中国全科医学 ›› 2024, Vol. 27 ›› Issue (20): 2505-2511.DOI: 10.12114/j.issn.1007-9572.2023.0614

• 论著·慢性病共病专题研究 • 上一篇    下一篇

老年共病患者就医延迟行为及其影响因素研究

王萧冉, 关新月, 张丹*()   

  1. 518055 广东省深圳市,清华大学医院管理研究院 清华大学深圳国际研究生院
  • 收稿日期:2023-10-10 修回日期:2023-12-20 出版日期:2024-07-15 发布日期:2024-04-08
  • 通讯作者: 张丹

  • 作者贡献:
    王萧冉、关新月、张丹提出主要研究目标,负责研究的构思与设计,研究的实施,撰写论文,问卷的发放与回收;王萧冉、关新月进行数据的收集与整理,统计学处理,图、表的绘制与展示;张丹进行论文的修订,负责文章的质量控制与审查,对文章整体负责,监督管理。
  • 基金资助:
    国家自然科学基金资助项目(72004112)

Patient Delay and Associated Factors in Older Adults with Multimorbidity

WANG Xiaoran, GUAN Xinyue, ZHANG Dan*()   

  1. Institute for Hospital Management of Tsinghua University/Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
  • Received:2023-10-10 Revised:2023-12-20 Published:2024-07-15 Online:2024-04-08
  • Contact: ZHANG Dan

摘要: 背景 随着我国人口老龄化的加剧,居民疾病谱发生变化,多种慢性病共存成为我国老年群体健康状况的常态。就医延迟是指个体在身体不适后由于各种主观或客观原因未能及时就医的行为,导致治疗效果下降、患者生存质量降低。目前,国内针对老年慢性病共病患者就医延迟行为及其影响因素的研究较少。目的 探讨老年慢性病共病患者就医延迟行为及其影响因素,为进一步改善老年共病患者就医延迟行为提供参考。方法 采用多阶段分层整群随机抽样法,选取2022年9—12月于广东省27个社区卫生服务中心就诊的符合条件的老年共病患者作为研究对象。采用自行设计的调查问卷收集患者的一般资料、疾病相关资料和就医延迟情况。采用多因素Logistic回归分析和基于CHAID算法的决策树模型分析老年共病患者就医延迟行为的影响因素。结果 共纳入研究对象998例,其中出现就医延迟行为243例(24.35%)。多因素Logistic回归分析结果显示,性别(女性:OR=0.701,95%CI=0.504~0.977,P=0.036)、户籍类型(农村:OR=0.590,95%CI=0.358~0.973,P=0.039)、医疗保险类型(城乡居民医疗保险:OR=2.660,95%CI=1.764~4.010,P<0.001)、疾病相关自我效能(低:OR=4.378,95%CI=2.079~9.217,P<0.001)、是否签约家庭医生(否:OR=2.277,95%CI=1.618~3.206,P<0.001)、自评健康状况(一般:OR=1.554,95%CI=1.073~2.250,P=0.020)是老年共病患者就医延迟行为的影响因素。决策树模型共3层,13个节点,共筛选出医疗保险类型、是否签约家庭医生、性别、自评健康状况、年龄5个影响因素。两种模型预测老年共病患者就医延迟行为的结果显示,多因素Logistic回归模型的受试者工作特征曲线下面积(AUC)为0.729,决策树模型的AUC为0.721。两种模型对老年共病患者就医延迟行为的预测效果的AUC比较,差异无统计学意义(Z=0.539,P=0.590)。结论 广东省老年共病患者就医延迟行为发生率为24.35%,医疗保险类型、家庭医生签约率、性别与疾病自评健康状况是老年共患者发生就医延迟行为的主要影响因素。应进一步完善医疗保障制度,提高家庭医生签约率与利用率,进而降低就医延迟行为发生率。

关键词: 慢性病共病, 就医延迟, 老年人, 广东省, Logistic回归分析, 决策树模型

Abstract:

Background

With the aggravation of population aging in China, the disease spectrum of the population has changed and the coexistence of multiple chronic diseases has become the norm for the health status of the older population in China. Patient delay refers to the behaviour of an individual who fails to seek medical care in a timely manner after becoming unwell for a variety of subjective or objective reasons, resulting in a decrease in the treatment effectiveness and a decrease in the quality of the patient's survival. At present, there are few researches on patient delay and the associated factors for elderly adults with multimorbidity in China.

Objective

To explore the patient delay and the associated factors for older adults with multimorbidity, so as to provide references to further reduce the incidence of patient delay.

Methods

Eligible elderly patients attending 27 community health centers in Guangdong Province from September to December 2022 were selected for the study using multi-stage stratified whole cluster random sampling method. A self-designed questionnaire was used to collect patients' general information, disease-related information and delays in seeking medical care. Multivariate Logistic regression analysis and a decision tree model based on the CHAID algorithm were used to analyse the influencing factors of patient delay in older adults with multimorbidity.

Results

A total of 998 patients were included in the study, of which 243 (24.35%) showed delays in seeking medical care. The multivariate Logistic regression results showed that gender (OR=0.701, 95%CI=0.504-0.977, P=0.036), type of household registration (OR=0.590, 95%CI=0.358-0.973, P=0.039), type of health insurance (OR=2.660, 95%CI=1.764-4.010, P<0.001), disease-related self-efficacy (OR=4.378, 95%CI=2.079-9.217, P<0.001), family doctor contract (OR=2.277, 95%CI=1.618-3.206, P<0.001) and self-reported health (OR=1.554, 95%CI=1.073-2.250, P=0.020) were the main factors influencing patient delay in older adults with multimorbidity (P<0.05). The decision tree model has 3 levels and 13 nodes, and a total of 5 influencing factors were screened, including type of health insurance, family doctor contract, gender, self-reported health and age. The results of the two models for predicting patient delay in older adults with multimorbidity showed that the area under receiver operating characteristic curve (AUC) was 0.729 for the multivariate Logistic regression model and 0.721 for the decision tree model. There was no significant difference in AUC between the two models for predicting patients delay in elderly patients with multimorbidity (Z=0.539, P=0.590) .

Conclusion

The incidence of patient delay in older adults with multimorbidity is 24.35% in Guangdong province, and the type of health insurance, the contracting rate of family doctors, gender, and self-reported health status are the main factors influencing patient delay in older adults with multimorbidity. The medical insurance system should be further improved to increase the contracting rate and utilization rate of family doctors in order to reduce the incidence of patient delay.

Key words: Multimorbidity, Patient delay, Aged, Guangdong province, Logistic regression analysis, Decision tree model