An Equity Analysis of Zero-Dose Children in India Using the National Family Health Survey Data: Status, Challenges, and Next Steps
Notice bibliographique
Résumé
Background While immunization programs across the world have made considerable progress, children and communities continue to be beyond the reach of healthcare services. Globally, they are now referred to as zero-dose (ZD) children (those who have not received a single dose of diphtheria, pertussis, and tetanus-containing vaccine). Pre-COVID-19 pandemic analyses suggest that nearly 50% of vaccine-preventable deaths occur among ZD children. Two-thirds of these children live in extremely poor households suffering from multiple deprivations including lack of access to reproductive health services, water, and sanitation. Hence, ZD children have now been prioritized as a key cohort for identification and integration with the health systems as we build back from the pandemic. Methodology Extracting data from the last two National Family Health Survey (NFHS) rounds (NFHS 4, 2015-2016 and NFHS 5, 2019-2021), this study aims to ascertain the status of ZD children aged 12-23 months in India, the challenges, and the necessary action agenda going forward. Data were analyzed for equity determinants such as gender, place of residence, religion, birth order, caste, and mother's schooling. Key determinants included the change in ZD prevalence at the national, state, and district levels; variations across equity parameters and states with maximum improvements; and disparity across these indicators. A correlation analysis was also conducted to understand the nature of the association between ZD prevalence and critical maternal and child health indicators. Results The overall ZD prevalence between the two rounds was reduced by 4.1% (10.5-6.4%). A total of 26 states in the country reported a ZD prevalence of <10% in NFHS 5 compared to 18 in NFHS 4. In total, 324 districts reported a ZD prevalence of <5%, and 145 districts reported a prevalence of >10%. The equity parameters reflected a slow-footed reduction among ZD for girl children, across urban geographies, firstborn children, mothers with 12 or more years of schooling, and children in families with the highest wealth quintiles. A negative correlation accentuated between the two NFHS rounds was established between first-trimester registration, four or more antenatal visits, institutional deliveries, and ZD prevalence. Conclusions The findings point toward sustained improvement across key equity parameters, however, challenges do exist. Moreover, the impact of the pandemic on immunization programs across the globe and in India is bound to halt and reverse the progress and potentiate further inequities. It is thus imperative that continued and augmented efforts are continued to identify, integrate, and immunize ZD children, families, and communities.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 ».