<![CDATA[Background: To optimize caesarean section (CS), Robson classification is useful for audit of CS rates within and across hospitals and regions. Valid conclusions are also possible by analyzing other characteristics of parturients that determine CS rates based on Robson groups. This study aimed to analyzed CS rate and determine the impact of parturients’ age and booking status on delivery by CS based on Robson classification. Subjects and Method: A cross-sectional descriptive study that classified parturients into Robson 10-groups using data from hospital records at the Federal Medical Centre Yenagoa (FMCY) in Bayelsa State, south-south, Nigeria. The dependent variable was mode of delivery. The independent variables were parturients’ age and booking status by Robson group. The study instrument was a predesigned spreadsheet used to collect real-time relevant data on all deliveries from patients’ hospital records. Descriptive statistics were presented using frequencies, percentages, mean and standard deviation. Chi-square, Exact test and logistic regression were used to determine association of parturients’ age and booking status with mode of delivery. Level of significance was p <0.05. Results: There were 556 deliveries during the study period and 269 CSs, giving a CS rate of 48.4%. Robson group 3 made the highest (27.9%) contribution to CS rate, followed by group 10 (22.3%), 5 (13.8%) and 1 (11.2%). The commonest indication for CS was cephalopelvic disproportion, followed by severe preeclampsia. Booked parturients in Robson groups 1 and 3 had 61.0% reduced odd (OR= 0.39; 95% CI = 0.15 to 0.99; p = 0.050) and 74.8% reduced odd (OR= 0.25; 95% CI= 0.14 to 0.45; p < 0.001) of delivery by CS, respectively. Conclusion: The CS rate at the FMCY was contributed largely by group 3, 10, 5 and 1 parturients. Using Robson classification, CS rate can be focused to targeted intervention to optimize CS. Keywords: caesarean section rate, hospital, Robson classification, Robson 10-group. Correspondence: Olakunle Ifeoluwa Makinde. Department of Obstetrics and Gynaecology, Federal Medical Centre Yenagoa, Bayelsa State, Nigeria. Email: olakunleife@gmail.com. Mobile: +2348032136315.]]>
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