Modeling Maternal Mortality in Ghana Using Hierarchical Models (2010-2013)
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Date
2015-06
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University of Ghana
Abstract
This thesis examined the appropriate model that best fit maternal mortality data in fifty six health facilities in the Central, Eastern, Greater Accra and Western regions of Ghana from 2010 to 2013 as well as factors contributing to this menace in the various health facilities. The study began with the application of a simple Poisson regression model which was extended to capture the hierarchical structure, overdispersion and zero-inflation. Expressions for the mean, variance and score equations were derived. All estimations were done in the maximum likelihood framework. In this thesis we show that all different extensions of the Poisson model is based on mixture of distributions. The overall best model showed that maternal mortality in the various health facilities in Ghana depends on the number of referrals (into and out of hospital facility), number of antenatal visits exceeding four, number of obstetric cases with HIV/AIDS, number of obstetric cases with malaria, number of medical doctors and number of midwives at the facility. We found that maternal mortality was significant in all four regions and for all years considered and that more pregnant women attend antenatal at clinics (Polyclinics, clinics, health centers and community health planning service centers) than in hospitals (general hospitals, regional hospitals and teaching hospitals) but more of them deliver in the hospitals than in the clinics. 2.3% and 12.9% of the pregnant women had HIV/AIDS and malaria respectively. Eclampsia and Pre-Eclampsia were the leading direct cause of maternal death while anaemia in pregnancy was second to a collection of other complications for the indirect cause. Finally, we estimated Ghana‟s maternal mortality ratio at 382 per 100000 live births.
Key words: Poisson regression model, hierarchical models, overdispersion, maternal mortality ratio, zero-inflated models.
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Thesis (MPhil.) - University of Ghana, 2015
Keywords
Hierarchical Models, Poisson Regression Model, Modeling, Zero-Inflated Models