Epidemiology of unintended pregnancies: regional insights from sub-Saharan Africa
Notice bibliographique
Résumé
Unintended pregnancy remains a significant public health issue globally, with sub-Saharan Africa experiencing a particularly severe burden. The aim of this study was to assess the prevalence and determinants of unintended pregnancy across 35 countries in sub-Saharan Africa. Data were obtained from the Demographic and Health Surveys (n = 373,298). Unintended pregnancies were defined as those that were either mistimed (i.e., occurring earlier than desired) or unwanted (i.e., not desired at all). Descriptive statistics were used to summarize the data, and multivariable logistic regression analysis was performed to examine the association between sociodemographic factors and unintended pregnancy. The analyses were conducted using Stata 16 software, with survey weights applied to account for the complex survey design. Descriptive analysis revealed that 27.6% of women reported their last pregnancy as unintended, with significant regional variation. The highest prevalence of unintended pregnancies was observed in South Africa (57.9%), while the lowest was in Burkina Faso (9.8%). In general, rural areas had a higher prevalence of unintended pregnancies compared to urban areas. Regression analysis identified significant sociodemographic factors associated with unintended pregnancy. Women in rural areas had lower odds of unintended pregnancy compared to urban residents (OR = 0.93, 95% CI 0.91–0.95). However, women with primary education were more likely to experience unintended pregnancies compared to those with no education (OR = 2.02, 95% CI 1.98–2.06), and those in the richest wealth quintile had significantly lower odds than those in the poorest quintile (OR = 0.81, 95% CI 0.79–0.84). Contraceptive use was also strongly associated with unintended pregnancy: women using no contraceptive method had 1.74 times higher odds of unintended pregnancy compared to those using modern methods (OR = 1.74, 95% CI 1.71–1.77). This study underscores the need for targeted, evidence-based interventions that address the sociodemographic factors contributing to unintended pregnancies in sub-Saharan Africa. Interventions should focus on improving access to contraceptive methods, promoting education, reducing socioeconomic disparitiesinequalities, and increasing media access to better inform reproductive health decisions. These efforts can help mitigate the high prevalence of unintended pregnancies in the region.
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 | 0,002 |
| 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 ».