Prevalence and predictors of taking tetanus toxoid vaccine in pregnancy: a cross-sectional study of 8,722 women in Sierra Leone
Notice bibliographique
Résumé
BACKGROUND: Immunization of women during pregnancy to protect them and their infants against tetanus, pertussis and influenza is recommended by the World health Organization (WHO). However, there is limited information about the coverage rate and associated factors in low-income countries. The aim of this study was to measure the prevalence and predictors of taking tetanus toxoid among pregnant women in Sierra Leone. METHODS: This study was based on the fifth round of Multiple Indicator Cluster Survey (MICS 5) conducted in Sierra Leone in 2017. In total 8722 women aged between 15 and 49 years were included in this study. Outcome variable was taking of Tetanus Toxoid vaccination during the last pregnancy. Data were analyzed using cross-tabulation and logistic regression methods. RESULTS: The overall prevalence of receiving TT immunization during women's last pregnancy was 96.3% and that of taking at least two doses was 82.12%. In the regression analysis, women from Mende ethnicity had a 0.48 fold lower chance of being immunized (OR = 0.480, 95% CI = 0.385,0.59768) than those from the other ethnicity. In addition, women who attended at least four ANC visits had higher odds of receiving TT vaccine (OR = 1.919, 95% CI = 1.639,2.245) compared to those who attended less ANC visits. Stratified by areas, this association was observed in both urban (OR = 2.661, 95% CI = 1.924,3.679) and rural areas (OR = 1.716, 95% CI = 1.430,2.059). Attending at least four ANC visits showed a positive association with receiving at least two doses TT (OR = 2.434, 95% CI = 1.711,3.464) in both urban (OR = 2.815, 95% CI = 1.413,5.610) and rural areas (OR = 2.216, 95% CI = 1.463,3.356) as well. CONCLUSION: Higher number of ANC visits, mass media exposure and higher wealth quintile increased the odds of receiving TT immunization. In addition, minimum two doses which were identified to reduce neonatal mortality. Therefore, immunization campaigns targeting improved utilization of healthcare and immunization services by women of childbearing age in Sierra Leone are strongly recommended.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 ».