Article ; Online: COVID-19 in Italy: An Analysis of Death Registry Data.
Journal of public health (Oxford, England)
2020 Volume 42, Issue 4, Page(s) 723–730
Abstract: ... the design of appropriate response policies.: Methods: We source daily death registry data for 4100 ... data on a host of co-factors of COVID-19 mortality, which we exploit in a differences-in-differences ... municipalities in Italy's north and match them to Census data. We augment the dataset with municipality-level ...
Abstract | Background: There are still many unknowns about COVID-19. We do not know its exact mortality rate nor the speed through which it spreads across communities. This lack of evidence complicates the design of appropriate response policies. Methods: We source daily death registry data for 4100 municipalities in Italy's north and match them to Census data. We augment the dataset with municipality-level data on a host of co-factors of COVID-19 mortality, which we exploit in a differences-in-differences regression model to analyze COVID-19-induced mortality. Results: We find that COVID-19 killed more than 0.15% of the local population during the first wave of the epidemic. We also show that official statistics vastly underreport this death toll, by about 60%. Next, we uncover the dramatic effects of the epidemic on nursing home residents in the outbreak epicenter: in municipalities with a high share of the elderly living in nursing homes, COVID-19 mortality was about twice as high as in those with no nursing home intown. Conclusions: A pro-active approach in managing the epidemic is key to reduce COVID-19 mortality. Authorities should ramp-up testing capacity and increase contact-tracing abilities. Adequate protective equipment should be provided to nursing home residents and staff. |
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MeSH term(s) | Age Factors ; COVID-19/mortality ; Female ; Homes for the Aged ; Humans ; Italy/epidemiology ; Male ; Nursing Homes ; Pandemics ; Registries ; Risk Assessment ; Risk Factors ; SARS-CoV-2 ; Survival Analysis |
Keywords | covid19 |
Language | English |
Publishing date | 2020-10-08 |
Publishing country | England |
Document type | Journal Article |
ZDB-ID | 2142082-8 |
ISSN | 1741-3850 ; 1741-3842 |
ISSN (online) | 1741-3850 |
ISSN | 1741-3842 |
DOI | 10.1093/pubmed/fdaa165 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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