomial regression analysis were conducted with software R v3.3.3.Results A total of 2 458 cases of notifiable communicable diseases were surveyed,and the overall reporting rate was 95.08％.Among these cases,97.74％ were reported timely.And among all the reporting cards,77.60％ were filled completely,61.24％ were filled accurately,95.27％ were consistent with network reporting,and 75.59％ were with personnel identification numbers.Results of multivariate log-binomial regression analysis indicated that self-check (PR =1.03,95％ CI:1.01-1.05) and staff training (PR =1.08,95％ CI:1.02-1.15)could improve the disease reporting effectively.Besides,qualified outpatient recording (PR =1.21,95％ CI:1.07-1.37),selfcheck (PR =1.09,95％ CI:1.03-1.14),reward and punishment system establishment (PR =2.03,95％ CI:1.49-2.78) and staff training (PR =1.18,95％ CI:1.02-1.37) can improve the completeness of reporting cards.The multivariate logistic regression analysis had consistent results,while with the increase of 1.00％ in the frequency of study event and the absolute difference between groups,compared with PR,the bias of OR estimated by logistic regression would increase by 0.65％ (95％CI:0.34％-0.95％) and 1.31％ (95％ CI:0.20％-2.41％) respectively.Conclusion The performance of notifiable communicable diseases reporting needs to be improved in medical institutions at county level and above in Guangxi through self-check,staff training,reward and punishment,qualified outpatient recording.In addition,the application of logbinomial regression analysis can be promoted in the quantitative estimation of relationship between exposure and outcome in cross-section study or cohort study. |
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| Keywords: | Communicable diseases Network reporting Log-binomial regression Logistic regression |
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