Medeiros Figueiredo Alexandre, Daponte-Codina Antonio, Moreira Marculino Figueiredo Daniela Cristina, Toledo Vianna Rodrigo Pinheiro, Costa de Lima Kenio, Gil-García Eugenia
Departamento de Promoción de la Salud, Universidade Federal da Paraíba, João Pessoa, Brasil; Programa de Posgrado en Ciencias de la Salud, Universidade Federal do Rio Grande do Norte, Natal, Brasil.
CIBER de Epidemiología y Salud Pública (CIBERESP), España; Observatorio de Salud y Medio Ambiente de Andalucía (OSMAN), Escuela Andaluza de Salud Pública, Granada, España.
Gac Sanit. 2021 Sep-Oct;35(5):445-452. doi: 10.1016/j.gaceta.2020.05.004. Epub 2020 May 30.
Analyze the evolution of the epidemic of COVID-19 after the alarm state and identify factors associated with the differences between the autonomous communities.
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.
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.
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.
分析警报状态后新冠疫情的演变情况，并确定与自治区之间差异相关的因素。
进行生态研究，将流行病学、人口统计学、环境以及卫生服务结构方面的变量用作解释变量。分析期为2020年3月15日（警报状态开始之日）至4月22日。发病率和死亡率为主要反应变量。使用斯皮尔曼相关系数和多元回归分析估计关联的强度。
警报状态发布时的发病率和死亡率与当前的发病率、死亡率及医院需求率相关。较高的平均气温与自治区当前较低的新冠发病率显著相关。同样，养老院中老年人比例较高与自治区当前较高的死亡率显著相关。
通过分析发病率和死亡率可以预测疫情的演变。较低的温度和居住场所中老年人的比例是与预后较差相关的因素。在决定实施遏制措施的时间和强度时必须考虑这些参数。从这个意义上讲，加强流行病学监测对于改善预测至关重要。
