Mapping the human immunodeficiency virus/acquired immunodeficiency syndrome epidemic in Namibia using Bayesian spatial modelling
| dc.contributor.author | Shikongo, Job | |
| dc.date.accessioned | 2022-06-29T10:52:53Z | |
| dc.date.available | 2022-06-29T10:52:53Z | |
| dc.date.issued | 2020 | |
| dc.description | A thesis submitted in fulfilment of the requirements for the degree of Master of Science (Biostatistics) | |
| dc.description.abstract | In order to develop an effective prevention response to the Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome (HIV/AIDS) epidemic in a particular area, a deeper understanding of the dynamics of the epidemic is required. Disease mapping is usually used to determine the spatial distribution of a disease prevalence and its risk factors in a particular area in order to device appropriate interventions. Although maps of HIV transmission are generally needed for planning, resources allocation, and monitoring and evaluation, such maps were currently not available in Namibia. This study developed a spatial model of HIV/AIDS epidemic in Namibia based on Bayesian methods (spatially unstructured and structured random effects) using the 2013 Namibia Demographic and Health Survey data. Furthermore, the study identified socio-economic demographic characteristics and sexual behavior that were associated with HIV/AIDS prevalence in Namibia. Specifically, spatial regression models were fitted using BayesX 3.0.2 to adjust for spatial random effects and non-random effects, and the Moran’s I statistic was calculated to test for the significance of autocorrelation between neighboring regions to show if they tend to cluster. The Moran's I statistic (0.120) was significant (p-value = 0.003) with a variance of 0.002 which stipulated that values that determine the strength of spatial dependence in neighboring regions tend to cluster. After adjusting for spatial random effects and non-random effects, results shows significant structured spatial effects with posterior mean ranging between (-0.423, 0.759) at regional level and (-0.687, 0.995) at constituency level. The socio-economic, demographic and cultural factors like non - condom use, wealth index (poor, middle, richer), marital status (living with partner) and gender (male) were significant in explaining the HIV prevalence in Namibia. Spatial clustering was observed in Khomas, Erongo and towards the regions in the northern parts of Namibia, namely Oshana and Ohangwena. The study recommends that the modelling of relative risk (as a function of spatial structure and spatial instructed random effects) in Namibia using Bayesian multi-scale models should be based on census data in order to identify definite spatial structures which would be exceedingly critical for both illustrative as well as policy implementation purposes. | en_US |
| dc.identifier.uri | http://hdl.handle.net/11070/3187 | |
| dc.language.iso | en | en_US |
| dc.publisher | University of Namibia | en_US |
| dc.subject | Human immunodeficiency virus | en_US |
| dc.subject | Acquired Immunodeficiency Syndrome | en_US |
| dc.subject | Socio-economic demographic | en_US |
| dc.subject | Spatial regression models | en_US |
| dc.title | Mapping the human immunodeficiency virus/acquired immunodeficiency syndrome epidemic in Namibia using Bayesian spatial modelling | en_US |
| dc.type | Thesis | en_US |