scholarly journals Estimating COVID-19-induced excess mortality in Lombardy, Italy

Author(s):  
Antonello Maruotti ◽  
Giovanna Jona-Lasinio ◽  
Fabio Divino ◽  
Gianfranco Lovison ◽  
Massimo Ciccozzi ◽  
...  
Keyword(s):  
2016 ◽  
Vol 25 (3) ◽  
pp. 294-316 ◽  
Author(s):  
Chik Collins ◽  
Ian Levitt

This article reports findings of research into the far-reaching plan to ‘modernise’ the Scottish economy, which emerged from the mid-late 1950s and was formally adopted by government in the early 1960s. It shows the growing awareness amongst policy-makers from the mid-1960s as to the profoundly deleterious effects the implementation of the plan was having on Glasgow. By 1971 these effects were understood to be substantial with likely severe consequences for the future. Nonetheless, there was no proportionate adjustment to the regional policy which was creating these understood ‘unwanted’ outcomes, even when such was proposed by the Secretary of State for Scotland. After presenting these findings, the paper offers some consideration as to their relevance to the task of accounting for Glasgow's ‘excess mortality’. It is suggested that regional policy can be seen to have contributed to the accumulation of ‘vulnerabilities’, particularly in Glasgow but also more widely in Scotland, during the 1960s and 1970s, and that the impact of the post-1979 UK government policy agenda on these vulnerabilities is likely to have been salient in the increase in ‘excess mortality’ evident in subsequent years.


2009 ◽  
pp. 091019190442039-22 ◽  
Author(s):  
Ian D Cameron ◽  
Jian Sheng Chen ◽  
Lyn M March ◽  
Judy M Simpson ◽  
Robert G Cumming ◽  
...  

Author(s):  
Augusto Cerqua ◽  
Roberta Di Stefano ◽  
Marco Letta ◽  
Sara Miccoli

AbstractEstimates of the real death toll of the COVID-19 pandemic have proven to be problematic in many countries, Italy being no exception. Mortality estimates at the local level are even more uncertain as they require stringent conditions, such as granularity and accuracy of the data at hand, which are rarely met. The “official” approach adopted by public institutions to estimate the “excess mortality” during the pandemic draws on a comparison between observed all-cause mortality data for 2020 and averages of mortality figures in the past years for the same period. In this paper, we apply the recently developed machine learning control method to build a more realistic counterfactual scenario of mortality in the absence of COVID-19. We demonstrate that supervised machine learning techniques outperform the official method by substantially improving the prediction accuracy of the local mortality in “ordinary” years, especially in small- and medium-sized municipalities. We then apply the best-performing algorithms to derive estimates of local excess mortality for the period between February and September 2020. Such estimates allow us to provide insights about the demographic evolution of the first wave of the pandemic throughout the country. To help improve diagnostic and monitoring efforts, our dataset is freely available to the research community.


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