Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-25T04:59:13.663Z Has data issue: false hasContentIssue false
  • English
  • Français

High-Amplitude Atlantic Hurricanes Produce Disparate Mortality in Small, Low-Income Countries

Published online by Cambridge University Press:  30 August 2016

Caleb Dresser ,
Jeroan Allison ,
John Broach ,
Mary-Elise Smith  and
Andrew Milsten
Caleb Dresser
Affiliation:
University of Massachusetts Medical School, Worcester, Massachusetts
Jeroan Allison
Affiliation:
Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts
John Broach
Affiliation:
Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Mary-Elise Smith
Affiliation:
Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
Andrew Milsten*
Affiliation:
Department of Emergency Medicine, University of Massachusetts Medical School, Worcester, Massachusetts.
*
Correspondence and reprint requests to Andrew Milsten, MD, MS, Department of Emergency Medicine, University of Massachusetts Medical School, 55 Lake Avenue North, Worcester, MA 01655 (e-mail: Andrew.Milsten@umassmemorial.org).
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives

Hurricanes cause substantial mortality, especially in developing nations, and climate science predicts that powerful hurricanes will increase in frequency during the coming decades. This study examined the association of wind speed and national economic conditions with mortality in a large sample of hurricane events in small countries.

Methods

Economic, meteorological, and fatality data for 149 hurricane events in 16 nations between 1958 and 2011 were analyzed. Mortality rate was modeled with negative binomial regression implemented by generalized estimating equations to account for variable population exposure, sequence of storm events, exposure of multiple islands to the same storm, and nonlinear associations.

Results

Low-amplitude storms caused little mortality regardless of economic status. Among high-amplitude storms (Saffir-Simpson category 4 or 5), expected mortality rate was 0.72 deaths per 100,000 people (95% confidence interval [CI]: 0.16–1.28) for nations in the highest tertile of per capita gross domestic product (GDP) compared with 25.93 deaths per 100,000 people (95% CI: 13.30–38.55) for nations with low per capita GDP.

Conclusions

Lower per capita GDP and higher wind speeds were associated with greater mortality rates in small countries. Excessive fatalities occurred when powerful storms struck resource-poor nations. Predictions of increasing storm amplitude over time suggest increasing disparity between death rates unless steps are taken to modify the risk profiles of poor nations. (Disaster Med Public Health Preparedness. 2016;10:832–837)

Keywords

hurricanedisasterpovertyclimate changemortality
Type
Original Research
Information
Disaster Medicine and Public Health Preparedness , Volume 10 , Issue 6 , December 2016 , pp. 832 - 837
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. EM-DAT. OFDA/CRED International Disaster Database. Université Catholique de Louvain, Brussels, Belgium. www.emdat.be. 2013. Accessed June 2013.Google Scholar
2. TFQCDM/WADEM. Health disaster management: guidelines for evaluation and research in the “Utstein Style.” Chapter 3: overview and concepts. Prehospital Disaster Med. 2002;17(Suppl 3):31-55.Google Scholar
3. Debacker, M, Hubloue, I, Dhondt, E, et al. Utstein-style template for uniform data reporting of acute medical response in disasters. PLoS Curr. Published online March 23, 2012. http://dx.doi.org/10.1371/4f6cf3e8df15a.CrossRefGoogle Scholar
4. Kahn, ME. The death toll from natural disasters: the role of income, geography, and institutions. Rev Econ Stat . 2005;87(2):271-284. http://dx.doi.org/10.1162/0034653053970339.Google Scholar
5. Rubin, O, Rossing, T. National and local vulnerability to climate-related disasters in Latin America: the role of social asset-based adaptation. Bull Lat Am Res. 2012;31(1):19-35. http://dx.doi.org/10.1111/j.1470-9856.2011.00607.x.Google Scholar
6. Price, GN. Hurricane Katrina: was there a political economy of death? Rev Black Polit Econ. 2008;35(4):163-180. http://dx.doi.org/10.1007/s12114-008-9033-z.CrossRefGoogle Scholar
7. IPCC. Summary for Policymakers. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom: Cambridge University Press; 2012:1-19.Google Scholar
8. Grinsted, A, Moore, JC, Jevrejeva, S. Projected Atlantic hurricane surge threat from rising temperatures. Proc Natl Acad Sci USA. 2013;110(14):5369-5373. http://dx.doi.org/10.1073/pnas.1209980110.CrossRefGoogle ScholarPubMed
9. Holland, G, Bruyere, C. Recent intense hurricane response to global climate change. Clim Dyn. 2014;42(3):617-627. http://dx.doi.org/10.1007/s00382-013-1713-0.Google Scholar
10. Hurricane Basics. National Oceanic and Atmospheric Administration website. http://www.nhc.noaa.gov/. May 1999. Accessed March 2013.Google Scholar
11. Historical Hurricane Tracks. National Oceanic and Atmospheric Administration Coastal Services Center. http://csc.noaa.gov/hurricanes/#. Accessed February 2013–March 2013.Google Scholar
12. Tropical Cyclone Reports, 1958-2011. National Weather Bureau, National Weather Service, and National Oceanic and Atmospheric Administration National Hurricane Center. http://www.nhc.noaa.gov/data/#tcr. Accessed May 2012-September 2012.Google Scholar
13. The World Bank. GDP per capita (constant 2000 US$) dataset. The World Bank website. http://data.worldbank.org. Accessed September 2012-February 2013.Google Scholar
14. The World Bank. Population, total dataset. The World Bank website. http://data.worldbank.org. Accessed September 2012-February 2013.Google Scholar
15. Cuzick, J. A Wilcoxon-type test for trend. Stat Med. 1985;4(1):87-90. http://dx.doi.org/10.1002/sim.4780040112.Google Scholar
16. Pan, W. Akaike’s information criterion in generalized estimating equations. Biometrics. 2001;57(1):120-125. http://dx.doi.org/10.1111/j.0006-341X.2001.00120.x.Google Scholar
17. Cui, J. QIC program and model selection in GEE analyses. Stata J. 2007;7(2):209-220.CrossRefGoogle Scholar
18. Xu, J, Long, S. Confidence intervals for predicted outcomes in regression models for categorical outcomes. Stata J. 2005;5(4):537-559.Google Scholar
19. Thomalla, F, Schmuck, H. “We all knew that a cyclone was coming”: disaster preparedness and the cyclone of 1999 in Orissa, India. Disasters. 2004;28(4):373-387. http://dx.doi.org/10.1111/j.0361-3666.2004.00264.x.Google Scholar
20. Bern, C, Sniezek, J, Mathbor, GM, et al. Risk factors for mortality in the Bangladesh cyclone of 1991. Bull World Health Organ. 1993;71(1):73-78.Google Scholar
21. Xiao, Y, Nilawar, U. Winners and losers: analysing post-disaster spatial economic demand shift. Disasters. 2013;37(4):646-668. http://dx.doi.org/10.1111/disa.12025.Google Scholar