Dataset Information

Caesarean section in Palestine using the Robson Ten Group Classification System: a population-based birth cohort study.

ABSTRACT:

Objective

To analyse the current situation of caesarean section in Palestine using the Robson Ten Group Classification System (TGCS).

Design

A population-based birth cohort study.

Setting

Obstetrical departments in three governmental hospitals in Gaza.

Participants

All women (18 908) who gave birth between 1 January 2016 and 30 April 2017.

Methods

The contributions of each group to the study population and to the overall rate of caesarean section were calculated, as well as the rate of caesarean section in each TGCS group. Differences in proportions between study hospitals were assessed by ?² test.

Main outcome measures

The main outcome was the contributions of each group to the overall caesarean section rate.

Results

The overall rate of caesarean section was 22.9% (4337 of 18 908), ranging from 20.6% in hospital 1 to 24.6% in hospital 3. The largest contributors to the overall caesarean section rate were multiparous women with single cephalic full-term pregnancy who had undergone at least one caesarean section (group 5, 42.6%), women with multiple pregnancies (group 8, 11.6%) and those with single cephalic preterm labour (group 10, 8.1%). Statistically significant differences in caesarean section rates between the study hospitals were observed in group 1 (nulliparous women with single cephalic full-term pregnancy and spontaneous labour), group 4 (multiparous with single cephalic full-term pregnancy with induced labour or prelabour caesarean section), group 5 (multiparous with single cephalic full-term pregnancy with previous caesarean section) and in group 7 (multiparous with breech presentation).

Conclusion

Women in groups 5, 8 and 10 were the largest contributors to the overall caesarean section rate in the study hospitals. Efforts to reduce the differences in obstetrical care between hospitals need to be directed towards increasing the proportion of vaginal births after caesarean section and by reducing primary caesarean section in multiple pregnancies and preterm labour.

SUBMITTER: Zimmo MW

PROVIDER: S-EPMC6224732 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Caesarean section in Palestine using the Robson Ten Group Classification System: a population-based birth cohort study.

Zimmo Mohammed Walid MW Laine Katariina K Hassan Sahar S Bottcher Bettina B Fosse Erik E Ali-Masri Hadil H Zimmo Kaled K Sørum Falk Ragnhild R Lieng Marit M Vikanes Åse Å

BMJ open 20181024 10

<h4>Objective</h4>To analyse the current situation of caesarean section in Palestine using the Robson Ten Group Classification System (TGCS).<h4>Design</h4>A population-based birth cohort study.<h4>Setting</h4>Obstetrical departments in three governmental hospitals in Gaza.<h4>Participants</h4>All women (18 908) who gave birth between 1 January 2016 and 30 April 2017.<h4>Methods</h4>The contributions of each group to the study population and to the overall rate of caesarean section were calculat ...[more]

PMID: 30361403

Similar Datasets

Project description:BackgroundWith an increasing rate of caesarean sections as well as rising numbers of multiple pregnancies, valid classifications for benchmarking are needed. The Robson classification provides a method to group cases with caesarean section in order to assess differences in outcome across regions and sites. In this study we set up a novel method of classification by using routinely collected health data. We hypothesize i that routinely collected health data can be used to apply complex medical classifications and ii that the Robson classification is capable of classifying mothers and their corresponding newborn into meaningful groups with regard to outcome.Methods and findingsThe study was conducted at the coding department and the department of obstetrics and gynecology Inselspital, University Hospital of Bern, Switzerland. The study population contained inpatient cases from 2014 until 2017. Administrative and health data were extracted from the Data Warehouse. Cases were classified by a Structured Query Language code according to the Robson criteria using data from the administrative system, the electronic health record and from the laboratory system. An automated query to classify the cases according to Robson could be implemented and successfully validated. A linkage of the mother's class to the corresponding newborn could be established. The distribution of clinical indicators was described. It could be shown that the Robson classes are associated to outcome parameters and case related costs.ConclusionsWith this study it could be demonstrated, that a complex query on routinely collected health data would serve for medical classification and monitoring of quality and outcome. Risk-stratification might be conducted using this data set and should be the next step in order to evaluate the Robson criteria and outcome. This study will enhance the discussion to adopt an automated classification on routinely collected health data for quality assurance purposes.

Project description:BackgroundCaesarean delivery (CD) rates are commonly used as an indicator of quality in obstetric care and risk adjustment evaluation is recommended to assess inter-institutional variations. The aim of this study was to evaluate whether the Ten Group classification system (TGCS) can be used in case-mix adjustment.MethodsStandardized data on 15,255 deliveries from 11 different regional centers were prospectively collected. Crude Risk Ratios of CDs were calculated for each center. Two multiple logistic regression models were herein considered by using: Model 1- maternal (age, Body Mass Index), obstetric variables (gestational age, fetal presentation, single or multiple, previous scar, parity, neonatal birth weight) and presence of risk factors; Model 2- TGCS either with or without maternal characteristics and presence of risk factors. Receiver Operating Characteristic (ROC) curves of the multivariate logistic regression analyses were used to assess the diagnostic accuracy of each model. The null hypothesis that Areas under ROC Curve (AUC) were not different from each other was verified with a Chi Square test and post hoc pairwise comparisons by using a Bonferroni correction.ResultsCrude evaluation of CD rates showed all centers had significantly higher Risk Ratios than the referent. Both multiple logistic regression models reduced these variations. However the two methods ranked institutions differently: model 1 and model 2 (adjusted for TGCS) identified respectively nine and eight centers with significantly higher CD rates than the referent with slightly different AUCs (0.8758 and 0.8929 respectively). In the adjusted model for TGCS and maternal characteristics/presence of risk factors, three centers had CD rates similar to the referent with the best AUC (0.9024).ConclusionsThe TGCS might be considered as a reliable variable to adjust CD rates. The addition of maternal characteristics and risk factors to TGCS substantially increase the predictive discrimination of the risk adjusted model.

Dataset Information

Caesarean section in Palestine using the Robson Ten Group Classification System: a population-based birth cohort study.

Objective

Design

Setting

Participants

Methods

Main outcome measures

Results

Conclusion

Publications

Caesarean section in Palestine using the Robson Ten Group Classification System: a population-based birth cohort study.

Similar Datasets

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