## 2 Methods
### Data Collection and Preparation  
The data for this study were sourced from the international EM-DAT (Emergency Events Database). EM-DAT is a comprehensive global database that tracks and records all types of natural and human-induced (technological) disasters. It is maintained and regularly updated by the Centre for Research on the Epidemiology of Disasters (CRED) at the University of Louvain, Belgium. A diverse range of sources, including UN agencies, non-governmental organizations, reinsurance firms, research institutions, and media outlets, were used to assemble the database (T  
Additionally, the category of injuries covers individuals who suffer from physical harm, trauma, or illness that needs medical attention as a direct outcome of the disaster. Lastly, fatalities represent the tragic loss of life resulting from natural hazards. Numerous advantages are offered by the EM-DAT database for researchers and decision-makers [81]. Its comprehensiveness and standardization have allowed for the long-term monitoring of natural and technological disasters on a global scale, making it a valuable tool for trend analysis and comparative studies across different regions [73; 82]. The database is made publicly accessible, provides wide geographical coverage, and is frequently utilized as a reference source in scientific research due to the breadth and quality of its data [83]. However, certain drawbacks are also associated with EM-DAT [84]. Despite its extensive nature, generalized information with limited details on specific events may be contained within the database, potentially restricting deeper analysis [83]. The quality of the data is dependent on the sources from which they are collected, which can lead to inconsistencies between different regions [78]. Additionally, the inclusion of events is limited to those meeting specific criteria, possibly resulting in the exclusion of smaller or less severe incidents. Older data may be affected by retrospective bias, and delays in the entry of the most recent events can impact the timeliness of current trend analysis [83; 85; 86]. For this study, a total of 25,836 disasters from 1900 to 2024 were analyzed. The data were cleaned and prepared for analysis using the Statistical Package for the Social Sciences (IBM SPSS Statistics, Version 26, New York, NY, USA) software after being downloaded. The data preparation process included several key steps: (a) identifying and removing duplicates and missing values to ensure data accuracy and consistency; (b) converting all data into a uniform format to facilitate easier analysis and interpretation, classifying disasters by type, intensity, geographical area, and period. During the data processing phase, all numerical values were standardized to ensure consistent interpretation of results across different time periods and geographic regions. Additionally, categorical data were coded to facilitate their integration into regression models and other quantitative analyses. More specifically stated, listwise deletion was employed to handle missing values in critical data points, while mean imputation was utilized as needed to preserve the integrity of the dataset. Outliers were detected through the use of z-scores, with those surpassing \(\pm\)3 standard deviations carefully examined and removed to avoid skewing the results. Duplicates, especially those associated with identical disaster events, were identified based on matching criteria across key variables and were subsequently eliminated to prevent double-counting. The assumptions underlying the data processing, such as the assumption that missing values were missing at random (MAR) and that the exclusion of outliers would not significantly alter the observed patterns, were also documented. These measures were implemented to enhance the reliability and validity of the analysis. Identification and Sourcing of Key Socio-Economic Indicators  
In this study, key socio-economic indicators with the potential to significantly influence disaster outcomes were identified.