Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a nationally representative survey.

BACKGROUND: Caesarean delivery has a significant role in reducing maternal and child death. However, unnecessary utilization has adverse health effects. This study aimed to assess the prevalence and associated factors of caesarean delivery in Bangladesh. METHODS: Data from the latest Bangladesh Multiple Indicator Cluster Survey (MICS, 2019) was used in this study. Since MICS data are hierarchical in nature, multilevel modelling was used. RESULTS: The prevalence of caesarean section (CS) was 67.4% among Bangladeshi women. Multilevel analysis suggests the age of the women, household wealth status, utilization of antenatal care (ANC) , delivery at a health facility and division were significantly associated with CS. Women who delivered in a private health facility had the highest odds for CS (odds ratio [OR] 10.35 [95% confidence interval {CI} 8.55 to 12.54]). Women 30-34 y of age had a 36% higher likelihood of CS compared with women 15-19 y of age (OR 1.36 [95% CI 1.03 to 1.79]). The odds of CS positively increased with household wealth status. Women who had at least one ANC visit had a 1.7 times higher possibility of CS (OR 1.70 [95% CI 1.26 to 2.30]). CONCLUSIONS: Policy guidelines on caesarean deliveries are urgently needed in Bangladesh to avoid unnecessary caesarean deliveries and protect mothers from the consequences.

Fuel Prediction and Reduction in Public Transportation by Sensor Monitoring and Bayesian Networks.

We exploit the use of a controller area network (CAN-bus) to monitor sensors on the buses of local public transportation in a big European city. The aim is to advise fleet managers and policymakers on how to reduce fuel consumption so that air pollution is controlled and public services are improved. We deploy heuristic algorithms and exhaustive ones to generate Bayesian networks among the monitored variables. The aim is to describe the relevant relationships between the variables, to discover and confirm the possible cause-effect relationships, to predict the fuel consumption dependent on the contextual conditions of traffic, and to enable an intervention analysis to be conducted on the variables so that our goals are achieved. We propose a validation technique using Bayesian networks based on Granger causality: it relies upon observations of the time series formed by successive values of the variables in time. We use the same method based on Granger causality to rank the Bayesian networks obtained as well. A comparison of the Bayesian networks discovered against the ground truth is proposed in a synthetic data set, specifically generated for this study: the results confirm the validity of the Bayesian networks that agree on most of the existing relationships.