University of Medicine and Pharmacy [301814]

University of Medicine and Pharmacy

“Carol Davila”, [anonimizat]:

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. Summary …………………………… 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 Mortality……………………………………………………………………………………………..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

[anonimizat](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) [anonimizat] 300 000 deaths from TB (range, 266 000–335 000) [anonimizat].

The severity of national epidemics varies widely among countries. Drug-resistant TB continues to be a public health crisis. [anonimizat] 2017, 558 000 people (range, 483 000–639 000) developed TB that was resistant to rifampicin (RR-TB), [anonimizat], and of these, 82% [anonimizat] (MDR-TB).

TB is an old disease that was once a death sentence. Effective drug treatments first became available in the 1940s, [anonimizat]-duce their burden of TB disease to very low levels.[anonimizat], the “end” of TB as an epidemic and major public health problem remains an aspiration rather than a reality. [anonimizat]-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 connections 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.

I.History of tuberculosis

1.1 Summary

Tuberculosis (TB) is one of the oldest diseases known to affect humanity, 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 1950s, 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 of TB, an important role was played by surgery,too.

The late Nineteenth century saw the introduction of the first useful measure 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.

1.2 Historical approach

Tuberculosis (TB) is one of the oldest diseases 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 disease 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 living and working conditions . Over the lyears, the disease felled an estimated one billion 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 agent, Mycobacterium tuberculosis. 5 This microbe was discovered and isolated in 1882 by a famous scientist Robert Koch (Fig.2), who was awarded the Nobel Prize for Medicine in 1905.

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 easily stain in tubercular lesions . [6-7] He implemented an innovative method of observing tubercular lesions using methylene-blue and a solidified medium of coagulated 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 also used nowadays in order 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. The 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.

II . Epidemiology

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 transmission but they usually have no epidemiologic significance.

Important determinants of the likelihood of transmission are: the 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 degree of infectiousness of the case.

It is considered that almost one-third of the world’s population have been exposed to MB and got potentially 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

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, as 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 deficiency in interleukin (IL)-12 promoting the T helper (Th) 1 response might be another factor in the increased susceptibility 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 individuals are infected with MTB but do not transmit the disease to others and do not present symptoms [21,24]. From latent 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 higher 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 were 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 hospitals or prisons. 22 Prevalence of the disease in such settings depends 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 usually 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 people with TB live in sub-Saharan Africa [22,26]. Differently,in 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 being 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 cases. 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 discovery of affordable and effective chemotherapy and is likely to have affected humans for most of their history. With 1.3 million TB deaths (including TB deaths in HIV-positive individuals) in 2012 18, TB and the human immunodeficiency virus (HIV) are the top causes of death from a single infectious agent worldwide [31,32]. TB is a killer among people in the most economically productive age groups 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 international public health studies since the early 1990s 35, following years of neglect during the 1980s 36.

III. Global Burden of Tuberculosis

Tuberculosis (TB) is one of the worldwide top 10 causes of death, and the cause from a single infectious agent (above HIV/AIDS).Millions of people continue to fall sick 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%), Indonesia (8%), the Philippines (6%), Bangladesh (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 mostly in high-income countries, 150–400 in most 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 TB (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 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 direct 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) disease 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 milestones and targets for reductions in the burden of TB disease have been set as part of the Sustainable Development Goals (SDGs) 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 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

WHO updates its estimates of the 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 .

3.1 TB incidence

Methods to estimate 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 proxy 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 strengthened 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 direct measure of TB incidence.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):

Results from TB prevalence surveys.

Incidence is estimated using estimates 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.

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 estimated number of cases in 2017 occurred 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 in 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)

The problem of national TB epidemics based on the annual number of incident TB cases relative to population size (the incidence 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 30 high TB burden countries (Fig. 8), and above 500 in a few countries including the Democratic People’s Republic of Korea, Lesotho, Mozambique, the Philippines and South Africa.

Among people living with HIV there was an estimated 9% of the 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 the rest of the world population, increasing with a decreasing prevalence of HIV in the general population.

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.

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 among HIV-negative people are described as TB deaths in the most recent version of the International classification of diseases (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 HIV-negative people can be measured 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 among 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.

Until 2008, WHO estimates of TB mortality used VR or mortality survey data for only three countries. This was substantially improved to 89 countries in 2009, although 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 data 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 WHO African Region has the greatest 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 from 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 above HIV/AIDS.Most of these deaths could be prevented with screening,early diagnosis and an appropriate treatment .For example, among people whose TB was detected, reported and treated in 2016, the treatment 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 global TB deaths among HIV-negative people, and for 27% of the combined total TB deaths in HIV-negative and HIV-positive people.

Globally, the number of TB deaths among HIV-negative people per 100 000 population was 17 in 2017, and 21 when TB deaths among HIV-positive people were included. There 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 trends in TB mortality, 2000–2017

Worldwide, the absolute number of deaths caused by TB among HIV-negative people has 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 period 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 the 30 high TB burden countries ,ranging from substantial reductions (more than 50%) since 2000 (e.g. in Cambodia China, Ethiopia, Myanmar, the Russian Federation and Vietnam) to limited changes (e.g. in Angola and Congo). High TB burden countries with rates of 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 substantially in recent years; for example, in Cambodia, Kenya, Namibia, South Africa, the United Republic of Tanzania and Zimbabwe .

3.3 Estimates of the disease 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 incident 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 about 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 surveillance of drug resistance among TB patients also allow estimation 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, there 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 disease duration.

Nonetheless, in an important subset of countries with 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). Findings 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 retained national TB prevalence surveys within its strategic 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 population) 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 informed 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 used 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 in Namibia and a repeat survey in Vietnam (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 countries (e.g. Ethiopia, Gambia, Nigeria, Sudan, Uganda and Zambia), prevalence per 100 000 population peaks among those aged 35–54 years. The male to female (M:F) ratio of cases for the same set of surveys shows a systematically higher burden of TB disease among 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 in detection and reporting, also suggests that there is a need for strategies to improve access to and use of health services among men.

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:

To evaluate the TB incidence

To evaluate the tuberculosis case detection

To assess the GNI Distribution

To assess the TB treatment rate success

To assess the relationship between TB treatment rate success and GNI

4.3. Hypothesis of the study :

Incidence of tuberculosis:

The highest incidence of TBC is in Romania,in comparison to: Afghanistan, Australia, Egypt,Germany,Israel,Kuwait,Lebanon,Moldova, and Somalia.

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.

The highest incidence of TBC is 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

Tuberculosis case detection was higher in Somalia, in comparison to the other countries: Afghanistan, Australia, Egypt,Germany,Israel,Kuwait,Lebanon,Moldova, and Romania.

The one that has the lowest case detection is the Sub-Saharan Africa

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,Kuwait,Lebanon,Moldova, and Romania.

Tuberculosis treatment success rate

The highest rate of the treatment success of tuberculosis was in Germany,in comparison to Afghanistan, Australia, Egypt,Somalia,Israel,Kuwait,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 strong 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

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 World Data Bank(https://data.worldbank.org/). The data was about the incidence of tuberculosis, the tuberculosis case detection, the 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 nonresidents . 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 the 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) in international statistics. While being conceptually identical, the precise calculation method has evolved at the same time as the name change.

A descriptive study 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 concomitantly 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 countries 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

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 several 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 between 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.

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 negative 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.

Results

Incidence of tuberculosis (per 100,000 people)

Incidence of tuberculosis is the estimated 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 time passing by, the incidence of tuberculosis started to decrease, in Somalia getting to a number of 266 cases, having a slow decrease 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 ) (Graph 1 )

Table I. Incidence of tuberculosis for :Afghanistan, Australia, Germany, Israel, Kuwait,Egypt, Lebanon, Moldova,Romania and Somalia

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 observed 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 . (Graph 2)

Table II Incidence of tuberculosis in the income groups for 2009-2017

Graph 2 Incidence of tuberculosis in the income groups for 2009-2017

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

Graph 3 Incidence of tuberculosis for different geographic zones based on the number of cases

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 only 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

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 . (Graph 6)

Table V. Percentage of tuberculosis case detection based on the geographic area during 2009-2017

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

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)(Graph 7)

Table VI Tuberculosis case detection in the income groups during 2009-2017

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

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(part 1)

Table VII. GNI distribution for : Afghanistan, Australia, Egypt,Germany, Israel, Kuwait, Lebanon, Moldova,Romania and Somalia during 2009-2017(part 2)

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

Table VIII GNI in the income groups for 2009-2017(part 1)

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

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 rate of tuberculosis treatment success rate was found in Somalia 63% in 2009. Table IX .

Table IX Tuberculosis treatment success for : Afghanistan, Australia, Egypt,Germany, Israel, Kuwait, Lebanon, Moldova,Romania and Somalia during 2009-2016

Graph 9 Tuberculosis treatment success in Afghanistan in 2016

Based on the region, 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

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 treatment success in the income groups from 2009-2016

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

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 scores (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

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 XIII.

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)

The mean of the tuberculosis treatment success rate was 85.125,while the mean of the GNI was 1202008875000 $. The value of R is 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 determination, 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

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 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 incomecountries and tuberculosis treatment success

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)

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

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.(Graph 16)

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 relationship 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)

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.(Graph 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 means 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 coefficient of determination, is 0.4176.The P-Value is .083417. The result is not significant at p < .01.

Table XVII .SOMALIA (GNI values and tuberculosis treatment success)

Graph 17 Correlation between income and tuberculosis treatment success in Somalia

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 TBC 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 tuberculosis.

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 Africa 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%.

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 in 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 treatment 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.worldbank.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.

The high income group a strong 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 against tuberculosis (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 lower 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 confirmed.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 has 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 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 value 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.

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-Saharan 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-Saharan 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 presents 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 groups 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 have a strong correlation with the treatment success rate of tuberculosis.

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 connected.

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 rates 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).

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