This is an applied study to investigate the association of selected socio-economic and demographic factors with the relative risk of tuberculosis (TB) prevalence in the Eastern Cape Province of South Africa and to produce disease maps for the spatial outlines of the disease in the province.
This is an ecological spatial study of TB prevalence in the Eastern Cape, a province in South Africa, during the year 2014. Three socio-economic indicators and three demographic factors, all calculated per sub-district, were used to assess their relationship with tuberculosis prevalence, using a Poisson regression model.
From the analysis, the best model included all the selected covariates of the proximal model with the spatial random effects. The improvement in the goodness-of-fit statistic when the spatial structure was included confirms the spatial pattern of population density and average household size.
The idea of assessing both the impact of covariates at the ecological level and spatial outlines in the same context should be encouraged in epidemiology to help with creating epidemiological surveillance systems (ESS) on a provincial basis for planning interventions and improvement of control programme efficiency.
Berkman LF, Kawachi I (2000) Social epidemiology. Oxford University, New York
Eastern Cape AIDS Council (2012) Provincial strategic plan for HIV and AIDS, STIs and TB 2012–2016
Eastern Cape Socio-Economic Consultative Council (2014) Buffalo City Metro-Eastern Cape socioeconomic profile. ECSECC
Hampel FR (1987) Data analysis and self-similar processes. In: Proceedings of the 46th Session of the International Statistical Institute book 4, Tokyo. International Statistical Institute 10 Voorburg, NL, 235–254
Kawachi I, Berkman LF (2003) Neighbourhoods and health. Oxford University Press, New York CrossRef
Krieger N, Waterman PD, Chen JT, Soobader MJ, Subramanian SV (2003) Monitoring socioeconomic inequalities in sexually transmitted infections, tuberculosis, and violence: geocoding and choice of area-based socioeconomic measures—the public health disparities geocoding project (US). Public Health Rep 118:240–260 CrossRefPubMedPubMedCentral
Lawn SD, Zumla AI (2011) Tuberculosis. Lancet 378(9785):57–72. https://doi.org/10.1016/S0140-6736(10)62173-3 CrossRefPubMed
National Department of Health (2010) Management of drug-resistant tuberculosis: policy guidelines http://www.tbonline.info/media/uploads/documents/mdr-tb_sa_2010.pdf. Accessed 4 Nov 2013
Smith I (2013) Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clin Microbiol Rev. https://doi.org/10.1128/CMR.16.3.463-496
Souza WV, Carvalho MS, Albuquerque Mde F, Barcellos CC, Ximenses RA (2007) Tuberculosis in intra-urban settings: a Bayesian approach. Trop Med Int Health 12(3):323–330
Spiegelhalter D, Best N, Carlin BP et al (2002) Bayesian measures of model complexity and fit. J R Stat Soc Ser B Methodol 64:539–583 CrossRef
Waaler HT (2002) Tuberculosis and poverty. Int J Tuberc Lung Dis 6:745–746 PubMed
World Health Organization (2010) Tuberculosis Fact sheet N°104″
- Disease mapping of tuberculosis prevalence in Eastern Cape Province, South Africa
- Springer Berlin Heidelberg