A model to forecast regional demand for COVID-19 related hospital beds

Ferstad, Johannes O; Gu, Angela; Lee, Raymond Ye; Thapa, Isha; Shin, Andrew; Salomon, Joshua A.; Shah, Nigam H.; Milstein, Arnold; Scheinker, David

doi:10.1101/2020.03.26.20044842

Cited by 31 publications

(42 citation statements)

References 9 publications

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“…There have been numerous previous studies which have aimed to forecast the number of hospital beds required for COVID-19 patients [16][17][18][19][20][21][22]54]. Many of these studies published so far have used point estimates, only originating from one study which often does not reflect the context of interest.…”

Section: Summarising Length Of Staymentioning

confidence: 99%

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COVID-19 length of hospital stay: a systematic review and data synthesis

Rees

Nightingale

Jafari

et al. 2020

Preprint

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Background: The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. Methods: We performed a systematic review to gather data on length of stay (LoS) of patients with COVID-19 in hospital and in ICU. We subsequently developed a method to generate LoS distributions which combines summary statistics reported in multiple studies, accounting for differences in sample sizes. Applying this approach we provide distributions for general hospital and ICU LoS from studies in China and elsewhere, for use by the community. Results: We identified 52 studies, the majority from China (46/52). Median hospital LoS ranged from 4 to 53 days within China, and 4 to 21 days outside of China, across 45 studies. ICU LoS was reported by eight studies -four each within and outside China -with median values ranging from 6 to 12 and 4 to 19 days, respectively. Our summary distributions have a median hospital LoS of 14 (IQR: 10-19) days for China, compared with 5 (IQR: 3-9) days outside of China. For ICU, the summary distributions are more similar (median (IQR) of 8 (5-13) days for China and 7 (4-11) days outside of China). There was a visible difference by discharge status, with patients who were discharged alive having longer LoS than those who died during their admission, but no trend associated with study date. Conclusion: Patients with COVID-19 in China appeared to remain in hospital for longer than elsewhere. This may be explained by differences in criteria for admission and discharge between countries, and different timing within the pandemic. In the absence of local data, the combined summary LoS distributions provided here can be used to model bed demands for contingency planning and then updated, with the novel method presented here, as more studies with aggregated statistics emerge outside China.

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Section: Summarising Length Of Staymentioning

confidence: 99%

“…4. The median and IQR for general hospital was estimated to be 14(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) for China and 5 (3-9) excluding China. This was also repeated for ICU LoS, with a median and IQR 8 (5-13) for China and 7 (4-11) outside China.…”

mentioning

confidence: 99%

COVID-19 length of hospital stay: a systematic review and data synthesis

Rees

Nightingale

Jafari

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In addition to this LoS, the TC method and the MS method can be used to estimate the length of stay until given outcomes such as discharge or death. [9] In this section, we compare estimates for these LoS. Again, pathways are disaggregated by whether the individual went via ICU.…”

Section: Declarationsmentioning

confidence: 99%

“…Day 0 is taken to be 23 February, which we have taken to be the start of the national outbreak for the UK. [9] A univariate AFT model is not fit for estimating LoS for competing hazards, such as for death and discharge.…”

Section: Declarationsmentioning

confidence: 99%

Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning

Vekaria¹,

orcid²,

Overton³

et al. 2020

Preprint

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Background: Predicting hospital length of stay (LoS) for patients with COVID-19 infection is essential to ensure that adequate bed capacity can be provided without unnecessarily restricting care for patients with other conditions. Here, we demonstrate the utility of three complementary methods for predicting LoS using UK national- and hospital-level data. Method: On a national scale, relevant patients were identified from the COVID-19 Hospitalisation in England Surveillance System (CHESS) reports. An Accelerated Failure Time (AFT) survival model and a truncation corrected method (TC), both with underlying Weibull distributions, were fitted to the data to estimate LoS from hospital admission date to an outcome (death or discharge) and from hospital admission date to Intensive Care Unit (ICU) admission date. In a second approach we fit a multi-state (MS) survival model to data directly from the Manchester University NHS Foundation Trust (MFT). We develop a planning tool that uses LoS estimates from these models to predict bed occupancy. Results: All methods produced similar overall estimates of LoS for overall hospital stay, given a patient is not admitted to ICU (8.4, 9.1 and 9.3 days for AFT, TC and MS, respectively). Estimates differ more significantly between the local and national level when considering ICU. National estimates for ICU LoS from AFT and TC were 12.4 and 13.4 days, whereas in local data the MS method produced estimates of 22.8 days. Conclusions: Given the complexity and partiality of different data sources and the rapidly evolving nature of the COVID-19 pandemic, it is most appropriate to use multiple analysis methods on multiple datasets. The AFT method accounts for censored cases, but does not allow for simultaneous consideration of different outcomes. The TC method does not include censored cases but does consider these different outcomes. The MS method can model complex pathways to different outcomes whilst accounting for censoring, but cannot handle non-random case missingness. Overall, we conclude that data-driven modelling approaches of LoS using these methods is useful in epidemic planning and management, and should be considered for widespread adoption throughout healthcare systems internationally where similar data resources exist.

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“…https://doi.org/10.1101/2020.04. 19.20072017 doi: medRxiv preprint Given that the test results and admissions data are available in nearly real time, health systems based monitoring of admission rates and the doubling time of hospitalized patient counts can provide accurate data for both public health planning and epidemiological modeling 3 .…”

Section: 77% Of Covid-19 Positive Patients Required An Icu Admissionmentioning

confidence: 99%

Measure what matters: counts of hospitalized patients are a better metric for health system capacity planning for a reopening

Kashyap

Gombar

Yadlowsky

et al. 2020

Preprint

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Responding to the COVID-19 pandemic requires accurate forecasting of health system capacity requirements using readily available inputs. We examined whether testing and hospitalization data could help quantify the anticipated burden on the health system given shelter-in-place (SIP) order.We find a marked slowdown in the hospitalization rate within ten days of SIP even as cases continued to rise. We also find a shift towards younger patients in the age distribution of those testing positive for COVID-19 over the four weeks of SIP. The impact of this shift is a divergence between increasing positive case confirmations and slowing new hospitalizations, both of which affects the demand on health systems.Without using local hospitalization rates and the age distribution of positive patients, current models are likely to overestimate the resource burden of COVID-19. It is imperative that health systems start using these data to quantify effects of SIP and aid reopening planning.(150 words / 150 words) All rights reserved. No reuse allowed without permission.

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A model to forecast regional demand for COVID-19 related hospital beds

Cited by 31 publications

References 9 publications

COVID-19 length of hospital stay: a systematic review and data synthesis

COVID-19 length of hospital stay: a systematic review and data synthesis

Hospital Length of Stay For COVID-19 Patients: Data-Driven Methods for Forward Planning

Measure what matters: counts of hospitalized patients are a better metric for health system capacity planning for a reopening

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