Multiple deprivations as drivers of suboptimal basic child vaccination in Latin America and the Caribbean: cross-sectional analysis of household survey data for 18,136 children across 211 regions in 15 countries
Notice bibliographique
Résumé
BACKGROUND: Latin America and the Caribbean (LAC) maintained high childhood vaccination coverage for 17 years but faced setbacks, increasing vulnerability to vaccine-preventable diseases. Despite signs of recovery, geographic inequalities and social deprivations persist. This study provides an up-to-date cross-sectional analysis of prevalence, subnational variation, and key determinants of suboptimal basic child vaccination (BCV). METHODS: We produced weighted estimates of suboptimal BCV prevalence at the national and subnational levels via harmonized data from household surveys spanning a 12-year period (2011-2022) in the LAC region. Six BCV-related outcomes were analysed: completely unvaccinated, no BCG, no DTP, no OPV, no MCV and not fully vaccinated. We employed a four-level mixed-effects logistic regression to analyse determinants of suboptimal BCV and to partition the total outcome variation over country, region, primary sample units (PSUs) and child‒mother‒household levels. Choropleth maps were used to illustrate the weighted mean prevalence of subnational regions for each outcome. Additionally, sensitivity analyses were performed to validate the findings and assess robustness. FINDINGS: A total of 18,136 children aged 12-23 months across 211 subnational regions in 15 LAC countries were analysed. The prevalence of suboptimal BCV ranged from 0.99% completely unvaccinated to 66% not fully vaccinated. Significant subnational disparities were observed: while all subnational regions in Cuba and Costa Rica had consistently low rates of completely unvaccinated children (< 3%), subnational regions or states such as Upper Takutu-Upper Essequibo and Mahaica-Berbice (Guyana) reported much higher rates, reaching 30.23% (95% CI: 9.52-50.94) and 26.56% (95% CI: 11.39-41.73), respectively. Maternal deprivation increased the risk of suboptimal BCV. The prevalence of completely unvaccinated children was significantly greater among those whose mothers did not have institutional delivery (3.35%; 95% CI: 3.07-3.63) than among those whose mothers had institutional delivery (0.74%; 95% CI: 0.70-0.79). The likelihood of suboptimal BCV outcomes increased as health services and socioeconomic deprivation intensified and intersected. CONCLUSIONS: In LACs, geographic inequalities and multiple deprivations increase the risk of suboptimal BCV. These countries should prioritize efforts to vaccinate children whose mothers lack access to one or more key health services, especially those from poor families.
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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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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 ».