Obstetric outcomes associated with induction of labour after caesarean section.

PURPOSE: To evaluate the efficiency and safety of inducing labour with oxytocin in women with a single prior Caesarean section, with particular focus on the Bishop score. METHODS: Between January 1, 2013 and March 31, 2017, we included all women with a singleton full-term pregnancy and single prior Caesarean section in this monocentric retrospective observational study. Women for whom vaginal delivery was not recommended and those who went into spontaneous labour were excluded. The choice between induction of labour and caesarean section was made by the obstetrician and the patient, taking into account both the patient's personal medical history and the clinical observations on admission to hospital. The primary outcome was the rate of vaginal delivery. RESULTS: Out of 966 women with no contraindication to trial of labour after previous caesarean delivery (TOLAC), 248 were induced, with a vaginal delivery rate of 58.5% (95% CI [52.06; 64.67]). This rate was 81.7% (67/82) among women with Bishop ≥6 and 47% (78/166) if Bishop was <6. Eight cases of uterine rupture were reported in the induction of labour group. Regarding maternal morbidity, this was the main difference between the caesarean section and the induction of labour groups (p=0.049). Neonatal morbidity was low in both groups. CONCLUSIONS: The rate of vaginal delivery after induction of labour with oxytocin infusion was satisfactory. Nevertheless, maternal morbidity and especially the risk of uterine rupture were not minor. It is thus essential before inducing labour to inform the woman about the rate of success of TOLAC and the risks of uterine rupture.

A generic method for improving the spatial interoperability of medical and ecological databases.

BACKGROUND: The availability of big data in healthcare and the intensive development of data reuse and georeferencing have opened up perspectives for health spatial analysis. However, fine-scale spatial studies of ecological and medical databases are limited by the change of support problem and thus a lack of spatial unit interoperability. The use of spatial disaggregation methods to solve this problem introduces errors into the spatial estimations. Here, we present a generic, two-step method for merging medical and ecological databases that avoids the use of spatial disaggregation methods, while maximizing the spatial resolution. METHODS: Firstly, a mapping table is created after one or more transition matrices have been defined. The latter link the spatial units of the original databases to the spatial units of the final database. Secondly, the mapping table is validated by (1) comparing the covariates contained in the two original databases, and (2) checking the spatial validity with a spatial continuity criterion and a spatial resolution index. RESULTS: We used our novel method to merge a medical database (the French national diagnosis-related group database, containing 5644 spatial units) with an ecological database (produced by the French National Institute of Statistics and Economic Studies, and containing with 36,594 spatial units). The mapping table yielded 5632 final spatial units. The mapping table's validity was evaluated by comparing the number of births in the medical database and the ecological databases in each final spatial unit. The median [interquartile range] relative difference was 2.3% [0; 5.7]. The spatial continuity criterion was low (2.4%), and the spatial resolution index was greater than for most French administrative areas. CONCLUSIONS: Our innovative approach improves interoperability between medical and ecological databases and facilitates fine-scale spatial analyses. We have shown that disaggregation models and large aggregation techniques are not necessarily the best ways to tackle the change of support problem.