Doctoral Degrees (Statistics)

Permanent URI for this collectionhttps://hdl.handle.net/10413/7126

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Measuring poverty and child malnutrition with their determinants from household survey data.
(2016) Habyarimana, Faustin.; Zewotir, Temesgen Tenaw.; Ramroop, Shaun.
The eradication of poverty and malnutrition is the main objective of most societies and policy makers. But in most cases, developing a perfect or accurate poverty and malnutrition assessment tool to target the poor households and malnourished people is a challenge for applied policy research. The poverty of households and malnutrition of children under five years have been measured based to money metric and this approach has a number of problems especially in developing countries. Hence, in this study we developed an asset index from Demographic and Health Survey data as an alternative method to measure poverty of households and malnutrition and thereby examine different statistical methods that are suitable to identify the associated factors. Therefore, principal component analysis was used to create an asset index for each household which in turn served as response variable in case of poverty and explanatory (known as wealth quintile) variable in the case of malnutrition. In order to account for the complexity of sampling design and the ordering of outcome variable, a generalized linear mixed model approach was used to extend ordinal survey logistic regression to include random effects and therefore to account for the variability between the primary sampling units or villages. Further, a joint model was used to simultaneously measure the malnutrition on three anthropometric indicators and to examine the possible correlation between underweight, stunting and wasting. To account for spatial variability between the villages, we used spatial multivariate joint model under generalized linear mixed model. A quantile regression model was used in order to consider a complete picture of the relationship between the outcome variable (poverty index and weight-for-age index) and predictor variables to the desired quantiles. We have also used generalized additive mixed model (semiparametric) in order to relax the assumption of normality and linearity inherent in linear regression models, where categorical covariates were modeled by parametric model, continuous covariates and interaction between the continuous and categorical variables by nonparametric models. A composite index from three anthropometric indices was created and used to identify the association of poverty and malnutrition as well as the factors associated with them. Each of these models has inherent strengths and weaknesses. Then, the choice of one depends on what a research is trying to accomplish and the type of data being used. The findings from this study revealed that the level of education of household head, gender of household head, age of household head, size of the household, place of residence and the province are the key determinants of poverty of households in Rwanda. It also revealed that the determinants of malnutrition of children under five years in Rwanda are: child age, birth order of the child, gender of the child, birth weight of the child, fever, multiple birth, mother’s level of education, mother’s age at the birth, anemia, marital status of the mother, body mass index of the mother, mother’s knowledge on nutrition, wealth index of the family, source of drinking water and province. Further, this study revealed a positive association between poverty of household and malnutrition of children under five years.
Statistical modelling on childhood anaemia, malaria and stunting in Malawi, Lesotho, and Burundi.
(2023) Gaston, Rugiranka Tony.; Ramroop, Shaun.; Habyarimana, Faustin.
The current research aimed to produce and expand statistical models in the discipline of biostatistics with a focus on childhood anaemia, malaria, and stunting. Malaria, anaemia, and stunting together continue to be public health issues worldwide in both industrialised and underdeveloped countries, particularly in children younger than 5 years (Osazuwa and Ayo, 2010; Kanchana et al., 2018). Malaria, anaemia, and stunting are dangerous, mostly in children from underdeveloped nations and they still remain the biggest contributor to morbidity and mortality. In addition, anaemia, malaria, and stunting are associated, and if not treated on time can damage children’s emotional, physical, mental status and poor performance at school (Gaston et al., 2022). The current study evaluates the link between anaemia, stunting, and malaria simultaneously. Furthermore, the study assessed whether socioeconomic, geographical, environmental, and child demographic variables have a significant effect on childhood malaria, anaemia, and stunting. This study used a national secondary cross-sectional data from Malawi Malaria Indicator Survey (MMIS); Lesotho Demographic Health Survey (LDHS); and Burundi Demographic Health Survey (BDHS). The data was collected based on multi-stage sampling, stratified, and cluster sampling with an unequal chance of sampling. It is for this reason we first used the survey logistic regression model in Chapter 3, which accounted for the complexity of sampling design and heterogeneity between observations from the same cluster. However, this model includes only the fixed effect and does not have the option of adding the random effect to model the correlation between observations. We extend the model in Chapter 4, to a generalised mixed additive model (GAMM) to include the random effect. The GAMM is also an extension of the generalised linear mixed model (GLMM) and enables the parametric fixed effects from GLMM to be modelled as a non-parametric model using the additive smooth function. These models were applied to single response variables, and we wanted to evaluate the relationship which might exist between anaemia, stunting, and malaria. We then explore the multivariate joint model under GLMM in Chapter 5 to simultaneously joint either malaria and anaemia or anaemia and stunting. Finally, we introduce a structural equation model (SEM) in Chapter 6, to evaluate the complex interrelationships between socioeconomics, demographics, and environmental factors, as well as their direct or indirect relationship with childhood malaria, anaemia and stunting co-morbidity. The previous chapters could not address these interrelationships among the variables of interest. Each model used in this study has its weaknesses and strengths which can depend on the goal of the xii researcher. However, the multivariate model under GLMM and the structural equation model were found to be more adaptive and attractive to researchers interested in innovative scientific research. The findings from this study revealed that the child’s nutrition status, age, the child with fever, diarrhoea, altitude, place of residence, toilet facility, access to electricity, children who slept under a mosquito bed net the night before the survey, mother's education level, and mother’s body mass index have a significant effect on both childhood anaemia and malaria. The age of a child, the mother’s educational status, place of residence, wealth index, and child weight at birth were the determinants of stunting or malnutrition. The findings also indicated that the geographical, geophysical, environmental, household and child demographic factors were statistically significant and have either a direct or an indirect effect on childhood co-morbidity factors. The geographical factors were statistically significant and had a positive direct effect on childhood malaria, anaemia, and stunting. The estimated indirect path for the impact of geophysical factors on childhood co-morbidity factors, as mediated by household factors was statistically significant and positive. However, the estimated indirect paths for the effect of geophysical factors on childhood co-morbidity factors, as mediated by environmental factors were statistically significant but negative. The child demographic factors revealed a direct statistically significant impact on childhood co-morbidity factors. Furthermore, the estimated indirect path effect on childhood comorbidity as mediated effect on household factors was statistically significant and negative. Moreover, household and environmental factors indicate a positive direct effect on childhood co-morbidity anaemia, malaria, and stunting. Finally, the results of this study revealed a positive relationship between stunting, anaemia, and malaria. This means that malaria, anaemia, and stunting increase or decrease in the same direction. Hence, controlling one or two between malaria, anaemia, and stunting can reduce the effect of other(s), which can assist the policymakers and government in the allocation of financial resources to fight against childhood comorbidity anaemia, malaria, and stunting. Furthermore, understanding the link between anaemia, malaria, and stunting other factors associated with them will assist in focusing on those areas and go a long way toward achieving the United Nations Sustainable Development Goals (SDGs3), known as the complete elimination of under-5 mortality by 2030.
Statistical study on childhood malnutrition and anaemia in Angola, Malawi and Senegal.
(2023) Khulu, Mthobisi Christian.; Ramroop, Shaun.; Habyarimana, Faustin.
Malnutrition and anaemia continue to be a concern to the future of developing countries. This thesis aimed to examine the risk factors associated with malnutrition and anaemia among under five-year-old children in Angola, Malawi and Senegal. Statistical models and techniques have improved over the years to give more insight into malnutrition and anaemia, in terms of demographic, socio-economic, environmental, and geographic factors. This thesis also assessed the spatial epidemiological overlaps between childhood malnutrition and anaemia diseases which can lead to various advantages in intervention planning, monitoring, controlling and total elimination of such diseases, especially in high-risk regions. This is a secondary data analysis where national representative data from the three countries was used. The Demographic and Health Survey data from Angola, Malawi and Senegal were merged to create a pooled sample which was then used for all the analyses conducted in this study. The relationship between exploratory variables to malnutrition and anaemia was assessed to obtain variables that explain the two outcomes. Consequently, a generalized linear mixed model was used to investigate the significance of the child-level, community-level and household-level factors to malnutrition and anaemia separately. The relationship between the two diseases was further examined using the three joint modelling approaches: (1) a joint generalised linear mixed model; (2) a structural equation model, and (3) a bivariate copula geo-additive model. For each model employed, the significant factors of both malnutrition and anaemia were identified. The GLMM results on malnutrition revealed that children’s place of residence, age, gender, mother’s level of schooling, wealth status, birth interval and birth order significantly explain malnutrition at the 5% level of significance. Whereas, the GLMM results on anaemia revealed that children‘s age, gender, mother’s level of schooling, wealth status and nutritional status significantly explain anaemia at 5% level of significance. The findings of copula geo-additive modelling of malnutrition and anaemia indicated that there is an association between malnutrition and anaemia. There was a strong association observed between malnutrition and anaemia in the north-west districts of Angola when compared to other districts. The results imply that the policymakers of Angola, Senegal and Malawi can control anaemia through the intervention of malnutrition controlling. The overall findings of this study provide meaningful insight to the policymakers of Angola, Malawi and Senegal which will lead to the implementation of interventions that can assist in achieving the Sustainable Development Goal (SDG) of 25 deaths per 1 000 live births by 2030. To properly eradicate all the causes of malnutrition and anaemia, programs such as parental education, financial education, children's dietary focus programs and mobile health facilities could add a significant value. The results also highlighted the national priority areas related to child-related factors, household factors and environmental factors for childhood malnutrition and anaemia morbidity control. It also provided policy makers with valuable geographical information for developing and implementing effective intervention. There is a greater need for partnership and collaboration among the studied countries to achieve the SGD target.

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Browsing Doctoral Degrees (Statistics) by Author "Habyarimana, Faustin."