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

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 interrel...

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Published in:Tropical medicine & international health 2022-04, Vol.27 (4), p.397-407
Main Authors: da Silva, Carlos Fabricio Assunção, Silva, Mayara Costa, dos Santos, Alex Mota, Rudke, Anderson Paulo, do Bonfim, Cristine Vieira, Portis, Gabriela Tobias, de Almeida Junior, Pedro Monteiro, Coutinho, Maria Beatriz de Santana
Format: Article
Language:English
Subjects:
Brazil - epidemiology
COVID-19
COVID-19 - epidemiology
Decision making
Economic analysis
Economic conditions
Economic Factors
Economics
geographical dimensions
geographically weighted regression
health inequalities
Humans
infectious disease
Municipalities
pandemic
Regression analysis
Regression models
Socioeconomic Factors
Spatial Analysis
Spatial distribution
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Summary: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.
ISSN:1360-2276
1365-3156
1365-3156
DOI:10.1111/tmi.13731