Global Research on Natural Disasters and Human Health: a Mapping Study Using Natural Language Processing Techniques

Purpose of Review This review aimed to systematically synthesize the global evidence base for natural disasters and human health using natural language processing (NLP) techniques. Recent Findings We searched Embase, PubMed, Scopus, PsycInfo, and Web of Science Core Collection, using titles, abstracts, and keywords, and included only literature indexed in English. NLP techniques, including text classification, topic modeling, and geoparsing methods, were used to systematically identify and map scientific literature on natural disasters and human health published between January 1, 2012, and April 3, 2022. We predicted 6105 studies in the area of natural disasters and human health. Earthquakes, hurricanes, and tsunamis were the most frequent nature disasters; posttraumatic stress disorder (PTSD) and depression were the most frequently studied health outcomes; mental health services were the most common way of coping. Geographically, the evidence base was dominated by studies from high-income countries. Co-occurrence of natural disasters and psychological distress was common. Psychological distress was one of the top three most frequent topics in all continents except Africa, where infectious diseases was the most prevalent topic. Summary Our findings demonstrated the importance and feasibility of using NLP to comprehensively map natural disasters and human health in the growing literature. The review identifies clear topics for future clinical and public health research and can provide an empirical basis for reducing the negative health effects of natural disasters.