A Model for Improved Estimates of Child Wasting in Yemen: Capturing the Effects of Conflict and Seasonal Variability In Incidence Estimates
Authors: Hussein Rana , Suprenant Mark , Al-Dheeb Najwa , Guerrero Saul , Rogers Eleanor , Shafique Fouzia , Dyson Meredith , Zaman Muhammad 
Published: November 2020

Abstract
Introduction: Conflict-affected settings - where starvation is often used as a weapon of war and deliveries of food restricted by warring parties, millions are displaced and economies are crippled, and health infrastructure destroyed - have become increasingly linked to high rates of wasting in children. In the case of Yemen, this relationship has become strikingly clear. The country’s ongoing civil war has severely restricted imports of food and disrupted livelihoods, worsening already high pre-war levels of food insecurity. Paired with outbreaks of disease and an overwhelmed and underfunded health system, this has brought rates of wasting in children under five - who are particularly vulnerable to and disproportionately impacted by wasting - to unprecedented levels, which continue to increase as the crisis worsens and aid becomes increasingly limited. In their planning of services to treat and prevent moderate and severe wasting in children, humanitarian agencies estimate expected caseload using a single, standard estimate. This calculation is inapplicable to the context of Yemen both because it is based on a global estimate and rates of incidence vary by context and because it does not capture variations in incidence due to seasonality, disease trends, and the general instability of a crisis setting. To address these limitations and provide more holistic and context-specific estimates of the incidence of wasting among children in Yemen, we developed a predictive Markov model. Computing context-specific values for the average duration of an episode of wasting, the model then considers how changes to other factors in Yemen – food insecurity levels and seasonal disease trends – cause incidence to vary.Methods: We developed a Markov model to estimate the monthly incidence and resultingly, prevalence, of moderate and severe wasting among under-five children in the governorate of Lahj, Yemen. Transition rates were estimated using a combination of monthly treatment analysis compiled by the Yemen Nutrition Cluster and provided by UNICEF, annual SMART survey prevalence estimates and other estimates from the literature. Through model calibration, context-specific values for the average duration of an episode of moderate and severe wasting, and respective incidence correction factors, were found. Local food insecurity levels and diarrheal disease rates – factors directly associated with the incidence of wasting – were introduced as adjustable parameters that would affect monthly incidence rates based on established mathematical relationships.Results: The calculated context-specific incidence correction factor for Yemen showed that previous estimates led to considerable underestimates of the burden of wasting. Adjusted annual caseloads for moderate and severe wasting were 29% and 40% higher, respectively, than the previously assumed values. Baseline values for the estimated duration of an episode of moderate and severe wasting were found to be 5.3 and 4.8 months, respectively. When these values were taken as the average duration of an episode of wasting and the model was run from September 2018 to October 2019, the model’s resulting estimates of the prevalence of moderate and severe wasting matched those recorded in the October 2019 SMART survey. By accounting for changes to underlying factors of wasting, the model produced outputs that reflect the variability of monthly incidence rates of wasting, mirroring the fluctuations seen in treatment admissions data.Conclusion: In this manuscript we propose a Markov model for more accurate and holistic estimates of the burden of wasting in children under five. By generating context specific incidence rates based on levels of food insecurity and seasonal disease trends, our estimates for the duration of an episode and thus caseload more accurately capture changing realities on the ground in Yemen. While we applied our model to Yemen, this model is highly flexible and may be used in other conflict-affected settings to allow health care workers to better predict and plan for expected cases of wasting.