Can severity of a humanitarian crisis be quantified? Assessment of the INFORM severity index

Abstract

Background: Those responding to humanitarian crises have an ethical imperative to respond most where the need is greatest. Metrics are used to estimate the severity of a given crisis. The INFORM Severity Index, one such metric, has become widely used to guide policy makers in humanitarian response decision making. The index, however, has not undergone critical statistical review. If imprecise or incorrect, the quality of decision making for humanitarian response will be affected. This analysis asks, how precise and how well does this index reflect the severity of conditions for people affected by disaster or war?

Results: The INFORM 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). 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 better quantify crisis severity.

Keywords: Factor analysis; Humanitarian crisis; Severity.

Fig. 1

A schematic of the GCSI conceptual framework for the complex crisis in Somalia (coded as SOM001 in the GCSI database). Each box represents a data point, each oval represents the aggregation of the boxes (or other ovals) preceding it (represented by an arrow), and each circle represents the aggregation into the GCSI pillars. Shapes with dashed values represented aggregated scores of sib-indicators and bold shapes are the aggregated final scores. Panel A shows the Impact of the Crisis. Panel B shows the Complexity of the Crisis. Panel C shows the Conditions of the People. In panel C, the Condition of the population in Need Score shows values that are scaled to 1,000,0000 people

Fig. 2

Second-order Confirmatory Factor Analysis (CFA), with factor loadings. The ovals reflect latent variables and the boxes reflect indicators. The dashed box contains the final first-order CFA

Fig. 3

The distribution of latent crisis severity scores. Figure 2A shows all crises (n = 172) and Fig. 2B is stratified by crisis type