Socioeconomic, demographic and environmental factors of child drownings in Northern Bangladesh
Notice bibliographique
Résumé
BACKGROUND: Drowning is the leading cause of death among children aged 0-17 years in rural Bangladesh, resulting in over 14 438 deaths annually-an average of 43 deaths per day. This study aims to identify socioeconomic, demographic and environmental factors linked to child drowning deaths in Northern Bangladesh-a region of high poverty, which is behind in overall socioeconomic indicators compared with other regions in the country. METHODS: We conducted a cross-sectional survey through purposive sampling to identify child fatal and non-fatal drownings among a total of 18 004 households, comprising 71 185 people, in 2 unions in Northern Bangladesh. Interviews were conducted between January and March 2024 with the households that experienced child drownings in the region. We employed a mixed-methods approach to data collection, using quantitative analysis to examine socioeconomic, demographic and environmental factors, alongside qualitative analysis to explore situational factors associated with drownings in the region. RESULTS: Through household visits, a total of 117 households were identified that faced child drowning incidents, comprising 84 fatal (71.8 %) and 33 non-fatal (28.2 %) drownings between 2018 and 2023. The households that faced drownings were comparatively of lower income groups, had lower rates of education and were mostly engaged in agriculture and other domestic work. In 2023, the number of drowning incidents was 34. Out of 117 drownings, 95% occurred between 9:00 and 15:00 hours, and more than 82% occurred between June and October. Out of 117 drowning incidents, approximately 97% of children did not know how to swim prior to the incident. Out of 117 respondents, 73.5% stated that they did not teach their child how to swim. Of those who taught their child to swim, the average age for learning to swim was 8.33 years. Out of 84 child drowning deaths, 75% were male and 25% were female, and the average age was 3.9 years. Out of the 84 fatal drowning deaths, 72.6% occurred in ponds. CONCLUSION: Identification of socioeconomic, demographic and environmental factors associated with child drownings will help to develop feasible prevention strategies and interventions in the region.
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 | 0,000 | 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».