Assessment of Inequalities in Coverage of Essential Reproductive, Maternal, Newborn, Child, and Adolescent Health Interventions in Kenya
Notice bibliographique
Résumé
Importance: Previous work has underscored subnational inequalities that could impede additional health gains in Kenya. Objective: To provide a comprehensive assessment of the burden, distribution, and change in inequalities in reproductive, maternal, newborn, child, and adolescent health (RMNCAH) interventions in Kenya from 2003 to 2014. Design, Setting, and Participants: This population-based cross-sectional study used data from the 2003, 2008, and 2014 Kenya Demographic and Health Surveys. The study included women of reproductive age (ages 15-49 years) and children younger than years, with national, regional, county, and subcounty level representation. Data analysis was conducted from April 2018 to November 2018. Exposures: Socioeconomic position that was derived from asset indices and presented as wealth quintiles. Urban and rural residence and regions of Kenya were also considered. Main Outcomes and Measures: Absolute and relative measures of inequality in coverage of RMNCAH interventions. Results: For this analysis, representative samples of 31 380 women of reproductive age and 29 743 children younger than 5 years from across Kenya were included. The RMNCAH interventions examined demonstrated pro-rich and bottom inequality patterns. The most inequitable interventions were skilled birth attendance, family planning needs satisfied, and 4 or more antenatal care visits, whereby the absolute difference in coverage between the wealthiest (quintile 5) and poorest quintiles (quintile 1) was 61.6% (95% CI, 60.1%-63.1%), 33.4% (95% CI, 31.9%-34.9%), and 31.0% (95% CI, 30.5%-31.6%), respectively. The most equitable intervention was early initiation of breastfeeding, with an absolute difference (quintile 5 minus quintile 1) of -7.9% (95% CI, -11.1% to -4.8%), although antenatal care (1 visit) and diphtheria-tetanus-pertussis immunization (3 doses) demonstrated the best combination of high coverage and low inequalities. Our geospatial analysis revealed significant socioeconomic disparities in the northern and eastern regions of Kenya that have translated to suboptimal intervention coverage. A significant gap remains for rural, disadvantaged populations. Conclusions and Relevance: Coverage of RMNCAH interventions has improved over time, but wealth and geospatial inequalities in Kenya are persistent. Policy and programming efforts should place more emphasis on improving the accessibility of health facility-based interventions, which generally demonstrate poor coverage and high inequalities, and focus on integrated approaches to maternal health service delivery at the community level when access is poor. Scaling up of health services for the urban and, in particular, rural poor areas and those residing in Kenya's former north eastern province will contribute toward achievement of universal health coverage.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 ».