Coverage and inequalities in maternal and child health interventions in Afghanistan
Notice bibliographique
Résumé
BACKGROUND: Afghanistan has made considerable gains in improving maternal and child health and survival since 2001. However, socioeconomic and regional inequities may pose a threat to reaching universal coverage of health interventions and further health progress. We explored coverage and socioeconomic inequalities in key life-saving reproductive, maternal, newborn and child health (RMNCH) interventions at the national level and by region in Afghanistan. We also assessed gains in child survival through scaling up effective community-based interventions across wealth groups. METHODS: Using data from the Afghanistan Multiple Indicator Cluster Survey (MICS) 2010/11, we explored 11 interventions that spanned all stages of the continuum of care, including indicators of composite coverage. Asset-based wealth quintiles were constructed using standardised methods, and absolute inequalities were explored using wealth quintile (Q) gaps (Q5-Q1) and the slope index of inequality (SII), while relative inequalities were assessed with ratios (Q5/Q1) and the concentration index (CIX). The lives saved tool (LiST) modeling used to estimate neonatal and post-neonatal deaths averted from scaling up essential community-based interventions by 90 % coverage by 2025. Analyses considered the survey design characteristics and were conducted via STATA version 12.0 and SAS version 9.4. RESULTS: Our results underscore significant pro-rich socioeconomic absolute and relative inequalities, and mass population deprivation across most all RMNCH interventions studied. The most inequitable are antenatal care with a skilled attendant (ANCS), skilled birth attendance (SBA), and 4 or more antenatal care visits (ANC4) where the richest have between 3.0 and 5.6 times higher coverage relative to the poor, and Q5-Q1 gaps range from 32 % - 65 %. Treatment of sick children and breastfeeding interventions are the most equitably distributed. Across regions, inequalities were highest in the more urbanised East, West and Central regions of the country, while they were lowest in the South and Southeast. About 7700 newborns and 26,000 post-neonates could be saved by scaling up coverage of community outreach interventions to 90 %, with the most gains in the poorest quintiles. CONCLUSIONS: Afghanistan is a pervasively poor and conflict-prone nation that has only recently experienced a decade of relative stability. Though donor investments during this period have been plentiful and have contributed to rebuilding of health infrastructure in the country, glaring inequities remain. A resolution to scaling up health coverage in insecure and isolated regions, and improving accessibility for the poorest and marginalised populations, should be at the forefront of national policy and programming efforts.
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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) | 0,000 | 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 ».