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Systematic Mapping of Global Research on Climate and Health Using Machine Learning

Background: The global literature on the links between climate change and human health is large, increasing exponentially, and no longer feasible to collate and synthesize using traditional systematic review approaches.  
 
Methods: We use machine learning and other data science methods to systematically identify and map the scientific literature on climate change and health published since 2013. We classify >16,000 relevant publications by field of climate research, climate drivers, health impact, date and geography. 
 
Findings: We show that climate-health literature is dominated by impact studies, with mitigation and adaptation responses and their co-benefits and co-risks remaining niche topics. Air quality and heat stress are the most frequently studied exposures, with all-cause mortality and infectious disease incidence being the most frequently studied health outcomes. Seasonality, extreme weather events, heat, and weather variability are the most frequently studied climate-related hazards. Geographically, the evidence base is dominated by studies from high-income countries and China, with very limited evidence from the majority of countries that suffer most from the health consequences of climate change.  
 
Interpretation: We demonstrate the importance and feasibility of using automated machine-learning pipelines for mapping the science on climate change and human health and generating valuable inputs into global climate and health assessments. We find major gaps in evidence on climate-health research for mental health, undernutrition, and maternal-child health, and very limited evidence overall from low income regions. The scant evidence on climate change response options is concerning and will significantly hamper the design of evidence-based pathways to reduce the effects on health of climate change. We argue that in the post-2015 Paris Agreement era of climate solutions much more attention should be given to climate adaptation and mitigation options and their effects on human health. 
 
Funding Statement: None to declare. 
 
Declaration of Interests: Dr. Berrang-Ford reports grants from Dfid-FCDO UK (Gov't), from null, during the conduct of the study; all other authors have nothing to declare.