基于CHAID模型的广州市某街道高血压患者生活方式影响因素分析

马超; 刘莉; 邓光璞; 向萍; 王媛媛; 方浩庭; 林璜; 陈一鸣; 朱宏

doi:10.16766/j.cnki.issn.1674-4152.002984

基于CHAID模型的广州市某街道高血压患者生活方式影响因素分析

doi: 10.16766/j.cnki.issn.1674-4152.002984

马超¹,
刘莉²,
邓光璞²,
向萍²,
王媛媛²,
方浩庭²,
林璜³,
陈一鸣¹,
朱宏^{1, 4, ,}

1.
南方医科大学卫生管理学院，广东广州 510515
2.
南方医科大学第一临床医学院，广东广州 510000
3.
南方医科大学公共卫生学院，广东广州 510080
4.
广东省卫生健康委员会，广东广州 510060

基金项目:

国家重点研发计划项目 2020YFC2006400

详细信息

通讯作者:
朱宏，E-mail：zhuhongnfyy@126.com

中图分类号: R544.1
计量
- 文章访问数: 333
- HTML全文浏览量: 115
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2022-10-19

CHAID model-based analysis of influencing factors of hypertensive patients' lifestyle on a street in Guangzhou

1.
School of Health Management, Southern Medical University, Guangzhou, Guangdong 510515, China

摘要

摘要: 目的了解广州市某街道高血压患者生活方式现状，分析其主要影响因素，为改善高血压患者生活方式提供参考依据。方法随机选取2021年1月1日—12月31日在广州市白云区某街道所辖18个社区中已建档随访且符合本研究筛选条件的高血压患者1 768例，收集其基本情况及生活方式信息。运用SPSS 25.0统计学软件进行CHAID模型分析。结果性别(χ²=126.030，P < 0.001)、婚姻状况(χ²=31.169，P < 0.001)、年龄(χ²=9.494，P=0.031)是吸烟的主要影响因素，70例未婚、离婚或丧偶的男性患者吸烟率最高，为45.7%；签订家庭医生(χ²=8.289，P=0.004)、性别(χ²=3.966，P=0.046)是过量饮酒的主要影响因素，528例未签订家庭医生的男性患者过量饮酒率为2.8%；文化程度(χ²=12.130，P=0.003)、婚姻状况(χ²=11.830，P=0.002)是饮食荤素均衡的主要影响因素，818例已婚的高文化水平患者饮食荤素均衡比例最优，为98.0%；职业类别(χ²=62.121，P < 0.001)、性别(χ²=8.937，P=0.003)、BMI(χ²=9.815，P=0.002)是体育锻炼的主要影响因素，201例BMI＜24的从业患者体育锻炼的比例最低，为49.8%。结论该地区高血压患者生活方式整体较好，但仍需通过加强心理疏导、加大宣教力度、人工智能赋能、提高家庭医生签约率等措施强化对重点人群的管理。
- 高血压 /
- 生活方式 /
- 影响因素 /
- 卡方自动交互检测
Abstract: Objective To understand the current status of lifestyle of hypertensive patients in a street in Guangzhou, and to analyze the main influencing factors to provide a reference basis for improving the lifestyle of hypertensive patients. Methods A total of 1 768 hypertensive patients who had been followed up in 18 communities under the jurisdiction of a street in Baiyun District, Guangzhou City, from January 1 to December 31, 2021, and who met the screening criteria of this study were randomly selected, and their basic conditions and lifestyle information were collected. Chi-squared automatic interaction detector (CHAID) model analysis was performed using SPSS 25.0 statistical software. Results Gender (χ²=126.030, P < 0.001), marital status (χ²=31.169, P < 0.001), and age (χ²=9.494, P=0.031) were the main influencing factors for smoking, with the 70 male patients who were unmarried, divorced, or widowed having the highest prevalence of smoking at 45.7%; signing up with a family doctor (χ²=8.289, P=0.004), gender (χ²=3.966, P=0.046) were the main influencing factors for excessive alcohol consumption, and 528 male patients who did not sign a family doctor had an excessive alcohol consumption rate of 2.8%; Educational level (χ²=12.130, P=0.003) and marital status (χ²=11.830, P=0.002) were the main influencing factors for a balanced diet of meat and vegetables, and 818 patients with high educational level who were married had the best proportion of balanced diet of meat and vegetables was 98.0%; Occupational category (χ²=62.121, P < 0.001), gender (χ²=8.937, P=0.003), and BMI (χ²=9.815, P=0.002) were the main influencing factors for physical exercise, and 201 practicing patients with BMI < 24 had the lowest proportion of physical exercise was 49.8%. Conclusion The overall lifestyle of hypertensive patients in a Guangzhou street is good, however, there is still a need to strengthen the management of key populations through strengthening psychological guidance, increasing education, empowering artificial intelligence, and increasing the contracting rate of family doctors.
- Hypertension /
- Lifestyle /
- Influencing factors /
- Chi-squared automatic interaction detector
利益冲突 无

HTML全文

图 1 吸烟影响因素的CHAID决策树模型

Figure 1. CHAID decision tree model of smoking influencing factors

下载: 全尺寸图片幻灯片

图 2 过量饮酒影响因素的CHAID决策树模型

Figure 2. CHAID decision tree model of Influencing Factors of excessive alcohol consumption

下载: 全尺寸图片幻灯片

图 3 饮食荤素均衡影响因素的CHAID决策树模型

Figure 3. CHAID Decision tree model of influencing Factors of balanced meat and vegetable diet

下载: 全尺寸图片幻灯片

图 4 体育锻炼影响因素的CHAID决策树模型

Figure 4. CHAID decision tree model of Influencing factors of physical exercise

下载: 全尺寸图片幻灯片

表 1 变量赋值表

Table 1. Variable assignment table

变量	赋值方法
吸烟(因变量)	否=0，是=1
过量饮酒(因变量)	否=0，是=1
饮食荤素均衡(因变量)	否=0，是=1
体育锻炼(因变量)	否=0，是=1
性别	男性=1，女性=2
年龄	30~39岁=1，40~49岁=2，50~59岁=3，60~69岁=4，≥70岁=5
BMI	＜24=1，≥24=2
婚姻状况	以“未婚”为参照，未婚=(0, 0), 已婚=(1, 0)，离婚、丧偶=(0, 1)
文化程度	小学及以下=1，初中=2，高中、中专、技校=3，大专及以上=4
职业类别	以“无职业”为参照，无职业=(0，0，0，0，0)
	办事人员和有关人员=(1，0，0，0，0)
	商业、服务业人员=(0, 1, 0，0，0)
	生产、运输设备操作人员及有关人员=(0，0，1，0，0)
	专业技术人员=(0，0，0，1，0)
	其他从业人员=(0，0，0，0，1)
家庭医生签约	否=0，是=1
医疗费用支付方式	以“其他支付方式”为参照，其他支付方式=(0，0，0，0)
	城镇职工医保=(1，0，0，0)
	城镇居民医保=(0，1，0，0)
	新农合=(0，0，1，0)
	全自费=(0，0，0，1)

下载: 导出CSV

参考文献(15)

[1]	黄文博, 冯娴, 姚元龙, 等. 全科医学视角下我国高血压患者的自我管理[J]. 中华全科医学, 2022, 20(7): 1085-1088. doi: 10.16766/j.cnki.issn.1674-4152.002529 HUANG B W, FENG X, YAO Y L, et al. Self-management of hypertensive patients in China from the perspective of general practice[J]. Chinese Journal of General Practice, 2022, 20(7): 1085-1088. doi: 10.16766/j.cnki.issn.1674-4152.002529
[2]	董惠玲, 吴炳义, 郭晓宇, 等. 基于健康促进理论高血压患者影响因素效应分析[J]. 中国公共卫生, 2021, 37(9): 1365-1370. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGW202109011.htm DONG H L, WU B Y, GUO X Y, et al. Influencing factors of hypertension among adult residents in Shandong province: a survey based on health promotion theory[J]. Chinese Journal of Public Health, 2021, 37(9): 1365-1370. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGGW202109011.htm
[3]	VALENZUELA P L, CARRERA-BASTOS P, GÁLVEZ B G, et al. Lifestyle interventions for the prevention and treatment of hypertension[J]. Nat Rev Cardiol, 2021, 18(4): 251-275. doi: 10.1038/s41569-020-00437-9
[4]	MEYDANLIOGLU A, AKCAN A, ONCEL S, et al. Prevalence of obesity and hypertension in children and determination of associated factors by CHAID analysis[J]. Arch Pediatr, 2022, 29(1): 30-35. doi: 10.1016/j.arcped.2020.10.017
[5]	杨莉莉. 基于决策树E-CHAID算法的小儿肺炎住院费用DRG路径研究[D]. 重庆: 中国人民解放军陆军军医大学, 2020. YANG L L. Research on DRG Path of Pediatric Pneumonia Hospitalization Costs Based on Decision Tree E-CHAID Algorithm[D]. Chongqing: Army Medical University, 2020.
[6]	高多多, 张爱莲, 任雯娟. 基于CHAID模型的某县农村居民生活方式影响因素分析[J]. 中国卫生统计, 2020, 37(5): 659-663. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT202005005.htm GAO D D, ZHANG A L, REN W J. Analysis of Influencing Factors of Rural Residents' Lifestyle in a County based on CHAID Model[J]. Chinese Journal of Health Statistics, 2020, 37(5): 659-663. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT202005005.htm
[7]	阚慧, 捷建旺, 靳娟, 等. 基于CHAID法广州市居民首诊医疗单位影响因素分析[J]. 现代预防医学, 2018, 45(6): 1034-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF201806020.htm KAN H, JIE J W, JIN J, et al. Analysis of influencing factors of first diagnosis institution among outpatients in Guangzhou Based on CHIAD[J]. Modern Preventive Medicine, 2018, 45(6): 1034-1038. https://www.cnki.com.cn/Article/CJFDTOTAL-XDYF201806020.htm
[8]	蒋妮, 程港, 贺思敏, 等. 基于决策树与多元线性回归模型的出生体重影响因素分析[J]. 中国卫生统计, 2022, 39(2): 202-206. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT202202009.htm JIANG N, CHENG G, HE S M, et al. Influencing Factors of Birth Weight based on Decision Tree and Multiple Linear Regression Model[J]. Chinese Journal of Health Statistics, 2022, 39(2): 202-206. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGWT202202009.htm
[9]	赵信星, 王长义, 戴舒红, 等. 南山区高血压患者吸烟和戒烟情况调查[J]. 预防医学, 2022, 34(5): 449-455. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYFX202205005.htm ZHAO X X, WANG C Y, DAI S H, et al. Status of smoking and smoking cessation among patients with hypertension in Nanshan District[J]. Preventive Medicine, 2022, 34(5): 449-455. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYFX202205005.htm
[10]	董晶, 马爱娟, 谢晨, 等. 2017年北京市18~79岁高血压人群饮酒行为及相关因素分析[J]. 中国健康教育, 2021, 37(11): 963-969. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJK202111002.htm DONG J, MA A J, XIE C, et al. Analysis on drinking behaviors and related factors among hypertensive population aged 18 to 79 in Beijing, 2017[J]. Chinese Journal of Health Education, 2021, 37(11): 963-969. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGJK202111002.htm
[11]	冯为, 姚建军, 李世明, 等. 无锡市主城区居民吸烟与心理行为特征的关系研究[J]. 中国全科医学, 2019, 22(7): 860-864. https://www.cnki.com.cn/Article/CJFDTOTAL-QKYX201907028.htm FENG W, YAO J J, LI S M, et al. Relationship between Smoking and Mental Health Related Behaviors among Residents in the Main Urban Areas of Wuxi[J]. Chinese General Practice, , 2019, 22(7): 860-864. https://www.cnki.com.cn/Article/CJFDTOTAL-QKYX201907028.htm
[12]	段力, 贺娟, 李淼晶. 成都市某社区居民高血压认知现状及影响因素分析[J]. 中华全科医学, 2022, 20(9): 1526-1530. doi: 10.16766/j.cnki.issn.1674-4152.002640 DUAN L, HE J, LI M J. Cognitive status and influencing factors of hypertension amongst residents resident in a community in Chengdu[J]. Chinese Journal of General Practice, 2022, 20(9): 1526-1530. doi: 10.16766/j.cnki.issn.1674-4152.002640
[13]	黄文, 陈奇峰, 李金, 等. 越城区老年人饮酒及过量饮酒调查[J]. 预防医学, 2021, 33(10): 1042-1045. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYFX202110020.htm HUANG W, CHEN Q F, L J, et al. Survey on alcohol consumption and overdrinking among elderly people in Yuecheng District[J]. Preventive Medicine, 2021, 33(10): 1042-1045. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYFX202110020.htm
[14]	杨继, 张垚, 张秋月, 等. 高血压病并发脑卒中的影响因素分析及风险预测模型的初步建立: 3 859例回顾性研究[J]. 中医杂志, 2021, 62(10): 868-873. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZYZ202110009.htm YANG J, ZHANG Y, ZHANG Q Y, et al. Analysis of factors influencing the complication of hypertensive disease with stroke and the preliminary development of risk prediction model-A retrospective study of 3 859 cases[J]. Journal of Traditional Chinese Medicine, 2021, 62(10): 868-873. https://www.cnki.com.cn/Article/CJFDTOTAL-ZZYZ202110009.htm
[15]	武明芬, 朱斌, 徐晓涵, 等. 北京地区高血压患者用药依从性及影响因素调查与研究[J]. 中国医院药学杂志, 2021, 41(21): 2255-2260. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYZ202121019.htm WU M F, ZHU B, XU X H, et al. Risk factors investigation on medication adherence of Chinese hypertension patients in Beijing[J]. Chinese Journal of Hospital Pharmacy, 2021, 41(21): 2255-2260. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGYZ202121019.htm