Browsing by Author "Roberts, Danielle Jade."

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Co-morbidity of childhood anaemia and malaria with a district-level spatial effect.
(2021) Roberts, Danielle Jade.; Zewotir, Temesgen Tenaw.
Anaemia and malaria are the leading causes of sub-Saharan African childhood morbidity and mortality. This thesis aimed to explore the risk factors as well as the complex relationship between anaemia and malaria in young children across the districts or counties of four contiguous sub-Saharan African countries, namely Kenya, Malawi, Tanzania and Uganda. Nationally representative data from the Demographic and Health Surveys conducted in all four countries was used. The observed prevalence of anaemia and malaria was 52.5% and 19.7%, respectively, with a 15.1% prevalence of co-infection. Machine learning based exploratory classification methods were used to gain insight into the relationships and patterns among the explanatory variables and the two responses. The administrative districts are the level at which public health decisions are made within each of the countries. Accordingly, the best linear unbiased predictor (BLUP) ranking and selection approach was adopted to investigate the district-level spatial effects, while controlling for child-level, household-level and environmental factors. Further to the geoadditive model, a generalised additive mixed model with a spatial effect based on the geographical coordinates of the sampled clusters within the districts was applied. The relationship between the two diseases was further explored using joint modelling approaches: a bivariate copula geoadditive model and shared component model. The child’s age, mother’s education level, household wealth index and cluster altitude were found to be significantly associated with both the anaemia and malaria status of the child. The results of this study can help policy makers target the correct set of interventions or prevent the use of incorrect interventions for anaemia and malaria control and prevention. This aids in the targeted allocation of limited district health system resources within each of these countries.
Factors associated with teenage pregnancy in Malawi.
(2020) Gumede, Sandile Innocent.; Roberts, Danielle Jade.; Desai, Arusha.
Teenage pregnancy is a challenge that society at large is faced with. This challenge is experienced primarily in developing countries, where an estimated 21 million girls aged between 15 and 19 years old become pregnant, with approximately 12 million giving birth in 2020. In 2018, the estimated average adolescent birth rate globally was 44 births per 1000 girls aged 15 to 19 years old. However, this rate in Malawi is significantly higher at 141. There are high health, social and economic costs of teenage pregnancy, and childbearing can lead to short and long term adverse consequences for the teen parents, the child and the community. Teenage pregnancies are more likely to occur in marginalized communities, commonly driven by poverty and a lack of education and employment opportunities. This study aimed at investigating the factors associated with pregnancy among young sexually active girls between the ages of 15 and 19 years old in Malawi. The study made use of data from a nationally representative survey, which resulted in an observed prevalence of pregnancy of 57.7% among the sexually active teenagers. Three statistical approaches were applied, namely a survey logistic regression model, a generalised linear mixed model and a spatial generalised linear mixed model. These approaches accounted for the complex survey design that was implemented during the data collection.The findings of the study outlined that age, the event of hearing of family planning on the radio, union type, socio-economic status, contraceptive use, and education level, among others, had a significant association with teenage pregnancy in Malawi. Such insight into the factors associated with and contributi ing to teenage pregnancy in Malawi can help all stakeholders develop policies and interventions that will address this challenge.
Joint modelling of child poverty and malnutrition in children aged 6 to 59 months in Malawi.
(2019) Dube, Lindani.; Roberts, Danielle Jade.
The objective of this study was to identify risk factors associated with poverty and malnutrition of children among the ages 6-to-59 months in the country of Malawi, making use of the joint model. By joint modelling, we refer to simultaneously analysing two or more response variables emanating from the same individual. Using the 2015/2016 Malawi Demographic and Health Survey, we jointly examine the relationship that exists between poverty and malnutrition of children among 6-to-59 months in Malawi. Jointly modelling these two outcome variables is appropriate since it is expected that people that live under poverty would have a poor nutrition system, and if a child is malnourished, the likelihood that they come from a poor family is greatly enhanced. Jointly modelling correlated outcomes can improve the efficiency of parameter estimates compared to fitting separate models for each outcome, as joint models have better control over type I error rates in multiple tests. A generalized linear mixed model (GLMM) was adopted and a Bayesian approach was used for parameter estimation. The potential risk factors considered in this study comprised of the childs age in months, gender of child, birth weight, birth order, mothers education level, head of household sex, language, household smoking habit, anaemic level, type of residence (urban or rural), region, toilet facility, source of drinking water, and multiple births. Each response was modelled separately as well as jointly and the results compared. The R package MCMCglmm was used in the analyses. The joint model revealed a positive association between malnutrition of children and poverty in the household.
Modelling depression in South Africa.
(2020) Ghoor, Tahzeeb.; Roberts, Danielle Jade.; Lougue, Siaka.
Depression is considered to be the leading cause of disability worldwide, with approximately 350 million individuals, of all ages, affected. The mental disorder is predominant in females and poverty is associated with an increased prevalence. The 12-month prevalence in South Africa is approximately 16.5%, with a lifetime prevalence of common mental disorders among adults of 38% (World Health Organization (WHO), 2017). In order to assist individuals in dealing with depression, it is important for such individuals to be identified at an early stage in order to provide them with the necessary support before their depression becomes unmanageable, thus putting them at risk for self-inflicted harm. The objective of this study was to investigate the prevalence and risk determinants of depression among South African individuals between the ages 15 to 49 years old and to determine which factors contribute the most to this mental illness. This study made use of data from the 2016 South African General Household Survey which was carried out using a multistage cluster sampling technique. The sample was not spread geographically in proportion to the population, but rather equally across the enumeration areas. The response variable of interest was binary, indicating whether an individual considered himself/herself depressed or not. Three statistical approaches were applied. The first was the survey logistic regression model which is a design-based approach. In this approach, parameter estimates and inferences were based on the sampling weights, and only inferences concerning the effects of certain covariates on the response variable were of interest. The second was a generalized linear mixed model which is a model-based approach. In this approach, interest was also on estimating and accounting for the proportion of variation in the response variable that was attributable to each of the multiple levels of sampling. This approach also accounted for possible correlations in the data where individuals in the same household or cluster tend to be more alike than those from other households or clusters. Lastly, a Bayesian network was applied to model the conditional dependence among the variables. This approach is a type of probabilistic graphical model that uses Bayesian inference for calculations of the probabilities. i The results indicated that substance abuse, the person’s perceived health status and gender were significantly associated with depression. Each of the three techniques were then used to classify the depression status of the individuals, and their performances in this classification were compared. The purpose of being able to classify an individual’s depression status, based on their individual and household factors, is to be able to identify a depressed individual in order to target them for intervention. The generalized linear mixed model proved to be the better performing technique in terms of classification. Thus, we recommend that when using data based on a complex survey design, this technique is considered in classifying the occurrence of an event of interest.
Prevalence and risk factors of malaria in children under the age of five years old in Uganda.
(2015) Roberts, Danielle Jade.; Matthews, Glenda Beverley.
Malaria is considered to be one of the main global health problems, with it causing close to a million deaths each year. Ninety percent of these deaths occur in Sub- Saharan Africa and 70% are of children under the age of 5 years. Uganda, ranked 6th worldwide in the number of malaria cases and 3rd in the number of malaria deaths in 2008, experiences weather conditions that often allow malaria transmission to occur all year round with only a few areas that experience low or unstable transmission. Malaria is the leading cause of morbidity in Uganda with 95% of the population at risk and it killing between 70,000 and 100,000 children every year. Children under the age of five years are among the most vulnerable to malaria infection as they have not yet developed any immunity to the disease. In order to apply successful implementations to eradicate malaria, there is a continuous need to understand the epidemiology and risk factors associated with the disease. Although a large number of studies done worldwide have identified a wide variety of risk factors; socioeconomic, environmental, demographic, and others, associated with malaria infection, there is still a great need to identify the influence of these factors in a local context to allow a successful formulation of a national malaria-control strategy. There have, however, been very few studies done in Uganda on malaria indicators and risk factors. These studies have also been specific to one community at a time. Most recent studies on malaria in Uganda have been hospital-based, investigating clinical malaria among young children and pregnant women. One of the aims of this thesis was to identify significant socio-economic, demographic and environmental risk factors associated with malaria infection, based on the result of a microscopy test conducted on 3,972 children under the age of five during a nationally represented Malaria Indicator Survey (MIS) done in Uganda in 2009. The MIS sample was stratified according to 10 regions of Uganda and was not spread geographically in proportion to the population, but rather equally across the regions. The survey consisted of a two-stage sample design where the first stage involved selecting clusters, with probability proportional to size, from a list of enumeration areas. The second stage involved systematic sampling of households from a list of households in each cluster. Surveys carried out using these sampling techniques are referred to as having complex survey designs. The response variable of interest is binary, indicating whether a child tested positive or negative for malaria. Logistic regression is commonly used to explore the relationship between a binary response variable and a set of explanatory variables. However, this method of analysis is not valid if the data come from complex survey designs. Failure to account for the complex design of a study may result in an overestimation of standard errors, therefore leading to incorrect results. There are many methods of dealing with this design of the study. Two such commonly used approaches are design-based and model-based statistical methods. A designed-based method, which involves the extension of logistic regression to complex survey designs, is survey logistic regression. For design-based methods, parameter estimates and inferences are based on the sampling weights, and only inferences concerning the effects of certain covariates on the response variable are of interest. However, model-based methods are used when interest is also on estimating the proportion of variation in the response variable that is attributable to each of the multiple levels of sampling. In this case, inference on the variance components of the model may also be of interest. Such methods include generalized linear mixed models and generalized estimating equations. This thesis discusses these three methods of analyzing complex survey designs and compares the results of each applied to the MIS data.
Risk factors and classification of diabetes in South Africa.
(2019) Grundlingh, Nina.; Zewotir, Temesgen Tenaw.; Roberts, Danielle Jade.
Diabetes prevalence has been seen to be on the increase in recent years, globally and in South Africa. The number of people with diabetes globally has risen from 108 million in 1980 to 442 million in 2014. It was estimated that, of the 1.8 million people between 20 and 79 years old with diabetes in South Africa in 2017, 84.8% were undiagnosed. Diabetes was the 2nd leading underlying cause of death in South Africa in 2016. Identifying risk factors for diabetes will assist in raising public awareness and assist public authorities to develop prevention programs. This study aimed to investigate the prevalence and risk factors associated with diabetes in the South African population aged 15 years and older, as well as explore various statistical methods of classifying a person’s diabetic status. This study made use of the South African Demographic Health Survey 2016 data which involved a two-stage sampling design. The study participants included 6442 individuals aged 15 years and older. Of the individuals sampled, 11%, 67% and 22% were found to be non-diabetic, pre-diabetic and diabetic, respectively. Classification methods, namely, a decision tree, random forest and Bayesian neural network, were used to assess classification of diabetic status based on the risk factors. Of the classification methods, the Bayesian neural network gave the highest accuracy (75.9%). These methods however, failed to account for the complex survey design and sampling weights. In addition, these methods are not able to provide the estimated effect that a risk factor has on the diabetic status. Regression models were employed to identify the significant risk factors. Due to the ordinal nature of diabetic status, initially the proportional odds model was fit. However, the proportional odds assumption was found to be violated. A multinomial generalized linear mixed model was fitted to account for the complexity of the design. However, the model’s residuals were found to be spatially autocorrelated. Accordingly, a spatial generalized additive mixed model, which accounts for the complexity of the survey structure as well as incorporates nonlinear spatial effects, was adopted. The highest accuracy from the regression models considered was obtained from this adjusted surface correlation model (accuracy = 70.8%). Individuals of the Black/African race were more likely to be diabetic (OR = 1.429; 95% CI: 1.032-1.978) than other races. Individuals taking high blood pressure medication were 1.444 times more likely to be diabetic than pre-diabetic (95% CI: 1.167-1.786) compared to those not taking high blood pressure medication.
Statistical analysis of the school attendance rate among under 20 South African learners.
(2020) Chabalala, Thabang Goodman.; Roberts, Danielle Jade.; Zewotir, Temesgen Tenaw.
School attendance is very crucial for the growth and development of the mindset of a child. The development of the mindset and provision of training to learners is an investment of a better future for the country. The government even made school attendance compulsory because of the fruits it bears in the future. But in the past, many studies have reflected a problem with school attendance and mostly the financial constrains appearing as the hindrance towards school attendance. Which is why the government has taken the initiative to make school attendance free for those who doesn’t afford to pay for it. This has reduced a greater number of individuals who had a wish to attend school but with no funds to pay for it and allowed an opportunity for those who need it. But still the country is experiencing individuals who are in school going age but not attending school. Some of these individuals are enrolled for school but choose not to attend. This brings many questions now about the factors affecting school attendance of learners. Which brings us to the aim of this study which is to identify factors affecting school attendance of learners at the basic education level. In identification of these factors, the study made use of different statistical mod- els which accommodate the binary response. The models used in the study include Correspondence Analysis(CA), Survey Logistic Regression(SLR), Generalized Lin- ear Mixed Model(GLMM) and Generalized Additive Mixed Model(GAMM). The results suggest that the likelihood of school non-attendance is associated with Northern Cape and Western Cape which are mostly dominated by Coloured/Indian/Asian race groups sharing ”Other” relationship to household head and have no parents presence. Moreover, the female learners with mothers not alive and coming from families with salaries and pension/grant as source of income are less likely to attend school. While learners coming from all other provinces except the two specified above, African/Black by race, sharing child/grandchild relationship to household head, have both parents alive, deviating from household with high wealth index z-score and have total income above R25000 are more likely to attend school. This is a clear indication that the initiatives which were applied by the government and results of the past studies have assisted in improving school attendance, but still more initiatives are needed to cover the areas which are still reflecting poor school attendance in order to meet the aims of the Millennium Development Goals.
Statistical models to analyse a baseline survey on rural KwaZulu-Natal adults’ HIV prevalence and associated risk factors.
(2020) Moodley, Kameshan.; Zewotir, Temesgen Tenaw.; Roberts, Danielle Jade.
South Africa is at the global epicentre of the HIV-AIDS pandemic. Though there has been an increase in prevention and control measures that has led to a significant reduction in HIV-AIDS mortality rates globally, South Africa has experienced a high share of the HIV burden. HIV-AIDS imposes a substantial economic burden on both individuals and governments. It has had a considerable effect on poverty by affecting potentially economically active citizens who would otherwise have entered the workforce and contributed to the local and national economy. This has hindered economic growth and development in South Africa. The 2016 UNAIDS Gap Report estimates that in 2015 there were seven million people living with HIV in South Africa and that this resulted in 180,000 AIDS related deaths in the same year. The same year saw an unprecedented 380,000 new reported infections. The prevalence of HIV-AIDS in South Africa remains high at 19.2% among the general population. This study was an investigation into the determinants of HIV in adults in the age group 15-49 years. The study used the HIV Incidence Provincial Surveillance System (HIPSS) to collect data between June 2014 and June 2015. The final data set comprised 9,804 observations and consisted of explanatory variables pertaining to individuals’ socio-economic, socio-demographic and behavioural circumstances. The response variable was binary indicating whether a participant tested positive or negative for HIV. Incorporating survey weights into the data owing to the complex sample design, necessitated the use multilevel regression procedures. To this end, survey logistic regression and the generalised linear mixed models were employed. The results emanating from these models revealed that factors encompassing socioi economic, demographic and selected behavioural characteristics were significantly associated with HIV prevalence in the study location. In some instances, it is possible that households in close proximity exhibit some similarities with the inevitable result of spatial autocorrelation requiring the use of geographically weighted regression techniques able to account for spatial autocorrelation. The application of a spatial multilevel model showed that the influence between households in close proximity is greater than between those further away, a phenomenon that would be ignored in conventional multilevel models.