Statistical models to study the BMI of under five children in Ethopia.

Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.Maternal and child malnutrition has long and short-term consequences on the health status of the people and on the country’s economy. It is among the major public health problems in Ethiopia. Worldwide, maternal and child malnutrition is an underlying cause for more than 3.5 million deaths each year. About 35% of the global disease burden is in under five children. Such a heavy burden requires an understanding of the nutritional status of the people, especially children under the age of five years and associated factors. Therefore, this study attempted to use possible statistical methods to estimate the effects of the risks related to the nutritional status of children. It also tried to identify the socio-economic and demographic factors that are associated with the BMI of under five children in Ethiopia. The study employed the 2016 Ethiopian Demographic and Health Survey data. A nationally representative sample of children under the age of five years was used to get information on weight and height measures of under five children. The BMI of children under five years of age was used as a response variable to fit weighted quantile regression. The covariates, age of a child, sex and other relevant socio-economic and demographic factors were used in the study. Following the quantile regression, the generalized linear models such as logistic regression model was applied after categorizing the response variable, BMI of under five children, into two categories namely normal and malnourished. Following binary logistic regression, an attempt to fit ordinal logistic regression was made. That means nutritional status was considered as ordinal outcome with four categories namely underweight, normal, overweight and obese. The findings and comparison of estimates using these different statistical methods with and without complex survey design were presented. The results revealed that methods that take into account the complex nature of the design, perform better than those that do not take this into account. It has also been found that age of a child, weight of child at birth, mother’s BMI, educational attainment of mother, region and wealth index were significantly associated with under five children’s nutritional status. Furthermore, the results are discussed and then a conclusion is made in the context of policy implication for Ethiopia.Refer to page i for two articles that were published from this thesis

Statistical modeling of acute HIV infection from a cohort of high-risk individuals in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.In this dissertation, longitudinal data modeling approaches to analyze data on CD4 cell counts measured repeatedly in HIV-infected patients enrolled in the Centre for the AIDS Programme of Research in South Africa are investigated. Longitudinal data, or repeated measurements data, is a specific form of multilevel data. In longitudinal studies, repeated observations are made on an individual on one or more outcomes, including covariates information at a baseline and over time. Mixedeffects models have become popular for modeling longitudinal data. This statistical procedure also permits the estimation of variability in hierarchically structured data and examines the impacts of factors at different levels. Since longitudinal studies are often faced with the incompleteness of the data due to partially observed subjects, the mixed-effects model is by its very nature able to deal with unbalanced data of this nature. Therefore, the study adopts the mixed-effects model and identifies whether specific clinical and sociodemographic factors present in the data influenced CD4 count in a cohort of HIV-infected patients. Since it is of great interest for a biomedical analyst or an investigator to correctly model the CD4 cell count or disease biomarkers of a patient in the presence of covariates or factors determining the disease progression over time, the Poisson regression approach, which explain variability in counts, is considered. The Poisson generalized mixed-effects models can be an appropriate choice for repeated count data. However, this model is not realistic because of the restriction that the mean and variance are equal. Therefore, the Poisson mixed-effects model is replaced by the negative binomial mixed-effects model. The later model effectively managed over-dispersion of the longitudinal data. We evaluate and compare the proposed models and their application to model CD4 cell counts of HIV-infected patients recruited in the study data set. The results reveal that the negative binomial mixedeffects model has appropriate properties and outperforms the Poisson mixed-effects model in terms of handling the over-dispersion of the data. Multiple imputation techniques are also used to handle missing values in the dataset to validate parameter estimates in modeling the negative binomial mixed-effects model by assuming a missing at random missingness. v To illustrate the full conditional distribution of the repeated outcome, a quantile mixed-effects model is employed. This gives greater inclusive statistical modeling than conventional ordinary mixed models. Quantile regression offers an invaluable tool to discern effects that would be missed by other conventional regression models, which are solely based on modeling conditional mean. The quantile regression model that assumes asymmetric Laplace distribution for the error term was applied to longitudinal CD4 count data. The exact maximum likelihood estimation of the covariate effects and variance-covariance elements in the quantile mixed-effects model was implemented using the Stochastic Approximation Expectation-Maximization algorithm. In the model, multiple random effects are also incorporated to consider the correlation among the observations. Thus, we obtain robust parameter estimates for various conditional distribution positions that communicate an inclusive and more complete picture of the effects. Furthermore, to get more insights into the functional relationship between the response variable and the covariates, the generalized additive mixed-effects models, such as the additive negative binomial mixed-effects model, a versatile model used to better understand and analyze complex nonlinear trajectories in an overdispersed longitudinal data, is applied. Following the additive negative binomial mixed-effects model, an attempt to fit additive quantile mixed-effects model, an efficient and flexible framework for nonparametric as well as parametric longitudinal forms of data analysis focused on features of the outcome beyond its central tendency, was made. The response variable at hand is a CD4 count of HIV-infected patients as a function of Highly Active Antiretroviral Therapy initiation and other relevant baseline characteristics of the patients. Thus, even though this is a biostatistics methodological dissertation research, some interesting clinical and sociodemographic findings are also discussed. Discussion and conclusion of the results from the proposed models with a suggestion of possible further research avenues completed the study

Factors affecting child malnutrition in Ethiopia

Background: One of the public health problems in developing countries is child malnutrition. An important factor for children’s well-being is good nutrition. Therefore, the malnutrition status of children under the age of five is an important outcome measure for children’s health. This study uses the proportional odds model to identify risk factors associated with child malnutrition in Ethiopia using the 2016 Ethiopian Demographic and Health Survey data.Methods: This study uses the 2016 Ethiopian Demographic and Health Survey results. Based on weight-for-height anthropometric index (Z-score) child nutrition status is categorized into four levels namely- underweight, normal, overweight and obese. Since this leads to an ordinal variable for nutrition status, an ordinal logistic regression (OLR)proportional odds model (POM) is an obvious choice for analysis.Results: The findings and comparison of results using the cumulative logit model with and without complex survey design are presented. The study results revealed that to produce the appropriate estimates and standard errors for data that were obtained from complex survey design, model fitting based on taking the survey sampling design into account is better. It has also been found that for children under the age of five, weight of a child at birth, mother’s age, mother’s Body Mass Index (BMI), marital status of mother and region (Affar, Dire Dawa, Gambela, Harari and Somali) were influential variables significantly associated with underfive children’s nutritional status in Ethiopia.Conclusion: This child’s age of a child, sex, weight of child at birth, mother’s BMI and region of residence were significant determinants of malnutrition of children under five years in Ethiopia. The effect of these determinants can be used to develop strategies for reducing child malnutrition in Ethiopia. Moreover, these findings show that OLR proportional odds model is appropriate assessing thedeterminants of malnutrition for ordinal nutritional status of underfive children in Ethiopia.Keywords: BMI, Ethiopian Demographic and Health Survey (EDHS), malnutrition, proportional odds model

Factors affecting child malnutrition in Ethiopia

Background: One of the public health problems in developing countries is child malnutrition. An important factor for children\u2019s well-being is good nutrition. Therefore, the malnutrition status of children under the age of five is an important outcome measure for children\u2019s health. This study uses the proportional odds model to identify risk factors associated with child malnutrition in Ethiopia using the 2016 Ethiopian Demographic and Health Survey data. Methods: This study uses the 2016 Ethiopian Demographic and Health Survey results. Based on weight-for-height anthropometric index (Z-score) child nutrition status is categorized into four levels namely- underweight, normal, overweight and obese. Since this leads to an ordinal variable for nutrition status, an ordinal logistic regression (OLR)proportional odds model (POM) is an obvious choice for analysis. Results: The findings and comparison of results using the cumulative logit model with and without complex survey design are presented. The study results revealed that to produce the appropriate estimates and standard errors for data that were obtained from complex survey design, model fitting based on taking the survey sampling design into account is better. It has also been found that for children under the age of five, weight of a child at birth, mother\u2019s age, mother\u2019s Body Mass Index (BMI), marital status of mother and region (Affar, Dire Dawa, Gambela, Harari and Somali) were influential variables significantly associated with underfive children\u2019s nutritional status in Ethiopia. Conclusion: This child\u2019s age of a child, sex, weight of child at birth, mother\u2019s BMI and region of residence were significant determinants of malnutrition of children under five years in Ethiopia. The effect of these determinants can be used to develop strategies for reducing child malnutrition in Ethiopia. Moreover, these findings show that OLR proportional odds model is appropriate assessing thedeterminants of malnutrition for ordinal nutritional status of underfive children in Ethiopia. DOI: https://dx.doi.org/10.4314/ahs.v19i2.13 Cite as: Yirga AA, Mwambi HG, Ayele DG, Melesse SF. Factors affecting child malnutrition in Ethiopia. Afri Health Sci.2019;19(2): 1897-1909. https://dx.doi.org/10.4314/ahs.v19i2.1