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中国防痨杂志 ›› 2019, Vol. 41 ›› Issue (8): 905-909.doi: 10.3969/j.issn.1000-6621.2019.08.019

• 论著 • 上一篇    下一篇

甘肃省天水市结核病分级诊疗和综合防治服务模式试点工作效果分析

李江红,张岚(),王军元,雷彩英,颜淑萍,刘晓兰,谈小燕,杨枢敏()   

  1. 甘肃省疾病预防控制中心结核病防制所(张岚、杨枢敏)
  • 收稿日期:2019-06-17 出版日期:2019-08-10 发布日期:2019-08-13
  • 通信作者: 张岚,杨枢敏 E-mail:459694599@qq.com;14405322791@qq.com

Analysis on the effect of the pilot of hierarchical diagnosis and treatment and comprehensive prevention and control service model of tuberculosis in Tianshui City, Gansu Province

Jiang-hong LI,Lan ZHANG(),Jun-yuan WANG,Cai-ying LEI,Shu-ping YAN,Xiao-lan LIU,Xiao-yan TAN,Shu-min YANG()   

  1. Department of Tuberculosis Prevention and Control, Tianshui Center for Disease Control and Prevention, Gansu Province, Tianshui 741000, China
  • Received:2019-06-17 Online:2019-08-10 Published:2019-08-13
  • Contact: Lan ZHANG,Shu-min YANG E-mail:459694599@qq.com;14405322791@qq.com

摘要:

目的 总结天水市结核病分级诊疗和综合防治服务模式试点县(区)工作经验,评估工作成效,为其在甘肃全省推广提供依据。方法 利用《中国疾病预防控制信息系统》的子系统《结核病管理信息系统》,统计天水市结核病分级诊疗和综合防治服务模式试点县(区)结核病患者发现、治疗、管理及耐药肺结核防治工作指标,比较试点前(2016年)和试点后(2018年)各指标完成情况。2016年5个试点县(区)报告肺结核患者537例,2018年报告肺结核患者799例。结果 2018年试点工作结束时,天水市建立了结核病分级诊疗体系,明确了市级和县(区)级定点医院和诊疗病种。2018年年底,试点县(区)肺结核可疑症状者和疑似患者的查痰率达到97.29%(4460/4584),肺结核患者病原学阳性率达到53.47%(347/649),基层医疗卫生机构肺结核患者管理率达到96.52%(748/775),肺结核患者治疗成功率达到94.53%(1623/1717),病原学阳性患者耐药性筛查率达到80.35%(229/285),耐药患者纳入治疗率达到81.25%(13/16),与基线指标[2016年分别为90.99%(7178/7889)、10.72%(185/1726)、89.80%(1550/1726)、90.03%(1554/1726)、52.31%(113/216)、30.00%(3/10)]相比,差异均有统计学意义(χ2值分别为184.67、495.85、32.32、24.35、44.58、4.84,P值分别为0.000、0.000、0.000、0.000、0.000、0.009)。 结论 天水市试点县(区)建立了结核病分级诊疗模式,完善了结核病防治保障政策,各项防治指标明显提升,为全面推广试点工作经验提供了初步的科学依据。

关键词: 结核, 医师诊疗模式, 试点项目, 评价研究, 小地区分析

Abstract:

Objective To summarize the experience of the pilot of hierarchical diagnosis and treatment and comprehensive prevention and control service model of tuberculosis and evaluate the effectiveness of the pilot in Tianshui, in order to provide evidence for the province’s promotion.Methods The data of the Tuberculosis Management Information System, a subsystem of the China Information System for Disease Control and Prevention were used to analyze the indicators of case detection, treatment, management and drug-resistant tuberculosis prevention and control of tuberculosis in the pilot counties (districts). The indicators were compared before (2016) and after the pilot (2018). Five pilot counties (districts) reported 537 cases of tuberculosis in 2016 and 799 cases in 2018.Results At the end of the pilot in 2018, a hierarchical diagnosis and treatment system was established in Tianshui, and designated hospitals and diseases were identified at prefecture and county (district) level. At the end of 2018, the sputum examination rate of patients with suspicious symptoms of tuberculosis and suspected patients in the pilot counties was 97.29% (4460/4584), and the pathogenic positive rate of tuberculosis patients was 53.47% (347/649), the management rate of tuberculosis patients in primary health care institutions was 96.52% (748/775), the treatment success rate of tuberculosis patients was 94.53% (1623/1717), the drug screening rate of pathogenic positive patients was 80.35% (229/285), and the treatment rate of patients with drug-resistant tuberculosis was 81.25% (13/16). Compared to baseline indicators (which were 90.99% (7178/7889), 10.72% (185/1726), 89.80% (1550/1726), 90.03% (1554/1726), 52.31% (113/216) and 30.00% (3/10) respectively), the differences were statistically significant (χ 2 values were 184.67, 495.85, 32.32, 24.35, 44.58, 4.84, P values were 0.000, 0.000, 0.000, 0.000, 0.000, 0.009). Conclusion The pilot counties (districts) in Tianshui have established a hierarchical diagnosis and treatment model for tuberculosis, improved the security policy of tuberculosis prevention and control, and significantly improved the prevention and control indicators, providing a preliminary scientific basis for the comprehensive promotion of pilot work experience.

Key words: Tuberculosis, Physician’s practice patterns;, Pilot projects, Evaluation studies, Small-area analysis