AbstractIntroduction: Recent ecologic data from WHO Department of Reproductive Health and Research including HRP show that when caesarean section rates inceases to 10% across a population (e.g. a country), the number of maternal and newborn deaths decrease. When the rate goes above 10%, there is no evidence that mortality rates improve. Due to differences in the cases and obstetric profile of mothers however, it is often difficult to determine an appropriate rate of caesarean section for individual health facilities. According to WHO, caesarean section rate is an auditable global standard for assessing, monitoring and comparing adequacy and appropriateness of and among healthcare facilities over time. In 2011, a systematic review of available classifications for CS concluded that the Robson classification (also called the 10-group classification) would be the best of its kind to audit caesarean sections. Since the system can be applied prospectively and its categories are totally inclusive and mutually exclusive, every woman that is admitted for delivery can be immediately classified based on these few basic characteristics, which are routinely collected worldwide in obstetric wards. A new mathematical model has now been launched to address this issue. Known as the C-Model, and developed by WHO RHR / HRP and partners, the tool is able to estimate the expected caesarean section rate in health facilities according to the characteristics of the population that they serve. The tool works as a calculator which can help obstetric teams, health system managers, health facilities, researchers and governments to produce a customized reference for the rate of caesarean sections. This data can therefore help people worldwide working across sectors to assess the use and / or overuse of caesarean sections in specific contexts. Objectives: To combine WHO C-Model and Robson group of classification in auditing the caesarean sections at a tertiary centre. Design: Retrospective cohort study. Setting: Father Muller Medical College Participants: All mothers delivered in Father Muller Medical College Hospital Labour room between May 2017 to July 2017. Method: All women were classified according to the Robson’s classification within which caesarean section rate was assessed. Then based on C-Model, probability of caesarean section was calculated for each group and compared with the existing caesarean section rates. Results: There were significant differences in the sizes of the groups of women and the incidences of events and outcomes within the Robson’s Classification. The largest group in the study belonged to Robson’s group 1( Primi with spontaneous onset of labor) ) . The largest contributor to caesarean section rates was by Group 5 (previous caesarean section). Highest caesarean section rate was found in groups 5 to 9. When compared to C-Model groups 1, 3 and 5 had 50-60% caesarean section rates higher while the other groups were lower than probability. Discussion: Robson’s group of classification and C-Model combination for auditing helps in auditing the caesarean section rate more specific to population. If we classify only according to Robson’s classification, we have to compare with international standards which may differ from local demography. For example, as per the study in Mangalore, Robson’s group 10 is significant contributor to Caesarean rates where as contribution by this group to overall caesarean section is less in other countries. Hence use of C-Model will give probability more specific to local demographics and will help in assessing the caesarean section rate more specific to local population. It will have all benefits of Robson classification with added benefit of demography based comparable standards. Conclusions: By using Robson’s classification, the group with highest contribution to caesarean section rate as well as with highest caesarean section within the group can be identified and compared with international standards. Using C-Model, the probability for each group can be calculated and the group with significant deviation can be concentrated on for reducing the caesarean section rates.