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Flattening the curve before it flattens us: hospital critical care capacity limits and mortality from novel coronavirus (SARS-CoV2) cases in US counties

Our take —

Based on data on critical care capacity across the US, a combination of substantial increases in the number of critical care beds and implementation of significant control measures including travel restrictions, self-quarantine, greater access to rapid testing, and behavior changes are needed to reduce the number of contacts to prevent hospitals from being overwhelmed leading to otherwise preventable deaths.

Study design

Modeling/Simulation

Study population and setting

A mathematical model previously developed by the authors was used to simulate the spatio-temporal dynamics of SARS-CoV-2 infections between February and March 24,2020 across 3108 counties in the US. The impact of control measures, including travel restrictions between areas, self-quarantine, and social distancing, was modeled by assuming 0, 25, and 50% reductions in contact patterns. The impact of testing and changes in health-seeking behavior were also considered. Hospital critical care supply was calculated for all continental US counties by linking and harmonizing data from multiple sources including: i) 2020 Centers for Medicare & Medicaid Services; ii) 2018 American Hospital Association (AHA) Annual Survey; iii) 2020 US DHHS Health Resources and Services Administration; and iv) 2017-2019 CMS Medicare Provider of Services. From a baseline of 30% availability of existing ICU beds, four surge capacity scenarios were considered. Excess deaths (the additional deaths compared to historical averages over the same time-frame) due to potential lack of critical care beds was assessed.

Summary of Main Findings

The authors estimated that up to 20,000 (with increased levels of control) and 11,000 (with increased critical care capacity) excess deaths could be averted over the period February 21 – March 24, 2020. Counties in the Northeast of the US were most affected by shortages in critical care beds, and thus projected to have the highest number of excess deaths. Counties in New York, Colorado, and Virginia were projected to exceed critical care limits within the 4 week study period even with high levels (50%) of contact reduction measures and high surge capacity. Spatial clustering of counties at risk of exceeding care capacity was observed in the very low surge capacity and 0% contact reduction scenario, highlighting regions that may require additional resources due to limited existing healthcare capacity. Urban areas were projected to exceed care capacity faster in every scenario compared to rural areas. Under some scenarios of low surge capacity, time to exceeding bed limits was as short as 1-2 weeks from the start of the study period. Urban counties in the Northeast US had the highest proportion of excess COVID-19 deaths that could have been averted with access to critical care.

Study Strengths

The study estimates the critical care capacity at county level from well-established data sources and links this explicitly to projected COVID-19 case numbers. By linking this to projected case numbers of COVID-19 over time and by counties, the level of control, the additional critical care capacity required, and the lead time before health services are overwhelmed can be estimated.

Limitations

Critical care capacity was only assessed in terms of physical equipment and hospital beds. Staffing requirements were not accounted for. Details of the model used to simulate COVID-19 cases across the US were not reported within the paper. These models were hypothetical planning scenarios that did not compare model trajectories to real data. In addition, the modeled scenarios were not tied to empirical data on the effectiveness of interventions in a way that facilitates comparison to real-world situations.

Value added

This was one of the first examinations of when US counties might exceed critical care capacity due to surges in COVID-19 cases which may help hospital and public health resourcing and planning.