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Factores asociados a la incidencia y la mortalidad por COVID-19 en las comunidades autónomas
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SESPAS. Published by Elsevier España, S.L.U.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260480/ https://www.ncbi.nlm.nih.gov/pubmed/32563533 http://dx.doi.org/10.1016/j.gaceta.2020.05.004 |
Sumario: | 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. |
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