Assessing factors associated with poor maternal mental health among mothers of children born small and sick at 24–47 months in rural Rwanda
Notice bibliographique
Résumé
BACKGROUND: Global investments in neonatal survival have resulted in a growing number of children with morbidities surviving and requiring ongoing care. Little is known about the caregivers of these children in low- and middle-income countries, including maternal mental health which can further negatively impact child health and development outcomes. We aimed to assess the prevalence and factors associated with poor maternal mental health in mothers of children born preterm, low birthweight (LBW), and with hypoxic ischemic encephalopathy (HIE) at 24-47 months of age in rural Rwanda. METHODS: Cross-sectional study of children 24-47 months born preterm, LBW, or with HIE, and their mothers discharged from the Neonatal Care Unit (NCU) at Kirehe Hospital between May 2015-April 2016 or discharged and enrolled in a NCU follow-up program from May 2016-November 2017. Households were interviewed between October 2018 and June 2019. Mothers reported on their mental health and their child's development; children's anthropometrics were measured directly. Backwards stepwise procedures were used to assess factors associated with poor maternal mental health using logistic regression. RESULTS: Of 287 total children, 189 (65.9%) were born preterm/LBW and 34.1% had HIE and 213 (74.2%) screened positive for potential caregiver-reported disability. Half (n = 148, 51.6%) of mothers reported poor mental health. In the final model, poor maternal mental health was significantly associated with use of violent discipline (Odds Ratio [OR] 2.29, 95% Confidence Interval [CI] 1.17,4.45) and having a child with caregiver-reported disability (OR 2.96, 95% CI 1.55, 5.67). Greater household food security (OR 0.80, 95% CI 0.70-0.92) and being married (OR = 0.12, 95% CI 0.04-0.36) or living together as if married (OR = 0.13, 95% CI 0.05, 0.37) reduced the odds of poor mental health. CONCLUSIONS: Half of mothers of children born preterm, LBW and with HIE had poor mental health indicating a need for interventions to identify and address maternal mental health in this population. Mother's poor mental health was also associated with negative parenting practices. Specific interventions targeting mothers of children with disabilities, single mothers, and food insecure households could be additionally beneficial given their strong association with poor maternal mental health.
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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.
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