A Complex Survey Data Analysis of TB Mortality in South Africa

Abstract

Many countries in the world record annual summary statistics such as economic indicators like Gross Domestic Product (GDP) and vital statistics for example the number of births and deaths. In this paper we focus on mortality data from various causes including Tuberculosis (TB). TB is an infectious disease caused by bacteria called  Mycobacterium tuberculosis. It is the main cause of death in the world among all infectious diseases (Herchline and Amorosa, 2010). An additional complexity is that HIV/AIDS acts as a catalyst to the occurrence of TB. People  infected with  mycobacterium tuberculosis alone have an approximately 10% life time risk of developing active TB, compared to 60% or more in persons co-infected with HIV and mycobacterium tuberculosis (Vaidynathan and Singh, 2003). In 2006, South Africa was ranked seventh highest by the World Health Organization (WHO, 2009) among  the 22 TB high burden countries in the world and fourth highest in Africa. The research work in this presentation  uses the 2007 Statistics South Africa (STATSSA) data on TB as the primary cause of death to build statistical  models that can be used to investigate factors associated with death due to TB. Logistic regression and generalized linear models (GLM) will be used to assess the effect of some risk factors or predictors to the probability of deaths associated with TB. This study will be guided by a theoretical approach to understanding factors associated with TB death. Of the 615312 deceased, (89%) died from natural death, (2%) were stillborn and (9%) from non-natural  death possibly accidents, murder, suicide. Among those who died from natural death and disease, (12%) died of TB.

Keywords: TB mortality, prevalence, HIV incidence, TB/HIV co-infection, survey data analysis, logistic regression model, and STATSSA South Africa.