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Effective response to a mass-casualty incident (MCI) entails the activation of hospital MCI plans. Unfortunately, there are no tools available in the literature to support hospital responders in predicting the proper level of MCI plan activation. This manuscript describes the scientific-based approach used to develop, test, and validate the PEMAAF score (Proximity, Event, Multitude, Overcrowding, Temporary Ward Reduction Capacity, Time Shift Slot [Prossimità, Evento, Moltitudine, Affollamento, Accorpamento, Fascia Oraria], a tool able to predict the required level of hospital MCI plan activation and to facilitate a coordinated activation of a multi-hospital network.
Three study phases were performed within the Metropolitan City of Milan, Italy: (1) retrospective analysis of past MCI after action reports (AARs); (2) PEMAAF score development; and (3) PEMAAF score validation. The validation phase entailed a multi-step process including two retrospective analyses of past MCIs using the score, a focus group discussion (FGD), and a prospective simulation-based study. Sensitivity and specificity of the score were analyzed using a regression model, Spearman’s Rho test, and receiver operating characteristic/ROC analysis curves.
Results of the retrospective analysis and FGD were used to refine the PEMAAF score, which included six items–Proximity, Event, Multitude, Emergency Department (ED) Overcrowding, Temporary Ward Reduction Capacity, and Time Shift Slot–allowing for the identification of three priority levels (score of 5-6: green alert; score of 7-9: yellow alert; and score of 10-12: red alert). When prospectively analyzed, the PEMAAF score determined most frequent hospital MCI plan activation (>10) during night and holiday shifts, with a score of 11 being associated with a higher sensitivity system and a score of 12 with higher specificity.
The PEMAAF score allowed for a balanced and adequately distributed response in case of MCI, prompting hospital MCI plan activation according to real needs, taking into consideration the whole hospital response network.
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerging infectious disease pandemic developed in Lombardy (northern Italy) during the last week of February 2020 with a progressive increase of patients presenting with serious clinical findings. Despite the efforts of the Central Italian Government, regional resources were rapidly at capacity. The solution was to plan the medical evacuation (MEDEVAC) of 119 critically ill patients (median age 61 years) to in-patient intensive care units in other Italian regions (77) and Germany (42). Once surviving patients were deemed suitable, the repatriation process concluded the assignment. The aim of this report is to underline the importance of a rapid organization and coordination process between different nodes of an effective national and international network during an emerging infectious disease outbreak and draw lessons learned from similar published reports.
To describe the health-care resources implemented during the Italian Formula 1 Grand Prix (F1GP) and to calculate the patient presentation rate (PPR) based on both real data and a prediction model.
Observational and descriptive study conducted from September 9 to September 11, 2022, during the Italian F1GP hosted in Monza (Italy). Maurer’s formula was applied to decide the number and type of health resources to be allocated. Patient presentation rate (PPR) was computed based on real data (PPR_real) and based on the Arbon formula (PPR_est).
Of 336,000 attendees, n = 263 requested medical assistance with most of them receiving treatment at the advanced medical post, and n = 16 needing transport to the hospital. The PPR_real was 51 for Friday, 78 for Saturday, 134 for Sunday, and 263 when considering the whole event as a single event. The PPR_est resulted in 85 for Friday, 93 for Saturday, 97 for Sunday, and 221 for the total population.
A careful organization of health-care resources could mitigate the impact of the Italian F1GP on local hospital facilities. The Arbon formula is an acceptable model to predict and estimate the number of patients requesting medical assistance, but further investigation needs to be conducted to implement the model and tailor it to broader categories of MGE.
Normative welfare economics commonly assumes that individuals’ preferences can be reliably inferred from their choices and relies on preference satisfaction as the normative standard for welfare. In recent years, several authors have criticized welfare economists’ reliance on preference satisfaction as the normative standard for welfare and have advocated grounding normative welfare economics on opportunities rather than preferences. In this paper, I argue that although preference-based approaches to normative welfare economics face significant conceptual and practical challenges, opportunity-based approaches fail to provide a more reliable and informative foundation for normative welfare economics than preference-based approaches. I then identify and rebut various influential calls to ground normative welfare economics on opportunities rather than preferences to support my qualified defence of preference-based approaches.
This paper provides a philosophical assessment of leading theory-based, evidence-based and coherentist approaches to the definition and the measurement of well-being. It then builds on this assessment to articulate a reformed division of labor for the science of well-being and argues that this reformed division of labor can improve on the proffered approaches by combining the most plausible tenets of theory-based approaches with the most plausible tenets of coherentist approaches. This result does not per se exclude the possibility that theory-based and coherentist approaches may be independently improved or amended in the years to come. Still, together with the challenges that affect these approaches, it strengthens the case for combining the most plausible tenets of those approaches.
On January 25, 2018 a 5-car train derailed in Pioltello, 10 kilometers North-East of Milano City. A standardized post-hoc form was distributed to the hospitals involved in the management of the victims and allowed for an evaluation of the response to the incident.
The management of the incident by EMS (Emergency Medical System) was effective in terms of organization of the scene and distribution of the patients, although the time for the first severe patient to reach the closest appropriate hospital was very long (2 hours). This can be partially explained by the extrication time.
None of the alerted hospitals exceeded their capacity, as patients were distributed carefully among the hospitals. The overall outcome was quite satisfactory; no deaths were reported except for those on scene. Some responding hospitals reported that there was an over-activation based on the services ultimately needed. However this is common in MCIs, as an over-activation is preferable to an under-estimation. To address this concern, as more data are available, activation should be scaled down based on a plan established prior to it; this mechanism of scaling down seems to have failed in this event.
It is of note that the highest performing hospitals underwent recently to an educational program on MCI management.
In recent years, several authors have called to ground descriptive and normative decision theory on neuropsychological measures of utility. In this article, I combine insights from the best available neuropsychological findings, leading philosophical conceptions of welfare, and contemporary decision theory to rebut these prominent calls. I argue for two claims of general interest to philosophers, choice modelers and policy makers. First, severe conceptual, epistemic, and evidential problems plague ongoing attempts to develop accurate and reliable neuropsychological measures of utility. Second, even if these problems are solved, neuropsychological measures of utility lack the potential to directly inform welfare analyses and policy evaluations.
This article examines the issue of whether consideration of so-called minimal models can prompt learning about real-world targets. Using a widely cited example as a test case, it argues against the increasingly popular view that consideration of minimal models can prompt learning about such targets. The article criticizes influential defenses of this view for failing to explicate by virtue of what properties or features minimal models supposedly prompt learning. It then argues that consideration of minimal models cannot prompt learning about real-world targets unless one supplements these models with additional information or presuppositions concerning such targets.
In traditional decision theory, utility is regarded as a mathematical representation of preferences to be inferred from agents’ choices. In the recent literature at the interface between economics, psychology and neuroscience, several authors argue that economists could develop more predictive and explanatory models by incorporating insights concerning individuals’ hedonic experiences. Some go as far as to contend that utility is literally computed by specific neural areas and urge economists to complement or substitute their notion of utility with some neuro-psychological construct. In this paper, I distinguish three notions of utility that are frequently mentioned in debates about decision theory and examine some critical issues regarding their definition and measurability. Moreover, I provide various empirical and conceptual reasons to doubt that economists should base decision theoretic analyses on some neuro-psychological notion of utility.