摘要
目的了解湖北省居民两周患病首诊机构选择状况及影响因素,为合理配置卫生资源和制定卫生政策提供依据。方法采用多阶段分层整群抽样方法抽取标本,入户进行问卷调查。运用多水平模型分析影响患者首诊机构选择的相关因素。结果湖北省居民两周患病首诊选择基层医疗机构的比例,城市为64.5%,农村为84.3%,各机构首诊比例随着医疗机构等级提高而降低。患者选择首诊机构与其来自地区(城市或农村,OR=0.463,95%CI:0.254~0.842)、年龄(OR=1.023,95%CI:1.010~1.036)、文化程度(OR>1.000)、病伤持续时间(OR=0.945,95%CI:0.917~0.973)、因病卧床时间(OR=0.854,95%CI:0.825~0.884)有关。结论湖北省居民两周患病基层医疗机构首诊比例较高,患者来自地区、年龄、文化程度和患病天数影响其首诊机构选择。
Objective To explore the selection of medical unit and the major influencing factors among residents in Hubei province, to allocate reasonably the health resources and provide reference for developing medical policy. Methods With the method of multi-stage stratified cluster sampling, household survey were done. The multilevel statistical model was used to analyze the influencing factors of the first diagnosed agencies. Results The proportions of residents who chose primary medical institutions as the first diagnosed agencies were 64.5 % in urban areas and 84.3 % in rural areas,and the visiting rate decreased as the level of health care institutions increased. The selection of first diagnosed agencies among patients were related to district （city or village,OR=0. 463,95%CI.0. 254-0. 842） ,age （OR= 1. 023,95%CI：1. 010-1. 036） ,the educational attainment （ORal. 000） ,illness duration in days （OR=0. 945,95 %CI.0. 917-0. 973） and number of days in bed （OR：O. 854,95%CI. 0. 825-0. 884）. Conclusion The residents who chose primary medical institutions as the first diagnosed agencies took a large proportion. District, age, the educational attainment and the illness duration in days had influence on the selection of the first diagnosed agencies among residents.
作者
何首杰
杨银梅
王伟忠
潘琦
燕虹
李十月
HE Shoujie1 ,YANG Yinmei1 ,WANG Weizhong1 ,PAN Qi2 ,YAN Hong1 ,LI Shiyue1(1. School of Health Sciences, Wuhan University, Wuhan , Hubei 430071, China ; 2. Department of Endocrinology, Beijing Hospital, Beijing 100730, Chin)
出处
《重庆医学》
CAS
2018年第13期1773-1776,1780,共5页
Chongqing medicine
基金
国家自然科学基金资助项目(81673196)
关键词
首诊机构
影响因素
多水平模型
first diagnosed agencies
influencing factors
multilevel statistical model
