Author Profile

Richard Garfield

Affiliation: ERRB, Centers for Disease Control, Atlanta, Georgia, USA

Team Lead, ERRB US Centers for Disease Control and Prevention and Professor Emeritus, Columbia University

Recent Posts

How Bad Is It? Usefulness of the “7eed Model” for Scoring Severity and Level of Need in Complex Emergencies

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Background: Humanitarian assistance is designated to save lives and alleviate suffering among people affected by disasters. In 2014, close to 25 billion USD was allocated to humanitarian assistance, more than 80% of it from governmental donors and EU institutions. Most of these funds are devoted to Complex Emergencies (CE). It is widely accepted that the needs of the affected population should be the main determinant for resource allocations of humanitarian funding. However, to date no common, systematic, and transparent system for needs-based allocations exists. In an earlier paper, an easy-to-use model, “the 7eed model”, based on readily available indicators that distinguished between levels of severity among disaster-affected countries was presented. The aim of this paper is to assess the usefulness of the 7eed model in regards to 1) data availability, 2) variations between CE effected countries and sensitivity to change over time, and 3) reliability in capturing severity and levels of need.

Method: We applied the 7eed model to 25 countries with CE using data from 2013 to 2015. Data availability and indicator value variations were assessed using heat maps. To calculate a severity score and a needs score, we applied a standardised mathematical formula, based on the UTSTEIN template. We assessed the model for reliability on previous CEs with a “known” outcome in terms of excess mortality.

Results: Most of the required data was available for nearly all countries and indicators, and availability increased over time. The 7eed model was able to discriminate between levels of severity and needs among countries. Comparison with historical complex disasters showed a correlation between excess mortality and severity score.

Conclusion: Our study indicates that the proposed 7eed model can serve as a useful tool for setting funding levels for humanitarian assistance according to measurable levels of need. The 7eed model provides national level information but does not take into account local variations or specific contextual factors. 

Who Is Worst Off? Developing a Severity-scoring Model of Complex Emergency Affected Countries in Order to Ensure Needs Based Funding

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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.