中国全科医学

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宁夏回族自治区农村居民慢性病共病的流行趋势及关联规则分析

张帆1,2,常建华1,2,杨娟1,2,乔慧1,2,谢永鑫1,2*   

  1. 1.750004 宁夏回族自治区银川市,宁夏医科大学公共卫生学院 2.750004 宁夏回族自治区银川市,宁夏环境因素与慢性病控制重点实验室
  • 收稿日期:2025-05-28 修回日期:2025-06-26 接受日期:2025-07-01
  • 通讯作者: 谢永鑫,副教授;E-mail:xieyongxin1991@163.com
  • 基金资助:
    国家自然科学基金资助项目(72264032,72164033);宁夏重点研发项目(引才专项)(2022BSB03082)

The Prevalence Trend and Association Rules of Chronic Disease Comorbidity among Rural Residents in Ningxia

ZHANG Fan1, 2, CHANG Jianhua1, 2, YANG Juan1, 2, QIAO Hui1, 2, XIE Yongxin1, 2*   

  1. 1.School of Public Health, Ningxia Medical University, Yinchuan 750004, China 2.Ningxia Key Laboratory of Environmental Factors and Chronic Disease Control, Yinchuan 750004, China
  • Received:2025-05-28 Revised:2025-06-26 Accepted:2025-07-01
  • Contact: XIE Yongxin, Associate professor; E-mail: xieyongxin1991@163.com

摘要: 背景 近年来,慢性病严重威胁人类健康,慢性病共病的患病率也在逐年上升。了解宁夏回族自治区农村居民慢性病共病的流行趋势及主要病种,有助于针对性地制定公共卫生政策,促进居民养成健康生活方式。目的 了解宁夏回族自治区农村居民10年间慢性病共病的动态流行趋势,挖掘其影响因素和共病模式,揭示其潜在影响因素的复杂关联,为制订慢性病防控策略与措施提供新思路。方法 本研究数据来源于宁夏回族自治区农村居民健康随访队列数据与国家自然科学基金项目数据的合并数据集,该数据集包含15年间的6期面板数据。本研究采用疾病计数法,从上述数据中筛选出2015—2024年10年间的4期(2015、2019、2022、2024年)人群队列,最终纳入年龄≥18岁且关键变量信息明确的70 667名农村居民作为研究对象。采用χ2 检验和趋势χ2 检验分析慢性病共病的流行趋势;基于2024年数据,采用Apriori算法分析慢性病共病模式;基于健康生态学模型,从个人特质、行为特征、人际网络、生活和工作条件、政策环境5个圈层选取自变量,构建多因素Logistic回归模型,以分析慢性病共病的影响因素。结果 2015—2024年宁夏回族自治区农村居民的慢性病共病患病率呈逐年增长趋势(P<0.05),2024年[9.97%(1 599/16 045)]较2015年[4.70%(977/10 775)]增长了5.27个百分点。在各亚组人群中,除年龄18~<45岁、自评健康非常好人群外,其他各亚组人群的慢性病共病患病率均呈逐年增长趋势(P<0.05)。采用Apriori算法得到20条强关联规则,其中14条关联规则与高血压有关,10条关联规则与脑血管疾病、椎间盘疾病、类风湿关节炎有关,9条关联规则与心血管疾病有关。多因素Logistic回归模型结果显示:≥2种慢性病共病和≥3种慢性病共病的影响因素基本一致,平均每天锻炼时长≥30 min、自评健康一般/很好/好是两种共病模式的保护因素(P<0.05),年龄≥45岁、BMI≥28.0 kg/m2 、一年内住院是两种共病模式的风险因素;另外,无抑郁是≥2种慢性病共病的保护因素(P<0.05),BMI在24.0~27.9 kg/m2 是≥2种慢性病共病的风险因素(P<0.05);平均每天睡眠时长6~8h、离家最近医疗点的距离1~5 km是≥3种慢性病共病的保护因素(P<0.05)。结论 宁夏回族自治区农村居民的慢性病共病患病率呈明显上升趋势,高血压是慢性病共病的核心疾病,居民慢性病共病受年龄、行为与生活方式、医疗可及性等多方面的影响。建议以重点疾病为切入口,加强对居民的健康教育,鼓励居民重视健康行为与生活方式,从而实现对慢性病共病的防控。

关键词: 慢性病共病, 患病率, 影响因素分析, 关联规则分析, 宁夏

Abstract: Background In recent years, chronic diseases have posed a serious threat to human health, with the prevalence of chronic disease comorbidity also increasing annually. Understanding the epidemiological trends and predominant types of chronic disease comorbidity among rural residents in Ningxia Hui Autonomous Region facilitates the development of targeted public health policies and promotes the adoption of healthy lifestyles. Objective To examine the dynamic epidemiological trends of chronic disease comorbidity among rural residents in Ningxia Hui Autonomous Region over a 10-year period, identify influencing factors and comorbidity patterns, reveal the complex interrelationships of underlying determinants, and provide new insights for formulating chronic disease prevention and control strategies and measures. Methods The data for this study were derived from a merged dataset combining the Ningxia Rural Residents Health Follow-up Cohort and the National Natural Science Foundation of China project data. This dataset comprises six waves of panel data spanning 15 years. Using the disease counting method, this study selected four population cohorts from the aforementioned data spanning the 10-year period from 2015 to 2024 (2015, 2019, 2022, and 2024). Ultimately, 70 667 rural residents aged ≥ 18 years with clearly defined key variable information were included as study subjects.Employed χ2 tests and trend χ2 tests to analyze the prevalence trends of chronic disease comorbidity. Based on 2024 data, utilized the Apriori algorithm to examine patterns of chronic disease comorbidity. Constructed a multifactorial logistic regression model using variables selected from five spheres—individual traits, behavioral characteristics, interpersonal networks, living and working conditions, and policy environment—grounded in the health ecology model to analyze the determinants of chronic disease comorbidity. Results From 2015 to 2024, the prevalence of chronic disease comorbidity among rural residents in Ningxia Hui Autonomous Region showed a year-on-year increasing trend (P<0.05). The prevalence in 2024[9.97% (1 599/16 045)] increased by 5.27 percentage points compared to 2015[4.70% (977/10 775)]. Among all subgroups, the prevalence of chronic disease comorbidity showed an annual upward trend in all groups except those aged 18-<45 years and those who self-rated their health as very good (P<0.05). The Apriori algorithm yielded 20 strong association rules, of which 14 were related to hypertension, 10 were associated with cerebrovascular disease, intervertebral disc disease, and rheumatoid arthritis, and 9 were linked to cardiovascular disease. Results from the multivariable Logistic regression model indicate that the risk factors for ≥ 2 chronic diseases and ≥ 3 chronic diseases are largely consistent. Daily exercise duration ≥ 30 minutes and self rated health as fair/very good/good serve as protective factors for both comorbidity patterns (P<0.05). Age ≥ 45 years, BMI ≥ 28.0 kg/m2 , and hospitalization within the past year are risk factors for both comorbidity patterns. Additionally, absence of depression was a protective factor for ≥ 2 chronic disease co-morbidities (P<0.05), while BMI between 24.0-27.9 kg/m2 was a risk factor for ≥ 2 co-morbidities (P<0.05). Average daily sleep duration of 6–8 hours and distance to nearest healthcare facility within 1-5 km were protective factors for ≥ 3 chronic disease co-morbidities (P<0.05). Conclusion The prevalence of chronic disease comorbidity among rural residents in Ningxia Hui Autonomous Region shows a marked upward trend, with hypertension being the core disease in chronic disease comorbidity. Resident chronic disease comorbidity is influenced by multiple factors including age, behavioral and lifestyle factors, and healthcare accessibility. It is recommended to focus on key diseases as entry points, strengthen health education for residents, and encourage residents to prioritize healthy behaviors and lifestyles, thereby achieving the prevention and control of chronic disease comorbidity.

Key words: Multiple chronic conditions, Prevalence, Root cause analysis, Association rule analysis, Ningxia

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