Magnitude and trends in socio-economic and geographic inequality in access to birth by cesarean section in Tanzania: evidence from five rounds of Tanzania demographic and health surveys (1996–2015)
Notice bibliographique
Résumé
Abstract Background Majority of maternal deaths are avoidable through quality obstetric care such as Cesarean Section (CS). However, in low-and middle-income countries, many women are still dying due to lack of obstetric services. Tanzania is one of the African countries where maternal mortality is high. However, there is paucity of evidence related to the magnitude and trends of disparities in CS utilization in the country. This study examined both the magnitude and trends in socio-economic and geographic inequalities in access to birth by CS. Methods Data were extracted from the Tanzania Demographic and Health Surveys (TDHSs) (1996–2015) and analyzed using the World Health Organization’s (WHO) Health Equity Assessment Toolkit (HEAT) software. First, access to birth by CS was disaggregated by four equity stratifiers: wealth index, education, residence and region. Second, we measured the inequality through summary measures, namely Difference (D), Ratio (R), Slope Index of Inequality (SII) and Relative Index of Inequality (RII). A 95% confidence interval was constructed for point estimates to measure statistical significance. Results The results showed variations in access to birth by CS across socioeconomic, urban-rural and regional subgroups in Tanzania from 1996 to 2015. Among the poorest subgroups, there was a 1.38 percentage points increase in CS coverage between 1996 and 2015 whereas approximately 11 percentage points increase was found among the richest subgroups within same period of time. The coverage of CS increased by nearly 1 percentage point, 3 percentage points and 9 percentage points among non-educated, those who had primary education and secondary or higher education, respectively over the last 19 years. The increase in coverage among rural residents was 2 percentage points and nearly 8 percentage points among urban residents over the last 19 years. Substantial disparity in CS coverage was recorded in all the studied surveys. For instance, in the most recent survey, pro-rich (RII = 15.55, 95% UI; 10.44, 20.66, SII = 15.8, 95% UI; 13.70, 17.91), pro-educated (RII = 13.71, 95% UI; 9.04, 18.38, SII = 16.04, 95% UI; 13.58, 18.49), pro-urban ( R = 3.18, 95% UI; 2.36, 3.99), and subnational (D = 16.25, 95% UI; 10.02, 22.48) absolute and relative inequalities were observed. Conclusion The findings showed that over the last 19 years, women who were uneducated, poorest/poor, living in rural settings and from regions such as Zanzibar South, appeared to utilize CS services less in Tanzania. Therefore, such subpopulations need to be the central focus of policies and programmes implemmentation to improve CS services coverage and enhance equity-based CS services utilization.
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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) | 0,001 | 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 ».