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The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: An analysis of environmental, demographic, and healthcare factors

机译:意大利地区和省份Covid-19病例(CFR)的决定因素:对环境,人口统计和医疗因素的分析

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The Italian government has been one of the most responsive to COVID-2019 emergency, through the adoption of quick and increasingly stringent measures to contain the outbreak. Despite this, Italy has suffered a huge human and social cost, especially in Lombardy. The aim of this paper is dual: ⅰ) first, to investigate the reasons of the case fatality rate (CFR) differences across Italian 20 regions and 107 provinces, using a multivariate OLS regression approach; and ⅱ) second, to build a "taxonomy" of provinces with similar mortality risk of COVID-19, by using the Ward's hierarchical agglomerative clustering method. I considered health system metrics, environmental pollution, climatic conditions, demographic variables, and three ad hoc indexes that represent the health system saturation. The results showed that overall health care efficiency, physician density, and average temperature helped to reduce the CFR. By the contrary, population aged 70 and above, car and firm density, air pollutants concentrations (NO_2,O_3, PM_(10), and PM_(2.5)), relative average humidity, COVID-19 prevalence, and all three indexes of health system saturation were positively associated with the CFR. Population density, social vertical integration, and altitude were not statistically significant. In particular, the risk of dying increases with age, as 90 years old and above had a three-fold greater risk than the 80-to-89 years old and four-fold greater risk than 70-to-79 years old. Moreover, the cluster analysis showed that the highest mortality risk was concentrated in the north of the country, while the lowest risk was associated with southern provinces. Finally, since prevalence and health system saturation indexes played the most important role in explaining the CFR variability, a significant part of the latter may have been caused by the massive stress of the Italian health system.
机译:意大利政府通过通过快速和日益严格的措施来遏制爆发,这是对2019年紧急情况最敏感的最敏感性。尽管如此,意大利遭遇了巨大的人类和社会成本,特别是在伦巴第。本文的目的是双重:Ⅰ)首先,调查意大利20个地区和107个省份的病情率(CFR)差异的原因,使用多元OLS回归方法; Ⅱ)第二,通过使用病房的分层凝聚聚类聚类方法,建立具有相似死亡率的“分类”具有相似的死亡率风险。我考虑了健康系统指标,环境污染,气候条件,人口变量和三个特设指标,代表了健康系统饱和度。结果表明,整体医疗保健效率,医师密度和平均温度有助于减少CFR。相反,70岁及以上人口,汽车和坚固的密度,空气污染物浓度(NO_2,O_3,PM_(10)和PM_(2.5)),相对平均湿度,Covid-19流行,以及所有三个健康指标系统饱和度与CFR呈正相关。人口密度,社会垂直整合和高度没有统计学意义。特别是,随着年龄90岁及以上的年龄垂死的风险比80至89岁的风险大3倍,风险高于70至79岁。此外,聚类分析表明,最高的死亡率风险集中在该国的北部,而风险最低与南部省份有关。最后,由于流行和健康系统饱和度指数在解释CFR变异方面发挥了最重要的作用,因此后者的重要部分可能是由意大利健康系统的大规模应力引起的。

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