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.

The recent female literacy and fertility levels in Kerala state are examined using the 2011 census data. Arriaga’s approach for estimation of age-specific fertility rates is undertaken to show the particularities of Kerala state and the best practices which made this state an example for other states in India as well as other places in the world, particularly developing countries. Women’s empowerment gets as much credit as physical facilities and family planning programs; this empowerment level of women is also related to their level of education.

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 less to sex data because of its biological differences between the genders.

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. 

Background: Breast cancer is one of the most common cancers among women. Breast cancer treatment strategies in Nigeria need urgent strengthening to reduce mortality rate because of the disease. This study aimed to determine the relationship between the ages at diagnosis and established the prognostic factors of modality of treatment given to breast cancer patient in Nigeria. Methods: The data was collected for 247 women between years 2011-2015 who had breast cancer in two different hospitals in Ekiti State, Nigeria. Model estimation is based on Bayesian approach via Markov Chain Monte Carlo. A multilevel model based on generalized linear mixed model is used to estimate the random effect. Results: The mean age of the patients (at the time of diagnosis) was 42.2 yr with 52% of the women aged between 35-49 yr. The results of the two approaches are almost similar but preference is given to Bayesian because the approach is more robust than the frequentist. Significant factors of treatment modality are age, educational level and breast cancer type. Conclusion: Differences in socio-demographic factors such as educational level and age at diagnosis significantly influence the modality of breast cancer treatment in western Nigeria. The study suggests the use of Bayesian multilevel approach in analyzing breast cancer data for the practicality, flexibility and strength of the method.