中国全科医学 ›› 2022, Vol. 25 ›› Issue (01): 87-93.DOI: 10.12114/j.issn.1007-9572.2021.00.321

所属专题: 社区卫生服务最新研究合集 老年人群健康最新文章合集 老年问题最新文章合集

• 论著·社区老年人群管理研究 • 上一篇    下一篇

决策树与Logistic回归模型在老年人社区养老意愿影响因素分析中的应用研究

闫蕊1, 赵守梅2, 张馨心2, 吕雨梅1,*   

  1. 1.150081 黑龙江省哈尔滨市,哈尔滨医科大学护理学院
    2.163319 黑龙江省大庆市,萨尔图区东安街道社区卫生服务中心
  • 收稿日期:2021-06-30 修回日期:2021-11-05 出版日期:2022-01-05 发布日期:2021-12-29
  • 通讯作者: 吕雨梅
  • 基金资助:
    2020年教育部人文社会科学研究项目(20YJAZH075)

Factors Associated with Older Adults' Intention to Use Community-based Elderly Carea Study Using the Decision Tree and Logistic Regression Models

YAN Rui1ZHAO Shoumei2ZHANG Xinxin2LYU Yumei1*   

  1. 1.School of NursingHarbin Medical UniversityHarbin 150081China

    2.Sarertu District Dongan Subdistrict Community Health CenterDaqing 163319China

    *Corresponding authorLYU YumeiAssociate professorMaster supervisorE-mail438866749@qq.com

  • Received:2021-06-30 Revised:2021-11-05 Published:2022-01-05 Online:2021-12-29

摘要: 背景 伴随我国人口老龄日益严重,养老问题日渐突出,社区养老可有效解决当前社会养老问题。 目的 运用决策树与Logistic回归模型探析老年人社区养老意愿的影响因素。 方法 2020年8—12月,依据便利抽样原则选取大庆市某3个社区为研究现场,方便选取符合入组标准的500例社区老年人为研究对象,进行问卷调查。问卷内容涵盖老年人一般资料、身心健康水平、家庭及社会支持等方面。采用决策树与Logistic回归模型分析老年人社区养老意愿的影响因素。 结果 共发放500份问卷,回收有效问卷489份,问卷有效回收率为97.8%。其中,159例(32.5%)社区老年人愿意选择社区养老。Logistic回归模型分析结果显示,养老观念、社区养老了解度、孤独感得分、代际关系得分是老年人社区养老意愿的影响因素(P<0.05);决策树模型分析结果显示,社区养老了解度、养老观念、孤独感得分、社会网络、健康自评、教育程度、居住方式为老年人社区养老意愿的影响因素(P<0.05)。Logistic回归模型与决策树模型筛出的前3位关键影响因素均为社区养老了解度、养老观念及孤独感得分。Logistic回归模型的灵敏度为94.34%,特异度为95.75%,受试者工作特征曲线(ROC曲线)下面积为0.985(0.974,0.996);决策树模型的灵敏度为88.05%,特异度为97.87%,ROC曲线下面积为0.980(0.968,0.992)。两个模型差异无统计学意义(Z=-0.625,P=0.268)。 结论 决策树与Logistic回归模型相结合,在老年人社区养老意愿影响因素研究中具有较高的运用价值。研究结果显示,老年人社区养老意愿偏低,提示未来可通过改变老年人传统养老观念、关注老年人身心健康、改善老年人家庭关系等方法提高老年人社区养老意愿。 该文的微信推文请扫描下方二维码查看! 

关键词: 老年人保健服务, 社区卫生服务, 社区养老, 决策树, Logistic回归分析, 影响因素分析

Abstract: Background

Community-based elderly care may effectively contribute to the handling of serious elderly care challenges brought by an increasingly aging population in China.

Objective

To explore the factors associated with older adults' intention to choose community-based elderly care using the decision tree and Logistic regression models.

Methods

This questionnaire survey was conducted in three communities selected from Daqing by convenient sampling from August to December 2020. 500 eligible community-dwelling older adults (≥60 years old) were selected as the research objects. The decision tree and Logistic regression models were used to explore factors associated with these older adults' intention to choose community-based elderly care via analyzing their demographics, self-rated physical and mental health, and family and social support collected by the survey.

Results

Altogether, 489 cases (97.8%) who effectively responded to the survey were included for analysis. The prevalence of intending to choose community-based elderly care in the respondents was 32.5% (159/489) . Logistic regression analysis revealed that the older adults' understanding level of community-based elderly care, views of elderly care, sense of loneliness, and intergenerational relationship had varying degrees of influence on their intention to choose community-based elderly care (P<0.05) . The decision tree analysis found that the older adults' understanding level of community-based elderly care, views of elderly care, sense of loneliness, social network, self-rated health, education level, and living status (alone or not) were key factors influencing their intention to choose community-based elderly care (P<0.05) . By both Logistic regression and decision tree analyses, understanding level of community-based elderly care, views of elderly care, and sense of loneliness were found to be three factors influencing older adults' intention to use community-based elderly care most. In exploring the factors associated with older adults' intention to choose community-based elderly care, Logistic regression analysis had an AUC of 0.985 (0.974, 0.996) with 94.34% sensitivity and 95.75% specificity, and decision tree analysis had an AUC of 0.980 (0.968, 0.992) with 88.05% sensitivity and 97.87% specificity, the performance of the two was similar (Z=-0.625, P=0.268) .

Conclusion

The combination of decision tree and Logistic regression model has high application value in the study of influencing factors of community pension willingness of the elderly. The prevalence of intending to use community-based elderly care was relatively low in Daqing older adults. To improve this, it is suggested to take actions to change older adults' traditional views of elderly care, to better their physical and mental health and family relationships.

Key words: Health services for the aged, Community health services, Community endowment, Decision tree, Logistic regression, Root cause analysis

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