Objective To generate a global reference for caesarean section (CS) rates at health facilities. Design Cross-sectional study. Setting Health facilities from 43 countries. Population/Sample Thirty eight thousand three hundred and twenty-four women giving birth from 22 countries for model building and 10 045 875 women giving birth from 43 countries for model testing. Methods We hypothesised that mathematical models could determine the relationship between clinical-obstetric characteristics and CS. These models generated probabilities of CS that could be compared with the observed CS rates. We devised a three-step approach to generate the global benchmark of CS rates at health facilities: creation of a multi-country reference population, building mathematical models, and testing these models. Main outcome measures Area under the ROC curves, diagnostic odds ratio, expected CS rate, observed CS rate. Results According to the different versions of the model, areas under the ROC curves suggested a good discriminatory capacity of C-Model, with summary estimates ranging from 0.832 to 0.844. The C-Model was able to generate expected CS rates adjusted for the case-mix of the obstetric population. We have also prepared an e-calculator to facilitate use of C-Model (www.who.int/reproductivehealth/publications/maternal-perinatal-health/c-model/en/). Conclusions This article describes the development of a global reference for CS rates. Based on maternal characteristics, this tool was able to generate an individualised expected CS rate for health facilities or groups of health facilities. With C-Model, obstetric teams, health system managers, health facilities, health insurance companies, and governments can produce a customised reference CS rate for assessing use (and overuse) of CS. Tweetable abstract The C-Model provides a customized benchmark for caesarean section rates in health facilities and systems.
|Number of pages||10|
|Journal||BJOG: An International Journal of Obstetrics and Gynaecology|
|Publication status||Published - 1 Feb 2016|
- caesarean delivery rates
- caesarean section rates
- logistic regression