Socioeconomic inequalities in modern contraceptive use among women in Benin: a decomposition analysis
Notice bibliographique
Résumé
BACKGROUND: Contraceptive use is crucial to achieving Sustainable Development Goal 3. Evidence of socioeconomic inequality in the use of modern contraceptives is essential to address the developing inequality in its utilisation given the low prevalence of contraceptive use among women in Benin. This study examined the socioeconomic inequalities in modern contraceptive use among women in Benin. METHODS: We performed a cross-sectional analysis of the 2017-18 Benin Demographic and Health Survey data. A weighted sample of 7,360 sexually active women of reproductive age was included in the study. We used a concentration curve to plot the cumulative proportion of women using modern contraception. Decomposition analysis was conducted to determine factors accounting for the socioeconomic disparities in modern contraceptive use. RESULTS: We noted that the richest women had higher odds of modern contraceptive use (adjusted odds ratio [aOR] = 1.67, CI = 1.22-2.30) compared to the poorest women. Other factors that showed significant associations with modern contraception use were age, marital status, religious affiliation, employment status, parity, women's educational level, and ethnicity. We found that modern contraceptive use is highly concentrated among the rich, with rich women having a higher propensity of using modern contraception relative to the poor. Also, the disadvantaged to modern contraceptive use included the poor, those aged 45-49, married women, those working, those with four or more live births, rural residents, and women of Bariba and related ethnicity. Conversely, favourable concentration in modern contraceptive use was found among the rich, women aged 20-24, the divorced, women with two live births, the highly educated, those with media exposure, and women of Yoruba and related ethnicity. CONCLUSION: The study has shown that wealthy women are more likely to utilize contraceptives than the poor. This is because wealthy women could afford both the service itself and the travel costs to the health facility, hence overcoming any economic barriers to using modern contraception. Other factors such as age, marital status, religion, employment status, parity, mother's educational level, and ethnicity were associated with contraceptive use in Benin. The Benin government and other stakeholders should develop family planning intercession techniques that address both the supply and demand sides of the equation, with a focus on reaching the illiterate and under-resourced population without admittance to modern contraception.
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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) | 0,001 | 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 | 0,000 | 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 ».