摘要
目的 了解山东省农村老年高血压患者社会资本与自评健康的关系,为有效提高农村老年高血压患者健康状况提供参考依据.方法 2021年6-7月,本研究采用多阶段分层随机抽样的方法在山东省菏泽、济南和威海抽取≥60周岁的老年高血压患者进行综合问卷调查,采用多因素非条件logistic回归分析法对其个人资源型社会资本与自评健康的关系及影响因素进行分析.结果 山东省950例农村老年高血压患者社会资本得分为(18.66±4.14)分;自评健康者514人(54.11％).多因素非条件logistic回归分析结果显示,在调整了性别、文化程度、医保类型、家庭年收入、职业状况、吸烟、饮酒、体育锻炼等混杂因素后,社会资本存量低的老年高血压患者自评健康状况差的概率为社会资本存量高的老年高血压患者的1.06倍(OR=1.06,95％CI:1.03～1.10).结论 山东省农村老年高血压患者社会资本处于中等偏上水平,社会资本对促进农村老年高血压患者的自评健康具有正向作用.
Abstract
Objective To examine the relationship between social capital and self-rated health among rural elderly people with hypertension in Shandong Province,so as to provide reference for effectively improving the health status of elderly hypertension patients in rural areas.Methods Using stratified multistage random sampling,hypertension patients aged 60 years and above were selected Heze,Jinan,and Weihai in Shandong Province from June to July,2021 for a comprehensive questionnaire survey.The relationship between their social capital and self-rated health and its influencing factors were analyzed using unconditional multivariable logistic regression analysis.Results The score of social capital among the 950 rural elderly patients with hypertension in Shandong Province was(18.66±4.14).There were 514 participants reported self-rated good health(54.11％).The results of unconditional multivariable logistic regression analysis demonstrated that compared to the rural elderly hypertensive patients with high level of social capital,the probability of reporting self-rated ill health in those with low level of social capital was 1.06 times(OR=1.06,95％CI:1.03-1.10)after adjusting for gender,education,type of health insurance,annual household income,occupation,smoking,alcohol drinking,and physical exercise.Conclusions The social capital of rural elderly patients with hypertension in Shandong Province is at medium level.Social capital has a positive effect on promoting self-rated health of rural elderly patients with hypertension.
基金项目
山东省自然科学基金面上项目(ZR2019MG013)
山东大学青年交叉科学创新群体文旅康养产业交叉融合创新研究团队(2020QNQT019)
NETL
NSTL
万方数据