华南预防医学 ›› 2025, Vol. 51 ›› Issue (5): 484-489.doi: 10.12183/j.scjpm.2025.0484

• 论著 •    下一篇

中老年农业工作者慢性病共病模式及卫生服务利用影响因素

柳蔚洲, 赵倩   

  1. 广州医科大学公共卫生学院,广东 广州 511436
  • 收稿日期:2024-11-18 发布日期:2025-06-27
  • 通讯作者: 赵倩,E-mail:zhaoqian121@126.com
  • 作者简介:柳蔚洲(2002—),男,大学本科,研究方向:预防医学
  • 基金资助:
    广东省医学科学技术研究基金项目(A2022231);广州市高等教育教学质量与教学改革工程一流课程项目(2023YLKC012)

Chronic disease comorbidity patterns and determinants of healthcare utilization among middle-aged and elderly agricultural workers

LIU Weizhou, ZHAO Qian   

  1. School of Public Health, Guangzhou Medical University, Guangzhou, Guangdong 511436, China
  • Received:2024-11-18 Published:2025-06-27

摘要: 目的 分析中国中老年农业工作者慢性病患病情况、共病模式及卫生服务利用的影响因素,为制定该人群的慢性病防控方案提供参考。方法 采用中国健康与养老追踪调查2018和2020年的中老年农业工作者数据,采用系统聚类、关联规则分析及广义线性混合效应模型探讨慢性病共病模式及卫生服务利用影响因素。结果 2018和2020年共纳入6 295名中老年农业工作者,慢性病共病率由51.52%(95% CI:50.27%~52.76%)增至55.87%(95% CI:54.63%~57.10%)(χ2=23.98,P<0.01)。疾病谱分析显示,关节炎或风湿病(2018:33.95%→2020:35.46%)、消化系统疾病(27.28%→29.29%)与高血压(26.93%→30.10%)持续占据慢性病共病率的前3位。经系统聚类分析,2018和2020年均识别出3类稳定的共病模式:代谢-循环系统群组、呼吸-肝肾多器官群组、神经-肿瘤复合群组。广义线性混合效应模型结果显示,在门诊利用方面,2020年(OR=1.32)、抑郁症状(OR=1.21)、高体力活动(OR=1.17)、男性(OR=0.84)、健康水平自评(一般:OR=0.53;良好:OR=0.32)、慢性病罹患数量(1种:OR=1.65;2种:OR=2.10;≥3种:OR=3.40)是影响因素;在住院服务利用方面,2020年(OR=1.33)、年龄(55~<65岁:OR=1.18;65~<75岁:OR=1.51;≥75岁:OR=2.35)、抑郁症状(OR=1.17)、高体力活动(OR=0.83)、健康水平自评(一般:OR=0.42;良好:OR=0.25)、慢性病罹患数量(1种:OR=1.70;2种:OR=2.33;≥3种:OR=3.85)是影响因素。结论 中老年农业工作者慢性病共病率呈显著上升趋势,共病疾病谱以关节炎或风湿病、消化系统疾病、高血压为主导,卫生服务利用呈现多维驱动机制。提示需构建共病网络与风险梯度干预的防控体系,通过健康行为优化与智能监测强化慢性病管理效能,为特殊职业群体制定精准防控策略提供理论依据。

关键词: 慢性病共病, 中老年人, 农业工作者, 卫生服务利用, 关联分析

Abstract: Objective To examine the prevalence, comorbidity patterns, and healthcare utilization determinants of chronic diseases among middle-aged and elderly agricultural workers in China, providing evidence for targeted chronic disease prevention strategies. Methods Data were extracted from the China Health and Retirement Longitudinal Study (CHARLS) for 2018 and 2020. Systematic clustering, association rule mining, and generalized linear mixed-effects models were used to analyze comorbidity patterns and healthcare utilization factors. Results Among 6 295 agricultural workers, the comorbidity prevalence increased significantly from 51.52% (95% CI: 50.27%-52.76%) in 2018 to 55.87% (95% CI: 54.63%-57.10%) in 2020 (χ2=23.98, P<0.01). Arthritis/rheumatism (2018: 33.95%→2020: 35.46%), digestive disorders (27.28%→29.29%), and hypertension (26.93%→30.10%) constituted the top three chronic conditions. Cluster analysis identified three stable comorbidity patterns: metabolic-circulatory, respiratory-hepatorenal multiorgan, and neuro-oncological complexes. Generalized linear mixed models revealed that outpatient utilization was associated with survey year (2020: OR=1.32), depressive symptoms (OR=1.21), high physical activity (OR=1.17), male sex (OR=0.84), self-rated health (fair: OR=0.53;good: OR=0.32), and comorbidity count (1 condition: OR=1.65;2: OR=2.10;≥3: OR=3.40). Inpatient service, utilization was associated with survey year (OR=1.33), age (55-<65: OR=1.18;65-<75: OR=1.51;≥75: OR=2.35), depressive symptoms (OR=1.17), high physical activity (OR=0.83), self-rated health (fair: OR=0.42;good: OR=0.25), and comorbidity count (1: OR=1.70;2: OR=2.33;≥3: OR=3.85). Conclusions The prevalence of comorbidities among middle-aged and elderly agricultural workers is increasing significantly, with arthritis/rheumatism, digestive disorders and hypertension constituting the predominant spectrum of comorbidities. Healthcare service utilization is driven by multifactorial determinants. This suggests the need to establish a prevention and control system based on comorbidity networks and risk gradient interventions. This system would enhance the effectiveness of chronic disease management through optimizing health behaviors and intelligent monitoring, and provide a theoretical basis for developing precise prevention and control strategies targeting special occupational groups.

Key words: Chronic disease comorbidity, Middle-aged and elderly, Agricultural workers, Healthcare utilization, Association analysis

中图分类号: 

  • R139