■在中国慢性病和危险因素监测中,使用了核心健康日措施来跟踪人群水平的健康状况;但是,它们不容易组合在一起,以创建总体健康相关生活质量(HRQOL)的摘要,限制此指示器的使用。
■本研究旨在根据中文版本的核心健康日指标(HRQOL-5)制定汇总评分,并将其用于估计中国人群的HRQOL及其决定因素。
■2018年11月至2019年5月,对潍坊市15岁以上常住人口进行了多阶段分层整群调查,调查人群健康状况和行为危险因素,山东省,中国。进行了探索性因素分析和验证性因素分析,以揭示HRQOL-5的潜在结构,然后通过计算其汇总分数来量化整体HRQOL。最后进行Tobit回归模型,找出汇总得分的影响因素。
■本研究共纳入26,269名参与者(男性:n=13,571,51.7%;平均年龄55.9,SD14.9岁)。共有71%(n=18,663)的受访者表示他们的总体健康状况良好或非常好。使用探索性因子分析提取一个汇总因子以捕获总体HRQOL。验证性因子分析进一步证实了这种单因素模型(塔克-刘易斯指数,比较拟合指数,且拟合优度指数>0.90;均方根误差近似为0.02)。多因素Tobit回归分析显示,年龄(β=-0.06),教育造诣(小学:β=0.72;初中:β=1.46;高中以上:β=2.58),平均收入(≥30,000日元[4200美元]:β=0.69),身体活动(β=0.75),饮酒(β=0.46),自我报告疾病(β=-6.36),自我报告损伤(β=-5.00)是影响HRQOL-5总分的主要因素。
■这项研究从HRQOL-5中构建了一个汇总评分,提供了人口水平HRQOL的综合表示。不同亚群汇总得分的差异可能有助于确定中国卫生规划的优先事项,以改善人口HRQOL。
UNASSIGNED: The core Healthy Days measures were used to track the population-level health status in the
China Chronic Disease and Risk Factor Surveillance; however, they were not easily combined to create a summary of the overall health-related quality of life (HRQOL), limiting this indicator\'s use.
UNASSIGNED: This study aims to develop a summary score based on the Chinese version of the core Healthy Days measures (HRQOL-5) and apply it to estimate HRQOL and its determinants in a Chinese population.
UNASSIGNED: From November 2018 to May 2019, a multistage stratified cluster survey was conducted to examine population health status and behavioral risk factors among the resident population older than 15 years in Weifang City, Shandong Province,
China. Both exploratory factor analyses and confirmatory factor analyses were performed to reveal the underlying latent construct of HRQOL-5 and then to quantify the overall HRQOL by calculating its summary score. Tobit regression models were finally carried out to identify the influencing factors of the summary score.
UNASSIGNED: A total of 26,269 participants (male: n=13,571, 51.7%; mean age 55.9, SD 14.9 years) were included in this study. A total of 71% (n=18,663) of respondents reported that they had excellent or very good general health. One summary factor was extracted to capture overall HRQOL using exploratory factor analysis. The confirmatory factor analysis further confirmed this one-factor model (Tucker-Lewis index, comparative fit index, and goodness-of-fit index >0.90; root mean square error of approximation 0.02). Multivariate Tobit regression analysis showed that age (β=-0.06), educational attainments (primary school: β=0.72; junior middle school: β=1.46; senior middle school or more: β=2.58), average income (≥¥30,000 [US $4200]: β=0.69), physical activity (β=0.75), alcohol use (β=0.46), self-reported disease (β=-6.36), and self-reported injury (β=-5.00) were the major influencing factors on the summary score of the HRQOL-5.
UNASSIGNED: This study constructs a summary score from the HRQOL-5, providing a comprehensive representation of population-level HRQOL. Differences in summary scores of different subpopulations may help set priorities for health planning in
China to improve population HRQOL.