Preventing malnutrition is one of the primary objectives of many humanitarian agencies, and household surveys are regularly employed to monitor food insecurity caused by political, economic, or environmental crises. Consumption frequencies for standard food groups are often collected to characterize the depth of food insecurity in a community and measure the impact of food assistance programs, producing a vector of bounded, correlated counts for each household. While aggregate indicators are typically used to summarize these results with a single statistic, they can be difficult to interpret and provide insufficient detail to judge the effectiveness of assistance programs. To address these limitations, we have developed a multivariate modeling framework for consumption frequency data. We introduce methods to update baseline models for the analysis of the smaller and more variable surveys typically collected in crisis settings, and we present an application of our approach to national consumption data collected in Yemen in 2014 and 2016 by the World Food Programme. The approach provides more nuanced and interpretable information about consumption changes in response to shocks and the effectiveness of humanitarian assistance.
Copyright: © 2023 Gorzycka-Sikora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.