Thesis (MSc. (Statistics)) -- University of Limpopo, 2022 / Under-Five Mortality (U5M) remains a major health challenge in most sub Saharan African countries including SouthAfrica,despite the significant progress made in child survival and the government’s efforts and commitment to reduce U5M. The failure of achieving the fourth Millennium Development Goal (MDG) by 2015 has led to an implementation of Sustainable Development Goal 3 (SDG3) which aims to have no more than 25 deaths per 1000 live births by 2030. To achieve this goal, more information is needed. Hence, the purpose of this study was to apply count models to identify the determinants of underfive mortality rate in South Africa. To identify these determinants, the study reviewed generalized linear models and utilised the 2016 South African Demographic and Health Survey data. The models studied were Logistic Regression (LR), Poisson Regression (PR) and Negative Binomial Regression (NBR). The findings revealed that baby postnatal check-up, child’s health prior discharge, child birth size, toilet facility, maternal education, province, residence and water source were significantly associated with U5M in South Africa. It was further concluded that children who are at high risk of dying before the age of five are those who did not attend their postnatal check-up within the first two months, those whose health was not checked prior discharge, whose birth size was very small, whose household utilised bucket toilets, who resided in Western Cape, North West and Mpumalanga province, who resided in urban areas as well as those whose household utilized piped, tube well and spring water as source of drinking water. / Department of Science and Technology(DST) National Research Foundation Centre of excellence in mathematical and statistical sciences(CoE-NasS) of South Africa.

Thesis (Ph.D. (Statistics)) -- University of Limpopo, 2022 / This thesis is based on application of frailty and copula models to under five child mortality data set in South Africa. The main purpose of the study was to apply sample splitting techniques in a survival analysis setting and compare clustered survival models considering left truncation to the under five child mortality data set in South Africa. The major contributions of this thesis is in the application of the shared frailty model and a class of Archimedean copulas in particular, Clayton-Oakes copula with completely monotone generator, and introduction of sample splitting techniques in a survival analysis setting. The findings based on shared frailty model show that clustering effect was sig nificant for modelling the determinants of time to death of under five children, and revealed the importance of accounting for clustering effect. The conclusion based on Clayton-Oakes model showed association between survival times of children from the same mother. It was found that the parameter estimates for the shared frailty and the Clayton-Oakes models were quite different and that the two models cannot be comparable. Gender, province, year, birth order and whether a child is part of twin or not were found to be significant factors affect ing under five child mortality in South Africa. / NRF-TDG Flemish Interuniversity Council Institutional corporation (VLIR-IUC) VLIR-IUC Programme of the University of Limpopo

The study and practice of epidemiology and public health benefit from the use of mortality statistics, such as mortality rates, which are frequently used as key health indicators. Furthermore, multiple causes of death (MCOD) data offer important information that could not possibly be gathered from other mortality data. This study aimed to describe the interrelationships between various causes of death in the United States in order to improve the understanding of the coexistence of MCOD and thereby improve public health and enhance longevity. The social support theory was used as a framework, and multivariate linear regression analyses were conducted to examine the coexistence of MCOD in approximately 80 million death cases across the United States from 1959 to 2005. The findings showed that in the United States, there is a statistically significant relationship between the number of coexisting MCOD, race, education, and the state of residence. Furthermore, age, gender, and marital status statistically influence the average number of coexisting MCOD. The results offer insights into how the number of coexisting MCOD vary across the United States, races, education levels, gender, age, and marital status and lay a foundation for further investigation into what people are dying from. The results have the long-term potential of helping public health practitioners identify individuals or communities that are at higher risks of death from a number of coexisting MCOD such that actions could be taken to lower the risks to improve people's wellbeing, enhance longevity, and contribute to positive social change.

Doctorat en sciences sociales, politiques et économiques / info:eu-repo/semantics/nonPublished