Related Experiment Video
Updated: Oct 2, 2025

An Experimental Paradigm for the Prediction of Post-Operative Pain PPOP
Published on: January 27, 2010
Multilevel analysis to identify the factors associated with caesarean section in Bangladesh: evidence from a
Md Sabbir Ahmed1, Mansura Islam2, Ishrat Jahan3
1Department of Community Health and Hygiene, Faculty of Nutrition and Food Science, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh.
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.
Related Concept Videos
Statistical Methods for Analyzing Epidemiological Data
Factorial Design
Cross-Sectional Research
Mechanistic Models: Compartment Models in Individual and Population Analysis
Comparing the Survival Analysis of Two or More Groups
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...

