Hospital bed capacity across in Tunisia hospital during the first 4 waves of the COVID-19 pandemic: A descriptive analysis

Background

In March 2020, the WHO declared COVID-19 as a pandemic, and Tunisia implemented a containment and targeted screening strategy. The country's public health policy has since focused on managing hospital beds.

Methods

The study analyzed the bed occupancy rates in public hospitals in Tunisia during the pandemic. The evolution of daily cases and nonpharmaceutical interventions (NPI) actions undertaken by the Tunisian Government were also analyzed. The study used 3 indices to assess bed flexibility: Ramp duration until the peak, ramp growth until the peak, and ramp rate until the peak. The study also calculated the time shift at the start and peak of each wave to evaluate the government's response efficacy.

Results

The study found that the evolution of the epidemic in Tunisia had 2 phases. The first phase saw the pandemic being controlled due to strong NPI actions, while the second phase saw a relaxation of measures and an increase in wave intensity. ICU bed availability followed the demand for beds, but ICU bed occupancy remained high, with a maximum of 97%. The government's response in terms of bed distribution and reallocation was slow. The study found that the most deadly wave by ICU occupied bed was the third wave due to a historical variant, while the fifth wave due to the delta variant was the most deadly in terms of cumulative death.

Conclusions

The study concluded that decision-makers could use its findings to assess their response capabilities in the current pandemic and future ones. The study highlighted the importance of flexible and responsive healthcare systems in managing pandemics.

Prediction of hospitalisations based on wastewater-based SARS-CoV-2 epidemiology

Wastewater-based epidemiology is widely applied in Austria since April 2020 to monitor the SARS-CoV-2 pandemic. With a steadily increasing number of monitored wastewater facilities, 123 plants covering roughly 70 % of the 9 million population were monitored as of August 2022. In this study, the SARS-CoV-2 viral concentrations in raw sewage were analysed to infer short-term hospitalisation occupancy. The temporal lead of wastewater-based epidemiological time series over hospitalisation occupancy levels facilitates the construction of forecast models. Data pre-processing techniques are presented, including the approach of comparing multiple decentralised wastewater signals with aggregated and centralised clinical data. Time‑lead quantification was performed using cross-correlation analysis and coefficient of determination optimisation approaches. Multivariate regression models were successfully applied to infer hospitalisation bed occupancy. The results show a predictive potential of viral loads in sewage towards Covid-19 hospitalisation occupancy, with an average lead time towards ICU and non-ICU bed occupancy between 14.8-17.7 days and 8.6-11.6 days, respectively. The presented procedure provides access to the trend and tipping point behaviour of pandemic dynamics and allows the prediction of short-term demand for public health services. The results showed an increase in forecast accuracy with an increase in the number of monitored wastewater treatment plants. Trained models are sensitive to changing variant types and require recalibration of model parameters, likely caused by immunity by vaccination and/or infection. The utilised approach displays a practical and rapidly implementable application of wastewater-based epidemiology to infer hospitalisation occupancy.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

[Forecasting models to guide intensive care COVID-19 capacities in Germany]

BACKGROUND

Time-series forecasting models play a central role in guiding intensive care coronavirus disease 2019 (COVID-19) bed capacity in a pandemic. A key predictor of future intensive care unit (ICU) COVID-19 bed occupancy is the number of new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections in the general population, which in turn is highly associated with week-to-week variability, reporting delays, regional differences, number of unknown cases, time-dependent infection rates, vaccinations, SARS-CoV‑2 virus variants, and nonpharmaceutical containment measures. Furthermore, current and also future COVID ICU occupancy is significantly influenced by ICU discharge and mortality rates.

METHODS

Both the number of new SARS-CoV‑2 infections in the general population and intensive care COVID-19 bed occupancy rates are recorded in Germany. These data are statistically analyzed on a daily basis using epidemic SEIR (susceptible, exposed, infection, recovered) models using ordinary differential equations and multiple regression models.

RESULTS

Forecast results of the immediate trend (20-day forecast) of ICU occupancy by COVID-19 patients are made available to decision makers at various levels throughout the country.

CONCLUSION

The forecasts are compared with the development of available ICU bed capacities in order to identify capacity limitations at an early stage and to enable short-term solutions to be made, such as supraregional transfers.

Impact of the COVID-19 pandemic on critical care utilization in Japan: a nationwide inpatient database study

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has disrupted critical care services worldwide. Examining how critical care systems responded to the COVID-19 pandemic on a national level will be useful in setting future critical care plans. The present study aimed to describe the utilization of critical care services before and during the COVID-19 pandemic using a nationwide Japanese inpatient administrative database.

METHODS

All patients admitted to an intensive care unit (ICU) or a high-dependency care unit (HDU) from February 9, 2019, to February 8, 2021, in the Japanese Diagnosis Procedure Combination inpatient database were included. February 9, 2020, was used as the breakpoint separating the periods before and during COVID-19 pandemic. Hospital and patient characteristics were compared before and during the COVID-19 pandemic. Change in ICU and HDU bed occupancy before and during the COVID-19 pandemic was evaluated using interrupted time-series analysis.

RESULTS

The number of ICU patients before and during the COVID-19 pandemic was 297,679 and 277,799, respectively, and the number of HDU patients was 408,005 and 384,647, respectively. In the participating hospitals (383 ICU-equipped hospitals and 460 HDU-equipped hospitals), the number of hospitals which increased the ICU and HDU beds capacity were 14 (3.7%) and 33 (7.2%), respectively. Patient characteristics and outcomes in ICU and HDU were similar before and during the COVID-19 pandemic except main etiology for admission of COVID-19. The mean ICU bed occupancy before and during the COVID-19 pandemic was 51.5% and 47.5%, respectively. The interrupted time-series analysis showed a downward level change in ICU bed occupancy during the COVID-19 pandemic (- 4.29%, 95% confidence intervals - 5.69 to - 2.88%), and HDU bed occupancy showed similar trends. Of 383 hospitals with ICUs, 232 (60.6%) treated COVID-19 patients in their ICUs. Their annual hospital case volume of COVID-19 ICU patients varied greatly, with a median of 10 (interquartile range 3-25, min 1, max 444).

CONCLUSIONS

The ICU and HDU bed capacity did not increase while their bed occupancy decreased during the COVID-19 pandemic in Japan. There was no change in clinicians' decision-making to forego ICU/HDU care for selected patients, and there was no progress in the centralization of critically ill COVID-19 patients.

Hospital preparedness during epidemics using simulation: the case of COVID-19

This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.

A simple method to validate medical bed number calculations

Doctors are often asked to make input into bed calculations but are often not provided with the necessary background to the potential flaws in such calculations. A simple method is presented which allows both inter- and intra-national comparison of bed numbers which are sensitive to both population age structure and the role of nearness-to-death in medical bed demand. Local adjustment will be required to account for the additional demand arising for hospitals servicing more deprived populations.

Impact of Remdesivir on the Treatment of COVID-19 During the First Wave in Spain

Introduction

Spain was one of the most affected countries during the first wave of COVID-19, having the highest mortality rate in Europe. The aim of this retrospective study is to estimate the impact that remdesivir—the first drug for COVID-19 approved in the EU—would have had in the first wave.

Methods

This study simulated the impact that remdesivir could have had on the Spanish National Health System (SNHS) capacity (bed occupancy) and the number of deaths that could have been prevented, based on two scenarios: a real-life scenario (without remdesivir) and an alternative scenario (with remdesivir). It considered the clinical results of the ACTT-1 trial in hospitalized patients with COVID-19 and pneumonia who required supplemental oxygen. The occupancy rates in general wards and ICUs were estimated in both scenarios.

Results

Remdesivir use could have prevented the admission of 2587 patients (43.75%) in the ICUs. It could have also increased the SNHS capacity in 5656 general wards beds and 1700 ICU beds, showing an increase in the number of beds available of 17.53% (95% CI 3.98%–24.42%) and 23.98% (95% CI 21.33%–28.22%), respectively, at the peak of the occupancy rates. Furthermore, remdesivir use could have prevented 7639 deaths due to COVID-19, which implies a 27.51% reduction (95% CI 14.25%–34.07%).

Conclusions

Remdesivir could have relieved the pressure on the SNHS and could have reduced the death toll, providing a better strategy for the management of COVID-19 during the first wave.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12325-021-01804-9.

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

BACKGROUND

Predicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.

METHODS

We obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.

RESULTS

In both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.

CONCLUSIONS

We identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.

TRIAL REGISTRATION

The ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital's control centre and is used in several Dutch hospitals during the second COVID-19 peak.

A Study of the Patient Acceptance Capacity of the Yamanashi Prefecture Medical System amid the Coronavirus Disease 2019 Pandemic

Introduction:

Whether healthcare providers can secure the number of beds that may be required during the coronavirus disease 2019 (COVID-19) pandemic remains unclear. This study aimed to determine the sufficiency of the hospital beds available to the healthcare system of Yamanashi, Japan, in accommodating hospitalized and severely ill patients during the COVID-19 pandemic.

Methods:

In total, 60 hospitals, with > 20 beds each, were included in this study (n = 10,684). However, beds in the psychiatric and tuberculosis wards (n = 2,295), nonoperational beds (n = 376), and beds for patients in the recovery and chronic phases (n = 3,494) were excluded. The projected occupancy rate was calculated based on the estimated number of patients, including severely ill patients requiring hospitalization during the COVID-19 pandemic. Based on the number of hospitalized patients, we created an adjusted model to calculate the mean occupancy rate of beds for each medical area in the prefecture (Model 1), which is free of areal occupancy rate biases. Moreover, we created an adjusted model that places severely ill patients in the two advanced acute hospitals in Yamanashi, thereby calculating the bed occupancy rates in other hospitals with > 200 beds (Model 2).

Results:

A total of 4,519 beds were analyzed. Although the existing infectious disease beds may not be able to accommodate the projected number of severely ill patients, the existing capacity can accommodate all patients projected to require hospitalization during the pandemic. In Model 1, the mean bed occupancy rate was 50%. Conversely, in Model 2, advanced acute hospital beds were insufficient for the projected number of severely ill patients, and the mean bed occupancy rate was 72.5%.

Conclusions:

Adjustment of patients across the medical area borders enables the existing hospital beds to accommodate the estimated number of patients requiring hospitalization or those who are severely ill.

Clinical characteristics and outcomes of critically ill patients with COVID-19 in Kobe, Japan: a single-center, retrospective, observational study

PURPOSE

Coronavirus disease 2019 (COVID-19) has placed a great burden on critical care services worldwide. Data regarding critically ill COVID-19 patients and their demand of critical care services outside of initial COVID-19 epicenters are lacking. This study described clinical characteristics and outcomes of critically ill COVID-19 patients and the capacity of a COVID-19-dedicated intensive care unit (ICU) in Kobe, Japan.

METHODS

This retrospective observational study included critically ill COVID-19 patients admitted to a 14-bed COVID-19-dedicated ICU in Kobe between March 3, 2020 and June 21, 2020. Clinical and daily ICU occupancy data were obtained from electrical medical records. The last follow-up day was June 28, 2020.

RESULTS

Of 32 patients included, the median hospital follow-up period was 27 (interquartile range 19-50) days. The median age was 68 (57-76) years; 23 (72%) were men and 25 (78%) had at least one comorbidity. Nineteen (59%) patients received invasive mechanical ventilation for a median duration of 14 (8-27) days. Until all patients were discharged from the ICU on June 5, 2020, the median daily ICU occupancy was 50% (36-71%). As of June 28, 2020, six (19%) died during hospitalization. Of 26 (81%) survivors, 23 (72%) were discharged from the hospital and three (9%) remained in the hospital.

CONCLUSION

During the first months of the outbreak in Kobe, most critically ill patients were men aged ≥ 60 years with at least one comorbidity and on mechanical ventilation; the ICU capacity was not strained, and the case-fatality rate was 19%.

Emergency department crowding and hospital transformation during COVID-19, a retrospective, descriptive study of a university hospital in Stockholm, Sweden

Objectives

COVID-19 presents challenges to the emergency care system that could lead to emergency department (ED) crowding. The Huddinge site at the Karolinska university hospital (KH) responded through a rapid transformation of inpatient care capacity together with changing working methods in the ED. The aim is to describe the KH response to the COVID-19 crisis, and how ED crowding, and important input, throughput and output factors for ED crowding developed at KH during a 30-day baseline period followed by the first 60 days of the COVID-19 outbreak in Stockholm Region.

Methods

Different phases in the development of the crisis were described and identified retrospectively based on major events that changed the conditions for the ED. Results were presented for each phase separately. The outcome ED length of stay (ED LOS) was calculated with mean and 95% confidence intervals. Input, throughput, output and demographic factors were described using distributions, proportions and means. Pearson correlation between ED LOS and emergency ward occupancy by phase was estimated with 95% confidence interval.

Results

As new working methods were introduced between phase 2 and 3, ED LOS declined from mean (95% CI) 386 (373–399) minutes to 307 (297–317). Imaging proportion was reduced from 29 to 18% and admission rate increased from 34 to 43%. Correlation (95% CI) between emergency ward occupancy and ED LOS by phase was 0.94 (0.55–0.99).

Conclusions

It is possible to avoid ED crowding, even during extreme and quickly changing conditions by leveraging previously known input, throughput and output factors. One key factor was the change in working methods in the ED with higher competence, less diagnostics and increased focus on rapid clinical admission decisions. Another important factor was the reduction in bed occupancy in emergency wards that enabled a timely admission to inpatient care. A key limitation was the retrospective study design.

COVID-19-Hospitalized Patients in Karnataka: Survival and Stay Characteristics

The information on the clinical course of coronavirus disease 2019 (COVID-19) and its correlates which are essential to assess the hospital care needs of the population are currently limited. We investigated the factors associated with hospital stay and death for COVID-19 patients for the entire state of Karnataka, India. A retrospective-cohort analysis was conducted on 445 COVID-19 patients that were reported in the publicly available media-bulletin from March 9, 2020, to April 23, 2020, for the Karnataka state. This fixed cohort was followed till 14 days (May 8, 2020) for definitive outcomes (death/discharge). The median length of hospital stay was 17 days (interquartile range: 15-20) for COVID-19 patients. Having severe disease at the time of admission (adjusted-hazard-ratio: 9.3 (3.2-27.3);P < 0.001) and being aged ≥ 60 years (adjusted-hazard-ratio: 11.9 (3.5-40.6);P < 0.001) were the significant predictors of COVID-19 mortality. By moving beyond descriptive (which provide only crude information) to survival analyses, information on the local hospital-related characteristics will be crucial to model bed-occupancy demands for contingency planning during COVID-19 pandemic.

Predictive model of length of stay in hospital among older patients

BACKGROUND

Most National Health Service (NHS) hospital bed occupants are older patients because of their frequent admissions and prolonged length of stay (LOS). We evaluated demographic and clinical factors as predictors of LOS in a single NHS Trust and derived an equation to estimate LOS.

METHODS

Stepwise logistic and linear regressions were used to predict prolonged LOS (upper-quintile LOS > 17 days) and LOS respectively, from demographic factors and acute and pre-existing conditions.

RESULTS

Of 374 (men:women = 127:247) admitted patients (20% to orthogeriatric, 69% to general medical and 11% to surgical wards), median age of 85 years (IQR = 78-90), 77 had acute first hip fracture; 297 had previous hip fracture (median time since previous fracture = 2.4 years) and 21 (7.1%) had recurrent hip fracture, with median time since first fracture = 2.4 years. Median LOS was 6.5 days (IQR = 1.8-14.8), and 38 (10.2%) died after 4.8 days (IQR = 1.6-14.3). Prolonged LOS was associated with discharge to places other than usual residence: OR = 3.1 (95% CI 1.7-5.7), acute stroke: OR = 10.1 (3.7-26.7), acute first hip fractures: OR = 6.8 (3.1-14.8), recurrent hip fractures: OR = 9.5 (3.2-28.7), urinary tract infection/pneumonia: OR = 4.0 (2.1-8.0), other acute fractures: OR = 9.8 (3.0-32.3) and malignancy: OR = 15.0 (3.1-71.8). Predictive equation showed estimated LOS was 11.6 days for discharge to places other than usual residence, 15 days for pre-existing or acute stroke, 9-14 days for acute and recurrent hip fractures, infections, other acute fractures and malignancy; these factors together explained 32% of variability in LOS.

CONCLUSIONS

A useful estimate of outcome and LOS can be made by constructing a predictive equation from information on hospital admission, to provide evidence-based guidance for resource requirements and discharge planning.

Converting acute inpatient take to outpatient take with fast-track assessment in internal medicine wards - a before-after study

Background

With an extensive rise in the number of acute patients and increases in both admissions and readmissions, hospitals are at times overcrowded and under immense pressure and this may challenge patient safety. This study evaluated an innovative strategy converting acute internal medicine inpatient take to an outpatient take. Here, acute patients, following referral, underwent fast-track assessment to the needed level of medical care as outpatients, directly in internal medicine wards.

Method

The two internal medicine wards at Diagnostic Centre, Silkeborg, Denmark, changed their take of acute patients 1st of March 2017. The intervention consisted of acute medical patients being received in medical examination chairs, going through accelerated evaluation as outpatients with assessment within one hour for either admission or another form of treatment. A before-and-after study design was used to evaluate changes in activity. All referred patients for 10 months following implementation of the intervention were compared with patients referred in corresponding months the previous year.

Results

A total of 5339 contacts (3632 patients) who underwent acute medical assessment (2633 contacts before and 2706 after) were included. Median hospital length-of-stay decreased from 32.6 h to 22.3 h, and the proportion of referred acute patients admitted decreased with 36.3% points from 94.5 to 58.2%. The median length-of-admission time for the admitted patients increased as expected after the intervention. The risk of being admitted, being readmitted as well as having a hospital length-of-time longer than 24 h, 72 h or 7 days, respectively, were significantly lower during the after-period in comparison to the before-period. Adverse effects, unplanned re-contacts, total contacts to general practice and mortality did not change after the intervention.

Conclusion

Assessing referred acute patients in medical examination chairs as outpatients directly in internal medicine wards and promoting an accelerated trajectory, reduced inpatient admissions and total length-of-stay considerably. This strategy seems effective in everyday acute medical patients and has the potential to ease the increasing pressure on the acute take for wards receiving acute medical patients.

Analysis of frequency, type of complications and economic costs of outlying patients in general and digestive surgery

INTRODUCTION

The shortage of available beds and the increase in Emergency Department pressure can cause some patients to be admitted in wards with available beds assigned to other services (outlying patients). The aim of this study is to assess the frequency, types of complications and costs of outlying patients.

METHODS

Using a retrospective cohort model, we analysed the 2015 general and digestive surgery records (source: Minimum Basic Data Set and economic database). After selecting all outlying patients, we compared the complications, length of stay, costs and consequences of complications against a randomized sample of non-outlying patients with the same DRG and date of episode for every outlying patient, obtaining one non-outlying patient for each selected outlying patient. Thirteen outlying patients with no non-outlying patient pair were excluded from the study.

RESULTS

From a total of 2,915 patients, 363 (12.45%) were outlying patients. A total of 350 outlying patients were analysed versus 350 non-outlying patients. There were no significant differences in complications (9.4 vs. 8.3%), length of stay (4.33 vs. 4.65 days) or costs (€3,034.12 vs. €3,223.27). Outlying patients men presented a significantly higher risk of complications compared to women (RR=2.10). Outlying patients presented complications after 2.5 or more days.

CONCLUSIONS

When outlying admissions become necessary, the selection of patients with less complex pathologies does not increase complications or their consequences (ICU admissions, readmissions, reoperations or mortality), hospital stays or costs. Only in cases of prolonged outlying stays of more than 2.5 days, or in males, may more complications appear. Therefore, male outliers should be avoided in general, and patients should be transferred to the proper ward if a length of stay beyond 2.5 days is foreseen.

Core components for effective infection prevention and control programmes: new WHO evidence-based recommendations

Health care-associated infections (HAI) are a major public health problem with a significant impact on morbidity, mortality and quality of life. They represent also an important economic burden to health systems worldwide. However, a large proportion of HAI are preventable through effective infection prevention and control (IPC) measures. Improvements in IPC at the national and facility level are critical for the successful containment of antimicrobial resistance and the prevention of HAI, including outbreaks of highly transmissible diseases through high quality care within the context of universal health coverage. Given the limited availability of IPC evidence-based guidance and standards, the World Health Organization (WHO) decided to prioritize the development of global recommendations on the core components of effective IPC programmes both at the national and acute health care facility level, based on systematic literature reviews and expert consensus. The aim of the guideline development process was to identify the evidence and evaluate its quality, consider patient values and preferences, resource implications, and the feasibility and acceptability of the recommendations. As a result, 11 recommendations and three good practice statements are presented here, including a summary of the supporting evidence, and form the substance of a new WHO IPC guideline.

The Sustained Rotavirus Vaccination Impact on Nosocomial Infection, Duration of Hospital Stay, and Age: The RotaBIS Study (2005-2012)

Introduction

The benefits of rotavirus (RV) vaccination in developed countries have focused on reductions in mortality, hospitalization and medical visits, and herd protection. We investigated other aspects related to RV-induced nosocomial infection, duration of hospital stay, age shift, and sustained vaccine impact (VI) over time.

Method

RotaBIS (Rotavirus Belgian Impact Study; ClinicalTrials.gov identifier, NCT01563146) annually collects retrospective data on hospitalization linked to RV testing in children up to 5 years old from 11 pediatric wards located all over Belgium. Data from 2005 to 2012 have been split in pre- (2005–2006) and post-vaccination (2007–2012) period. Information was collected on age, gender, RV test result, nosocomial infection caused by RV and duration of hospital stay.

Results

Over the 6-year period after the introduction of the RV vaccine, an 85% reduction in nosocomial infections was observed (221 in 2005 to 33 in 2012, p < 0.001). A significant reduction of almost 2 days in average duration of hospital stay per event was observed overall (7.62 days in 2005 to 5.77 days in 2012, p < 0.001). The difference is mainly explained by the higher reduction in number of nosocomial infections. A pronounced age shift (+24%, p < 0.01) of RV nosocomial infection to infants ≤2 months old was observed, increasing with length of post-vaccination period. VI was maintained over the follow-up (±79% VI per birth cohort). A decrease was seen depending on age, 85% (95% CI 76–91%) in the youngest to 63% (95% CI 22–92%) in the oldest age group.

Conclusion

The higher reduction in nosocomial infection may affect the overall average duration of hospital stay for RV infection. No change in VI by birth cohort, but a reduction by age group was observed. These findings could be important for decision-makers considering the introduction of universal mass RV vaccination programs.

Trial registration

ClinicalTrials.gov identifier, NCT01563146.

Funding

GlaxoSmithKline Biologicals SA (Rixensart, Belgium).

Primary triage nurses do not divert patients away from the emergency department at times of high in-hospital bed occupancy - a retrospective cohort study

BACKGROUND

Emergency department (ED) overcrowding is frequently described in terms of input- throughput and output. In order to reduce ED input, a concept called primary triage has been introduced in several Swedish EDs. In short, primary triage means that a nurse separately evaluates patients who present in the Emergency Department (ED) and either refers them to primary care or discharges them home, if their complaints are perceived as being of low acuity. The aim of the present study is to elucidate whether high levels of in-hospital bed occupancy are associated with decreased permeability in primary triage. The appropriateness of discharges from primary triage is assessed by 72-h revisits to the ED.

METHODS

The study is a retrospective cohort study on administrative data from the ED at a 420-bed hospital in southern Sweden from 2011-2012. In addition to crude comparisons of proportions experiencing each outcome across strata of in-hospital bed occupancy, multivariate models are constructed in order to adjust for age, sex and other factors.

RESULTS

A total of 37,129 visits to primary triage were included in the study. 53.4 % of these were admitted to the ED. Among the cases referred to another level of care, 8.8 % made an unplanned revisit to the ED within 72 h. The permeability of primary triage was not decreased at higher levels of in-hospital bed occupancy. Rather, the permeability was slightly higher at occupancy of 100-105 % compared to <95 % (OR 1.09 95 % CI 1.02-1.16). No significant association between in-hospital bed occupancy and the probability of 72-h revisits was observed.

CONCLUSIONS

The absence of a decreased permeability of primary triage at times of high in-hospital bed occupancy is reassuring, as the opposite would have implied that patients might be denied entry not only to the hospital, but also to the ED, when in-hospital beds are scarce.

Paediatric burn unit in Portugal: Beds needed using a bed-day approach

INTRODUCTION

Despite the high burden of children with burns, there is not a paediatric burn unit (PBU) in Portugal. We aimed to estimate the Portuguese health care providing needs on paediatric burns.

METHODS

We performed a nation-wide retrospective study, between 2009 and 2013, among less than 16 years-old inpatients with burns that met the transfer criteria to a burn unit in Portugal. A bed-day approach was used, targeting an occupancy rate of 70-75%, and possible locations were studied. The primary outcome was the number of beds needed, and secondary outcomes were the overload and revenue for each possible number of beds in a PBU.

RESULTS

A total of 1155 children met the transfer criteria to a burn unit, representing a total of 17,371 bed-days. Occupancy rates of 11-bed, 12-bed, 13-bed and 14-bed PBU were, respectively, 79.7%, 75.3%, 71.0% and 66.8%. The 13-bed PBU scenario would represent an overload of 523 bed-days, revenue of more than 5 million Euros and a ratio of 1 PBU bed per 123,409 children.

CONCLUSIONS

Using a groundbreaking approach, the optimal number of PBU beds needed in Portugal is 13. However, as half of the patients who met burn transfer criteria are not transferred, this bed number might be overestimated if this pattern maintains, despite the underestimation with our method approach. If a PBU is to be created the preferable location is Porto. Cost-effectiveness studies should be performed.

Influence of ICU-bed availability on ICU admission decisions

BACKGROUND

The potential influence of bed availability on triage to intensive care unit (ICU) admission is among the factors that may influence the ideal ratio of ICU beds to population: thus, high bed availability (HBA) may result in the admission of patients too well or too sick to benefit, whereas bed scarcity may result in refusal of patients likely to benefit from ICU admission.

METHODS

Characteristics and outcomes of patient admitted in four ICUs with usual HBA, defined by admission refusal rate less than 11 % because of bed unavailability, were compared to patients admitted in six ICUs with usual low bed availability (LBA), i.e., an admission refusal rate higher than 10 % during a 90-day period.

RESULTS

Over the 90 days, the mean number of days with no bed available was 30 ± 16 in HBA units versus 48 ± 21 in LBA units (p < 0.01). The proportion of admitted patients was significantly higher in the HBA (80.1 %; n = 659/823) than in the LBA units [61.6 %: n = 480/779; (p < 0.0001)]. The proportion of patients deemed too sick to benefit from admission was higher in LBA (9.0 %; n = 70) than in the HBA (6.3 %; n = 52) units (p < 0.05). The HBA group had a significantly greater proportion of patients younger than 40 years of age (22.5 %; n = 148 versus 14 %; n = 67 in LBA group; p < 0.001) and higher proportions of patients with either high or low simplified acute physiologic score II values.

CONCLUSIONS

Bed availability affected triage decisions. Units with HBA trend to admit patients too sick or too well to benefit.

[Trends in ambulatory surgical procedures in Catalonia (Spain), 2001-2011]

OBJECTIVE

To analyse the trend in ambulatory surgery procedures in Catalonia (Spain) hospitals with regard to the activity in inpatient care units and structural resources in surgery.

METHODS

A descriptive study was performed using data from the Statistics of Health Facilities with Inpatient Care of the Health Department of the Catalan Government from 2001 to 2011. Data from acute care hospitals were analysed and were classified in public and private hospitals.

DISCUSSION

The percentage of ambulatory surgical procedures increased by 63.2% and the percentage of inpatient surgery decreased by 23.5% (this trend was more pronounced in public hospitals). This result coincided with a decrease of structural resources in surgery (beds and operating rooms) and with an improvement in inpatient surgical activity (a decrease in the mean length of stay and bed occupancy rate in all hospitals). Structural surgery resources were optimized and efficiency was improved in surgery inpatient care units.

Associations between in-hospital bed occupancy and unplanned 72-h revisits to the emergency department: a register study

Background

A possible downstream effect of high in-hospital bed occupancy is that patients in the emergency department (ED) who would benefit from in-hospital care are denied admission. The present study aimed at evaluating this hypothesis through investigating associations between in-hospital bed occupancy at the time of presentation in the ED and the probability for unplanned 72-hour (72-h) revisits to the ED among patients discharged at index. A second outcome was unplanned 72-h revisits resulting in admission.

Methods

All visits to the ED of a 420-bed emergency hospital in southern Sweden between 1 January 2011 and 31 December 2012, which did not result in admission, death, or transfer to another hospital were included. Revisiting fractions were computed for in-hospital occupancy intervals <85%, 85% to 90%, 90% to 95%, 95% to 100%, 100% to 105%, and ≥105%. Multivariate models were constructed in an attempt to take confounding factors from, e.g., presenting complaints, age, referral status, and triage priority into account.

Results

Included in the study are 81,878 visits. The fraction of unplanned 72-h revisits/unplanned 72-h revisits resulting in admission was 5.8%/1.4% overall, 6.2%/1.4% for occupancy <85%, 6.4%/1.5% for occupancy 85% to 90%, 5.8%/1.4% for occupancy 90% to 95%, 6.0%/1.6% for occupancy 95% to 100%, 5.4%/1.6% for occupancy 100% to 105%, and 4.9%/1.4% for occupancy ≥105%.

In the multivariate models, a trend to lower probability of unplanned 72-h revisits was observed at occupancy ≥105% compared to occupancy <95% (OR 0.88, CI 0.76 to 1.01). No significant associations between in-hospital occupancy at index and the probability of making unplanned 72-h revisits resulting in admission were observed.

Conclusions

The lack of associations between in-hospital occupancy and unplanned 72-h revisits does not support the hypothesis that ED patients are inappropriately discharged when in-hospital beds are scarce. The results are reassuring as they indicate that physicians are able to make good decisions, also while resources are constrained.

ICU service in Taiwan

Background

The aim of the study was to understand the current status of intensive care unit (ICU) in order to optimize the resources achieving the best possible care.

Methods

The study analyzed the status of ICU settings based on the Taiwan National Health Insurance database between March 2004 and February 2009.

Results

A total of 1,028,364 ICU patients were identified. The age was 65 ± 18 years, and 61% of the patients were male. The total ICU bed occupancy rate was 83.8% which went up to 87.3% during winter. The ICU bed occupancy was 94.4% in major medical centers. The ICU stay was 6.5 ± 0.5 days, and the overall ICU mortality rate was 20.2%. The hospital stay was 16.4 ± 16.8 days, and the average cost of total hospital stay was approximately US$5,186 per patient.

Conclusions

The rate of ICU bed occupancy was dependent on seasonal changes, and it reached near full capacity in major medical centers in Taiwan. The ICU beds were distributed based on the categories of hospitals in order to achieve a reasonable cost efficiency. ICU faces many challenges to maintain and improve quality care because of the increasing cost of state-of-the-art technologies and dealing with aging population.

Optimal occupancy in the ICU: a literature review

INTRODUCTION

In intensive care, occupancy is a commonly used measure. There is inconsistency however in its measurement and optimal occupancy targets need to be defined. The objectives of this literature review were to explore how occupancy is measured, reported, and interpreted and investigate optimal occupancy levels for ICUs.

METHOD

A literature search was performed using the Medline, Embase and CINAHL databases and citation tracking identified additional relevant articles. Articles published since 1997, written in English and focused on the adult ICU setting were included. As a result, 16 articles were selected for this review.

RESULTS

Although it was apparent there was no commonly accepted or used method for calculating ICU occupancy, methods described as more accurate enumerate actual patient hours in the ICU, use operational (and preferably fully staffed) beds as the denominator, and are calculated daily. Issues pertaining to the utility, interpretation, and reporting of ICU occupancy measures were identified and there were indications that optimal ICU occupancy rates were around 70-75%. It was evident however that setting a uniform target figure for all ICUs would be problematic as there are a range of factors both at the unit and the hospital level that impact occupancy figures and optimal occupancy levels.

IMPLICATIONS

This literature review informed the recommendation of a proposed method for calculating ICU occupancy which provides a realistic measure of occupied bed hours as a percentage of available beds. Despite the importance of gaining an understanding of ICU occupancy at the local and broader health system levels, there are a number of unknown factors that require further research. Appropriate occupancy targets, impact of unavailable beds, and the intrinsic and extrinsic factors on occupancy measurement are a few examples of where more information is required to adequately inform ICU monitoring, planning and evaluation activities.