结核与肺部疾病杂志 ›› 2024, Vol. 5 ›› Issue (1): 58-63.doi: 10.19983/j.issn.2096-8493.2024010

• 论著 • 上一篇    下一篇

2016—2022年北京市通州区老年肺结核患者就诊延迟情况及影响因素分析

杨超, 王晶(), 唐桂林, 高汉青, 王斌   

  1. 北京市通州区疾病预防控制中心结核病防治所,北京 101100
  • 收稿日期:2023-12-09 出版日期:2024-02-20 发布日期:2024-02-02
  • 通信作者: 王晶,Email:tzjfs2008yangchao@126.com

Analysis of patient delay and its influencing factors among elderly pulmonary tuberculosis patients in Tongzhou District of Beijing, 2016—2022

Yang Chao, Wang Jing(), Tang Guilin, Gao Hanqing, Wang Bin   

  1. nstitute of Tuberculosis Prevention and Control, Tongzhou District Center for Disease Prevention and Control, Beijing 101100, China
  • Received:2023-12-09 Online:2024-02-20 Published:2024-02-02
  • Contact: Wang Jing,Email:tzjfs2008yangchao@126.com

摘要:

目的: 分析2016—2022年北京市通州区老年人群(≥60岁)肺结核就诊延迟现状及影响因素,为今后有效减少老年肺结核患者就诊延迟提供科学依据。方法: 从“中国疾病预防控制信息系统”的子系统“结核病信息管理系统”收集北京市通州区2016—2022年老年肺结核患者(963例)的基本信息,包括病案信息和诊疗情况等,运用描述性统计方法分析患者就诊延迟分布情况及变化趋势。采用多因素logistic回归模型分析患者就诊延迟的影响因素。结果: 2016—2022年北京市通州区老年肺结核患者就诊延迟中位数(四分位数)为5(0,32)d,平均就诊延迟率为40.08%(386/963)。就诊延迟中位数由2016年的12(0,37)d下降至2022年的0(0,24)d。就诊延迟率由2016年的34.39%(54/157)上升至2019年的50.42%(60/119),差异有统计学意义( χ 2=7.605,P=0.006),再下降至2022年的30.99%(44/142),差异有统计学意义( χ 2=7.138,P=0.008),呈先升后降的趋势。多因素logistic回归分析显示,登记年度在2020—2022年为就诊延迟的保护因素(OR=0.667,95%CI:0.508~0.876),非城区居住为就诊延迟的危险因素(OR=1.368,95%CI:1.037~1.804)。结论: 2016—2022年北京市通州区老年肺结核患者就诊延迟呈先升后降的趋势,就诊延迟与患者的登记年度和现住址有关,应针对其实施有针对性的干预策略。

关键词: 结核,肺, 因素分析, 北京市

Abstract:

Objective: To analyze the current situation and influencing factors of patient delay among elderly (≥60 years old) pulmonary tuberculosis (PTB) in Tongzhou District of Beijing from 2016 to 2022, and to provide scientific basis for reducing the patient delay in the future. Methods: Data of 963 cases of PTB patients in the elderly people in Tongzhou District of Beijing from 2016 to 2022 were extracted from the Tuberculosis Management Information System of China Information System for Disease Control and Prevention, including medical records, diagnosis and treatment information, and so on. Descriptive statistics was used to analyze the distribution and trend of patient delay. Influencing factors of patient delay was analyzed by multivariable logistic regression. Results: From 2016 to 2022, the median and quartile of patient delay days was 5 (0,32) days. The average patient delay rate was 40.08% (386/963). The median of patient delay days went down from 12 (0,37) days in 2016 to 0 (0,24) days in 2022. The patient delay rate changed from 34.39% (54/157) in 2016 to 50.42% (60/119) in 2019, showing a upward trend ( χ t r e n d 2=7.605,P=0.006), and then decreased to 30.99% (44/142) in 2022, showing a downward trend ( χ t r e n d 2=7.138,P=0.008). Multivariable logistic regression analysis showed that patient registered during 2020—2022 (OR=0.667,95%CI:0.508-0.876) was the protective factor for patient delay. Non-urban area (OR=1.368,95%CI:1.037-1.804) was the risk factor for patient delay. Conclusion: The patient delay showed an upward-then-downward trend from 2016 to 2022. The patient delay was related to year of registration and current address. It is necessary to perform targeted intervention measures.

Key words: Tuberculosis, pulmonary, Factor analysis, Beijing

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