A complex survey data analysis of TB and HIV mortality in South Africa.
| dc.contributor.advisor | Thomas, Achia. | |
| dc.contributor.advisor | Mwambi, Henry G. | |
| dc.contributor.author | Murorunkwere, Joie Lea. | |
| dc.date.accessioned | 2013-06-11T08:57:01Z | |
| dc.date.available | 2013-06-11T08:57:01Z | |
| dc.date.created | 2012 | |
| dc.date.issued | 2012 | |
| dc.description | Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2012. | en |
| dc.description.abstract | Many countries in the world record annual summary statistics such as economic indicators like Gross Domestic Product (GDP) and vital statistics for example the number of births and deaths. In this thesis we focus on mortality data from various causes including Tuberculosis (TB) and HIV. TB is an infectious disease caused by bacteria called Mycobacterium tuberculosis. It is the main cause of death in the world among all infectious diseases. An additional complexity is that HIV/AIDS acts as a catalyst to the occurrence of TB. Vaidyanathan and Singh revealed that people infected with mycobacterium tuberculosis alone have an approximately 10% life time risk of developing active TB, compared to 60% or more in persons co-infected with HIV and mycobacterium tuberculosis. South Africa was ranked seventh highest by the World Health Organization among the 22 TB high burden countries in the world and fourth highest in Africa. The research work in this thesis uses the 2007 Statistics South Africa (STATSSA) data on TB and HIV as the primary cause of death to build statistical models that can be used to investigate factors associated with death due to TB. Logistic regression, Survey Logistic regression and generalized linear models (GLM) will be used to assess the effect of risk factors or predictors to the probability of deaths associated with TB and HIV. This study will be guided by a theoretical approach to understanding factors associated with TB and HIV deaths. Bayesian modeling using WINBUGS will be used to assess spatial modeling of relative risk and spatial prior distributions for disease mapping models. Of the 615312 deceased, 546917 (89%) died from natural death, 14179 (2%) were stillborn and 54216 (9%) from non-natural death possibly accidents, murder, suicide. Among those who died from natural death and disease, 65052 (12%) died of TB and 13718 (2%) died of HIV. The results of the analysis revealed risk factors associated with TB and HIV mortality. | en |
| dc.identifier.uri | http://hdl.handle.net/10413/9122 | |
| dc.language.iso | en_ZA | en |
| dc.subject | Tuberculosis--Mortality--South Africa. | en |
| dc.subject | HIV infections. | en |
| dc.subject | Tuberculosis--South Africa--Public opinion--Statistics. | en |
| dc.subject | Theses--Applied statistics. | en |
| dc.title | A complex survey data analysis of TB and HIV mortality in South Africa. | en |
| dc.type | Thesis | en |