Elective repeat cesarean delivery in women eligible for trial of labor in Brazil

OBJECTIVE

To assess the hospital, maternal, and obstetric characteristics associated with elective repeat cesarean delivery (ERCD) among women eligible for trial of labor after cesarean (TOLAC) delivery in Brazil.

METHODS

The present data were retrieved from the Birth in Brazil study, a national hospital-based cohort study conducted during 2011-2012. Data were collected from medical records and by interview. Univariate and hierarchical multiple logistic regression analyses were performed to analyze factors associated with ERCD among women with a previous cesarean delivery who were eligible for TOLAC.

RESULTS

Among 2295 women considered eligible for TOLAC, 1516 (66.1%) had an ERCD; the overall cesarean delivery rate was 79.4%. In the private sector, almost all deliveries (95.3%) were performed by ERCD. In the public sector, ERCD was associated with socioeconomic (more years of schooling), obstetric (women's preference, no previous vaginal delivery, macrosomia), and hospital (mixed hospital, location in noncapital city, fewer than 1500 deliveries per year) characteristics.

CONCLUSION

The ERCD rate in Brazil was high even in a low-risk group, indicating that nonclinical factors may be driving the decision for cesarean delivery. Efforts aiming to reduce cesarean deliveries in Brazil should target women with a previous cesarean delivery.

Risk groups in children under six months of age using self-organizing maps

Fetal and infant growth tends to follow irregular patterns and, particularly in developing countries, these patterns are greatly influenced by unfavorable living conditions and interactions with complications during pregnancy. The aim of this study was to identify groups of children with different risk profiles for growth development. The study sample comprised 496 girls and 508 boys under six months of age from 27 pediatric primary health care units in the city of Rio de Janeiro, Brazil. Data were obtained through interviews with the mothers and by reviewing each child's health card. An unsupervised learning, know as a self-organizing map (SOM) and a K-means algorithm were used for cluster analysis to identify groups of children. Four groups of infants were identified. The first (139) consisted of infants born exclusively by cesarean delivery, and their mothers were exclusively multiparous; the highest prevalences of prematurity and low birthweight, a high prevalence of exclusive breastfeeding and a low proportion of hospitalization were observed for this group. The second (247 infants) and the third (298 infants) groups had the best and worst perinatal and infant health indicators, respectively. The infants of the fourth group (318) were born heavier, had a low prevalence of exclusive breastfeeding, and had a higher rate of hospitalization. Using a SOM, it was possible to identify children with common features, although no differences between groups were found with respect to the adequacy of postnatal weight. Pregnant women and children with characteristics similar to those of group 3 require early intervention and more attention in public policy.