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中国防痨杂志 ›› 2021, Vol. 43 ›› Issue (5): 506-512.doi: 10.3969/j.issn.1000-6621.2021.05.017

• 论著 • 上一篇    下一篇

2014—2019年北京市通州区学校肺结核患者就诊和诊断延误影响因素分析

解艳涛*, 高汉青, 吴越, 王赛赛, 康万里, 刘洋()   

  1. 101149 首都医科大学附属北京胸科医院流行病学研究室(解艳涛、王赛赛、康万里、刘洋);北京市通州区疾病预防控制中心结核病防治所(高汉青、吴越)
  • 收稿日期:2021-03-17 出版日期:2021-05-10 发布日期:2021-04-30
  • 通信作者: 刘洋 E-mail:lygyl1973@126.com

Influencing factors of patient delay and diagnosis delay among tuberculosis patients in schools of Tongzhou District, Beijing,2014—2019

XIE Yan-tao*, GAO Han-qing, WU Yue, WANG Sai-sai, KANG Wan-li, LIU Yang()   

  1. *Epidemiology Research Office, Beijing Chest Hospital, Capital Medical University, Beijing 101149, China
  • Received:2021-03-17 Online:2021-05-10 Published:2021-04-30
  • Contact: LIU Yang E-mail:lygyl1973@126.com

摘要:

目的 探索北京市通州区学校肺结核患者就诊延误、诊断延误及其影响因素,为制定干预措施提供理论依据。方法 以2014—2019年北京市通州区辖区学校的152例初治肺结核患者为研究对象,采用回顾性研究方法收集《中国结核病管理信息系统》病案数据、北京市统一制定的学校肺结核患者的个案调查表数据和学校相关数据,采用单因素χ2检验及多因素logistic回归分析方法,分析学校肺结核患者的就诊延误(从出现症状至首次到医疗机构就诊的时间间隔超过14d)和诊断延误(从首次就诊至被确诊为肺结核的时间间隔超过14d)的影响因素。 结果 2014—2019年北京市通州区学校肺结核患者从出现症状到就诊的天数中位数(四分位数)为4.0(0.0,11.0)d,从初次就诊到确诊的天数中位数(四分位数)为10.5(6.0,19.0)d;就诊延误率为16.4%(25/152),诊断延误率为34.9%(53/152);多因素logistic回归分析结果显示,以执行晨午检为对照,未执行晨午检的学校肺结核患者就诊延误的风险高[OR(95%CI)=26.900(3.188~226.978)];以被动发现为对照,健康体检和密切接触者筛查发现的学校肺结核患者的就诊延误的风险低[OR(95%CI)=0.049(0.005~0.436)和OR(95%CI)=0.088(0.010~0.802)];相对于第四季度发病,第二季度和第三季度发病的学校肺结核患者的就诊延误的风险低[OR(95%CI)=0.089(0.020~0.391)和OR(95%CI)=0.169(0.036~0.801)];以首诊在定点医疗机构就诊为对照,首诊在非定点医疗机构就诊的学校肺结核患者诊断延误的风险高[OR(95%CI)=2.638(1.203~5.785)];以被动发现为对照,密切接触者筛查发现的学校肺结核患者的诊断延误风险低[OR(95%CI)=0.169(0.037~0.785)]。结论 北京市通州区学校肺结核患者的就诊延误、诊断延误的影响因素包括患者、医疗机构和学校方面的因素,对于上述延误的影响因素要加强干预,在提升非定点医疗机构结核病诊断水平和发现意识的同时,重点关注学校晨午检制度、定期健康体检制度等学校相关因素的干预,减少延误的发生。

关键词: 结核, 院校, 延误就诊, 延误诊断, 因素分析,统计学

Abstract:

Objective To explore the influencing factors of patient delay, diagnosis delay in pulmonary tuberculosis (PTB) patients in schools of Tongzhou District of Beijing, and to provide evidence for strengthening intervention measures. Methods One hundred fifty-two new PTB patients detected from 2014 to 2019 in schools of Tongzhou District of Beijing were selected as study subjects. Retrospective study method was used to collect the medical record data from the Chinese TB Management Information System, the case investigation data using uniform questionairre across Beijing and other school-related data.Chi-squared test and multivariate logistic regression were used to analyze the influencing factors of patient delay (from symptom onset to seeking health care >14 days), diagnosis delay(from seeking medical service to diagnosis >14 days) in those PTB patients. Results The median days (quartiles) from symptom onset to seeking health care was 4.0 (0.0,11.0) days while the median days (quartiles) from seeking medical service to diagnosis was 10.5 (6.0,19.0) days. The proportions of patient delay, diagnosis delay among all patients were 16.4% (25/152),34.9% (53/152), respectively. Multivariate logistic regression analysis showed that, the risk of patient delay for PTB patients in schools without morning and afternoon symptom examination was higher (OR (95%CI)=26.900 (3.188-226.978)) than those in schools with this practice; in contrast to passive detection, the risk of patient delay was lower among school PTB patients identified by health examination (OR (95%CI)=0.049 (0.005-0.436)) and by close contact screening (OR (95%CI)=0.088 (0.010-0.802)); patients whose symptoms onset in the second and third quarter of year had a lower risk of patient delay than that in the fourth quarter (OR (95%CI)=0.089 (0.020-0.391),OR (95%CI)=0.169 (0.036-0.801)); the risk of diagnosis delay was higher for patients who were firstly diagnosed in non-TB-designated medical institutions than in special TB medical institutions (OR (95%CI)=2.638(1.203-5.785)); compared with passive detection, the risk of diagnosis delay of patients detected by close contact screening was lower (OR (95%CI)=0.169(0.037-0.785)). Conclusion The influencing factors of patient delay, diagnosis delay of PTB patients in schools of Tongzhou District of Beijing were factors about patients, medical institutions and schools. Intervention should be strengthened targeting at those influencing factors. While improving the diagnosis ability and awareness of detecting TB in non-TB-designated medical institutions, we should focus on interventions for school related factors such as morning and afternoon symptom examination system and regular health examination system to reduce the chance of delay.

Key words: Tuberculosis, Schools, Patient delay, Delayed diagnosis, Factor analysis, statistics