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To examine whether post-outbreak early-phase interventions by emergency response teams (ERTs) in long-term care facilities (LTCFs) contribute to containment with lower incidence and case-fatality rate of COVID-19 and analyse the required assistance. Records from 59 LTCFs (28 hospitals, 15 nursing homes, and 16 homes) assisted by ERTs after the COVID-19 outbreak, between May 2020 and January 2021, were used for the analysis. Incidence and case-fatality rates among 6,432 residents and 8,586 care workers were calculated. The daily reports of ERTs were reviewed, and content analysis was performed. Incidence rates among residents and care workers with early phase (<7 days from onset) interventions (30·3%, 10·8%) were lower than those with late phase (≥7 days from onset) interventions (36·6%, 12·6%) (p<0·001, p = 0·011, respectively). The case-fatality rate among residents with early-phase and late-phase interventions were 14·8% and 16·9%, respectively. ERT assistance in LTCFs was not limited to infection control but extended to command and coordination assistance in all studied facilities. Assistance in the facility's operational governance from the early phase of an outbreak in LTCFs contributed to a significant decline in incidence rate and case fatality rate among LTCF residents and care workers in facilities.

Introduction Disasters and accidents have occurred with increasing frequency in recent years. Primary disasters have the potential to result in mass casualty events involving crush syndrome (CS) and other serious injuries. Prehospital providers and emergency clinicians stand on the front lines of these patients’ evaluation and treatment. However, the bulk of our current knowledge, derived from historical data, has remained unchanged for over ten years. In addition, no evidence-based treatment has been established to date. Objective This narrative review aims to provide a focused overview of, and update on, CS for both prehospital providers and emergency clinicians. Discussion CS is a severe systemic manifestation of trauma and ischemia involving soft tissue, principally skeletal muscle, due to prolonged crushing of tissues. Among earthquake survivors, the reported incidence of CS is 2–15%, and mortality is reported to be up to 48%. Patients with CS can develop cardiac failure, kidney dysfunction, shock, systemic inflammation, and sepsis. In addition, late presentations include life-threatening systemic effects such as hypovolemic shock, hyperkalemia, metabolic acidosis, and disseminated intravascular coagulation. Immediately beginning treatment is the single most important factor in reducing the mortality of disaster-situation CS. In order to reduce complications from CS, early, aggressive resuscitation is recommended in prehospital settings, ideally even before extrication. However, in large-scale natural disasters, it is difficult to diagnose CS, and to reach and start treatments such as continuous administration of massive amounts of fluid, diuresis, and hemodialysis, on time. This may lead to delayed diagnosis of, and high on-site mortality from, CS. To overcome these challenges, new diagnostic and therapeutic modalities in the CS animal model have recently been advanced. Conclusions Patient outcomes can be optimized by ensuring that prehospital providers and emergency clinicians maintain a comprehensive understanding of CS. The field is poised to undergo significant advances in coming years, given recent developments in what is considered possible both technologically and surgically; this only serves to further emphasize the importance of the field, and the need for ongoing research.

Objectives Translation of the Professional Fulfillment Index (PFI) into Japanese would be more useful than the currently developed scales for appropriately measuring burnout and professional fulfillment in healthcare professionals. This study aimed to develop the Japanese version of the PFI and examine its internal consistency, structural validity, and convergent validity in healthcare professionals. Methods Healthcare professionals in Japan were recruited online. The survey was conducted from October to November 2022. Internal consistency was tested using Cronbach's α. Structural validity was tested using confirmatory factor analysis (CFA) and exploratory factor analysis (EFA). Convergent validity was tested using Pearson's correlation coefficients, which were calculated between each score of the PFI scale and burnout (the Japanese Burnout Scale: JBS), depressive symptoms (the Patient Health Questionnaire‐9), and QOL (the General Health Questionnaire‐12). Results The Cronbach's alpha was .91 in professional fulfillment, .80 in burnout: work exhaustion, .90 in burnout: interpersonal disengagement, and .89 in burnout: total score. Confirmatory factor analysis demonstrated a modest fit, and EFA yielded a three‐factor structure the same as the original PFI. The all three subscales and total score of burnout were significantly correlated with the scores of all the scales (p < .001; e.g., burnout: work exhaustion correlated emotional exhaustion in JBS, r = .71). Conclusions The Japanese version of the PFI demonstrated acceptable high internal consistency, structural validity, and convergent validity of the scale with a three‐factor structure the same as in the original PFI. The Japanese version of PFI proved to be reliable and valid for use in healthcare professionals.

The World Health Organization (WHO) and its partners established the WHO Thematic Platform for Health Emergency and Disaster Risk Management Research Network (HEALTH EDRM RN) in 2016 to respond to the increasing burden of recent health emergencies and disasters. The mission of the HEALTH EDRM RN, whose secretariat is located at the WHO Kobe Centre (WKC), is to promote global research collaboration and strengthen research activities to inform policies and programs by generating new evidence to manage health risks associated with all types of emergencies and disasters. With the strong support and involvement of all WHO regional offices, the HEALTH EDRM RN now works with more than 200 global experts and partners to pursue its mission. The first Core Group Meetings of the HEALTH EDRM RN were held on 17–18 October 2019, and concluded with the HEALTH EDRM RN-activity priorities to (1) promote operational research to better meet the needs of emergency- and disaster-exposed individuals and communities and efforts to translate science to policies and programs and (2) strengthen the research capacity of the Health EDRM community. In collaboration with the Japanese Association for Disaster Medicine, the WKC held a workshop on 21 February 2020, in which 20 Japanese experts from different research fields participated to further discuss these two points. This paper summarizes the discussion at the workshop.

Introduction:Hospitals experiencing a COVID-19 outbreak are in a similar situation to those affected by natural disasters, with a breakdown in command and coordination, shortage of personnel and supplies, and increased stress among staff. In Japan, when a COVID-19 outbreak occurs, the first step is for the hospital or health center to respond. However, if the local authorities are unable to respond, the Ministry of Health, Labour and Welfare dispatch Disaster Medicine Assistance Team (DMAT) by request of the local government to facilitate early recovery. This study will examine the effectiveness of early phase support by DMAT.Method:Patients and healthcare workers in 31 hospitals supported by DMAT after an outbreak occurred between April 2020 and January 2021 were included in the study. Attack rate and case fatality rate for patients and the attack rate for healthcare workers were analyzed for each of the two groups: those that started support less than ten days after the first positive case and those that started support more than ten days after the first positive case.Results:For hospitals that started support in less than ten days, the attack rate was 27.9%, the case fatality rate was 17.4% for patients, and the attack rate for healthcare workers was 9.7%. For hospitals that took more than ten days to start support, the attack rate was 44.8%, the case fatality rate was 23.1% for patients, and the attack rate for healthcare workers was 14.3%. The attack rate (p<0.001) and case fatality rate (p=0.011) for patients and attack rate for healthcare workers (p<0.001) were significantly lower in hospitals that started support in less than ten days.Conclusion:Early intervention of DMAT support using natural disaster support techniques for hospitals experiencing an outbreak reduced the attack rate and case fatality rate for patients and healthcare workers.

Background/Introduction:The Minimum Data Set (MDS) has allowed governments of disaster-affected countries to collect, examine, and evaluate standardized medical data from Emergency Medical Teams in real-time. However, little study has been conducted on the use of MDS data to predict health care needs.Objectives:This research proposes an outlook on the use of machine learning and MDS data to predict the need for medical care in disaster-affected areas.Method/Description:The characteristics of the data collected by MDS and the optimal machine learning model were discussed.Results/Outcomes:The primary causes of disease after disasters are trauma (MDS Nos. 4–8), which frequently occurs immediately after a disaster, and infectious diseases (MDS Nos. 9–18), which can increase due to decreasing hygiene conditions. Furthermore, certain infectious diseases can spread quickly because of living in congested evacuation centers, and early detection is crucial.Therefore, predicting the need for medical care in a disaster area is complicated and requires a combination of many machine-learning models. Data-driven methods are mostly linear approaches and cannot capture the dynamics of infectious disease transmission. Additionally, statistical models depend heavily on assumptions, making real-time infection prediction challenging. Thus, deep learning is employed to model without losing the temporal component.Conclusion:Real-time prediction of health care needs using machine learning and MDS can be useful to policymakers by enabling them to better deploy and allocate health care resources, which is useful to patients and front-line health care providers. More detailed predictions for regions and diseases are also anticipated.

Trauma informed care (TIC) is an important approach for people who have experienced trauma. Although psychological first aid (PFA) may be effective training in TIC, no study reported an association between PFA training and TIC. This study aimed to investigate the association between PFA training and attitudes toward TIC among health care professionals in Japan. Japanese health care professionals were recruited for a survey conducted from May 21 to June 18, 2021. TIC was assessed by the Attitudes Related to Trauma Informed Care Scale 10-item short form (ARTIC-10). A question about PFA training participation was originally developed through discussion among researchers. Univariate and multiple linear regression analyses were used to examine the association between the PFA experience and ARTIC-10. In total, 484 (3.6%) health care professionals completed all questions. Among them, 77 (15.9%) had experienced PFA training. Univariate and multiple linear regression analyses showed that PFA experience (B = 0.19, 95% CI: 0.02-0.36, = 0.03; B = 0.17, 95% CI: 0.01-0.34, = 0.04) was significantly associated with ARTIC-10. This study was the first to show an association between PFA training and attitudes toward TIC among health care professionals, which was a significant result for future research on PFA training, TIC, and trauma care.

We conducted a systematic review to determine the prevalence and characteristics of earthquake-associated head injuries for better disaster preparedness and management. We searched for all publications related to head injuries and earthquakes from 1985 to 2018 in MEDLINE and other major databases. A search was conducted using \"earthquakes,\" \"wounds and injuries,\" and \"cranio-cerebral trauma\" as a medical subject headings. Included in the analysis were 34 articles. With regard to the commonly occurring injuries, earthquake-related head injury ranks third among patients with earthquake-related injuries. The most common trauma is lower extremity (36.2%) followed by upper extremity (19.9%), head (16.6%), spine (13.1%), chest (11.3%), and abdomen (3.8%). The most common earthquake-related head injury was laceration or contusion (59.1%), while epidural hematoma was the most common among inpatients with intracranial hemorrhage (9.5%) followed by intracerebral hematoma (7.0%), and subdural hematoma (6.8%). Mortality rate was 5.6%. Head injuries were found to be a commonly occurring trauma along with extremity injuries. This knowledge is important for determining the demands for neurosurgery and for adequately managing patients, especially in resource-limited conditions.

The Diamond Princess cruise ship, carrying 3711 passengers and crew members, docked at Yokohama Port in Japan on February 3, 2020. A quarantine was immediately instituted because 1 passenger who had disembarked in Hong Kong was confirmed to have tested positive for coronavirus disease 2019 (COVID-19). After the quarantine began, all passengers and crew were tested using the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) polymerase chain reaction assay on the ship, 696 of whom tested positive. In total, 769 patients, including 696 with COVID-19, required transport to a hospital. The Japan Disaster Medical Assistance Team (DMAT) successfully picked up and safely transported the COVID-19 patients using a novel classification system to prioritize patients. The Japan DMAT transported 203 patients to hospitals in Kanagawa and another 566 patients to hospitals in 15 different prefectures.

Background: Standardized health-data collection enables effective disaster responses and patient care. Emergency medical teams use the Japan Surveillance in Post-Extreme Emergencies and Disasters (J-SPEED) reporting template to collect patient data. EMTs submit data on treated patients to an EMT coordination cell. The World Health Organization’s (WHO) EMT minimum dataset (MDS) offers an international standard for disaster data collection. Goal: The goal of this study was to analyze age and gender distribution of medical consultations in EMT during disasters. Methods: Data collected from 2016 to 2020 using the J-SPEED/MDS tools during six disasters in Japan and Mozambique were analyzed. Linear regression with data smoothing via the moving average method was employed to identify trends in medical consultations based on age and gender. Results: 31,056 consultations were recorded: 13,958 in Japan and 17,098 in Mozambique. Women accounted for 56.3% and 55.7% of examinees in Japan and Mozambique, respectively. Children accounted for 6.8% of consultations in Japan and 28.1% in Mozambique. Elders accounted for 1.32 and 1.52 times more consultations than adults in Japan and Mozambique, respectively. Conclusions: Study findings highlight the importance of considering age-specific healthcare requirements in disaster planning. Real-time data collection tools such as J-SPEED and MDS, which generate both daily reports and raw data for in-depth analysis, facilitate the validation of equitable access to healthcare services, emphasize the specific needs of vulnerable groups, and enable the consideration of cultural preferences to improve healthcare provision by EMTs.