Importance of patient bed pathways and length of stay differences in predicting COVID-19 hospital bed occupancy in England

Comparison of clinical outcomes in hospitalized patients with COVID-19 or non-COVID-19 community-acquired pneumonia in a prospective observational cohort study

PURPOSE

Coronavirus disease 2019 (COVID-19) and non-COVID-19 community-acquired pneumonia (NC-CAP) often result in hospitalization with considerable risks of mortality, ICU treatment, and long-term morbidity. A comparative analysis of clinical outcomes in COVID-19 CAP (C-CAP) and NC-CAP may improve clinical management.

METHODS

Using prospectively collected CAPNETZ study data (January 2017 to June 2021, 35 study centers), we conducted a comprehensive analysis of clinical outcomes including in-hospital death, ICU treatment, length of hospital stay (LOHS), 180-day survival, and post-discharge re-hospitalization rate. Logistic regression models were used to examine group differences between C-CAP and NC-CAP patients and associations with patient demography, recruitment period, comorbidity, and treatment.

RESULTS

Among 1368 patients (C-CAP: n = 344; NC-CAP: n = 1024), C-CAP showed elevated adjusted probabilities for in-hospital death (aOR 4.48 [95% CI 2.38-8.53]) and ICU treatment (aOR 8.08 [95% CI 5.31-12.52]) compared to NC-CAP. C-CAP patients were at increased risk of LOHS over seven days (aOR 1.88 [95% CI 1.47-2.42]). Although ICU patients had similar in-hospital mortality risk, C-CAP was associated with length of ICU stay over seven days (aOR 3.59 [95% CI 1.65-8.38]). Recruitment period influenced outcomes in C-CAP but not in NC-CAP. During follow-up, C-CAP was linked to a reduced risk of re-hospitalization and mortality post-discharge (aOR 0.43 [95% CI 0.27-0.70]).

CONCLUSION

Distinct clinical trajectories of C-CAP and NC-CAP underscore the need for adapted management to avoid acute and long-term morbidity and mortality amid the evolving landscape of CAP pathogens.

Hospital capacities and response to COVID-19 pandemic surges in Iran: A quantitative model-based study

The coronavirus 2019 (COVID-19) pandemic resulted in serious limitations for healthcare systems, and this study aimed to investigate the impact of COVID-19 surges on in-patient care capacities in Iran employing the Adaptt tool. Using a cross-sectional study design, our study was carried out in the year 2022 using 1-year epidemiologic (polymerase chain reaction-positive COVID-19 cases) and hospital capacity (beds and human resource) data from the official declaration of the pandemic in Iran in February 2020. We populated several scenarios, and in each scenario, a proportion of hospital capacity is assumed to be allocated to the COVID-19 patients. In most of the scenarios, no significant shortage was found in terms of bed and human resources. However, considering the need for treatment of non- COVID-19 cases, in one of the scenarios, it can be observed that during the peak period, the number of required and available specialists is exactly equal, which was a challenge during surge periods and resulted in extra hours of working and workforce burnout in hospitals. The shortage of intensive care unit beds and doctors specializing in internal medicine, infectious diseases, and anesthesiology also requires more attention for planning during the peak days of COVID-19.

Empirical distributions of time intervals between COVID-19 cases and more severe outcomes in Scotland

A critical factor in infectious disease control is the risk of an outbreak overwhelming local healthcare capacity. The overall demand on healthcare services will depend on disease severity, but the precise timing and size of peak demand also depends on the time interval (or clinical time delay) between initial infection, and development of severe disease. A broader distribution of intervals may draw that demand out over a longer period, but have a lower peak demand. These interval distributions are therefore important in modelling trajectories of e.g. hospital admissions, given a trajectory of incidence. Conversely, as testing rates decline, an incidence trajectory may need to be inferred through the delayed, but relatively unbiased signal of hospital admissions. Healthcare demand has been extensively modelled during the COVID-19 pandemic, where localised waves of infection have imposed severe stresses on healthcare services. While the initial acute threat posed by this disease has since subsided with immunity buildup from vaccination and prior infection, prevalence remains high and waning immunity may lead to substantial pressures for years to come. In this work, then, we present a set of interval distributions, for COVID-19 cases and subsequent severe outcomes; hospital admission, ICU admission, and death. These may be used to model more realistic scenarios of hospital admissions and occupancy, given a trajectory of infections or cases. We present a method for obtaining empirical distributions using COVID-19 outcomes data from Scotland between September 2020 and January 2022 (N = 31724 hospital admissions, N = 3514 ICU admissions, N = 8306 mortalities). We present separate distributions for individual age, sex, and deprivation of residing community. While the risk of severe disease following COVID-19 infection is substantially higher for the elderly and those residing in areas of high deprivation, the length of stay shows no strong dependence, suggesting that severe outcomes are equally severe across risk groups. As Scotland and other countries move into a phase where testing is no longer abundant, these intervals may be of use for retrospective modelling of patterns of infection, given data on severe outcomes.

Hospital length of stay throughout bed pathways and factors affecting this time: A non-concurrent cohort study of Colombia COVID-19 patients and an unCoVer network project

Predictions of hospital beds occupancy depends on hospital admission rates and the length of stay (LoS) according to bed type (general ward -GW- and intensive care unit -ICU- beds). The objective of this study was to describe the LoS of COVID-19 hospital patients in Colombia during 2020-2021. Accelerated failure time models were used to estimate the LoS distribution according to each bed type and throughout each bed pathway. Acceleration factors and 95% confidence intervals were calculated to measure the effect on LoS of the outcome, sex, age, admission period during the epidemic (i.e., epidemic waves, peaks or valleys, and before/after vaccination period), and patients geographic origin. Most of the admitted COVID-19 patients occupied just a GW bed. Recovered patients spent more time in the GW and ICU beds than deceased patients. Men had longer LoS than women. In general, the LoS increased with age. Finally, the LoS varied along epidemic waves. It was lower in epidemic valleys than peaks, and decreased after vaccinations began in Colombia. Our study highlights the necessity of analyzing local data on hospital admission rates and LoS to design strategies to prioritize hospital beds resources during the current and future pandemics.

Estimating distribution of length of stay in a multi-state model conditional on the pathway, with an application to patients hospitalised with Covid-19

Multi-state models are used to describe how individuals transition through different states over time. The distribution of the time spent in different states, referred to as 'length of stay', is often of interest. Methods for estimating expected length of stay in a given state are well established. The focus of this paper is on the distribution of the time spent in different states conditional on the complete pathway taken through the states, which we call 'conditional length of stay'. This work is motivated by questions about length of stay in hospital wards and intensive care units among patients hospitalised due to Covid-19. Conditional length of stay estimates are useful as a way of summarising individuals' transitions through the multi-state model, and also as inputs to mathematical models used in planning hospital capacity requirements. We describe non-parametric methods for estimating conditional length of stay distributions in a multi-state model in the presence of censoring, including conditional expected length of stay (CELOS). Methods are described for an illness-death model and then for the more complex motivating example. The methods are assessed using a simulation study and shown to give unbiased estimates of CELOS, whereas naive estimates of CELOS based on empirical averages are biased in the presence of censoring. The methods are applied to estimate conditional length of stay distributions for individuals hospitalised due to Covid-19 in the UK, using data on 42,980 individuals hospitalised from March to July 2020 from the COVID19 Clinical Information Network.

Assessing the impacts of COVID-19 vaccination programme's timing and speed on health benefits, cost-effectiveness, and relative affordability in 27 African countries

BACKGROUND

The COVID-19 vaccine supply shortage in 2021 constrained roll-out efforts in Africa while populations experienced waves of epidemics. As supply improves, a key question is whether vaccination remains an impactful and cost-effective strategy given changes in the timing of implementation.

METHODS

We assessed the impact of vaccination programme timing using an epidemiological and economic model. We fitted an age-specific dynamic transmission model to reported COVID-19 deaths in 27 African countries to approximate existing immunity resulting from infection before substantial vaccine roll-out. We then projected health outcomes (from symptomatic cases to overall disability-adjusted life years (DALYs) averted) for different programme start dates (01 January to 01 December 2021, n = 12) and roll-out rates (slow, medium, fast; 275, 826, and 2066 doses/million population-day, respectively) for viral vector and mRNA vaccines by the end of 2022. Roll-out rates used were derived from observed uptake trajectories in this region. Vaccination programmes were assumed to prioritise those above 60 years before other adults. We collected data on vaccine delivery costs, calculated incremental cost-effectiveness ratios (ICERs) compared to no vaccine use, and compared these ICERs to GDP per capita. We additionally calculated a relative affordability measure of vaccination programmes to assess potential nonmarginal budget impacts.

RESULTS

Vaccination programmes with early start dates yielded the most health benefits and lowest ICERs compared to those with late starts. While producing the most health benefits, fast vaccine roll-out did not always result in the lowest ICERs. The highest marginal effectiveness within vaccination programmes was found among older adults. High country income groups, high proportions of populations over 60 years or non-susceptible at the start of vaccination programmes are associated with low ICERs relative to GDP per capita. Most vaccination programmes with small ICERs relative to GDP per capita were also relatively affordable.

CONCLUSION

Although ICERs increased significantly as vaccination programmes were delayed, programmes starting late in 2021 may still generate low ICERs and manageable affordability measures. Looking forward, lower vaccine purchasing costs and vaccines with improved efficacies can help increase the economic value of COVID-19 vaccination programmes.

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England

The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.

COVID-19, the disease caused by SARS-CoV-2, leads to a high proportion of cases requiring admission to hospital. Coupled with the high burden of infections worldwide, this put substantial pressure on healthcare systems. To enable public health systems to cope with the high levels of demand, forecasting models are vital. These models enable public health managers to plan their workloads accordingly. Here, we developed EpiBeds, which combines an epidemic model with a model for patient flow through hospitals. By fitting this model to data from England, EpiBeds has been used to provide short-term forecasts of hospital admissions and bed demand weekly throughout the COVID-19 pandemic. In this paper, we describe the motivation behind the structure of EpiBeds, how the model is fitted to data, and report the estimates of the key parameters throughout the pandemic. We then evaluate the performance of EpiBeds by comparing generated forecasts to future data points, finding good agreement between the forecasts and data.

The determinants of patient care manager role and the implementation of COVID-19 clinical pathway: a cross-sectional study

Objective

This study aims to determine the factors associated with patient care manager role and the implementation of the clinical pathway among nurses in private hospitals.

Methods

This study was conducted from January-July 2021 using the cross-sectional approach. The sample consisted of 168 nurses working in a private hospital in Surabaya City, East Java, Indonesia. Meanwhile, the data were collected using the Patient Care Manager Role Scale (PCMRS) and analyzed by multiple logistic regression to find the correlation between the variables.

Results

A higher percentage of nurses namely 64.3% had compliance in COVID-19 clinical pathways with an average PCMRS score of 27.81 ± 2.43. Nurses with a high-level patient care manager role level had a significant compliance risk with odds ratio [OR] 440.137, 95% confidence interval [CI] [51.850-3736.184], and p-value = 0.000 compared to those with a low role.

Conclusion

The role of patient care manager and compliance with COVID-19 clinical pathways correlated significantly. Based on the results, several actions are needed for the early identification of patient service managers' roles to ensure compliance with COVID-19 clinical pathways and reduce the number of cases in Indonesia.

Optimal COVID-19 lockdown strategies in an age-structured SEIR model of Northern Ireland

An age-structured SEIR model simulates the propagation of COVID-19 in the population of Northern Ireland. It is used to identify optimal timings of short-term lockdowns that enable long-term pandemic exit strategies by clearing the threshold for herd immunity or achieving time for vaccine development with minimal excess deaths.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Developing Pulmonary Rehabilitation for COVID-19: Are We Linked with the Present Literature? A Lexical and Geographical Evaluation Study Based on the Graph Theory

The Coronavirus Disease 2019 (COVID-19) pandemic is a severe ongoing global emergency. Despite high rates of asymptomatic patients, in many cases, the infection causes a rapid decline in pulmonary function due to an acute respiratory distress-like syndrome, leading to multi-organ failure and death. To date, recommendations about rehabilitation on COVID-19 are based on clinical data derived from other similar lung diseases. Rehabilitation literature lacks a standard taxonomy, limiting a proper evaluation of the most effective treatments for patients after COVID-19 infection. In this study, we assessed the clinical and rehabilitative associations and the geographical area involved in interstitial lung diseases (ILD) and in COVID-19, by a mathematical analysis based on graph theory. We performed a quantitative analysis of the literature in terms of lexical analysis and on how words are connected to each other. Despite a large difference in timeframe (throughout the last 23 years for ILD and in the last 1.5 years for COVID-19), the numbers of papers included in this study were similar. Our results show a clear discrepancy between rehabilitation proposed for COVID-19 and ILD. In ILD, the term “rehabilitation” and other related words such as “exercise” and “program” resulted in lower values of centrality and higher values of eccentricity, meaning relatively less importance of the training during the process of care in rehabilitation of patients with ILD. Conversely, “rehabilitation” was one of the most cited terms in COVID-19 literature, strongly associated with terms such as “exercise”, “physical”, and “program”, entailing a multidimensional approach of the rehabilitation for these patients. This could also be due to the widespread studies conducted on rehabilitation on COVID-19, with Chinese and Italian researchers more involved. The assessment of the terms used for the description of the rehabilitation may help to program shared rehabilitation knowledge and avoid literature misunderstandings.