Home birth and associated factors in Nigeria: A comparative study of rural and urban settings—Analysis of national population-based data
Notice bibliographique
Résumé
INTRODUCTION: Nigeria currently has the highest maternal mortality ratio and one of the highest neonatal mortality rates worldwide. Home birth-childbirth outside health facilities, often without skilled attendance or timely access to emergency obstetric care-may contribute to these disproportionate and avoidable adverse maternal and neonatal outcomes. National estimates often mask substantial sub-national disparities. This study examines the prevalence of home birth and associated factors across national, rural, and urban settings in Nigeria. METHODS: We analysed data from the nationally representative cross-sectional Nigeria Demographic and Health Survey 2018, guided by Andersen's Behavioural Model. Multivariable logistic regression was used to examine the associations between home birth and various predictor variables at the national level, as well as separately for rural and urban areas in Nigeria. RESULTS: Nationally, 58.1% (95% CI: 56.5, 59.7) of mothers gave birth at home, with prevalence twice as high in rural areas (72.4%, 95% CI: 70.7, 74.0) compared to urban areas (36.1%, 95% CI: 33.6, 38.7) (p < 0.001). The North-West region had the highest home birth prevalence both nationally (83.6%, 95% CI: 81.5, 85.6) and in rural (89.4%, 95% CI: 87.6, 91.0), and urban (66.6%, 95% CI: 60.5, 72.2) areas (p < 0.001). The South-East recorded the lowest prevalence in rural areas (16.2%, 95% CI: 11.0, 23.3), while the South-West had the lowest in urban areas (16.7%, 95% CI: 14.1, 19.7) (p < 0.001). At the national level and across all settings, factors such as low maternal and husband's education, poor household wealth, fewer than eight antenatal contacts, higher birth order, Hausa-Fulani ethnicity, and limited exposure to media (radio and television) and the internet were associated with higher odds of home birth. In rural areas, additional predictors included difficulty obtaining permission, distance to health facilities, limited decision-making autonomy, and significant regional disparities, especially in the North and South-South regions. In urban areas, young maternal age, Islamic religion, financial barriers, and poor or middle household wealth were uniquely associated with higher odds of home birth. CONCLUSION: Home birth remains highly prevalent in Nigeria, particularly in rural settings and in the northern and South-South regions, where prevalence is disproportionately high. Reducing home births requires a comprehensive approach that addresses the interplay of factors identified in this study. From a social justice and health determinants perspective, these factors are interconnected and can influence both access to and use of services. In rural areas, policies should enhance women's decision-making autonomy, reduce distance barriers, and address region-specific challenges (e.g., insecurity in northern regions). In urban areas, it is essential to address financial barriers, support young mothers, and provide culturally and religiously sensitive care. Nationally, efforts should focus on improving education, expanding and strengthening antenatal care, and increasing access to media and the internet. From an equity perspective, interventions must be tailored to specific contexts to reduce unsafe home births and ensure that all mothers, regardless of location, have equitable access to skilled, respectful, and high-quality childbirth care.
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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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».