Association between water, sanitation and hygiene (WASH) and child undernutrition in Ethiopia: a hierarchical approach
Notice bibliographique
Résumé
BACKGROUND: Undernutrition is a significant public health challenge and one of the leading causes of child mortality in a wide range of developing countries, including Ethiopia. Poor access to water, sanitation, and hygiene (WASH) facilities commonly contributes to child growth failure. There is a paucity of information on the interrelationship between WASH and child undernutrition (stunting and wasting). This study aimed to assess the association between WASH and undernutrition among under-five-year-old children in Ethiopia. METHODS: A secondary data analysis was undertaken based on the Ethiopian Demographic and Health Surveys (EDHS) conducted from 2000 to 2016. A total of 33,763 recent live births extracted from the EDHS reports were included in the current analysis. Multilevel logistic regression models were used to investigate the association between WASH and child undernutrition. Relevant factors from EDHS data were identified after extensive literature review. RESULTS: The overall prevalences of stunting and wasting were 47.29% [95% CI: (46.75, 47.82%)] and 10.98% [95% CI: (10.65, 11.32%)], respectively. Children from households having unimproved toilet facilities [AOR: 1.20, 95% CI: (1.05,1.39)], practicing open defecation [AOR: 1.29, 95% CI: (1.11,1.51)], and living in households with dirt floors [AOR: 1.32, 95% CI: (1.12,1.57)] were associated with higher odds of being stunted. Children from households having unimproved drinking water sources were significantly less likely to be wasted [AOR: 0.85, 95% CI: (0.76,0.95)] and stunted [AOR: 0.91, 95% CI: (0.83, 0.99)]. We found no statistical differences between improved sanitation, safe disposal of a child's stool, or improved household flooring and child wasting. CONCLUSION: The present study confirms that the quality of access to sanitation and housing conditions affects child linear growth indicators. Besides, household sources of drinking water did not predict the occurrence of either wasting or stunting. Further longitudinal and interventional studies are needed to determine whether individual and joint access to WASH facilities was strongly associated with child stunting and wasting.
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 tête enseignante, 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 ».