Hostname: page-component-586b7cd67f-rcrh6 Total loading time: 0 Render date: 2024-11-26T03:00:27.407Z Has data issue: false hasContentIssue false
  • English
  • Français

The Yew Disaster Severity Index: A New Tool in Disaster Metrics

Published online by Cambridge University Press:  02 January 2019

Ying Ying Yew ,
Rafael Castro Delgado ,
David James Heslop  and
Pedro Arcos González
Ying Ying Yew*
Affiliation:
Universidad De Oviedo-Campus El-Cristo, Unit for Research in Emergency and Disaster, Faculty of Medicine, Oviedo, Spain
Rafael Castro Delgado
Affiliation:
Universidad De Oviedo-Campus El-Cristo, Unit for Research in Emergency and Disaster, Faculty of Medicine, Oviedo, Spain
David James Heslop
Affiliation:
University of New South Wales, School of Public Health and Community Medicine, Sydney, New South Wales, Australia
Pedro Arcos González
Affiliation:
Universidad De Oviedo-Campus El-Cristo, Unit for Research in Emergency and Disaster, Faculty of Medicine, Oviedo, Spain
*
Correspondence: Ying Ying Yew, RN, MPH Universidad De Oviedo-Campus El-Cristo Unit for Research in Emergency and Disaster Faculty of Medicine ES E-33006 Oviedo, Spain E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives

The Richter Scale measures the magnitude of a seismic occurrence, but it does not feasibly quantify the magnitude of the “disaster” at the point of impact in real humanitarian needs, based on United Nations International Strategy for Disaster Reduction (UNISDR; Geneva, Switzerland) 2009 Disaster Terminology. A Disaster Severity Index (DSI) similar to the Richter Scale and the Mercalli Scale has been formulated; this will quantify needs, holistically and objectively, in the hands of any stakeholders and even across timelines.

Background

An agreed terminology in quantifying “disaster” matters; inconsistency in measuring it by stakeholders posed a challenge globally in formulating legislation and policies responding to it.

Methods

A quantitative, mathematical calculation which uses the median score percentage of 100% as a baseline, indicating the ability to cope within the local capacity, was used. Seventeen indicators were selected based on the UNISDR 2009 disaster definition of vulnerability and exposure and holistic approach as a pre-condition. The severity of the disaster is defined as the level of unmet needs. Thirty natural disasters were tested, retrospectively, and non-parametric tests were used to test the correlation of the DSI score against the indicators.

Results

The findings showed that 20 out of 30 natural disasters tested fulfilled the inability to cope, within local capacity in disaster terminology. Non-parametric tests showed that there was a correlation between the 30 DSI scored and the indicators.

Conclusion

By computing a median fit percentage score of 100% as the ability to cope, and the correlation of the 17 indicators, in this DSI Scale, 20 natural disasters fitted into the disaster definition. This DSI will enable humanitarian stakeholders to measure and compare the severity of the disaster objectively, as well as enable future response to be based on needs.

YewYY, Castro DelgadoR, HeslopDJ, Arcos GonzálezP. The Yew Disaster Severity Index: A New Tool in Disaster Metrics. Prehosp Disaster Med. 2019;34(1):8–19.

Keywords

disaster severity scoreexposurehazardnatural disastervulnerability
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 34 , Issue 1 , February 2019 , pp. 8 - 19
Copyright
© World Association for Disaster and Emergency Medicine 2019 

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.)

Footnotes

Conflicts of interest: none

References

1. The United Nations Office for Disaster Risk Reduction-UNISDR, Geneva Switzerland. Terminology on disaster risk reduction. http://www.unisdr.org/files/7817_UNISDRTerminologyEnglish.pdf. Published 2009. Accessed June 6, 2016.Google Scholar
2. USAID Office of US Foreign Disaster Assistance. Washington, DC USA. OFDA Factsheet. https://www.usaid.gov/sites/default/files/documents/1866/OFDA_fact_sheet_01-21-2016.pdf. Published 2016. Accessed March 2, 2016.Google Scholar
3. Guha-Sapir, D, Below, R, Hoyois, PH. EM-DAT: The CRED/OFDA International Disaster Database. https://www.emdat.be. Université Catholique de Louvain; Brussels, Belgium. Accessed March 2, 2016.Google Scholar
4. Centre for Research for Epidemiology for Disaster (CRED) & United Nations Office for Disaster Risk Reduction (UNISDR). The Human Cost of Weather-Related Disasters 1995-2015. https://www.unisdr.org/2015/docs/climatechange/COP21_WeatherDisastersReport_2015_FINAL.pdf. Published 2015. Accessed March 2, 2016.Google Scholar
5. US Geology Survey. Earthquake Hazards Program: Earthquake Glossary- Richter Scale. http://earthquake.usgs.gov/learn/glossary/?term=Richter%20scale. Published 2016. Accessed April 7, 2016.Google Scholar
6. US Geological Survey (USGS). The Modified Mercalli Intensity Scale. https://earthquake.usgs.gov/learn/topics/mercalli.php. Accessed February 15, 2018.Google Scholar
7. US Geological Survey (USGS). Earthquake Magnitude versus Intensities Comparison. https://earthquake.usgs.gov/learn/topics/mag_vs_int.php. Accessed February 15, 2018.Google Scholar
8. US Department of Commerce. National Oceanic and Atmospheric Administration, Storm Prediction Centre. Beaufort Wind Scale. Sir Francis Beaufort. http://www.spc.noaa.gov/faq/tornado/beaufort.html. Accessed February 16, 2018Google Scholar
9. Benini, A. A Note for ACAPS Severity and Priority. Their Measurement in Rapid Needs Assessments. Assessment Capacities Project (ACAP); 2013: 1-59. http://aldo-benini.org/Level2/HumanitData/Benini_forACAPS_SeverityAndPriority_2013.pdf. Accessed June 5, 2016.Google Scholar
10. The John Hopkins University (Baltimore, Maryland USA) and the International Federation of Red Cross and Red Crescent Societies (IFRC). Public Health Guide for Emergencies. 2nd edition. Geneva, Switzerland: IFRC; 2008: 24-36.Google Scholar
11. Boer, JD. Order in Chaos: Modelling Medical Disaster Management. Free University Hospital (VUMC). Amsterdam, The Netherlands. PreventionWeb. http://www.preventionweb.net/files/1936_VL206210.pdf. Published 1999. Accessed June 31, 2016.Google Scholar
12. Wirasinghe, SC, Caldera, HJ, Durage, SW, Ruwanpura, JY. “Preliminary Analysis and Classification of Natural Disasters: Fatality Based Disaster Classification.” Paper presented at 3rd World Conference of Disaster Risk Reduction; Sendai, Japan: 14-18th March 2015. http://www.wcdrr.org/conference/events/462. Accessed June 6, 2016.Google Scholar
13. MunichRE. Munich, Germany. https://www.munichre.com/en/homepage/index.html. Accessed March 5, 2016Google Scholar
14. Welle, T, Birkmann, J. World Risk Index. Alliance Development Works and United Nations University – Institute for Environment and Human Security (UNU-EHS). http://weltrisikobericht.de/wp-content/uploads/2016/08/WorldRiskReport_2015.pdf. Published 2016. Accessed March 8, 2016.Google Scholar
15. Bayram, JD, Kysia, R, Kirsch, TD. Disaster metrics: a proposed quantitative assessment tool in complex humanitarian emergencies-the Public Health Impact Severity Scale (PHISS). PLoS Currents. 2012;1:4.Google Scholar
16. Sphere Association (Geneva, Switzerland). The Sphere Handbook: Humanitarian Charter and Minimum standards in Humanitarian Response. 3rd edition. United Kingdom: Practical Action Publishing; 2011:1-393.Google Scholar
17. Arcos González, P, Castro Delgado, R, Mahabir, R, Ferrero Fernández, E. The feasibility of applying the Disaster Severity Score: the case of Spain. J Intensive & Critical Care. 2016;2(2):1-6.Google Scholar
18. Eriksson, A, Ohlsén, YK, Garfield, R, von Schreeb, J. Who is worst off? Developing a severity-scoring model of complex emergency affected countries in order to ensure needs-based funding. PLoS Currents. 2015;1:7.Google Scholar
19. Maslow, AH. A theory of human motivation. Psychological Review. 1943;50(4):370-396.Google Scholar
20. Guha-Sapir, D, Below, R, Hoyois, PH. EM-DAT: The CRED/OFDA International Disaster Database. Classification. http://www.emdat.be/classification. Université Catholique de Louvain; Brussels, Belgium. Accessed March 5, 2016.Google Scholar
21. Dewhurst, M. Trustee of the Penny on-Trust. http://www.pennyon.com/. Accessed March 1, 2016.Google Scholar
22. ReliefWeb by UN Office for the Coordination of Humanitarian Affairs (OCHA). Geneva, Switzerland/New York City, New York USA. http://reliefweb.int. Published 2016. Accessed March 5, 2016.Google Scholar
23. Google Scholar, Google LLC Services. Mountain View, California USA. Updated October 25, 2017. https://scholar.google.com. Accessed October 25, 2017.Google Scholar
24. MEDLINE/PubMed Resources. Bethesda, Maryland USA: National Library of Medicine. Updated July 23, 2018. https://www.nlm.nih.gov/bsd/medline.html. Accessed April 20, 2018.Google Scholar
25. ResearchGate GmbH. Berlin, Germany. https://www.researchgate.net. Accessed October 25, 2016.Google Scholar
26. Global Assessment Report on Disaster Risk Reduction: Revealing risk, redefining development. http://www.preventionweb.net/english/hyogo/gar/2011/en/home/executive.html. Published 2011. Accessed March 5, 2016.Google Scholar
27. United Nations Office for Disaster Risk Reduction (UNISDR). Geneva, Switzerland. http://www.unisdr.org. Published 2016. Accessed March 5, 2016.Google Scholar
28. United Nations Office for the Coordination of Humanitarian Affairs (UNOCHA). http://www.unocha.org. Published 2016. Accessed March 5, 2016.Google Scholar
29. Garfield, R. Measuring humanitarian emergencies. Disaster Med Public Health Prep. 2007;1(2):110-116.Google Scholar
30. Federal Emergency Management Agency (FEMA). US Homeland Security: Hazard, Risk and Vulnerability Analysis training handout. http://www.training.fema.gov/hiedu/docs/hazrm/handout%205-8b.pdf. Accessed May 2, 2016.Google Scholar
31. Pearce, Laurie, Hightower, H, Konkin, B, Megalos, S, Pernu, J. British Columbia Hazard, Risk, and Vulnerability Analysis. Vancouver, BC: DPRC, University of British Columbia. Accessed May 2, 2016.Google Scholar
32. Burgess, PA. Optimal shift duration and sequence: recommended approach for short-term emergency response activations for public health and emergency management-physiology of Circadian Rhythmicity. Am J Pub Health. 2007;97(1):S88-S92.Google Scholar
33. Jeffrey, CH, Michael, R, Michael, WY. Nobel Prize in Physiology or Medicine: Discoveries of molecules mechanism that control circadian rhythm. https://www.nobelprize.org/nobel_prizes/medicine/laureates/2017/advanced-medicineprize2017.pdf. Accessed January 2, 2018.Google Scholar
34. ReliefWeb. Taiwan (China) – Earthquake. http://reliefweb.int/disaster/eq-2016-000012-twn. Accessed May 2, 2016.Google Scholar
35. Boer, JD. Definition and classification of disasters: introduction of a disaster severity scale. J Emerg Med. 1990;8(5):591-595.Google Scholar
36. Harper, D. Online Etymology Dictionary: Topography. http://www.etymonline.com/index.php?term=topography. Accessed May 3, 2016.Google Scholar
37. Centre for International Earth Science Information Network - CIESIN - Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, New York USA: NASA Socioeconomic Data and Applications Centre (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed May 4, 2016.Google Scholar
38. United Nations Economic and Social Affairs, Population Division. World Population Prospects. http://beta.sedac.ciesin.columbia.edu/downloads/maps/gpw-v4/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals/gpw-v4-un-adjusted-population-count-global-2015.pdf. Accessed May 3, 2016.Google Scholar
39. US Congressional Research Service. Japan’s Earthquake and Tsunami: Economic Effects and Implications for the US. https://www.fas.org/sgp/crs/row/R41702.pdf. Published April 6, 2011. Accessed May 3, 2016.Google Scholar
40. GoTenna. Brooklyn, New York USA. http://www.gotenna.com/pages/how-it-works. Accessed May 3, 2016.Google Scholar
41. World Bank Data. Washington, DC USA. New country classifications by income level. http://blogs.worldbank.org/opendata/new-country-classifications-2016. Published 2016. Accessed July 2, 2016.Google Scholar
42. Centre for Research on the Epidemiology of Disasters (CRED). The Human Cost of Natural Disasters: A Global Perspective. http://www.cred.be/node/1355. Accessed June 14, 2016.Google Scholar
43. Transparency International. Berlin, Germany. Corruption Perception Index. https://www.transparency.org/cpi2015. Published January 2016. Accessed March 6, 2016.Google Scholar
44. Sphere Association. Geneva, Switzerland. The Sphere Project, Sphere Handbook 2011. Humanitarian Charter and Minimum standards in Humanitarian Response. Sanitation Minimum Standards: Minimum Numbers of Toilets at Public Places and Institutions in Disaster Situations. https://www.spherestandards.org/handbook/. Accessed June 3, 2016.Google Scholar
45. Food and Agriculture Organization of United Nations. Rome, Italy. Food Security Indicators. http://www.fao.org/economic/ess/ess-fs/ess-fadata/en/#.Ww7Cs0iFPIV. Accessed June 3, 2016.Google Scholar
46. Guha-Sapir, D, Below, R, Hoyois, PH. EM-DAT: The CRED/OFDA International Disaster Database. Advanced Search. Université Catholique de Louvain: Brussels, Belgium. http://www.emdat.be/advanced_search/index.html. Accessed June 1, 2016.Google Scholar
47. World Health Organization. Health Statistics and Information Systems. Monitoring the building blocks of health systems: a handbook of indicators and their measurement strategies. http://www.who.int/healthinfo/systems/WHO_MBHSS_2010_full_web.pdf?ua=1. Geneva, Switzerland: WHO; 2010. Accessed June 3, 2016.Google Scholar
48. Guha-Sapir, D, Below, R, Hoyois, PH. EM-DAT: The CRED/OFDA International Disaster Database. Glossary. Université Catholique de Louvain: Brussels, Belgium. http://www.emdat.be/Glossary. Accessed July 1, 2016.Google Scholar
49. United Nations Office for Disaster Risk Reduction (UNISDR). Hyogo Framework for Action (2005-2015): Building the Resilience of Nations and Communities to Disasters. https://www.unisdr.org/files/1037_hyogoframeworkforactionenglish.pdf. Published 2007. Accessed June 15, 2016.Google Scholar
50. United Nations Office for Disaster Risk Reduction (UNISDR). Sendai Framework for Disaster Risk Reduction (2015-2030): Making the Difference for Poverty, Health and Resilience. https://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf. Published 2015. Accessed June 15, 2016.Google Scholar
51. StatPlus v5. AnalystSoft Inc. Walnut, California USA. https://www.analystsoft.com/en/products/statplus. Downloaded/Accessed July 8, 2016.Google Scholar