Preparedness and Response Strategies for Chemical, Biological, Radiological, and Nuclear Incidents in the Middle East and North Africa: An Artificial Intelligence-Enhanced Delphi Approach.

OBJECTIVE

Chemical, biological, radiological, and nuclear (CBRN) incidents require meticulous preparedness, particularly in the Middle East and North Africa (MENA) region. This study evaluated CBRN response operational flowcharts, tabletop training scenarios methods, and a health sector preparedness assessment tool specific to the MENA region.

METHODS

An online Delphi survey engaging international disaster medicine experts was conducted. Content validity indices (CVIs) were used to validate the items. Consensus metrics, including interquartile ranges (IQRs) and Kendall's W coefficient, were utilized to assess the panelists' agreement levels. Advanced artificial intelligence computing methods, including sentiment analysis and machine-learning methods (t-distributed stochastic neighbor embedding [t-SNE] and k-means), were used to cluster the consensus data.

RESULTS

Forty experts participated in this study. The item-level CVIs for the CBRN response flowcharts, preparedness assessment tool, and tabletop scenarios were 0.96, 0.85, and 0.84, respectively, indicating strong content validity. Consensus analysis demonstrated an IQR of 0 for most items and a strong Kendall's W coefficient, indicating a high level of agreement among the panelists. The t-SNE and k-means identified four clusters with greater European response engagement.

CONCLUSIONS

This study validated essential CBRN preparedness and response tools using broad expert consensus, demonstrating their applicability across different geographic areas.