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本文引用的文献

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The socio-economic determinants of COVID-19: A spatial analysis of German county level data.新冠疫情的社会经济决定因素:基于德国县级数据的空间分析
Socioecon Plann Sci. 2021 Dec;78:101083. doi: 10.1016/j.seps.2021.101083. Epub 2021 May 14.
2
Demographic and socio-economic factors, and healthcare resource indicators associated with the rapid spread of COVID-19 in Northern Italy: An ecological study.人口统计学和社会经济因素,以及与意大利北部 COVID-19 快速传播相关的医疗保健资源指标:一项生态学研究。
PLoS One. 2020 Dec 28;15(12):e0244535. doi: 10.1371/journal.pone.0244535. eCollection 2020.
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Regional and global contributions of air pollution to risk of death from COVID-19.空气污染对 COVID-19 死亡风险的区域和全球贡献。
Cardiovasc Res. 2020 Dec 1;116(14):2247-2253. doi: 10.1093/cvr/cvaa288.
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Spatial analysis of COVID-19 spread in Iran: Insights into geographical and structural transmission determinants at a province level.伊朗 COVID-19 传播的空间分析:省级层面地理和结构传播决定因素的见解。
PLoS Negl Trop Dis. 2020 Nov 18;14(11):e0008875. doi: 10.1371/journal.pntd.0008875. eCollection 2020 Nov.
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A country level analysis measuring the impact of government actions, country preparedness and socioeconomic factors on COVID-19 mortality and related health outcomes.一项国家级分析,衡量政府行动、国家准备情况和社会经济因素对新冠肺炎死亡率及相关健康结果的影响。
EClinicalMedicine. 2020 Aug;25:100464. doi: 10.1016/j.eclinm.2020.100464. Epub 2020 Jul 21.
6
COVID-19 Pandemic and Burden of Non-Communicable Diseases: An Ecological Study on Data of 185 Countries.新冠疫情与非传染性疾病负担:一项基于185个国家数据的生态学研究
J Stroke Cerebrovasc Dis. 2020 Sep;29(9):105089. doi: 10.1016/j.jstrokecerebrovasdis.2020.105089. Epub 2020 Jun 25.
7
Human mobility and coronavirus disease 2019 (COVID-19): a negative binomial regression analysis.人口流动与 2019 年冠状病毒病(COVID-19):负二项回归分析。
Public Health. 2020 Aug;185:364-367. doi: 10.1016/j.puhe.2020.07.002. Epub 2020 Jul 10.
8
Rich at risk: socio-economic drivers of COVID-19 pandemic spread.富人面临风险:新冠疫情传播的社会经济驱动因素
Clin Mol Allergy. 2020 Jul 1;18:12. doi: 10.1186/s12948-020-00127-4. eCollection 2020.
9
Impacts of social and economic factors on the transmission of coronavirus disease 2019 (COVID-19) in China.社会经济因素对中国2019冠状病毒病(COVID-19)传播的影响
J Popul Econ. 2020;33(4):1127-1172. doi: 10.1007/s00148-020-00778-2. Epub 2020 May 9.
10
Importance of collecting data on socioeconomic determinants from the early stage of the COVID-19 outbreak onwards.重视从 COVID-19 疫情早期开始收集社会经济决定因素相关数据。
J Epidemiol Community Health. 2020 Aug;74(8):620-623. doi: 10.1136/jech-2020-214297. Epub 2020 May 8.

《COVID-19 大流行的社会经济、人口和医疗保健决定因素:对西班牙的生态研究》。

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

机构信息

Alfonso X University, Madrid, Spain.

出版信息

BMC Public Health. 2021 Mar 29;21(1):606. doi: 10.1186/s12889-021-10658-3.

DOI:10.1186/s12889-021-10658-3
PMID:33781245
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8006121/
Abstract

BACKGROUND

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.

METHODS

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.

RESULTS

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".

CONCLUSIONS

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.

摘要

背景

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。

方法

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。

结果

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。

结论

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。