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Enregistrement W2971289387 · doi:10.1371/journal.pmed.1002892

Impact on child acute malnutrition of integrating small-quantity lipid-based nutrient supplements into community-level screening for acute malnutrition: A cluster-randomized controlled trial in Mali

2019· article· en· W2971289387 sur OpenAlexfundno aff
Lieven Huybregts, Agnès Le Port, Elodie Becquey, Amanda Zongrone, Francisco M. Barba, Rahul Rawat, Jef L Leroy, Marie T. Ruel

Notice bibliographique

RevuePLoS Medicine · 2019
Typearticle
Langueen
DomaineNursing
ThématiqueChild Nutrition and Water Access
Établissements canadiensnon disponible
Organismes subventionnairesConsortium of International Agricultural Research CentersGlobal Affairs CanadaInternational Fine Particle Research Institute
Mots-clésMalnutritionSevere Acute MalnutritionMedicineRandomized controlled trialProtein–energy malnutritionCluster (spacecraft)Malnutrition in childrenEnvironmental healthPediatricsInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Community-based management of acute malnutrition (CMAM) has been widely adopted to treat childhood acute malnutrition (AM), but its effectiveness in program settings is often limited by implementation constraints, low screening coverage, and poor treatment uptake and adherence. This study addresses the problem of low screening coverage by testing the impact of distributing small-quantity lipid-based nutrient supplements (SQ-LNSs) at monthly screenings held by community health volunteers (CHVs). Screening sessions included behavior change communication (BCC) on nutrition, health, and hygiene practices (both study arms) and SQ-LNSs (one study arm). Impact was assessed on AM screening and treatment coverage and on AM incidence and prevalence. METHODS AND FINDINGS: A two-arm cluster-randomized controlled trial in 48 health center catchment areas in the Bla and San health districts in Mali was conducted from February 2015 to April 2017. In both arms, CHVs led monthly AM screenings in children 6-23 months of age and provided BCC to caregivers. The intervention arm also received a monthly supply of SQ-LNSs to stimulate caregivers' participation and supplement children's diet. We used two study designs: i) a repeated cross-sectional study (n = approximately 2,300) with baseline and endline surveys to examine impacts on AM screening and treatment coverage and prevalence (primary study outcomes) and ii) a longitudinal study of children enrolled at 6 months of age (n = 1,132) and followed monthly for 18 months to assess impact on AM screening and treatment coverage and incidence (primary study outcomes). All analyses were done by intent to treat. The intervention significantly increased AM screening coverage (cross-sectional study: +40 percentage points [pp], 95% confidence interval [CI]: 32, 49, p < 0.001; longitudinal study: +28 pp, 95% CI: 23, 33, p < 0.001). No impact on treatment coverage or AM prevalence was found. Children in the intervention arm, however, were 29% (95% CI: 8, 46; p = 0.017) less likely to develop a first AM episode (incidence) and, compared to children in comparison arm, their overall risk of AM (longitudinal prevalence) was 30% (95% CI: 12, 44; p = 0.002) lower. The intervention lowered CMAM enrollment by 10 pp (95% CI: 1.9, 18; p = 0.016), an unintended negative impact likely due to CHVs handing out preventive SQ-LNSs to caregivers of AM children instead of referring them to the CMAM program. Study limitations were i) the referral of AM cases by our research team (for ethical reasons) during monthly measurements in the longitudinal study might have interfered with usual CMAM activities and ii) the outcomes presented by child age also reflect seasonal variations because of the closed cohort design. CONCLUSIONS: Incorporating SQ-LNSs into monthly community-level AM screenings and BCC sessions was highly effective at improving screening coverage and reducing AM incidence, but it did not improve AM prevalence or treatment coverage. Future evaluation and implementation research on CMAM should carefully assess and tackle the remaining barriers that prevent AM cases from being correctly diagnosed, referred, and adequately treated. TRIAL REGISTRATION: ClinicalTrials.gov NCT02323815.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Essai randomisé · Signal consensuel: Essai randomisé
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0030,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,043
Tête enseignante GPT0,348
Écart entre enseignants0,305 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeEssai randomisé
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations50
Publié2019
Routes d'admission1
Résumé présentoui

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