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

Holly Ching Yu Lam, Andy Haines, Glenn McGregor, Emily Ying Yang Chan and Shakoor Hajat
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Holly Ching Yu Lam: Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
Andy Haines: Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK
Glenn McGregor: Department of Geography, Durham University, Durham DH1 3LE, UK
Emily Ying Yang Chan: Jockey Club School of Public Health and Primary Care, The Chinese University of Hong Kong, Hong Kong, China
Shakoor Hajat: Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine, London WC1H 9SH, UK

IJERPH, 2019, vol. 16, issue 17, 1-16

Abstract: 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.

Keywords: El Niño Southern Oscillation; natural disasters; number of people affected; El Niño; La Niña; Oceanic Niño Index (ONI) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
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