Alfonso X University, Madrid, Spain.
BMC Public Health. 2021 Mar 29;21(1):606. doi: 10.1186/s12889-021-10658-3.
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.
2019 年冠状病毒病(COVID-19)大流行对全球的卫生、经济和政治系统构成了重大挑战。了解影响大流行的社会经济、人口和卫生决定因素对利益相关者具有重要意义。本生态研究的目的是分析不同的社会经济、人口和医疗保健决定因素对西班牙各地区 COVID-19 第一波死亡率和估计累积发病率的影响。
我们通过多元线性回归使用最小二乘法对 17 个西班牙地区(自治区)的现有数据进行了生态研究。为此,我们使用两个不同的因变量进行了分析:每 100 万居民的死亡率的对数和估计的累积发病率。该研究有 12 个解释变量。
在应用逐步向后多元分析后,我们获得了一个死亡率模型,该模型有九个不同水平的显著变量,一个估计累积发病率模型有七个显著变量。其中,六个变量在两个模型中均具有统计学意义且符号相同:“养老院床位”、“超过 100 张床位的养老院比例”、“人均 GDP 对数”、“飞机乘客”、“城市人口比例”和“岛屿地区”虚拟变量。
每个地区的不同社会经济、人口和医疗保健决定因素对采用相同初始措施控制大流行的地区的 COVID-19 死亡率和估计累积发病率有重大影响。