Assessment of malaria transmission in Kenya using multilevel logistic regression.

Background: Kenya has a lower malaria incidence in comparison to other African malaria-endemic nations. Malaria is a significant public health concern in the country. The malaria indicator survey (MIS) data were analyzed using the logistic regression model.

Application of quantile mixed-effects model in modeling CD4 count from HIV-infected patients in KwaZulu-Natal South Africa.

Background: The CD4 cell count signifies the health of an individual's immune system. The use of data-driven models enables clinicians to accurately interpret potential information, examine the progression of CD4 count, and deal with patient heterogeneity due to patient-specific effects. Quantile-based regression models can be used to illustrate the entire conditional distribution of an outcome and identify various covariates effects at the respective location.

Determinants of the type of health care sought for symptoms of Acute respiratory infection in children: analysis of Ghana demographic and health surveys.

Background: Globally, acute respiratory infection (ARI) is a leading cause of infant and childhood morbidity and mortality. Currently, it is estimated that 50 million cases of childhood ARI are untreated. In this study, we identified determinants of the type of treatment sought for symptoms of childhood acute respiratory infection (ARI), including non-treatment, amongst a nationally representative sample of children under five years in Ghana.

Additive quantile mixed effects modelling with application to longitudinal CD4 count data.

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. Quantile regression for mixed-effects models has become practical for longitudinal data analysis due to the recent computational advances and the ready availability of efficient linear programming algorithms. Recently, quantile regression has also been extended to additive mixed-effects models, providing 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.

Modelling CD4 counts before and after HAART for HIV infected patients in KwaZulu-Natal South Africa.

Background: This study aims to make use of a longitudinal data modelling approach to analyze data on the number of CD4+cell counts measured repeatedly in HIV-1 Subtype C infected women enrolled in the Acute Infection Study of the Centre for the AIDS Programme of Research in South Africa.

Methodology: This study uses data from the CAPRISA 002 Acute Infection Study, which was conducted in South Africa. This cohort study observed N=235 incident HIV-1 positive women whose disease biomarkers were measured repeatedly at least four times on each participant.

Determinants of Under-Five Children Body Mass Index in Sudan; Application of Quantile Regression: A Systematic Review.

Background: One of the health challenges in Sub-Saharan countries is child malnutrition. Body Mass Index (BMI) can be defined as a measure of nutritional status. Examining the determinants of under-five children's BMI is a significant subject that needs to be studied.

Negative binomial mixed models for analyzing longitudinal CD4 count data.

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 mixed-effects models (PMM) 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.

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.

Survival analysis of under-five mortality using Cox and frailty models in Ethiopia.

Background: The risk of a child dying before reaching 5 years of age is highest in sub-Saharan African countries. But in Ethiopia, under-five mortality rates have shown a substantial decline.

Methods: For this study, the Cox regression model for fixed and time-dependent explanatory variables was studied for under-five mortality in Ethiopia.

Comparison of under-five mortality for 2000, 2005 and 2011 surveys in Ethiopia.

Background: Though the socio-economic situation of the Ethiopian household is improving along with the decrease in under-five child mortality. But, under-five mortality is still one of the major problems. Identification of the risk factors change over time which mismatches with the diminishing rate of under-five mortality is important to address the problems.

Indirect child mortality estimation technique to identify trends of under-five mortality in Ethiopia.

Background: In sub-Saharan African countries, the chance of a child dying before the age of five years is high. The problem is similar in Ethiopia, but it shows a decrease over years.

Methods: The 2000; 2005 and 2011 Ethiopian Demographic and Health Survey results were used for this work.

Determinants of fertility in Ethiopia.

Background: The most important elements to determine the rate of population growth is fertility. Fertility is the main element to affect the welfare of mother. The survival of a child can be affected by high fertility and shorter birth intervals.

Structured additive regression models with spatial correlation to estimate under-five mortality risk factors in Ethiopia.

Background: The risk of a child dying before reaching five years of age is highest in Sub-Saharan African countries. But Child mortality rates have shown substantial decline in Ethiopia. It is important to identify factors affecting under-five mortality.

Using Rasch modeling to re-evaluate rapid malaria diagnosis test analyses.

The objective of this study was to demonstrate the use of the Rasch model by assessing the appropriateness of the demographic, social-economic and geographic factors in providing a total score in malaria RDT in accordance with the model's expectations. The baseline malaria indicator survey was conducted in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia by The Carter Center in 2007. The result shows high reliability and little disordering of thresholds with no evidence of differential item functioning.

Semiparametric models for malaria rapid diagnosis test result.

Background: More than 75% of the total of Ethiopia is malarious. Therefore, malaria is a leading public health problem in Ethiopia. This study aims to identify socio-economic, geographic and demographic factors contributing to the spread of malaria and is based on the results of a malaria Rapid Diagnosis Test survey.

Spatial distribution of malaria problem in three regions of Ethiopia.

Background: The transmission of malaria is the leading public health problem in Ethiopia. From the total area of Ethiopia, more than 75% is malarious. The aim of this study was to identify socio-economic, geographic and demographic risk factors of malaria based on the rapid diagnosis test (RDT) survey results and produce the prevalence map of the area illustrating variation in malaria risk.

Prevalence and risk factors of malaria in Ethiopia.

Background: More than 75% of the total area of Ethiopia is malarious, making malaria the leading public health problem in Ethiopia. The aim of this study was to investigate the prevalence rate and the associated socio-economic, geographic and demographic factors of malaria based on the rapid diagnosis test (RDT) survey results.

Methods: From December 2006 to January 2007, a baseline malaria indicator survey in Amhara, Oromiya and Southern Nation Nationalities and People (SNNP) regions of Ethiopia was conducted by The Carter Center.