Optimizing prevention and community-based management of severe malnutrition in children
Notice bibliographique
Résumé
In this issue of PLOS Medicine, Matt Hitchings and colleagues detail the findings from their prospective cluster-randomized crossover trial conducted across 10 health centers in Sokoto, Nigeria, to assess the nutritional recovery in children with uncomplicated severe acute malnutrition (SAM) receiving monthly follow-up compared to the standard weekly follow-up schedules [1].In almost 4,000 children so allocated, the nutritional recovery at 3 months' follow-up was lower in the monthly follow-up group (52.4%) compared to the standard weekly group (58.8%), with higher cumulative mortality at 3 months (8.5% versus 6.2% with the standard weekly follow-up).In contrast, rates of default and relapse were significantly lower among SAM children allocated to monthly follow-up.The authors, while urging caution in adopting a modified schedule of monthly follow-up visits in such children, also recognize the trade-off of simplicity and ease of operations in some settings where weekly follow-up visits are not feasible.Despite global progress in improving maternal and child undernutrition, the high burden of severe malnutrition persists.Recent estimates show a small reduction (from 15AU : AU : Pleasenotet .9% to 14.2%) in wasting prevalence in low-income countries, and a slight increase (from 3.3% to 4.7%) in middle-income countries, although overall almost 50 million children aged under 5 years still remain wasted worldwide [2].This burden of SAM has most likely been exacerbated during the recent Coronavirus Disease 2019 (COVIDAU : PleasenotethatCOVID À 19hasbeendefineda -19) pandemic, with an estimated additional 6.7 million children becoming wasted in 2020 [3].Within this large number of wasted children are those with SAM who are triaged to facilitybased nutritional rehabilitation if seriously ill, or community-based treatment regimens if stable.The development of standardized management protocols for children with SAM with ready-to-use therapeutic foods (RUTFs) represents one of the greatest advances in treating such children at scale and reducing the mortality associated with the condition [4].However, given the general context where childhood SAM clusters, such as those affected by extreme poverty, climate change, conflict, and involving displaced populations, major challenges remain in optimizing SAM management.These include relatively high rates of relapse [5], and associated residual mortality with severe malnutrition, often exceeding 10% in some settings [6].Strategies are thus needed to optimize community case management aimed at simplifying the treatment regimen for SAM, reducing defaults and relapse rates among affected children.Such real-life evaluations of management strategies for severe malnutrition among at-risk children are few and far between, and most welcome.The global evidence base for the management of SAM in various settings is still mixed, with wide variations in recovery or relapse rates
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,012 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,003 |
| Science ouverte | 0,004 | 0,002 |
| Intégrité de la recherche | 0,011 | 0,018 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».