基于截断最小绝对离差模型慢性病患者健康相关生命质量及其影响因素研究:以甘肃、河北、四川、浙江为例

Health-related Quality of Life and Its Influencing Factors in Chronic Disease Patients:a CLAD Regression Analysis of the Survey Data from Gansu,Hebei,Sichuan and Zhejiang

背景当前慢性病已成为困扰威胁我国居民生命健康的一类重要疾病,严重降低患者的健康相关生命质量(HRQoL),给患者带来了沉重的疾病负担。目的计算甘肃、河北、四川、浙江4省慢性病患者健康效用值,评估4省慢性病患者HRQoL并探讨其影响因素。方法于2017年5月—2018年5月,采用分层抽样与随机抽样结合的方法,选取4省慢性病患者为研究对象,采用问卷和五维健康评定(EQ-5D-5L)量表对健康效用值进行调查,通过描述性统计、Tobit模型、截断最小绝对离差(CLAD)模型对其HRQoL及其影响因素进行评估分析。结果4省慢性病患者的健康效用值在[-0.3910,1]。4省不同特征慢性病患者的健康效用值存在差异,不同年龄、婚姻状况、就业状况、月收入水平、BMI、医疗保险类型慢性病患者的健康效用值比较,差异有统计学意义(P<0.05)。Tobit回归结果显示:年龄、婚姻状况是4省慢性病患者HRQoL的影响因素(P<0.05);CLAD回归结果显示:年龄、婚姻状况、就业状况,BMI,是否拥有健康档案是4省慢性病患者HRQoL的影响因素(P<0.05)。结论本研究中对于慢性病患者HRQoL影响因素分析,CLAD模型的回归效果要优于Tobit模型。除了户籍、婚姻状况、就业状况等社会人口学因素,BMI、过去一年内是否进行过体检、慢性病数量、医保报销比例、是否有健康档案等因素值得被重点关注。不同省份应根据省内实际特征,制定相应的卫生服务政策。

Background Chronic diseases have become an important kind of diseases that haunt and threaten the health of residents,which seriously reduce the health-related quality of life in the suffered ones,and bring a heavy disease burden on them.Objective To calculate the health utility value,and assess the health-related quality of life and its influencing factors in chronic disease patients in four provinces(Gansu,Hebei,Sichuan and Zhejiang).Methods From May 2017 to May 2018,by use of stratified sampling and random sampling,chronic disease patients from four provinces were enrolled,and were surveyed with a questionnaire developed by our research group for collecting sociodemographics and healthcare behaviors and utilization data,and with the Chinese version of EQ-5 D-5 L for assessing the health utility value.Health-related quality of life and its influencing factors were analyzed by descriptive statistical analysis,Tobit model,and censored least absolute deviations(CLAD)model.Results The health utility value of patients with chronic diseases in the four provinces ranged from-0.3910 to 1.Personal characteristics were associated with the differences in health utility value(P<0.05).To be specific,health utility value differed significantly by age,marital status,employment status,monthly income,BMI and type of medical insurance(P<0.05).Tobit regression results showed that age and marital status were the influencing factors of health-related quality of life(P<0.05).And CLAD regression results showed that age,marital status,employment status,BMI,and whether having a health file were the influencing factors of health-related quality of life(P<0.05).Conclusion Our study found that CLAD regression analysis was superior to Tobit regression analysis in terms of identifying the factors affecting the health-related quality of life in chronic disease patients.Besides household register,marital status,and employment status,other sociodemographic factors such as BMI,whether having a medical examination in the past year,number of chronic diseases,and ratio of treatment cost reimbursed by medical insurance are worthy of special attention.In addition,regional healthcare policies should be developed based on the local practical characteristics.