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BackgroundCaesarean section rates continue to increase worldwide with uncertain medical consequences. Auditing and analysing caesarean section rates and other perinatal outcomes in a reliable and continuous manner is critical for understanding reasons caesarean section changes over time.MethodsWe analyzed data on 97,095 women delivering in 120 facilities in 8 countries, collected as part of the 2004-2005 Global Survey on Maternal and Perinatal Health in Latin America. The objective of this analysis was to test if the "10-group" or "Robson" classification could help identify which groups of women are contributing most to the high caesarean section rates in Latin America, and if it could provide information useful for health care providers in monitoring and planning effective actions to reduce these rates.ResultsThe overall rate of caesarean section was 35.4%. Women with single cephalic pregnancy at term without previous caesarean section who entered into labour spontaneously (groups 1 and 3) represented 60% of the total obstetric population. Although women with a term singleton cephalic pregnancy with a previous caesarean section (group 5) represented only 11.4% of the obstetric population, this group was the largest contributor to the overall caesarean section rate (26.7% of all the caesarean sections). The second and third largest contributors to the overall caesarean section rate were nulliparous women with single cephalic pregnancy at term either in spontaneous labour (group 1) or induced or delivered by caesarean section before labour (group 2), which were responsible for 18.3% and 15.3% of all caesarean deliveries, respectively.ConclusionThe 10-group classification could be easily applied to a multicountry dataset without problems of inconsistencies or misclassification. Specific groups of women were clearly identified as the main contributors to the overall caesarean section rate. This classification could help health care providers to plan practical and effective actions targeting specific groups of women to improve maternal and perinatal care.

Cluster-based studies involving aggregate units such as hospitals or medical practices are increasingly being used in healthcare evaluation. An important characteristic of such studies is the presence of intracluster correlation, typically quantified by the intracluster correlation coefficient (ICC). Sample size calculations for cluster-based studies need to account for the ICC, or risk underestimating the sample size required to yield the desired levels of power and significance. In this article, we present values for ICCs that were obtained from data on 97,095 pregnancies and 98,072 births taking place in a representative sample of 120 hospitals in eight Latin American countries. We present ICCs for 86 variables measured on mothers and newborns from pregnancy to the time of hospital discharge, including 'process variables' representing actual medical care received for each mother and newborn. Process variables are of primary interest in the field of implementation research. We found that overall, ICCs ranged from a minimum of 0.0003 to a maximum of 0.563 (median 0.067). For maternal and newborn outcome variables, the median ICCs were 0.011 (interquartile range 0.007-0.037) and 0.054 (interquartile range 0.013-0.075) respectively; however, for process variables, the median was 0.161 (interquartile range 0.072-0.328). Thus, we confirm previous findings that process variables tend to have higher ICCs than outcome variables. We demonstrate that ICCs generally tend to increase with higher prevalences (close to 0.5). These results can help researchers calculate the required sample size for future research studies in maternal and perinatal health.