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Socioeconomic, demographic and healthcare determinants of the COVID-19 pandemic: an ecological study of Spain

机译:Covid-19大流行的社会经济,人口和医疗保健决定因素:西班牙的生态研究

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The coronavirus disease 2019 (COVID-19) pandemic has posed a major challenge to health, economic and political systems around the world. Understanding the socioeconomic, demographic and health determinants affecting the pandemic is of interest to stakeholders. The purpose of this ecological study is to analyse the effect of the different socioeconomic, demographic and healthcare determinants on the mortality rate and estimated cumulative incidence of COVID-19 first wave in the Spanish regions. From the available data of the 17 Spanish regions (Autonomous Communities), we have carried out an ecological study through multivariate linear regression using ordinary least squares. To do this, we conducted an analysis using two distinct dependent variables: the logarithm of mortality rate per 1,000,000 inhabitants and the estimated cumulative incidence. The study has 12 explanatory variables. After applying the backward stepwise multivariate analysis, we obtained a model with nine significant variables at different levels for mortality rate and a model with seven significant variables for estimated cumulative incidence. Among them, six variables are statistically significant and of the same sign in both models: “Nursing homes beds”, “Proportion of care homes over 100 beds”, “Log GDP per capita”, “Aeroplane passengers”, “Proportion of urban people”, and the dummy variable “Island region”. The different socioeconomic, demographic and healthcare determinants of each region have a significant effect on the mortality rate and estimated cumulative incidence of COVID-19 in territories where the measures initially adopted to control the pandemic have been identical.
机译:2019年冠状病毒疾病(Covid-19)大流行为世界各地的健康,经济和政治制度构成了重大挑战。了解影响大流行的社会经济,人口和健康决定因素对利益相关者感兴趣。该生态研究的目的是分析不同社会经济,人口统计学和医疗决定簇对西班牙地区Covid-19第一波的死亡率和估计累积发生率的影响。从17个西班牙地区(自主社区)的可用数据,我们通过使用普通最小二乘来通过多变量线性回归进行了生态研究。为此,我们使用两个不同的依赖变量进行了分析:每1000,000名居民死亡率和估计累积发病率的对数。该研究有12个解释性变量。在施加后向逐步多变量分析后,我们在不同水平下获得了九个显着变量的模型,用于死亡率和七种显着变量的模型,用于估计累积发病率。其中,六个变量在统计上显着,两种型号相同的标志:“护理家庭床”,“护理房屋比例超过100张床”,“人均”GDP人均“,”飞机乘客“,”城市人数“比例” “,和虚拟变量”岛屿区域“。每个地区的不同社会经济,人口和医疗决定簇对地区的死亡率和估计的Covid-19估计累计发生率有显着影响,其中最初通过控制大流行的措施相同。

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