University of Bergamo, Department of Management, Economics and Quantitative Methods, via dei Caniana 2, 24127 Bergamo, Italy.
Sci Total Environ. 2021 Feb 10;755(Pt 1):142523. doi: 10.1016/j.scitotenv.2020.142523. Epub 2020 Sep 24.
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, O, PM, and PM), 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.
意大利政府是对 COVID-19 疫情反应最迅速的政府之一,通过采取快速且日益严格的措施来控制疫情。尽管如此,意大利还是付出了巨大的人力和社会代价,尤其是在伦巴第大区。本文的目的有两个:i)首先,使用多元 OLS 回归方法,研究意大利 20 个地区和 107 个省之间的病死率(CFR)差异的原因;ii)其次,使用 Ward 的层次凝聚聚类方法构建具有 COVID-19 相似死亡率风险的省份“分类法”。我考虑了卫生系统指标、环境污染、气候条件、人口变量以及代表卫生系统饱和状态的三个专门指标。结果表明,整体医疗保健效率、医生密度和平均温度有助于降低 CFR。相反,70 岁及以上人口、汽车和公司密度、空气污染物浓度(NO、O、PM 和 PM)、相对平均湿度、COVID-19 流行率以及卫生系统饱和的三个指标均与 CFR 呈正相关。人口密度、社会垂直整合度和海拔高度则没有统计学意义。特别是,死亡风险随着年龄的增长而增加,90 岁及以上人群的死亡风险是 80-89 岁人群的三倍,是 70-79 岁人群的四倍。此外,聚类分析表明,高死亡率风险集中在该国北部,而低死亡率风险与南部省份有关。最后,由于患病率和卫生系统饱和指数在解释 CFR 变异性方面发挥了最重要的作用,后者的很大一部分可能是由意大利卫生系统的巨大压力造成的。