University of Medicine and Pharmacy [603239]

University of Medicine and Pharmacy
“Carol Davila”, București
Faculty of Medicine
LICENSE DIPLOMA

Assessing the relationship between
the success rate in the treatment of tuberculosis
and incomes at the population level

SCIENTIFIC CO -ORDINATOR :
Prof. Dr. DANA GALITA MINCA

TUTOR:
Dr. Bogdan Pana

Graduate:
OSMAN MUHAMMAD

2019

Table of contents
Introduction ……………………………………………………………………… Pag. 1
General part
I. History of tuberculosis ………………………………………………. Pag. 2
1.1. Summar y …………………………… Pag. 2
1.2. Historical approach …………………………………………………. Pag. 3
1.3. Isolation and staining of the Koch bacillus …………………………. Pag. 4

II.Epidemiology ……………………………………………………………………. ………………………. .Pag 5

III. Globar burden of TB……………………………………………………………………. ……………. Pag 9
3.1 TB incidence………………………………………………………………………………………… ……Pag 12
3.2 TB Mort ality………………………………………………………………………………… …………..Pag 19
3.3 Estimates of the disease burden caused by MDR/RR -TB ………………………. ………Pag 22
3.4 National TB prevalence surveys…………………………………………………………….. ……Pag 22

Personal part
4.1. Aim of the study ……………………………………………………………… Pag. 24
4.2. Objectives of the study ………………………………………………………… Pag. 24
4.3. Hypothesis of the study ……………………………………………………….. Pag. 24
4.4. Material and methods …………………………………………………………. Pag. 26
4.4.1. Study design ………………………………………………………………… Pag. 26

4.4.2. Population of the study ……………………………………………………… Pag. 27
4.4.3. Tool of the study ……………………… ……………………………………. Pag. 28
4.5. Results ……………………………………………………… ………………….. Pag. 29
4.6 .Discussions … ……………………………………………………………………Pag 58

Conclusions ………………………………………………………………………. Pag.62

References ………………………………………………………………………… Pag. 64

1
Introduction
Worldwide, TB (tuberculosis) is one of the top 10 causes of death and the leading cause from
a single infectious agent (above HIV/AIDS). Millions of people continue to fall sick with TB each
year. In 2017, TB caused an estimated 1.3 million deaths (range, 1.2 –1.4 million) among HIV –
negative people and there were an additional 300 000 deaths from TB (range, 266 000 –335 000)
among HIV -positive people.
The severity of national epidemics varies widely among countries. Drug -resistant TB
continues to be a public health crisis. The best e stimate is that, worldwide in 2017, 558 000 people
(range, 483 000 –639 000) developed TB that was resistant to rifampicin (RR -TB), the most
effective first -line drug, and of these, 82% had multidrug -resistant TB (MDR -TB).
TB is an old disease that was once a death sentence. Effective drug treatments first became
available in the 1940s, and in combination with social and economic development they allowed
countries in western Europe, North America and some other parts of the world to re -duce their
burden of TB disease to very low levels.For most countries, however, the “end” of TB as an
epidemic and major public health problem remains an aspiration rather than a reality. Urgent action
is required to improve the coverage and quality of diagnosis, treatm ent and care for people with
drug-resistant TB.
Closing gaps in detection and treatment requires much higher coverage of drug
susceptibility testing among people diagnosed with TB, reducing underdiagnosis of TB, models
of care that make it easier to access and continue treatment, new diagnostics, and new medicines
and treatment regimens with higher efficacy and better safety .
The success rate in the treatment of tuberculosis and the consequences that come from
this,can lead to many connec tions with the incomes at the population level .Taking into account
this relationship between treatment and population level ,it may bring a better understanding of
the disease itself.

2
I.History of tuberculosis
1.1 Summary
Tuberculosis (TB) is one of the oldest diseases known to affect h umanity, and it is still a
major public health problem for the world . It is caused by the bacillus Mycobacterium
tuberculosis (MT), isolated in 1882 by Robert Koch. Until the 19 50s, X rays were used as a
method of diagnostic screening together with the tuberculin skin sensitivity test. In the diagnosis
and also in the treatment o f TB, an important role was played by surgery ,too.
The late Nineteenth century saw the introduction of the first useful m easure against
tuberculosis: the tuberculosis sanatorium, Subsequently, Albert Calmette and Camille Guérin
(Fig.1) used a non -virulent MT strain to produce a live attenuated vaccine. In the 1980s and
1990s, the incidence of tuberculosis was proved to be a major opportunistic infection in people
with HIV infection and AIDS; for this reason, a strategy based on improving drug treatment,
diagnostic instruments and prevention was needed.

Fig.1 Albert Calmette and Camille Guerin
Source: https://www.drbrianstork.com/wp -content/uploads/BCG -VACCINE1.jpg

3
1.2 Historical approach
Tuberculosis (TB) is one of the oldest dis eases known to affect humanity.1 It is caused by the
bacillus Mycobacterium tuberculosis (MT): the bacilli usually multiply in the lungs and are
spread by air -borne droplets from infected persons. 2 The disea se reached epidemic proportions.
In the past, MT was called the "thief of youth" because it displayed a predilection for young
people and in most cases, it proved fatal, with a death proportion in the pre -chemotherapy era:
roughly 65% at 5 years, especially when it was associated to malnutrition, poor livi ng and
working conditions . Ove r the l years, the disease felled an estimated one bill ion people
worldwide, and today is the second greatest cause of mortality among infectious diseases in the
world after human immunodeficiency virus (HIV) .[1-4] Villemin was the first to prove that
human tuberculosis was a contagious disease transmitted by an infectious age nt, Mycobacterium
tuberculosis . 5 This microbe was discovered and isolate d in 1882 by a famous scientist Robert
Koch (Fig.2) , who was awarded the Nobe l Prize for Medicine in 1905.

Fig.2 Robert Koch
Source: https://www.britannica.com/biography/Robert -Koch

4
1.3 Isolation and staining of the Koch bacillus
Robert Koch observed that the alcohol -methylene blue staining developed by Karl Weigert in
1875 did not easi ly stain in tubercular lesions . [6-7] He implemented an innovative method of
observing tubercular lesions using methylene -blue and a solidified medium of coagulat ed bovine
serum heated to 40°C . 8 Using this , Koch reported that bacteria and animal tissues stained
brown, whereas tubercular bacteria were detected very easily on microscopy , as they appeared
bright blue . 9 This innovation empowered Koch to prove that the various manifestations of
tuberculosis, such as pulmonary, extra -pulmonary and meningeal tubercular diseases, scrofula
had a common cause . [4, 10-15]
In 1882, Franz Ziehl introduced carbol fuschin instead of aniline, and Friedrich Neelson
substituted sulphuric acid for another compound: nitric acid, which stained the bacillus a brighter
red; this was known as the Ziehl -Neelson (ZN) or acidalcohol -fast bacillus (AAFB), since, once
stained, the MT bacillus lipidic wall resists decoloring by acid and alcohol; for this reason, this
technique is als o used nowadays in or der to isolate the MT bacillus 12. With the time passing by ,
microscopy continued to be a gold standard isolation method, and the diagnosis was made with
with high specificity. Moreover, it was simple to use and cheap. 16
For the improval of sensitivity, other methods of concentration, such as centrifugation, N -acetyl
cysteine -sodium hydroxide, bleach, ammonium sulfate or chitin, were tested and used in
countries with a high prevalence of the disease. T he classical ZN staining was replaced by
Kinvoun staining and immunofluorescence. 16 During the last years, other cold staining methods
were used to diagnose tuberculosis, such as Gabbet’s method and a modified two -reagent cold
staining method, which were easier to perform and less time consuming . 17 Currently, the
fluorescent stain auramine is used to better detect positive smears.

5
II . E pidemiology
TB(Tuberculosis) is airborne and contagious . The risk of acquiring a Mycobacterium tuberculosis
infection is usually determined by exogenous factors. TB is most transmitted by droplet nuclei
from a person with infectious pulmonary TB (Fig.3) .This droplet are are aerosolized by speaking
and also coughing or sneezing (Fig.4 )(Fig.5) . There are also other routes of transmissi on but they
usually have no epide miologic significance.

Important determinants of the likelihood of transmission are: t he probability of contact with a
person who has or had an infectious form of TB, the closure and the duration of that contact the
shared environment , and the degre e of infectiousness of the case.
It is considered that almost one-third of the world’s population have been exposed to MB and got
poten tially infected . 18 Not all the persons will be infected,only a small amount of people will
become sick,but there are still people at risk of falling ill from TB: people with weakened immune
systems ,people with silicosis (among other morbidities) ,people living with HIV.

For the peope without HIV demonstrated ,the risk of death after 10 years was reported between
53% and 86%, with a weighted mean of 70% 19 , compared with 3% of HIV -uninfected treated
tuberculosis patients expected to die because of TB. 20 TB is a disease of of the poverty that
affects most of the time young adults in their productive years living in the developing world . 18

6

Fig.3 Cycle of bacterial infection
Source: http://www.museumofhealthcare.ca/explore/exhibits/breath/etiology.html

Fig.4 Route of transmission Fig.5 Route of transmission
Source: https://www.slideshare.net/Hamdi0Alturkey/pulmonary -tuberculosis -34642377

7
There are groups that are in a higher risk of getting infected.These are: young adults (more
commonly males), health care workers who are around the disease frequently those in
developing countries, people whose immune systems are weak, a s in those who have HIV or
smoking [21,22]. Because HIV-TB comorbidity has been widely studied ,it was proved that TB is
the leading cause of death in those infected with HIV [22,24]. Individuals and those who reside in
areas where malnutrition is prevalent are more likely to get infected.21 The host's own de ficiency
in interleukin (IL) -12 promoting the T helper (Th) 1 response might be another f actor in the
increased suscepti bility to infection . 25 There are other conditions that can bring a higher risk for
MTB infection.These are:ageing,diabetes,long -term use of corticosteroids, polymorphism in
vitamin D, polymorphism in IL -12 and IFN -g genes receptors .
MTB infection is produced through inhalation inhalation of infectious aerosol particles released
from close contacts [24,26 ]. A lot of individuals who inhale MTB can bring an effective response
leading to successful inhibition in the development of MTB, and as a result the bacteria is
becoming dormant; this condition is often referred to as latent tuberculosis or LTBI 27
:immunocompetent latent ind ividuals are infected with MTB but do not transmit the disease to
others and do not present symptoms [21,24]. From lat ent infection, the infection can become active.23
Some cases of LTBI are at risk to progress from infection to active (primary) TB. 24 People with
HIV and people that are taking immunosuppressing medication have a hig her risk of developing
active TB. .
The World Health Organization (WHO) reported that “one-third of the world's population has
been infected with TB ” . 21 Holding true to Robert Koch's statement that the disease is deadlier
than the plaque or cholera 24 , about 9 million people w ere infected with TB and about 1.5 million
succumbed to the disease in 2013 . 21 In 2004, TB was responsible for 2.5% of all deaths in the
world. 26 It is thought that infection rates are higher in areas such as hos pitals or prisons. 22
Prevalence of the disease in such s ettings de pends on innate immunity, virulence, and
susceptibility.
Even though TB can be present in any country, a majority of those deaths reported, about 95%,
occurred in low – and middle -income countries where the resources are usua lly limited, with a
majority of cases appearing in countries such as India and China [21,22].People with HIV are the
population with the greatest risk for getting infected with TB, and more than 50% of HIV -infected

8
people with TB live in sub -Saharan Africa [22,26]. Differently,i n the United States, only 10% of
people with TB are infected with HIV; in 2008, only 12, 904 TB cases were reported with the
incidence be ing 4.2 per 100,000. While a lot of diagnostic advancements were being made in the
past four years, 80% of TB cases worldwide are concentrated in twenty -two countries 21 . There
are also twenty -seven countries including India, China, and Russia, that are responsible for about
85% of MDR TB case s. 22 Unlucky , more recent data is not available due to the limits of global
surveillance and reporting systems.26 It is still obscure the reason why some people can aquire
disease while other cannot.,even though they are exposed to the same risk factors. [23,25]
Because a majority of people with TB have latent infection 24, the evolution and development
of new screening tools and diagnostic tests s has become necessary in order to have a better control
over the disease [27,28 ]. Interferon -gamma release assays (IGRAs) are used to diagnose LTBI . The
tuberculin skin test (TST) is still an option because is a cost-effective test. 27 The TST and IGRA
work by measuring the response of T cells to TB antigens . 24
Tuberculosis (TB) [29,30] is still a major cause of death in the entire world despite the dis covery of
affordable and effective chemothera py and is likely to have affected hum ans for most of their
history . With 1.3 million TB deaths (includi ng TB deaths in HIV -positive in dividuals) in 2012 18,
TB and the human immunodeficiency virus (HIV) are the top causes of death from a sin gle
infectious agent worldwide [31,32 ]. TB is a killer among people in the most economically productive
age gro ups and also among people living with HIV 33 ,and even those cured from TB can be left
with sequelae that can definitely reduce their quality of life 34. Knowing these facts has brought
TB control high on the in ternational public health studies since the early 1990s 35, following years
of neglect during the 198 0s 36.

9
III. Global Burden of Tuberculosis
Tuberculosis (TB) is one of the worldwide top 10 c auses of death, and the cause from a
single infe ctious agent (above HIV/AIDS).M illions of people continue to fall s ick with the
disease each year.
In 2017, TB caused an estimated 1.3 million deaths in the HIV-negative people, and there
were an additional 300 000 deaths from TB among HIV -positive people. There were an
estimated 10.0 million new cases of equivalent to 133 cases per 100 000 population.
TB affects all age groups and a lot of countries, but overall the best estimates for 2017 were
that 90% of cases were adults (aged ≥15 years), 64% were male, 9% were people living with
HIV (72% of them in Africa) and two thirds were in eight countries: China (9%), Pakistan (5%),
Nigeria (4%), India (27%), In donesia (8%), the Philippines (6%), Bangla desh (4%) and South
Africa (3%) .
The severity of national epidemics varies widely. In 2017, there were less than 10 new
cases per 100 000 population found most ly in high-income countries, 150 –400 in mos t of the
30 high TB burden countries, and above 500 in a few countries including Mozambique, the
Philippines and South Africa.
Globally in 2017, there were an estimated 558 000 new cases of tuberculosis -drug
resistant cases -rifampicin -resistant T B (RR -TB), and almost half of the cases were from :
India (24%), China (13%) and the Russian Federation (10%). Among RR -TB cases, an
estimated 82% had multidrug -resistant TB (MDR -TB) and this can bring an important problem
for the public health studies .
3.There are a lot of new cases and also previously treated cases with MDR/RR -TB. Even
though it is knows that among HIV infected people it will always be a high increase of risk for
TB, over the years it has been estimated that the percentage has fallen, the cases decreasing in
number.
The TB mortality rate (TB deaths among HIV -negative people per 100 000 population per
year) is falling at about 3% per year, and the best estimate for the overall reduction during
2000 –2017 is 42 %. The fastest declines in mortality rates in the 5 -year period 2013 –2017 were

10
in the WHO European Region and WHO South -East Asia Region (11% and 4% per year,
respectively).
Worldwide, TB incidence (new cases per 100 000 population per year) is starting to
decrease with 2% per year.
In 2017, the best estimate of the proportion of people with TB who died from the disease
(the case fatality ratio, CFR) was 16%, down from 23% in 2000. The CFR needs to fall to
10% by 2020 to reach the first milestones of the End TB Strategy. There is considerable
country variation in the CFR demonstrating large inequalities among countries in access to
TB diagnosis and treatment.
Vital registration systems and national notification need to be empowered towards the
goal of dir ect measurement of TB incidence and mortality in all countries. National TB
prevalence surveys provide an approach to directly measuring the burden of TB disease in
an important subset of high TB burden countries .

The burden of tuberculosis (TB) disea se can be measured in terms of:
incidence – the number of new and relapse cases of TB arising in a given time period, usually
1 year;

prevalence – the number of cases of TB at a given point in time; and

mortality – the number of deaths caused by TB in a given time period, usually 1 year.

Global miles tones and targets for reductions in the bur den of TB disease have been set as part of
the Sustainable Development Goals (S DGs) and WHO’s End TB Strategy . SDG 3 includes a
target to end the global TB epidemic by 2030, with TB incidence (new and relapse cases per
100000 population per year) defined as the indicator for measurement of progress. The 2030
targets set in the End TB Strategy are an 80% reduction in TB incidence, compared with levels in

11
2015 and a 90% reduction in TB deaths. Targets for 2035 and milestones for 2020 and 2025 have
also been defined ( Table I).

Table I . Targets for percentage reductions in TB disease burden set in WHO’s End TB
Strategy
MILESTONES TARGETS
INDICATORS 2020 2025 2030 2035
Percentage reduction in the
absolute number of TB
deaths per year 35% 75% 90% 95%
Percentage reduction in the
TB incidence rate (new and
relapse cases per 100 000
population per year) 20% 50% 80% 90%
Source: Global Tuberculosis Report 2018

WHO updates its estimates of t he burden of TB disease every year , using the latest analytical
methods and available data. Since 2006, concerted efforts have been made to improve the available
data and methods used, under the umbrella of the WHO Global Task Force on TB Impact
Measurement .

12
3.1 TB incidence

Methods to e stimate TB incidence
TB incidence has never been measured at national level because this would require long -term
studies among large cohorts (hundreds of thousands) of people, which would involve high costs
and challenging logistics. Notifications of TB cases provide a good pr oxy indication of TB
incidence in countries that have a high-performance surveillance systems (e.g. with little
underreporting of diagnosed cases), and in which the access and the quality of health care means
that there will be few cases that will not be diagnosed.

The last goal is to directly measure TB incidence from TB notifications in all countries. This
requires better quantification of underreporting (i.e. the number of cases that are somehow missed
by surveillance systems) a combination of st rengt hened surveillance, and universal health
coverage. A TB surveillance checklist developed by the WHO Global Task Force on TB Impact
Measurement defines the objectives and standards that need to be met in order for the data to
provide a dir ect measure of TB inci dence.37 By August 2018, a total of 68 countries, including 26
of the 30 high TB burden countries had completed the checklist, often in association with a
national TB epidemiological review ( Fig. 5 ).

Methods currently used by WHO to estimate TB incidence can be grouped into four major
categories, ( Fig. 6 ):

13

Fig 5 Epidemiological Reviews
Source: Global Tuberculosis Report 2018

14

Fig 6 Main methods used to estimate TB Incidence
Source: Global Tuberculosis Report 2018

Results from TB prevalence surveys.
Incidence is estimated using estima tes of the duration of disease and prevalence survey results
with the latter derived from a model that accounts for the impact of HIV co -infection and
antiretroviral therapy (ART) on the distribution of disease duration. This method is used for 22
countries, of which 22 have national survey data and one – India – has a survey in one state. The
23 countries accounted for 60% of the estimated global number of incident cases in 2017 .

The underlying principle for the WHO Global Task Force on TB Impact Measurement since
its establishment in 2006 has been that estimates of the level of and trends in TB disease burden
should be based on direct measurements from routine surveillance and surveys as much as
possible, as opposed to indirect estimates that rely on modelling and expert opinion.

15
Estimates of TB incidence in 2017

Globally in 2017, there were an estimated 10.0 million incident cases of TB , equivalent to 133
cases per 100 000 population.
Most of the estim ated number of cases in 2017 oc curred in the WHO South -East Asia Region
(44%), the WHO African Region (25%) and the WHO Western Pacific Region (18%); smaller
proportions of cases occurred i n the WHO Eastern Mediterranean Region (7.7%), the WHO
Region of the Americas (2.8%) and the WHO European Region (2.7%). The 30 high TB burden
countries accounted for 87% of all estimated incident cases worldwide, and eight of these countries
accounted for two thirds of the global total: India (27%), China (9%), Indonesia (8%), the
Philippines (6%), Pakistan (5%), Nigeria (4%), Bangladesh (4%) and South Africa (3%) ( Fig.7 )

Fig 7 Estimated TB incidence in 2017,for countries with at least 100.000 incident cases
Source: Global Tuberculosis Report 2018

The problem of national TB epidemics based on the annual number of incident TB cases relative
to population size (the inc idence rate) varied widely among countries in 2017. There were under
10 incident cases per 100 000 population in most high -income countries, 150 –400 in most of the

16
30 high TB burden countries ( Fig. 8 ), and above 500 in a few countries including the Democratic
People’s Republic of Korea, Lesotho, Mozambique, t he Philippines and South Africa.

Fig 8 Estimated TB Incidence rates,2017
Source: Global Tuberculosis Report 2018

Among people living with HIV there was a n estimated 9% of t he incident TB cases in 2017 .
The proportion of TB cases co -infected with HIV was highest in countries in the WHO African
Region, exceeding 50% in parts of southern Africa ( Fig. 9 ).
The risk of developing TB in the 37 million people living with HIV was much higher than the
risk in th e rest of the world population , increasing with a decreasing prevalence of HIV in the
general population.

17

Fig 9 .Estimated HIV prevalence in new and relapse TB cases,2017
Source: Global Tuberculosis Report 2018

Consistent with previous global TB reports, the number of incident cases decreases , in both
absolute terms and per capita ( Fig. 10 , Fig. 11 ).
Globally, the average rate of decline in the TB incidence rate was 1.5% per year in the period
2000−2017, and 1.8% between 2016 and 2017.
This needs to accelerate to 4 –5% per year by 2020 and to 10% per year by 2025, to achieve the
milestones for reductions in cases and deaths set in the End TB Strategy .

18

Fig. 10 Global trends in the estimated number of incident TB cases and the number of TB
deaths
Source: Global Tuberculosis Report 2018

Fig 11 Global trends in estimated TB incidence and mortality rates, 2000 -2007
Source: Global Tuberculosis Report 2018

19
The fastest declines in the 5 -year period 2013 –2017 were in the WHO European Region .In the
same period, also an amazing reduction (4–8% per year) occurred in southern Africa , following
a peak in the HIV epidemic and the expansion of TB and HIV care and prevention, and in the
Russian Federation following intensified efforts to reduce the burden of TB .

3.2 TB mortality
Deaths from TB amo ng HIV -negative people are described as TB deaths in the most recent version
of the International classification of di seases (ICD -10). When an HIV -positive person dies from
TB, the of death is classified as HIV. For consistency with these international classifications, this
section makes a clear distinction between TB deaths in HIV -negative people and TB deaths in
HIV-positive people.

Methods to estimate TB mortality
TB mortality among H IV-negative people can be meas ured directly using data from national vital
registration (VR) systems, provided that these systems have high coverage and that causes of death
are accurately determined and coded according to ICD -10. Sample VR systems covering
representative areas of the country can provide an interim solution. Mortality surveys can also be
used to estimate deaths caused by TB.
In 2017, most countries with a high burden of TB lacked national or sample VR systems, and few
had conducted mortality surveys. In the absence of VR systems or mortality surveys, TB mortality
can be estimated as the product of TB incidence and the case fatality ratio (CFR), or through
ecological modelling using mortality data from countries with VR systems.
TB mortality a mong HIV -positive people is very hard to measure, even when VR systems are in
place, because this deaths among HIV -positive people are coded as HIV deaths, and contributory
causes (e.g. TB) are often not reliably assessed and recorded. TB deaths among HIV -positive
people were estimated as the product of TB incidence and the CFR, with the latter accounting for
the protective effect of ART.

20

Until 2008, WHO estimates of TB mortality used VR or mortality survey data for only three
countries. This was substantially impro ved to 89 countries in 2009, al though most of the data were
from countries in the WHO European Region and the WHO Region of the Americas, which were
not having too many case detections. For the current report, VR or mortality survey da ta were
used for 127 countries , which collectively accounted for 57% of the estimated number of TB
deaths (among HIV -negative people) globally in 2017. For 19 countries, analyses of VR data and
resulting estimates of TB deaths published by the Institute of Health Metrics and Evaluation
(IHME) at the University of Washington, USA, were used. The W HO African Region has the
great est need to introduce or strengthen VR systems in which causes of death are classified
according to ICD -10.

Estimates of TB mortality in 2017
There were an estimated 1.3 million deaths from TB among HIV -negative people in 2017 and an
additional 300 000 deaths fro m TB among HIV -positive people.
TB is the tenth leading cause of death worldwide, and since 2011 it has been the leading cause
of death from a single infectious agent, ranking a bove HIV/AIDS. Most of these deaths could be
prevented wi th screening, early diagnosis and an appropri ate treatment .For example, among
people whose TB was detected, reported and treated in 2016, the treat ment success rate was 82%
globally ; and in high -income countries with universal health coverage, the proportion of people
who die from TB can be under 5% .
Most of TB deaths among HIV -negative people occurred in the WHO African Region and the
WHO South -East Asia Region in 2017; these regions accounted for 85% of the combined total of
TB deaths in HIV -negative and HIV -positive people. India accounted for 32% of gl obal TB deaths
among HIV -negative people, and for 27% of the combined total TB deaths in HIV -negative and
HIV-positive people.

21
Globally, the number of TB deaths among HIV -negative people per 100 000 population was 17
in 2017, and 21 when TB deaths among H IV-positive people were included. T here was
considerable variation among countries , ranging from less than one TB death per 100 000
population in many high -income countries, to 40 or more deaths per 100 000 population in much
of the WHO African Region and in four high TB bur -den countries in Asia (the Democratic
People’s Republic of Korea, Indonesia, Myanmar and Papua New Guinea).

Estimated tr ends in TB mortality, 2000 –2017
Worldwide , the absolute number of deaths caused by TB among HIV -negative people h as been
falling since 2000, from a best estimate of 1.8 million in 2000 to 1.3 million in 2017 ( Fig. 10 ), a
reduction of 29%. The reduction since 2015 was 5%. The TB mortality rate (TB deaths among
HIV-negative people per 100 000 population per year) fell by 42% globally between 2000 and
2017 ( Fig. 11 ), and by 3.2% between 2016 and 2017.38
Mortality rates among HIV -negative people have been falling in all six WHO regions since
2000, but the rate of decline varies . For example, in the 5 -year pe riod 2013 –2017, the fastest
average rates of decline were in the WHO European Region (11% per year) and the WHO South –
East Asia Region (4.3% per year), and the slowest rate was in the WHO African Region (1.7% per
year).
Trends also vary markedly among t he 30 high TB burden countries , ranging from substantial
reductions (more than 50%) since 2000 (e.g. in Cambodia China, Ethiopia, Myanmar, t he Russian
Federation and Vietn am) to limited changes (e.g. in Angola and Congo). High TB burden countries
with rates o f decline in TB deaths among HIV -negative people that exceeded 6% per year in the
5-year period 2013 –2017 included the Russian Federation (13% per year), Ethiopia (12% per
year), Sierra Leone (10% per year), Kenya (8% per year) and Viet Nam (8% per year) .
Globally, the number of TB deaths among HIV -positive people has fallen by 44% since 2000,
from 534 000 (range, 460 000 –613 000) in 2000 to 300 000 (range, 266 000 –335 000) in 2017,
and by 20% since 2015. Most of this reduction was in the WHO African Region . In several high
TB burden countries, the number of deaths caused by TB among HIV -positive people has fallen

22
substantially in recent years; for example, in Cambodia, Kenya, Namibia, South Africa, the United
Republic of Tanzania and Zimbabwe .

3.3 Estimates of the dis ease burden caused by MDR/RR -TB
Globally in 2017, an estimated 3.5% (95% confidence interval [CI]: 2.5 –4.7%) of new cases
and 18% (95% CI: 6.3 –34%) of previously treated cases had MDR/RR -TB .39
There were an estimated 558 000 i ncident cases of MDR/RR -TB in 2017. As before, however,
the proportion of cases estimated to have MDR -TB was 82% (460 000 out of 560 000) .The
countries with the largest numbers of MDR/RR -TB cases were China, India and the Russian
Federation .There were ab out 230 000 deaths from MDR/RR -TB in 2017, similar to the best
estimate for 2016 that was published in the 2017 edition of the WHO global TB report.
Data compiled from surveys and continuous s urveil lance of drug resistance among TB patients
also allow est imation of the number of MDR/RR -TB cases among notified TB patients with
pulmonary TB. These are the MDR/RR -TB cases that could be detected if all notified patients
were tested for drug resistance using WHO -recommended diagnostic tests. Globally in 2017, t here
were an estimated 330 000 MDR/RR -TB cases among notified TB patients.

3.4 National TB prevalence surveys
The prevalence of TB disease is not an indicator in the SDGs or a high -level indicator of the End
TB Strategy, and no global target has been set for the period 2016 –2035. Furthermore, indirect
estimates of prevalence suffer from considerable uncertainty, because they are derived from
estimates of incidence and assumptions about dis ease duration.
Nonetheless, in an important subset of countries wit h a large proportion of the world’s TB
burden, national TB prevalence surveys continue to provide the best method for measuring the
burden of TB disease (both in absolute terms and to assess trends when repeat surveys are done,
and by age and sex). Finding s can inform assessment of actions needed to reduce this burden as
well as estimates of TB incidence ( Fig. 11 ), thus contributing to the monitoring of progress towards
SDG and End TB Strategy targets. The WHO Global Task Force on TB Impact Measurement has

23
retained national TB prevalence surveys within its strateg ic areas of work for 2016 –2020 , and has
defined the group of countries where they continue to be relevant as those with a relatively high
burden of TB (about 150 incident cases per 100 000 populati on) that do not yet have health,
national notification and VR systems of the quality and coverage required to provide reliable and
routine direct measurements of the number of TB cases and deaths. The most recent example of a
prevalence survey that has inf ormed understanding of trends in TB disease burden, estimates of
TB incidence and identification of actions required to reduce the burden of TB disease is the 2016
survey in the Philippines.38,39
An unprecedented number of surveys were implemented in 2007 –2015, a period in which the
WHO Global Task Force on TB Impact Measurement defined national TB prevalence surveys in
22 global focus countries as one of its three strategic areas of work . Between 2007 and the end of
2017, a total of 25 surveys that u sed the screening and diagnostic methods recommended in the
second edition of the WHO handbook on prevalence surveys40 were completed. No surveys were
completed in 2017; however, in early 2018, field operations were completed in the first national
survey i n Namib ia and a repeat survey in Vietn am (following a first survey in 2007).
In Asia and some African countries (e.g. Ghana, Malawi, Rwanda, the United Republic of
Tanzania and Zimbabwe), prevalence increases with age. However, in several African countri es
(e.g. Ethiopia, Gambia, Nigeria, Sudan, Uganda and Zambia), prevalence per 100 000 population
peaks among those aged 35 –54 years. The male to fema le (M:F) ratio of cases for the same set of
surveys sh ows a systematically higher burden of TB disease amon g men. The ratio of prevalence
to notifications (P:N) from surveys implemented in 2007 –2017 suggest that women are accessing
available diagnostic and treatment services more effectively than men. The higher disease burden
in men, combined with larger gaps i n detection and reporting, also suggests that there is a need for
strategies to improve access to and use of health services among men.

24
Personal part
4.1. Aim of the study :
The aim of this study is to provide information on the association between tuberculosis
treatment -the success rate and GNI-an indicator related to income per different countries.

4.2. Objectives of the study:

1. To evaluate the TB incidence
2. To evaluate the tuberculosis case detection
3. To assess the GNI Distribution
4. To assess th e TB treatment rate success
5. To assess the relationship between TB treatment rate success and GNI

4.3. Hypothesis of the study :
Incidence of tuberculosis:
1. The highest incidence of TBC is in Romania,in comparison to: Afghanistan, Australia,
Egypt,Germany,Israel,Kuwait,Lebanon,Moldova, and Somalia .
2. The highest incidence of TBC can be found in the lower middle income countries in
comparison to other groups of income:low income,high income and upper middle
income.
3. The highest incidence of TBC i s in Sub-Saharan Africa in comparison to other regions
from the world : East Asia & Pacific , Middle East & North Africa , Europe & Central
Asia and South Asia
Tuberculosis case detection
4. Tuberculosis case detection was higher in Somalia, in comparison to the other countries :
Afghanistan, Australia, Egypt,Germany,Israel,Kuw ait,Lebanon,Moldova, and Romania.
 The one that has the lowest case detection is the Sub -Saharan Africa

25
 TBC case detection was found mostly in the high income group
GNI Distribution
 The highest GNI indicator is in Germany ,in comparison to Afghanistan, Australia,
Egypt,Somalia ,Israel,Kuw ait,Lebanon,Moldova, and Romania.
Tuberculosis treatment success rate
 The highest rate of the treatment success of tuberculosis was in Germany ,in comp arison
to Afghanistan, Australia, Egypt,Somalia ,Israel,Kuw ait,Lebanon,Moldova, and Romania
 The highest treatment success rate was found in East Asia & Pacific
 The lowest treatment success rate was found in the low income group.

Comparison on GNI and Tuberculosis treatment success rate
 The high income group has the highest rate of success of the TBC treatment
 There is no correlation between the upper middle income group and the treatment success
of tuberculosis
 The lower middle income group has a str ong negative correlation with the tuberculosis
treatment success
 The low income group has a negative correlation with the tuberculosis treatment success
 Germany has a strong positive correlation with the treatment success of TBC
 Somalia has a positive correlation with the treatment success of TBC

26
4.4. Material and methods

4.4.1.The study design :
For evaluating the association between the success rate of the treatment of tuberculosis and GNI
we conduct an observational transverse descriptive study (cross -section study) between 1 July
2009 to 1 July 2017 . Data was taken from Worl d Data Bank( https://data.worldbank.org/ ). The
data was about the incidence of tuberculosis, the tuberculosis case detection, th e rate of success
of the tuberculosis treatment and about the GNI .
The gross national income (GNI), previously known as gross national product (GNP ), is the total
domestic and foreign output claimed by residents of a country, consisting of gross domestic
product ( GDP ), plus factor incomes earned by foreign residents, minus income earned in the
domestic economy by nonres idents . Comparing GNI to GDP shows the degree to which a
nation's GDP represents domestic or international activity. GNI has gradually replaced GNP in
international statistics. While being conceptually identical, it is calculated differently. GNI is the
basis of calculation of the largest part of contributions to the budget of the European Union .
Gross national product (GNP ) is the market value of all the goods and services produced in one
year by labor and property supplied by the citizens of a country. Unlike gross domestic product
(GDP), which defines production based on the geographical location of production, GNP
indicates allocated production based on location of ownership. In fact it calculates income by the
location of ownership and residence, and so its name is also th e less ambiguous gross national
income . GNP is an economic statistic that is equal to GDP plus any income earned by residents
from overseas investments minus income earned within the domestic economy by overseas
residents. GNP does not distinguish between qualitative improvements in the state of the
technical arts (e.g., increasing computer processing speeds), and quantitative increases in goods
(e.g., number of computers produced), and considers both to be forms of " economic growth ".
When a country's capital or labour resources are employed outside its borders, or when a foreign
firm is operating in its territory, GDP and GNP can produce different measures of total output.
The term gross national income (GNI) has gradually replaced the Gross national product (GNP)

27
in international statistics. While being conceptually identical, the precise calculation method has
evolved at the same time as the name change.

A descriptive stu dy allows comparison among countries and populations. It provides basis for
services planning, prediction and evaluation. a population sample is selected and actively
examined through interview +/ – clinical/lab examination, in order to identify concomitant ly
exposures (risk factors) and effects (diseases).
Data from World Data Bank brings a lot of facilities. It allows an easier access to a larger part of
the population. It is a cheap tool that is easily shared on the internet and allows the collection and
analysis of data from the population.
For these reasons we chose to use the online site World Data Bank in order to collect data about
the population.

4.4.2.Population of the study:
We collected data about the population of each country. The countri es from which the data was
taken were : Afghanistan,Australia,Egypt,Germany,Israel,Kuwait,Lebanon,Moldova,Romania
and Somalia. These countries are included in different categories as, based on the GNI
distribution and based on the geographic zone. We also took general data about the world
divided in categories of GNI and geographic areas as to have a better look of the real
association of the rate of success of TBC treatment and the GNI distribution.

We grouped the countries based on the geographic area:
South Asia -Afghanistan
Sub-Saharan Africa -Somalia
East Asia & Pacific -Australia
Middle East & North Africa – Israel ;Kuwait; Egypt; Lebanon
Europe & Central Asia -Moldova ;Romania ;Germany

28

We also grouped the countries based on the GNI Distribution :
Low income -Somalia ;Afghanistan
High income -Australia; Germany; Israel ; Kuwait
Upper middle income – Lebanon ; Romania
Lower middle income – Egypt ; Moldova

4.4.3 Tool of study:

We used the internet in order to have a connection through https://data.worldbank.org/
Information was searched using indicators about the subject like :
 Geographic area
 tuberculosis incidence
 tuberculosis case detection  tuberculosis treatment success
 GNI
After getting the information from the internet, we used Microsoft Word and Microsoft Excel
2013 in order to work on descriptive statistics. We calculated the mean , the correlation
coefficients and the p value for the association between each category of the GNI distribution
and the rate of success of the treatment for TBC. Correlation coefficients are used in statistics to
measure how strong a relationship is between two variables. There are s everal types of
correlation coefficient, one of them being Pearson’s correlation (also called Pearson’s R) is a
correlation coefficient commonly used in linear regression.
Correlation coefficient formulas are used to find how strong a relationship is betwe en data.
(Fig 12 ) The formulas return a value between -1 and 1, where:
 1 indicates a strong positive relationship.
 -1 indicates a strong negative relationship.
 A result of zero indicates no relationship at all.

29
Fig. 12 Correlation coefficient

Meaning
 A correlation coefficient of 1 means that for every positive increase in one variable, there
is a positive increase of a fixed proportion in the other. A correlation coefficient of -1
means that for every positive increase in one variable, there is a negat ive decrease of a
fixed proportion in the other.
 Zero means that for every increase, there is not a positive or negative increase. The two
just are not related.

4.5 Results
Incidence of tuberculosis (per 100,000 people)
Incidence of tuberculosis is the esti mated number of new and relapse tuberculosis cases arising
in a given year, expressed as the rate per 100,000 population. All forms of TB are included,
including cases in people living with HIV.
Having the data collected from World Data Bank, we observed that the highest incidence of
tuberculosis was found in Somalia with a number of 286 cases during the year of 2009. With the

30
time passing by, the incidence of tuberculosis started to decrease, in Somalia getting to a
number of 266 cases, having a slow d ecrease in the number of cases.
On the second place for the high incidence is Afghanistan ,with a number of 189 cases during
all the period of 2009 -2017 and on the third place for the high incidence is Moldova ,having in
2009 a number of 126 cases.
The country that has almost no cases of new or relapsed tuberculosis cases is Israel, one of the
high income countries with only 3.2 cases in 2017.(Table I ) (G raph 1 )

Table I. Incidence of tuberculosis for :Afghanistan, Australia, Germany, Israel, Kuwa it,Egypt,
Lebanon, Moldova,Romania and Somalia
Country
Name Period

2009 2010 2011 2012 2013 2014 2015 2016 2017
Afghanistan
Number of cases 189 189 189 189 189 189 189 189 189
Australia 6.8 6.5 6.3 6.6 6.2 6.5 6.1 6.6 6.8
Germany 5.9 5.8 5.8 5.7 5.9 6.1 8 8.1 7.5
Israel 5.5 5.3 6.3 7.6 4.5 5.2 4 3.5 3.2
Kuwait 38 37 24 25 22 22 22 24 27
Lebanon 14 14 12 15 15 14 13 12 12
Moldova 126 116 119 124 127 115 102 101 95
Romania 115 105 97 92 89 86 82 74 72
Somalia 286 286 286 286 285 274 274 270 266
Egypt 19 18 17 17 16 15 15 14 13

31
Graph 1 Incidence of tuberculosis during 2009 -2017 for: Afghanistan, Australia, Germany,
Israel, Kuwait, Egypt, Lebanon, Moldova,Romania and Somalia

Comparing the data from World Data Bank, we also wanted to see how the numbers of each
category for the GNI and Geographic distribution looked like.
Taking into account the incidence of tuberculosis for the entire world that divided by GNI, it
can be ob served that most of the cases were presented in the Lower middle and Low income
with a number of 222 and 206 new or relapsed cases(per 100.000 people).(Table II ).
The only category that has few cases of tuberculosis is the High income category . (Gr aph 2)

050100150200250300350
2009 2010 2011 2012 2013 2014 2015 2016 2017Cases of Tuberculosis
(per 100,000 people)Incidence of Tuberculosis
Afghanistan Australia Germany Israel Kuwait
Lebanon Moldova Romania Somalia Egypt, Arab Rep.

32
Table II Incidence of tuberculosis in the income groups for 2009 -2017
Country
Name Period

2009 2010 2011 2012 2013 2014 2015 2016 2017
High
income
Number of cases 16 15 15 14 14 13 13 13 12
Upper
middle
income 93 88 87 85 81 78 75 71 69
Lower
middle
income 257 252 248 244 240 236 231 227 222
Low
income 257 250 242 236 227 222 216 211 206

Graph 2 Incidence of tuberculosis in the income groups for 2009 -2017
050100150200250300
2009 2010 2011 2012 2013 2014 2015 2016 2017Number of cases
YearsIncidence of tuberculosis (per 100,000 people)
High income Upper middle income Lower middle income Low income

33
Dividing the world by geographic areas, it can be observed that most of the cases discovered
were from Sub -Saharan Africa with a number of 333 cases in 2009 and South Asia with a
number of 248 cases also in 2009. The data from the entire World brings data from all the
regions and describes a slow decrease in the number of cases of TBC. (Table III) (Graph 3)
Table III Incidence of tuberculosis from different geographic zones
Geographic area Period
2009 2010 2011 2012 2013 2014 2015 2016 2017
South Asia
Number of cases 248 243 238 233 229 224 220 215 210
East Asia &
Pacific 170 148 144 143 142 139 137 136 134
Europe &
Central Asia 53 45 43 41 38 37 35 33 32
Sub-Saharan
Africa 333 319 310 302 292 281 272 261 246
Middle East &
North Africa 46 36 36 35 34 33 33 33 32
World 170 157 153 151 148 145 142 139 136

Graph 3 Incidence of tuberculosis for different geographic zones based on the number of cases
050100150200250300350
2009 2010 2011 2012 2013 2014 2015 2016 2017Number of cases
Years
East Asia & Pacific Europe & Central Asia Sub-Saharan Africa
Middle East & North Africa World South Asia

34
TBC CASE DETECTION (%, all forms)

About the tuberculosis case detection, all the cases were described as a percentage. During the
period of 2009 -2017, there were countries that presented the same percentage 87 % of case
detection on TBC forms (Table IV) (Graph 4)
Those countries were Australia, Germany, Israel, Kuwait, Lebanon, Moldova and Romania.
Most of the countries are from the High income areas.
The one that proved to have the lowest percentage of detection was found in Somalia during the
year of 2009 with o nly 33% detection.(Table IV)

Table IV Percentage of TBC Case Detection for 2009 -2017 in : Afghanistan, Australia,
Egypt,Germany, Israel, Kuwait, Lebanon, Moldova,Romania and Somalia

Country Name Period
2009 2010 2011 2012 2013 2014 2015 2016 2017
Afghanistan
Percentage(%) 49 51 50 49 51 51 56 64 70
Australia 87 87 87 87 87 87 87 87 87
Egypt, Arab
Rep. 63 62 60 57 54 53 58 61 63
Germany 87 87 87 87 87 87 87 87 87
Israel 87 87 87 87 87 87 87 87 87
Kuwait 87 87 87 87 87 87 87 87 87
Lebanon 87 87 87 87 87 87 87 87 87
Moldova 87 87 87 87 87 87 87 87 87
Romania 87 87 87 87 87 87 87 87 87
Somalia 33 29 33 33 35 35 37 37 42

35

Graph 4 Tuberculosis case detection for the countries with the same percentage of detection(%
,all forms) fduring 2009 -2017

Taking into discussion the TBC case detection based on the geographic areas, most of the cases
were discovered in Europe &Central Asia with 89 % cases in 2009 , followed by Middle East &
North Africa with 78 % cases ,also in 2009. (Graph 5) The one that has the lowest case
detection is the Sub -Saharan Africa with only 49% -50% during the period of 2011 -2015.(Table
V)
In the World, the TBC is detected in 64 % of the cases, being discovered in more than half of
the cases . (Gra ph 6)

87%13%Tuberculosis case detection (%, all forms) for
Australia, Germany, Israel, Kuwait, Lebanon, Moldova and
Romania
Case detection (% all
forms)
Not detected

36
Table V. Percentage of tuberculosis case detection based on the geographic area during 2009 –
2017
Geographic
area Period
2009 2010 2011 2012 2013 2014 2015 2016 2017
East Asia &
Pacific
Percentage(%) 60 60 62 63 63 63 64 67 68
Europe &
Central Asia 89 87 87 87 91 87 88 88 87
Middle East &
North Africa 78 77 77 77 76 76 74 75 75
Sub-Saharan
Africa 51 52 50 50 49 50 50 52 52
South Asia 46 46 46 46 46 57 59 64 66
World 54 55 55 55 55 59 60 63 64

Graph 5.Tuberculosis case detection in the geographic areas (evolution through 2009 -2017)

0102030405060708090100
2009 2010 2011 2012 2013 2014 2015 2016 2017PERCENTAGE(%)
YEARSTBC CASE DETECTION (%, ALL FORMS)
World Europe & Central Asia Middle East & North Africa
Sub-Saharan Africa East Asia & Pacific South Asia

37
Graph 6 Tuberculosis case detection in the world

In the GNI Distribution, the TBC case detection was found mostly in the high income group
with a percentage of 88% ,followed by the upper middle income group with a percentage of
82% all during the year of 2017.(Table VI)(G raph 7)
Table VI Tuberculosis case detection in the income groups during 2009 -2017
GNI
Distribution Period
2009 2010 2011 2012 2013 2014 2015 2016 2017
High
income
Percentage(%) 89 89 89 89 88 86 88 88 88
Low
income 52 53 54 54 54 54 56 58 60
Lower
middle
income 45 45 46 46 46 52 54 58 60
Upper
middle
income 82 82 82 80 81 81 81 81 82

64%36%TBC CASE DETECTION IN THE WORLD
Detected
Not detected

38
Graph 7 Tuberculosis case detection in the income groups for each year (2009 -2017)

GNI Distribution
Grouping the countries based on the income, most of them were from the high income
group(40%) and the others were divided equally (20%) (Graph 8)
Graph 8 The world divided by income groups

0102030405060708090100
2009 2010 2011 2012 2013 2014 2015 2016 2017Percentage(%)
YearsTBC CASE DETECTION (%, ALL FORMS)
High income Upper middle income Low income Lower middle income
40%
20%
20%
20%Distribution of the countries by percentage
High income
Upper middle income
Lower middle income
Low income

39
GNI is different from country to country. During the years, it has been seen an improve in the
GNI for every country . The highest GNI is found in Germany, followed by Australia. The
lowest GNI is found in Somalia. (Table VII)

Table VII. GNI distribution for : Afghanistan, Australia, Egypt,Germany, Israel, Kuwait,
Lebanon, Moldova,Romania and Somalia during 2009 -2017(par t 1)
Country name Period
2009 2010 2011 2012 2013
Afghanistan
GNI($) 41174067764 47883270733 48940525323 56348385997 60580430923
Australia 8.42099E+11 8.31622E+11 8.98285E+11 9.42265E+11 1.03355E+12
Egypt, Arab
Rep. 7.63963E+11 7.96236E+11 8.22212E+11 8.58143E+11 8.88805E+11
Israel 2.00851E+11 2.15563E+11 2.34225E+11 2.44686E+11 2.69734E+11
Kuwait 2.43335E+11 2.37103E+11 2.6191E+11 2.8799E+11 2.96876E+11
Lebanon 63521870572 68984943489 71578309882 75038478834 78267879820
Moldova 13299430405 14787863158 16102758557 16731882437 18551742601
Romania 3.30773E+11 3.38276E+11 3.5476E+11 3.73164E+11 3.87062E+11
Somalia 39174067764 40174067664 41173066564 46174067764 47374067764
Germany 3.27E+12 3.51E+12 3.59E+12 3.73E+12 3.89E+12

40
Table VII. GNI distribution for : Afghanistan, Australia, Egypt,Germany, Israel, Kuwait,
Lebanon, Moldova,Romania and Somalia during 2009 -2017(part 2)
Country name Period
2014 2015 2016 2017
Afghanistan
GNI($) 63120259411 65323494743 67379050560 70416279905
Australia 1.06985E+12 1.07965E+12 1.09206E+12 1.16007E+12
Egypt, Arab
Rep. 9.32947E+11 9.90986E+11 1.0512E+12 1.1081E+12
Israel 2.78042E+11 2.98147E+11 3.14569E+11 3.30295E+11
Kuwait 3.09382E+11 3.18783E+11 3.3696E+11 3.44617E+11
Lebanon 80653925682 81868280499 83870624120 87258840773
Moldova 19686641104 19234326618 20373146728 21610786014
Romania 4.05393E+11 4.18648E+11 4.57928E+11 5.08134E+11
Somalia 51174067764 54174063950 57173062752 59174067764
Germany 3.27E+12 3.51E+12 3.59E+12 3.73E+12

Dividing the world based on the income, it can be seen the differences between the years and
between the catego ries of the income. Using this table(Table VIII) we will correlate the rate of
success of the treatment of TBC with the GNI distribution.

41
Table VIII GNI in the income groups for 2009 -2017(part 1)
GNI Period
2009 2010 2011 2012 2013
High
income
GNI($) 4.40145E+13 4.61485E+13 4.83879E+13 5.01539E+13 5.21309E+13
Low income 9.41961E+11 1.02431E+12 1.07302E+12 1.15311E+12 1.24428E+12
Lower
middle
income 1.19138E+13 1.29732E+13 1.39804E+13 1.49669E+13 1.60151E+13
Upper
middle
income 2.62536E+13 2.86075E+13 3.14002E+13 3.3838E+13 3.59702E+13

Table VIII GNI in the income groups for 2009 -2017(Part 2)

GNI Period
2014 2015 2016 2017
High income
GNI($) 5.39637E+13 5.5721E+13 5.72479E+13 5.94243E+13
Low income 1.33337E+12 1.39214E+12 1.45388E+12 1.55781E+12
Lower middle
income 1.73031E+13 1.85512E+13 1.98339E+13 2.12927E+13
Upper middle
income 3.84231E+13 3.99907E+13 4.22941E+13 4.52199E+13

42
Tuberculosis treatment success rate (% of new cases)
Tuberculosis treatment success rate in the countries taken into discussion presents the countries
with the highest rate of success : Kuwait with 97% of new cases during the year of 2014,
Afghanistan with 93% cases during 2016 (Graph 9 ) and Egypt with 91% of new cases in 2009.
The lowest rat e of tuberculosis treatment success rate was found in Somalia 63% in 2009. T able
IX .
Table IX Tuberculosis treatment success for : Afghanistan, Australia, Egypt,Germany, Israel,
Kuwait, Lebanon, Moldova,Romania and Somalia during 2009 -2016
Country
Name Period
2009 2010 2011 2012 2013 2014 2015 2016
Afghanistan
Percentage(%) 86 86 88 88 88 87 88 93
Australia 80 82 78 82 85 79 73 78
Egypt, Arab
Rep. 91 89 88 88 86 84 85 87
Germany
76 76 73 74 67 63 65 70
Israel 88 84 78 81 84 89 83 79
Kuwait 86 89 87 87 82 97 87 87
Lebanon 85 81 78 71 71 76 83 85
Moldova 69 67 73 76 80 79 80 83
Romania 87 84 86 85 85 85 85 86
Somalia 63 74 86 88 86 86 84 88

43
Graph 9 Tuberculosis treatment success in Afghanistan in 2016

Based on the regi on, the highest treatment success was found in East Asia & Pacific, with rates
of 89% in 2016 (Table X) and with above 90 % during the other years. ( Graph 10 )
Table X Tuberculosis treatment success based on the geographic regions
Country Name Period
2009 2010 2011 2012 2013 2014 2015 2016
East Asia & Pacific
Percentage(%) 90 91 91 90 91 90 90 89
Europe & Central
Asia 75 74 73 76 76 76 77 77
Middle East & North
Africa 87 86 84 83 86 85 87 87
South Asia 89 89 90 88 89 79 78 76
Sub-Saharan Africa 76 73 79 81 79 81 83 82

93%7%Tubercuosis treatment success rate in Afghanistan
(% of new cases)
Treatment success
Treatment non-success

44
Graph 10 .East Asia&Pacific Tuberculosis treatment success in 2016

Knowing the division of the income in the world, the high income category was not found to
have a good rate of success of the treatment of tuberculosis (Graph 11) During the year of
2016,the percentage of success varied from 73% to 87% (table XI)

Table XI . Tuberculosis tre atment success in the income groups from 2009 -2016
Country Name Period
2009 2010 2011 2012 2013 2014 2015 2016
High income
Percentage(%) 67 68 68 72 72 71 71 73
Low income 82 83 85 86 86 86 86 87
Lower middle income 87 87 87 87 87 81 81 79
Upper middle income 84 81 87 87 87 86 87 87

91%
9%East Asia & Pacific Tuberculosis treatment success in
2016
Success
Not successful

45
Graph 11 Tuberculosis treatment success in the income groups for each year (from 2009 -2016)

0102030405060708090100
2009 2010 2011 2012 2013 2014 2015 2016percentage of treatment success
yearsTuberculosis treatment success rate (%of new cases)
High income Low income Lower middle income Upper middle income

46
Comparison between GNI and Tuberculosis treatment success rate

High income countries and tuberculosis treatment success
The values for GNI and also for the success rates were written in Table XII.
The values were taken from 2009 -2016. The income values from the GNI were considered X
values,while the values from the tuberculosis treatment success were considerated Y values.
(Graph 12) The mean for the X values was 50971037500000 $ and the mean for the Y Values
was 70.25.
The value of R is 0.8589.This is a strong positive correlation, which means that high X variable
scores go with high Y variable sco res (and vice versa),the high income countries have a strong
correlation with the treatment success rate of tuberculosis.The value of R2, the coefficient of
determination, is 0.7377.The P -Value is .006301. The result is significant at p < .01.
Table XII .GNI –high income countries and tuberculosis treatment success
GNI -High income (X values) ( $) Tuberculosis treatment success rate (Y
values)
4.40145E+13 67
4.61485E+13 68
4.83879E+13 68
5.01539E+13 72
5.21309E+13 72
5.39637E+13 71
5.5721E+13 71
5.72479E+13 73

47
Graph 12 Correlation between high income countries and tuberculosis treatment success

Low income countries and tuberculosis treatment success

The values for GNI and also for the success rates of the tuberculosis treatment were written in
Table XII I.
The values were taken from 2009 -2016. The income values from the GNI were considered Y
values,while the values from the tuberculosis treatment success were considerated X
values. (Graph 13)

48
The mean of the tuberculosis treatment success rate was 85.125,while the mean of the GNI was
1202008875000 $. The value of R i s 0.8788. This is a strong positive correlation, which means
that high rates of success of the tuberculosis treatment go with high GNI vasriables (and vice
versa), the tuberculosis treatment success rate and the GNI of the low income countries are
connected. The value of R2, the coefficient of determ ination, is 0.7723. The P -Value is .004056.
The result is significant at p < .01.

Table XIII .GNI low income countries and tuberculosis treatment success

Tuberculosis treatment success rate (X values) ( $) Low income countries (Y values)
82 9.41961E+11
83 1.02431E+12
85 1.07302E+12
86 1.15311E+12
86 1.24428E+12
86 1.33337E+12
86 1.39214E+12
87 1.45388E+12

49
Graph 13. Correlation between low income countries and tuberculosis treatment success

Upper middle income countries and tuberculosis treatment success

The values for GNI and also for the success rates of the tuberculosis treatment were written in
Table XIV.
The values were taken from 2009 -2016. The income values from the GNI were considered X
values,while the values from the tuberculosis treatment success were considerated Y
values. (Graph 14)

The mean of the GNI values was 34597175000000 $ , while the mean for the tuberculosis
treatment success rate was 85.75. The value of R is 0.6639. This is a moderate positive

50
correlation, which means there is a tendency for high GNI variable go with high rates of success
variable (and vice versa). This being said, the correlation between the two, is a weak one. The
value of R2, the coefficient of determination, is 0.4408.The P -Value is .072658. The result is not
significant at p < .01
Table XIV .GNI upper middle income countries and tuberculosis treatment success

GNI-Upper middle income countries (X
values) ( $) Tuberculosis treatment success rate (Y values)
2.62536E+13 84
2.86075E+13 81
3.14002E+13 87
3.3838E+13 87
3.59702E+13 87
3.84231E+13 86
3.99907E+13 87
4.22941E+13 87

51
Graph 14. Correlation between upper middle income countries and tuberculosis treatment
success

Lower middle income and tuberculosis treatment success rate

The values for GNI and also for the success rates of the tuberculosis treatment were written in
Table XV.
The values were taken from 2009 -2016. The income values from the GNI were considered X
values,while the values from the tuberculosis treatment success were considerated Y
values.(Graph 15)

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The mean for the GNI values was 15700000000000 $ ,while the mean for the rate of treatment
success was 84.5. The value of R is -0.8859.This is a strong negative correlation, which means
that high GNI values go with low rates of (and vice versa).The value of R2, the coefficient of
determination, is 0.7848.The P -Value is .003394. The result is significant at p < .01.

Table XV. GNI lower middle income countries and tuberculosis treatment success rate

GNI-Lower middle income (X values) ( $) Tuberculosis treatment success rate (Y
values)
1.19E+13 87
1.3E+13 87
1.4E+13 87
1.5E+13 87
1.6E+13 87
1.73E+13 81
1.86E+13 81
1.98E+13 79

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Graph 15. Correlation between lower middle income countries and tuberculosis treatment
success

Germany

The values for GNI and also for the success rates of the tuberculosis treatment were written in
Table XVI.
The values were taken from 2009 -2016. The income values from the GNI were considered X
values,while the values from the tuberculosis treatment success were considerated Y
values.(G raph 16 )

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The mean value of the GNI for Germany was 3545000000000 $ , while the mean of the
success rate of tuberculosis treatment was 70.5. The value of R is 0.0108. Although technically a
positive correlation, the relationsh ip between your variables is weak ( the nearer the value is to
zero, the weaker the relationship). The value of R2, the coefficient of determination, is
0.0001. The P -Value is .979752. The result is not significant at p < .01 .

Table XVI .GERMANY (GNI values and tuberculosis treatment success)
GNI (X values) ( $) Tuberculosis treatment success (Y values )
3.27E+12 76
3.51E+12 76
3.59E+12 73
3.73E+12 74
3.89E+12 67
3.27E+12 63
3.51E+12 65
3.59E+12 70

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Graph 16 Correlation between income and tuberculosis treatment success in Germany

Somalia
The values for GNI and also for the success rates of the tuberculosis treatment were written in
Table XVII .
The values were taken from 2009 -2016. The income values from the GNI were considered X
values,while the values from the tuberculosis treatment success were considerated Y
values.(G raph 17 )
The mean of the GNI values for Somalia was 47073816498.25 $ while the mean of the success
rate of the tuberculosis treatment was 81.875. The value of R is 0.6462. This is a moderate
positive correlation, which mean s there is a tendency for high GNI variables go with high
success rates of the tuberculosis treatment and vice versa). The value of R2, the coefficie nt of
determination, is 0.4176. The P -Value is .0 83417. The result is not significant at p < .01.

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Table XVII .SOMALIA (GNI values and tuberculosis treatment success)
GNI(X values ) ( $) Tuberculosis treatment success ( Y
values)
3.92E+10 63
4.02E+10 74
4.12E+10 86
4.62E+10 88
4.74E+10 86
5.10E+10 86
5.40E+10 84
5.70E+10 88

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Graph 17 Correlation between income and tuberculosis treatment success in Somalia

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4.6 Discussion

The highest incidence of TBC is in Romania
 This aspect was not confirmed .The highest incidence was in Somalia during the year
2009 with 286 cases,even though Romania is an endemic country.Also,with the time
passing by,the incidence of tuberculosis started to decrease.

The highest incidence of TB C can be found in the lower middle income countries
 This aspect was confirmed.But the data from the worlddatabank.com proved that it
should also be included the lower income countries to get the right countries that have
the highest incidence of TBC.

The highest incidence of TBC was found in the Sub -Saharan Africa
 It was proved that the Sub -Saharan Africa has the highest incidence of tuberculosis .
This region and also South Asia have both more than 200 new or relapsed cases(per
100.000 people) of tu berculosis.

TBC case detection was higher in Somalia than in the other countries
 This was not confirmed. Having the data,it was observed that Somalia was on the
contrary ,the one of the countries that has the lowest case detection.

The Sub -Saharan Afri ca has the lowest case detection
 This was confirmed .The Sub -Saharan Africa had the lowest case detection during the
period of 2011 -2015 with hardly 50%.

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TBC case detection was found mostly in the high income group
 This is totally true. It was observed that the high income group had 88 -89% TBC case
detection (%all forms).This group was followed by the upper middle income group that
was also proved to have an increased percentage of TBC forms.

The highest GNI was i n Germany
 This is totally true. The country that has the highest GNI distribution is Germany ,a
country that belongs to the high income group of countries.There was an improve in the
GNI for each and every country during the years.

The highest rate of tre atment success was in Germany.
 This aspect was not at all confirmed.The countries that had a high rate of treatment
success were:Kuwait and Afghanistan.Somalia was one of the countries with the lowest
treatment success.

The highest rate of treatment success was in East Asia & Pacific.
 Data collected from data.world bank .org, proved that the highest rate of succes was in
East Asia&Pacific.Rates found in this area are more than 89%.

The lowest rate of treatment success is found in low income countries
 This was not at all confirmed. On the contrary,the low income countries and also the
upper middle income countries had had the best rate of success. Both of them had over
80% success,more exactly 87% rate of success against tuberculosis.

60

The high in come group a st rong correlation with the rate of success of the TBC treatment
 This is totally true. The value of R is 0.8589.This is a strong positive correlation, which
means that high GNI variable scores go with high rates of success agains t tuberculos is
(and vice versa).The P -Value is .006301. The result is significant at p < .01.

There is no correlation between the upper middle income group and the rate of treatment
success against tuberculosis
 This aspect was not confirmed .The value of R is 0.6639.This is a moderate positive
correlation, which means there is a tendency for high GNI variable scores go with high
rates of success against tuberculosis(and vice versa).The P -Value is .072658.

The l ower middle income group has a strong negative correlation with the tuberculosis treatment
success
 The value of R is -0.8859.This is a strong negative correlation, which means that high
GNI variable scores go with low rates of success against tuberculosis(and vice versa).The
value of R2, the coefficient of determination, is 0.7848.The P -Value is .003394. The
result is significant at p < .01.

The low income group has a negative correlation with the tuberculosis treatment success
 This is not c onfirmed.The value of R is 0.8788.This is a strong positive correlation,
which means that high GNI variable scores go with high rates of success against
tuberculosis(and vice versa). The P -Value is .004056. The result is significant at p < .01.

Germany ha s a strong positive correlation with the treatment success of TBC
 This is partially confirmed.The value of R is 0.0108.Although technically a positive
correlation, the relationship between your variables is weak ( nb. the nearer the value is to

61
zero, the weaker the relationship).The value of R2, the coefficient of determination,
is 0.0001.The P -Value is .979752. The result is not significant at p < .01.

Somalia has a positive correlation with the treatment success of TBC
 This was confirmed.The v alue of R is 0.6462.This is a moderate positive correlation,
which means there is a tendency for high GNI variable scores go with high rates of
success against tuberculosis(and vice versa). The value of R2, the coefficient of
determination, is 0.4176.The P -Value is .083417. The result is not significant at p < .01.

62
Conclusion
 The highest incidence of tuberculosis was found in Somalia and taking into account the
incidence of tuberculosis for the entire world divided by GNI, most of the cases were
presented in the Lower middle and Low income countries. Dividing the world by
geographic areas, most of the cases discovered were from Sub -Sahar an Africa .
 The one that proved to have the lowest percentage of detection was found in Somalia .
 There were countries as : Australia, Germany, Israel, Kuwait, Lebanon, Moldova and
Romania that had the same percentage of TBC case detection during the years . Most of
the countries are from the High income areas.
 Taking into discussion the TBC case detection based on the geographic areas, most of the
cases were discovered in Europe &Central Asia and the one that has the lowest case
detection is the Sub -Sahar an Africa .In the World, the TBC is discovered in more than
half of the cases .
 In the GNI Distribution, the TBC case detection was found mostly in the high income
group,followed by the upper middle income group .
 GNI is different from country to country. During the years, it has been seen an improve in
the GNI for every country . The highest GNI is found in Germany, followed by Australia.
The lowest GNI is found in Somalia.
 Tuberculosis treatment success rate in the countries taken into discussion present s the
countries with the highest rate of success : Kuwait , Afghanistan and Egypt . The lowest
rate of tuberculosis treatment success rate was found in Somalia . Based on the region,
the highest treatment success was found in East Asia & Pacific.The grou ps that had the
best rate of succes against tuberculosis were the low income and the upper middle income
categories.
 For high income countries, it was calculated to correlation coefficient and as a result ,it
was observed that the high income countries ha ve a strong correlation with the treatment
success rate of tuberculosis .

63
 For low income countries,calculating the correlation coefficient,as a result we had a
strong positive correlation, which means that high rates of success of the tuberculosis
treatment go with high GNI variables (and vice versa), the tuberculosis treatment
success rate and the GNI of the low income countries are connec ted.
 For the upper middle income countries, the correlation coefficient calculated expressed a
moderate positive correlation, which means there is a tendency for high GNI variable go
with high success rates of the tuberculosis treatment (and vice versa) .This being said,
the correlation between the two, is a weak one.
 The ones that established a negative correlation were the lower middle income countries
with the success rates of the tuberculosis treatment, which means that high GNI values go
with low rat es of treatment success (and vice versa).
 For Germany,the country with the highest income ,it was calculated the correlation
between TBC treatment success and GNI .Although technically ,was found a positive
correlation, the relationship between GNI and the rates of treatment succes is weak .
 For Somalia,the country with the lowest income ,it was also calculated the correlation
between treatment success and GNI. It was found a moderate positive correlation, which
means there is a tendency for high GNI variable scores go with high rates of treatment
success (and vice versa).

64
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