Impact of COVID-19 on the indigenous population of Brazil: a geo-epidemiological study

Abstract

This study aimed to analyse the geographical distribution of COVID-19 and to identify highrisk areas in space and time for the occurrence of cases and deaths in the indigenous population of Brazil. This is an ecological study carried out between 24 March and 26 October 2020 whose units of analysis were the Special Indigenous Sanitary Districts. The Getis-Ord General G and Getis-Ord Gi* techniques were used to verify the spatial association of the phenomena and a retrospective space−time scan was performed. There were 32041 confirmed cases of COVID-19 and 471 deaths. The non-randomness of cases (z score = 5.40; p <0.001) and deaths (z score = 3.83; p <0.001) were confirmed. Hotspots were identified for cases and deaths in the north and midwest regions of Brazil. Sixteen high-risk space−time clusters were identified for the occurrence of cases with a higher RR=21.23 (p <0.001) and four risk clusters for deaths with a higher RR=80.33 (p <0.001). These clusters were identified from 22 May and were active until 10 October 2020. The results indicate critical areas in the indigenous territories of Brazil and contribute to better directing the actions of control of COVID-19 in this population.

Epidemiological characteristics and temporal-spatial analysis of overseas imported dengue fever cases in outbreak provinces of China, 2005–2019

“…Spatial autocorrelation analysis [ 25 ], which includes global autocorrelation and local autocorrelation, can be used to analyze the spatial correlation of a variable. In this study, we used global Moran's I [ 26 , 27 ] and Getis-Ord General G analysis [ 28 , 29 ] to estimate the global autocorrelation. The value range of Moran's I index is between −1 and 1.…”

Spatiotemporal pattern of Covid-19 outbreak in Turkey

“…In this context, spatial autocorrelation was employed to examine whether Covid-19 has noteworthy global or local spatial autocorrelation features (Xiong et al, 2020). Global or local measures of spatial autocorrelation have been used to investigate the spatial patterns and define hot spots and cold spots of the epidemic in many researches (Alves et al, 2021;Castro et al, 2021;Das et al, 2021;Ghosh & Cartone, 2020;Islam et al, 2021;Kuznetsov & Sadovskaya, 2021;Maithani et al, 2020;Maroko et al, 2020). Spatial statistical methods have also been employed in previous years to comprehend spatial patterns of epidemic risks (Bailey, 2001;Bailey et al, 2011;Cao et al, 2010;Cromley, 2003;Ge et al, 2016;Hu et al, 2013;Tsai et al, 2009;Wang et al, 2008;Wen et al, 2006).…”

Impact of the COVID-19 Pandemic on Infectious Diseases in Brazil: A Case Study on Dengue Infections

“…They are an at-risk group, having had a history of being disproportionately burdened by infectious diseases [10]. During the H1N1 influenza pandemic in 2009, the death rate was 4.5 times higher among the Indigenous population than other ethnic groups and 6.5 times higher during the COVID-19 pandemic [11]. The same disparity exists among Pardo and Black Brazilians, who present a higher risk of death from COVID-19 infection than other racial groups.…”