Impact of Tuberculosis in South Africa

The assignment is based on a report that concludes with the selection of variables to test hypotheses related to tuberculosis in South Africa. The report uses information gathered from world bank sources to analyze the impact of tuberculosis in South Africa, including percentage rates of detected cases and treatment successful rates. The assignment also includes references to books, journals, and online resources that discuss tuberculosis in South Africa.

PUBLIC HEALTH INTELLIGENCE

TABLE OF CONTENTS
Epidemiology of Tuberculosis among individual more than 18 years in African Countries
between 2005-2015..........................................................................................................................1
Context.............................................................................................................................................1
Demographics..............................................................................................................................2
METHODS/ DATA SOURCE........................................................................................................3
RESULTS........................................................................................................................................4
DISCUSSION................................................................................................................................10
Strength......................................................................................................................................11
Limitations.................................................................................................................................11
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

Epidemiology of Tuberculosis among individual more than 18 years in
African Countries between 2005-2015
Tuberculosis is an infectious disease which causes damages to the lungs. This is disease is
2nd biggest killer and almost 1.8 million patients have died and 10.4 million are still struggling
from this bacterial infection (Tackling the Tuberculosis Epidemic in sub-Saharan Africa, 2015).
It mainly affects the lungs but it can also affect the other parts of body. There are two kinds of
infections which is being spread world wide through interacting with an infectious person. Thus,
mainly in African country the ratio of TB infected individual and mortality rate was higher such
as it raised to 3% from 2015. It is because the rate of TB cases and mortality had affected the
nation due to poverty, inappropriate medical facilities as well as vaccination against such
disease. Along with this, use of poor technology in diagnosing the disease as well as in adequate
vaccination have affected this ratio (TB Statistics - Incidence, prevalence, high burden, 2018).
Latent Tuberculosis is the type of infection in which bacteria remains on inactive state in
the body. It generally does not cause any symptoms as well as they are not contagious.
Moreover, with no any reaction, symptoms etc. they can cause damage to the body as well as
they can be active anytime. Active Tuberculosis is serious bacterial type of infection in which
service user experiences several symptoms as well as is easily spread from one person to another.
Therefore, it is highly risky and can cause health issues to mass population.
Context
Therefore, on the basis of such type of TB bacteria on which it can be estimated there are
chances of having 1/3rd of population is being infected with Latent TB. In any part of the body,
there will be chances of having this inactive bacterium. Therefore, there are only 10% chances of
Latent TB bacteria to get active (All you need to know about tuberculosis, 2018). It is riskier for
the people who have poor immune system such as people surviving with the life-threatening
diseases such as HIV, Diabetes as well as are malnourished. Along with this, people who smoke
regularly have chances to develop TB (Giri et.al., 2018). diseases. It is infectious and therefore
spread through air around infected person via laugh, cough, sneeze, etc. It is caused by a type of
bacterium called Mycobacterium tuberculosis. It's spread when a person with active TB disease
in their lungs coughs or sneezes and someone else inhales the expelled droplets, which contain
TB bacteria.
1

Demographics
There have been impacts of this disease in individuals of all age group. It is the most
common diseases mainly in Asian and African countries. In accordance with the recent surveys
made by National Institute for Communicable diseases, in 2012 there were 80% of general
young population in developing countries have been infected with this disease. The sample in
such survey was on selected on cluster basis for 349 as planned individual on which 169002 as
patient aged between 18-24 years and 44-65 years. There have been higher chances (20%) of
having this infection due to the air pollution as well as various factors which have spread it
quickly. In most of the adults, this ratio is comparatively higher as per habit of smoking which
damages their immune system as well as invites various diseases.
Figure 1 Tuberculosis impacts in South Africa
(Source: South African Tuberculosis Drug Resistance Survey 2012–14, 2015)
Global impacts of Tuberculosis:
This is among the most common infectious cause of death which have affected worldwide
population especially backwards areas of Asia and African countries. In accordance with the
survey of 2006 which have reflected that there were 9.2 million cases where 1.7 million had been
died due to TB and in 2017 there were 1,600,000 deaths on which 234,000 were children below
16 years. Thus, on which the major cases had been found in Asia while in Africa there have been
2

higher impacts of population. It is because of higher prevalence of HIV infection in the countries
were higher (The Global Fight Against HIV/AIDS, Tuberculosis, and Malaria: Current Status
and Future Perspectives, 2018). Moreover, there have been s various cases of TB-HIV infection
have been spread all over the world.
METHODS/ DATA SOURCE
In reference with analysing the impacts of Tuberculosis on South Africa there have been
collection of effective data set which will be effective and adequate in determining the current
situation of this disease (Budgell and et.al., 2018). However, as per the cluster survey there have
been selection of the data base on the basis of cases detected rate in TB and Treatment success
rates from 2005 to 2015 (Tuberculosis case detection rate (%, all forms), 2017).
Therefore, there will be use of various techniques in measures to analyse the hypothesis and
relationship between these two variables. It states the percentage of cases detected each years
and Treatment success rates in South Africa.
Raw data: Percentage of rate of case detected and treatment success rate in South Africa:
Years
Case detection rate
(in%) per 100,000 of population
Treatment success rate
(in%) per 100,000 of population
2005 59 69
2006 64 70
2007 65 71
2008 71 73
2009 73 68
2010 73 53
2011 75 77
2012 68 77
2013 68 78
2014 67 78
2015 64 81
By considering the data base there have been creation of two hypothesis which will be
tasted by researchers such as:
Hypothesis:
3

Null Hypothesis: There is no mean significance relationship between Case detected rate
and Treatment success rate
Alternative Hypothesis: There is a mean significance relationship between Case detected
rate and Treatment success rate
To analyse the relationship between these two variables there will be use of various
mathematical techniques which can be adequate and effective in identifying the outcomes.
Therefore, it includes tools such as:
Descriptive statistics:
This is the method of analysing the reliability of the data set which is being examined by
the researcher. Thus, it consists of statistical tools which were being measured on the basis of
analysing mean, mode, median, standard deviation etc. However, In the present report there will
be consideration on the data of TB case detected rate and treatment success rate in South Africa
from 2005 to 2015.
Regression analysis:
This is the techniques of identifying the relationship between the variables. This, the
relationship between two or more variables will be identified by denoting them as dependent as
well as independent variables (Beyers and Gie, 2018). In the present report, there will be use of
detected case rate as independent variable while dependent variable as Treatment success rate.
However, such analysis will be helpful in bringing the adequate analysis over the relationship
between these two data bases.
Correlation:
There will be detection of the mutual relationship and correlation between more than two
or 2 variables. Thus, the range of outcomes is must be between -1 to +1 which will be effective
in identifying the positive and negative relationship among the variables (Maraba and et.al.,
2018). Thus, inn the present research to analyse the impacts of tuberculosis in South Africa
where detected case and treatment successful rate have been considered to be examined.
Chi-Square:
This technique is used for testing hypothesis of the data base which will be useful to detect
and analysing relationship between data set as per hypothesis (Ismail and et.al., 2018).
RESULTS
Descriptive statistics
4

Descriptive Statistics
N Minimu
m
Maximu
m
Mean Std.
Deviation
case detection rate 11 59 75 67.91 4.805
Treatment success
rate 11 53 81 72.27 7.708
Valid N (listwise) 11
Interpretation: on the basis of analysing the descriptive statistics of the data base which has
been considered by the researcher in examining the impacts of Tuberculosis in South Africa in
2005 to 2015. Thus, the reliability of the data set has been analysed through this statistical
technique. There has been consideration on information such as case detection rate and treatment
success rate of South Africa. In accordance with the mean value of these variables where case
detected rate has been measured on the average of 67.91 while treatment successful rate as 72.27.
Thus, as per such results it can be ascertained that vaccination and medical facilities in this
nation have been improved while there has been reduction in the rat6e of TB patients.
Regression analysis:
Descriptive Statistics
Mean Std.
Deviation
N
Treatment success rate 72.27 7.708 11
case detection rate 67.91 4.805 11
Correlations
Treatment
success rate
case detection
rate
Pearson Correlation Treatment success rate 1.000 -.218
5

case detection rate -.218 1.000
Sig. (1-tailed) Treatment success rate . .260
case detection rate .260 .
N Treatment success rate 11 11
case detection rate 11 11
Model Summary
Model R R
Square
Adjusted
R Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .218a .047 -.058 7.930 .047 .449 1 9 .520
a. Predictors: (Constant), case detection rate
ANOVAa
Model Sum of
Squares
df Mean Square F Sig.
1
Regression 28.223 1 28.223 .449 .520b
Residual 565.959 9 62.884
Total 594.182 10
a. Dependent Variable: Treatment success rate
b. Predictors: (Constant), case detection rate
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. 95.0% Confidence
Interval for B
B Std.
Error
Beta Lower
Bound
Upper
Bound
6

1
(Constant) 96.014 35.519 2.703 .024 15.664 176.364
case
detection rate -.350 .522 -.218 -.670 .520 -1.530 .831
a. Dependent Variable: Treatment success rate
Interpretation: In considering the regression analysis of the data base which has have
presented the relationship between percentage of case detection rate and treatment successful rate
of the organisation. Thus, on which model summary of the data set has been analysed where R
value is 0.047 while R Square as -0.058 that defines -5.8% of relationship among the variable’s.
Thus, in accordance with such analysis on which it can eb said that there are negative relations is
among the variables. In addition, as per ascertaining the Significant value of the data base which
have presented the value as 0.520 is more than the P level such as 0.05. In this regard, there will
be acceptance to the Null hypothesis, there is no mean significance relationship between Case
detected rate and Treatment success rate.
Correlation:
Descriptive Statistics
Mean Std.
Deviation
N
case detection rate 67.91 4.805 11
Treatment success
rate 72.27 7.708 11
Correlations
case
detection rate
Treatment
success rate
case detection rate Pearson
Correlation 1 -.218
Sig. (2-tailed) .520
7

N 11 11
Treatment success
rate
Pearson
Correlation -.218 1
Sig. (2-tailed) .520
N 11 11
Interpretation: As per analysing correlation analysis over data base on which it has been
ascertained that these variables have presented negative outcomes through observation.
However, it has presented the outcomes as -0.218 Thus, the relation is negative among these
variables. Overall, the changes incurred in one variable would not affect the another one.
Chi-Square
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
case detection rate *
Treatment success rate 11 100.0% 0 0.0% 11 100.0%
case detection rate * Treatment success rate Crosstabulation
Count
Treatment success rate Total
53 68 69 70 71 73 77 78 81
case
detection
rate
59 0 0 1 0 0 0 0 0 0 1
64 0 0 0 1 0 0 0 0 1 2
65 0 0 0 0 1 0 0 0 0 1
67 0 0 0 0 0 0 0 1 0 1
68 0 0 0 0 0 0 1 1 0 2
8

71 0 0 0 0 0 1 0 0 0 1
73 1 1 0 0 0 0 0 0 0 2
75 0 0 0 0 0 0 1 0 0 1
Total 1 1 1 1 1 1 2 2 1 11
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Pearson Chi-Square 60.500a 56 .317
Likelihood Ratio 38.891 56 .960
Linear-by-Linear
Association .475 1 .491
N of Valid Cases 11
a. 72 cells (100.0%) have expected count less than 5. The
minimum expected count is .09.
Symmetric Measures
Value Asymp. Std.
Errora
Approx. Tb Approx. Sig.
Interval by
Interval Pearson's R -.218 .265 -.670 .520c
Ordinal by
Ordinal Spearman Correlation -.170 .354 -.518 .617c
N of Valid Cases 11
a. Not assuming the null hypothesis.
b. Using the asymptotic standard error assuming the null hypothesis.
c. Based on normal approximation.
9

Interpretation: As per detecting the significant relationship among the variables. Thus, in
consideration with this, there have been demonstration of significant value which is based on
analysing the outcomes on the P level of values. Therefore, here Significant value have been
derived as 0.317, 0.960 and 0.491 which are comparatively higher than 0.05. Moreover, in this
case, there is no mean significance relationship between Case detected rate and Treatment
success rate.
DISCUSSION
To analyse the impacts of this bacterial disease in South Africa there have been
ascertainment of effective outcomes. However, this study has detected that the impacts of
Tuberculosis in Africa has been reduced from past years. It is mainly due to implication of new
technologies in medical sector to detect and diagnose the disease. Drug resistance rate has been
controlled by the government which have presented them the adequate outcomes as the
maximum number of patients have been cured with this disease. There have been increment in
the vaccination system which have involved drugs like DOTS etc.
Figure 2 Percentage change in CDR and TSR rate of South Africa
By considering the above presented graphical presentation which have defined the changes
in the case detection rate and treatment successful rate in Tuberculosis impacts in South Africa.
Therefore, it has presented the outcomes as Case detection rate has been reduced since 2005. It
was highest at 2009 which was the most challenging situation for the government of South
Africa in term of controlling this disease. Along with this, there have been similar reduction in
10

the treatment success rate. Thus, the medical facilities in this country have been improved since
past years. Therefore, the government is being capable for mitigating the risks of this bacterial
disease.
However, in relation with analysing the outcomes which has have presented that there is no
relationship among the Case detection rate and treatment success rate of the organisation. Thus,
in aspect with such outcomes. The changes incurred in one variable will not affect the another
one. Moreover, there will not be any impacts of such changes. To overcome with this disease the
government has to be more attentive and committed towards making appropriate analysis over
the data base.
Strength
In relation with analysing the strength of the research project on which it can be said that,
government in South Africa has to be converted regarding managing the medical issues as the
results are reflecting negative outcomes. It will be motivating in terms of making effective
changes in the medical sector.
Limitations
The limitation of analysed outcomes has been determined by the researcher as there is
requirement of addressing a large number of sample size of the years of observation is required
to be more. Along with this, the outcomes determined by such observation and analysis are not
satisfying with reference to make better ascertainment of the data base.
CONCLUSION
On the basis of above report, it can be concluded that impacts of tuberculosis in South
Africa. However, on the basis of which there have been selection of various variables which
have been test through implicating the hypothesis in detecting the outcomes. Information which
were gathered through world bank source have been used to analyse impact of Tuberculosis in
South Africa. It was consisted with the percentage rate of detected cased and treatment
successful rates.
11

REFERENCES
Books and Journals
Beyers, N. and Gie, R., 2018. Tuberculosis in South Africa before, during, and after World War
II. In Tuberculosis and War (Vol. 43, pp. 130-133). Karger Publishers.
Budgell, E.P. and et.al., 2018. The costs and outcomes of paediatric tuberculosis treatment at
primary healthcare clinics in Johannesburg, South Africa. South African Medical
Journal. 108(5). pp.423-431.
Giri, A. and et.al., 2018. Polymorphisms in Rv3806c (ubiA) and the upstream region of embA in
relation to ethambutol resistance in clinical isolates of Mycobacterium tuberculosis from
North India. Tuberculosis. 108. pp.41-46.
Ismail, N. A. and et.al., 2018. Prevalence of drug-resistant tuberculosis and imputed burden in
South Africa: a national and sub-national cross-sectional survey. The Lancet Infectious
Diseases. 18(7). pp.779-787.
Maraba, N. and et.al., 2018. Linkage to care among adults being investigated for tuberculosis in
South Africa: pilot study of a case manager intervention. BMJ open. 8(5). p.e021111.
Online
All you need to know about tuberculosis. 2018. [Online]. Available through :<
https://www.medicalnewstoday.com/articles/8856.php>.
South African Tuberculosis Drug Resistance Survey 2012–14. 2015. [Online]. Available
through :<http://www.nicd.ac.za/assets/files/K-12750%20NICD%20National%20Survey
%20Report_Dev_V11-LR.pdf >.
The Global Fight Against HIV/AIDS, Tuberculosis, and Malaria: Current Status and Future
Perspectives. 2018. [Online]. Available through :<
https://academic.oup.com/ajcp/article/131/6/844/1760847>.
Tuberculosis case detection rate (%, all forms). 2017. [Online]. Available through :<
https://data.worldbank.org/indicator/SH.TBS.DTEC.ZS>.
Tackling the Tuberculosis Epidemic in sub-Saharan Africa. 2015. [Online]. Available through :<
https://www.sciencedirect.com/science/article/pii/S1201971214017573>.
TB Statistics - Incidence, prevalence, high burden. 2018. [Online]. Available through :<
https://www.tbfacts.org/tb-statistics/ >.
12

13