Flexible statistical modelling of the determinants of childhood anaemia in Tanzania and Angola.
| dc.contributor.advisor | Ramroop, Shaun. | |
| dc.contributor.advisor | Mwambi, Henry Godwell. | |
| dc.contributor.author | Ndlangamandla, Qondeni. | |
| dc.date.accessioned | 2022-10-31T08:38:07Z | |
| dc.date.available | 2022-10-31T08:38:07Z | |
| dc.date.created | 2020 | |
| dc.date.issued | 2020 | |
| dc.description | Masters Degree. University of KwaZulu-Natal, Pietermaritzburg. | en_US |
| dc.description.abstract | Anaemia is one of the major causes of morbidity and mortality in children aged five or less in Africa, affecting 25% of the world’s population. In developing countries, it accounts for more than 89% of the disease burden. Although anaemia affects all population groups, the more vulnerable groups are children under five years of age and women of reproductive age (15–49 years) compared to any other age group. According to the World Health Organization’s 2008 report, 50% of anaemia cases in Africa were associated with insufficient consumption of iron (iron deficiency anaemia). This study aims to determine the factors associated with childhood anaemia in Tanzania and Angola. For us to serve our aim, the Tanzania Demographics and Health Survey (TDHS) and the Angola Demographics and Health Survey (ADHS) data sets were fitted to several statistical models that could robustly model the response variable, anaemia, which is binary. Survey Logistic Regression (SLR), which is under the class of Generalized Linear Models (GLM), fits because of its robustness, not only in modelling dichotomous responses, but also in it ability to deal with data that assumes complex survey designs. The SLR model was extended by a Generalized additive mixed model (GAMM), which was fitted to relax the assumption of normality and to fit other terms non-parametrically. Furthermore, to cater for the effect of spatial effect and spatial variability, a Spatial Generalized linear mixed model (SGLMM) was fitted to the two data sets to help in the investigation of factors that are spatially related to childhood anaemia. The SLR and SGLMM models were fitted using the SAS software (PROC SURVEYLOGISTIC and PROC GLIMMIX, respectively), while the GAMM model was fitted using the statistical-R software. Moreover, smooth maps were produced for the outcome variable using ARCGIS software for the purpose of identifying the hot spots of childhood anaemia in the country. Our aim for this study was successfully achieved. After the three models were fitted into the two data sets, they revealed that the factors that were highly associated with childhood anaemia in both countries are: the highest level of education of caretakiers (mothers), child gender, age of the child and stunting status. The models also revealed that the standard of living in Tanzania has a significant effect in childhood anaemia | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/21042 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Survey Logistic Regression. | en_US |
| dc.subject.other | Spatial generalized linear mixed models. | en_US |
| dc.subject.other | Adjusted odds ratios. | en_US |
| dc.subject.other | Generalized additive mixed models. | en_US |
| dc.title | Flexible statistical modelling of the determinants of childhood anaemia in Tanzania and Angola. | en_US |
| dc.type | Thesis | en_US |