Page 11 - 4-Windhoek (11 April 2024)(SESSION 1) Graduation e-BOOK

School of Science
FACULTY OF AGRICULTURE,
ENGINEERING & NATURAL SCIENCES

DOCTOR OF PHILOSOPHY IN SCIENCE

CANDIDATE: MBONGO Laina

CANDIDATE’S DISSERTATION
STATISTICAL MODELLING OF THE ASSOCIATION
BETWEEN DIETARY DIVERSITY, DIETARY PATTERNS AND
NON-COMMUNICABLE DISEASES IN NAMIBIA.

The doctoral study was undertaken and completed under the
supervision of Prof. Lawrence Kazembe (University of Namibia (UNAM),
Department of Computing, Mathematics and Statistical Sciences)
as the Main Supervisor and Prof. Lillian Pazvakawambwa (University
of Namibia (UNAM), Department of Computing, Mathematics and
Statistical Sciences) as the Co-Supervisor.
The main objective of this study was to explore the linkages between
human dietary patterns, dietary diversity, and the prevalence of non-
communicable diseases.
The study focused on dietary diversity by using different count models. The
household dietary diversity score presented a mean score of 6.5, suggesting
a moderate diverse diet, with less consumption of food made from beans/
lentils; eggs; fruits/vegetables and more consumption of starch food.
Determinants for household dietary diversity included educational level,
sex of head of household and main source of income (p-value <0.005).
The study further used bivariate modelling approaches to analyze the
food consumption patterns. The results found that, whereas the monthly
consumption of food was more on the non-convenience foods, the
purchases of convenience was frequent on a weekly basis and in multiple
food sources. Moreover, the study employed copula joint modelling of
food security indicators. The findings show that AIC of the untruncated
(conditional/marginal) Poisson regression model was lower and thus
proved to fit the data better. The Frank Copula and Bivariate Normal
Copula best fitted the data of establishing the relationship between HFIP
and HDDS, and between HFIP and MIHFP respectively. Lastly, the study
analyzed multiple indicators-multiple causes examining the relationship
between foods consumed and non-communicable disease. Principal
Component Analysis (PCA) and Structural Equation Models (SEM) were
used as data reduction methods to derive dietary patterns. Fruits, foods
such as condiments/tea/coffee and potatoes, yams, cassava, or any
foods made from roots and tubers accounted for majority of the variation.
The study recommends strengthened advocacy for consumption
of healthy and diverse diets in the country to slow down and arrest
proliferation of non-communicable diseases.

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