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陕西省慢性病行为危险因素聚类特征分析
引用本文:王维华,飒日娜,邱琳,王艳平,刘蓉,胡志平,陶雅丽,刘莹.陕西省慢性病行为危险因素聚类特征分析[J].中国慢性病预防与控制,2021(1):18-22,28.
作者姓名:王维华  飒日娜  邱琳  王艳平  刘蓉  胡志平  陶雅丽  刘莹
作者单位:陕西省疾病预防控制中心慢病科;民航西安医院心内科
摘    要:目的本研究旨在探讨陕西省慢性病行为危险因素的流行、分布及聚集性等特征,为陕西省慢性病的防控提供参考。方法于2015年采用多阶段整群随机抽样的方法抽取陕西省10个监测点18岁及以上的常住居民6330人作为调查对象,调查吸烟、饮酒、饮食、身体活动和超重或肥胖5种慢性疾病的行为危险因素。使用SPSS 25.0进行统计分析,所有率均进行加权处理。计算5种行为危险因素的流行和聚类模式,采用有序logistic回归分析行为危险因素影响因素。结果陕西省18岁及以上居民5种行为危险因素流行率从高到低依次为:水果和蔬菜摄入不足率(55.88%)、超重或肥胖率(46.82%)、现在吸烟率(28.30%)、体力活动不足率(24.99%)和有害饮酒率(2.21%)。54.58%的陕西省18岁及以上居民有2种及以上的行为危险因素,其中水果蔬菜摄入不足(15.53%)和超重或肥胖(12.70%)是陕西省成年人群中最主要的行为危险因素模式。有序logistic回归分析结果显示,女性(OR=0.251,95%CI:0.250~0.251)、受教育程度高(小学OR=0.913,95%CI:0.910~0.915;初中OR=0.863,95%CI:0.861~0.865;高中OR=0.846,95%CI:0.844~0.849;大专及以上OR=0.699,95%CI:0.696~0.701)和家庭年收入较高(中等收入OR=0.672,95%CI:0.671~0.674;高收入OR=0.651,95%CI:0.649~0.653)者行为危险因素个数较少,高龄(45~59岁OR=1.262,95%CI:1.259~1.264;60岁及以上OR=1.314,95%CI:1.310~1.317)、农村(OR=1.488,95%CI:1.486~1.491)、已婚或同居(OR=1.203,95%CI:1.199~1.206)及离婚或丧偶或分居者(OR=1.747,95%CI:1.737~1.758)行为危险因素个数较多。结论陕西省居民中慢性病行为危险因素发生率较高,需针对男性、老年、受教育程度低、低收入人群及农村地区人群采取积极的公共卫生干预措施来减少慢性病危险因素的发生,进而减少和减缓慢性病的发生和发展。

关 键 词:行为危险因素  流行特征  聚集模式

Analysis on clustering pattern of behavioral risk factors of chronic disease in Shaanxi Province
WANG Wei-hua,SA Ri-na,QIU Lin,WANG Yan-ping,LIU Rong,HU Zhi-ping,TAO Ya-li,LIU Ying.Analysis on clustering pattern of behavioral risk factors of chronic disease in Shaanxi Province[J].Chinese Journal of Prevention and Control of Chronic Non-Communicable Diseases,2021(1):18-22,28.
Authors:WANG Wei-hua  SA Ri-na  QIU Lin  WANG Yan-ping  LIU Rong  HU Zhi-ping  TAO Ya-li  LIU Ying
Institution:(Shaanxi Provincial Center for Disease Control and Prevention,Xi'an,Shaanxi Province 710054,China;不详)
Abstract:Objective To explore the prevalence,distribution,and clustering pattern of behavioral risk factors(BRFs)of chronic disease in Shaanxi Province,and to provide the reference for prevention and control of chronic diseases in Shaanxi Province.Methods In 2015,the multi-stage cluster random sampling method was used to select 6330 permanent residents(≥18 years old)from 10 surveillance sites in Shaanxi Province as the subjects,the investigation was performed for 5 BRFs(smoking,drinking,diet,physical activity and overweight or obesity)of chronic diseases.The prevalence and clustering patterns of five BRFs were calculated,all rates were treated with weighting.The ordinal logistic regression was used to analyze the influencing factors of five BRFs.The used software was SPSS 25.0.Results The order of the five BRFs in residents(≥18 years old)of Shaanxi Province was insufficient fruit and vegetable intake(55.88%),overweight or obesity(46.82%),current smoking(28.30%),physical inactivity(24.99%)and harmful drinking rate(2.21%).The proportion of residents(≥18 years old)with≥2 BRFs was 54.58%,among which the fruit and vegetable intake(15.53%)and overweight or obesity(12.70%)were the most important BRFs patterns in adult residents of Shaanxi Province.The ordinal logistic regression showed that females(OR=0.251,95%CI:0.250-0.251),subjects with higher education level(primary school,OR=0.913,95%CI:0.910-0.915;junior middle school,OR=0.863,95%CI:0.861-0.865;senior middle school,OR=0.846,95%CI:0.844-0.849;≥college,OR=0.699,95%CI:0.696-0.701),and higher annual family income(middle income,OR=0.672,95%CI:0.671-0.674;high income,OR=0.651,95%CI:0.649-0.653)had less numbers of BRFs;but the subjects with high age(45-59 years old,OR=1.262,95%CI:1.259-1.264;≥60 years old,OR=1.314,95%CI:1.310-1.317),rural areas(OR=1.488,95%CI:1.486-1.491),marital status(married or cohabit,OR=1.203,95%CI:1.199-1.206;divorced or widowed or separated,OR=1.747,95%CI:1.737-1.758)had more numbers of BRFs.Conclusion The prevalence of BRFs for chronic diseases was high among residents in Shaanxi Province.The measures of active public health intervention should be taken for males,seniors,rural residents,residents with low education level and low income to reduce the occurrence and development BRFs of chronic diseases.
Keywords:Behavioral risk factors  Prevalence characteristics  Clustering pattern
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