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

Area of Science:

• Environmental Health
• Public Health
• Computational Linguistics

Background:

• The growing body of literature on natural disasters and human health necessitates systematic synthesis.
• Understanding the global research landscape is crucial for identifying knowledge gaps and future research directions.

Purpose of the Study:

• To systematically synthesize the global evidence on natural disasters and human health.
• To map the scientific literature using natural language processing (NLP) techniques.
• To identify trends in disaster types, health outcomes, coping mechanisms, and geographical focus.

Main Methods:

• Systematic literature search across multiple databases (Embase, PubMed, Scopus, PsycInfo, Web of Science).
• Application of NLP techniques: text classification, topic modeling, and geoparsing.
• Analysis of literature published between January 1, 2012, and April 3, 2022.

Main Results:

• Identified 6105 studies on natural disasters and human health.
• Most frequent disasters: earthquakes, hurricanes, tsunamis. Most studied outcomes: posttraumatic stress disorder (PTSD) and depression.
• Mental health services were the most common coping strategy. Research predominantly from high-income countries.
• Psychological distress frequently co-occurred with natural disasters globally, except in Africa where infectious diseases were prevalent.

Conclusions:

• NLP is effective for mapping large-scale scientific literature on natural disasters and health.
• Findings highlight key research areas and provide an empirical basis for public health interventions.
• Identifies a need for more research from low- and middle-income countries and on infectious disease impacts in Africa.