This paper describes the design and function of an application that enables vulnerable people to provide medical information for use in disasters, and presents the results of an initial test of its usability in Nankoku, Japan. The application consists of two parts: K-DiPS Solo, a smartphone app, and K-DiPS Online, a web application for disaster management by local governments. We asked vulnerable people or their family caregivers to enter medical information into the app on their smartphones and connected this information to a local government application as a demonstration of a disaster response solution that manages information. We targeted a group of 14 healthy older people. The user information that they entered into the app was stored in the cloud via the communication system of the mobile phone. A ledger of vulnerable people for use in the event of a disaster was automatically created on the web application using the information supplied by the individuals. Local government staff corrected the location information, if necessary, by dragging points plotted on a map. This disaster response solution was shown to connect individuals to government offices, and to enable a consistent flow of information from patient details to stocking of supplies, and for simulation, training, and response during disasters.

Background: The number of suicides in Japan increased during the COVID-19 pandemic. Predicting the number of suicides is important to take timely preventive measures. Objective: This study aims to clarify whether the number of suicides can be predicted by suicide-related search queries used before searching for the keyword “suicide.” Methods: This study uses the infoveillance approach for suicide in Japan by search trends in search engines. The monthly number of suicides by gender, collected and published by the National Police Agency, was used as an outcome variable. The number of searches by gender with queries associated with “suicide” on “Yahoo! JAPAN Search” from January 2016 to December 2020 was used as a predictive variable. The following five phrases highly relevant to suicide were used as search terms before searching for the keyword “suicide” and extracted and used for analyses: “abuse”; “work, don’t want to go”; “company, want to quit”; “divorce”; and “no money.” The augmented Dickey-Fuller and Johansen tests were performed for the original series and to verify the existence of unit roots and cointegration for each variable, respectively. The vector autoregression model was applied to predict the number of suicides. The Breusch-Godfrey Lagrangian multiplier (BG-LM) test, autoregressive conditional heteroskedasticity Lagrangian multiplier (ARCH-LM) test, and Jarque-Bera (JB) test were used to confirm model convergence. In addition, a Granger causality test was performed for each predictive variable. Results: In the original series, unit roots were found in the trend model, whereas in the first-order difference series, both men (minimum tau 3: −9.24; max tau 3: −5.38) and women (minimum tau 3: −9.24; max tau 3: −5.38) had no unit roots for all variables. In the Johansen test, a cointegration relationship was observed among several variables. The queries used in the converged models were “divorce” for men (BG-LM test: P=.55; ARCH-LM test: P=.63; JB test: P=.66) and “no money” for women (BG-LM test: P=.17; ARCH-LM test: P=.15; JB test: P=.10). In the Granger causality test for each variable, “divorce” was significant for both men (F104=3.29; P=.04) and women (F104=3.23; P=.04). Conclusions: The number of suicides can be predicted by search queries related to the keyword “suicide.” Previous studies have reported that financial poverty and divorce are associated with suicide. The results of this study, in which search queries on “no money” and “divorce” predicted suicide, support the findings of previous studies. Further research on the economic poverty of women and those with complex problems is necessary.

In the current study, we sought to identify special needs and safe evacuation conditions for children with neurodevelopmental disorders (CNDs) along Japan’s tsunami-prone Pacific coast. A survey and spatial analysis were used to collect data of CNDs (n = 47) and their caregivers. Areas predicted to be flooded in a tsunami, as well as evacuation routes to emergency shelters for vulnerable people (ESVPs), were mapped using geographic information systems (GIS). Our results showed that five professional staff were needed to support 33 CNDs requiring 135.9 m² of ESVP space. Critical safety factors were altitude, vertical evacuation, accessibility to ESVPs, and nonexistence of estuaries in the direction of evacuation. GIS-based spatial analysis and evacuation modeling for disaster preparedness and training plans that involve nurses are essential.

The importance of active learning has continued to increase in Japan. The authors conducted classes for first-year students who entered the nursing program using the problem-based learning method which is a kind of active learning. Students discussed social topics in classes. The purposes of this study were to analyze the post-class essay, describe logical and critical thinking after attended a Problem-Based Learning (PBL) course. The authors used Mayring’s methodology for qualitative content analysis and text mining. In the description about the skills required to resolve social issues, seven categories were extracted: (recognition of diverse social issues), (attitudes about resolving social issues), (discerning the root cause), (multi-lateral information processing skills), (making a path to resolve issues), (processivity in dealing with issues), and (reflecting). In the description about communication, five categories were extracted: (simple statement), (robust theories), (respecting the opponent), (communication skills), and (attractive presentations). As the result of text mining, the words extracted more than 100 times included “issue,” “society,” “resolve,” “myself,” “ability,” “opinion,” and “information.” Education using PBL could be an effective means of improving skills that students described, and communication in general. Some students felt difficulty of communication resulting from characteristics of Japanese.

Tsunamis cause direct damage to property and destroy infrastructure. In addition, power outages can lead to death, especially for patients who rely on medical equipment requiring a power supply. Recently, Nankai Trough Earthquakes have been predicted, and much effort has been put into developing countermeasures in Japan. Kochi City on Shikoku Island is expected to suffer in the event of a large tsunami. The present study identifies individuals living in Kochi who need evacuation assistance and depend on electrical medical devices, simulates evacuation behavior and inundation during a tsunami using a geographic information system (GIS), and considers the usefulness of such a GIS. We asked caregivers, including visiting nurses, to introduce us to homecare recipients who rely on a ventilator, an endotracheal suction device, or other medical devices requiring electric power. We received introductions to 52 homecare recipients. Using a GIS, we plotted the area of predicted inundation and the locations of homecare recipients, nursing stations, and welfare evacuation shelters. We predicted evacuation routes, and then analyzed the time difference between the time required for evacuation and tsunami arrival at a welfare evacuation shelter. To measure the effects of the main parameters, we conducted both one-way and multi-way sensitivity analysis. In the event of a tsunami, eight of the homecare recipients living in the forecasted inundation areas in Kochi may face delayed evacuation. Among homecare recipients facing a high possibility of escape delay, 95.2% lived more than 1,800 m from the nearest welfare evacuation shelter. We found that individual evacuation behavior can be simulated by specifying the residence of a homecare recipient and the evacuation route using a GIS.

On 1 January 2024, a large earthquake occurred in Japan’s Noto region. Many buildings collapsed as a result of violent shaking. Electricity and water supplies were cut off, and communications were disrupted. On 5 January, four days after the earthquake, we visited Noto and conducted disaster-relief activities. This report integrates and discusses the results of the site visits, information broadcasts by public institutions, and previous research. Evacuation centers lacked water and proper sanitation, leading to health issues, including infectious diseases. Disaster Medical Assistance Teams (DMAT) were delayed in implementing infection control measures. Isolated evacuation centers faced communication and supply challenges. Infrastructure restoration, power supply, and toilet facilities at evacuation centers were delayed because of geographical challenges. It is important to have a team that can determine and carry out the necessary activities on site, even without instructions from the DMAT. It is believed to be effective to decide in advance how volunteer teams and the private sector will conduct their activities, assuming that they will be unable to contact public institutions during a disaster. In large-scale disasters, evacuees must operate evacuation centers autonomously. To achieve this, it is necessary for residents to regularly come together as a community. Systematically recording and accumulating these experiences will contribute to improved disaster prevention and mitigation planning. We hope that the experiences we obtained through the abovementioned disaster will be useful for preparing for future disasters.