International Day Of Women in Statistics and Data Science: Full Schedule

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10:00 UTC

S208 - Optimal Model selection for incidence of Birth Asphyxia: NICU Centers in Greater Accra Region.

Tuesday October 6, 2026 10:00 - 10:30 UTC

Abstract
Birth asphyxia remains a major contributor to neonatal morbidity and mortality in low- and middle-income countries, particularly in sub-Saharan Africa. This study investigated the determinants of birth asphyxia among newborns using a Quasi-Poisson regression model to account for overdispersion in the count data. Secondary data comprising neonatal and maternal records were analysed using descriptive statistics, correlation analysis, and inferential modelling. The Quasi-Poisson model selected after diagnostic assessment confirmed overdispersion in the response variable, making it more appropriate than the standard Poisson model. The Quasi-Poisson regression model for predicting birth asphyxia is given as : Birth Asphyxia = 1.742 +0.385(Birth Weight) + 0.012(Mothers Age)− 0.088(Gestational Age) + 0.0401(Sex)+ 0.301(Mode of Delivery) + 0.158(Parity)+ 0.067(SURVIVE)
The results showed that birth weight, gestational age, mode of delivery, maternal age, parity, and fetal presentation were significant predictors of birth asphyxia. Specifically, lower birth weight and shorter gestational age were associated with a higher incidence of birth asphyxia, while caesarean delivery and abnormal fetal presentation increased the likelihood of adverse birth outcomes.
Keywords: Birth asphyxia, Quasi-Poisson regression, Gestational age, Neonatal outcomes, Maternal health, Ghana.
Authors:
1. Selina Dadzie (Mphil)
2. Irene Kafui Vorsah Amponsah (PhD)

Speakers

Selina Dadzie

Student

Bio
Miss Selina Dadzie is a graduate student in Statistics at the University of Cape Coast, Ghana, where she is pursuing a Master of Philosophy (MPhil) in Statistics. Her research focuses on “Optimal Model Selection for Incidence of Birth Asphyxia: NICU Centers in Accra”, with particular... Read More →

Organizers

Selina Dadzie

Student

Tuesday October 6, 2026 10:00 - 10:30 UTC
Zoom Room #2

Plenary Talk, Technical Statistical Session

Country Ghana
Session ID 208

International Day Of Women in Statistics and Data Science

10:00 UTC

Selina Dadzie

Selina Dadzie

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