| Literature DB >> 32936443 |
Maurício Polidoro1, Francisco de Assis Mendonça2, Stela Nazareth Meneghel3, Alan Alves-Brito3, Marcelo Gonçalves4, Fernanda Bairros3, Daniel Canavese3.
Abstract
The current health, political, and environmental crisis ongoing in Brazil and the advances of the impacts of COVID-19 in traditional populations (as indigenous and quilombolas) are not yet prioritized in the scientific production about the novel coronavirus. We performed spatial correlation analysis to map the clusters and outliers of COVID-19 in South of Brazil to identify indigenous and quilombolas communities impacted right now in the pandemic. We show that communities located nearby metropolitan areas and mid-sized cities are the most impacted by the COVID-19 and the advance of the transmission to inner states may intensify the ongoing historical process of elimination of indigenous and quilombolas people. We call for a global response to the indigenous and quilombolas situation in Brazil, pointing to the need of more analysis in the country.Entities:
Keywords: Brazil; COVID-19; Indigenous; Quilombolas
Mesh:
Year: 2020 PMID: 32936443 PMCID: PMC7493698 DOI: 10.1007/s40615-020-00868-7
Source DB: PubMed Journal: J Racial Ethn Health Disparities ISSN: 2196-8837
Population residing in selected geographical units per race/color, 2010
| Scale | Race/color | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Caucasian | Black* | Asian | Mixed** | Indigenous | ||||||
| n | % | N | % | n | % | n | % | n | % | |
| Brazil | 91,051,646 | 47.7 | 14,517,961 | 7.6 | 2,084,288 | 1.0 | 82,277,333 | 43.1 | 817,963 | 0.4 |
| North | 3,720,168 | 23.4 | 1,053,053 | 6.6 | 173,509 | 1.0 | 10,611,342 | 66.8 | 305,873 | 1.9 |
| Northeast | 15,627,710 | 29.4 | 5,058,802 | 9.5 | 631,009 | 1.1 | 31,554,475 | 59.4 | 208,691 | 0.3 |
| Southeast | 44,330,981 | 55.1 | 6,356,320 | 7.9 | 890,267 | 1.1 | 28,684,715 | 35.6 | 97,960 | 0.1 |
| South | 21,490,997 | 78.4 | 1,109,810 | 4.0 | 184,904 | 0.6 | 4,525,979 | 16.5 | 74,945 | 0.2 |
| Midwest | 5,881,790 | 41.8 | 939,976 | 6.6 | 204,599 | 1.4 | 6,900,822 | 49.0 | 130,494 | 0.9 |
| Paraná State | 7,344,122 | 70.3 | 330,830 | 3.1 | 123,205 | 1.1 | 2,620,378 | 25.0 | 25,915 | 0.2 |
| Santa Catarina State | 5,246,868 | 83.97 | 183,857 | 2.94 | 26,017 | 0.42 | 775,558 | 12.4 | 16,041 | 0.2 |
| Rio Grande do Sul State | 8,900,007 | 83.22 | 595,123 | 5.57 | 35,682 | 0.33 | 1,130,043 | 10.5 | 32,989 | 0.3 |
*preto; **pardo. Source: Brazilian Institute of Geography and Statistics [27]
Information on health conditions in selected geographical units, 2010
| Scale | Life expectancy at birth (2010) | Child mortality rate (2010) | Percent of people in extreme poverty (2010) |
|---|---|---|---|
| Brazil | 74.4 | 16.7 | 6.6 |
| North | 72.2 | 18.4 | 12.8 |
| Northeast | 71.7 | 22.7 | 14.9 |
| Southeast | 75.2 | 14.3 | 2.3 |
| South | 75.5 | 12.3 | 1.6 |
| Midwest | 75.2 | 15.7 | 2.8 |
| Paraná state | 74.8 | 13.0 | 1.9 |
| Santa Catarina state | 76.6 | 11.5 | 1.0 |
| Rio Grande do Sul state | 75.3 | 12.3 | 1.9 |
Source: Brazilian Institute of Geography and Statistics [28]
Fig. 1Indigenous special health districts (coverage area)
Fig. 2Indigenous and quilombola territories and the coverage area of the indigenous special health districts
Concentration of clusters and outliers in indigenous communities of southern Brazilian states, 2020
| Scale | Indigenous communities | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| N | N Cluster HH | % Cluster HH | N Outlier HL | % Outlier HL | N Outlier LH | % Outlier LH | N Cluster LL | % Cluster LL | |
| Paraná State | 29 | 1 | 3.4 | 0 | 0 | 0 | 0.0 | 4 | 13.8 |
| Santa Catarina State | 29 | 9 | 31.0 | 5 | 17.2 | 11 | 37.9 | 0 | 0 |
| Rio Grande do Sul | 44 | 6 | 13.6 | 4 | 9.1 | 4 | 9.1 | 5 | 11.4 |
Source: Authors, based on data by the Ministry of Health
Fig. 3Indigenous and quilombola territories and the Global Moran’s I
Concentration of clusters and outliers in quilombola communities of southern Brazilian states, 2020
| Scale | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| N | N Cluster HH | % Cluster HH | N Outlier HL | % Outlier HL | N Outlier LH | % Outlier LH | N Cluster LL | % Cluster LL | |
| Paraná | 34 | 0 | 0 | 0 | 0 | 4 | 11.8 | 0 | 0 |
| Santa Catarina | 6 | 2 | 33.3 | 0 | 0 | 0 | 0 | 1 | 16.7 |
| Rio Grande do Sul | 131 | 11 | 8.4 | 2 | 1.5 | 1 | 0.8 | 7 | 5.3 |
Source: Authors, based on data by the Ministry of Health