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Masters Degrees (Statistics)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7127

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Browsing Masters Degrees (Statistics) by Author "Bovu, Andisiwe."

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    Statistical models to determine factors affecting under-five child mortality in South Africa.
    (2020) Bovu, Andisiwe.; Melesse, Sileshi Fanta.
    The level of under-five child mortality is an important indicator of economic, social and health development of the nation. In the last two decades, substantial progress has been made in improving under-five child mortality globally, with deaths dropping among children under the age of five years from approximately 12 million in 1990 to about 6.3 million in 2015. However, significant strides to address the key risk factors are still needed in the Sub-Saharan Africa region if they are to achieve the Sustainable Development Goals 2030. The key objective of the study is to identify key factors associated with mortality of children under the age of five years in South Africa. In order to identify these factors, the study used different statistical models that accommodate a binary response variable. Models used include Logistic Regression, Survey Logistic Regression, Generalized Linear Mixed Models and Generalized Additive Models. Although logistic regression is useful in modelling data with a dichotomous outcome, it is not suitable for modelling data obtained through a complex survey that incorporates weights, stratification and clustering. Survey logistic regression is used to model the relationship between binary dependent and the set of explanatory variables by making use of the sampling design information. In this case, the inclusion of random effects in the model results in generalized linear mixed models (GLMM). These models are an extension of linear mixed models that allow response variable from different distributions, such as binary responses. One can think of GLMM as an extension of generalized linear models (e.g. logistic regression) that combine both features of fixed and random effects. These statistical models assume linearity parametric form for the explanatory variable. However, this assumption of linear independence of response on covariates may not hold. Hence, we introduce generalized additive models (GAM). The GAM models show some non-linear relationship between the response variable and some covariates. The results showed that, the size of child at birth, breastfeeding, birth order number, ethnicity, number of children 5 under, total children ever born, source of drinking water and province were significantly associated with under-five child mortality. The study concludes that prolonged breastfeeding, improved health services and source of water are among the main factors to decline under-five child mortality further. Therefore, the study suggests that there is a need to strengthen child health interventions in South Africa to reduce the under-five mortality rate even more in order to achieve sustainable development goals (SDG) 2030.