Hospital ward patient lifting tasks

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.

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Modeling aerosol transmission of SARS-CoV-2 from human-exhaled particles in a hospital ward

Environmental Science and Pollution Research ◽

10.1007/s11356-021-14519-9 ◽

2021 ◽

Author(s):

Lip Huat Saw ◽

Bey Fen Leo ◽

Norefrina Shafinaz Md Nor ◽

Chee Wai Yip ◽

Nazlina Ibrahim ◽

...

Keyword(s):

Hospital Ward

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Simulate and sense force exertions during virtual patient transfer tasks

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/1071181320641507 ◽

2020 ◽

Vol 64 (1) ◽

pp. 2092-2096

Author(s):

Ken Chen ◽

Rebecca Widmayer ◽

Karen B. Chen

Keyword(s):

Muscle Activity ◽

Fine Tuning ◽

Activity Levels ◽

Patient Lifting ◽

Perceived Workload ◽

Specific Muscle ◽

Muscle Groups ◽

Forceful Exertion ◽

Lifting Task ◽

Transfer Tasks

Virtual reality (VR) is commonplace for training, yet simulated physical activities in VR do not require trainees to engage and contract the muscle groups normally engaged in physical lifting. This paper presents a muscle activity-driven interface to elicit the sensation of forceful, physical exertions when lifting virtual objects. Users contracted and attained predefined muscle activity levels that were calibrated to user-specific muscle activity when lifting the physical counterpart. The overarching goal is to engage the appropriate muscles, and thereby encourage and elicit behaviors normally seen in the physical environment. Activities of 12 key muscles were monitored using electromyography (EMG) sensors while they performed a three-part patient lifting task in a Cave Automatic Virtual Environment. Participants reported higher task mental loads and less physical loads for the virtual lift than the physical lift. Findings suggest the potential to elicit sensation of forceful exertion via EMG feedback but needed fine-tuning to offset perceived workload.

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Workplace violence from the perspective of hospital ward managers in Sweden: a qualitative study

Journal of Nursing Management ◽

10.1111/jonm.13423 ◽

2021 ◽

Author(s):

Jenny Jakobsson ◽

Karin Örmon ◽

Hanne Berthelsen ◽

Malin Axelsson

Keyword(s):

Qualitative Study ◽

Workplace Violence ◽

Hospital Ward

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Association between Frailty and 90-Day Outcomes amongst the Chinese Population: A Hospital-Based Multicentre Cohort Study

Gerontology ◽

10.1159/000514948 ◽

2021 ◽

pp. 1-9

Author(s):

Jing Jiao ◽

Na Guo ◽

Lingli Xie ◽

Qiaoyan Ying ◽

Chen Zhu ◽

...

Keyword(s):

Cohort Study ◽

Adverse Outcomes ◽

Hospital Ward ◽

Adverse Health Outcomes ◽

Multicentre Cohort Study ◽

Older Inpatients ◽

Ward Clustering ◽

Clustering Effect ◽

The Relationship ◽

Multicentre Cohort

Introduction: Frailty has gained increasing attention as it is by far the most prevalent geriatric condition amongst older patients which heavily impacts chronic health status. However, the relationship between frailty and adverse health outcomes in China is far from clear. This study explored the relation between frailty and a panel of adverse health outcomes. Methods: We performed a multicentre cohort study of older inpatients at 6 large hospitals in China, with two-stage cluster sampling, from October 2018 to April 2019. Frailty was measured according to the FRAIL scale and categorized into robust, pre-frail, and frail. A multivariable logistic regression model and multilevel multivariable negative binomial regression model were used to analyse the relationship between frailty and adverse outcomes. Outcomes were length of hospitalization, as well as falls, readmission, and mortality at 30 and 90 days after enrolment. All regression models were adjusted for age, sex, BMI, surgery, and hospital ward. Results: We included 9,996 inpatients (median age 72 years and 57.8% male). The overall mortality at 30 and 90 days was 1.23 and 1.88%, respectively. At 30 days, frailty was an independent predictor of falls (odds ratio [OR] 3.19; 95% CI 1.59–6.38), readmission (OR 1.45; 95% CI 1.25–1.67), and mortality (OR 3.54; 95% confidence interval [CI] 2.10–5.96), adjusted for age, sex, BMI, surgery, and hospital ward clustering effect. At 90 days, frailty had a strong predictive effect on falls (OR 2.10; 95% CI 1.09–4.01), readmission (OR 1.38; 95% CI 1.21–1.57), and mortality (OR 6.50; 95% CI 4.00–7.97), adjusted for age, sex, BMI, surgery, and hospital ward clustering effect. There seemed to be a dose-response association between frailty categories and fall or mortality, except for readmission. Conclusions: Frailty is closely related to falls, readmission, and mortality at 30 or 90 days. Early identification and intervention for frailty amongst older inpatients should be conducted to prevent adverse outcomes.

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