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ObjectivesTo compare the cross-sectional robustness of commonly used volumetric software and effects of lesion filling in multiple sclerosis (MS).MethodsNine MS patients (six females; age 38±13 years, disease duration 7.3±5.2 years) were scanned twice with repositioning on three MRI scanners (Siemens Aera 1.5T, Avanto 1.5T, Trio 3.0T) the same day. Volumetric T1-weighted images were processed with FreeSurfer, FSL-SIENAX, SPM and SPM-CAT before and after 3D FLAIR lesion filling with LST. The whole-brain, grey matter (GM) and white matter (WM) volumes were calculated with and without normalisation to the intracranial volume or FSL-SIENAX scaling factor. Robustness was assessed using the coefficient of variation (CoV).ResultsVariability in volumetrics was lower within than between scanners (CoV 0.17–0.96% vs. 0.65–5.0%, p<0.001). All software provided similarly robust segmentations of the brain volume on the same scanner (CoV 0.17–0.28%, p=0.076). Normalisation improved inter-scanner reproducibility in FreeSurfer and SPM-based methods, but the FSL-SIENAX scaling factor did not improve robustness. Generally, SPM-based methods produced the most consistent volumetrics, while FreeSurfer was more robust for WM volumes on different scanners. FreeSurfer had more robust normalised brain and GM volumes on different scanners than FSL-SIENAX (p=0.004). MS lesion filling changed the output of FSL-SIENAX, SPM and SPM-CAT but not FreeSurfer.ConclusionsConsistent use of the same scanner is essential and normalisation to the intracranial volume is recommended for multiple scanners. Based on robustness, SPM-based methods are particularly suitable for cross-sectional volumetry. FreeSurfer poses a suitable alternative with WM segmentations less sensitive to MS lesions.Key Points• The same scanner should be used for brain volumetry. If different scanners are used, the intracranial volume normalisation improves the FreeSurfer and SPM robustness (but not the FSL scaling factor).• FreeSurfer, FSL and SPM all provide robust measures of the whole brain volume on the same MRI scanner. SPM-based methods overall provide the most robust segmentations (except white matter segmentations on different scanners where FreeSurfer is more robust).• MS lesion filling with Lesion Segmentation Toolbox changes the output of FSL-SIENAX and SPM. FreeSurfer output is not affected by MS lesion filling since it already takes white matter hypointensities into account and is therefore particularly suitable for MS brain volumetry.

Lowering the response times of fire and rescue services (FRS) can reduce costs and the risk of fatalities in emergencies. Ensuring everyone can be reached by the FRS as quickly as possible is therefore a key objective for planners, who often rely on estimated response times to know where response times are long. To be reliable, such estimations need to be validated against real response times. Therefore, this study investigates the spatial patterns of FRS in Sweden in 2018, and compares how estimated and real response times correspond, as well as what factors explain under- or overestimation of the real times. Network analysis performed in a Geographical Information System (GIS) and regression modelling underpin the methodology. In most FRS events (81.3%), estimated response times are shorter than real response times with an average of 83.6 s. Both real and estimated response times are highest in rural municipalities. However, on average, underestimations are larger in urban municipalities. Response times also tend to be underestimated in municipalities with formalized cooperation agreements. Joint organization of FRS resources is thus not necessarily an effective way to reduce response times, with implications for the way FRS is delivered. This study highlights the heterogeneous spatial patterns of difference between estimated and real response times, and shows the importance of being event and area specific when planning the FRS system.

Unmanned aerial vehicles (UAVs) can bring many benefits, particularly in emergency response and disaster management. However, they also induce negative effects, such as noise and visual pollution, risk, and integrity concerns. In this work, we study visual pollution, developing a quantitative measure that can calculate the visual pollution from one or multiple UAVs. First, the Analytic Hierarchy Process was utilized in an expert workshop to find and rank factors relevant to visual pollution. Then an image-based questionnaire targeted at the general public was used to find relations between the factors. The results show that the two main factors causing visual pollution are the number of UAVs and the distance between a UAV and the observer. They also show that while a UAV used for emergency medical services is as polluting as any other UAV, it is easier to tolerate this pollution. Based on the questionnaire results, two visual pollution functions were developed that can be used when carrying out path planning for one or multiple UAVs. When combining this function with other existing measures for noise pollution, and ground and air risk, it is possible to find paths that will give as little negative impact as possible from urban air mobility.

Planning and decision making in emergency response systems face new challenges due to climate changes and the increased risk of multiple or compound natural hazards. This is especially the case in areas with inexperience of such events. The aim of this paper is to identify important activities concerning planning and decision-making during responses to natural hazards, and their potential need for decision support. The knowledge base of the study consists of interviews with 12 representatives from the Swedish emergency response system, supplemented by documents covering policies, operations, and responsibility. Thematic coding was applied to the interview data for identification of important planning and decision activities. Needs of decision support were identified by applying activity theory to the identified activities. We found needs of decision support connected to eight identified key activities concerning consequence analysis, national reinforcements, and resource management. The results illuminate a lack of technology to support response activities during both single and multiple natural hazards. The findings can inform policy makers of emergency response of where to concentrate the development of tools for collaborative preparedness and response work to cope with future challenges from natural hazards.

Background Assuring that emergency health care (EHC) is accessible is a key objective for health care planners. Conventional accessibility analysis commonly relies on resident population data. However, the allocation of resources based on stationary population data may lead to erroneous assumptions of population accessibility to EHC. Method Therefore, in this paper, we calculate population accessibility to emergency departments in Sweden with a geographical information system based network analysis. Utilizing static population data and dynamic population data, we investigate spatiotemporal patterns of how static population data over- or underestimates population sizes derived from temporally dynamic population data. Results Our findings show that conventional measures of population accessibility tend to underestimate population sizes particularly in rural areas and in smaller ED’s catchment areas compared to urban, larger ED’s—especially during vacation time in the summer. Conclusions Planning based on static population data may thus lead to inequitable distributions of resources. This study is motivated in light of the ongoing centralization of ED’s in Sweden, which largely depends on population sizes in ED’s catchment areas.

We discuss a widely used air traffic flow management formulation. We show that this formulation can lead to a solution where air delays are assigned to flights during their take-off which is prohibited in practice. Although air delay is more expensive than ground delay, the model may assign air delay to a few flights during their take-off to save more on not having as much ground delay. We present a modified formulation and verify its functionality in avoiding incorrect solutions.

Evacuation preparedness includes ensuring proper infrastructure, resources and planning for moving people from a dangerous area to safety. This is especially important and challenging during mass gatherings, such as large concerts. In this paper, we present the Emergency Exit Layout Problem (EELP) which is the problem of locating a given number of emergency exits and deciding their width such that the time it takes to evacuate the crowd from an arena is minimized. The EELP takes into account the geography of the arena and its surroundings, as well as the number of pedestrians in the crowd and the distribution of these within the arena. The EELP is formulated as a two-stage stochastic mixed integer linear program to handle the uncertainty related to the location of the possible incidents and the distribution of the pedestrians. Two cases are studied, a large concert planned at the Leangen trotting track in Trondheim and a smaller indoor arena. For each case, the EELP is solved for different scenarios, and the suggested layouts are evaluated using an agent-based simulation model. In particular, the potential of incorporating detailed assessment regarding the location and probability of specific incidents and the distribution of pedestrians are investigated. The computational study shows that making a more detailed risk assessment has little effect on the large concert, but a significant impact on the location of the emergency exits for the smaller indoor case. The results also indicate that it is more important to consider the location and probability of specific incidents rather than the pedestrian distribution.

SARS-CoV-2 uses ACE2, an inhibitor of the Renin-Angiotensin-Aldosterone System (RAAS), for cellular entry. Studies indicate that RAAS imbalance worsens the prognosis in COVID-19. We present a consecutive retrospective COVID-19 cohort with findings of frequent pulmonary thromboembolism (17%), high pulmonary artery pressure (60%) and lung MRI perfusion disturbances. We demonstrate, in swine, that infusing angiotensin II or blocking ACE2 induces increased pulmonary artery pressure, reduces blood oxygenation, increases coagulation, disturbs lung perfusion, induces diffuse alveolar damage, and acute tubular necrosis compared to control animals. We further demonstrate that this imbalanced state can be ameliorated by infusion of an angiotensin receptor blocker and low-molecular-weight heparin. In this work, we show that a pathophysiological state in swine induced by RAAS imbalance shares several features with the clinical COVID-19 presentation. Therefore, we propose that severe COVID-19 could partially be driven by a RAAS imbalance. The SARS-CoV-2 receptor ACE2 is involved in the Renin-Angiotensin-Aldosterone System (RAAS). Over-activation of RAAS in swine results in a disease state similar to that of COVID-19 in human patients, suggesting that COVID-19 pathophysiology may be driven, at least in part, by an imbalance of this hormonal system.

Background Treatment with mechanical thrombectomy (MT) remains inaccessible for many patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO) and under‐utilization prevails across healthcare systems. Increasing the number of thrombectomy centers and ambulance helicopters may alleviate these issues. Aim This study aims to determine the most effective combination of optimally located ambulance helicopters and thrombectomy centers for the economically constrained healthcare system. Methods This nation‐wide, observational study analyses anonymized patient‐level registry data stretching over a 6‐year study period in Sweden. It combines optimization modeling with cost‐effectiveness analysis to generate combinations of optimally located thrombectomy centers and ambulance helicopters to compare with the current eight locations of thrombectomy centers in Sweden and no ambulance helicopters. The analysis extends to evaluate the cost‐effectiveness of increasing the number of thrombectomy centers and ambulance helicopters when the current eight locations remain fixed. Results The most cost‐effective solution comprises 11 thrombectomy centers and 14 ambulance helicopters, corresponding to densities of 1.05 and 1.34 per one million inhabitants, respectively. It yields an estimated annual incremental net monetary benefit (INMB) close to €13.6 million. In the extended scenario analysis, the most cost‐effective solution comprised nine thrombectomy centers and 13 ambulance helicopters, with an estimated annual INMB of €3.8 million. Conclusions The most cost‐effective combination of optimally located thrombectomy centers and ambulance helicopters brings about substantial health gains for patients with AIS due to LVO, compared with the current eight locations of thrombectomy centers in Sweden and ambulance helicopters.

Background Organized volunteer initiatives can reduce response times and improve outcomes in emergencies such as cardiac arrests or fires. Retention of volunteers is important to maintain good coverage and capabilities. The current study explores factors underlying volunteers’ motivation to continue as volunteers. Methods Data from 5347 active volunteers were collected through an online survey. An exploratory factor analysis was used to identify underlying factors that were then used in a regression analysis to predict intention to continue as a volunteer. Group differences based on, among others, number of alarms and prior professional experience in emergency response were explored. Results The results showed that the factors community, self-image, and competence were the strongest positive predictors for the motivation to continue, whereas alarm fatigue and negative experience were the strongest negative predictors. Volunteers with professional background had higher competence and lower Alarm fatigue. Volunteers from rural areas and small cities had higher community than those in large cities. Conclusions Alarm fatigue can make it hard to retain volunteers, which could be addressed using improved dispatch algorithms. Support after dispatch is important to prevent negative experiences. Finally, increased competence, e.g. through education and training, can improve volunteer’s motivation to continue.