Spatial variability in the risk of death from COVID-19 in Italy
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Abstract
OBJECTIVES: Italy has been badly affected by the COVID-19 pandemic and has one of the highest death tolls. We analyzed the severity of COVID-19 across all 20 Italian regions. METHOD: We manually retrieved the daily cumulative numbers of laboratory-confirmed cases and deaths attributed to COVID-19 in each region, and estimated the crude case fatality ratio and time delay-adjusted case fatality ratio (aCFR). We then assessed the association between aCFR and sociodemographic, health care and transmission factors using multivariate regression analysis. RESULTS: The overall aCFR in Italy was estimated at 17.4%. Lombardia exhibited the highest aCFR (24.7%), followed by Marche (19.3%), Emilia Romagna (17.7%) and Liguria (17.6%). Our aCFR estimate was greater than 10% for 12 regions. Our aCFR estimates were statistically associated with population density and cumulative morbidity rate in a multivariate analysis. CONCLUSION: Our aCFR estimates for Italy as a whole and for seven out of the 20 regions exceeded those reported for the most badly affected region in China. These findings highlight the importance of social distancing to suppress transmission to avoid overwhelming the health care system and reduce the risk of death.
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SciScore for 10.1101/2020.04.01.20049668: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study is not exempted from limitations. The preferential ascertainment of severe cases bias in COVID-19 may have spuriously increased our estimate of CFR,25 which is a frequent caveat in this type of studies.26, 27 Similarly, for a disease like COVID-19 where transmission is characterized by a rapid growth phase in case incidence, …
SciScore for 10.1101/2020.04.01.20049668: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:Our study is not exempted from limitations. The preferential ascertainment of severe cases bias in COVID-19 may have spuriously increased our estimate of CFR,25 which is a frequent caveat in this type of studies.26, 27 Similarly, for a disease like COVID-19 where transmission is characterized by a rapid growth phase in case incidence, but the infection-death time is long (ranges from 2 to 8 weeks),8 our CFR estimate could have been affected by delayed reporting bias.25, 28 Similarly, our data on number of cases reflects the date of reporting and not the date of onset of illness.
Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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