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Understanding how geographic, demographic and treatment history impact health outcomes of patients with multi-drug-resistant tuberculosis in Pakistan, 2014–2017

Published online by Cambridge University Press:  30 September 2020

F. Iqbal*
Affiliation:
Harvard Medical School, Harvard University, Boston, USA Brigham and Women's Hospital, Boston, USA
M. K. Defer*
Affiliation:
School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, Waterloo, Canada
A. Latif
Affiliation:
National Tuberculosis Control Program Pakistan, Islamabad, Pakistan
H. Hadi
Affiliation:
National Tuberculosis Control Program Pakistan, Islamabad, Pakistan
*
Author for correspondence: F. Iqbal, E-mail: fiqbal1@bwh.harvard.edu; M.K. Defer, mirandadefer@gmail.com
Author for correspondence: F. Iqbal, E-mail: fiqbal1@bwh.harvard.edu; M.K. Defer, mirandadefer@gmail.com
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Extract

Tuberculosis (TB) is one of the top 10 leading causes of morbidity and mortality worldwide [1]. In 2017, approximately 10 million people were infected with TB and 1.3 million patients faced mortality [1]. Patients with active TB can infect up to 10–15 people over a year. There is a greater risk of transmission in overcrowded areas with limited air ventilation including large family units, prisons and slums [1, 2]. Without proper diagnosis and treatment, roughly 45% of non-HIV positive TB patients face mortality [1]. With the help of global organizations and national TB treatment and control programmes, the global incidence of TB is declining by approximately 2% each year [1]. The World Health Organization (WHO) TB-strategy aims to end the TB epidemic and encourages partners to fund national TB programmes to improve diagnosis and treatment of TB. The goal is to ultimately decrease death rates by 90% and decrease incidence rates by 80% [1]. To achieve these goals, the decline in TB incidence needs to reach approximately 4–5% per year [1]. The WHO 2018 TB report identified multidrug resistant TB (MDR-TB) as the leading factor hindering that goal [1]. The incidence and spread of MDR-TB has drastically increased, where approximately 558 000 new cases of MDR-TB were diagnosed in 2017 causing more than 230 000 deaths globally [1]. MDR-TB is identified by resistance to the two most powerful anti-TB treatment drugs including isoniazid and rifampicin [3]. Patients with MDR-TB are required to start second-line anti-TB drugs (SLDs), which are limited, expensive, less effective and more toxic [1,2]. Therapy duration is one of the major limitations of second-line treatments, which may require up to two years of consistent use. Since TB affects mostly developing countries, long treatment durations and associated costs become a major challenge. In 2015, 15% of new TB cases were reported as MDR-TB, which drastically increased to 24% by 2017 [1]. Even with significant improvements in molecular tests and diagnostic methods, MDR-TB is still on the rise where the success rate of treatments is between 50 and 60% [1]. Additional characteristics including socioeconomic and sociocultural factors need to be considered when targeting and treating patients with MDR-TB.

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Type
Original Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press
Figure 0

Fig. 1. Study flow diagram of patients with MDR-TB, Pakistan, 2014–2017.

Figure 1

Table 1. Counts, Percentages and Differences in Demographic Factors for MDR-TB Patients With Favourable and Unfavourable Outcomes

Figure 2

Table 2. Effects of Socioeconomic Status on Patient Treatment Outcome

Figure 3

Table 3. Effects of Patient Treatment History Per Province on Treatment Outcomes

Figure 4

Table 4. Unadjusted and Adjusted Odds Ratios for 50 km Distance to Treatment Site Predicting Favourable/Unfavourable Outcomes of MDR-TB Patients

Figure 5

Fig. 2. Choropleth map representing favorable patient outcomes based on Pakistan’s provinces and states.

Supplementary material: File

Iqbal et al. supplementary material

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