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广西壮族人群慢性病共病影响因素和模式分析

唐静梅, 刘鑫, 莫秋燕, 刘颖春, 黄东萍, 刘顺, 曾小云, 仇小强, 余红平, 温秋萍

唐静梅, 刘鑫, 莫秋燕, 刘颖春, 黄东萍, 刘顺, 曾小云, 仇小强, 余红平, 温秋萍. 广西壮族人群慢性病共病影响因素和模式分析[J]. 广西医科大学学报, 2023, 40(12): 2084-2092. DOI: 10.16190/j.cnki.45-1211/r.2023.12.023
引用本文: 唐静梅, 刘鑫, 莫秋燕, 刘颖春, 黄东萍, 刘顺, 曾小云, 仇小强, 余红平, 温秋萍. 广西壮族人群慢性病共病影响因素和模式分析[J]. 广西医科大学学报, 2023, 40(12): 2084-2092. DOI: 10.16190/j.cnki.45-1211/r.2023.12.023
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Tang Jingmei, Liu Xin, Mo Qiuyan, Liu Yingchun, Huang Dongping, Liu Shun, Zeng Xiaoyun, Qiu Xiaoqiang, Yu Hongping, Wen Qiuping. Analysis of the influencing factors and patterns of comorbidity among the Zhuang population in Guangxi[J]. Journal of Guangxi Medical University, 2023, 40(12): 2084-2092. DOI: 10.16190/j.cnki.45-1211/r.2023.12.023
Citation: Tang Jingmei, Liu Xin, Mo Qiuyan, Liu Yingchun, Huang Dongping, Liu Shun, Zeng Xiaoyun, Qiu Xiaoqiang, Yu Hongping, Wen Qiuping. Analysis of the influencing factors and patterns of comorbidity among the Zhuang population in Guangxi[J]. Journal of Guangxi Medical University, 2023, 40(12): 2084-2092. DOI: 10.16190/j.cnki.45-1211/r.2023.12.023
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广西壮族人群慢性病共病影响因素和模式分析

  • 1. 广西医科大学公共卫生学院, 南宁 530021;

  • 2. 广西医科大学附属肿瘤医院, 南宁 530021;

  • 3. 广西壮族自治区卫生健康委员会肿瘤分子医学重点 [培育] 实验室, 南宁 530021;

  • 4. 区域性高发肿瘤早期防治研究教育部重点实验室 [广西医科大学], 南宁 530021;

  • 5. 广西区域性高发肿瘤早期防治研究重点实验室, 南宁 530021

基金项目: 

国家重点研发计划资助项目（No.2017YFC0907103）；广西自然科学基金重点项目（No.2018GXNSFDA050012）；广西壮族自治区卫生健康委员会肿瘤分子医学重点（培育）实验室资助项目（No.ZPTJ2020001）；上海吴孟超医学科技基金会资助项目（No.JJHXM-2019042）

详细信息
    通讯作者:

    余红平,E-mail:yuhongping@stu.gxmu.edu.cn

    温秋萍,E-mail:wenqiuping@stu.gxmu.edu.cn

  • 中图分类号: R195.4

Analysis of the influencing factors and patterns of comorbidity among the Zhuang population in Guangxi

  • 1. School of Public Health, Guangxi Medical University, Nanning 530021, China;

  • 2. Guangxi Medical University Cancer Hospital, Nanning 530021, China;

  • 3. Key Cultivated Laboratory of Cancer Molecular Medicine, Health Commission of Guangxi Zhuang Autonomous Region, Nanning 530021, China;

  • 4. Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor [Gaungxi Medical University], Ministry of Education, Nanning 530021, China;

  • 5. Guangxi Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor, Nanning 530021, China

摘要

    摘要
  • 摘要   目的：了解广西地区35~74岁壮族人群慢性病共病的患病情况及影响因素，为有针对性地进行慢性病管理与防控提供依据。方法：本研究数据源于“广西少数民族自然人群慢性病前瞻性队列研究”项目。该项目于2017—2019年对广西壮族自治区内35~74岁壮族人群采用方便抽样的方法进行调查。调查内容为包括了社会人口学特征、生活方式等信息在内的问卷调查、体格检查和血生化检测。采用多因素非条件logistic回归分析法分析慢性病共病的影响因素，用优势分析法计算影响因素对慢性病共病患病的贡献度，关联规则分析通过Apriori算法来完成，用于分析共病模式，并绘制共病网络图。结果：12 411例35~74岁壮族人群中共有慢性病患者7 806例，共病患者3 178例，慢性病共病率为25.61%。多因素非条件logistic回归分析结果显示，男性（OR=2.24，95%CI=2.02~2.49）、45~59岁（OR=1.91，95%CI=1.68~2.17）、≥60岁（OR=3.11，95%CI=2.72~3.56）、饮酒（OR=1.37，95%CI=1.23~1.53）、饮茶（OR=1.21，95%CI=1.08~1.36）及超重或肥胖（OR=3.00，95%CI=2.75~3.28）与慢性病共病高风险相关，而体力劳动工作者较非体力劳动者慢性病共病风险更低（OR=0.85，95%CI=0.77~0.94）。优势分析结果显示，对共病患病影响排前3位的因素分别为超重或肥胖（46.17%）、≥60岁（18.21%）、男性（21.74%）。在共病网络中，高血压患病率最高，处于中心的最大节点，关联规则分析筛选出9种强关联的共病模式，其中置信度和提升度最高的二元和三元模式均包含糖尿病。结论：广西地区壮族人群的慢性病共病影响因素主要涉及个体特征及生活行为因素，相关医疗卫生机构应根据不同群体特征加强慢性病共病高危人群的不良生活行为因素干预，并对慢性病共病防治进行规范化管理，提高人群健康水平。
    Abstract   Objective: To understand the prevalence and influencing factors of comorbidity among Zhuang population aged 35-74 in Guangxi and provide a basis for targeted management, prevention, and control of chronic diseases. Methods: Data of the present study were sourced from "Prospective Cohort Study of Chronic Diseases in Guangxi Ethnic Minority Natural Population" project, which conducted a survey on Zhuang population aged 35-74 in Guangxi Zhuang Autonomous Region from 2017 to 2019 by a convenient sampling method. The survey included questionnaire investigation(e.g., demographic characteristics and lifestyle), physical examination, and blood biochemical testing. Multivariable unconditional logistic regression analysis was used to analyze the influencing factors of comorbidity, and dominance analysis was used to estimate the contribution of influencing factors to the prevalence of comorbidity. Association rule analysis was performed by Apriori algorithm to analyze comorbidity patterns and generate the comorbidity network. Results: There were 7, 806 patients with chronic diseases and 3, 178 patients with comorbidity among 12, 411 Zhuang population aged 35-74. The rate of comorbidity was 25.61%. Multivariable unconditional logistic regression analysis showed that men(OR=2.24, 95% CI=2.02-2.49), 45-59 years old(OR=1.91, 95% CI=1.68-2.17), ≥ 60 years old(OR=3.11, 95% CI=2.72-3.56), drinking alcohol(OR=1.37, 95% CI=1.23-1.53), drinking tea(OR=1.21, 95% CI=1.08-1.36), and overweight or obesity(OR=3.00, 95% CI=2.75-3.28) were associated with higher risk of comorbidity, while manual labor workers had a lower risk of comorbidity than non-manual workers(OR=0.85, 95% CI=0.77-0.94).The results of advantage analysis showed that the top three influencing factors of comorbidity were overweight or obesity(46.17%), ≥ 60 years old(18.21%) and male(21.74%). Hypertension had the highest prevalence and caught the largest node in the center of the comorbidity network. Association rule analysis screened nine strongly associated comorbidity patterns, among which the association rules of binary and ternary patterns with the highest confidence and lift were all including diabetes. Conclusion: The influencing factors of comorbidity among the Zhuang population in Guangxi mainly involve individual characteristics and lifestyle. Medical and health institutions should strengthen intervention on unhealthy lifestyle among high-risk population with comorbidity based on different characteristics, and standardize the prevention and treatment of comorbidity to improve the health level of the population.
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  • 收稿日期:  2023-11-15
  • 网络出版日期:  2024-01-30

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