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巴西小地区 COVID-19 死亡和感染的空间模式。

Spatial pattern of COVID-19 deaths and infections in small areas of Brazil.

机构信息

College of Philosophy and Human Sciences (IFCH), University of Campinas (UNICAMP), Campinas, Brazil.

Department of Statistics, Federal University of Technology, Akure, Nigeria.

出版信息

PLoS One. 2021 Feb 11;16(2):e0246808. doi: 10.1371/journal.pone.0246808. eCollection 2021.

Abstract

As of mid-August 2020, Brazil was the country with the second-highest number of cases and deaths by the COVID-19 pandemic, but with large regional and social differences. In this study, using data from the Brazilian Ministry of Health, we analyze the spatial patterns of infection and mortality from Covid-19 across small areas of Brazil. We apply spatial autoregressive Bayesian models and estimate the risks of infection and mortality, taking into account age, sex composition of the population and other variables that describe the health situation of the spatial units. We also perform a decomposition analysis to study how age composition impacts the differences in mortality and infection rates across regions. Our results indicate that death and infections are spatially distributed, forming clusters and hotspots, especially in the Northern Amazon, Northeast coast and Southeast of the country. The high mortality risk in the Southeast part of the country, where the major cities are located, can be explained by the high proportion of the elderly in the population. In the less developed areas of the North and Northeast, there are high rates of infection among young adults, people of lower socioeconomic status, and people without access to health care, resulting in more deaths.

摘要

截至 2020 年 8 月中旬,巴西是 COVID-19 大流行病例和死亡人数第二高的国家,但存在较大的地区和社会差异。在这项研究中,我们使用巴西卫生部的数据,分析了巴西小地区 COVID-19 感染和死亡的空间模式。我们应用空间自回归贝叶斯模型,并考虑到年龄、人口性别构成以及描述空间单位健康状况的其他变量,估计了感染和死亡率的风险。我们还进行了分解分析,以研究年龄构成如何影响各地区死亡率和感染率的差异。我们的结果表明,死亡和感染呈空间分布,形成集群和热点,特别是在亚马逊北部、东北部沿海和东南部地区。该国东南部地区(主要城市所在地)的高死亡率风险可以用人口中老年人的比例较高来解释。在北部和东北部欠发达地区,年轻人、社会经济地位较低的人和无法获得医疗保健的人感染率较高,导致死亡人数较多。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f70c/7877657/a568e8545399/pone.0246808.g001.jpg

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