Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Who Is Worst Off?: Developing a Severity-scoring Model of Complex Emergency Affected Countries in Order to Ensure Needs Based Funding
Department of Public Health Sciences, Health system and policy, Karolinska Institute.
The Swedish Red Cross University College, Department of Technology and Welfare.
ERRB, Centers for Disease Control, Atlanta, Georgia, USA.
Department of Public Health Sciences, Health System and Policy, Karolinska Institute.
2015 (English)In: PLOS Currents, ISSN 2157-3999, E-ISSN 2157-3999, no November 3Article in journal (Refereed) Published
Abstract [en]

Background: Disasters affect close to 400 million people each year. Complex Emergencies (CE) are a category of disaster that affects nearly half of the 400 million and often last for several years. To support the people affected by CE, humanitarian assistance is provided with the aim of saving lives and alleviating suffering. It is widely agreed that funding for this assistance should be needs-based. However, to date, there is no model or set of indicators that quantify and compare needs from one CE to another. In an effort to support needs-based and transparent funding of humanitarian assistance, the aim of this study is to develop a model that distinguishes between levels of severity among countries affected by CE.

Methods: In this study, severity serves as a predictor for level of need. The study focuses on two components of severity: vulnerability and exposure. In a literature and Internet search we identified indicators that characterize vulnerability and exposure to CE. Among the more than 100 indicators identified, a core set of six was selected in an expert ratings exercise. Selection was made based on indicator availability and their ability to characterize preexisting or underlying vulnerabilities (four indicators) or to quantify exposure to a CE (two indicators). CE from 50 countries were then scored using a 3-tiered score (Low-Moderate, High, Critical). 

Results: The developed model builds on the logic of the Utstein template. It scores severity based on the readily available value of four vulnerability and four exposure indicators. These are 1) GNI per capita, PPP, 2) Under-five mortality rate, per 1 000 live births, 3) Adult literacy rate, % of people ages 15 and above, 4) Underweight, % of population under 5 years, and 5) number of persons and proportion of population affected, and 6) number of uprooted persons and proportion of population uprooted.

Conclusion: The model can be used to derive support for transparent, needs-based funding of humanitarian assistance. Further research is needed to determine its validity, the robustness of indicators and to what extent levels of scoring relate to CE outcome.

Place, publisher, year, edition, pages
2015. no November 3
National Category
Public Health, Global Health, Social Medicine and Epidemiology
Identifiers
URN: urn:nbn:se:rkh:diva-1966PubMedID: 26635996OAI: oai:DiVA.org:rkh-1966DiVA: diva2:871951
Available from: 2015-11-17 Created: 2015-11-16 Last updated: 2015-12-18Bibliographically approved

Open Access in DiVA

No full text

Other links

PubMedhttp://currents.plos.org/disasters/article/who-is-worst-off-developing-a-severity-scoring-model-of-complex-emergency-affected-countries-in-order-to-ensure-needs-based-funding/

Search in DiVA

By author/editor
Ohlsén, Ylva Kristina
By organisation
Department of Technology and Welfare
In the same journal
PLOS Currents
Public Health, Global Health, Social Medicine and Epidemiology

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 40 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf