Analyzing Overdispersed Skilled Antenatal Care Visits of Pregnant Women in Bangladesh Using Generalized Poisson Regression Model

Md Muddasir Hossain Akib, B. Pal
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Abstract

It is common to use the Poisson regression model (PRM) for analyzing the count data. The major limitation of this PRM is that the mean and variance of the response variable need to be equal. However, in the actual dataset, the response variable’s variance may exceed the mean, introducing overdispersion in the dataset. In this paper, the generalized Poisson regression model (GPRM) has been used in the modeling and analysis of the skilled antenatal care (ANC) count data extracted from the Bangladesh Demographic and Health Survey (BDHS) 2017-18. The findings of this study have revealed several socioeconomic and demographic variables that significantly impact the skilled ANC visits. Dhaka Univ. J. Sci. 71(1): 69-75, 2023 (Jan)
利用广义泊松回归模型分析孟加拉国孕妇过度分散的熟练产前护理就诊情况
通常使用泊松回归模型(PRM)来分析计数数据。该PRM的主要限制是响应变量的均值和方差需要相等。然而,在实际数据集中,响应变量的方差可能会超过平均值,从而在数据集中引入过分散。本文使用广义泊松回归模型(GPRM)对2017-18年孟加拉国人口与健康调查(BDHS)中提取的熟练产前护理(ANC)计数数据进行建模和分析。本研究的发现揭示了几个社会经济和人口统计学变量显著影响熟练的ANC访问。达卡大学学报(自然科学版),71(1):69- 75,2023 (1)
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