意大利地区和省份中COVID-19病死率（CFR）的决定因素：环境、人口统计学和卫生保健因素分析
The determinants of COVID-19 case fatality rate (CFR) in the Italian regions and provinces: an analysis of environmental, demographic, and healthcare factors
摘要：
意大利政府一直是应对COVID-19紧急情况最积极的政府之一，通过采取快速和日益严格的措施来遏制疫情。尽管如此，意大利还是遭受了巨大的人力和社会代价，尤其是在伦巴第大区。本文的目的是双重的：i）首先，使用多变量OLS回归方法调查意大利20个地区和107个省之间病死率（CFR）差异的原因; ii）其次，使用Ward分层凝聚聚类方法建立具有相似COVID-19死亡风险的省份分类。我考虑了卫生系统指标、环境污染、气候条件、人口统计变量和代表卫生系统饱和度的三个临时指标。结果显示，整体医疗效率、医师密度、平均温度有助于降低病死率。相反，70岁及以上人口、汽车和公司密度、空气污染物（NO2、O3、PM10和PM2. 5）水平、相对平均湿度、COVID-19患病率以及卫生系统饱和度的所有三个指标与CFR呈正相关。人口密度、社会垂直整合和海拔没有统计学意义。特别是，死亡的风险随着年龄的增长而增加，因为90岁及以上的风险比80至89岁的风险高出三倍，比70至79岁的风险高出四倍。此外，聚类分析表明，死亡风险最高的地区集中在该国北部，而风险最低的地区与南部省份有关。最后，由于患病率和卫生系统饱和度指数在解释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。
英文摘要：
The Italian government has been one of the most responsive to COVID-19 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 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, level of air pollutants (NO2, O3, PM10, and PM2.5), 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.
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