Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis most commonly affecting lungs. Due to the low detection, possible drug-resistance and coinfections with other diseases, it remains one of the largest global public health problem. TB is considered as a disease of poverty and health inequalities, often associated with HIV infections. Iveta Angelova Nikolova, PhD University of architecture, civil engineering and geodesy, Sofia

1. Effectiveness of tuberculosis screening among a high-risk population: recommendation proposal for
reducing TB incidence in India
Iveta Angelova Nikolova, PhD
University of architecture, civil engineering and geodesy, Sofia
Email: iveta.nikolova@abv.bg
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis most
commonly affecting lungs. Due to the low detection, possible drug-resistance and coinfections with
other diseases, it remains one of the largest global public health problem. TB is considered as a disease
of poverty and health inequalities, often associated with HIV infections. For example, it is estimated
that homeless population has 10 to 85 times higher incidence rate compared to the general population
(Haddad, Wilson, Ijaz, & et al., 2005). Even in developed countries, the incidence rates can exceed 700-
800 per 100.000 population (Story, Murad, Verheyen, & et al., 2007). Homeless people are more likely
to have TB than non-homeless with the odds rate (OR) equals to about 6 (Tan de Bibiana, Rossi, Rivest,
& et al., 2011). Nonetheless, not only homeless people are at risk of contracting tuberculosis. The
World Health Organization (WHO) estimates that even one third of the world population is infected
with TB and 5-10% of latent cases will develop leading to TB. HIV infection increases the risk of
progression of latent TB infection to active TB disease. TB and HIV forms the deadly synergy increasing
risk of death many times. In 2019 the WHO reported approximately 10 million cases of TB and 1.2
million deaths among HIV-negative people worldwide, and an additional 208.000 deaths among HIV-
positive people. TB causes more adult deaths than any other diseases. Thus, reducing the disease
incidence and mortality are one of the leading aims of international health organizations. In 2014 and
2015 the WHO and the United Nations (UN) committed to end the TB epidemic by adapting and
expanding the WHO's End TB Strategy and the UN Sustainable Development Goals (SDGs). The strategy
contains milestones and targets that should be achieved by 2030 (WHO, 2020).
As per the WHO TB report, the estimated incidence of TB in India in 2019 was approximately
2.64 million cases (approx. 193 per 100.000 population) and 446.000 deaths including almost 10.000
HIV-positive. The number of TB cases accounts for about a quarter of the world's TB burden. To
eliminate TB in India, in 2017 several organizations approved an ambitious National Strategic Plan
(NSP) for 2017-2025 for TB Elimination (India TB Report, 2020). After three years of implementation of
the NSP, it has been renamed as National Tuberculosis Elimination Programme. The program is a
guidance for the National and State Governments, Development Partners, Civil Society Organizations,
International Agencies, Research Institutes, Private Sector and others that provides framework for the
activities that are relevant to TB elimination during the period 2017-2025. It aims to direct attention
to the most important interventions and activities which can bring significant changes in the incidence,
prevalence and mortality of TB in India. These goals and strategies are in addition to the activities
already ongoing in the country and are in line with other health interventions, such as WHO's End TB
Strategy and UN's SDGs. The vision of the NSP is TB-Free India with zero deaths, disease and poverty
due to TB by 2025. The main goals are 80% reduction in TB incidence rate and 90% reduction in TB
mortality (the same as WHO and UN plan to achieve by 2030). The objective is to find all drug sensitive
and drug resistant TB cases, seek undiagnosed TB in high-risk populations, prevent the emergency of
TB in susceptible populations and provide adequate financial resources.
Based on the data provided by the WHO, the rate of TB decline in India is too slow to meet the
2025 goals. Since 2016, the incidence rate declines by about 3% annually and by 11% compared with

2. 2015. What is more, the current efforts yield inadequate decline to meet the 2030 End TB targets. To
obtain the assumed goals, the decline is to be more than 15% annually. New additional interventions
are required to accelerate the decline of TB incidence. In Figure 1, trend curve for TB incidence rate in
India is presented together with two curves depicting required trends for achieving the WHO TB End
and NSP targets. The trend curve was obtained based on actual data for TB incidence in India in 2015-
2019.
Rapid detection of TB,
mapping of high-risk groups,
systematic screening and active case
finding (ACF) for active TB optimize
treatment and improve outcomes
(Dye & Williams, 2008). The WHO
recommends the Directly Observed
Treatment Short-Course, known as
DOTS, as the TB control strategy,
which is the most cost-effective way
to stop the TB spread in high-
incidence communities. In India since
2007-2008 approximately 20 million
symptomatic persons are screened
by microscopy annually, which
initiates about 1.5 million persons on
TB treatment. In 2019 early case
detection resulted in almost 63.000
additional TB cases. In 2017 more
than 700.000 patients were tested by
rapid molecular tests which resulted
in almost 40.000 Rifampicin
resistant/MDR-TB patients (India TB Report, 2020). Detecting TB cases early by finding symptomatic
people may result in a significant reduction of TB burden. The additional reduction can be achieved
when ACF is carried out in non-symptomatic people, in particular in high-risk populations.
Interventions among HIV-positive people to reduce the burden of TB are recommended by the
WHO. HIV testing of TB patients is now routine, as HIV prevalence among incident TB patients is
estimated to be 4%. Although the risk of developing TB among people living with HIV and AIDS is 21
times higher, they are not the only one group which can be considered as a high-risk group of
developing TB (Romaszko, Bucinski, Kuchta, & Bednarski, 2013). It is estimated that 2-6% of TB cases
is among homeless people (Radiaga, Raoult, & Brouqui, 2008) (Haddad, Wilson, Ijaz, & et al., 2005),
where a higher percentage is found in high-developed countries. The mortality among homeless is
lower than among HIV-positive patients, but still is twice that of the general population (Ranzani,
Carvalho, Waldman, & Rodruguez, 2016). In (Choinski, Bodzioch, & Forys, 2019) it was shown that that
homeless people may transmit the disease more intensively that the others – not only between
themselves, but also to the general population. In (Romaszko, Siemaszko, Bodzioch, Bucinski, &
Doboszynska, 2016) (Bodzioch, Choinski, & Forys, 2019) it was shown that homeless people can be
treated as a high-risk population of TB transmission and carrying out ACF campaigns among them give
better results than in the general population. It suggests that reducing the incidence among homeless
people may be considered as an additional protocol for TB eradication. ACF among non-symptomatic
may be less economical effective if it is not conducted in relevant target groups. According to the
Figure 1. Incidence rate per 100.000 population in 2015-2030 plotted against
three different strategies. Blue points depict the actual data for TB incidence
in India. Blue curve depicts the trend in TB incidence rate. Red one is the WHO
End TB trend curve, which is required to achieve the 2020 and 2025
milestones and 2030 target. Green one depicts the NSP trend.

3. simulations performed in the paper,
ACF among homeless is strongly
justified also by economic reasons.
Thus, screening and testing of active
TB among homeless, even non-
symptomatic, should be discussed to
potentially update recommendations
for health care and prevention.
Regular monitoring and review of
interventions protocols are essential
to control the disease.
In order to investigate the
effectiveness of TB screening among
homeless, we follow the concept of
(Romaszko, Siemaszko, Bodzioch,
Bucinski, & Doboszynska, 2016) and
(Bodzioch, Choinski, & Forys, 2019).
We divide the population of India
into two subpopulations: non-
homeless and homeless, assuming
that 2% of TB cases is among homeless. Comparing the data to the model proposed (Bodzioch,
Choinski, & Forys, 2019), we are able to investigate the effectiveness of ACF for different detection
rates. We define the detection rate as the fraction of detected cases per one infected per year. In
Figure 2 the actual trend curve together with the WHO End TB target curve are shown. The area
between these two curves represents the additional number of TB cases compared to WHO target;
approx. 2.2 million until 2025 and 6.9 million until 2030. Additionally, the 10-year predicted TB
incidence curve for detection rate of 0.2 is shown. Predictions for ranges 0.05-0.35 and 0.1-0.3 are also
depicted. Dependence of the decline of the incidence rate on the detection rate and duration of
detection is shown in Figure 3(a) In a longer time horizon, the same decrease can be achieved for lower
values of the detection rate. This is important from the economic point of view. In particular, the
Figure 2. Number of TB cases. Blue curve represents number of infected under
the actual trends, while the red one depicts the required trends by the WHO
End TB strategy. The dashed-blue curve represents the predictions of TB cases
when ACF among homeless is applied with detection rate equals to 0.2 per
one infected per year. Grey regions indicate predictions for detection rate
between 0.05 and 0.35, while the dark-grey region – for 0.1-0.3.
(b) (b)
Figure 3. (a) Dependence of the incidence rate decline on the detection rate and duration of detection. Colors represent the
decline in the incidence rate. (b) Dependence of the decline in the total number of TB cases per one detected case on the
detection rate and duration of detection. Colors represent the reduction in the total number of infected in the general
population per one detected in the homeless population.

4. effectiveness of ACF decreases with the number of people tested, usually giving 1 active case for 10-
15 tested persons. In (Romaszko, Siemaszko, Bodzioch, Bucinski, & Doboszynska, 2016), it is shown
that each identified homeless person reduces the incidence of TB in the general population of 3–4
individuals within 1 year and up to 20 individuals within 5 years. Based on demographic and epidemic
data for India, the impact of identification of infected homeless people on the decline of TB incidence
seems to be more significant. Figure 3(b) shows the dependence of the reduction of number of TB
cases per one detected case among homeless on the detection rate and duration of detection. It is
clearly visible that lower detection rates give better results per one identified active case in a longer
time horizon. As the presented dependences are highly non-linear, they give valuable information
about the effectiveness of ACF in time. On the basis of Figure 2 and Figure 3 one can observe that
increasing the detection rate above 0.3 significantly reduces the efficiency of ACF. The total decline in
the TB incidence for detection rate between 0.1 and 0.3 is estimated to be 0.6-1.3 million cases during
5 years and up to 3.5 million during 10 years. One should note that the detection rate of 0.2 (assuming
2% of TB cases among homeless) means approx. 10.000 identified and 100.000-150.000 tested
homeless people annually. In order to maximize the effectiveness of ACF activities, the detection rate
should be gradually increased, starting from relatively small values and then, when its influence on the
incidence rate decline diminishes, ACF in other groups should be activated, which may provide the
synergy effect.
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