Factores socioambientales que contribuyen a alta incidencia de COVID-19 en ciudad fronteriza del norte de Uruguay
DOI:
https://doi.org/10.17058/reci.v14i1.18597Palabras clave:
COVID-19, Spatial-Temporal Analysis, Uruguay, Social Mobility, Socioeconomic Level, Population DensityResumen
Justificación y Objetivos: el estudio se realizó en la ciudad de Rivera, situada en el norte del país en la frontera con Brasil. La enfermedad progresó lentamente durante 2020, con brotes posteriores seguidos de un rápido aumento de la incidencia. El objetivo fue explorar la relación entre la distribución espacial de los casos de COVID-19 en una ciudad binacional y variables como nivel socioeconómico, densidad poblacional y patrones de movilidad, con el objetivo de informar políticas públicas. Métodos: se realizó un estudio exploratorio entre agosto 2020 y enero 2021 con datos del Ministerio de Salud, considerando semanas epidemiológicas. Las variables explicativas consideradas fueron densidad poblacional, nivel socioeconómico y movilidad. Se identificaron tres periodos temporales desde agosto 2020 hasta enero 2021. Se analizo la autocorrelación espacial empleando el Índice de Moran y estadística Gi* (Getis & Ord). Mediante el análisis de cluster jerárquico, fue posible identificar grupos homogéneos de segmentos censales. Resultados: se georreferenciaron un total de 1.846 casos. Mediante análisis de cluster jerárquico, se identificaron siete grupos homogéneos. Para el nivel alto socioeconómico, la movilidad es el factor explicativo de una mayor incidencia de casos. Mientras que, para para el grupo de nivel bajo, la densidad de la población fue el factor explicativo de las diferencias en la presentación de la enfermedad. Conclusión: la población a ser priorizada en esta ciudad corresponde a aquellas zonas con mayor densidad poblacional y donde se incrementa la movilidad. El análisis territorial a pequeña escala genera información para la construcción de política local, ante una crisis sanitaria, que la hace más eficaz.
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Derechos de autor 2024 Marcel Achkar, Mariana Gomez-Camponovo, Nicolas Perez , Eleuterio Umpierrez
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