巴西伯南布哥州社会经济因素的空间分析及其与 COVID-19 病例的关系。
Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil.
机构信息
Department of Cartographic and Survey Engineering, Federal University of Pernambuco, Recife, Brazil.
Center of Agroforestry Sciences and Technologies, Federal University of Southern Bahia, Itabuna, Brazil.
出版信息
Trop Med Int Health. 2022 Apr;27(4):397-407. doi: 10.1111/tmi.13731. Epub 2022 Feb 11.
OBJECTIVES
To analyse the spatial distribution of rates of COVID-19 cases and its association with socio-economic conditions in the state of Pernambuco, Brazil.
METHODS
Autocorrelation (Moran index) and spatial association (Geographically weighted regression) models were used to explain the interrelationships between municipalities and the possible effects of socio-economic factors on rates.
RESULTS
Two isolated clusters were revealed in the inner part of the state in sparsely inhabited municipalities. The spatial model (Geographically Weighted Regression) was able to explain 50% of the variations in COVID-19 cases. The variables proportion of people with low income, percentage of rented homes, percentage of families in social programs, Gini index and running water had the greatest explanatory power for the increase in infection by COVID-19.
CONCLUSIONS
Our results provide important information on socio-economic factors related to the spread of COVID-19 and can serve as a basis for decision-making in similar circumstances.
目的
分析巴西伯南布哥州 COVID-19 病例率的空间分布及其与社会经济条件的关系。
方法
使用自相关（莫兰指数）和空间关联（地理加权回归）模型来解释各市镇之间的相互关系，以及社会经济因素对发病率的可能影响。
结果
在该州内部人口稀少的市镇发现了两个孤立的集群。空间模型（地理加权回归）能够解释 COVID-19 病例变化的 50%。收入低的人口比例、出租房屋的百分比、社会计划中的家庭百分比、基尼指数和自来水等变量对 COVID-19 感染的增加具有最大的解释力。
结论
我们的结果提供了与 COVID-19 传播有关的社会经济因素的重要信息，可以为类似情况下的决策提供依据。
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