Spatial analysis of socio-economic factors and their relationship with the cases of COVID-19 in Pernambuco, Brazil.

Autor: da Silva CFA; Department of Cartographic and Survey Engineering, Federal University of Pernambuco, Recife, Brazil., Silva MC; Department of Cartographic and Survey Engineering, Federal University of Pernambuco, Recife, Brazil., Dos Santos AM; Center of Agroforestry Sciences and Technologies, Federal University of Southern Bahia, Itabuna, Brazil., Rudke AP; Department of Sanitary and Environmental Engineering, Federal University of Minas Gerais, Belo Horizonte, Brazil.; Federal University of Technology - Paraná, Londrina, Brazil., do Bonfim CV; Social Research Department, Joaquim Nabuco Foundation, Recife, Brazil.; Postgraduate Program in Collective Health, Federal University of Pernambuco, Recife, Brazil., Portis GT; Federal Institute of Goiás, Goiânia, Brazil., de Almeida Junior PM; Department of Statistics, Center of Nature and Exact Sciences, Federal University of Pernambuco, Recife, Brazil., Coutinho MBS; Department of Statistics, Center of Nature and Exact Sciences, Federal University of Pernambuco, Recife, Brazil.
Jazyk: angličtina
Zdroj: Tropical medicine & international health : TM & IH [Trop Med Int Health] 2022 Apr; Vol. 27 (4), pp. 397-407. Date of Electronic Publication: 2022 Feb 11.
DOI: 10.1111/tmi.13731
Abstrakt: 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.
(© 2022 John Wiley & Sons Ltd.)
Databáze: MEDLINE
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