摘要
目的:分析北京市居民传染病健康素养水平现状及其影响因素。方法:采用多阶段分层抽样的方法对北京市6个区县18岁以上居民进行抽样,通过问卷调查的方法进行现场调查,调查内容包括传染病健康素养的3个维度(知识、行为、技能)的相关指标,根据答案给各个指标赋值。应用非条件Logistic回归分析探索传染病健康素养的影响因素。结果:此次调查共抽取13 287人,回收有效问卷13 001份。北京市居民具备传染病健康素养的比例为9.9%,郊区人口(6.6%)、60岁以上老年人(6.4%)、文盲或半文盲的居民(0.8%)、农民(4.5%)、生产及运输工人(4.3%)和家务及待业人员(4.5%)等人群具备传染病健康素养的比例较低。多因素非条件Logistic分析显示,在控制了其他因素后,年龄、文化程度、职业、身体状况(自我感知)、地区(城郊)等是影响居民传染病健康素养高低的重要因素。结论:北京市居民传染病健康素养水平较低,应针对不同对象特征采取积极措施。
Objective : To assess the status and associated influence factors of health literacy relating to infectious diseases in Beijing. Methods: A multi-stage stratified sampling method was carried out to select the sample population in Beijing, which were adults aged over 18 years. The questionnaire survey was used to collect the information of adult health literacy relating to infectious diseases, and the answers were scored. Logistic regression model was used to analyze the influence factors of health literacy relating to infectious diseases. Results: The samples were 13 287 people, and the valid questionnaires were 13 001. In the study, 9.9% of the respondents were classified with enough health literacy relating to infectious diseases, and the low level of health literacy was observed among the respondents living in rural area (6.6%), elderly aged over 60 years (6.4%), poor-educated people (0.8%), farmers (4.5%), workers (4.3%), house-holders and people waiting for employment (4.5%). Multiple Logistic analyses indicated that age, educational status, occupation, self-reported health status, and regions (urban or rural area) were the influence factors associated with the level of health literacy. Conclusion: The residents living in Beijing were considered to be with low level of health literacy relating to infectious disea- ses, and more measures should be taken to improve it.
出处
《北京大学学报(医学版)》
CAS
CSCD
北大核心
2012年第4期607-611,共5页
Journal of Peking University:Health Sciences
关键词
健康知识
态度
实践
传染病
危险因素
健康教育
LOGISTIC模型
Health knowledge, attitudes, practice
Communicable diseases
Risk factors
Health education
Logistic models