Bayesian Modelling of Tuberculosis Risk Factors in South Africa 2014

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

Methodological approach of the spatial distribution of maternal mortality in Burkina Faso and explanatory factors associated

Philosophiae Doctor - PhDMaternal mortality is one of the most important problems related to the reproductive health. This is why the reduction by three quarters of maternal mortality by 2015 has been fixed as target No. 5 of the Millennium Development Goals (MDGs). Achieving this goal requires an annual decline of 5.5% of maternal mortality between 1990 and 2015. Unfortunately, the reduction as estimated in 1997 was less than 1% per year. Africa is the continent most affected by this problem. In 2010, the number of maternal mortality in the world was estimated to 287 000 and Africa was hosting more than 52 % (148 000) of the occurrence in the world In Burkina Faso, maternal mortality ratio decreased from 566 in 1991 to 484 in 1998 and 341 in 2010 according to the DHS data while the census estimate was 307 in 2006 and United Nation agencies provided the number of 300 maternal deaths per 100 000 live births in 2010. Statistics provided by the different sources vary considerably. This situation creates confusion among data users. In addition, researches made on the issue remain very insufficient because of the complexity of the issue, lack of data and poor quality of existing data on maternal mortality. This study has been initiated to fill the gap of knowledge about the determinants and estimates of maternal mortality at national and sub-national levels. Results of this research highlighted explanatory factors of maternal mortality at national and regional level with a focus on factors of regional disparities. Findings also provided estimate by adjusting the census 2006 data from missingness and incoherences, improving the census method and testing different other methods. Finally, projection of maternal mortality level is made from 2006 to 2050

Maternal mortality in Burkina Faso: a method from population census 2006

Background: Estimating maternal mortality level is constantly challenging researchers and planners both in rich and poor countries. In developing countries, particularly in Burkina Faso where the registration system is not working properly, censuses and surveys are the main providers of maternal mortality estimates. However, censuses provide more reliable data about maternal mortality especially at sub-national level. Strength of this situation, the census 2006 of Burkina Faso collected information about maternal mortality. Unfortunately, the census also under reported the phenomenon. In this regard, a methodology was developed to provide adjusted estimates of the phenomenon. Methods: This paper aims to assess the census 2006 estimates of maternal mortality through a critical review of the questionnaire, data quality, adjustment technique and outputs. Incoherencies, duplicated cases and missing data were the key aspects of the data quality assessment. The assumptions and outputs of the method were examined and comparison made with existent estimates. Results: Findings highlighted weaknesses regarding the assumptions of the method and showed that the levels of the phenomenon were still under-estimated. In this research, propositions have been made concerning data cleaning, situations of adjustment coefficients less than 1 and the problem of weak assumptions. Findings led to a MMRatio of 331 [293-402] maternal deaths per 100 000 live births. Conclusion: The level of maternal mortality as published in the census 2006 report (MMRatio of 307) is acceptable because falling in the range 293-402. However, the questionnaire, data and method used needed improvements.Web of Scienc

Tuberculosis risk factors in South Africa, 2008 to 2017: A Generalised Estimating Equations approach

Background: Although, death due to tuberculosis has been on the decline. In 2016, 124 000 people died of tuberculosis in South Africa and the disease was declared the leading cause of death by Statistics South Africa. Continued efforts to use research to create a nation free of tuberculosis are underway.  Methods: A repeated measures investigation was performed with the aim of identifying the persistent predictors and the long-term patterns of tuberculosis infection in South Africa for the period 2008 to 2017. The most suitable Generalised Estimating Equations that describe the population average probability of infection over time were applied to a sample of respondents taken from the National Income Dynamics Survey data, wave 1 to wave 5. The response variable was binary with the outcome of interest being the respondents that self-reported to have been diagnosed with tuberculosis. To improve estimation efficiency, the best working correlation matrix for this data was selected.  Results: We used a sample of 8510 individuals followed for five waves, of these, 3.7%, 2.54%, 4.15%, 5.72% and 5.99% for waves 1, 2, 3, 4 and 5 respectively, reported to have been diagnosed with tuberculosis. Findings revealed that the independent working correlation matrix with the model-based standard error estimates gave the most robust results for the repeated measures tuberculosis data in South Africa. Furthermore, over the years, the average probability of being diagnosed with tuberculosis was positively associated with being single, male, middle-aged (30- 59 years), black African, unemployed, smoking, lower education levels, lack of regular exercise, asthma, suffering from other diseases, lack of access to improved sanitation, lower household income and expenditure.  Conclusion: The probabilities of tuberculosis infection are independent within individuals over time. The inequalities in socioeconomic status in South Africa caused the poor to be more at risk of tuberculosis over time from 2008 to 2017.&nbsp

An assessment of the age reporting in Tanzania population census

The objective of this paper is to provide data users with a worldwide assessment of the age reporting in the Tanzania Population Census 2012 data. Many demographic and socio-economic data are age-sex attributed. However, a variety of irregularities and misstatements are noted with respect to age-related data and sex ratio data because of its biological differences between the genders. Noting the misstatement / misreporting, inconsistence of age data regardless of its significant importance in demographic and epidemiological studies, this study assess the quality of the 2012 Tanzania Population and Housing Census data relative to age. Data were downloaded from Tanzania National Bureau of Statistics. Age heaping and digit preference were measured using summary indices viz., Whipple‟s index, Myers‟ blended index, and Age-Sex Accuracy index. The recorded Whipple‟s index for both sexes was 154.43, where males had the lower index of about 152.65 while females had the higher index of about 156.07. For Myers‟ blended index, the prefrences were at digits „0‟ and „5‟ while avoidance were at digits „1‟ and „3‟ for both sexes. Finally, the age-sex index stood at 59.8 where the sex ratio score was 5.82, and the age ratio scores were 20.89 and 21.4 for males and female respectively. The evaluation of the 2012 Population Housing Censes data using the demographic techniques has qualified the data as of poor quality as a result of systematic heaping and digit preferences/avoidances in recorded age. Thus, innovative methods in data collection along with measuring and minimizing errors using statistical techniques should be used to ensure accuracy of age data

Linkage between ACE2 Gene Polymorphisms and SARS-CoV-2 infection in Burkina Faso, sub-Saharan Africa

The ACE2 gene polymorphisms (rs143936283, rs146676783, and rs4646116) in infected and noninfected persons by SARS-CoV-2 in Burkina Faso. Our cross-sectional study population comprised 137 SARS-CoV-2 infected persons and 181 non-infected persons. Three ACE2 gene polymorphisms rs143936283, rs146676783, and rs4646116, were genotyped using the real-time PCR standard TaqMan allelic discrimination technique. The association between SARS-CoV-2 infection and the polymorphisms were evaluated by a binary logistic regression. There was no association between the polymorphisms rs143936283, rs4646116 haplotypes, and SARS-CoV-2 infection in our study population. However, in the female population, the heterozygous genotype CT of rs146676783 increased by two and half the risk (OR=2.58 95%CI (1.2-5.48), p= 0.014) of being infected by SARS-CoV-2. Additionally, carrying the homozygous minor allele (genotype TT) of rs146676783 increased by more than five and half the risk (OR=5.57 95%CI (1.64-18.78), p=0.006) of being infected by SARS-CoV-2 among females. This study showed that the ACE2 gene variant rs146676783 was associated with an increased risk of being infected by SARS-CoV-2 in females, suggesting a need for further investigation to contribute to a better understanding of the African COVID-19 enigma

Bayesian generalized linear mixed modeling of Tuberculosis using informative priors.

TB is rated as one of the world's deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014

WinBUGS’ output of autocorrelation.

<p>WinBUGS’ output of autocorrelation.</p

Model summary for the year 2014.

<p>Model summary for the year 2014.</p