Keywords
factores socio-demográficos
modelos lineales generalizados (GLM)
regresión de Poisson
regresión ponderada geográficamente (GWR)
Costa Rica
COVID-19
socio-demographic factors
generalized linear models (GLM)
Poisson regression
geographically weighted regression (GWR)
Costa Rica.
How to Cite
Abstract
Introduction: The coronavirus disease (COVID-19) has spread among the population of Costa Rica and has had a great world impact. However, there are important geographic differences in mortality from COVID-19 between the different regions in the world and within Costa Rica. Objective: The main objective of this article was to explore the effect of some sociodemographic factors on COVID-19 mortality in the cantons of Costa Rica, from a geographical perspective. Methods: Data on mortality from COVID-19 and sociodemographic information were obtained for the cantons of Costa Rica. The classical epidemiological Poisson regression model of the family of generalized linear models (GLM) is compared with the geographically weighted regression model (GWR). Results: Compared to the GLM regression model, a significantly lower Akaike Information Criterion (AIC) was obtained in the GWR model (927.1 in GLM versus 358.4 in GWR). The cantons with a higher population density, higher material well-being, lower number of population by health service units and that are located near the Pacific coasts of Costa Rica had a higher risk of mortality from COVID-19. Conclusions: There are potential effects of sociodemographic factors on COVID-19 mortality, however the findings and methodology of this study could guide other countries to help a better understanding of the local transmission of COVID-19 and design a focused and specific intervention strategy. for those countries.
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