scholarly journals Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Christopher Martin ◽  
Stuart McDonald ◽  
Steve Bale ◽  
Michiel Luteijn ◽  
Rahul Sarkar
Keyword(s):  
Intensive Care ◽  
National Level ◽  
Direct Consequence ◽  
Hospital Ward ◽  
Model Parameters ◽  
Capacity Model ◽  
New Variant ◽  
Pessimistic Scenario ◽  
Demand And Capacity ◽  
Excess Deaths

Abstract Background This paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. Methods Compartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, were modelled and compared with the subsequent observations to the 3rd February. Results The key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the ‘Optimistic’ scenario. Several of the ‘Middling’ scenario applications resulted in excess deaths—up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the ‘Pessimistic’ scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7). Conclusions Mortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the ‘Optimistic’ scenario. The ‘Middling’ scenario could result in some excess deaths—up to a 0.7% increase relative to the total number of deaths. The ‘Pessimistic’ scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase). Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.

scholarly journals Construction of a Demand and Capacity Model for Intensive Care and Hospital Ward Beds, and Mortality From Covid-19

2021 ◽  
Author(s):  
Christopher Martin ◽  
Stuart McDonald ◽  
Steve Bale ◽  
Michiel Luteijn ◽  
Rahul Sarkar
Keyword(s):  
Intensive Care ◽  
National Level ◽  
Direct Consequence ◽  
Hospital Ward ◽  
Model Parameters ◽  
Capacity Model ◽  
New Variant ◽  
Pessimistic Scenario ◽  
Demand And Capacity ◽  
Excess Deaths

Abstract BackgroundThis paper describes a model for estimating COVID-19 related excess deaths that are a direct consequence of insufficient hospital ward bed and intensive care unit (ICU) capacity. MethodsCompartmental models were used to estimate deaths under different combinations of ICU and ward care required and received in England up to late April 2021. Model parameters were sourced from publicly available government information and organisations collating COVID-19 data. A sub-model was used to estimate the mortality scalars that represent increased mortality due to insufficient ICU or general ward bed capacity. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, were modelled and compared with the subsequent observations to the 3rd February. ResultsThe key output was the demand and capacity model described. There were no excess deaths from a lack of capacity in the ‘Optimistic’ scenario. Several of the ‘Middling’ scenario applications resulted in excess deaths - up to 597 deaths (0.6% increase) with a 20% reduction compared to best estimate ICU capacity. All the ‘Pessimistic’ scenario applications resulted in excess deaths, ranging from 49,178 (17.0% increase) for a 20% increase in ward bed availability, to 103,735 (35.8% increase) for a 20% shortfall in ward bed availability. These scenarios took no account of the emergence of the new, more transmissible, variant of concern (b.1.1.7).ConclusionsMortality is increased when hospital demand exceeds available capacity. No excess deaths from breaching capacity would be expected under the ‘Optimistic’ scenario. The ‘Middling’ scenario could result in some excess deaths - up to a 0.7% increase relative to the total number of deaths. The ‘Pessimistic’ scenario would have resulted in significant excess deaths. Our sensitivity analysis indicated a range between 49,178 (17% increase) and 103,735 (35.8% increase).Given the new variant, the pessimistic scenario appeared increasingly likely and could have resulted in a substantial increase in the number of COVID-19 deaths. In the event, it would appear that capacity was not breached at any stage at a national level with no excess deaths. it will remain unclear if minor local capacity breaches resulted in any small number of excess deaths.

scholarly journals Construction of a demand and capacity model for intensive care and hospital ward beds, and mortality from COVID-19

2021 ◽  
Author(s):  
Stuart McDonald ◽  
Chris Martin ◽  
Steve Bale ◽  
Michiel Luteijn ◽  
Rahul Sarkar
Keyword(s):  
Sensitivity Analysis ◽  
Intensive Care ◽  
Hospital Admissions ◽  
Direct Consequence ◽  
Model Parameters ◽  
Sensitivity Testing ◽  
Best Estimate ◽  
New Variant ◽  
Pessimistic Scenario ◽  
Excess Deaths

AbstractBackgroundThis paper describes the construction of a model used to estimate the number of excess deaths that could be expected as a direct consequence of a lack of hospital bed and intensive care unit (ICU) capacity.MethodsA series of compartmental models was used to estimate the number of deaths under different combinations of care required (ICU or ward), and care received (ICU, ward or no care) in England up to the end of April 2021. Model parameters were sourced from publicly available government information, organisations collating COVID-19 data and calculations using existing parameters. A compartmental sub-model was used to estimate the mortality scalars that represent the increase in mortality that would be expected from a lack of provision of an ICU or general ward bed when one is required. Three illustrative scenarios for admissions numbers, ‘Optimistic’, ‘Middling’ and ‘Pessimistic’, are described showing how the model can be used to estimate mortality rates under different scenarios of capacity.ResultsThe key output of our collaboration was the model itself rather than the results of any of the scenarios. The model allows a user to understand the excess mortality impact arising as a direct consequence of capacity being breached under various scenarios or forecasts of hospital admissions. The scenarios described in this paper are illustrative and are not forecasts.There were no excess deaths from a lack of capacity in any of the ‘Optimistic’ scenario applications in sensitivity analysis.Several of the ‘Middling’ scenario applications under sensitivity testing resulted in excess deaths directly attributable to a lack of capacity. Most excess deaths arose when we modelled a 20% reduction compared to best estimate ICU capacity. This led to 597 deaths (0.7% increase).All the ‘Pessimistic’ scenario applications under sensitivity analysis had excess deaths. These ranged from 49,219 (19.4% increase) when we modelled a 20% increase in ward bed availability over the best-estimate, to 103,845 (40.9% increase) when we modelled a 20% shortfall in ward bed availability below the best-estimate. The emergence of a new, more transmissible variant (VOC 202012/01) increases the likelihood of real world outcomes at, or beyond, those modelled in our ‘Pessimistic’ scenario.The results can be explained by considering how capacity evolves in each of the scenarios. In the Middling scenario, whilst ICU capacity may be approached and even possibly breached, there remains sufficient ward capacity to take lives who need either ward or ICU support, keeping excess deaths relatively low. However, the Pessimistic scenario sees ward capacity breached, and in many scenarios for a period of several weeks, resulting in much higher mortality in those lives who require care but do not receive it.ConclusionsNo excess deaths from breaching capacity would be expected under the unadjusted ‘Optimistic’ assumptions of demand. The ‘Middling’ scenario could result in some excess deaths from breaching capacity, though these would be small (0.7% increase) relative to the total number of deaths in that scenario. The ‘Pessimistic’ scenario would certainly result in significant excess deaths from breaching capacity. Our sensitivity analysis indicated a range between 49,219 (19.4% increase) and 103,845 (40.9% increase) excess deaths.Without the new variant, exceeding capacity for hospital and ICU beds did not appear to be the most likely outcome but given the new variant it now appears more plausible and, if so, would result in a substantial increase in the number of deaths from COVID-19.

scholarly journals Transfers from intensive care unit to hospital ward: a multicentre textual analysis of physician progress notes

Critical Care ◽  
2018 ◽  
Vol 22 (1) ◽  
Author(s):  
Kyla N. Brown ◽  
Jeanna Parsons Leigh ◽  
Hasham Kamran ◽  
Sean M. Bagshaw ◽  
Rob A. Fowler ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Textual Analysis ◽  
Hospital Ward ◽  
Progress Notes

A multi-center prospective cohort study of patient transfers from the intensive care unit to the hospital ward

2017 ◽  
Vol 43 (10) ◽  
pp. 1485-1494 ◽  
Author(s):  
Henry T. Stelfox ◽  
Jeanna Parsons Leigh ◽  
Peter M. Dodek ◽  
Alexis F. Turgeon ◽  
Alan J. Forster ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Hospital Ward ◽  
Patient Transfers

scholarly journals Recurrence and mortality 1 year after hospital admission for non-fatal self-harm: a nationwide population-based study

2019 ◽  
Vol 29 ◽  
Author(s):  
A. Vuagnat ◽  
F. Jollant ◽  
M. Abbar ◽  
K. Hawton ◽  
C. Quantin
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Drug Overdose ◽  
Psychiatric Inpatient ◽  
National Level ◽  
Significant Risk ◽  
Medical Decision ◽  
Short Term ◽  
Predictive Values ◽  
Self Harm

Abstract Aims A large number of people present each day at hospitals for non-fatal deliberate self-harm (DSH). Examination of the short-term risk of non-fatal recurrence and mortality at the national level is of major importance for both individual medical decision-making and global organisation of care. Methods Following the almost exhaustive linkage (96%) of two national registries in France covering 45 million inhabitants (i.e. 70% of the whole population), information about hospitalisation for DSH in 2008–2009 and vital status at 1 year was obtained. Individuals who died during the index hospital stay were excluded from analyses. Results Over 2 years, 136,451 individuals were hospitalised in medicine or surgery for DSH. The sample comprised 62.8% women, median age 38 in both genders, with two peaks at 16 and 44 years in women, and one peak at 37 years in men. The method used for DSH was drug overdose in 82.1% of cases. Admission to an intensive care unit occurred in 12.9%. Following index hospitalisation, 71.3% returned home and 23.7% were transferred to a psychiatric inpatient care unit. DSH recurrence during the following year occurred in 12.4% of the sample, within the first 6 months in 75.2%, and only once in 74.6%. At 1 year, 2.6% of the sample had died. The overall standardised mortality ratio was 7.5 but reached more than 20 in young adults. The causes were natural causes (35.7%), suicide (34.4%), unspecified cause (17.5%) and accident (12.4%). Most (62.9%) deaths by suicide occurred within the first 6 months following index DSH. Violent means (i.e. not drug overdose) were used in 70% of suicide cases. Concordance between means used for index DSH and for suicide was low (30% overall), except for drug overdose. Main suicide risk factors were older age, being male, use of a violent means at index DSH, index admission to an intensive care unit, a transfer to another medical department or to a psychiatric inpatient unit, and recurrence of DSH. However, these factors had low positive predictive values individually (below 2%). Conclusions Non-fatal DSH represent frequent events with a significant risk of short-term recurrence and death from various causes. The first 6 months following hospital discharge appear to be a critical period. Specific short-term aftercare programs targeting all people with a DSH episode have to be developed, along other suicide prevention strategies.

scholarly journals Clinical risk conditions for acute lung injury in the intensive care unit and hospital ward: a prospective observational study

Critical Care ◽  
2007 ◽  
Vol 11 (5) ◽  
pp. R96 ◽  
Author(s):  
Niall D Ferguson ◽  
Fernando Frutos-Vivar ◽  
Andrés Esteban ◽  
Federico Gordo ◽  
Teresa Honrubia ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Acute Lung Injury ◽  
Lung Injury ◽  
Observational Study ◽  
Prospective Observational Study ◽  
Hospital Ward ◽  
Clinical Risk

scholarly journals Capacity and Delay Estimation for Roundabouts Using Conflict Theory

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Zhaowei Qu ◽  
Yuzhou Duan ◽  
Hongyu Hu ◽  
Xianmin Song
Keyword(s):  
Queuing Theory ◽  
High Volume ◽  
Conflict Theory ◽  
Model Parameters ◽  
Delay Model ◽  
Highway Capacity ◽  
Highway Capacity Manual ◽  
Traffic Characteristics ◽  
Capacity Model ◽  
Proposed Model

To estimate the capacity of roundabouts more accurately, the priority rank of each stream is determined through the classification technique given in the Highway Capacity Manual 2010 (HCM2010), which is based on macroscopical analysis of the relationship between entry flow and circulating flow. Then a conflict matrix is established using the additive conflict flow method and by considering the impacts of traffic characteristics and limited priority with high volume. Correspondingly, the conflict relationships of streams are built using probability theory. Furthermore, the entry capacity model of roundabouts is built, and sensitivity analysis is conducted on the model parameters. Finally, the entrance delay model is derived using queuing theory, and the proposed capacity model is compared with the model proposed by Wu and that in the HCM2010. The results show that the capacity calculated by the proposed model is lower than the others for an A-type roundabout, while it is basically consistent with the estimated values from HCM2010 for a B-type roundabout.

scholarly journals Excess Mortality Associated With COVID-19 by Demographic Group: Evidence From Florida and Ohio

2021 ◽  
pp. 003335492110415
Author(s):  
Troy Quast ◽  
Ross Andel
Keyword(s):  
Excess Mortality ◽  
Prediction Interval ◽  
National Level ◽  
Age Groups ◽  
Black Population ◽  
Infection Rates ◽  
Black Adults ◽  
Demographic Groups ◽  
Younger Age ◽  
Excess Deaths

Objective COVID-19 mortality varies across demographic groups at the national level, but little is known about potential differences in COVID-19 mortality across states. The objective of this study was to estimate the number of all-cause excess deaths associated with COVID-19 in Florida and Ohio overall and by sex, age, and race. Methods We calculated the number of weekly and cumulative excess deaths among adults aged ≥20 from March 15 through December 5, 2020, in Florida and Ohio as the observed number of deaths less the expected number of deaths, adjusted for population, secular trends, and seasonality. We based our estimates on death certificate data from the previous 10 years. Results The results were based on ratios of observed-to-expected deaths. The ratios were 1.17 (95% prediction interval, 1.14-1.21) in Florida and 1.15 (95% prediction interval, 1.11-1.19) in Ohio. Although the largest number of excess deaths occurred in the oldest age groups, in both states the ratios of observed-to-expected deaths were highest among adults aged 20-49 (1.21; 95% prediction interval, 1.11-1.32). The ratio of observed-to-expected deaths for the Black population was especially elevated in Florida. Conclusions Although excess deaths were largely concentrated among older cohorts, the high ratios of observed-to-expected deaths among younger age groups indicate widespread effects of COVID-19. The high levels of observed-to-expected deaths among Black adults may reflect in part disparities in infection rates, preexisting conditions, and access to care. The finding of high excess deaths among Black adults deserves further attention.

scholarly journals A Bayesian hierarchical model with integrated covariate selection and misclassification matrices to estimate neonatal and child causes of death

2021 ◽  
Author(s):  
Amy R. Mulick ◽  
Shefali Oza ◽  
David Prieto-Merino ◽  
Francisco Villavicencio ◽  
Simon Cousens ◽  
...  
Keyword(s):  
Random Effects ◽  
Fixed Effects ◽  
Causes Of Death ◽  
National Level ◽  
Selection Procedure ◽  
Statistical Modelling ◽  
Model Parameters ◽  
Covariate Selection ◽  
Country Level ◽  
Nationally Representative

SummaryReducing neonatal and child mortality is a global priority. In countries without comprehensive vital registration data to inform policy and planning, statistical modelling is used to estimate the distribution of key causes of death. This modelling presents challenges given that the input data are few, noisy, often not nationally representative of the country from which they are derived, and often do not report separately on all of the key causes. As more nationally representative data come to be available, it becomes possible to produce country estimates that go beyond fixed-effects models with national-level covariates by incorporating country-level random effects. However, the existing frequentist multinomial model is limited by convergence problems when adding random effects, and had not incorporated a covariate selection procedure simultaneously over all causes. We report here on the translation of a fixed effects, frequentist model into a Bayesian framework to address these problems, incorporating a misclassification matrix with the potential to correct for mis-reported as well as unreported causes. We apply the new method and compare the model parameters and predicted distributions of eight key causes of death with those based on the previous, frequentist model.

scholarly journals Probability of elimination for COVID-19 in Aotearoa New Zealand

2020 ◽  
Author(s):  
Rachelle N Binny ◽  
Shaun C Hendy ◽  
Alex James ◽  
Audrey Lustig ◽  
Michael J Plank ◽  
...  
Keyword(s):  
New Zealand ◽  
Model Parameters ◽  
Alert System ◽  
Control Effectiveness ◽  
Aotearoa New Zealand ◽  
High Detection ◽  
Community Transmission ◽  
Pessimistic Scenario ◽  
Different Levels ◽  
Clinical Cases

On 25th March 2020, New Zealand implemented stringent lockdown measures (Alert Level 4, in a four-level alert system) with the goal of eliminating community transmission of COVID-19. Once new cases are no longer detected over consecutive days, the probability of elimination is an important measure for informing decisions on when certain COVID-19 restrictions should be relaxed. Our model of COVID-19 spread in New Zealand estimates that after 2-3 weeks of no new reported cases, there is a 95% probability that COVID-19 has been eliminated. We assessed the sensitivity of this estimate to varying model parameters, in particular to different likelihoods of detection of clinical cases and different levels of control effectiveness. Under an optimistic scenario with high detection of clinical cases, a 95% probability of elimination is achieved after 10 consecutive days with no new reported cases, while under a more pessimistic scenario with low case detection it is achieved after 22 days.

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