Associations between hospital occupancy, intensive care unit transfer delay and hospital mortality

Disaster Preparedness and Hospital Safety in State Hospitals in Lima (Peru)

INTRODUCTION

Peru's health infrastructures, particularly hospitals, are exposed to disaster threats of different natures. Traditionally, earthquakes have been the main disaster in terms of physical and structural vulnerability, but the coronavirus disease 2019 (COVID-19) pandemic has also shown their functional vulnerability. Public hospitals in Lima are very different in terms of year constructed, type of construction, and number of floors, making them highly vulnerable to earthquakes. In addition, they are subject to a high demand for care daily. Therefore, if a major earthquake were to occur in Lima, the hospitals would not have the capacity to respond to the high demand.

OBJECTIVE

The aim of this study was to analyze the Hospital Safety Index (HSI) in hospitals in Lima (Peru).

MATERIALS AND METHODS

This was a cross-sectional observational study of 18 state-run hospitals that met the inclusion criteria; open access data were collected for the indicators proposed by the Pan American Health Organization (PAHO) Version 1. Associations between variables were calculated using the chi-square test, considering a confidence level of 95%. A P value less than .05 was considered to determine statistical significance.

RESULTS

The average bed occupancy rate was 90%, the average age was 70 years, on average had one bed per 25,126 inhabitants, and HSI average score was 0.36 with a vulnerability of 0.63. No association was found between HSI and hospital characteristics.

CONCLUSION

Most of the hospitals were considered Category C in earthquake and disaster safety, and only one hospital was Category A. The hospital situation needs to be clarified, and the specific deficiencies of each institution need to be identified and addressed according to their own characteristics and context.

Patterns in the Pandemic: Disproportionate Patient Burdens Among Regional Hospitals

Study objective

To examine the distribution of hospitalized COVID-19 patients among adult acute care facilities in the Greater Philadelphia area and identify factors associated with hospitals carrying higher burdens of COVID-19 patients.

Methods

In this observational descriptive study, we obtained self-reported daily COVID-19 inpatient censuses from 28 large (>100 beds) adult acute care hospitals in the Greater Philadelphia region during the initial wave of the COVID-19 pandemic (March 23, 2020, to July 28, 2020). We examined hospitals based on their size, location, trauma certification, median household income, and reliance on public insurance. COVID-19 inpatient burdens (ie, beds occupied by COVID-19 patients), relative to overall facility capacity (ie, total beds), were reported and assessed using thresholds established by the Institute of Health Metrics and Evaluation to approximate the stress induced by different COVID-19 levels.

Results

Maximum (ie, peak) daily COVID-19 occupancy averaged 27.5% (SD 11.2%) across the 28 hospitals. However, there was dramatic variation between hospitals, with maximum daily COVID-19 occupancy ranging from 5.7% to 50.0%. Smaller hospitals remained above 20% COVID-19 capacity for longer (small hospital median 27.5 days [interquartile range {IQR}: 4 to 32]; medium hospital median 18.5 days [IQR: 0.5 to 37]; large hospital median 13 days [IQR: 6 to 32]). Trauma centers reached 20% capacity sooner (median 19 days [IQR: 16-25] versus nontrauma median 30 days [IQR: 20 to 128]), remained above 20% capacity for longer (median 31 days [IQR: 11 to 38]; nontrauma median 8 days [IQR: 0 to 30]), and had higher observed burdens relative to their total capacity (median 5.8% [IQR: 2.4% to 8.3%]; nontrauma median 2.5% [IQR: 1.6% to 2.8%]). Urban location was also predictive of higher COVID-19 patient burden (urban median 3.8% [IQR: 2.6% to 6.7%]; suburban median 2.2% [IQR: 1.5% to 2.8%]). Heat map analyses demonstrated that hospitals in lower-income areas and hospitals in areas of higher reliance on public insurance also exhibited substantially higher COVID-19 occupancy and longer periods of higher COVID-19 occupancy.

Conclusion

Substantial discrepancies in COVID-19 inpatient burdens existed among Philadelphia-region adult acute care facilities during the initial COVID-19 surge. Trauma center status, urban location, low household income, and high reliance on public insurance were associated with both higher COVID-19 burdens and longer periods of high occupancy. Improved data collection and centralized sharing of pandemic-specific data between health care facilities may improve resource balancing and patient care during current and future response efforts.

Demand for Intensive Care beds and patient classification according to the priority criterion

OBJECTIVE

to assess the demand for Intensive Care Unit beds as well as the classification of the patients for admission, according to the priority system.

METHOD

a retrospective and cross-sectional study, developed from January2014 to December2018 in two Intensive Care Units for adults of a university hospital. The sample consisted of the requests for vacancies according to the priority system(scale from 1 to 4, where 1 is the highest priority and 4 is no priority), registered in the institution's electronic system.

RESULTS

a total of 8,483 vacancies were requested, of which 4,389(51.7%) were from unitB. The highest percentage in unitA was of Priority2 patients(32.6%); and Priority1 was prevalent in unitB(45.4%). The median lead time between request and admission to unitA presented a lower value for priority1 patients(2h57) and a higher value for priority4 patients(11h24); in unitB, priority4 patients presented shorter time(5h54) and priority3 had longer time(11h54). 40.5% of the requests made to unitA and 48.5% of those made to unitB were fulfilled, with 50.7% and 48.5% of these patients being discharged from the units, respectively.

CONCLUSION

it is concluded that the demand for intensive care beds was greater than their availability. Most of the patients assisted were priorities1 and2, although a considerable percentage of those classified as priorities3 and4 is observed.

Access to intensive care in 14 European countries: a spatial analysis of intensive care need and capacity in the light of COVID-19

Purpose

The coronavirus disease 2019 (COVID-19) poses major challenges to health-care systems worldwide. This pandemic demonstrates the importance of timely access to intensive care and, therefore, this study aims to explore the accessibility of intensive care beds in 14 European countries and its impact on the COVID-19 case fatality ratio (CFR).

Methods

We examined access to intensive care beds by deriving (1) a regional ratio of intensive care beds to 100,000 population capita (accessibility index, AI) and (2) the distance to the closest intensive care unit. The cross-sectional analysis was performed at a 5-by-5 km spatial resolution and results were summarized nationally for 14 European countries. The relationship between AI and CFR was analyzed at the regional level.

Results

We found national-level differences in the levels of access to intensive care beds. The AI was highest in Germany (AI = 35.3), followed by Estonia (AI = 33.5) and Austria (AI = 26.4), and lowest in Sweden (AI = 5) and Denmark (AI = 6.4). The average travel distance to the closest hospital was highest in Croatia (25.3 min by car) and lowest in Luxembourg (9.1 min). Subnational results illustrate that capacity was associated with population density and national-level inventories. The correlation analysis revealed a negative correlation of ICU accessibility and COVID-19 CFR (r = − 0.57; p < 0.001).

Conclusion

Geographical access to intensive care beds varies significantly across European countries and low ICU accessibility was associated with a higher proportion of COVID-19 deaths to cases (CFR). Important differences in access are due to the sizes of national resource inventories and the distribution of health-care facilities relative to the human population. Our findings provide a resource for officials planning public health responses beyond the current COVID-19 pandemic, such as identifying potential locations suitable for temporary facilities or establishing logistical plans for moving severely ill patients to facilities with available beds.

Electronic supplementary material

The online version of this article (10.1007/s00134-020-06229-6) contains supplementary material, which is available to authorized users.