This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Teke Apalata
Walter Sisulu University
Corresponding Author
License:
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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 rates of TB deaths, TB case finding index, TB treatment failure, HIV associated TB deaths, TB defaulter rate, and new TB smear positive cases. Analysis of variance (ANOVA) and Post-Hoc Bonferroni pairwise tests 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 TB incidence rate reduced by 80% at the end of the evaluation. After adjusting for confounders, expenditure per patient day equivalent (PDE) and PHC expenditure per capita were significantly and independently associated with the decline of incident TB cases (adjusted R2 = 60%; ρ = 0.002) following the equation: Y= (- 209× Expenditure per PDE) + (- 0.191 × PHC expenditure per capita). CDA showed that TB cases in the most socio-economically deprived communities (quintile 1) were significantly more likely to have high HIV and TB co-infection death rates 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.
Keywords
TB incidence, index of multiple deprivations, HIV associated TB deaths, healthcare expenditures
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This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Research article
Teke Apalata
Walter Sisulu University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 14 Jan, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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 rates of TB deaths, TB case finding index, TB treatment failure, HIV associated TB deaths, TB defaulter rate, and new TB smear positive cases. Analysis of variance (ANOVA) and Post-Hoc Bonferroni pairwise tests 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 TB incidence rate reduced by 80% at the end of the evaluation. After adjusting for confounders, expenditure per patient day equivalent (PDE) and PHC expenditure per capita were significantly and independently associated with the decline of incident TB cases (adjusted R2 = 60%; ρ = 0.002) following the equation: Y= (- 209× Expenditure per PDE) + (- 0.191 × PHC expenditure per capita). CDA showed that TB cases in the most socio-economically deprived communities (quintile 1) were significantly more likely to have high HIV and TB co-infection death rates 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.
Figure 1
Figure 2
Figure 3
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