Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.
Estimating weekly excess mortality at sub-national level in Italy during the COVID-19 pandemic.
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DOI：
10.1371/journal.pone.0240286
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发表时间：
2020
期刊：
影响因子：
3.7
通讯作者：
Baio G
中科院分区：
文献类型：
--
作者：
Blangiardo M;Cameletti M;Pirani M;Corsetti G;Battaglini M;Baio G
在这项研究中，我们首次对意大利COVID-19大流行期间超额死亡率的时空差异进行了全面分析。我们对7904个意大利城市的全因死亡率数据采用了基于人群的设计。我们根据2016-2019年的前四个月估算了每个城市按性别划分的每周死亡率，同时根据年龄、局部时间趋势和温度的影响进行了调整。然后，基于模拟的时空趋势，我们预测了2020年同期各城市的全因周死亡率和死亡率。伦巴第从2月底开始的死亡率高于预期，共有23 946人（23 013人至24 786人）超额死亡。西北和东北地区滞后一周，从3月初开始死亡率较高，总死亡人数分别为6,942人（6,142至7,667人）和8,033人（7,061至9,044人）。我们还观察到在城市层面上存在明显的地理差异。在男性方面，在大流行高峰期，贝加莫市（伦巴第）的超额比例最大，为88.9%（81.9%至95.2%）。与此同时，据估计，佩萨罗市（意大利中部）的男性死亡率高出84.2%(73.8%至93.4%)，与该地区其他地区形成鲜明对比，后者没有显示出死亡人数过多的证据。我们在次国家层面对COVID-19大流行期间的超额死亡率进行了全面概率分析，表明在空间和时间上存在直接和间接的差异影响。我们的模型可以用来帮助政策制定者在当地采取措施，以控制医疗保健系统的负担，并减少社会和经济后果。此外，该框架可用于实时死亡率监测，持续监测当地时间趋势，并标记死亡率偏离预期范围的地点和时间，这可能表明大流行的第二波。
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.
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影响因子：
168.9
作者：
Bennett, James E.;Li, Guangquan;Ezzati, Majid
通讯作者：
Ezzati, Majid
DOI：
10.1093/biostatistics/kxy038
发表时间：
2020-07-01
期刊：
Biostatistics (Oxford, England)
影响因子：
--
作者：
Boulieri A;Bennett JE;Blangiardo M
通讯作者：
Blangiardo M
影响因子：
1
作者：
BESAG, J;YORK, J;MOLLIE, A
通讯作者：
MOLLIE, A
影响因子：
19
作者：
Michelozzi, Paola;De'Donato, Francesca;Davoll, Marina
通讯作者：
Davoll, Marina
DOI：
10.1111/rssa.12178
发表时间：
2017-01-01
影响因子：
2
作者：
Boulieri, Areti;Liverani, Silvia;Blangiardo, Marta
通讯作者：
Blangiardo, Marta