Abstract
米白冰,赵亚玲,党少农,李强,杨睿海,颜虹.应用分位数回归对陕西省汉中市农村居民健康相关生命质量及其影响因素的分析[J].Chinese journal of Epidemiology,2015,36(10):1148-1152
应用分位数回归对陕西省汉中市农村居民健康相关生命质量及其影响因素的分析
Quantile regression analysis of health-related quality of life of rural residents in Shaanxi and its associated factors
Received:March 02, 2015  
DOI:10.3760/cma.j.issn.0254-6450.2015.10.024
KeyWord: 健康相关生命质量  影响因素  分位数回归  农村居民
English Key Word: Health-related quality of life  Influencing factors  Quantile regression  Rural resident
FundProject:国家自然科学基金(81230016);美国中华医学基金会(08-925)
Author NameAffiliationE-mail
Mi Baibing Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi'an 710061, China  
Zhao Yaling Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi'an 710061, China  
Dang Shaonong Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi'an 710061, China  
Li Qiang Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi'an 710061, China  
Yang Ruihai Department of Cardiovascular Diseases, Hanzhong People's Hospital  
Yan Hong Department of Epidemiology and Biostatistics, School of Public Health, Health Science Center of Xi'an Jiaotong University, Xi'an 710061, China xjtu_yh.paper@aliyun.com 
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Abstract:
      目的 应用分位数回归分析陕西省汉中市农村居民健康调查数据,探讨当地居民健康相关生命质量(HRQOL)的分布特点和影响因素,并展示分位数回归应用于HRQOL分析的价值。方法 使用横断面调查获得的2 737名被调查者的资料,采用SF-36量表评估被调查者的HRQOL现状,应用分位数回归模型分析精神健康状况(MCS)和躯体健康状况(PCS)得分,并了解HRQOL状态及其影响因素。结果 汉中市农村居民HRQOL分布情况与国内其他地区类似,但不同分位点MCS和PCS得分的影响因素及其影响程度有差异。整体而言,婚姻状况、教育程度、体力活动、既往疾病史对MCS和PCS得分存在显著影响。结论 了解汉中市农村居民HRQOL分布特征及其影响因素,可有针对性地采取措施提升当地居民的HRQOL。
English Abstract:
      Objective This study aimed to apply quantile regression to study Hanzhong rural residents health survey data, explore the local distribution characteristics of health-related quality of life (HRQOL) and influencing factors and present the value of quantile regression applying in analysis of HRQOL. Methods In this cross-sectional population-based study, we evaluated the HRQOL of 2 737 subjects through filling Short-Form Health Survey (SF-36). Quantile regression model was used to compare MCS and PCS scores and evaluate the associated factors. Results With different quantiles MCS and PCS score, the associated factors and influence degree were different. In general, the influences of marital status, educational level, physical activity, history of disease and HRQOL in the part of the percentile scores were significant. Conclusion Analysis of the distribution of HRQOL of rural residents in Hanzhong and influencing factors would benefit the improvement of HRQOL of local residents.
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