How Healthcare Congestion Increases Covid-19 Mortality: Evidence from Lombardy, Italy*
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Abstract
The Covid-19 pandemic has caused generous and well-developed healthcare systems to collapse. This paper quantifies how much system congestion may have increased mortality rates, using distance to the ICU as a proxy for access to emergency care.
Methods
We match daily death registry data for almost 1,500 municipalities in Lombardy, Italy, to data on geographical location of all ICU beds in the region. We then analyze how system congestion increases mortality in municipalities that are far from the ICU through a differences-in-differences regression model.
Findings
We find that Covid-19 mortality is up to 60% higher in the average municipality – which is 15 minutes driving away from the closest ICU – than in a municipality with an ICU in town. This difference is larger in areas and in days characterized by an abnormal number of calls to the emergency line.
Interpretation
We interpret these results as suggesting that a sudden surge of critical patients may have congested the healthcare system, forcing emergency medical services to prioritize patients in the most proximate communities in order to maximize the number of lives saved. Through some back-of-the-envelope calculations, we estimate that Lombardy’s death toll from the first Covid-19 outbreak could have been 25% lower had all municipalities had ready access to the ICU. Drawing a lesson from Lombardy’s tale, governments should strengthen the emergency care response and palliate geographical inequalities to ensure that everyone in need can receive critical care on time during new outbreaks.
Funding
No funding.
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SciScore for 10.1101/2020.10.27.20221085: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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…
SciScore for 10.1101/2020.10.27.20221085: (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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.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.
- Thank you for including a protocol registration statement.
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