中国全科医学 ›› 2024, Vol. 27 ›› Issue (02): 208-216.DOI: 10.12114/j.issn.1007-9572.2023.0396

所属专题: 共病最新文章合集

• 论著·慢性病共病专题研究 • 上一篇    下一篇

基于健康生态学模型的广东省老年共病患者患慢性病数量影响因素研究

李林瑾1, 肖丽勤2, 张丹1,*()   

  1. 1518055 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
    2518055 广东省深圳市前海蛇口自贸区医院
  • 收稿日期:2023-06-16 修回日期:2023-08-15 出版日期:2024-01-15 发布日期:2023-10-23
  • 通讯作者: 张丹

  • 作者贡献:张丹、李林瑾提出主要研究目标,负责研究的构思与设计,研究的实施,撰写论文;李林瑾、肖丽勤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)

Study on the Factors Affecting the Number of Chronic Diseases among Elderly Comorbidity Patients in Guangdong Province Based on the Model of Ecological Health

LI Linjin1, XIAO Liqin2, ZHANG Dan1,*()   

  1. 1Institute for Hospital Management of Tsinghua University/Tsinghua Shenzhen International Graduate School, Shenzhen 518055, China
    2Shenzhen Qianhai Shekou Free Trade Zone Hospital, Shenzhen 518055, China
  • Received:2023-06-16 Revised:2023-08-15 Published:2024-01-15 Online:2023-10-23
  • Contact: ZHANG Dan

摘要: 背景 近年来我国老年共病患者数是持续上升。随着慢性疾病数量的增加,会给老年人带来不同程度的健康损失。目前分析老年共病患者患慢性病数量的多层次因素研究尚少。 目的 结合慢性病的病因和发病特点,利用健康生态学模型,从不同层面探讨影响老年共病患者患慢性病数量的因素,为我国社区老年共病患者管理和防治提供依据。 方法 2023年2月,采用多阶段分层整群随机抽样的方法,抽取广东省社区老年(≥60岁)共病患者为调查对象。采用《老年共病患者现况及影响因素调查问卷》进行面对面询问调查,该问卷基于健康生态学模型包含个人特质层、行为特征层、人际关系层、生活和工作条件层、政策环境层。以老年共病患者患慢性病数量为因变量,根据5个层次纳入自变量,进行无序多因素Logistic回归分析。 结果 共发放问卷1 000份,回收有效问卷987份,有效回收率为98.7%。987例老年共病患者中,同时患2种慢性病346例(35.1%),同时患3种慢性病456例(46.2%),同时患>3种慢性病185例(18.7%)。无序多因素Logistic回归分析结果显示,以同时患2种慢性病的老年共病患者为对照,患慢性病的时间<6年和6~10年、本地城镇户口是老年共病患者患3种慢性病的危险因素(P<0.05),OR(95%CI)分别为2.100(1.284~3.435)、1.948(1.201~3.158)、4.103(1.496~11.250);每天均可以保证至少6 h睡眠、自评健康状况比较好、每天服药1~3种类型、经常参加社会活动、初中及以下和高中/中专学历、有城镇职工医疗保险/城乡居民医疗保险是老年共病患者患3种慢性病的保护因素(P<0.05),OR(95%CI)分别为0.528(0.322~0.867)、0.570(0.325~0.998)、0.385(0.261~0.569)、0.348(0.208~0.582)、0.412(0.175~0.972)、0.486(0.298~0.790)、0.392(0.242~0.634);男性、1周运动3次以下是老年共病患者患3种以上慢性病的危险因素(P<0.05),OR(95%CI)分别为2.563(1.634~4.021)、2.990(1.429~6.256);每天均可以保证至少6 h睡眠、自评健康状况比较好和一般、每天服药1~3种类型、年平均收入≤3万元和>3~5万元、有城镇职工医疗保险/城乡居民医疗保险是老年共病患者患3种以上的慢性病的保护因素(P<0.05),OR(95%CI)分别为0.300(0.159~0.565)、0.247(0.125~0.487)、0.448(0.240~0.837)、0.288(0.178~0.467)、0.318(0.155~0.654)、0.489(0.293~0.816)、0.416(0.229~0.755)。 结论 广东省老年共病患者同时患2~3种慢性病的比例较高(占80%以上)。影响老年共病患者患慢性病数量的因素复杂,包括性别、患慢性病时间、运动情况、睡眠情况、自评健康状况、服药情况、户籍类型、子女或家人督促吃药或锻炼情况、收入情况、受教育程度和医保类型,且不同共病数量的危险因素差异较大。因此,应该从不同层面采取相应的干预措施,减少老年共病患者患慢性病数量,提高其健康水平。

关键词: 慢性病共病, 老年共病, 患病数量, 患病率, 健康生态学模型, 多因素Logistic回归分析, 广东

Abstract:

Background

The number of elderly comorbidity patients in our country is continuously increasing. With the accumulation of chronic diseases, older adults experience varying degrees of health loss. Currently, there is a lack of research analyzing the multi-level factors influencing the number of chronic conditions in elderly comorbidity patients.

Objective

To explore the factors influencing the number of chronic conditions in elderly patients from different levels combining with the etiology and characteristics of chronic diseases based on the health ecology model, so as to provide evidence for the management and prevention of chronic diseases in community-dwelling elderly comorbidity patients in our country.

Methods

In February 2023, a multi-stage stratified cluster random sampling method was used to select community-dwelling elderly (≥60 years old) comorbidity patients in Guangdong province as the survey subjects. A face-to-face interview was conducted using the "Survey Questionnaire on the Status and Influencing Factors of Elderly Patients with Multiple Chronic Conditions", which was based on the health ecology model and included five levels of individual trait, behavioral characteristic, interpersonal relationship, living and working conditions, and policy environment. The number of chronic conditions in elderly comorbidity patients was considered as the dependent variable, and an unordered multivariate Logistic regression analysis was conducted by incorporating independent variables according to the five levels.

Results

A total of 1 000 questionnaires were distributed, and 987 valid questionnaires were collected, with a recovery rate of 98.7%. Among the 987 elderly comorbidity patients, 346 (35.1%) had two concurrent chronic diseases, 456 (46.2%) had three concurrent chronic diseases, and 185 (18.7%) had more than three concurrent chronic diseases. The results of unordered multivariate logistic regression analysis showed that, compared to elderly patients with two concurrent chronic diseases, disease duration less than 6 years and 6-10 years, local urban household were risk factors for elderly patients with three concurrent chronic diseases (P<0.05), with OR (95%CI) values of 2.100 (1.284-3.435), 1.948 (1.201-3.158), and 4.103 (1.496-11.250), respectively. Having at least 6 hours of sleep daily, self-rating good health status, taking 1-3 types of medication daily, regularly participating in social activities, level of junior high school or below and high school/secondary school, and having urban employee medical insurance/rural resident medical insurance were protective factors for elderly patients with three concurrent chronic diseases (P<0.05), with OR (95%CI) values of 0.528 (0.322-0.867), 0.570 (0.325-0.998), 0.385 (0.261-0.569), 0.348 (0.208-0.582), 0.412 (0.175-0.972), 0.486 (0.298-0.790), and 0.392 (0.242-0.634), respectively. Being male, exercising less than 3 times a week were risk factors for elderly patients with more than three concurrent chronic diseases (P<0.05), with OR (95%CI) values of 2.563 (1.634-4.021), 2.990 (1.429-6.256), respectively. Having at least 6 hours of sleep daily, self-rating good and fair health status, taking 1-3 types of medication daily, having an annual average income below ≤30 000 and >30 000-50 000 yuan, and having urban employee medical insurance/rural resident medical insurance were protective factors for elderly patients with more than three concurrent chronic diseases (P<0.05), with OR (95%CI) values of 0.300 (0.159-0.565), 0.247 (0.125-0.487), 0.448 (0.240-0.837), 0.288 (0.178-0.467), 0.318 (0.155-0.654), 0.489 (0.293-0.816), and 0.416 (0.229-0.755), respectively.

Conclusion

The proportion of elderly comorbidity patients having 2-3 types of chronic diseases is relatively high in Guangdong province, accounting for over 80%. The factors influencing the number of chronic conditions in elderly comorbidity patients are complex, including gender, duration of disease, physical activity, sleep quality, self-rated health status, medication adherence, household registration type, supervision by children or family members in medication adherence or exercise, income level, educational level, and type of medical insurance. Moreover, there are significant differences in the risk factors across different comorbidity counts. Therefore, corresponding intervention measures should be implemented at different levels to reduce the number of chronic conditions in elderly comorbidity patients and improve their overall health level.

Key words: Chronic disease comorbidity, Elderly comorbidity, Number of diseases, Prevalence, Health ecology model, Multifactor Logistic regression analysis, Guangdong