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Time-Series Study of Associations between Rates of People Affected by Disasters and the El Niño Southern Oscillation (ENSO) Cycle.

Holly Ching Yu LamAndrew HainesGlenn McGregorEmily Ying-Yang ChanShakoor Hajat
Published in: International journal of environmental research and public health (2019)
The El Niño Southern Oscillation (ENSO) is a major driver of climatic variability that can have far reaching consequences for public health globally. We explored whether global, regional and country-level rates of people affected by natural disasters (PAD) are linked to ENSO. Annual numbers of PAD between 1964-2017 recorded on the EM-DAT disaster database were combined with UN population data to create PAD rates. Time-series regression was used to assess de-trended associations between PAD and 2 ENSO indices: Oceanic Niño Index (ONI) and multivariate El Niño Index (MEI). Over 95% of PAD were caused by floods, droughts or storms, with over 75% of people affected by these three disasters residing in Asia. Globally, drought-related PAD rate increased sharply in El Niño years (versus neutral years). Flood events were the disaster type most strongly associated with El Niño regionally: in South Asia, flood-related PAD increased by 40.5% (95% CI 19.3% to 65.6%) for each boundary point increase in ONI (p = 0.002). India was found to be the country with the largest increase in flood-related PAD rates following an El Niño event, with the Philippines experiencing the largest increase following La Niña. Multivariate ENSO Index (MEI)-analyses showed consistent results. These findings can be used to inform disaster preparedness strategies.
Keyphrases
  • public health
  • metal organic framework
  • transition metal
  • high frequency
  • emergency department
  • machine learning
  • climate change
  • big data
  • deep learning