A Spatiotemporal Tool to Project Hospital Critical Care Capacity and Mortality From COVID-19 in US Counties
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Abstract
Objectives. To create a tool to rapidly determine where pandemic demand for critical care overwhelms county-level surge capacity and to compare public health and medical responses.
Methods. In March 2020, COVID-19 cases requiring critical care were estimated using an adaptive metapopulation SEIR (susceptible‒exposed‒infectious‒recovered) model for all 3142 US counties for future 21-day and 42-day periods from April 2, 2020, to May 13, 2020, in 4 reactive patterns of contact reduction—0%, 20%, 30%, and 40%—and 4 surge response scenarios—very low, low, medium, and high.
Results. In areas with increased demand, surge response measures could avert 104 120 additional deaths—55% through high clearance of critical care beds and 45% through measures such as greater ventilator access. The percentages of lives saved from high levels of contact reduction were 1.9 to 4.2 times greater than high levels of hospital surge response. Differences in projected versus actual COVID-19 demands were reasonably small over time.
Conclusions. Nonpharmaceutical public health interventions had greater impact in minimizing preventable deaths during the pandemic than did hospital critical care surge response. Ready-to-go spatiotemporal supply and demand data visualization and analytics tools should be advanced for future preparedness and all-hazards disaster response.
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SciScore for 10.1101/2020.04.01.20049759: (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:However, a major limitation is that the models presented here use data on physical infrastructure but do not account for staffing or ventilator supplies. Healthcare workers, especially those involved in critical care, are at high risk for COVID-19 infection and thus there may be staffing shortages that reduce the utility of the critical …
SciScore for 10.1101/2020.04.01.20049759: (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:However, a major limitation is that the models presented here use data on physical infrastructure but do not account for staffing or ventilator supplies. Healthcare workers, especially those involved in critical care, are at high risk for COVID-19 infection and thus there may be staffing shortages that reduce the utility of the critical care beds that could be gained under surge responses. There have already been reports of hospitals being unable to accept patients, not because of lack of beds but due to lack of staff to cover those beds. Some states, like New York, are currently recruiting retired healthcare workers to assist with staffing shortfalls, an approach that might be generally applicable in alleviating shortfalls during the current epidemic. These retirees are, however, generally older and can be particularly vulnerable to poor outcomes from COVID-19. Our models also cannot account for the innovation, ingenuity and perseverance of medical staff, many of whom are trained to work in crisis situations. It is likely that medical staff will find solutions that are unanticipated by our models, that can subsequently be included as they become known and more widely applied across healthcare systems. Future analyses should also incorporate counts of ventilators in addition to critical care beds. Our models also did not account for heterogeneities arising from specific high-risk communities in different localities. For instance, places with large elderly populations or high ...
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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