Statistical Service, Istituto Superiore di Sanità, 00161 Rome, Italy.
Department of Statistical Sciences, La Sapienza University, 00185 Rome, Italy.
Int J Environ Res Public Health. 2023 Feb 5;20(4):2812. doi: 10.3390/ijerph20042812.
Excess mortality (EM) can reliably capture the impact of a pandemic, this study aims at assessing the numerous factors associated with EM during the COVID-19 pandemic in Italy.
Mortality records (ISTAT 2015-2021) aggregated in the 610 Italian Labour Market Areas (LMAs) were used to obtain the EM P-scores to associate EM with socioeconomic variables. A two-step analysis was implemented: (1) Functional representation of EM and clustering. (2) Distinct functional regression by cluster.
The LMAs are divided into four clusters: 1 low EM; 2 moderate EM; 3 high EM; and 4 high EM-first wave. Low-Income showed a negative association with EM clusters 1 and 4. Population density and percentage of over 70 did not seem to affect EM significantly. Bed availability positively associates with EM during the first wave. The employment rate positively associates with EM during the first two waves, becoming negatively associated when the vaccination campaign began.
The clustering shows diverse behaviours by geography and time, the impact of socioeconomic characteristics, and local governments and health services' responses. The LMAs allow to draw a clear picture of local characteristics associated with the spread of the virus. The employment rate trend confirmed that essential workers were at risk, especially during the first wave.
超额死亡率（EM）可以可靠地捕捉大流行的影响，本研究旨在评估意大利 COVID-19 大流行期间与 EM 相关的众多因素。
使用汇总了 610 个意大利劳动力市场区域（LMA）的死亡率记录（ISTAT 2015-2021），获得 EM P 分数，将 EM 与社会经济变量相关联。实施了两步分析：（1）EM 的功能表示和聚类。（2）按簇进行不同的功能回归。
LMA 分为四个簇：1 低 EM；2 中 EM；3 高 EM；和 4 高 EM-第一波。低收入与 EM 簇 1 和 4 呈负相关。人口密度和 70 岁以上人口比例似乎对 EM 没有显著影响。床位可用性在第一波期间与 EM 呈正相关。就业率在第一波和第二波期间与 EM 呈正相关，当疫苗接种运动开始时，这种相关性变为负相关。
聚类显示了不同地理和时间的不同行为、社会经济特征的影响以及地方政府和卫生服务的反应。LMA 允许描绘与病毒传播相关的当地特征的清晰图景。就业率趋势证实，基本工作人员面临风险，尤其是在第一波期间。
