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Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic

机译:在Covid-19大流行期间估算意大利亚国家层面的每周过度死亡率

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In this study we present the first comprehensive analysis of the spatio-temporal differences in excess mortality during the COVID-19 pandemic in Italy. We used a population-based design on all-cause mortality data, for the 7,904 Italian municipalities. We estimated sex-specific weekly mortality rates for each municipality, based on the first four months of 2016–2019, while adjusting for age, localised temporal trends and the effect of temperature. Then, we predicted all-cause weekly deaths and mortality rates at municipality level for the same period in 2020, based on the modelled spatio-temporal trends. Lombardia showed higher mortality rates than expected from the end of February, with 23,946 (23,013 to 24,786) total excess deaths. North-West and North-East regions showed one week lag, with higher mortality from the beginning of March and 6,942 (6,142 to 7,667) and 8,033 (7,061 to 9,044) total excess deaths respectively. We observed marked geographical differences also at municipality level. For males, the city of Bergamo (Lombardia) showed the largest percent excess, 88.9% (81.9% to 95.2%), at the peak of the pandemic. An excess of 84.2% (73.8% to 93.4%) was also estimated at the same time for males in the city of Pesaro (Central Italy), in stark contrast with the rest of the region, which does not show evidence of excess deaths. We provided a fully probabilistic analysis of excess mortality during the COVID-19 pandemic at sub-national level, suggesting a differential direct and indirect effect in space and time. Our model can be used to help policy-makers target measures locally to contain the burden on the health-care system as well as reducing social and economic consequences. Additionally, this framework can be used for real-time mortality surveillance, continuous monitoring of local temporal trends and to flag where and when mortality rates deviate from the expected range, which might suggest a second wave of the pandemic.
机译:在这项研究中,我们在意大利Covid-19大流行期间,第一次综合分析了在Covid-19大流行期间的过度死亡率。我们在全源地死亡率数据上使用了基于人口的设计,为7,904名意大利市。根据2016 - 2019年前四个月,我们估计每个市政当局的性别每周死亡率,同时调整年龄,局部时间趋势和温度效果。然后,根据建模的时空趋势,我们预测了2020年同期的全面每周死亡和处死人均死亡率。伦巴第比率比2月底的死亡率高于预期,23,946名(23,013至24,786)人的总死亡人数。西北和东北地区显示一周滞后,从3月初和6,942名(6,142至7,667)和8,033(7,061至9,044)的死亡率分别较高。我们观察了在市政层面的标志的地理差异。对于雄狮,贝加莫市(伦巴第城市)显示出最大的过量百分比,88.9%(81.9%至95.2%),在大流行的峰值。在与该地区其他地区的斯塔克造影中,也估计超过84.2%(73.8%至93.4%)的同时估计,同时,与该地区的其他地区的鲜明对比,这并没有显示出过度死亡的证据。在亚国家一级的Covid-19流行病中,我们提供了对多重死亡率的完全概率分析,在空间和时间内表达了差异直接和间接的影响。我们的型号可用于帮助政策制定者在本地目标措施,以遏制保健系统的负担,并降低社会和经济后果。此外,该框架可用于实时死亡率监测,持续监测局部时间趋势,并在死亡率偏离预期范围的地点以及当可能表明大流行的第二波的地点和地点时。

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