Predictors of tuberculosis incidence and the effects of multiple deprivation indices on tuberculosis management in OR Tambo district over a 5-year period

Abstract

Background This study investigated the associations between socio-economic deprivation and tuberculosis (TB) treatment outcomes, alongside well-known TB risk factors. The effects of healthcare expenditures and their growth on trends in TB incidence from 2009 to 2013 were also assessed. Methods Secondary data analysis was performed on data obtained from various sources including governmental, non-governmental and research institutions. Indicators for TB treatment outcomes included TB death rate, TB rate among the household contacts of the Index TB cases, TB treatment failure, HIV associated TB death rate, TB defaulter rate, and new TB smear positive cases. Analysis of variance (ANOVA) and Turkey’s tests for post-hoc analysis were used to compare means of variables of interest considering a type I error rate of 0.05. Regression models and canonical discriminant analysis (CDA) were used to explore the associations between trends in TB incidence and independent TB predictors. During CDA, Fischer’s linear functions, Eigen values, and Mahalanobis distances were determined with values of Wilk’s Lambda closer to zero being the evidence for well discriminated patient groups. Data analysis was performed using SPSS® statistical software version 23.0 (Chicago, IL). Results In total, 62 400 records of TB notification were analyzed for the period 2009–2013. The average TB incidence rate over a 5-year period was 298 cases per 100,000 inhabitants per year. The incidence of TB was reduced by 79.70% at the end of the evaluation as compared to the baseline data in 2009. Multiple linear regression analysis showed that the Expenditure per patient day equivalent (PDE) and PHC expenditure per capita were significantly and independently associated with the decline of TB incidence (adjusted R2 = 60%; ρ = 0.002) following the equation: Y = (- 209× Expenditure per PDE) + (- 0.191 × PHC expenditure per capita). CDA showed that in the most socio-economically deprived communities (quintile 1), HIV associated TB death rates were significantly more likely to be higher as compared to the least socio-economically deprived group (quintile 5) [Eigen value (12.95), function coefficient (1.49) > (.77); Wilk’s Lambda = .019, p < .0001]. Conclusions Although TB control programs in OR Tambo district have averted thousands of TB incident cases, their effects on HIV associated TB deaths among the most deprived communities remain insignificant. There is an urgent need for strengthening integration of TB/HIV services in most deprived settings.

Developing a Model for Integrating of Tuberculosis, Human Immunodeficiency Virus and Primary Healthcare Services in Oliver Reginald (O.R) Tambo District, Eastern Cape, South Africa

“…Such obstacles could exist at the facility and health system levels and include a lack of specific instructions on which service is to be integrated in which department and how; insufficient supervisory on integrated training; and stock-outs of TB resources including drugs and ARVs supplier [29,30]. If clients believe that integrated services require lengthy wait times, poor privacy, and little opportunity for questioning during consultations, integration may be less beneficial from their perspective [31].…”

“…This is a potentially effective model for SA to address the high prevalence of HIV, TB, and HIV-TB coinfections, as well as limited human resources [4]. Although this is not the first empirical discussion of TB-HIV integration in South Africa, it is, as far as we know, the first attempt to examine operational issues of TB-HIV integration from the perspective of nurses at the facility level [26][27][28][29][30][31]. This paper describes the development of a district-based model for comprehensive and integrated TB and HIV care that meets the needs of the region.…”

Developing a Model for Integrating of Tuberculosis, Human Immunodeficiency Virus and Primary Healthcare Services in Oliver Reginald (O.R) Tambo District, Eastern Cape, South Africa

“…Therefore, the term eigenvalue can be termed as characteristic value, characteristic root, proper values or latent roots as well [32] Canonical discrimination Analysis (CDA): It builds a predictive model for group membership. One primary purpose of CDA is to separate classes (populations) in a lower dimensional discriminant space [32]. Mahalanobis distance: It is the distance of the test point from the centre of mass divided by the width of the ellipsoid in the direction of the test point.…”

“…Mahalanobis distance: It is the distance of the test point from the centre of mass divided by the width of the ellipsoid in the direction of the test point. It can be used to determine whether a sample is an outlier, whether a process is in control or whether a sample is a member of a group or not [32] Posthoc analysis: Post hoc in Latin means 'after this'. Simply put, a post-hoc analysis refers to a statistical analysis specified after a study has been concluded and the data collected.…”

“…Simply put, a post-hoc analysis refers to a statistical analysis specified after a study has been concluded and the data collected. A post-hoc test is done to identify exactly which groups differ from each other [32] Regression Analysis: Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest [32] Model: A model is a symbolic depiction of reality, which provides a schematic representation of some relationships among phenomena and uses symbols or diagrams to represent an idea [34]. For the purpose of this study, a model refers to a schematic presentation of the integration of TB and…”