Article Text

Original research
Hospital bed capacity and usage across secondary healthcare providers in England during the first wave of the COVID-19 pandemic: a descriptive analysis
  1. Bilal Akhter Mateen1,2,3,
  2. Harrison Wilde4,
  3. John M Dennis5,
  4. Andrew Duncan2,6,
  5. Nick Thomas5,7,
  6. Andrew McGovern5,7,
  7. Spiros Denaxas2,3,8,
  8. Matt Keeling9,
  9. Sebastian Vollmer2,4
  1. 1Warwick Medical School, University of Warwick, Coventry, UK
  2. 2The Alan Turing Institute, London, UK
  3. 3Institute of Health Informatics, University College London, London, UK
  4. 4Department of Statistics, University of Warwick, Coventry, UK
  5. 5The Institute of Biomedical & Clinical Science, University of Exeter, Exeter, UK
  6. 6Department of Statistics, Imperial College London, London, UK
  7. 7Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
  8. 8Health Data Research UK, London, UK
  9. 9The Zeeman Institute for Systems Biology & Infectious Disease Epidemiology Research, University of Warwick, Coventry, UK
  1. Correspondence to Dr Bilal Akhter Mateen; bilal.mateen{at}


Objective In this study, we describe the pattern of bed occupancy across England during the peak of the first wave of the COVID-19 pandemic.

Design Descriptive survey.

Setting All non-specialist secondary care providers in England from 27 March27to 5 June 2020.

Participants Acute (non-specialist) trusts with a type 1 (ie, 24 hours/day, consultant-led) accident and emergency department (n=125), Nightingale (field) hospitals (n=7) and independent sector secondary care providers (n=195).

Main outcome measures Two thresholds for ‘safe occupancy’ were used: 85% as per the Royal College of Emergency Medicine and 92% as per NHS Improvement.

Results At peak availability, there were 2711 additional beds compatible with mechanical ventilation across England, reflecting a 53% increase in capacity, and occupancy never exceeded 62%. A consequence of the repurposing of beds meant that at the trough there were 8.7% (8508) fewer general and acute beds across England, but occupancy never exceeded 72%. The closest to full occupancy of general and acute bed (surge) capacity that any trust in England reached was 99.8% . For beds compatible with mechanical ventilation there were 326 trust-days (3.7%) spent above 85% of surge capacity and 154 trust-days (1.8%) spent above 92%. 23 trusts spent a cumulative 81 days at 100% saturation of their surge ventilator bed capacity (median number of days per trust=1, range: 1–17). However, only three sustainability and transformation partnerships (aggregates of geographically co-located trusts) reached 100% saturation of their mechanical ventilation beds.

Conclusions Throughout the first wave of the pandemic, an adequate supply of all bed types existed at a national level. However, due to an unequal distribution of bed utilisation, many trusts spent a significant period operating above ‘safe-occupancy’ thresholds despite substantial capacity in geographically co-located trusts, a key operational issue to address in preparing for future waves.

  • COVID-19
  • intensive & critical care
  • public health
  • health policy

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  • Twitter @Bilal_A_Mateen, @HarrisonDWilde, @john_den_, @SpirosDenaxas

  • Contributors Based on the CRediT taxonomy, the authors of this study made contributions to this manuscript in the following ways: conceptualisation (BAM and SV); data curation (HW and SV); methodology (HW, BAM and SV); formal analysis (HW, BAM, JMD, SD and SV); project administration and supervision (BAM, MK and SV); visualisation (HW, BAM, JMD, NT, AM, AD and SV); resources (SV and MK); established data access (MK); writing the original draft (BAM and HW); and reviewing and editing the draft (all authors). The corresponding author and the senior author (SV) had full access to all data and attest to the integrity of the analysis. The decision to submit for publication was agreed by all authors. BAM and SV act as guarantors of the work as presented.

  • Funding The study was funded by UK Research and Innovation. BAM, SV and SD are supported by The Alan Turing Institute (EPSRC grant EP/N510129). JMD is supported by an Independent Fellowship funded by Research England’s Expanding Excellence in England (E3) fund. SV is supported by the University of Warwick IAA funding. HW is supported by the Feuer International Scholarship in Artificial Intelligence. JMD, NT and AM are supported by the NIHR Exeter Clinical Research Facility.

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  • Competing interests AM declares previous research funding from Eli Lilly and Company, Pfizer and AstraZeneca. SV declares funding from IQVIA. All other authors declare no competing interests.

  • Patient consent for publication Not required.

  • Ethics approval Data used in this study were made available through an agreement between the University of Warwick and the Scientific Pandemic Influenza Group on Modelling (SPI-M), which were acting on behalf of the British Government. The study was reviewed and approved by the Warwick BSREC (BSREC 120/19-20).

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement Data may be obtained from a third party and are not publicly available. Trust-level data will eventually be published by NHS England as a freely accessible data resource, but outputs have been delayed by the COVID-19 pandemic. For expedited or more granular access, requests will need to be made directly to NHS England (contact via All codes for this study are available on request.

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