Introduction: Since 1902, disasters in the Northern Triangle of Central America, which consists of the countries Guatemala, Honduras, and El Salvador, have caused over one-hundred-thousand deaths, affected millions of people, and caused tens of billions of dollars in damages. Understanding the nature and frequency of these events will allow stakeholders to decrease both the acute damages and the long-term deleterious consequences of disasters.
Study Objective: This study provides a descriptive analysis of all disasters recorded in the Emergency Events Database (EM-DAT) affecting Guatemala, Honduras, and El Salvador from 1902-2022.
Methods: Data were collected and analyzed from the EM-DAT, which categorizes disasters by frequency, severity, financial cost, distribution by country, burden of death, number of people affected, financial cost by country, and type of disasters most prevalent in each country. Results are presented as absolute numbers and as a percentage of the overall disaster burden. These trends are then graphed over the time period of the database.
Results: The EM-DAT recorded 359 disasters in the Northern Triangle from 1902 through 2022. Meteorologic events (floods and storms) were the most common types of disaster (44%), followed by transport accidents (13%). Meteorologic events and earthquakes were the most severe, as measured by deaths (62%), people affected (60%), and financial cost (86%). Guatemala had the greatest number of disasters (45%), deaths (68%), and affected people (52%). The financial costs of the disasters were evenly distributed between the three countries.
Conclusion: Meteorologic disasters are the most common and most severe type of disaster in the Northern Triangle. Earthquakes and transport accidents are also common. As climate change causes more severe storms in the region, disasters are likely to increase in severity as well. Governments and aid organizations should develop disaster preparedness and mitigation strategies to lessen the catastrophic effects of future disasters. Missing data limit the conclusions of this study to general trends.
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