Background: Technological disasters in Asia have significant public health and environmental implications, but there is limited epidemiological analysis of these events. This study aims to characterize the epidemiological profile of technological disasters in Asia from 2000 to 2021, focusing on morbidity and mortality trends.
Methods: A retrospective descriptive observational analysis was conducted using data from emergency events database (EM-DAT), DesInventar, NatCAt, and Sigma. The study categorized disasters into transport, industrial, and miscellaneous accidents. Statistical analyses were used to examine frequencies, trends, and correlations among the different disaster types.
Results: From 2000 to 2021, Asia experienced 2333 technological disasters, with transport accidents being the most frequent (55.77%), followed by industrial (26.10%) and miscellaneous accidents (18.13%). The overall trend showed a statistically significant decrease in the frequency of these disasters and in average mortality and injury rates. The study highlighted the varying impact of different disaster types, with industrial accidents causing the highest fatality and affected rates despite being less frequent than transport accidents.
Conclusions: The study indicates a declining trend in the frequency and severity of technological disasters in Asia, reflecting improved safety measures and disaster management. However, the high impact of industrial accidents underscores the need for targeted prevention strategies.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1111/phn.13419 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
The PANDEM-2 Simulation Exercise: Training the Coordinated Response to a Large-Scale Pandemic in 2 European Public Health Emergency Operations Centers.
Disaster Med Public Health Prep
January 2025
Robert Koch Institute, Berlin, Germany.
Objective: In the course of the EU funded Pandemic Preparedness and Response (PANDEM-2) project, a functional exercise (FX) was conducted to train the coordinated response to a large-scale pandemic event in Europe by using new IT solutions developed by the project. This report provides an overview of the steps involved in planning, conducting, and evaluating the FX.
Methods: The FX design was based on the European Centre for Disease Prevention and Control (ECDC) simulation exercise cycle for public health settings and was carried out over 2 days in the German and Dutch national public health institutes (PHI), with support from other consortium PHIs.
How much time to figure out how to get where? Route planning and subjective stress under time pressure.
PLoS One
January 2025
Department of Psychology, Tufts University, Medford, Massachusetts, United States of America.
From a daily commute to military operations in hostile territory and natural disaster responses, people frequently move from place to place. Cognition (e.g.
View Article and Find Full Text PDFHealthcare Application of Failure Mode and Effect Analysis (FMEA): Is There Room in the Infectious Disease Setting? A Scoping Review.
Healthcare (Basel)
January 2025
Emergency Medical Teams, Country Readiness Strengthening Department, World Health Organization, 1211 Geneva, Switzerland.
: Failure mode and effect analysis (FMEA) is a valuable risk analysis tool aimed at predicting the potential failures of a system and preventing them from occurring. Since its initial use, it has also recently been applied to the healthcare setting, which has been made progressively more complex by technological developments and new challenges. Infection prevention and control (IPC) is an area that requires effective strategies.
View Article and Find Full Text PDFRevolutionizing Health Care: The Transformative Impact of Large Language Models in Medicine.
J Med Internet Res
January 2025
Department of Cardiology and Institute of Vascular Medicine, Key Laboratory of Molecular Cardiovascular Science, Ministry of Education, Peking University Third Hospital, Beijing, China.
Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs' evolution through improved computing power and data.
View Article and Find Full Text PDFPerennial disaster patterns in Central Europe since 2000 and implications for hospital preparedness planning - a cross-sectional analysis.
Sci Rep
January 2025
Heidelberg University, Medical Faculty Heidelberg, Center for Pediatrics and Adolescent Medicine, Pediatric Neurology and Metabolic Medicine, Im Neuenheimer Feld 430, 69120, Heidelberg, Germany.
The goal of this analysis is to describe seasonal disaster patterns in Central Europe in order to raise awareness and improve hospital disaster planning and resilience, particularly during peak events. Hospitals are essential pillars of a country's critical infrastructure, vital for sustaining healthcare services and supporting public well-being-a key issue of national security. Disaster planning for hospitals is crucial to ensure their functionality under special circumstances.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!