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Mid‐upper arm circumference (MUAC) < 11.5 cm and weight‐for‐height Z‐score (WHZ) < −3 are used for screening for severe acute malnutrition (SAM). Underweight and concurrent wasting and stunting may better target those at the highest risk of mortality. We compared anthropometric outcomes in children enrolled in a trial of antibiotics for SAM based on categories of baseline anthropometry, including indicators for programme admission (WHZ < −3, MUAC < 11.5) and alternative indicators (weight‐for‐age Z‐score [WAZ] < −3, concurrent wasting and stunting [WHZ < −3 and height‐for‐age Z‐score < −3]). Participants were followed weekly until nutritional recovery and at 8 weeks. We evaluated changes in weight gain (g/kg/day), MUAC, and WHZ in children admitted by admissions criteria (MUAC only, WHZ only, or MUAC and WHZ) and by underweight or concurrent wasting and stunting. Of 301 admitted children, 100 (33%) were admitted based on MUAC only, 41 (14%) WHZ only, and 160 (53%) both MUAC and WHZ, 210 (68%) were underweight and 67 (22%) were concurrently wasted/stunted. Low MUAC and low WHZ children had the lowest probability of nutritional recovery (17% vs. 50% for MUAC‐only and 34% for WHZ‐only). There was no difference in weight gain velocity or WHZ by admissions criteria (WHZ and/or MUAC). Underweight and concurrently wasted/stunted children had lower MUAC and WHZ at 8 weeks compared with those who were not underweight or concurrently wasted and stunted. Children with both low MUAC and low WHZ had the worst outcomes. Relying on MUAC alone may miss children who have poor outcomes. Other indicators, such as WAZ, may be useful for identifying vulnerable children. Key messages: Mid‐upper arm circumference (MUAC) and weight‐for‐height Z‐score (WHZ) are currently used for severe acute malnutrition (SAM) screening, but alternative indicators such as weight‐for‐age Z‐score (WAZ) may be additional options.Children admitted to the nutritional program based on both low MUAC and low WHZ had the worst outcomes, including the lowest probability of nutritional recovery and the worst anthropometric outcomes.While MUAC identified most children with SAM, WHZ may provide additional information about poor outcomes beyond MUAC alone.WAZ may be a useful alternative indicator to identify children who would benefit from a therapeutic feeding program without requiring height measurement. [ABSTRACT FROM AUTHOR]

Several recently developed methods have the potential to harness machine learning in the pursuit of target quantities inspired by causal inference, including inverse weighting, doubly robust estimating equations and substitution estimators like targeted maximum likelihood estimation. There are even more recent augmentations of these procedures that can increase robustness, by adding a layer of cross‐validation (cross‐validated targeted maximum likelihood estimation and double machine learning, as applied to substitution and estimating equation approaches, respectively). While these methods have been evaluated individually on simulated and experimental data sets, a comprehensive analysis of their performance across real data based simulations have yet to be conducted. In this work, we benchmark multiple widely used methods for estimation of the average treatment effect using ten different nutrition intervention studies data. A nonparametric regression method, undersmoothed highly adaptive lasso, is used to generate the simulated distribution which preserves important features from the observed data and reproduces a set of true target parameters. For each simulated data, we apply the methods above to estimate the average treatment effects as well as their standard errors and resulting confidence intervals. Based on the analytic results, a general recommendation is put forth for use of the cross‐validated variants of both substitution and estimating equation estimators. We conclude that the additional layer of cross‐validation helps in avoiding unintentional over‐fitting of nuisance parameter functionals and leads to more robust inferences. [ABSTRACT FROM AUTHOR]