Measuring the Caesarean Risk Factors in Bangladesh by Using Binary Logistic Regression Model

Rahaman, Sabrina and Hossain, Md. Murad and Ankhi, Fahima Akter and Roy, Madhusudan (2019) Measuring the Caesarean Risk Factors in Bangladesh by Using Binary Logistic Regression Model. Asian Journal of Probability and Statistics, 3 (3). pp. 1-11. ISSN 2582-0230

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Abstract

Caesarean section (CS) has been on the rise worldwide and Bangladesh is no exception. In Bangladesh, the CS rate, which includes both institutional and community-based deliveries, has increased from about 3% in 2000 to about 24% in 2014. Rather than numerous impediments, cesarean conveyances are most basic among woman’s however it is not clinically advocated. For enhancing the maternal wellbeing status, it is basic to decide the risk components of cesarean conveyance. The primary focal point of this examination is to research and recognize the cesarean risk factors in the entire territory of Bangladesh. For this investigation, we have gathered auxiliary information from the Bangladesh Demographic and Health Survey (BDHS) 2014. This dataset has one record for every eligible woman as defined by the household schedule. It contains 17864 data which is collected in the woman's questionnaire plus some variables from the household. For the examination, a chi-square test was performed to identify the significant association between conveyance type (cesarean/non-cesarean) and socio-demographic and financial factor's individual. A binary logistic regression was completed to recognize the most effective factors on cesarean conveyance. We found that 5 factors (i.e respondent age, respondent highest education level, husband’s occupation, type of place of residence, wealth index) were measurably connected with conveyance type out of 13 chance elements. From this investigation, it is obvious to us that the above powerful factors may influences the mother's wellbeing status in Bangladesh.

Item Type: Article
Subjects: STM Digital Library > Mathematical Science
Depositing User: Unnamed user with email support@stmdigitallib.com
Date Deposited: 13 Apr 2023 06:14
Last Modified: 24 Jun 2024 04:35
URI: http://archive.scholarstm.com/id/eprint/869

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