Stroke outcomes research requires risk-adjustment for stroke severity, but this measure is often unavailable. The Passive Surveillance Stroke SeVerity (PaSSV) score is an administrative data-based stroke severity measure that was developed in Ontario, Canada. We assessed the geographical and temporal external validity of PaSSV in British Columbia (BC), Nova Scotia (NS) and Ontario, Canada.

We used linked administrative data in each province to identify adult patients with ischemic stroke or intracerebral hemorrhage between 2014-2019 and calculated their PaSSV score. We used Cox proportional hazards models to evaluate the association between the PaSSV score and the hazard of death over 30 days and the cause-specific hazard of admission to long-term care over 365 days. We assessed the models’ discriminative values using Uno’s c-statistic, comparing models with versus without PaSSV.

We included 86,142 patients (n = 18,387 in BC, n = 65,082 in Ontario, n = 2,673 in NS). The mean and median PaSSV were similar across provinces. A higher PaSSV score, representing lower stroke severity, was associated with a lower hazard of death (hazard ratio and 95% confidence intervals 0.70 [0.68, 0.71] in BC, 0.69 [0.68, 0.69] in Ontario, 0.72 [0.68, 0.75] in NS) and admission to long-term care (0.77 [0.76, 0.79] in BC, 0.84 [0.83, 0.85] in Ontario, 0.86 [0.79, 0.93] in NS). Including PaSSV in the multivariable models increased the c-statistics compared to models without this variable.

PaSSV has geographical and temporal validity, making it useful for risk-adjustment in stroke outcomes research, including in multi-jurisdiction analyses.

Events, specifically those where excessive alcohol consumption is common, pose a risk to increase alcohol-related presentations to emergency departments (EDs). Limited evidence exists that synthesizes the impact from events on alcohol-related presentations to EDs.

This integrative review aimed to synthesize the literature regarding the impact events have on alcohol-related presentations to EDs.

An integrative literature review methodology was guided by the Preferred Reporting Items of Systematic Reviews and Meta-Analysis (PRISMA) Guidelines for data collection, and Whittemore and Knafl’s framework for data analysis. Information sources used to identify studies were MEDLINE, CINAHL, and EMBASE, last searched May 26, 2021.

In total, 23 articles describing 46 events met criteria for inclusion. There was a noted increase in alcohol-related presentations to EDs from 27 events, decrease from eight events, and no change from 25 events. Public holidays, music festivals, and sporting events resulted in the majority of increased alcohol-related presentations to EDs. Few articles focused on ED length-of-stay (LOS), treatment, and disposition.

An increase in the consumption of alcohol from holiday, social, and sporting events pose the risk for an influx of presentations to EDs and as a result may negatively impact departmental flow. Further research examining health service outcomes is required that considers the impact of events from a local, national, and global perspective.

The evidence shows that the need for emergency evacuation in hospitals has arisen. Designing an emergency evacuation decision making tool increases the confidence of hospital managers in the decision made. Therefore, this study was aimed at the development, and the psychometric properties, of the decision-making scale for emergency hospital evacuation in disasters.

This study was done in 2 phases of qualitative study and literature review and designing and psychometric properties of the instrument. After development of the primary item pool, the psychometric properties of the questionnaire were evaluated. In this regard, face and content validity, internal consistency (Alpha’s Cronbach), reliability (ICC), and stability were assessed.

In the validity stage of the instrument, 4 items were removed. Also, 4 items were modified and 2 items were merged. The number of items was thus decreased to 64. After CVI calculation, 5 items were removed, 4 items were modified, and 2 items were merged. As a result of this, the number of items decreased to 58 items. The scale has good reliability and stability.

It seems that the instrument could be useful in decision-making for emergency hospital evacuation in disasters.

Ethiopian policy and strategy aim to make health care systems capable of dealing with emergencies. However, Ethiopian health care still lacks a comprehensive “all-hazard” approach and a disaster preparedness program. Thus, this study aimed to assess the level of disaster preparedness in selected public hospitals for mass-casualty incidents (MCIs) in Amhara Regional State, Northwest Ethiopia.

A descriptive cross-sectional study was conducted at general and comprehensive specialized hospitals (CSHs) in Amhara Regional State, Ethiopia using a World Health Organization (WHO) hospital emergency response checklist that included a domain on mass-casualty management (MCM) adapted from a literature review.

Seventeen (17) hospitals were evaluated (response rate: 81%). Five (29.4%) were teaching hospitals (tertiary health care) and 12 (70.5%) were non-teaching (secondary health care) hospitals. With an average mean of 97.3 (SD = 33.68; range 31-160), most hospitals under WHO required an Acceptable level of preparedness. Two were at an Unacceptable (0-67) level of preparedness, 12 (70.5%) hospitals were at an Insufficient (68-134) state, while the other three had an Acceptable (135-192) level of preparedness.

The preparedness level of hospitals is Insufficient for potential MCIs in this region and needs prior attention in implementing existing strategic guidelines to develop and activate hospital disaster plans if and when needed.

Receiving bad news about one’s health can be devastating, yet little is known about how the therapeutic nature of the environment where bad news is delivered affects the experience. The current study aimed to explore how patients and their families were affected by the language and the built, natural, social, and symbolic environments when receiving bad news, through the Therapeutic Landscapes theoretical framework.

Patients diagnosed with a life-limiting illness living in regional Victoria who had a hospital admission within 24 months and a diagnostic/prognostic conversation were invited to participate, as well as a family member who witnessed the conversation. Participants were recruited through social media and snowballing, resulting in 14 online semi-structured interviews being conducted between November 2021 and March 2022, audio-recorded, and transcribed verbatim. Reflexive thematic analysis was used to develop the themes.

Fourteen semi-structured interviews were conducted with women aged between 30 and 77 years. Interviews lasted between 45 and 120 minutes, with an average of 69 minutes, and were conducted online or via mobile phone. Four central themes were developed: “Hearing bad news for the first time,” “Preferences for having hard conversations,” “Creating a sense of safety for ongoing care,” and “The therapeutic nature of the ward.”

This body of work will help inform practice and future policy regarding bad news delivery and the design and aesthetics of environments where bad news is delivered. It is essential that bad news is delivered within a quiet, calm, and emotionally safe environment within a supportive therapeutic relationship.

Alternative options to hospital care like home care or local health centers (LHCs) are being advocated. However, no study has measured citizens’ preferences (who will finance these services via taxation) for these options.

We measured (i) citizens’ preferences for these services, that is, respondents stated where they would like to get the treatment; (ii) the strength of their preference.

A computerized survey composed of (i) a decision aid to inform respondents about the three options; (ii) three scenarios, from light-to-heavy care, that respondents should rank from the most to the least preferred option of care. (iii) a contingent valuation survey (CVS) to assess how much respondents were willing to pay for their preferred option (except for hospital care if chosen, because it is the default option and free). (iv) a socio-demographic questionnaire.

Data were collected from a representative sample of citizens living in the Rhône–Alps Region (n = 800). The heavier the care was, the more respondents preferred hospital care. Willingness to pay for additional taxation per household/month varied from €13.9 for light care in LHC to €19.1 for heavy home care. The small number of protesting respondents and outliers, and the close correlation between preferences, income, and WTP supports the validity of the CVS.

In France, for cancer, not all citizens would prefer to be treated at home rather than in a hospital. Only less than a quarter would prefer LHC. These results show the mismatch between public health policies and the citizens’ preferences.

To predict the short-term mortality of the serum lactate level and the National Early Warning Score + lactate (NEWS+L) at the time of first admission to the emergency department in COVID-19 patients.

This retrospective analysis was performed by screening the data of COVID-19 patients over a 6-month period (from January 15, 2021, to June 15, 2021). The demographic, comorbidities, vital parameters, and lactate values, as well as C- reactive protein (CRP), blood urea nitrogen (BUN), and 28-day mortality data were recorded.

A total of 70 patients were included in our study. The median (25th - 75th percentile) age was 58 (47.3 - 73.5) years, and 33 (47.1%) patients were female. The mean lactate value was 1.6 (1.2 - 1.98) mmol/L, the mean NEWS was 6 (4-7.75), and the mean NEWS+L was 7.24 ± 2.54. Mortality occurred in 13 (18.2%) of the 70 patients at 28 days. Lactate, NEWS, and NEWS+L had no significant relationship with mortality. None of these parameters was able to predict mortality (P = 0.132, 0.670, and 0.994, respectively).

Our findings showed that the NEWS+L, NEWS, and lactate level could not predict short-term mortality in COVID-19 patients at the time of first admission.

This research evaluated the resilience of 6 tertiary and rural health facilities within a single Australian Health Service, using the World Health Organization (WHO) Hospital Safety Index (HSI). This adaptation of the HSI was compared with existing national accreditation and facility design Standards to assess disaster preparedness and identify opportunities for improvement.

This cross-sectional descriptive study surveyed 6 hospitals that provide 24/7 emergency department and acute inpatient services. HSI assessments, comprising 151 previously validated criteria, were conducted by Health Service engineers and facility managers before being externally reviewed by independent disaster management professionals.

All facilities were found to be highly disaster resilient, with each recording high HSI scores. Variances in structure, architectural safety, continuity of critical services supply, and emergency plans were consistently identified. Power and water supply vulnerabilities are common to previously reported vulnerabilities in health facilities of developing countries.

Clinical, engineering, and disaster management professionals assessed 6 Australian hospitals using the WHO HSI with each facility scoring highly, genuine vulnerabilities and practical opportunities for improvement were identified. This application of the WHO HSI, intended for use primarily in developing countries and disaster-affected regions, complimented and extended the existing Australian national health service accreditation and facility design Standards. These results support the expansion of existing assessment tools used to assess Australian health facility disaster preparedness and resilience.

An increasing number of children, adolescents and adults with intellectual disabilities and/or autism are being admitted to general psychiatric wards and cared for by general psychiatrists.

The aim of this systematic review was to consider the likely effectiveness of in-patient treatment for this population, and compare and contrast differing models of in-patient care.

A systematic search was completed to identify papers where authors had reported data about the effectiveness of in-patient admissions with reference to one of three domains: treatment effect (e.g. length of stay, clinical outcome, readmission), patient safety (e.g. restrictive practices) and patient experience (e.g. patient or family satisfaction). Where possible, outcomes associated with admission were considered further within the context of differing models of in-patient care (e.g. specialist in-patient services versus general mental health in-patient services).

A total of 106 studies were included and there was evidence that improvements in mental health, social functioning, behaviour and forensic risk were associated with in-patient admission. There were two main models of in-patient psychiatric care described within the literature: admission to a specialist intellectual disability or general mental health in-patient service. Patients admitted to specialist intellectual disability in-patient services had greater complexity, but there were additional benefits, including fewer out-of-area discharges and lower seclusion rates.

There was evidence that admission to in-patient services was associated with improvements in mental health for this population. There was some evidence indicating better outcomes for those admitted to specialist services.

A danger threatening hospitals is fire. The most important action following a fire is to urgently evacuate the hospital during the shortest time possible. The aim of this study was to predict the duration of emergency evacuation following hospital fire using machine-learning algorithms.

In this study, the real emergency evacuation duration of 190 patients admitted to a hospital was predicted in a simulation based on the following 8 factors: the number of hospital floors, patient preparation and transfer time, distance to the safe location, as well as patient’s weight, age, sex, and movement capability. To design and validate the model, we used statistical models of machine learning, including Support Vector Machines Random Forest, Naive Bayes Classifier, and Artificial Neural Network.

Data analysis showed that based on the Area Under the Curve, precision, and sensitivity values of 99.5%, 92.4%, and 92.1%, respectively, the Random Forest model showed a better performance compared to other models for predicting the duration of hospital emergency evacuation during fire.

Predicting evacuation duration can provide managers with accurate information and true analyses of these events. Therefore, health policy makers and managers can promote preparedness and responsiveness during fire by predicting evacuation duration and developing appropriate plans using machine learning models.