Evacuation at Home Delayed the First Medical Intervention in Minamisanriku Town after the 2011 Great East Japan Earthquake.

INTRODUCTION: In Japan, evacuation at home is expected to increase in the future as a post-disaster evacuation type due to the pandemic, aging, and diverse disabilities of the population. However, more disaster-related indirect deaths occurred in homes than in evacuation centers after the 2011 Great East Japan Earthquake (GEJE). The health risks faced by evacuees at home have not been adequately discussed. STUDY OBJECTIVE: This study aimed to clarify the gap in disaster health management for evacuees at home compared to the evacuees at the evacuation centers in Minamisanriku Town, which lost all health care facilities after the 2011 GEJE. METHODS: This was a retrospective cross-sectional and quasi-experimental study based on the anonymized disaster medical records (DMRs) of patients from March 11 through April 10, 2011, that compared the evacuation-at-home and evacuation-center groups focusing on the day of the first medical intervention after the onset. Multivariable Cox regression analysis and propensity score (PS)-matching analysis were performed to identify the risk factors and causal relationship between the evacuation type and the delay of medical intervention. RESULTS: Of the 2,838 eligible patients, 460 and 2,378 were in the evacuation-at-home and evacuation-center groups, respectively. In the month after the onset, the evacuation-at-home group had significantly lower rates of respiratory and mental health diseases than the evacuation-center group. However, the mean time to the first medical intervention was significantly delayed in the evacuation-at-home group (19.3 [SD = 6.1] days) compared to that in the evacuation-center group (14.1 [SD = 6.3] days); P <.001). In the multivariable Cox regression analysis, the hazard ratio (HR) of delayed medical intervention for evacuation-at-home was 2.31 with a 95% confident interval of 2.07-2.59. The PS-matching analysis of the adjusted 459 patients in each group confirmed that evacuation at home was significantly associated with delays in the first medical intervention (P <.001). CONCLUSION: This study suggested, for the first time, the causal relationship between evacuation at home and delay in the first medical intervention by PS-matching analysis. Although evacuation at home had several advantages in reducing the frequencies of some diseases, the delay in medical intervention could exacerbate the symptoms and be a cause of indirect death. As more evacuees are likely to remain in their homes in the future, this study recommends earlier surveillance and health care provision to the home evacuees.

Emergency flood detection using multiple information sources: Integrated analysis of natural hazard monitoring and social media data.

Extreme weather events are occurring more frequently as a result of climate change. In October 2019, eastern Japan was hit by Hagibis, a large and high-speed typhoon. This unprecedented typhoon caused the evacuation of over 4000 people, injured more than 300 people, and damaged more than 98,000 dwellings throughout the affected area. Because floods are one of the most devastating natural disasters in Asia, providing an effective early warning system (EWS) is critical to reducing disaster impacts. However, warnings based only on natural hazard monitoring do not offer sufficient protection. Integrating natural hazard monitoring and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. We analyzed time-series data including rainfall intensity, 90-min-effective rainfall, and river water level as well as Twitter data related to disaster events during the 5-day period from 11 to 15 October, focusing on the most affected areas in Japan. The analysis included more than 60,000 tweets. Our analysis confirmed the utility of the statistical approach of outbreak detection with social media data in the early detection and local identification of multiple-flood events, and the results from the municipality-level analyses show that tweet frequencies related to the flood disaster ontological categories were significantly correlated to temporal variations in the hazard monitoring data. Thus, flood detection at the administrative level using social media data combined with current hazard monitoring data can enable a decision-driven EWS design. Interactive approaches for decision-making and knowledge production should continue to be considered in the face of climate-change-induced disasters.