Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil

被引:41
|
作者
Raymundo, Carlos Eduardo [1 ]
Oliveira, Marcella Cini [2 ]
Eleuterio, Tatiana de Araujo [1 ,3 ]
Andre, Suzana Rosa [4 ]
da Silva, Marcele Goncalves [2 ]
Queiroz, Eny Regina da Silva [1 ]
Medronho, Roberto de Andrade [1 ,2 ]
机构
[1] Univ Fed Rio de Janeiro, Inst Estudos Saude Colet, Rio De Janeiro, RJ, Brazil
[2] Univ Fed Rio de Janeiro, Fac Med, Rio De Janeiro, RJ, Brazil
[3] Univ Estado Rio de Janeiro, Dept Enfermagem Saude Publ, Rio De Janeiro, RJ, Brazil
[4] Univ Fed Rio de Janeiro, Escola Enfermagem Anna Nery, Rio De Janeiro, RJ, Brazil
来源
PLOS ONE | 2021年 / 16卷 / 03期
关键词
D O I
10.1371/journal.pone.0247794
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil's municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic's spread in the country. Methods This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). Findings The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Discussion Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.
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页数:16
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