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Abstract : A Smooth Test of Goodness-of-Fit for the Weibull Distribution: An Application to an HIV Retention Data
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Abstract: In this study, we fit the two-parameter Weibull distribution to an HIV retention data and assess the fit using a smooth test of goodness-of-fit. The smooth test described here is a score test and is derived as an extension of the Neyman’s smooth test. Simulations are conducted to compare the power of the smooth test with the power of each of three empirical goodness-of-fit tests for the Weibull distribution. Results show that the smooth tests of order three and four are more powerful than the three empirical goodness-of-fit tests. For validation, we used retention data from an HIV care setting in Kenya. Keywords: Goodness-of-fit, Loss to follow-up, Neyman’s smooth test, Retention in HIV care, Weibull distribution. |
Abstract : Improving the Efficiency of Outpatient Services at Benue State University Teaching Hospital using the Queuing Theory
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Abstract: Introduction: Long client waiting time is a characteristic of poor performance of the health care delivery and is a major challenge for healthcare services all over the world, especially in developing countries. The study was aimed at developing a model that optimizes performance of the general outpatient department of the Benue State University Teaching Hospital, Makurdi, Benue State Nigeria. Methodology: Data was collected through observation and interviews with doctors at the general outpatient clinic of the Benue State University Teaching Hospital. The average number of clients seen per day was calculated by determining the average of daily attendants for five consecutive working days. The data obtained was used to create a five capacity scenarios using the queuing theory software. Result: The Average Daily Attendance (ADA) was 73.2 clients while the Average Daily Arrival Rate was 10.47 clients per hour. There were six doctors working on any given day in the clinic and a doctor spends an average of 16.2 minutes per patient, representing an average of 3.7 patients per hour. The model showed that the optimum system performance can be achieved with four doctors (with 70.7% server utilization rate, average of 1.065 clients on the queue and 0.102 hours waiting time). Conclusion: Four doctors working at the same time at the general outpatient clinic is required for optimal performance. The queuing theory should be used regularly at GOPD BSUTH and in all health facilities experiencing long queues to optimize operational efficiency. Keywords: Waiting time, Client satisfaction, server, performance, Makurdi. |
Abstract : Predictors of High Blood Pressure in South African Children: Quantile Regression Approach
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Abstract: Objective: To identify predictors of blood pressure (BP) in children and explore the predictors` effects on the conditional quantile functions of systolic blood pressure and diastolic blood pressure. Methods: A secondary data analysis was performed using data from the South African National Income Dynamics Study (2014-2015). From this particular secondary data, data for children aged between 10-17 years were extracted for analysis. The variables used in the study were systolic blood pressure (SBP), diastolic blood pressure (DBP), body mass index (BMI), age, smoking, alcohol consumption, exercises, gender and race. Two parameter estimation methods were used, ordinary least squares (OLS) and quantile regression (QR). Results: BMI had positive statistically significant estimated OLS and conditional quantile functions with both the BP measures except the 95th quantile for SBP. Age had also positive statistically significant estimated OLS and QR coefficients except for the 95th percentile, with both DBP and SBP respectively. Gender was found to be inversely related to both DBP and SBP except the 10th quantile for DBP. Race was partially significant to DBP. Smoking, alcohol consumption and exercises did not present any statistically significant relations with both DBP and SBP for all the estimated OLS and QR coefficients. Conclusion: BMI, age, gender and partially race were found to be predictors of BP in South African children using both OLS and QR techniques. Exercises, smoking and alcohol consumption did not present any statistically significant relations with both DBP and SBP probably because few participants exercise regularly, smoke and drink alcohol to bring out a significant change in both BP measurements. Keywords: Body Mass Index (BMI), Diastolic Blood Pressure (DBP), Systolic Blood Pressure (SBP), Ordinary Least Squares Regression (OLS), Quantile Regression (QR). |
Abstract : Model Based Sparse Feature Extraction for Biomedical Signal Classification
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Abstract: This article focuses on model based sparse feature extraction of biomedical signals for classification problems, which stems from sparse representation in modern signal processing. In the presented work, a novel approach based on sparse principal component analysis (SPCA) is proposed to extract signal features. This method involves partitioning signals and utilizing SPCA to select only a limited number of signal segments in order to construct signal principal components during the training stage. For signal classification purposes, a set of regression models based on sparse principal components of the selected training signal segments is constructed. Within this approach, model residuals are estimated and used as signal features for classification. The applications of the proposed approach are demonstrated by using both the synthetic data and real EEG signals. The high classification accuracy results suggest that the proposed methods may be useful for automatic event detection using long-term observational signals. keywords: Sparse Principal Component Analysis, Sparse Feature Extraction, Signal Classification, Long-term Signals. Keywords: Sparse Principal Component Analysis, Sparse Representation, Signal Classification, Long-term Signals. |
Abstract : Bayesian Modelling of Tuberculosis Risk Factors in South Africa 2014
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Abstract: Background: Although the number of deaths has declined since 2007, Tuberculosis (TB) continues to be the number one cause of death in South Africa. To create a country free of TB, there is need for continued research to explore models that will provide the Department of Health with new interventions. Aim: This study was aimed at identifying the risk factors of active self-reported TB prevalence for South Africa in 2014. Methods: The Frequentist Logistic Regression (FLR) approach was applied on a sample of 19213 individuals taken from the National Income Dynamics Survey (NIDS) wave data. Bayesian analysis with non-informative priors were used to model Wave 1 to 3 data and elicitation of the obtained posterior density parameters by averaging done to obtain the informative priors used to model wave 4. The wave 4 results obtained under the two estimation approaches were compared as well as the results for non-informative and informative priors. Results: The findings show that self-reported TB prevalence is higher than the reported 1%, Human Immuno Deficiency Virus (HIV) remains a major threat to TB and Eastern Cape is the province mostly affected by TB with Limpopo recording the least prevalence. Poor living conditions and lower socio-economic conditions continue to be drivers of TB whilst English illiteracy, lack of Secondary/Tertiary education, alcohol consumption, marital status, gender and age groups also influence TB progression to disease. FLR yielded similar results to Bayesian with non-informative priors whilst the results are more precise for informative priors. Conclusion: This study identified individuals and communities at risk of developing active TB disease in South Africa. Keywords: Tuberculosis, HIV, South Africa, National Income Dynamics Survey, Bayesian analysis, Frequentist Logistic Regression. |