Application of machine learning methods for predicting under-five mortality: analysis of Nigerian demographic health survey 2018 dataset.

Background: Under-five mortality remains a significant public health issue in developing countries. This study aimed to assess the effectiveness of various machine learning algorithms in predicting under-five mortality in Nigeria and identify the most relevant predictors.

Methods: The study used nationally representative data from the 2018 Nigeria Demographic and Health Survey.

Impact of ambient air pollution exposure during pregnancy on adverse birth outcomes: generalized structural equation modeling approach.

Background: Air pollution and several prenatal factors, such as socio-demographic, behavioural, physical activity and clinical factors influence adverse birth outcomes. The study aimed to investigate the impact of ambient air pollution exposure during pregnancy adjusting prenatal risk factors on adverse birth outcomes among pregnant women in MACE birth cohort.

Methods: Data for the study was obtained from the Mother and Child in the Environment (MACE) birth cohort study in Durban, South Africa from 2013 to 2017.

The spatial modification effect of predictors on household level food insecurity in Ethiopia.

Household food insecurity remains highly prevalent in developing countries (including in Ethiopia) and it has been recognized as a serious public health problem. Several factors such as demographic, economic, social, and clinical factors influence household food insecurity, and these vary geographically. In this work, we investigate the geographical modification of the effect of several factors on chronic food insecurity.

Spatiotemporal Variation and Predictors of Unsuppressed Viral Load among HIV-Positive Men and Women in Rural and Peri-Urban KwaZulu-Natal, South Africa.

Unsuppressed HIV viral load is an important marker of sustained HIV transmission. We investigated the prevalence, predictors, and high-risk areas of unsuppressed HIV viral load among HIV-positive men and women. Unsuppressed HIV viral load was defined as viral load of ≥400 copies/mL.

Identifying Potential Factors Associated with High HIV viral load in KwaZulu-Natal, South Africa using Multiple Correspondence Analysis and Random Forest Analysis.

Background: Sustainable Human Immunodeficiency Virus (HIV) virological suppression is crucial to achieving the Joint United Nations Programme of HIV/AIDS (UNAIDS) 95-95-95 treatment targets to reduce the risk of onward HIV transmission. Exploratory data analysis is an integral part of statistical analysis which aids variable selection from complex survey data for further confirmatory analysis.

Methods: In this study, we divulge participants' epidemiological and biological factors with high HIV RNA viral load (HHVL) from an HIV Incidence Provincial Surveillance System (HIPSS) sequential cross-sectional survey between 2014 and 2015 KwaZulu-Natal, South Africa.

Decomposing the urban-rural inequalities in the utilisation of maternal health care services: evidence from 27 selected countries in Sub-Saharan Africa.

Background: There has been a substantial improvement in reducing maternal mortality in the Sub-Saharan African region. The vast rural-urban gap in maternal health outcomes, however, is obscured by this average achievement. This study attempts to measure the contribution of identified risk factors to describe the average rural-urban difference in the use of antenatal care, health facilities for delivery, and health professional assistance at delivery.

Association of medical male circumcision and sexually transmitted infections in a population-based study using targeted maximum likelihood estimation.

Background: Epidemiological theory and many empirical studies support the hypothesis that there is a protective effect of male circumcision against some sexually transmitted infections (STIs). However, there is a paucity of randomized control trials (RCTs) to test this hypothesis in the South African population. Due to the infeasibility of conducting RCTs, estimating marginal or average treatment effects with observational data increases interest.

Modelling children ever born using performance evaluation metrics: A dataset.

Predicting the number of total children ever born in a country is a key component for proper implementation of economic growth policy. Here, performance metrics were used to predict models that appropriately describe the factors that affect children ever born. A comparison of 60% training and 40% validation, 70% training and 30% validation, 80% training and 20% validation also 90% training and 10% validation was performed respectively to examine the three models' behaviours (Poisson regression, Negative Binomial regression and Generalized Poisson regression) with RMSE, R, MAE and MSE as performance metrics.

Effects of prenatal exposure factors on birth outcomes through mediation of favorable fetal growth conditions using structural equation modeling.

Background: Birth weight, birth length, and gestational age are major indicators of newborn health. Several prenatal exposure factors influence the fetal environment. The aim of the study was to investigate the effect of prenatal exposure factors, including socio-demographic, behavioural, dietary, physical activity, clinical and environmental on birth outcomes through the mediation of Favourable Fetal Growth Conditions (FFGC).

The impact of antenatal care on under-five mortality in Ethiopia: a difference-in-differences analysis.

Background: Sub-Saharan Africa, as opposed to other regions, has the highest under-five mortality rates yet makes the least improvement in reducing under-five mortality. Despite the decline, Ethiopia is among the top ten countries contributing the most to global under-five mortalities. This article examines the impact of the number of antenatal care and the timing of first antenatal care on child health outcomes.

Multilevel ordinal model for CD4 count trends in seroconversion among South Africa women.

Background: Ordinal health longitudinal response variables have distributions that make them unsuitable for many popular statistical models that assume normality. We present a multilevel growth model that may be more suitable for medical ordinal longitudinal outcomes than are statistical models that assume normality and continuous measurements.

Methods: The data is from an ongoing prospective cohort study conducted amongst adult women who are HIV-infected patients in Kwazulu-Natal, South Africa.

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model.

Background: HIV infected patients may experience many intermediate events including between-event transition throughout their follow up. Through modelling these transitions, we can gain a deeper understanding of HIV disease process and progression and of factors that influence the disease process and progression pathway. In this work, we present transition-specific parametric multi-state models to describe HIV disease process and progression.

The differential effect of maternal dietary patterns on quantiles of Birthweight.

Background: Maternal dietary habits during pregnancy are considered essential for development and growth of the fetus as well as maternal health. It has an effect on the birthweight of infants. However, little is known about the effect of dietary patterns on birthweight in urban South Africa.

Exploratory Data Analysis of Adverse Birth Outcomes and Exposure to Oxides of Nitrogen Using Interactive Parallel Coordinates Plot Technique.

We propose that a parallel coordinates plot can be used to study multidimensional data particularly to explore discovery of patterns across the variables. This can assist researchers from the health sciences to visualize their cohort data with interactive data analysis. The study used data from Mother and Child in the Environment birth cohort in Durban, South Africa for the period 2013 to 2017 retrospectively registered.

Modeling Viral Suppression, Viral Rebound and State-Specific Duration of HIV Patients with CD4 Count Adjustment: Parametric Multistate Frailty Model Approach.

Introduction: Combination antiretroviral therapy has become the standard care of human immunodeficiency virus (HIV)-infected patients and has further led to a dramatically decreased progression probability to acquired immune deficiency syndrome (AIDS) for patients under such a therapy. However, responses of the patients to this therapy have recorded heterogeneous complexity and high dynamism. In this paper, we simultaneously model long-term viral suppression, viral rebound, and state-specific duration of HIV-infected patients.

The spatial modification of the non-linear effects of ambient oxides of nitrogen during pregnancy on birthweight in a South African birth cohort.

Birthweight is strongly associated with infant mortality and is a major determinant of infant survival. Several factors such as maternal, environmental, clinical, and social factors influence birthweight, and these vary geographically, including across low, middle, and economically advanced countries. The aim of the study was to investigate the geographical modification of the effect of oxides of nitrogen exposure on birthweight adjusted for clinical and socio-demographic factors.

Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model.

Background: CD4 cell and viral load count are highly correlated surrogate markers of human immunodeficiency virus (HIV) disease progression. In modelling the progression of HIV, previous studies mostly dealt with either CD4 cell counts or viral load alone. In this work, both biomarkers are in included one model, in order to study possible factors that affect the intensities of immune deterioration, immune recovery and state-specific duration of HIV-infected women.

Modeling Differential Effects of Maternal Dietary Patterns across Severity Levels of Preterm Birth Using a Partial Proportional Odds Model.

Preterm birth is a common cause of death worldwide of children under the age of five years. This condition is linked with short and long term neonatal morbidity and mortality. Maternal nutrition during pregnancy has a profound effect on fetal growth and development and subsequently also on the incidence of preterm birth.

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model.

Background: Patients infected with HIV may experience a succession of clinical stages before the disease diagnosis and their health status may be followed-up by tracking disease biomarkers. In this study, we present a joint multistate model for predicting the clinical progression of HIV infection which takes into account the viral load and CD4 count biomarkers.

Methods: The data is from an ongoing prospective cohort study conducted among antiretroviral treatment (ART) naïve HIV-infected women in the province of KwaZulu-Natal, South Africa.

Multivariate multilevel modeling of quality of life dynamics of HIV infected patients.

Background: Longitudinal quality of life (QoL) is an important outcome in many chronic illness studies aiming to evaluate the efficiency of care both at the patient and health system level. Although many QoL studies involve multiple correlated hierarchical outcome measures, very few of them use multivariate modeling. In this work, we modeled the long-term dynamics of QoL scores accounting for the correlation between the QoL scores in a multilevel multivariate framework and to compare the effects of covariates across the outcomes.

Trends and Adaptive Optimal Set Points of CD4 Count Clinical Covariates at Each Phase of the HIV Disease Progression.

In response to invasion by the human immunodeficiency virus (HIV), the self-regulatory immune system attempts to restore the CD4 count fluctuations. Consequently, many clinical covariates are bound to adapt too, but little is known about their corresponding new optimal set points. It has been reported that there exist few strongest clinical covariates of the CD4 count.

Covariate random effects on the CD4 count variation during HIV disease progression in women.

To investigate the variation in CD4 count between HIV positive patients due to clinical covariates at each phase of the HIV disease progression. The Centre for the AIDS Programme of Research in South Africa (CAPRISA) conducted different studies in which female patients were initially enrolled in HIV negative cohorts (phase 1). Seroconverts were further followed-up weekly to fortnightly visits up to 3 months (phase 2: acute infection), monthly visits from 3 to 12 months (phase 3: early infection), quarterly visits thereafter (phase 4: established infection) until antiretroviral therapy (ART) initiation (phase 5).

An Evaluation to Determine the Strongest CD4 Count Covariates during HIV Disease Progression in Women in South Africa.

Introduction: Past endeavours to deal with the obstacle of expensive Cluster of Difference 4 (CD4) count diagnostics in resource-limited settings have left a long trail of suggested continuous CD4 count clinical covariates that turned out to be a potentially important integral part of the human immunodeficiency virus (HIV) treatment process during disease progression. However, an evaluation to determine the strongest candidates among these CD4 count covariates has not been well documented.

Methods: The Centre for the AIDS Programme of Research in South Africa (CAPRISA) initially enrolled HIV-negative (phase 1) patients into different study cohorts.

Model development including interactions with multiple imputed data.

Background: Multiple imputation is a reliable tool to deal with missing data and is becoming increasingly popular in biostatistics. However, building a model with interactions that are not specified a priori, in the presence of missing data, presents a challenge. On the one hand, the interactions are needed to impute the data, while on the other hand, the data is needed to identify the interactions.

Factors affecting first-month adherence to antiretroviral therapy among HIV-positive adults in South Africa.

This study explores the influence of baseline factors on first-month adherence to highly active antiretroviral therapy (HAART) among adults. The study design involved a review of routinely collected patient information in the CAPRISA AIDS Treatment (CAT) programme, at a rural and an urban clinic in KwaZulu-Natal Province, South Africa. The records of 688 patients enrolled in the CAT programme between June 2004 and September 2006 were analysed.