About: Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas
Creator	Daniela, Codina Daponte, Antonio Gil-García, Eugenia Medeiros De Figueiredo, Alexandre Moreira, Cristina De Figueiredo, Marculino Kenio Costa De Lima, Vianna Toledo, Rodrigo
source	Elsevier; Medline; PMC; WHO
abstract	RESUMEN Objetivo: Analizar la evolución de la epidemia de COVID-19 después del estado de alarma e identificar factores asociados a las diferencias entre las comunidades autónomas. Método: Estudio ecológico que utilizó variables epidemiológicas, demográficas, ambientales y sobre la estructura de los servicios sanitarios como variables explicativas. El periodo de análisis fue desde el 15 de marzo (inicio del estado de alarma) hasta el 22 de abril de 2020. Las tasas de incidencia y de mortalidad fueron las variables respuesta principales. La magnitud de las asociaciones se ha estimado mediante el coeficiente de correlación de Spearman y el análisis de regresión múltiple. Resultados: Las tasas de incidencia y de mortalidad en el momento del decreto del estado de alarma se asocian con las tasas de incidencia, mortalidad y demanda hospitalaria actuales. Las temperaturas medias más altas se asocian significativamente con una menor incidencia actual de COVID-19. Asimismo, una mayor proporción de personas mayores en residencias se asocia significativamente a una mortalidad actual más elevada. Conclusión: Es posible predecir la evolución de la epidemia a través del análisis de la incidencia y de la mortalidad. Las temperaturas más bajas y la elevada proporción de personas mayores en residencias son factores asociados a un peor pronóstico. Estos parámetros deben ser considerados en las decisiones sobre el momento y la intensidad de la implantación de las medidas de contención. En este sentido, fortalecer la vigilancia epidemiológica es esencial para mejorar las predicciones. ABSTRACT Objective: Analyze the evolution of the epidemic of COVID-19 after the alarm state and identify factors associated with the differences between the autonomous communities. Method: Ecological study that used epidemiological, demographic, environmental and variables on the structure of health services as explanatory variables. The analysis period was from March 15th (the start of the alarm state) until April 22nd, 2020. Incidence and mortality rates were the main response variables. The magnitude of the associations has been estimated using the Spearman correlation coefficient and multiple regression analysis. Results: Incidence and mortality rates at the time of decree of alarm status are associated with current incidence, mortality and hospital demand rates. Higher mean temperatures are significantly associated with a lower current incidence of COVID-19 in the autonomous communities. Likewise, a higher proportion of older people in nursing homes is significantly associated with a higher current mortality in the autonomous communities. Conclusion: It is possible to predict the evolution of the epidemic through the analysis of incidence and mortality. Lower temperatures and the proportion of older people in residences are factors associated with a worse prognosis. These parameters must be considered in decisions about the timing and intensity of the implementation of containment measures. In this sense, strengthening epidemiological surveillance is essential to improve predictions.
has issue date	2020-05-30(xsd:dateTime)
bibo:doi	10.1016/j.gaceta.2020.05.004
bibo:pmid	32563533
has license	els-covid
sha1sum (hex)	816f560609c0185ba99f4c81878915050e2dcf75
schema:url	https://doi.org/10.1016/j.gaceta.2020.05.004
resource representing a document's title	Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas
has PubMed Central identifier	PMC7260480
has PubMed identifier	32563533
schema:publication	Gac Sanit
resource representing a document's body	covid:816f560609c0185ba99f4c81878915050e2dcf75#body_text
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