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Currently, child acute malnutrition continues to be a serious public health problem, and although its most fatal consequences are well known, its associated factors still need to be studied in more depth in different contexts. The objective of the present study is to determine the association between socioeconomic variables and acute malnutrition severity in rural emergency contexts of Niger and Mali. The present study consists of a secondary analysis of controlled trials. Data related to a total of 1447 treated children (6–59 months of age) were considered, for whom the Variable Selection Using Random Forests (VSURF) algorithm was applied to create interpretation and prediction random forest models (considering 86 variables). In Mali and Niger, the prediction models agree in pointing out aspects related to the water source and the work activity of caregivers as some of the main risk factors for developing severe acute malnutrition. However, the interpretation models highlight important heterogeneity, with the distance to the health center being the greatest exponent of this situation, being the most important factor in Niger while disappearing in Mali. The prediction accuracy in the interpretation model was 68.0% in Niger and 79.80% in Mali, while the prediction model reached similar rates of 63.17% and 75.63%, respectively. Machine learning techniques have proven to be a valid tool to interpret and predict the degree of severity of acute malnutrition based on socioeconomic characteristics, including complex interrelationships. The results obtained point out different aspects to be addressed to prevent and minimize the effects of acute malnutrition. Acute malnutrition remains a critical health issue in rural emergency contexts of Niger and Mali. Using machine learning (VSURF algorithm), we analyzed socioeconomic risk factors in 1447 children. Water sources and caregivers' work emerged as key predictors. However, regional disparities exist, highlighting the need for context‐specific interventions to mitigate malnutrition severity. Summary Machine learning approaches are an effective tool to fight against child acute malnutrition. Random forest models correctly identify most severe cases of child acute malnutrition only using socioeconomic/environmental variables. The factors that have been identified as most associated with an increase in the severity of malnutrition are those related to water, access to health care, and socioeconomic status. The underlying factors of acute malnutrition were different in Mali and Niger and these contextual differences reveal the importance of tailoring medical interventions to optimize case diagnosis in emergency contexts and the orientation of public policies.

Evidence on the cost of acute malnutrition treatment, particularly with regards to simplified approaches, is limited. The objective of this study was to determine the cost of acute malnutrition treatment and how it is influenced by treatment protocol and programme size. We conducted a costing study in Kabléwa and N’Guigmi, Diffa region, where children with acute malnutrition aged 6–59 months were treated either with a standard or simplified protocol, respectively. Cost data were collected from accountancy records and through key informant interviews. Programme data were extracted from health centre records. In Kabléwa, where 355 children were treated, the cost per child treated was USD 187.3 (95% CI: USD 171.4; USD 203.2). In N’Guigmi, where 889 children were treated, the cost per child treated was USD 110.2 (95% CI: USD 100.0; USD 120.3). Treatment of moderate acute malnutrition was cheaper than treatment of severe acute malnutrition. In a modelled scenario sensitivity analysis with an equal number of children in both areas, the difference in costs between the two locations was reduced from USD 77 to USD 11. Our study highlighted the significant impact of programme size and coverage on treatment costs, that cost can differ significantly between neighbouring locations, and that it can be reduced by using a simplified protocol.

Background A non-randomized controlled trial, conducted from June 2018 to March 2019 in two rural communes in the health district of Mayahi in Niger, showed that including community health workers (CHWs) in the treatment of severe acute malnutrition (SAM) resulted in a better recovery rate (77.2% vs. 72.1%) compared with the standard treatment provided solely at the health centers. The present study aims to assess the cost and cost-effectiveness of the CHWs led treatment of uncomplicated SAM in children 6–59 months compared to the standard national protocol. Methods To account for all relevant costs, the cost analysis included activity-based costing and bottom-up approaches from a societal perspective and on a within-trial time horizon. The cost-effectiveness analysis was conducted through a decision analysis network built with OpenMarkov and evaluated under two approaches: (1) with recovery rate and cost per child admitted for treatment as measures of effectiveness and cost, respectively; and (2) assessing the total number of children recovered and the total cost incurred. In addition, a multivariate probabilistic sensitivity analysis was carried out to evaluate the effect of uncertainty around the base case input data. Results For the base case data, the average cost per child recovered was 116.52 USD in the standard treatment and 107.22 USD in the CHWs-led treatment. Based on the first approach, the CHWs-led treatment was more cost-effective than the standard treatment with an average cost per child admitted for treatment of 82.81 USD vs. 84.01 USD. Based on the second approach, the incremental cost-effectiveness ratio of the transition from the standard to the CHWs-led treatment amounted to 98.01 USD per additional SAM case recovered. Conclusions In the district of Mayahi in Niger, the CHWs-led SAM treatment was found to be cost-effective when compared to the standard protocol and provided additional advantages such as the reduction of costs for households. Trial registration : ISRCTN with ID 31143316. https://doi.org/10.1186/ISRCTN31143316

Background In 2015, the Ministry of Health in Mali included the treatment of severe acute malnutrition (SAM) into the package of activities of the integrated Community Case Management (iCCM). This paper aims to analyze the impact of including community health workers (CHWs) as treatment providers outside the Health Facilities (HFs) on the coverage of SAM treatment when scaling up the intervention in the three largest districts of the Kayes Region in Mali. Methods A baseline coverage assessment was conducted in August 2017 in the three districts before the CHWs started treating SAM. The end-line assessment was conducted one year later, in August 2018. Coverage was assessed by the standardized methodology called Semi-Quantitative Evaluation of Access and Coverage (SQUEAC). The primary outcome was treatment coverage and other variables evaluated were the geographical distribution of the HFs, CHW’s sites and overlapping between both health providers, the estimation of children with geographical access to health care and the estimation of children screened for acute malnutrition in their communities. Results Treatment coverage increased in Kayes (28.7–57.1%) and Bafoulabé (20.4–61.1%) but did not in Kita (28.4–28.5%). The decentralization of treatment has not had the same impact on coverage in all districts, with significant differences. The geospatial analyses showed that Kita had a high proportion of overlap between HFs and/or CHWs 48.7% (39.2–58.2), a high proportion of children without geographical access to health care 70.4% (70.1–70.6), and a high proportion of children not screened for SAM in their communities 52.2% (51.9–52.5). Conclusions Working with CHWs in SAM increases treatment coverage, but other critical aspects need to be considered by policymakers if this intervention model is intended to be scaled up at the country level. To improve families’ access to nutritional health care, before establishing decentralized treatment in a whole region it must be considered the geographical location of CHWs. This previous assessment will avoid overlap among health providers and ensure the coverage of all unserved areas according to their population densities need. Trial registration: ISRCTN registry with ID 1990746. https://doi.org/10.1186/ISRCTN14990746

Geographical and economic access barriers to health facilities (HF) have been identified as some of the most important causes of the low coverage of severe acute malnutrition (SAM) treatment. The objective of this study is to assess the effectiveness and coverage of SAM treatment delivered by community health workers (CHWs) in the Guidimakha region in Mauritania, compared to the HF based approach. This study was a nonrandomized controlled trial, including two rural areas. The control group received outpatient treatment for uncomplicated SAM from HF, whilst the intervention group received outpatient treatment for uncomplicated SAM from HF or CHWs. A total of 869 children aged 6–59 months with SAM without medical complications were included in the study. The proportion of cured children was 82.3% in the control group, and 76.4% in the intervention group, we found no significant difference between the groups. Coverage in the intervention zone increased from 53.6% to 71.7%. In contrast, coverage remained at approximately 44% in the control zone from baseline to end-line. This study is the first to demonstrate in Mauritania that the decentralization model of CHWs treating SAM improves acute malnutrition treatment coverage and complies with the international quality standards for community treatment of acute malnutrition. The non-randomized study design may limit the quality of the evidence, but these results could be used by political decision-makers as a first step in revising the protocol for acute malnutrition management.

Background Acute malnutrition treatment coverage remains low worldwide, causing significant morbidity and mortality. Decentralisation of treatment to Community Health Worker (CHW) sites has shown to be an effective strategy to improve access and increase coverage, but evidence on the cost and cost-effectiveness of this approach as well the use of simplified treatment protocols in conflict settings is lacking. The objective of this study was to determine cost per child treated as well as the cost-effectiveness of the hybrid model of treatment delivery (where treatment is provided at both health facilities and CHW sites) using either a standard protocol (Intervention 1) or simplified protocol (Intervention 2) compared to standard treatment at health facilities only (Control) in the conflict affected region of Gao in Northern Mali. Methods This economic evaluation was part of a three-arm cluster randomized controlled trial which enrolled 2038 children with moderate and severe acute malnutrition. Outcomes assessed were cost per child treated as well as average and incremental cost-effectiveness ratios for cost per child cured and disability adjusted life year (DALY) averted. A within study trial horizon, from March 2020 to July 2021, was used. Cost data were collected from accountancy records and through key informant interviews using a societal perspective. Treatment admission and outcome data were obtained from the main trial. Results In the base case scenario the cost per child treated was 272 US$, 179 US$ and 210US$ in the Control, Intervention 1 and 2 groups, respectively. Cost per child cured was 356 US$ in the Control, 219 US$ in the Intervention 1 and 226 US$ Intervention 2 groups. Ready-to-use therapeutic foods (RUTF) costs among SAM children treated with a simplified protocol were 5.7 US$ less per child. The average cost per DALY averted was 173.1 US$ in the Control compared to 60.3 US$ in the Intervention 1 and 53 US$ in the Intervention 2. Conclusion This study shows that involving CHWs in acute malnutrition treatment reduces the cost per child treated and is a cost-effective strategy, due to lower treatment costs and greater coverage in the decentralised model. Switching to a simplified protocol in a conflict setting can lead to cost savings particularly in terms of RUTF, and should be considered where weight-based admission, monitoring or dosage is not possible or RUTF stocks are running low. Trial registration The study protocol was registered under reference ISRCTN-60,973,756 on the 15th of October 2020.

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.

Background: the aim of this study is to evaluate the effectiveness and coverage of a simplified protocol that is implemented in health centers (HCs) and health posts (HPs) for children who are suffering from severe acute malnutrition (SAM) in the humanitarian context of Diffa. Methods: We conducted a non-randomized community-controlled trial. The control group received outpatient treatment for SAM, without medical complications, at HCs and HPs with the standard protocol of community management of acute malnutrition (CMAM). Meanwhile, with respect to the intervention group, the children with SAM received treatment at the HCs and HPs through a simplified protocol wherein the mid-upper arm circumference (MUAC) and the presence of edema were used as the admission criteria, and the children with SAM were administered doses of fixed ready-to-use therapeutic food (RUTF). Results: A total of 508 children, who were all under 5 years and had SAM, were admitted into the study. The cured proportion was 87.4% in the control group versus 96.6% in the intervention group (p value = 0.001). There was no difference between the groups in the length of stay, which was 35 days, but the intervention group used a lower quantity of RUTF—70 sachets versus 90 sachets, per child cured. Coverage increases were observed in both groups. Discussion: the simplified protocol used at the HCs and HPs did not result in worse recovery and resulted in fewer discharge errors compared to the standard protocol.

Severe acute malnutrition (SAM) affects over 16.6 million children worldwide. The integrated Community Case Management (iCCM) strategy seeks to improve essential health by means of nonmedical community health workers (CHWs) who treat the deadliest infectious diseases in remote rural areas where there is no nearby health center. The objective of this study was to assess whether SAM treatment delivered by CHWs close to families' locations may improve the early identification of cases compared to outpatient treatment at health facilities (HFs), with a decreased number complicated cases referred to stabilization centers, increased anthropometric measurements at admission (closer to the admission threshold) and similarity in clinical outcomes (cure, death, and default). The study included 930 children aged 6 to 59 months suffering from SAM in the Kita district of the Kayes Region in Mali; 552 children were treated by trained CHWs. Anthropometric measurements, the presence of edema, and other medical signs were recorded at admission, and the length of stay and clinical outcomes were recorded at discharge. The results showed fewer children with edema at admission in the CHW group than in the HF group (0.4% vs. 3.7%; OR = 10.585 [2.222-50.416], p = 0.003). Anthropometric measurements at admission were higher in the CHW group, with fewer children falling into the lowest quartiles of both weight-for-height z-scores (20.2% vs. 31.5%; p = 0.002) and mid-upper arm circumference (18.0% vs. 32.4%; p<0.001), than in the HF group. There was no difference in the length of stay. More children in the CHW group were cured (95.9% vs. 88.7%; RR = 3.311 [1.772-6.185]; p<0.001), and there were fewer defaulters (3.7% vs. 9.8%; RR = 3.345 [1.702-6.577]; p<0.001) than in the HF group. Regression analyses demonstrated that less severe anthropometric measurements at admission resulted in an increased probability of cure at discharge. The study results also showed that CHWs provided more integrated care, as they diagnosed and treated significantly more cases of infectious diseases than HFs (diarrhea: 36.0% vs. 18.3%, p<0.001; malaria: 41.7% vs. 19.8%, p<0.001; acute respiratory infection: 34.8% vs. 25.2%, p = 0.007). The addition of SAM treatment in the curative tasks that the CHWs provided to the families resulted in earlier admission and more integrated care for children than those associated with HFs. CHW treatment also achieved better discharge outcomes than standard community treatment.

The present study aimed to assess the effectiveness and impact on treatment coverage of integrating severe acute malnutrition (SAM) treatment at the health hut level by community health workers (CHWs). This study was a non-randomized controlled trial, including two rural communes in the health district of Mayahi: Maïreyreye (control) and Guidan Amoumoune (intervention). The control group received outpatient treatment for uncomplicated SAM from health facilities (HFs), while the intervention group received outpatient treatment for uncomplicated SAM from HFs or CHWs. A total of 2789 children aged 6–59 months with SAM without medical complications were included in the study. The proportion of cured children was 72.1% in the control group, and 77.2% in the intervention group. Treatment coverage decreased by 8.3% in the control area, while the group of CHWs was able to mitigate that drop and even increase coverage by 3%. This decentralized treatment model of acute malnutrition with CHWs allowed an increase in treatment coverage while maintaining a good quality of care. It also allowed the early inclusion of children in less severe conditions. These results may enhance the Niger Ministry of Health to review the management of SAM protocol and allow CHWs to treat acute malnutrition.