Browsing by Author "Mbongo, Laina Tulipomwene"

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    Food innsecurity and quality of life in informal settlements of Katutura, Windhoek, Namibia
    (University of Namibia, 2017) Mbongo, Laina Tulipomwene
    Globally, with rapid urbanization in most cities of the World, more people are now living in urban areas than in rural areas. The housing backlog coupled with a shortage of housing subsidies means that for many there is no alternative but to live in informal settlements. Urban food insecurity is often overlooked since at aggregate level, economic and social conditions in urban areas are much better than those in rural areas. The urban poor often live in neighborhoods with poor sanitation, high environmental pollution and therefore high and chronic exposure to health hazards. Such unhealthy living conditions aggravate food insecurity and quality of life of individuals. This study explored linkages between food insecurity and quality of life in informal settlements in Windhoek, Namibia and determine how socio economic factors mediate and explain both. A cross-sectional quantitative design through a two-stage cluster survey sampling was applied. The sample size of the study was 416 respondents selected from 40 Primary Sampling Units (PSU’s). The research instrument used in this study was the face to face administered questionnaire, distributed to 416 respondents. The study measured three (3) indicators of food security namely, Household Food Insecurity Access Scores (HFIAS), Household Dietary Diversity Score (HDDS) and Months of inadequate Household Food Provisioning (MIHFP). Lived Poverty Index (LPI) and World Health Organization Quality of Life (WHOQoL) instruments were used to measure quality of life. Bivariate analysis using cross-tabulations and chi-square tests, were also carried out to relate explanatory variables to food insecurity and quality of life. A binary logistic regression was used to relate quality of life status with food security, controlling for confounders like the bio-demographic and socio-economic variables. Ordinal logistic regression model was used to investigate the effect of household characteristics to food insecurity and quality of life. This study found that food insecure households in informal settlements are associated with poor quality of life. The study found that food insecure households in the informal settlements as measured by the HFIAP is 63%. Food insecure households have average levels of intake in any foods made from beans, peas, lentils, or nuts (54.1%); eggs (47.3%); potatoes, yams, manioc, cassava or any other foods made from roots or tubers (49.5%); vegetables (54.3%) and fruits (48.8%). Households in the informal settlements had less food in the months of August, November and January. Low level of education, large family size, female headed household and low/lack of income were associated with increased food insecurity. The four domains of quality of life were also assessed and it was found that the highest mean score was observed for domain 2 (Psychological health, mean= 63.8508) and domain 1 (Physical health, mean= 63.8018). The lowest mean score was observed for Domain 4 (Environmental health, mean= 37.1803). Furthermore, it was found that there is a statistical significant relationship between food insecure households and quality of life (Physical health, Psychological health, Social relationships and Environmental health, P< 0.005). It is concluded that poor quality of life is associated with high levels of food insecurity in the informal settlements of Katutura and thus a linkage between food insecurity and quality of life exists. Thus, to improve food insecurity, there is also a need to focus on advancing quality of life of individuals. Further dedicated quality of life and food insecurity research in informal settlements is required.
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    Statistical modelling of the association between dietary diversity, dietary patterns and non-communicable diseases in Namibia
    (University of Namibia, 2024) Mbongo, Laina Tulipomwene; Kazembe, Lawrence
    Globalization coupled with urbanization has placed a significant pressure on the food systems of many developing countries. This has led to lifestyle changes that have become one of the most important influences on dietary patterns. The nutritional transition has affected the dietary pattern and nutrient intake greatly and has led to a rise in the purchases and consumption of processed and convenience foods. Analysis in nutritional epidemiology typically examined diseases in relation to a single or a few nutrients or foods. However, people do not eat isolated nutrients. Instead, they eat meals consisting of a variety of foods with complex combinations of nutrients. The high degree of inter-correlation among nutrients as well as among foods makes it difficult to attribute effects to single dietary components. Dietary patterns can influence health and the risk of developing chronic conditions. Therefore, to gain full understanding of the relationship between diet and the development of non-communicable diseases (NCD), it is desirable to use several methodological approaches. The main objective of this study was to explore the linkages between dietary patterns, dietary diversity and prevalence of non-communicable diseases. Specifically, the study aimed at: (i) applying count models on dietary diversity in Namibia, (ii) using bivariate count modelling approach in analyzing convenience and non-convenience consumption food preference in Windhoek, (iii) applying copula joint modelling of food insecurity indicators with application to food insecurity prevalence (FIP), household dietary diversity score (HDDS) and months of inadequate household food provisioning (MIHFP), (iv) fitting multiple indicators-multiple-cause modelling to examine the relationship between foods consumed and non-communicable diseases. The analysis used two representative survey data, namely the AFSUN-HCP Household Food Security Baseline Survey (2016) and Namibian Household and Income Expenditure (NHIES) of 2015/2016. 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 iii modelling approaches to analyze the food consumption patterns. The results found that, whereas the consumption of food monthly 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, we 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 concluded that the usage of appropriate methods for specific data types is very critical. Generalized Poisson Regression models through the usage copula approaches are best to analyze jointly two outcomes in order to test for significant relationships between high-level hierarchical effects (e.g., random effects). Specifically, the bivariate normal and the Frank Copula were found to fit the data best. The unique nature of the bivariate normal model is that it does not allow for a different dependence structure between the outcomes while the frank copula does not have tail dependence and it can model both positive and negative dependencies as the normal copula. SEM and PCA’s were used as data reduction methods. Lastly, the study concludes that food and nutrition insecurity is a major threat to the development of the country and the study recommends for strengthened advocacy for consumption of healthy and diverse diets in the country in order to slow down and arrest proliferation of non-communicable diseases