What Makes a Humanitarian Crisis Severe and Can Severity be Quantified? Assessment of the Global Severity Crisis Index

Background: Humanitarian response actors evaluate crisis severity with the INFORM Severity Index, a publicly available metric. This index, however, has not undergone critical statistical review. If imprecise or incorrect, humanitarian response may be negatively impacted. Methods: The INFROM Severity Index is calculated from 35 publicly available indicators, which conceptually reflect the severity of each crisis We used 172 unique global crises from the INFORM SEVERITY Index database that occurred January 1 to November 30, 2019, or were ongoing by this date. We applied exploratory factor analysis (EFA) to determine common factors within the dataset. We then applied a second-order confirmatory factor analysis (CFA) to predict crisis severity as a latent construct. Model fit was assessed via chi-square goodness-of-fit statistic, Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA). Results: The EFA models suggested a 3- or 4- factor solution, with 46% and 53% variance explained in each model, respectively. The final CFA was parsimonious, containing three factors comprised of 11 indicators, with reasonable model fit (Chi-squared=107, with 40 degrees of freedom, CFI=0.94, TLI=0.92, RMSEA=0.10). In the second-order CFA, the magnitude of standardized factor-loading on the societal governance latent construct had the strongest association with the latent construct of crisis severity (0.73), followed by the humanitarian access/safety construct (0.56). Conclusions: A metric of crisis-severity is a critical step towards improving humanitarian response, but only when it reflects real-life conditions. Our work is a first step in refining an existing framework to quantify crisis severity.