Abstract: Background The control of hypertension is still unsatisfactory，as the number of patients continues to increase in China，and self-management plays a positive role in the prevention of relevant complications and reducing the economic burden of the disease. The influencing factors of prevalence and self-management are varied，but few studies have identified path coefficients and indirect effects between factors. Objective The aim of this study was to analyse the prevalence of hypertension and self-management and their influencing factors in rural Dayao County， Yunnan Province. Methods A multistage stratified random sampling method was used to select 2 499 rural residents aged ≥ 35 years from Dayao County. Each participant received a questionnaire survey and underwent physical examination. Principal component analysis was used to construct the index of socioeconomic position（SEP），whereas structural equation modelling（SEM）was used to analysis the possible influencing factors of the prevalence of hypertension and self-management. Results Among the surveyed population，the prevalence of hypertension was 53.7%，and the rate of compliance to anti-hypertensive drugs，self-monitoring of blood pressure and taking measures to control hypertension was 84.5%，82.0% and 88.3%，and was 52.4%，82.2%，80.8% and 87.8% for males and 55.0%，86.8%，83.2% and 88.0% for females，respectively. The prevalence of hypertension increased with age（χ2 trend=224.142，p<0.001), whereas the rate of self-monitoring of blood pressure decreased (χ2 trend=4.012，p<0.05). A greater prevalence of hypertension was observed in those with lower education levels and SEP（χ2 =28.036，χ2 trend=12.147，p<0.001）. Individuals with good access to medical services had a higher rate of self-monitoring of blood pressure than their counterparts did（χ2 =10.137，P<0.05）. The result of SEM indicated that the following factors had a direct statistically significant effect on the prevalence of hypertension：SEP（-0.43），body shape（including overweight or obesity and central obesity status）（0.16），physical inactivity（0.06），and family history of hypertension（0.15）. However indirect effects on the prevalence of hypertension were observed for sex（0.23，through SEP）and age（0.35，through SEP and physical inactivity）. Similarly，the results also indicated that SEP（0.20），alcohol consumption（-0.17），and conditions of hypertensive patients（including the course of disease and complications）（0.53）had a statistically significant direct effect on the prevalence of self-management，while gender（0.06）had an indirect effect on prevalence of self-management through alcohol consumption. Conclusion There is a relatively high prevalence of hypertension and an overall high level of self-management in rural Dayao County. Future health education and management regarding hypertension should be strengthened for the elderly and individuals with low SEP and unhealthy lifestyles.

Key words: Hypertension, Prevalence, Self-management, Root Cause Analysis, Structural equation modeling

摘要： 背景 我国高血压控制情况仍不理想，患者数量持续增加，自我管理有助于预防相关并发症，减轻疾病负担，高血压患病和自我管理的影响因素复杂多样，但目前缺乏明确各因素间路径大小及间接作用的研究。目的分析云南省大姚县农村居民高血压患病和自我管理现状及其影响因素。方法 于2022年7月采用多阶段分层随机抽样方法抽取云南省大姚县 35 岁及以上的农村常住居民作为研究对象。采用自行设计的问卷以一对一的方式进行现场调查。问卷内容包括基本人口学特征（性别、年龄、文化程度、家庭年人均收入、医疗服务可及性）、吸烟、饮酒、体力活动、高血压家族史、高血压患病情况以及自我管理情况（遵医嘱服药、自我监测血压及近 2 周内采取降压措施）等。采用主成分分析法（PCA）构建调查对象的社会经济地位（SEP），采用结构方程模型（SEM）分析高血压患病和自我管理的影响因素。结果 本次调查共发放问卷 2 526 份，回收有效问卷 2 499 份，有效回收率为 98.9%。云南省大姚县高血压患病率、遵医嘱服药率、自我监测血压率和采取降压措施率分别为 53.7%、84.5%、82.0% 和 88.3%，其中男性分别为 52.4%、82.2%、80.8% 和 87.8%，女性分别为 55.0%、86.8%、83.2% 和 88.0%。高血压患病率随着年龄的增加而上升（χ2 趋势 =224.142，p<0.001）；自我监测血压率随年龄增加而下降（χ2 趋势 =4.012，P<0.05）；文化程度和社会经济地位越低者其高血压患病率越高（χ2 趋势 =28.036，χ2 趋势 =12.147，p<0.001）；医疗服务可及性较好者其自我监测血压率较高（χ2 =10.137，P<0.05）。SEM 结果显示，SEP、体型（包括超重或肥胖和中心性肥胖）、缺乏体力活动以及高血压家族史对高血压患病产生直接作用，路径系数分别为 -0.43、0.16、0.06 和 0.15；性别通过 SEP、年龄通过 SEP 和缺乏体力活动对高血压患病产生间接作用，路径系数分别为 0.23 和 0.35；SEP、有饮酒行为以及高血压患者的病情（包括患病年限和并发症）对自我管理产生直接作用，路径系数为 0.20、-0.17 和 0.53；性别通过饮酒行为对高血压自我管理产生间接作用，路径系数为 0.06。结论 大姚县高血压患病率较高，自我管理情况总体较好。应加强老年人、低社会经济地位和不良生活方式者的高血压健康教育和健康管理工作。

关键词: 高血压, 患病率, 自我管理, 影响因素分析, 结构方程模型