Impact of a simplified treatment protocol for moderate acute malnutrition with a decentralized treatment approach in emergency settings of Niger

Sánchez-Martínez, Luis Javier; Charle-Cuéllar, Pilar; Gado, Abdoul Aziz; Dougnon, Abdias Ogobara; Sanoussi, Atté; Ousmane, Nassirou; Lazoumar, Ramatoulaye Hamidou; Toure, Fanta; Vargas, Antonio; Hernández, Candela Lucía; López-Ejeda, Noemí

doi:10.3389/fnut.2023.1253545

Cited by 3 publications

References 41 publications

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Effectiveness of acute malnutrition treatment with a simplified, combined protocol in Central African Republic: An observational cohort study

Heymsfield,

Tausanovitch,

Christian

et al. 2024

Maternal & Child Nutrition

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A simplified, combined protocol admitting children with a mid‐upper‐arm circumference (MUAC) of <125 mm or oedema to malnutrition treatment with ready‐to‐use therapeutic food (RUTF) uses two sachets of RUTF per day of those with MUAC < 115 mm and/or oedema and one sachet of RUTF per day for those with MUAC 115–<125 mm. This treatment previously demonstrated noninferior programmatic outcomes compared with standard treatment and high recovery in a routine setting. We aimed to observe the protocol's effectiveness in a routine setting at scale, in two health districts of the Central African Republic through an observational cohort study. The pilot enrolled children for 1 year in consortium by the Ministry of Health and nongovernmental partners. A total of 7909 children were admitted to the simplified, combined treatment. Treatment resulted in an 81.2% overall recovery, with a mean length of stay (LOS) of 38.7 days and a mean RUTF consumption of 43.4 sachets per child treated. Among children admitted with MUAC < 115 mm or oedema, 67.9% recovered with a mean LOS of 48.1 days and consumed an average of 70.9 RUTF sachets. Programme performance differed between the two districts, with an overall defaulting rate of 31.1% in the Kouango‐Grimari health district, compared to 8.2% in Kemo. Response to treatment by children admitted with severe acute malnutrition (SAM) by MUAC and SAM by oedema was similar. The simplified, combined protocol resulted in a satisfactory overall recovery and low RUTF consumption per child treated, with further need to understand defaulting in the context.

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Effectiveness of acute malnutrition treatment with a simplified, combined protocol in Central African Republic: An observational cohort study

Heymsfield,

Tausanovitch,

Christian

et al. 2024

Maternal & Child Nutrition

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Bayesian mixed effect models to account for environmental modulators of acute malnutrition treatment in children

Sánchez-Martínez,

Faes,

Charle-Cuéllar

et al. 2024

Preprint

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Acute child malnutrition is a global public health problem influenced by very diverse factors, including socioeconomic and dietary aspects, but also seasonal and geographic factors. The present study is a secondary analysis that attempts to characterize which variables have influenced the Middle Upper-Arm Circumference (MUAC) upon admission and the Length of Stay (LOS) for treatment recovery. The sample of children analysed was 852. Initially, data cleaning and a reduction of the dimensionality of dietary diversity were carried out. A selection of the importance of the variables using the Watanabe Akaike Information Criteria (WAIC) was carried out prior to the adjustment of Bayesian mixed effects models, with the variables of travel time to health site and week of admission as random factors, on the MUAC and LOS variables. Clear differences were seen between both contexts. Highlighting significant interactions of travel time in Niger while the seasonal effect stood out in Mali. The MUAC models identified a positive effect of age in both contexts, and in Niger, influences of diet diversity, comorbidities, breastfeeding and vaccination appeared. On the other hand, the LOS models highlighted the severity upon admission, and in Niger also factors related to the treatment protocol and the distance to the water source, while in Mali, the quality of water was more decisive. The present study shows the importance of considering acute child malnutrition from a multidimensional and complex approach, where diverse factors (biological, socioeconomic, ecological, etc.) can influence directly or as modulators of the disease and its treatment.

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Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol

Sánchez-Martínez,

Charle-Cuéllar,

Gado

et al. 2024

Nutrients

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Background/Objectives: Child acute malnutrition is a global public health problem, affecting 45 million children under 5 years of age. The World Health Organization recommends monitoring weight gain weekly as an indicator of the correct treatment. However, simplified protocols that do not record the weight and base diagnosis and follow-up in arm circumference at discharge are being tested in emergency settings. The present study aims to use machine learning techniques to predict weight gain based on the socio-economic characteristics at admission for the children treated under a simplified protocol in the Diffa region of Niger. Methods: The sample consists of 535 children aged 6–59 months receiving outpatient treatment for acute malnutrition, for whom information on 51 socio-economic variables was collected. First, the Variable Selection Using Random Forest (VSURF) algorithm was used to select the variables associated with weight gain. Subsequently, the dataset was partitioned into training/testing, and an ensemble model was adjusted using five algorithms for prediction, which were combined using a Random Forest meta-algorithm. Afterward, Receiver Operating Characteristic (ROC) curves were used to identify the optimal cut-off point for predicting the group of individuals most vulnerable to developing low weight gain. Results: The critical variables that influence weight gain are water, hygiene and sanitation, the caregiver’s employment–socio-economic level and access to treatment. The final ensemble prediction model achieved a better fit (R2 = 0.55) with respect to the individual algorithms (R2 = 0.14–0.27). An optimal cut-off point was identified to establish low weight gain, with an Area Under the Curve (AUC) of 0.777 at a value of <6.5 g/kg/day. The ensemble model achieved a success rate of 84% (78/93) at the identification of individuals below <6.5 g/kg/day in the test set. Conclusions: The results highlight the importance of adapting the cut-off points for weight gain to each context, as well as the practical usefulness that these techniques can have in optimizing and adapting to the treatment in humanitarian settings.

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Impact of a simplified treatment protocol for moderate acute malnutrition with a decentralized treatment approach in emergency settings of Niger

Cited by 3 publications

References 41 publications

Effectiveness of acute malnutrition treatment with a simplified, combined protocol in Central African Republic: An observational cohort study

Effectiveness of acute malnutrition treatment with a simplified, combined protocol in Central African Republic: An observational cohort study

Bayesian mixed effect models to account for environmental modulators of acute malnutrition treatment in children

Using Machine Learning to Fight Child Acute Malnutrition and Predict Weight Gain During Outpatient Treatment with a Simplified Combined Protocol

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