• Scoping Review
  • Open access
  • Published: 23 September 2022

Interventions pathways to reduce tuberculosis-related stigma: a literature review and conceptual framework

  • Charlotte Nuttall 1   na1 ,
  • Ahmad Fuady   ORCID: orcid.org/0000-0003-0030-0524 2 , 3 , 4   na1 ,
  • Holly Nuttall 1 ,
  • Kritika Dixit 5 , 6 ,
  • Muchtaruddin Mansyur 2 &
  • Tom Wingfield   ORCID: orcid.org/0000-0001-8433-6887 1 , 5 , 7 , 8  

Infectious Diseases of Poverty volume  11 , Article number:  101 ( 2022 ) Cite this article

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Prevention of tuberculosis (TB)-related stigma is vital to achieving the World Health Organisation’s End TB Strategy target of eliminating TB. However, the process and impact evaluation of interventions to reduce TB-stigma are limited. This literature review aimed to examine the quality, design, implementation challenges, and successes of TB-stigma intervention studies and create a novel conceptual framework of pathways to TB-stigma reduction.

We searched relevant articles recorded in four scientific databases from 1999 to 2022, using pre-defined inclusion and exclusion criteria, supplemented by the snowball method and complementary grey literature searches. We assessed the quality of studies using the Crowe Critical Appraisal Tool, then reviewed study characteristics, data on stigma measurement tools used, and interventions implemented, and designed a conceptual framework to illustrate the pathways to TB-stigma reduction in the interventions identified.

Of 14,259 articles identified, eleven met inclusion criteria, of which three were high quality. TB-stigma reduction interventions consisted mainly of education and psychosocial support targeted predominantly toward three key populations: people with TB, healthcare workers, and the public. No psychosocial interventions for people with TB set TB-stigma reduction as their primary or co-primary aim. Eight studies on healthcare workers and the public reported a decrease in TB-stigma attributed to the interventions. Despite the benefits, the interventions were limited by a dearth of validated stigma measurement tools. Three of eight studies with quantitative stigma measurement questionnaires had not been previously validated among people with TB. No qualitative studies used previously validated methods or tools to qualitatively evaluate stigma. On the basis of these findings, we generated a conceptual framework that mapped the population targeted, interventions delivered, and their potential effects on reducing TB-stigma towards and experienced by people with TB and healthcare workers involved in TB care.

Conclusions

Interpretation of the limited evidence on interventions to reduce TB-stigma is hampered by the heterogeneity of stigma measurement tools, intervention design, and outcome measures. Our novel conceptual framework will support mapping of the pathways to impacts of TB-stigma reduction interventions.

Graphical Abstract

literature review on tuberculosis

Stigma experienced by people with or affected by tuberculosis (TB)—henceforth termed TB-stigma—remains one of the major challenges in TB control [ 1 , 2 ]. TB-stigma has been shown to delay health-seeking behaviour [ 3 ]. This challenge has been aggravated by the COVID-19 pandemic which has restricted access to healthcare, reduced the number of people notified with TB, and been associated with an increase in TB mortality [ 1 , 4 , 5 ]. TB-stigma has also been shown to reduce treatment compliance, and negatively impact on TB treatment outcomes [ 6 , 7 ]. The prevalence of TB-stigma varies geographically and, in specific subpopulations, has been estimated to affect up to 80% of people with TB [ 8 , 9 ]. Therefore, in the context of TB control, TB-stigma is one of the major social determinants of health and contributes to compounding health inequalities [ 1 , 10 , 11 ].

For these reasons, the Global Fund and UN high-level meeting highlighted TB-Stigma as one of the most significant barriers to reaching the World Health Organization (WHO) End TB goal of eliminating TB by 2050 and called on the international community to “promote and support an end to stigma and all forms of discrimination” [ 12 , 13 , 14 ]. Despite this, few resources have been mobilised to address this issue [ 15 ]. In part, this is due to inherent difficulties in the identification and measurement of TB-Stigma and the complexity and limited evidence base relating to stigma-reduction interventions [ 16 ].

Measuring stigma is vital to understand its determinants, prevalence and assess the effectiveness of stigma-reduction interventions [ 17 ]. Multiple scales and tools exist that assess health-related stigma [ 18 ]. To be robust and reliable, these tools should have been validated in the community or population in which they are to be used and then refined to ensure they are accurate, specific, and reliable. In 2018, the KNCV Tuberculosis Foundation created a TB-Stigma Handbook, which provides examples of how the limited available set of existing tools can best be applied to measure and evaluate stigma [ 19 ]. However, most studies to date have used either disparate, invalidated tools or solely qualitative measures of stigma. This has made it difficult to broaden our understanding of the determinants and consequences of TB-Stigma [ 15 , 20 ] (Box 1 ).

In addition, despite recognition of the global importance of TB-stigma, there has been limited critical appraisal in the literature of the few existing interventions aimed at reducing TB-stigma. The single related systematic review on TB-Stigma by Sommerland et al. focused on the effectiveness of stigma-reduction interventions [ 17 ]. Measuring and reducing TB-Stigma is complex. It involves interrelated, heterogeneous system structures, and multiple approaches from the individual to societal level [ 16 ]. Therefore, it is critical to evaluate not only the scale but also the challenges and successes in the design and implementation processes of interventions to reduce TB-stigma. These evaluations will help identify the weaknesses in current TB-stigma intervention design and delivery in order to refine these interventions for future implementation and scale-up.

We reviewed studies reporting interventions to reduce TB-Stigma. The review appraised the study design and stigma measurement tools, identified their challenges and successes, and evaluated their pathways to impact on TB-Stigma. We then developed a conceptual framework for the TB-Stigma reduction pathway to support researchers to successfully design and deliver impactful TB-stigma reduction interventions.

Box 1 Types of TB-stigma [ 18 ]

Enacted (or experienced) stigma encompasses the range of behaviours directly experienced by a person with TB.

Anticipated stigma is the expectation and fear of discrimination and behaviour of others towards a person if they are diagnosed and/or unwell with TB, which has an impact on health-seeking behaviour, whether enacted stigma occurs or not.

Internalised (or self) stigma is when those diagnosed and/or unwell with TB may accept a negative stereotype about people with TB and potentially act in a way that endorses this stereotype.

Secondary or external stigma is negative attitude towards family members, caregivers, friends, or TB healthcare workers because they are associated with, live with, or have close contact with people with TB.

This study was a systematic literature review. A preliminary scoping search was conducted to ensure that all relevant key terms were identified, and the final search strategy refined.

Search terms and management of search results

The following search terms were used within four databases (CINAHL Complete, Medline Complete, Global Health and PubMed): (TB OR Tubercul* OR “Mycobacterium tuberculosis infections”) AND (stigma* OR discrimin* OR “social stigma” OR barrier* OR attitude* OR “social discrimination” OR marginalisation OR “psychosocial impact” OR “socioeconomic impact” OR shame OR “social isolation” OR “social inclusion” OR prejudice OR perception OR “self-esteem”) AND (interven* OR strateg* OR pathway* OR education OR “psychosocial intervention*” OR “psychoemotional intervention*” OR “socioeconomic intervention*” OR “social support” OR “patient support” OR “training workshop*” OR “counselling”) were searched. In addition, the “snowballing” method of reference tracking and searches of Google Scholar and the WHO database for grey literature were used to identify additional articles that may have been overlooked by the initial search strategy. Searches were limited to December 31, 2021. Citations of the articles identified from the searches were exported into Endnote X9 (Camelot UK Bidco Limited/Clarivate, UK). Duplicates were then identified and removed using the duplicates tool in Endnote X9. The titles and abstracts of the remaining articles were read through and screened for relevance independently by three reviewers (CN, HN, AF). Where there were unresolved disagreements, a fourth senior reviewer finalized screening for inclusion or exclusion (TW). We applied the inclusion and exclusion criteria, documented the reasons for article exclusion, and identified the relevant articles for full-text review. Finally, supplemental manual review of the reference lists of the selected full-text articles was performed to identify any further articles for inclusion.

Selection criteria

Eligible studies included those that reported the implementation and evaluation of TB-stigma reduction interventions amongst people with TB and their households, healthcare workers, and the general public. Included study designs were intervention studies with randomised controlled trials, non-randomised controlled trials, quasi-experimental studies, mixed-methods studies, qualitative studies, cohort studies, case-control studies, and cross-sectional studies. The review was restricted to articles that were written in English. Articles were excluded if they did not report measurement of stigma.

Critical appraisal

Critical appraisal was undertaken by three reviewers (CN, HN, AF) using the “Crowe Critical Appraisal Tool” (CCAT), Version 1.4, to determine the quality of each study [ 21 ]. The CCAT was selected as it has been proven to be reliable and valid for the analysis of multiple studies of heterogenous design and implementation approaches, and can reduce rater bias [ 22 ]. To further reduce researcher bias, each individual assessment was cross-checked. A fourth reviewer (TW) resolved any discrepancies. A priori, and in line with published guidance [ 21 ], a pragmatic decision was taken by the study team that articles with a CCAT score between 75% and 100% would be deemed high quality, 50% and 74% moderate, and below 50% to be low quality.

Data extraction and synthesis

Data on country and region of intervention, target population, type of stigma studied, the scale or tool used to assess stigma, intervention activities, challenges and successes of intervention, the impact of intervention, and reported changes to TB practice and policy were collated and tabulated. Further information on the intervention including format, content, outcomes (both reported and intended if different) and detail on how the intervention reduced TB-stigma (theory explicitly stated in the main text or implied in objectives or methods) were also tabulated. Qualitative details were lifted directly from the text and copied into the data-extraction table. The articles were read carefully for similar and recurring themes and concepts. The concepts were then organised to determine any contradictory concepts, which were then removed. A conceptual framework was then created to organise the variables and concepts perceived by the research team to contribute to the pathways by which an intervention successfully reduced TB-stigma. The intention of the novel conceptual framework was to support researchers to design, develop, and implement a successful and sustainable stigma-reduction intervention in current and future studies [ 23 ]. With respect to stigma measurement tools and scales, these were evaluated through collection of data including: the tool or scale used; implementation methods; methods to reduce bias and ensure validity; internal and external validation and piloting prior to use; comparison of stigma scores before and after the intervention or between study groups; types of stigma assessed; whether the tool was adapted from a previously validated tool; and the described limitations of the tool. Any required data that was missing from the published papers was collected by directly contacting the corresponding author of the paper.

The search yielded 14,244 articles with 15 further articles identified from other sources including grey literature. After removal of duplicate articles, 10,954 were screened, 54 of which met the study inclusion criteria. Following the full-text eligibility assessment, 43 further articles were excluded (Additional file 1 ). The remaining 11 articles were included for critical appraisal in the systematic review (Fig.  1 ).

figure 1

PRISMA flow diagram of study identification, screening, and inclusion in review. Other sources of articles refer to those identified through the Stop TB Partnership website, KNCV Tuberculosis foundation database, and the snowball method

Quality assessment

The median CCAT score for quality of studies was 24/40 (range 15–38) (Additional file 1 ). Three studies were classified as high quality [ 24 , 25 , 26 ]. The predominant reasons for lower quality scoring were lack of details relating to methods and study protocols including sampling frames and ethical approval.

Study characteristics

There was marked heterogeneity in the study characteristics in terms of study aims, designs, population and region/sites, and type of stigma measured (Table 1 ). The studies were conducted in low- ( n  = 1) [ 27 ], middle- ( n  = 9) [ 24 , 25 , 26 , 28 , 29 , 30 , 31 , 32 , 33 ], and high-income ( n  = 1) countries [ 34 ]. Two studies were conducted in the same country (Peru) [ 30 , 31 ], and these studies were linked with some overlap of study team members and co-authors. The studies were targeted at a variety of different populations including people with TB and MDR-TB and their households ( n  = 5), HCWs ( n  = 3), and the public ( n  = 3).

Study population, aims and intervention

Six studies applied interventions targeted towards people with TB. Five of the studies aimed to improve TB treatment compliance and completion through psychosocial support interventions, which were TB clubs or support groups ( n  = 3) [ 28 , 29 , 30 ], nurse support ( n  = 1) [ 31 ], and household counselling ( n  = 1) [ 26 ], while one study focused on improving TB knowledge [ 33 ].

TB clubs involved group meetings of people diagnosed with TB to discuss their experiences and provide mutual support to encourage each other through their illness and treatment. Other studies initiated patient-centred home visits by HCWs to complement the TB clubs [ 30 ], provided individualised emotional support from community nurses who informed and educated people with TB and their households about TB [ 31 ], and implemented a household counselling intervention delivered by nurses and trained counsellors [ 26 ]. All of the studies tailored towards people with TB captured stigma related to being diagnosed with TB. The assessment focused on measuring enacted and internalised stigma and, where possible, the influence of such stigma on TB treatment success rates. Two of the studies also involved family members of people with TB: one to evaluate stigma [ 26 ] and another to evaluate people with TB and their family members’ TB knowledge following delivery of educational videos while waiting at TB outpatient clinic appointments [ 33 ].

Two studies evaluated stigma among HCWs using workshops focused on distinct aspects of stigma [ 24 , 34 ]. One delivered nationwide TB training workshops to educate HCWs on TB, stigma and human rights to improve knowledge on TB and reduce TB-stigma towards people with TB [ 34 ]. In another, there was a focus on healthcare workers who were themselves stigmatised by other HCWs [ 24 ]. This study measured external or secondary stigma, in which HCWs experience negative attitudes or rejection because of the care they have given to people with TB.

Three studies assessed anticipated TB-Stigma among the public: two in an adult population and one in an adolescent population. All the studies measured anticipated TB-stigma using before-after intervention designs. Two studies applied health education programs in the community (mass information programs and health promotion at mass gatherings) [ 27 , 32 ]. Another study delivered training to students and evaluated whether the training reduced their levels of anticipated TB-stigma [ 25 ].

Stigma measurement tools

Eight studies used quantitative questionnaires to measure stigma (Table 2 ) [ 24 , 25 , 26 , 27 , 28 , 32 , 33 , 34 ]. The format of the questionnaires to measure stigma varied widely including the number of questions asked (range 3–14 questions). Three of the questionnaires were adapted from tools that were not specific to any particular disease and had been previously validated but not among people with TB [ 28 , 29 , 34 ]. For example, Macq et al . adapted their questionnaire from the Boyd Ritsher Mental Illness stigma scale and pre-tested it 2 years before the intervention study to improve its internal validity [ 28 ]. One study piloted the tools in six different communities (four Zambian and two South African) with six different languages (Nyanja, Bemba, Tonga, isiXhosa, Afrikaans and English) [ 26 ]. Four other studies piloted their questionnaires in a single population each [ 24 , 25 , 27 , 28 , 35 ].

Most studies ( n  = 7) applied the tool before and after a stigma-reduction or related intervention with the time period between the first and second application varying from 4 weeks to 18 months [ 24 , 25 , 26 , 28 , 32 , 33 , 34 ]. One quantitative study did not evaluate stigma before the intervention [ 27 ]. The study was an evaluation of an extensive mass health education programme that was implemented for 2–3 years in one case study area and compared to another control study area with a limited health education programme.

Four studies used qualitative methods, such as focus group discussions, interviews and observation, to evaluate stigma [ 24 , 29 , 30 , 31 ]. No studies used previously validated methods or tools to qualitatively evaluate stigma. However, the qualitative approaches focused less on measurement of stigma and more on exploring how people with TB attempted to combat the stigma perceived by themselves [ 29 ] or by other people [ 30 ], and how HCWs who work with people with TB struggled to deal with stigmatisation from other HCWs [ 24 ].

Challenges, successes, and outcomes

Implementation and delivery challenges and process indicators such as fidelity, acceptability, and feasibility were infrequently measured or reported in the studies. One study caused a positive change to national practice with the production of a manual to expand the intervention to a wider population [ 28 ]. Three studies reported that a success of the intervention was that sustainable changes had been made within the study site communities [ 26 , 29 , 33 ]. However, there was no objective way to measure or verify these changes from the data presented within the study articles.

Four of the studies explicitly stated that their intervention was limited by geographical challenges including some populations not being reached by the intervention [ 25 , 27 , 28 , 32 ]. This limited the external validity of the data. Three studies mentioned challenges concerning maintenance and sustainability of the programmes, including identifying participants to take part in the intervention and motivating people to continue engaging with the intervention [ 30 , 31 , 34 ]. Another study mentioned that inviting all HCWs to participate in a workshop about TB was problematic because hospitals were busy and understaffed [ 24 ]. The intervention itself was also challenged by issues relating to professional rank, position, and social status of different HCWs, which was perceived as limiting open discussion about the optimal ways to address stigma between HCWs (Table 3 ).

Pathways to impact of TB-stigma interventions

Synthesising learning from the interventions and outcomes, we found distinct pathways to reduce stigma depending on the population targeted by the intervention. We created a novel conceptual framework to illustrate these pathways (Fig.  2 ).

figure 2

Among people with TB, stigma is a negative effect of being ill with TB, diagnosed with TB, and being on TB treatment. Stigma towards people with TB can develop in three other populations: the public, TB-related HCWs, and other HCWs. The interventions for people with TB improved TB knowledge, reduced myth and misconception related to TB, increased confidence of people with TB, and thereby reduced internalised stigma experienced by people with TB [ 28 , 29 , 30 , 31 , 33 ]. These effects directly supported people with TB to comply with and complete TB treatment. Another study showed that, although household counselling was not specifically designed to, or found to, reduce TB-Stigma [ 26 ], health counsellors can help households manage the consequence of TB-Stigma. The main challenge in providing household counselling, particularly in communities with high levels of TB-stigma, is that the visits themselves may trigger anticipated, internalised, or enacted stigma.

Training to TB HCWs improved the HCWs’ knowledge, attitude, and practice towards people with TB [ 34 ], which may contribute to improved TB care. Conversely, our review found that TB-related HCWs were often stigmatised by other HCWs [ 24 ]. Training HCWs who care for people with TB to educate other HCWs is likely to support dissemination of knowledge and accurate information about TB-Stigma in their workplace. Although the training failed to reduce external or secondary stigma, there was a potential spill over effect that TB-related HCWs could have used the campaign materials to educate people living in their neighbourhood.

Interventions targeted towards the general public had positive impacts on knowledge, attitudes and practice related to TB as measured by knowledge, attitude, and practice (KAP) and stigma scores [ 25 , 27 , 32 ]. Mass TB education to the public was expected to increase TB knowledge and remove TB misconceptions which could result in improved community attitudes and reduced stigma towards people with TB. However, the evidence found in this review suggested that misconceptions about TB persisted, even worsened, if health education through pamphlets or posters were too short and failed to convey accurate public health messages [ 32 ].

This literature review found that, despite the global importance of addressing TB-Stigma, there is a paucity of high-quality studies evaluating interventions to reduce TB-stigma. Intervention design across studies was heterogeneous, but education about TB frequently featured as a core intervention activity. Assessment of the impact of interventions on TB-stigma reduction was limited by a lack of well-validated tools to measure stigma. The novel conceptual framework highlighted that people with TB may experience stigma from three different populations around them: the public, TB HCWs, and other HCWs. The ideal and possibly most synergistic interventions to reduce TB-Stigma would be optimized by delivering interventions targeted towards more than one key populations at the same time.

There has been an increasing awareness of the importance of combatting TB-Stigma in recent years. This study may not have captured stigma-reduction interventions or programs at local, sub-national, or national levels due to such programs not being executed, evaluated, or reported systematically, which makes them difficult to review and compare. Among the limited studies identified, this review found that research on TB-stigma was often hampered by suboptimal design and methods for implementation and evaluation.

This review complements the Sommerland et al . paper[ 17 ] and extends its findings through a specific focus on the tools used to measure stigma reduction, a qualitative evaluation of the potential reasons underlying the success or failure of the interventions, and the creation of a novel conceptual framework of the pathways to intervention impact. By this approach, this review highlights that most TB-stigma intervention studies used tools that lacked appropriate validation. This finding is consistent with other published studies [ 36 , 37 , 38 , 39 ]. Using reliable, validated stigma measurement tools and methods is important and helps measure stigma accurately and consistently. It also enables the comparison of impact evaluation of stigma reduction interventions across studies and contexts. There is a list of available tools that can be used and validated [ 19 ], including Van Rie’s TB-Stigma Scale [ 40 ], one of the most adapted questionnaires to assess stigma [ 38 ], which may support researchers and implementers designing TB-stigma reduction programmes in the future.

Using qualitative approaches to measure TB-stigma has both strengths and limitations. This approach cannot precisely assess the reduction of stigma after the intervention. However, it can help explore more profound dimensions of stigma and its impact on people with TB and their households. For example, qualitative studies have been able to elicit key emotional responses to TB-Stigma, including shock, fear of being isolated or abandoned by a spouse, shame related to becoming weak and incapable of working, worry relating to loneliness, and desperation related to thoughts about TB-related death [ 29 , 30 ]. Therefore, interventions incorporating mixed methods process evaluations would be both prudent and beneficial.

The conceptual framework we have generated can support understanding of the pathways through which interventions successfully reduce stigma and the parties affected. It is notable that interventions rarely have stigma-reduction as a primary aim or objective. Rather, programs often cite stigma reduction as a bridge towards TB treatment compliance, completion, and success. This may be short-sighted: besides improving treatment completion, psychosocial support is also important to prevent or alleviate anxiety, depression, and mental illness, which are well established correlates of being affected by TB [ 41 , 42 ]. Not having stigma reduction as a primary or even co-primary outcome may have contributed to a lack of focus on using validated instruments to measure stigma. Given that global TB policy strongly recommends interventions to reduce TB-stigma [ 43 , 44 ], it is vital that appropriately validated tools be used to measure stigma and that reduction of stigma be considered as a key individual-level outcome for people affected by TB.

The framework shows that the interventions on TB HCWs are also critical and may have potential spill over effects to other healthcare workers and the general public. TB HCWs often face secondary stigma and may be at risk of a psychosocial impact of TB themselves, particularly in areas with high co-prevalence of HIV/AIDS and TB [ 45 , 46 ]. TB-stigma interventions for HCWs can be challenging to implement because they may encounter power structure problems against colleagues with higher professional rank, position, and status [ 24 ]. However, HCWs are well placed to convey anti-stigma messages to their surrounding communities and should be empowered to do so.

The framework also emphasises the potential role for synergy across different pathways of TB-stigma reduction to enhance the effectiveness of interventions. For example, improvements in community and HCWs’ KAP are likely to reduce enacted stigma. Concurrently improving people with TB’s KAP is also expected to mitigate internalised and anticipated stigma. This implies that the ideal and possibly most synergistic intervention would be optimized by delivering interventions to more than one key population. TB clubs, for example, often only benefit people with TB. Stigma-reduction activities and interventions should aim to be more inclusive where possible with HCWS and/or community members being encouraged to attend and participate in TB club meetings, helping to act on all three forms of stigma.

Given that social and economic determinants and consequences of TB are now recognised as significant limiting factors for ending TB, it is notable that there is no recognised national or global indicator for TB-stigma [ 47 ]. Currently, there is a global indicator of TB-related catastrophic costs. This indicator has already proven highly useful within research and National TB Programme activities [ 48 ] and has led to the creation of a WHO database of the financial burden of TB for more than twenty countries [ 1 ]. The same approach should be taken to document the psychosocial burden of TB-stigma across countries. We would strongly advocate for a unified, adaptable global TB-stigma indicator. It could support research and activities to gather data on the prevalence of stigma in different countries or regions. It would also eventually garner further resource investment and scientific interest and heighten much-needed advocacy in this field.

This study has several limitations. First, due to the heterogeneity of study designs, quality, interventions, and tools used, it was not possible to quantitatively determine the effectiveness of interventions. While this was a weakness, the focus was on qualitative assessment of the studies. Second, only articles written in English were included, therefore, some relevant literature may have been missed, particularly of research performed in high TB burden countries in which English is not the first language. Third, paper quality was moderate, and some papers had missing data, which—despite contacting corresponding authors—was not made available for analysis. As our analysis suggested that most papers included were of only low or moderate quality, the power of this review to make conclusions about study impacts is limited [ 49 ]. Fourth, we were not able to include studies of complex interventions that aimed to minimise both the psychosocial (e.g. stigma) and economic (e.g. catastrophic costs) of TB, such as the randomised-controlled HRESIPT and CRESIPT studies in Peru [ 50 , 51 , 52 ]. While TB treatment, prevention, and economic outcomes from these studies are available, the impact of these interventions on stigma is not yet known. While publication bias is a limitation of any review of the published literature, we attempted to mitigate this bias through a comprehensive grey literature review. Despite the comprehensive evaluation of stigma reduction interventions, this review and framework could not capture the complexity of stigma, which—from a social perspective—involves a set of interrelated, heterogeneous system structures [ 16 ].

Despite the global importance of addressing TB-Stigma, there is a paucity of high-quality studies evaluating interventions to reduce TB-stigma. The novel conceptual framework highlighted that people with TB may experience internalized stigma and anticipated or enacted stigma from three different populations around them: the public, TB HCWs, and other HCWs. Interventions for people with TB can effectively provide psychosocial support for treatment completion. Interventions on HCWs can enhance the support provided for people with TB, and interventions for the public have the potential to reduce community-level stigma toward people with TB. The ideal and possibly most synergistic intervention would be optimized by delivering interventions to more than one key population. Finally, our findings reinforce that it is vital to promote stigma as an indicator in national and international TB strategies to strengthen the development and evaluation of stigma-reducing interventions.

Availability of data and materials

Data generated or analysed during this study are included in this published article and its additional information files.

World Health Organization. Global tuberculosis report 2021. Geneva: World Health Organization; 2021.

Google Scholar  

Datiko DG, Jerene D, Suarez P. Stigma matters in ending tuberculosis: nationwide survey of stigma in Ethiopia. BMC Public Health. 2020;20(1):190.

Article   PubMed   PubMed Central   Google Scholar  

Teo AKJ, Singh SR, Prem K, Hsu LY, Yi S. Duration and determinants of delayed tuberculosis diagnosis and treatment in high-burden countries: a mixed-methods systematic review and meta-analysis. Respir Res. 2021;22(1):251.

Pai M, Kasaeva T, Swaminathan S. COVID-19’s devastating effect on tuberculosis care—a path to recovery. N Engl J Med. 2022;386:1490–3.

Article   CAS   PubMed   Google Scholar  

Chapman HJ, Veras-Estévez BA. Lessons learned during the COVID-19 pandemic to strengthen TB Infection control: a rapid review. Glob Health Sci Pract. 2021;9(4):964–77.

Chang SH, Cataldo JK. A systematic review of global cultural variations in knowledge, attitudes and health responses to tuberculosis stigma. Int J Tuberc Lung Dis. 2014;18(2):168–73.

Article   PubMed   Google Scholar  

Munro SA, Lewin SA, Smith HJ, Engel ME, Fretheim A, Volmink J. Patient adherence to tuberculosis treatment: a systematic review of qualitative research. PLoS Med. 2007;4(7): e238.

Courtwright A, Turner AN. Tuberculosis and stigmatization: pathways and interventions. Public Health Rep. 2010;125(4_suppl):34–42.

Chowdhury MRK, Rahman MS, Mondal MNI, Sayem A, Billah B. Social impact of stigma regarding tuberculosis hindering adherence to treatment: a cross sectional study involving tuberculosis patients in Rajshahi City, Bangladesh. Jpn J Infect Dis. 2015;68(6):461–6.

Bonadonna LV, Saunders MJ, Zegarra R, Evans C, Alegria-Flores K, Guio H. Why wait? The social determinants underlying tuberculosis diagnostic delay. PLoS One. 2017;12(9):e0185018.

Craig GM, Daftary A, Engel N, O’Driscoll S, Ioannaki A. Tuberculosis stigma as a social determinant of health: a systematic mapping review of research in low incidence countries. Int J Infect Dis. 2017;56:90–100.

World Health Organization. The end TB strategy. Geneva: World Health Organization; 2015.

The Global Fund. Tuberculosis and human right. In: The UN General Assembly high-level meeting on ending TB. The Global Fund; 2018.

United Nations. United Nations high-level meeting on the fight against tuberculosis. New York: United Nations; 2018.

Macintyre K, Bakker MI, Bergson S, Bhavaraju R, Bond V, Chikovore J, et al. Defining the research agenda to measure and reduce tuberculosis stigmas. Int J Tuberc Lung Dis. 2017;21(11):S87–96.

Article   Google Scholar  

Pescosolido BA, Martin JK. The stigma complex. Ann Rev Sociol. 2015;41(1):87–116.

Sommerland N, Wouters E, Mitchell EMH, Ngicho M, Redwood L, Masquillier C, et al. Evidence-based interventions to reduce tuberculosis stigma: a systematic review. Int J Tuberc Lung Dis. 2017;21(11):S81–6.

Partnership STB. TB stigma assessment: implementation handbook. Geneva: Stop TB Partnership; 2019.

Challenge TB. TB stigma measurement guidance. Den Haag: KNCV; 2018.

Hartog K, Hubbard CD, Krouwer AF, Thornicroft G, Kohrt BA, Jordans MJD. Stigma reduction interventions for children and adolescents in low- and middle-income countries: systematic review of intervention strategies. Soc Sci Med. 2020;246: 112749.

Crowe M. Crowe Critical Appraisal Tool (CCAT) v 1.4. Queensland; 2013.

Crowe M, Sheppard L. A general critical appraisal tool: an evaluation of construct validity. Int J Nurs Stud. 2011;48(12):1505–16.

Thapa S, Hannes K, Cargo M, Buve A, Aro AR, Mathei C. Building a conceptual framework to study the effect of hiv stigma-reduction intervention strategies on HIV test uptake: a scoping review. J Assoc Nurses AIDS Care. 2017;28(4):545–60.

Sommerland N, Masquillier C, Rau A, Engelbrecht M, Kigozi G, Pliakas T, et al. Reducing HIV- and TB-stigma among healthcare co-workers in South Africa: results of a cluster randomised trial. Soc Sci Med. 2020;266: 113450.

Idris NA, Zakaria R, Muhamad R, Nik Husain NR, Ishak A, Wan Mohammad WMZ. The effectiveness of tuberculosis education programme in Kelantan, Malaysia on knowledge, attitude, practice and stigma towards tuberculosis among adolescents. Malays J Med Sci. 2020;27(6):102–14.

PubMed   PubMed Central   Google Scholar  

Bond V, Floyd S, Fenty J, Schaap A, Godfrey-Faussett P, Claassens M, et al. Secondary analysis of tuberculosis stigma data from a cluster randomised trial in Zambia and South Africa (ZAMSTAR). Int J Tuberc Lung Dis. 2017;21(11):49–59.

Croft RP, Croft RA. Knowledge, attitude and practice regarding leprosy and tuberculosis in Bangladesh. Lepr Rev. 1999;70(1):34–42.

CAS   PubMed   Google Scholar  

Macq J, Solis A, Martinez G, Martiny P. Tackling tuberculosis patients’ internalized social stigma through patient centred care: an intervention study in rural Nicaragua. BMC Public Health. 2008;8:154–154.

Demissie M, Getahun H, Lindtjørn B. Community tuberculosis care through “TB clubs” in rural North Ethiopia. Soc Sci Med. 2003;56(10):2009–18.

Acha J, Sweetland A, Guerra D, Chalco K, Castillo H, Palacios E. Psychosocial support groups for patients with multidrug-resistant tuberculosis: five years of experience. Glob Public Health. 2007;2(4):404–17.

Chalco K, Wu DY, Mestanza L, Muñoz M, Llaro K, Guerra D, et al. Nurses as providers of emotional support to patients with MDR-TB. Int Nurs Rev. 2006;53(4):253–60.

Balogun M, Sekoni A, Meloni ST, Odukoya O, Onajole A, Longe-Peters O, et al. Trained community volunteers improve tuberculosis knowledge and attitudes among adults in a periurban community in southwest Nigeria. Am J Trop Med Hyg. 2015;92(3):625–32.

Wilson JW, Ramos JG, Castillo F, Castellanos EF, Escalante P. Tuberculosis patient and family education through videography in El Salvador. J Clin Tuberc Other Mycobact Dis. 2016;4:14–20.

Wu PS, Chou P, Chang NT, Sun WJ, Kuo HS. Assessment of changes in knowledge and stigmatization following tuberculosis training workshops in taiwan. J Formos Med Assoc. 2009;108(5):377–85.

Wouters E, Rau A, Engelbrecht M, Uebel K, Siegel J, Masquillier C, et al. The development and piloting of parallel scales measuring external and internal HIV and tuberculosis stigma among healthcare workers in the Free State Province, South Africa. Clin Infect Dis. 2016;62(suppl_3):S244–54.

Sommerland N, Wouters E, Mitchell EMH, Ngicho M, Redwood L, Masquillier C, et al. Evidence-based interventions to reduce tuberculosis stigma: a systematic review. Int J Tuberc Lung Dis. 2017;21(11):81–6.

Sengupta S, Banks B, Jonas D, Miles MS, Smith GC. HIV interventions to reduce HIV/AIDS stigma: a systematic review. AIDS Behav. 2011;15(6):1075–87.

Bergman A, McNabb K, Farley JE. A systematic review and psychometric appraisal of instruments measuring tuberculosis stigma in Sub-Saharan Africa. Stigma Health. 2021. https://doi.org/10.1037/sah0000328 .

MacKenzie SB, Podsakoff PM, Podsakoff NP. Construct measurement and validation procedures in MIS and behavioral research: integrating new and existing techniques. MIS Q. 2011;35(2):293–334.

Van Rie A, Sengupta S, Pungrassami P, Balthip Q, Choonuan S, Kasetjaroen Y, et al. Measuring stigma associated with tuberculosis and HIV/AIDS in southern Thailand: exploratory and confirmatory factor analyses of two new scales. Trop Med Int Health. 2008;13(1):21–30.

Koyanagi A, Vancampfort D, Carvalho AF, DeVylder JE, Haro JM, Pizzol D, et al. Depression comorbid with tuberculosis and its impact on health status: cross-sectional analysis of community-based data from 48 low- and middle-income countries. BMC Med. 2017;15(1):209–209.

Naidu T, Pillay SR, Ramlall S, Mthembu SS, Padayatchi N, Burns JK, et al. Major depression and stigma among individuals with multidrug-resistant tuberculosis in South Africa. Am J Trop Med Hyg. 2020;103(3):1067–71.

Courtwright A, Turner AN. Tuberculosis and stigmatization: pathways and interventions. Public Health Rep. 2010;125(Suppl 4):34–42.

Baral SC, Karki DK, Newell JN. Causes of stigma and discrimination associated with tuberculosis in Nepal: a qualitative study. BMC Public Health. 2007;7(1):211.

Wouters E, Sommerland N, Masquillier C, Rau A, Engelbrecht M, van Rensburg AJ, et al. Unpacking the dynamics of double stigma: how the HIV-TB co-epidemic alters TB stigma and its management among healthcare workers. BMC Infect Dis. 2020;20(1):106.

Rau A, Wouters E, Engelbrecht M, Masquillier C, Uebel K, Kigozi G, et al. Towards a health-enabling working environment—developing and testing interventions to decrease HIV and TB stigma among healthcare workers in the Free State, South Africa: study protocol for a randomised controlled trial. Trials. 2018;19(1):351.

Lönnroth K, Glaziou P, Weil D, Floyd K, Uplekar M, Raviglione M. Beyond UHC: monitoring health and social protection coverage in the context of tuberculosis care and prevention. PLoS Med. 2014;11(9): e1001693.

Viney K, Islam T, Hoa NB, Morishita F, Lönnroth K. The financial burden of tuberculosis for patients in the Western-Pacific region. Trop Med Infect Dis. 2019;4(2):94.

Article   PubMed Central   Google Scholar  

Peters SE, Johnston V, Coppieters MW. Interpreting systematic reviews: looking beyond the all too familiar conclusion. J Hand Ther. 2014;27(1):1–3.

Wingfield T, Tovar MA, Huff D, Boccia D, Montoya R, Ramos E, et al. Socioeconomic support to improve initiation of tuberculosis preventive therapy and increase tuberculosis treatment success in Peru: a household-randomised, controlled evaluation. Lancet. 2017;389:S16.

Wingfield T, Tovar MA, Huff D, Boccia D, Montoya R, Ramos E, et al. A randomized controlled study of socioeconomic support to enhance tuberculosis prevention and treatment, Peru. Bull World Health Organ. 2017;95(4):270.

Wingfield T, Tovar MA, Huff D, Boccia D, Montoya R, Ramos E, et al. The economic effects of supporting tuberculosis-affected households in Peru. Eur Respir J. 2016;48(5):1396–410.

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Acknowledgements

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TW is supported by grants from the Wellcome Trust, UK (209075/Z/17/Z), the Medical Research Council, Department for International Development, and Wellcome Trust (Joint Global Health Trials, MR/V004832/1), and a Dorothy Temple Cross Tuberculosis International Collaboration Grant from the Medical Research Foundation, UK. AF and TW are recipients of a Royal Society of Tropical Medicine and Hygiene early career research grant.

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Charlotte Nuttall and Ahmad Fuady joint first authors with equal contribution

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Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, UK

Charlotte Nuttall, Holly Nuttall & Tom Wingfield

Department of Community Medicine, Faculty of Medicine, Universitas Indonesia, 10310, Jakarta, Indonesia

Ahmad Fuady & Muchtaruddin Mansyur

Department of Public Health, Erasmus MC University Medical Center Rotterdam, 3015GD, Rotterdam, The Netherlands

Ahmad Fuady

Primary Health Care Research and Innovation Center, Indonesian Medical Education and Research Institute, Faculty of Medicine Universitas Indonesia, 10430, Jakarta, Indonesia

Social Medicine, Infectious Diseases, and Migration (SIM) Group, Department of Public Health Sciences, Karolinska Institute, 10653, Stockholm, Sweden

Kritika Dixit & Tom Wingfield

Birat Nepal Medical Trust, Lazimpat Road, Lazimpat, Kathmandu, 44600, Nepal

Kritika Dixit

Departments of International Public Health and Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, L3 5QA, UK

Tom Wingfield

Tropical and Infectious Disease Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, L7 8XP, UK

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CN and AF led the construction and write-up of the manuscript. TW advised throughout the process of constructing the study and was the lead editor of the manuscript. HN was the second reviewer of papers. KD and MM reviewed the manuscript and provided substantial comments. All authors read and approved the final manuscript.

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Nuttall, C., Fuady, A., Nuttall, H. et al. Interventions pathways to reduce tuberculosis-related stigma: a literature review and conceptual framework. Infect Dis Poverty 11 , 101 (2022). https://doi.org/10.1186/s40249-022-01021-8

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Global prevalence and burden of multidrug-resistant tuberculosis from 1990 to 2019

  • Hengliang Lv 1 , 2   na1 ,
  • Xin Zhang 1 , 2   na1 ,
  • Xueli Zhang 3   na1 ,
  • Junzhu Bai 1 , 2 ,
  • Shumeng You 1 , 2 ,
  • Xuan Li 1 , 4 ,
  • Shenlong Li 2 ,
  • Yong Wang 1 , 2 , 4 ,
  • Wenyi Zhang 1 , 2 , 4 &
  • Yuanyong Xu 1 , 2  

BMC Infectious Diseases volume  24 , Article number:  243 ( 2024 ) Cite this article

Metrics details

Tuberculosis(TB) remains a pressing public health challenge, with multidrug-resistant tuberculosis (MDR-TB) emerging as a major threat. And healthcare authorities require reliable epidemiological evidence as a crucial reference to address this issue effectively. The aim was to offer a comprehensive epidemiological assessment of the global prevalence and burden of MDR-TB from 1990 to 2019.

Estimates and 95% uncertainty intervals (UIs) for the age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized disability-adjusted life years rate (ASR of DALYs), and age-standardized death rate (ASDR) of MDR-TB were obtained from the Global Burden of Disease (GBD) 2019 database. The prevalence and burden of MDR-TB in 2019 were illustrated in the population and regional distribution. Temporal trends were analyzed by using Joinpoint regression analysis to calculate the annual percentage change (APC), average annual percentage change (AAPC) and its 95% confidence interval( CI ).

The estimates of the number of cases were 687,839(95% UIs: 365,512 to 1223,262), the ASPR were 8.26 per 100,000 (95%UIs: 4.61 to 15.20), the ASR of DALYs were 52.38 per 100,000 (95%UIs: 22.64 to 97.60) and the ASDR were 1.36 per 100,000 (95%UIs: 0.54 to 2.59) of MDR-TB at global in 2019. Substantial burden was observed in Africa and Southeast Asia. Males exhibited higher ASPR, ASR of DALYs, and ASDR than females across most age groups, with the burden of MDR-TB increasing with age. Additionally, significant increases were observed globally in the ASIR (AAPC = 5.8; 95% CI : 5.4 to 6.1; P  < 0.001), ASPR (AAPC = 5.9; 95% CI : 5.4 to 6.4; P  < 0.001), ASR of DALYs (AAPC = 4.6; 95% CI : 4.2 to 5.0; P  < 0.001) and ASDR (AAPC = 4.4; 95% CI : 4.0 to 4.8; P  < 0.001) of MDR-TB from 1990 to 2019.

Conclusions

This study underscored the persistent threat of drug-resistant tuberculosis to public health. It is imperative that countries and organizations worldwide take immediate and concerted action to implement measures aimed at significantly reducing the burden of TB.

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Introduction

Tuberculosis (TB) is a highly contagious disease caused by the Mycobacterium tuberculosis ( Mtb ), primarily transmitted through the air [ 1 ]. It is characterized by a long incubation period, lack of early symptoms, and ease of transmission [ 2 ]. TB continued to be a significant global health concern, ranking as the second leading cause of infectious disease mortality, resulting in 1.4 million deaths in 2021 [ 3 ]. The expenses of TB diagnostic, treatment and prevention services in low and middle-income countries were estimated at US$ 5.4 billion in 2021 [ 1 ], which has brought heavy economic and social burden to the countries around the world. Nevertheless, estimated in 27 countries from the country-specific models, which suggested that there could be further increases in the incidence and deaths of TB [ 1 ].

Multidrug-resistant TB (MDR-TB), defined as TB caused by Mtb bacilli resistant to rifampicin and isoniazid, represents a major threat to global TB control [ 4 ]. In 2021, there were an estimated 450,000 cases of MDR-TB, representing a 3.1% increase from the previous year [ 1 ]. About 464,000 global cases of rifampicin-resistant TB, 78% of which were MDR-TB was noticed at global in 2019 [ 5 ], and approximately 25% of deaths related to TB can be attributed to antimicrobial drug resistance [ 6 ]. Relevant studies have shown that MDR-TB is a debilitating disease that can give rise to severe and secular physical [ 7 ], mental [ 8 ], and financial sequelae [ 9 ]. Although countries have been expanding diagnostic capacity, detecting more patients with MDR-TB over recent years, a large number of cases has still been reported in some countries of Central Asia and Eastern Europe, such as China, India, and Russia [ 1 , 10 , 11 ]. In addition, MDR-TB imposes a significant burden on healthcare systems, with treatment costs 20 times higher than those of drug-susceptible TB [ 12 ]. Given its impact, MDR-TB deserves increased attention and prioritization globally.

The continuous dissemination of MDR-TB poses one of the most challenging and urgent obstacles to global TB control efforts [ 13 ]. Enhanced surveillance and data collection are crucial for assessing the risk of MDR-TB transmission. Understanding the global prevalence of MDR-TB will be helpful in the optimal allocation of limited resources to control its spread. The aim was to provide epidemiological evidence to the department of health by describing the prevalence of MDR-TB globally, utilizing data from the Global Burden of Disease (GBD) study.

Data source

For the present study, we acquired the MDR-TB data from the GBD 2019 database, available at http://ghdx.healthdata.org/gbd-results-tool . The GBD 2019 database is widely recognized for its valuable, systematic, and comprehensive approach in collecting and analyzing epidemiological data, which provides standardized measures of incidence, prevalence, mortality, DALYs (disability-adjusted life years), and other indicators for 369 injuries and diseases across 204 countries and territories, spanning the years from 1990 to 2019. Statistical code used for GBD estimation is publicly available online from http://ghdx.healthdata.org/gbd-2019/code . Supplement 1 [ 14 ] provides some details on the methods used to model MDR-TB. The methodology, data inputs and processing used in GBD 2019 have been extensively described in other high-quality studies [ 14 , 15 , 16 ], related information of TB has been utilized to described previously as well [ 17 , 18 ].

We concentrated on the differences of MDR-TB between sexes, ages, and regions. And the estimates and 95% uncertainty intervals (UIs) for the number of cases, age-standardized prevalence rate (ASPR), age-standardized incidence rate (ASIR), age-standardized disability-adjusted life years (ASR of DALYs), and age-standardized death rate (ASDR) of MDR-TB were extracted from GBD 2019.

Disease defination and description

The section on drug-resistant TB in the GBD database primarily focuses on the following four aspects: (i) human immunodeficiency virus(HIV)/acquired immune deficiency syndrome(AIDS)-MDR TB without extensive drug resistance(abbreviated to MDR-TB throughout the other part of this study), (ii) HIV/AIDS-Extensive drug-resistant TB, (iii) MDR-TB, and (iv) Extensive drug-resistant TB. This study specifically focuses on the research of MDR-TB. Multidrug-resistant TB (MDR-TB), defined as TB caused by Mtb bacilli resistant to rifampicin and isoniazid [ 4 ]. The study provided a comprehensive description of MDR-TB by examining multiple disease indicators, encompassing prevalence, DALYs rate, and death rate in different population subgroups, including gender and age, globally in 2019, additionally, regarding regional distribution of MDR-TB, the number of cases, ASPR, ASR of DALYs, and ASDR were adopted to compare the differences in the burden of disease across countries and territories in 2019.

Temporal trend analysis

Joinpoint regression analysis [ 19 ] was employed to analyze the temporal trends of ASIR, ASPR, ASR of DALYs, and ASDR from 1990 to 2019 at the global/region level. This method allows the identification of significant joinpoints, which are points indicating substantial changes in the trend. The analysis divides the trend into multiple subsegments, and the annual percentage change (APC) with a 95% confidence interval ( CI ) is calculated for each subsegment. Additionally, the average annual percentage change (AAPC) is used to summarize the overall change trends from 1990 to 2019. A positive APC/AAPC estimation along with the lower boundary of its 95% CI greater than zero indicates an upward trend during a specific period. Conversely, if the APC/AAPC estimation and the upper boundary of its 95% CI are both below zero, a declining trend is observed. If neither of these conditions is met, the trend is considered stable [ 20 , 21 ].

Statistical analysis

We utilized bar graphs to visually represent the disparities of MDR-TB among different sexes and age groups. World maps were employed to demonstrate the geographic variances in the age-standardized rate in 2019. Furthermore, to calculate the APC and AAPC, we employed Joinpoint regression software and line charts were employed to depict the disease trends from 1990 to 2019. All statistical analyses and visualizations were executed using R software (version 4.2.1) and Joinpoint regression software (version 4.9.1.0). We considered a P  < 0.05 as significant.

ASPR of MDR-TB

Based on the data extracted from the GBD 2019 database, some meaningful findings are listed below. The prevalence of MDR-TB was higher in men (9.39/100,000; 95% UIs: 4.99 to 16.79) compared to women (7.95/100,000; 95% UIs: 4.29 to 13.69). The highest prevalence was noted in the 75–79 years age group (19.74/100,000; 95% UIs: 8.20 to 39.86) (Fig.  1 A), and males had a higher prevalence rate than females across most age groups (Fig.  1 A). The number of MDR-TB was 687,839(95% UIs: 365,512 to 1223,262) at global in 2019, and Fig.  2 A showed the top three cases were in India, China and Pakistan. The ASPR of MDR-TB was 8.26 per 100,000 population (95% UIs: 4.61 to 15.20) at global, and the Sub-Saharan Africa showed a higher burden than other continents, the country with the highest ASPR was Somalia (48.86/100,000; 95% UIs: 13.62 to 140.00), while Slovenia had the lowest ASPR (0.01/100,000; 95% UIs: 0.00 to 0.03) (Fig.  2 B) across 204 countries in 2019. The global ASPR of MDR-TB showed a significant increasing trend from 1990 to 2019 (AAPC = 5.9, 95% CI : 5.4 to 6.4; P  < 0.001) (Fig.  3 B; Table  1 ), with the most enormous changes occurring during 1990–1992 (APC = 60.3; 95% CI : 52.3 to 68.7; P  < 0.001). However, there was a decline in the trend between 2005 and 2013 (APC = -2.7; 95% CI : -3.0 to -2.5; P  < 0.001), followed by a subsequent rise (Table  2 ).

figure 1

Age and sex distribution of ASPR (a) , ASR of DALYs (b) and ASDR of MDR-TB (c) of MDR-TB at global in 2019. ASPR:age-standardized prevalence rate; ASR of DALYs: age-standardized of Disability-adjusted life years rate; ASDR:age-standardised death rate

figure 2

Number of case (a) , ASPR (b) , ASR of DALYs (c) and ASDR (d) of MDR-TB of MDR-TB by country in 2019. ASPR:age-standardized prevalence rate; ASR of DALYs: age-standardized of Disability-adjusted life years rate; ASDR:age-standardised death rate

figure 3

Joinpoint regression analysis of ASIR (a) , ASPR (b) , ASR of DALYs (c) and ASDR (d) of MDR-TB at the global from 1990 to 2019. ASIR:age-standardized incidence rate; ASPR:age-standardized prevalence rate; ASR of DALYs: age-standardized of Disability-adjusted life years rate; ASDR:age-standardised death rate

ASIR of MDR-TB from 1990 to 2019

Joinpoint regression analysis was also conducted to analyze the annual age-standardized incidence rates (ASIR) of MDR-TB from 1990 to 2019. The main findings revealed a substantial upward trend in the ASIR of MDR-TB (AAPC = 5.8; 95% CI : 5.4 to 6.1; P  < 0.001), which was consistent for both males (AAPC = 5.8; 95% CI : 5.4 to 6.2; P  < 0.001) (Fig.  3 A; Table  1 ) and females (AAPC = 5.6; 95% CI : 5.3 to 6.0; P  < 0.001) (Fig.  3 A; Table  1 ). The increasing trend was interrupted around 2005, followed by a downward trend from 2005 to 2015 (APC = -2.9; 95% CI : -3.0 to -2.7; P  < 0.001) (Table  2 ).

ASR of DALYs of MDR-TB

The age-standardized rate (ASR) of disability-adjusted life years (DALYs) for MDR-TB was 52.38 per 100,000 population (95%UIs: 97.60 to 22.64) in 2019. Males generally exhibited higher DALYs than females across most age groups. The most significant DALYs occurred in the 70–74 years age group for men, while for women, it was highest in the 80–84 years age group (Fig.  1 B). Similar to the ASPR, the Sub-Saharan Africa exhibited a high burden of ASR of DALYs, Somalia had the highest ASR of DALYs (1010.92/100,000; 95%UIs: 230.54 to 2778.92), while Slovenia had the lowest ASR of DALYs (0.02/100,000; 95%UIs: 0.00 to 0.07) (Fig.  2 C). From 1990 to 2019, the ASR of DALYs for both sexes demonstrated a year-by-year upward trend (AAPC = 4.6, 95% CI : 4.2 to 5.0; P  < 0.001) (Fig.  3 C; Table  1 . The increasing trend was interrupted around 2003, followed by a downward trend from 2003 to 2011 (APC = -3.7, 95% CI : -3.9 to -3.4; P  < 0.001)(Table  2 ), subsequently, the ASR of DALYs continued to decline until 2019.

ASDR of MDR-TB

Across 204 countries and regions in 2019, the death rate of MDR-TB was higher in men (1.83/100,000; 95%UIs: 0.75 to 3.51) compared to women (0.93/100,000; 95% UIs: 0.37 to 1.81) (Fig.  1 C). The most significant death rate occurred in the 90–94 years age group for men, while for women, it was highest in the 80–84 years age group (Fig.  1 C). The age-standardized death rate (ASDR) ascribable to MDR-TB was 1.36 per 100,000 population (95% UIs: 0.54 to 2.59). The Sub-Saharan Africa showed a high burden of ASDR as well, and Somalia had the highest ASDR (31.90/100,000; 95% UIs: 7.12 to 88.59), while countries like Slovenia, Bermuda, and Andorra had the lowest ASDR (0.00/100,000; 95% UIs: 0.00 to 0.00) (Fig.  2 D). Joinpoint regression analysis indicated a considerable rising trend in the global ASDR of MDR-TB from 1990 to 2019 (AAPC = 4.4, 95% CI : 4.0 to 4.8; P  < 0.001) (Fig.  3 D; Table  1 ). The increasing trend was interrupted around 2003, followed by a downward trend from 2003 to 2011 (APC = -3.9, 95% CI : -4.2 to -3.7; P  < 0.001). Subsequently, the rate of increase slowed down from 2011 to 2019 (APC = -2.5, 95% CI : -2.8 to -2.3; P  < 0.001) (Table  2 ).

In this study, we analyzed the data from GBD 2019 to describe the population and regional distribution of the burden of MDR-TB at the national and global levels in 2019. We also examined the temporal trends from 1990 to 2019. The main strengths of this study contain a large data base covering populations worldwide, a wide time span, and comprehensive geographic coverage.

We observed that males had higher prevalence, DALYs, and death rate than females in most age groups. This finding was consistent with the Global TB reports provided by the WHO [ 1 , 4 , 5 ], which reported a higher number of TB cases among men. Men tend to be more closely associated with smoking, alcohol abuse, and long-term work pressure, which have been widely associated with an elevated risk of TB according to other studies [ 22 , 23 , 24 , 25 ]. After the onset of TB, inadequate and prolonged treatment combined with drug misuse can result in the development of drug resistance within the body, further exacerbating the challenges associated with treatment. The proportion of ASIR, ASPR, ASR of DALYs, and ASDR increased with age, reflecting the higher burden of TB and MDR-TB in older individuals. Research conducted by Zhang Ting and colleagues [ 26 ] has indicated that the global burden of TB primarily affects the middle-aged and elderly population (aged 40–60). The prolonged use of anti-TB drugs, coupled with a natural decline in immune function as individuals age [ 27 ], often leads to a higher susceptibility to MDR-TB among the elderly. Therefore, in the prevention and control of MDR-TB, this study recommended intensifying screening efforts among TB patients to ensure early detection, timely diagnosis, and prompt treatment. TB patients should adhere to the principles of early, appropriate, combination, regular, and comprehensive medication, in order to prevent the progression to drug resistance. It is worth noting that our study provided a more in-depth analysis of population information compared to other studies [ 28 , 29 ], specifically, we have compared the burden of MDR-TB in different age groups, taking into account the variations between sexes.

Regarding the region distribution of MDR-TB, our findings showed the top three cases were in India, China and Pakistan which was consistent with the WHO reports [ 4 , 5 , 30 ]. These regions have reported the highest incidence rates of TB, which can be attributed to the ecnomics and undernutrition [ 1 , 4 , 5 ], certainly, other social determinants should be taken into consideration as well, such as education, medical techniques, occupation, and social class [ 31 , 32 ]. However, when we included the total population of the country for burden estimation, we discovered that the Sub-Saharan Africa, particularly in Somalia, had a significantly larger rate compared to other regions. Although the number of cases in this region was not obvious, the proportion of patients in relation to the total population of their country was significant, which would impose a serious burden on their country. Other related study [ 26 ] have also indicated that, there were higher mortality rates due to TB in the eastern and western regions of Sub-Saharan Africa in 2019. This finding highlights the urgent need to pay attention to the issue of MDR-TB in the African region as well. A recent study has demonstrated that the implementation of effective social protection and poverty eradication programs could potentially lead to a substantial reduction in the incidence and mortality rates of TB [ 33 ]. Hence, it is imperative for these countries to prioritize the allocation of healthcare resources, particularly focusing on impoverished regions, reducing hunger, and developing social protection programs specifically targeting underprivileged households with TB patients.

In addition, the Joinpoint regression analysis provides temporal trends rather than the overall percentage change trends from 1990 to 2019, which offers more accurate and detailed information than others [ 34 , 35 ]. All four indicators exhibited an overall ascending tendency from 1990 to 2019, with a subsequent decline from 2005 to 2019. This trend was consistent with the global tuberculosis report [ 5 ], which indicated a decline in the number of deaths and incidence rate of TB but not at a rate sufficient to achieve the 2020 milestone of a 20% reduction. Meanwhile, Lange emphasized that despite the global decrease in the burden of TB and improvements in treatment rates, MDR-TB remains a substantial global health threat, characterized by high mortality rates [ 36 ]. Therefore, within the field of TB prevention and control, it is essential to recognize MDR-TB as a critical component. The focus should not only be on reducing the incidence of TB but also on efforts to decrease the number of drug-susceptible TB cases transitioning to drug-resistant forms.

Some limitations should be acknowledged. The data used in this study, derived from GBD 2019, provides estimated values that depend on the quality of the underlying data, which may vary across regions, leading to potential underestimations of the burden of MDR-TB in certain areas. Currently, the GBD dataset is only updated until the year 2019, and the findings of this study exclusively examine the prevalence of MDR-TB infections up to and including 2019. Further research is warranted to investigate the post-2019 trends in MDR-TB prevalence and the impact of the coronavirus disease 2019 pandemic on MDR-TB dynamics. Furthermore, this study did not conduct specific analyses of countries with high MDR-TB burdens, which would require further investigation.

In conclusion, our analysis of GBD 2019 data has revealed significant disparities in the global distribution of MDR-TB, encompassing variations in population, regions, and temporal. It is evident that the MDR-TB stayed on a persistent threat to public health, and concerted global action is necessary to address this challenge. Countries and health organizations should adopt a multi-faceted approach tailored to their specific circumstances, aiming to identify suitable policies and strategies to collectively work towards the goal of “Ending TB”.

Data availability

The datas used were publicly for this study. The website of the data is: https://vizhub.healthdata.org/gbd-results/ .

Abbreviations

multidrug-resistant tuberculosis

Mycobacterium tuberculosis

human immunodeficiency virus

acquired immune deficiency syndrome

uncertainty intervals

age-standardized incidence rate

age-standardized prevalence rate

age-standardized disability-adjusted life years rate

age-standardized death rate

annual percentage change

average annual percentage change

confidence interval

Bagcchi S. WHO’s Global Tuberculosis Report 2022. Lancet Microbe. 2023;4:e20.

Article   PubMed   Google Scholar  

Margarit A, Simó S, Rozas L, Deyà-Martínez À, Barrabeig I, Gené A, et al. [Adolescent tuberculosis; a challenge and opportunity to prevent community transmission]. Pediatr (Barc). 2017;86:110–4.

Article   Google Scholar  

Salari N, Kanjoori AH, Hosseinian-Far A, Hasheminezhad R, Mansouri K, Mohammadi M. Global prevalence of drug-resistant tuberculosis: a systematic review and meta-analysis. Infect Dis Poverty. 2023;12:57.

Article   PubMed   PubMed Central   Google Scholar  

Global tuberculosis report 2023. Licence: CC BY-NC-SA 3.0 IGO. Geneva: World Health Organization; 2023.

Google Scholar  

Global. tuberculosis report 2020. https://www.who.int/publications-detail-redirect/9789240013131 . Accessed 7 Jun 2023.

Furin J, Cox H, Pai M, Tuberculosis. Lancet. 2019;393:1642–56.

Powers M, Sanchez TR, Welty TK, Cole SA, Oelsner EC, Yeh F, et al. Lung function and respiratory symptoms after tuberculosis in an American Indian Population. The strong heart study. Ann Am Thorac Soc. 2020;17:38–48.

Psychiatric issues in. the management of patients with multidrug-resistant tuberculosis - PubMed. https://pubmed.ncbi.nlm.nih.gov/15182146/ . Accessed 7 Jun 2023.

Wang Y, McNeil EB, Huang Z, Chen L, Lu X, Wang C, et al. Household financial burden among multidrug-resistant tuberculosis patients in Guizhou province, China: a cross-sectional study. Med (Baltim). 2020;99:e21023.

Mazurek GH, Jereb J, Lobue P, Iademarco MF, Metchock B, Vernon A, et al. Guidelines for using the QuantiFERON-TB gold test for detecting Mycobacterium tuberculosis infection, United States. MMWR Recomm Rep. 2005;54 RR–15:49–55.

Baya B, Achenbach CJ, Kone B, Toloba Y, Dabitao DK, Diarra B, et al. Clinical risk factors associated with multidrug-resistant tuberculosis (MDR-TB) in Mali. Int J Infect Dis. 2019;81:149–55.

Global tuberculosis report. 2016. https://www.who.int/publications-detail-redirect/9789241565394 . Accessed 7 Jun 2023.

Multidrug-Resistant Tuberculosis and Extensively Drug-Resistant Tuberculosis - PubMed. https://pubmed.ncbi.nlm.nih.gov/25918181/ . Accessed 7 Jun 2023.

Collaborators G. 2019 D and I. Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet (London, England). 2020;396:1204.

Vos T, Flaxman AD, Naghavi M, Lozano R, Michaud C, Ezzati M, et al. Years lived with disability (YLDs) for 1160 sequelae of 289 diseases and injuries 1990–2010: a systematic analysis for the global burden of Disease Study 2010. Lancet. 2012;380:2163–96.

GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49.

Ma J, Vongpradith A, Ledesma JR, Novotney A, Yi S, Lim K, et al. Progress towards the 2020 milestones of the end TB strategy in Cambodia: estimates of age and sex specific TB incidence and mortality from the global burden of Disease Study 2019. BMC Infect Dis. 2022;22:904.

Zou Z, Liu G, Hay SI, Basu S, Belgaumi UI, Dhali A, et al. Time trends in tuberculosis mortality across the BRICS: an age-period-cohort analysis for the GBD 2019. eClinicalMedicine. 2022;53:101646.

Kim HJ, Fay MP, Feuer EJ, Midthune DN. Permutation tests for joinpoint regression with applications to cancer rates. Stat Med. 2000;19:335–51.

Article   CAS   PubMed   Google Scholar  

Wang L, Lv H, Zhang X, Zhang X, Bai J, You S, et al. Global prevalence, burden and trend in HIV and drug-susceptible tuberculosis co-infection from 1990 to 2019 and prediction to 2040. Heliyon. 2023;10:e23479.

Zhang X, Lv H, Chen X, Li M, Zhou X, Jia X. Analysis of ischemic stroke burden in Asia from 1990 to 2019: based on the global burden of disease 2019 data. Front Neurol. 2023;14:1309931.

Imtiaz S, Shield KD, Roerecke M, Samokhvalov AV, Lönnroth K, Rehm J. Alcohol consumption as a risk factor for tuberculosis: meta-analyses and burden of disease. Eur Respir J. 2017;50:1700216.

Lönnroth K, Williams BG, Stadlin S, Jaramillo E, Dye C. Alcohol use as a risk factor for tuberculosis– a systematic review. BMC Public Health. 2008;8:289.

Lin H-H, Ezzati M, Chang H-Y, Murray M. Association between Tobacco Smoking and active tuberculosis in Taiwan: prospective cohort study. Am J Respir Crit Care Med. 2009;180:475–80.

Sadeghi K, Poorolajal J, Doosti-Irani A. Prevalence of modifiable risk factors of tuberculosis and their population attributable fraction in Iran: a cross-sectional study. PLoS ONE. 2022;17:e0271511.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Zhang T, Zhang J, Wei L, Liang H, Zhang J, Shi D, et al. The global, regional, and national burden of tuberculosis in 204 countries and territories, 1990–2019. J Infect Public Health. 2023;16:368–75.

Bansal A, Arora S. MDR Tuberculosis in elderly. Indian J Tuberc. 2022;69(Suppl 2):267–71.

Girum T, Muktar E, Lentiro K, Wondiye H, Shewangizaw M. Epidemiology of multidrug-resistant tuberculosis (MDR-TB) in Ethiopia: a systematic review and meta-analysis of the prevalence, determinants and treatment outcome. Trop Dis Travel Med Vaccines. 2018;4:5.

Iem V, Dean A, Zignol M, Vongvichit P, Inthavong D, Siphanthong S, et al. Low prevalence of MDR-TB in Lao PDR: results from the first national anti-tuberculosis drug resistance survey. Tropical Med Int Health. 2019;24:421–31.

Article   CAS   Google Scholar  

Global. tuberculosis report 2018. https://www.who.int/publications-detail-redirect/9789241565646 . Accessed 7 Jun 2023.

Rengganis Wardani DWS, Wahono EP. Spatio-temporal dynamics of tuberculosis clusters in Indonesia. Indian J Community Med. 2020;45:43–7.

PubMed   PubMed Central   Google Scholar  

Najafizada M, Rahman A, Taufique Q, Sarkar A. Social determinants of multidrug-resistant tuberculosis: a scoping review and research gaps. Indian J Tuberc. 2021;68:99–105.

Martial NT, Mubarik S, Yu C. Long-term trends of tuberculosis incidence and mortality in four Central African countries. Sci Rep. 2021;11:16624.

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Garrido MdaS, Bührer-Sékula S, de Souza AB, Ramasawmy R, Quincó P, de Monte L. Temporal distribution of tuberculosis in the state of Amazonas, Brazil. Rev Soc Bras Med Trop. 2015;48(Suppl 1):63–9.

Jantarabenjakul W, Supradish Na Ayudhya P, Suntarattiwong P, Thepnarong N, Rotcheewaphan S, Udomsantisuk N, et al. Temporal trend of drug-resistant tuberculosis among Thai children during 2006–2021. IJID Reg. 2022;5:79–85.

Lange C, Chesov D, Heyckendorf J, Leung CC, Udwadia Z, Dheda K. Drug-resistant tuberculosis: an update on disease burden, diagnosis and treatment. Respirology. 2018;23:656–73.

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Acknowledgements

The authors express their gratitude to the Institute for Health Metrics and Evaluation for sharing valuable GBD data.

This study was funded by grants from the National Natural Science Foundation of China (12031010), the Special Grant for the Prevention and Control of Infectious Diseases (2018ZX10713003). The funders had no role in study design, data collection and analysis, the decision to publish, or the preparation of the manuscript.

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Hengliang Lv, Xin Zhang and Xueli Zhang contributed equally to this work.

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Department of Epidemiology, School of Public Health, China Medical University, Shenyang, China

Hengliang Lv, Xin Zhang, Junzhu Bai, Shumeng You, Xuan Li, Yong Wang, Wenyi Zhang & Yuanyong Xu

Chinese PLA Center for Disease Control and Prevention, Beijing, China

Hengliang Lv, Xin Zhang, Junzhu Bai, Shumeng You, Shenlong Li, Yong Wang, Wenyi Zhang & Yuanyong Xu

Changchun University of Chinese Medicine, Changchun, China

Xueli Zhang

Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Hefei, China

Xuan Li, Yong Wang & Wenyi Zhang

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HL, XZ and ZXL: conceptualization, formal analysis, and writing— original draft preparation. JB: software and writing—review and editing. SY: methodology. XL: validation and visualization. SL and YW: software and funding acquisition. WZ and YX: investigation, resources, and supervision. All authors contributed to the article and approved the submitted version.

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  • Multidrug resistant tuberculosis
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Measuring health-related quality of life in tuberculosis: a systematic review

  • Fawziah Marra 2 &
  • Carlo A Marra 1 , 3  

Health and Quality of Life Outcomes volume  7 , Article number:  14 ( 2009 ) Cite this article

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Introduction

Tuberculosis remains a major public health problem worldwide. In recent years, increasing efforts have been dedicated to assessing the health-related quality of life experienced by people infected with tuberculosis. The objectives of this study were to better understand the impact of tuberculosis and its treatment on people's quality of life, and to review quality of life instruments used in current tuberculosis research.

A systematic literature search from 1981 to 2008 was performed through a number of electronic databases as well as a manual search. Eligible studies assessed multi-dimensional quality of life in people with tuberculosis disease or infection using standardized instruments. Results of the included studies were summarized qualitatively.

Twelve original studies met our criteria for inclusion. A wide range of quality of life instruments were involved, and the Short-Form 36 was most commonly used. A validated tuberculosis-specific quality of life instrument was not located. The findings showed that tuberculosis had a substantial and encompassing impact on patients' quality of life. Overall, the anti-tuberculosis treatment had a positive effect of improving patients' quality of life; their physical health tended to recover more quickly than the mental well-being. However, after the patients successfully completed treatment and were microbiologically 'cured', their quality of life remained significantly worse than the general population.

Tuberculosis has substantially adverse impacts on patients' quality of life, which persist after microbiological 'cure'. A variety of instruments were used to assess quality of life in tuberculosis and there has been no well-established tuberculosis-specific instrument, making it difficult to fully understand the impact of the illness.

The assessment of patient reported outcomes (PROs) has become more accepted and valued in the disease management and outcome evaluation. Health-related quality of life (HRQL) is a complex type of PRO that evaluates health status. HRQL broadly describes how well individuals function in daily lives and their own perception of well-being in physical, psychological, and social aspects [ 1 , 2 ]. Although traditional clinical and biological indicators are often intrinsically related to patients' quality of life, they fail to represent one's self-perceived function and well-being in everyday life settings. It is known that patients with chronic diseases place a high value on their mental and social well-being as well as pure physical health [ 3 ]. As a result, HRQL has become an area of increasing interest and has been evaluated in many diseases, including tuberculosis (TB). To measure HRQL, two kinds of instruments are often used: generic and disease-specific [ 1 , 2 , 4 ]. Generic instruments are developed to cover the common and important aspects of health and can be used to assess and compare HRQL across different health conditions and sub-populations [ 1 , 4 ]. In contrast, disease- or condition-specific instruments are designed to reflect unique problems most relevant to a given disease and/or its treatment [ 1 , 4 ]. Theoretically, disease-specific instruments are more precise and more sensitive to small but potentially important differences or changes on HRQL, compared to generic instruments [ 1 , 4 ]. One special category of generic HRQL instruments assesses "preferences" for certain health states [ 2 ]. These instruments summarize quality of life into a single utility score, reflecting the 'value' people place on a health state, anchored at 0 (death) and 1 (full health). [ 2 ]. Health utility measurements are often used in health economic studies.

Although effective therapy has long been available, TB remains a major public health threat globally, with one third of the world's population infected [ 5 , 6 ]. Many aspects of TB along with its treatment could potentially compromise patients' HRQL. For example, the standard anti-TB therapy consists of four medications and takes at least 6 to 9 months to complete, with serious risks of adverse reactions [ 6 – 8 ]. In some communities, TB patients are perceived as a source of infection and the resultant social rejection and isolation leads to a long-term impairment on patients' psychosocial well-being [ 9 – 14 ]. Many TB patients also report to experience negative emotions, such as anxiety and fear [ 13 , 14 ]. However, the current goal of TB management is to achieve microbiological 'cure' and there has been little effort taken to consider patients' HRQL. In 2004, Chang et. al. published a review summarizing the English medical literature on the quality of life in TB patients [ 15 ]. At that time, the authors were unable to locate studies measuring HRQL using standardized instruments. Over the past few years, more effort has been dedicated to this research field. Therefore, the present review was performed to identify published original studies utilizing structured HRQL instruments.

The objectives of this review were: (1) to identify HRQL instruments used in TB research; (2) to better understand the impact of TB disease or infection and the associated treatment on patients' HRQL; and (3) to examine demographic, socio-economic, and clinical factors associated with HRQL outcomes in TB patients.

Search strategies for identification of potential studies

A systematic literature search was performed using the following electronic databases: Medline (1950-present), EMBASE (1980-present), Cochrane Register of Controlled Trials (CENTRAL), CINAHL, PsycINFO, and HaPI (1985-present). Key word searching and/or subject searching were performed, if applicable. The following keywords were used: tuberculosis (TB), Quality of Life (QoL), Quality Adjusted Life Years (QALY), health utility, health status, life quality , and well-being . The limit feature was used to select human studies published between 1981 and 2008 written in English or Chinese (traditional or simplified). The last time electronic database search was conducted during July 22, 2008. The reference sections of the following key journals were manually searched for relevant articles: International Journal of Tuberculosis and Lung Disease, Chest, Quality of Life Research , and Health and Quality of Life Outcomes . Reference lists of included studies, review articles, letters, and comments were checked afterwards. We did not contact the authors of identified studies or relevant experts to locate unpublished studies. Each stage of the literature searching process is illustrated in Figure 1 .

figure 1

Inclusion and exclusion criteria

All clinical trials and observational studies where multi-dimensional HRQL was evaluated, either as a primary or secondary outcome, using structured HRQL instruments were considered in this review. Participants were those diagnosed with active TB disease or latent TB infection (LTBI), regardless of the site and stage of the disease and the treatment status. There were no limitations on age, gender, race, the origin of birth, and other socio-economic status.

For the purpose of this review, HRQL was defined as patients' self-evaluations of the impact of either active TB disease or LTBI and the associated treatments on their physical, mental, and social well-being and functioning. The following requirements for HRQL measurement were set a priori for studies to be included in this review: (1) one multi-dimensional HRQL instrument or a combination of single-dimensional instruments had to be used to capture the broad framework of HRQL; (2) the HRQL instruments could be either generic or disease (or condition) -specific; (3) the origin of the applied instruments had to be identifiable and traceable; (4) the HRQL instruments had to have psychometric properties such as reliability and validity reported from previous studies or were assessed in the specific study being reviewed; (5) HRQL outcomes had to be self-reported by the specific participant, but HRQL measurement that were completed with help from proper proxies, such as family members and caregivers, were also accepted.

Studies were excluded if (1) HRQL was evaluated using qualitative methodologies, such as focus groups; or (2) only one single dimension of HRQL (e.g., depression) was assessed; or (3) HRQL was assessed using instruments designed for the specific study without psychometric properties evaluated and reported; or (4) a modified version of a previously validated instruments (e.g., SF-36) was used as the psychometric properties of the original instrument could be changed by the modification.

Data extraction

If the study was included in this review, the following information was collected: study design, inclusion and exclusion criteria of subjects, included subjects' socio-demographic characteristics and clinical features, HRQL instrument(s) used, the origin and structure of HRQL instrument(s), administration of HRQL instrument(s), and HRQL outcomes and validation results.

The literature search identified 2540 articles which were narrowed to 26 [ 9 – 14 , 16 – 35 ] (Figure 1 ). After reviewing the full texts, 14 studies were further excluded for various reasons: 6 studies used qualitative methodologies [ 9 – 14 ]; 2 studies measured only one single dimension of HRQL [ 16 , 17 ]; 1 study [ 18 ] used the Short-Form 36 (SF-36) but the response options of SF-36 were modified to 3 levels (i.e., the same as before, better, and worse) without providing validation data; 1 study [ 19 ] used one single question from a structured instrument; 1 study was a duplicate and the earlier version was excluded [ 20 , 21 ]; 1 study [ 22 ] used a generic instrument, the General Quality of Life Interview (GQOLI-74), however, no relevant references were provided to track the origin and the psychometric properties of this instrument; 2 articles [ 23 , 24 ] were published from the same study, and therefore only included as one study for the review; another 2 articles, Marra et. al. [ 25 ] and Guo et. al. [ 26 ], reported longitudinal and cross-sectional results from one same study respectively, and thus only one study was counted for the review. Therefore, a total of 12 original studies were included in this review [ 21 , 23 , 25 , 27 – 35 ] and an overview is presented in Additional file 1 .

Of the 12 included studies, one was published in 1998 [ 27 ] and the remaining 11 were published after 2001 [ 21 , 23 , 25 , 28 – 35 ]. Nine studies were published in English and 3 in Chinese [ 27 , 29 , 33 ]. The included studies were carried out within different countries: 3 in China [ 27 – 29 ]; 1 in both China and southern Thailand [ 33 ]; 2 in India [ 21 , 35 ]; 2 in Turkey [ 30 , 31 ]; 2 in Canada [ 23 – 26 ]; and 2 in the USA [ 32 , 34 ]. Seven of the included studies were cross-sectional [ 27 , 29 – 31 , 33 – 35 ] and 4 were prospective cohort studies [ 21 , 23 , 25 , 28 ]. The remaining one study was a randomized controlled trial (RCT) [ 32 ], but only baseline HRQL assessment data was reported in the published article. Among the 12 studies, three studies included a comparison group either from the general population [ 28 ] or from a "healthy" non-TB sample [ 27 , 29 ]; one study used the normative data from the Canadian population as the reference group [ 23 , 24 ]; two studies included people with LTBI as controls [ 25 , 34 ]; one study compared TB patients with a group of chronic obstructive pulmonary disease (COPD) patients [ 31 ]; and the remaining 5 studies did not include proper comparison groups. Sample size (i.e., number of subjects included in the statistical analysis) varied among the 12 studies, from 46 to 436. Only one study [ 23 ] reported how the sample size was estimated statistically. A wide range of TB patients were included in this review: pulmonary TB and extra-pulmonary TB, active TB disease and LTBI, and current TB and previously treated TB.

To measure multiple-dimensional HRQL, a variety of instruments were involved in the included studies (Additional file 2 ). As a result, it was not possible to statistically summarize the results and thus a qualitative synthesis approach was taken for this review.

HRQL instruments used in the included studies

Nine studies included generic multi-dimensional instruments with or without specific single-dimensional ones, one study used a newly developed TB-specific multi-dimensional instrument [ 21 ], and two studies used a battery of single-dimensional instruments [ 31 , 33 ].

Generic HRQL instruments

The SF-36 was used in 6 studies with different language versions [ 23 – 28 , 33 ]. It consists of 36 items which are aggregated into 8 subscales, including physical functioning (PF), role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role-emotional (RE), and mental health (MH) [ 36 ]. From the 8 subscales, the physical component summary (PCS) and mental component summary (MCS) scores can be also calculated [ 36 ]. Duyan et. al. used the 24-item Quality of Life Questionnaire (QLQ), which covers 7 domains, including living conditions, finances, leisure, family relations, social life, health, and access to health care [ 30 ]. The 24-item QLQ was first presented by Greenley et. al. in 1997 [ 37 ]. Finally, the long Medical Outcome Study (MOS) core questionnaire was used in Pasipanodya et. al. [ 34 ]. This is a generic instrument covering multiple dimensions, including physical function, social function, general health, vitality, and limitations due to physical and emotional functioning [ 38 ]. The well-known SF-36 was developed and evolved based on a subset of items from the MOS core questionnaire [ 38 ].

Specific HRQL instruments

Dhingra and Rajpal measured HRQL with the DR-12, a new TB-specific instrument, which was developed in India and first published in 2003 [ 20 ]. It is composed of 12 items, among which 7 cover TB symptoms (i.e., cough and sputum, haemoptysis, fever, breathlessness, chest pain, anorexia, and weight loss) and 5 relate to socio-psychological and exercise adaptation (i.e., emotional symptoms/depression, interest in work, household activities, exercise activities, and social activities) [ 20 , 21 ]. All response options are presented on 3-point scales and equal weights are given to each item when calculating the two domain scores and the total score [ 20 , 21 ]. The St. George Respiratory Questionnaire (SGRQ) used in Pasipanodya et. al. [ 34 ] is a widely used specific instrument designed for measuring HRQL in patients with chronic obstructive pulmonary disease (COPD) and other types of respiratory diseases. Three domain (symptom, activity, and impacts) scores and a total score can be generated [ 39 ]. It was developed at the St. George's Hospital Medical School at the UK and has been translated into various languages [ 39 ].

Yang et. al. used two single-dimensional instruments, the Chinese version Symptoms Checklist 90 (SCL-90) and Social Support Rating Scale (SSRS) [ 29 ]. The SCL-90 is a 90-item symptom inventory designed mainly to evaluate a broad range of psychological problems and symptoms, including 9 dimensions: somatization, obsessive-compulsive behaviour, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid ideation, and psychoticism [ 40 ]. The 10-item SSRS was used to measure the self-perceived availability and use of social support services [ 27 ]. The study by Aydin and Ulusahin used two single-dimensional instruments, the General Health Questionnaire 12 (GHQ-12) and Brief Disability Questionnaire (BDQ) [ 31 ]. GHQ-12 is a short version of the GHQ-60, which was developed for screening non-psychotic psychiatric disorders in the general population [ 41 ]. The BDQ, derived from the MOS short form general health survey, is used to measure patients' physical and social disability level [ 42 ]. Marra et. al. [ 25 ] used the Beck Depression Inventory (Beck-DI), along with the SF-36 and a couple of health utility instruments. The Beck-DI is a 21-item instrument, designed to measure the symptoms and degree of depression [ 43 ].

In the USA study, a series of instruments or questions were used to assess TB-infected homeless individuals' self-perceived physical health, psychological profile, emotional well-being, social support, and health care access and use [ 32 ]. Examples included the 5-item Mental Health Index (MHI-5) and the Center for Epidemiological Studies Depression Scale (CES-D) [ 32 ].

Health utility instrument

Health utility, one generic measure of HRQL, reflects subjective preferences for health states and also provides quantitative estimates of HRQL under certain health states [ 2 ]. The two studies [ 23 – 26 ] conducted in Canada applied various health utility assessment techniques among TB patients, including the Health Utility Index (HUI), EuroQol (EQ-5D), Short-Form 6D (SF-6D), Visual Analogue Scale (VAS), and Standard Gamble (SG). HUI, SF-6D, and EQ-5D are multi-attribute health status classification systems that indirectly measure preferences for health states [ 2 ]. SG and VAS are to directly obtain individuals' preferences using different techniques.

HUI currently consists of HUI-2 and HUI-3 [ 44 ]. HUI-2 and HUI-3 are derived from the same questionnaire but HUI-2 has 7 domains (sensation, mobility, emotion, cognition, self-care, pain, and fertility) and HUI-3 contains 8 domains (vision, hearing, speech, ambulation, dexterity, emotion, cognition, and pain). EQ-5D consists of 5 domains, including mobility, self-care, usual activity, pain, and anxiety/depression [ 2 ]. SF-6D is derived from a subset of SF-36 questions. It has 6 dimensions including physical functioning, role limitations, social functioning, pain, mental health, and vitality [ 45 ].

The SG is a classic technique to obtain individual preferences for health outcomes, based on the theory of von Neumann and Morgenstern [ 2 ]. In the study by Dion et. al., the respondent was offered a choice between the certain outcome of a particular health state and a hypothetical gamble, with relative possibilities of perfect health and immediate death varying. The gamble was terminated when the respondent was indifferent to the choice between the given health state and the gamble. The VAS used by Dion et. al. was a 100 cm "feeling thermometer", marked at each end by word descriptions as "immediate death" and "perfect health". The respondents were asked to put a mark at the point that represents their current health status [ 23 , 24 ]. Similarly, a 10 cm length of horizontal line (anchored at 0 cm = death and 10 cm = perfect health) was used by Marra et. al. [ 25 ] as VAS.

Psychometric properties of HRQL instruments in tuberculosis

The SF-36 was used in 6 studies, and overall it showed acceptable validity and reliability. Chamla [ 28 ] validated the Chinese version SF-36 among active pulmonary TB patients and the general population in China. The reliability was tested by Cronbach's α, ranging form 0.88 to 0.97 for the eight SF-36 subscales. All 36 questions of the SF-36 had internal item consistency coefficients between 0.56 and 0.86. In Dion et. al. [ 23 , 24 ], the reliability of SF-36 was evaluated among a mixture of TB patients, including 25 with LTBI, 17 with active TB on treatment, and 8 with previously treated TB. The internal consistency of the SF-36 responses was strong, with coefficients of 0.86–0.92 for the two summary scores and 0.73–0.94 for the subscale scores. The test-retest reliability (2-week interval) of SF-36 was tested by calculating Intraclass Correlation (ICC) coefficients: 0.66–0.79 for the two SF-36 summary scores. He et. al. [ 33 ] also reported good reliability of the Chinese version SF-36 (Cronbach' α > 0.7) among the two groups of TB patients from China and Thailand.

Validity of the SF-36 was evaluated by examining the correlations between SF-36 outcomes with other external variables, including clinical criteria, responses from other HRQL measures, and physician's evaluations. It was reported that SF-36 scores were able to discriminate between TB patients with different severity levels [ 21 , 26 ] and between patients at different stages of treatment (i.e., the start, middle, and end of the treatment) [ 21 , 25 , 28 ]. In Guo et. al. [ 26 ], the correlations between SF-36 summary scores (PCS and MCS) and four utility instruments (SF-6D, HUI-2, HUI-3, and VAS) were tested by calculating Spearman's coefficients. SF-6D scores were strongly correlated with both PCS and MCS (0.79, 0.80), and HUI-2, HUI-3, and VAS scores were more strongly correlated with PCS (0.59, 0.66, and 0.67) than with MCS (0.37, 0.48, and 0.59). Similarly, in the study by Dion et. al. [ 23 , 24 ], SF-36 scores were observed moderately correlated with EQ-5D and VAS scores, but poorly correlated with SG scores (Pearson coefficients < 0.2). Wang et. al. [ 27 ] reported that patient-reported SF-36 scores were well correlated with physician proxy-reported Quality of Life Index (QLI) and Karnofsky Performance Status (KPS) scores, with correlation coefficients of 0.78 and 0.89 respectively. However, it was not reported which type of correlation coefficient was calculated.

The structural validity of SF-36 was tested in two studies, but the results were not consistent. In Chamala [ 28 ], factor analysis was applied to evaluate the 2-dimensional model of the SF-36. Two factors (physical health and mental health) were extracted and subjected to orthogonal rotation using the Varimax method. The observed pattern of correlations between the 8 subscales and the 2 factors supported the authors' prior hypothesis. For example, it was reported that the 4 physical subscales (PF, RP, BP, and GH) were correlated strongly with the physical health factor, but only poorly correlated with the mental health factor. On the other hand, the 4 mental subscales (MH, RE, SF, and VT) were strongly correlated with the mental health factor, but not the physical factor. He et. al. [ 33 ] used principle component analysis to test the structural validity of SF-36. However, the results showed that the 8 subscales were not well independent, and there were overlapping items between different subscales. For example, RE and RP subscales were both strongly correlated among the two groups of patients (correlation coefficient 0.82 and 0.77). Based on their findings, the authors concluded that the SF-36 did not show satisfactory construct validity in the studied TB patients.

The application of SF-36 among TB patients also revealed some problems. In the study by Dion et. al. [ 23 , 24 ], SF-36 subscales demonstrated a remarkable ceiling effect problem. Over 50% participants with concurrent or previous TB reported the highest scores for 5 of SF-36 subscales (PF, RP, RE, BP, and SF).

Ceiling and floor effects are a common problem for the application of health utility instruments in TB. In Dion et. al. [ 23 , 24 ], 42–53% participants reported the best possible EQ-5D health state. Guo et. al. also observed ceiling and/or floor effect problems with three commonly used health utility instruments. HUI-2 and HUI-3 suffered from a serious ceiling effect problem, both in global score and single dimension level. For example, 25% of active TB patients scored 1.0 (perfect health) using the HUI-2 and 98% of them reported the best level of hearing for HUI-3. SF-6D, on the other hand, was primarily limited by its narrow range of available utility values, from 0.30 to 1.0. Health states at the lower end may not be adequately represented by the SF-6D. Despite these problems with the application among TB patients, some positive aspects of these utility instruments were also observed. For example, these utility instruments showed moderate to strong correlations with the SF-36 responses as stated before [ 23 , 24 , 26 ]. Guo et. al. [ 26 ] also reported moderate to strong agreement among SF-6D, HUI-2, HUI-3, and VAS, using ICC: the overall ICC coefficient among these 4 instruments was 0.65 and paired ICC coefficients ranged from 0.53 to 0.67. In addition, these four utility instruments were all able to discriminate between TB patients with different severity levels.

Pasipanodya et. al. [ 34 ] administered the lung disease-specific SGRQ among people with treated pulmonary TB disease or LTBI. Test-retest reliability of the SGRQ was examined by ICC coefficients, 0.93 for the total score and 0.83–0.91 for subscale scores. Internal consistency was tested by Cronbach's α, at 0.93. To evaluate its validity, SGRQ responses were correlated with a previously validated MOS core questionnaire and a couple of clinical pulmonary function tests, such as the forced vital capacity (FVC). Overall, SGRQ scores and MOS scores agreed on similar health constructs and diverged on dissimilar constructs. Low but significant correlations were observed between SGRQ scores and pulmonary function test results (-0.12 to -0.29, p < 0.05). On the other hand, a ceiling effect problem for SGRQ was observed. In both treated pulmonary TB patients and people with LTBI, the distribution of SGRQ scores was skewed toward higher HRQL. In addition, considering varied levels of reading and understanding in English in respondents, different language versions of SGRQ were used, but the potential impact of combining results from these on HRQL outcomes was not known.

Dhingra and Rajpal [ 21 ] applied the new TB-specific instrument, DR-12, among TB patients under directly observed therapy (DOT). It was reported that, at the beginning of treatment, DR-12 scores demonstrated significant differences between pulmonary and extra-pulmonary TB patients, and between sputum positive and sputum negative patients. Over the treatment period, higher DR-12 score gains were observed among patients who positively responded to the treatment compared to those who did not. Based on these evidences, the authors came to the conclusion that DR-12 had strong construct validity in the studied population. However, the clinical criteria or indicators were not well defined in the published work. All comparisons were performed by using paired or unpaired t-tests. Potential confounders such as socio-demographic and clinical variables were not controlled in the final data analysis.

Impact of tuberculosis on HRQL

Overall, active TB disease had significant and encompassing impacts on patients' HRQL. Using the SF-36, Chamla [ 28 ] found that, compared to the general population, people with active TB disease scored significantly lower on PF, RP, GH, BP, and VT (p < 0.05), but no significant differences were observed on RE, SF, and MH subscales (p > 0.05). In general, physical health subscales were more affected than mental ones. Dion et. al. [ 23 , 24 ] also found active TB patients scored significantly lower in SF-36 PCS scores, but not in MCS scores, when compared to people with LTBI and those with previously treated TB disease. In terms of health utility outcomes, Dion et. al. found that active TB patients scored significantly lower in VAS (median 92.5 VS. 97.5, p = 0.02) and SG (median 80.0 VS. 90.0, p = 0.002) than others at the baseline assessment. However, no significant difference was observed in EQ-5D scores between active TB patients and others. It is likely that the small sample size and the heterogeneous composition of subjects could have prevented the authors from detecting the small but important differences in the sample. Wang et. al. [ 27 ] found that active TB patients reported lower scores (p < 0.01) across all SF-36 subscales than healthy non-TB people, with RP and RE being most affected. Marra et. al. and Guo et. al. [ 25 , 26 ] found that, compared to those with LTBI, people with active TB scored significantly lower at all SF-36 subscales, SF-6D, HUI-2, HUI-3, and VAS. In contrast, SF-36 scores among people with LTBI before the preventative therapy were very similar to the U.S. norm references.

In the study by Marra et. al. [ 25 ], Beck-DI scores showed substantial impairment on mental well-being in active TB patients, compared to people with LTBI. However, many aspects of the Beck-DI (such as fatigue) can also be symptoms of TB and might not be necessarily indicative of mental health impairments. Aydin and Ulusahin [ 31 ] compared TB patients to COPD patients and found that TB patients had a lower prevalence of depression and anxiety and a lower level of disability, suggested by GHQ-12 and BDQ scores. The authors postulated that the chronic duration of COPD and the older age of the COPD patients may result in a higher prevalence of psychological impairments. Within TB patients, multi-drug resistant TB patients reported the worst disability level, according to BDQ outcomes. Yang et. al. [ 29 ] found that pulmonary TB patients reported more psychological symptoms listed in the SCL-90 and a lower degree of social support using SSRS compared to healthy controls. However, SCL-90 scores did not show significant correlation with SSRS scores, which is not consistent with the established relationship between social support and health [ 46 ], as discussed by the authors.

The impaired HRQL experienced by TB patients may be a reflection of socio-demographic status (e.g., age, gender, and socio-economic status) and other underlying co-morbid conditions, besides TB and its treatment. A few included studies explored the relationship between socio-demographic features and clinical factors and HRQL in TB patients. In general, the findings were consistent, but some discrepancies existed. Yang et. al. [ 29 ] and Nyamatihi et. al. [ 32 ] observed that females were more likely to report poorer health than males, especially on mental health problems, such as depression and anxiety. Chamla [ 28 ] and Guo et. al. [ 26 ] found older people tended to have poorer HRQL than younger ones. But Duyan et. al. [ 30 ] did not find significant associations between gender, age and HRQL in TB patients. On the other hand, they [ 30 ] found that better HRQL was correlated with higher income, higher education, better housing conditions, better social security, and closer relationships with family members and friends. Some clinical factors that were observed to correlate with poorer HRQL in TB patients include size of pulmonary TB infection, duration of TB disease, reactivation of previous TB infection, number of symptoms before treatment, development of hemoptysis, hospitalization, underlying chronic conditions, anemia, and count of white blood cells before treatment [ 27 , 28 ].

Effect of anti-tuberculosis treatment on HRQL

Chamla [ 28 ], Dhingra and Rajpal [ 21 ], and Marra et. al. [ 25 ] prospectively measured active TB patients' HRQL at the start, middle, and end of treatment. In the study by Chamla [ 28 ], after the anti-TB treatment, significant improvement was observed in all physical health subscales of the SF-36 (PF, RP, BP, and GH, p < 0.05); two mental health subscales, RE and SF (p < 0.05), improved significantly, but not VT and MH (p > 0.05). During the treatment, RP, VT and MH scores decreased after the initial 2 months and but showed overall improvement at the end of the treatment, while all other subscale scores showed gradual increase over the treatment [ 28 ]. Dhingra and Rajpal [ 21 ] observed a gradual improvement on DR-12 scores in active TB patients over the course of the treatment. Overall, a more identifiable improvement was observed in symptom scores than that in socio-psychological and exercise adaptation scores. Consistently, Marra et. al. [ 25 ] also found significant HRQL improvement in active TB patients over the 6 months of treatment, using SF-36 and Beck-DI.

Although anti-TB treatment improved HRQL overall, active TB patients still had poorer HRQL at the end of the treatment compared to the general population or people with LTBI, especially in psychological well-being and social functioning. Chamla [ 28 ] observed that, at the end of the treatment, active TB patients still scored significantly lower at RP, VT, and MH subscales compared to general population comparisons. Marra et. al. [ 25 ] found that, after the 6 month of treatment, active TB patients scored significantly lower at SF-36 PCS and MCS summary scores compared to people with LTBI. An interesting finding by Marra et. al. [ 25 ] is that, after the preventive treatment, MCS scores among people with LTBI decreased significantly, while PCS scores remained unchanged. Pasipanodya et. al. [ 34 ] measured HRQL among pulmonary TB patients who completed at least 20 weeks of treatment, using the SGRQ. Compared with those with LTBI, treated TB patients had lower SGRQ scores. Those with better lung functions and/or born in the U.S. (against foreign-born) tended to have better HRQL outcomes. No gender difference was observed in SGRQ scores.

Muniyandi et. al. [ 35 ] assessed the HRQL in a sample of previous TB patients one year after successful completion of treatment. 40% of these people reported persistent symptoms, such as breathlessness, cough, chest pain, and occasional fever. The authors calculated three SF-36 component scores: the physical well-being, mental well-being, and social well-being. Based on their results, there was no gender difference on physical well-being score; but females scored much lower at mental and social well-being scores. Compared with younger people, older ones had significantly lower physical and mental well-being scores, but not the social score. They also presented the U.S. general population norms for the three component scores and concluded that TB patients' HRQL returned to normal level one year after the completion of treatment. However, the way of calculating the three SF-36 component scores is not commonly seen in literatures, and the reference regarding the U.S. general population norms provided in the published paper cannot be located.

HRQL has been appreciated as an important health outcome measure in clinical research. We identified 12 original studies where multi-dimensional HRQL was assessed among people with TB disease or infection using structured instruments around the world. We found that TB and its treatment have a significant impact on patients' quality of life from various aspects and this impact tends to persist for a long time even after the successful completion of treatment and the microbiological 'cure' of the disease.

The results suggest that TB disease has a negative and encompassing impact on active TB patients' self-perceived health status in physical, psychological, and social aspects. Overall, the anti-TB treatment showed positive effect on improving patients' HRQL. It appeared that physical health seemed to be more affected by the disease but improved more quickly after the treatment, while the impairment on mental well-being tended to persist for a longer term [ 21 , 28 ]. However, even after the active TB patients successfully completed the treatment and were considered microbiologically 'cured', their HRQL remained poor as compared to the general population [ 23 – 25 , 28 ]. The ongoing HRQL impairment may be partly due to the persistent physical symptoms and residual physiological damages from the disease and/or the treatment. Furthermore, a few qualitative studies [ 9 – 14 , 16 – 18 ] have shown that the social stigma attached to the diagnosis of TB in some cultures is significant. People with TB may feel isolated from their family and friends or experience the fear and anxiety of being known by others about their diagnosis. All these consequential impairments also need to be 'cured' and may take a long recovery time.

Most studies have focused on assessing HRQL in active TB patients. Although people with LTBI do not present with clinical disease or symptoms, they are likely to be subjected to the same social and psychological impacts as active TB patients. The knowledge of a deadly and stigmatized disease lying dormant in his/her body may also induce anxiety and fear. As Marra et. al. [ 25 ] observed that, after receiving 6 months of preventive therapy with isoniazid, the mental well-being of people with LTBI decreased significantly.

HRQL assessment in TB research is still a new area, and a valid and reliable TB-specific instrument is much needed. Currently, a wide range of HRQL instruments were utilized in the literature. The SF-36 was the most frequently used instrument and it appeared to be a valid and reliable tool to be used in TB. Although the SF-36 has been used extensively to assess both population health and specific health conditions for various medical conditions, as a generic health assessment instrument, it offers little information to help understand the unique experiences among TB patients, such as social stigma and anti-TB treatment related ADRs.

Our review identified one TB-specific HRQL instrument, DR-12, which was developed in India [ 20 , 21 ]. Unfortunately, its validation study was not conducted in a systematic fashion and the current evidence provided was not convincing. Further applications and appropriate methodologies are needed to show DR-12 is a psychometrically sound HRQL instrument feasible and valid for TB patients. In addition, the DR-12 is actually designed specifically for pulmonary TB patients, judging from its item content. TB can affect almost any part of the human body, and in Canada, about 40% of active TB diseases would present as extra-pulmonary TB [ 47 ]. Different types of TB disease would have very different clinical presentations and affect people's function differently. This may be a challenge when developing a TB-specific HRQL instrument.

It should be also noted that most TB patients have very different cultural and socio-demographic backgrounds compared with the population in which many of these instruments were originally developed. Also, in the studies done in Canada and the USA [ 24 – 26 , 32 , 34 ], most TB patients were foreign-born and the instruments were normally self-administered in the English language which would not have been the respondents' first language. Thus, the results of these studies may not be valid if careful translation and cultural adaptation of the instrument was not done to accommodate the multi-cultural population.

Particular attention should be given to some methodological issues on assessing HRQL among people with active TB disease or LTBI. To comprehensively examine the impact of TB and its treatment on patients' HRQL, it is very important to include a proper comparison group from a similar demographic and socio-economic background. When conducting the study, researchers are recommended to seek statistical consultation regarding proper sample size estimating, missing data handling, and adjusting for potential confounders, such as socio-demographic status and presence of co-morbidities. Another concern is the lack of interpretation of HRQL outcomes in terms of clinical meaningfulness. Statistical significance is a useful way to interpret the result, but it fails to relate the HRQL outcome with clinical relevance. As such, more work needs to be done to relate changes in HRQL assessment in TB to concepts such as the minimal clinical difference [ 48 , 49 ].

Our review of the literature shows that TB diminishes patients' HRQL, as measured by various instruments. However, due to the heterogeneity of HRQL measurements, it was difficult to assimilate results across studies. A few studies used the SF-36 which appeared to be a valid instrument in the measurement of HRQL in TB. Other instruments require further psychometric testing to determine their suitability in measurement in this context. Our review suggests that HRQL assessment in people with TB is a growing research area and a psychometrically sound TB-specific HRQL instrument is lacking. A critical step in the future would be to design an applicable, reliable, and valid TB-specific HRQL instrument. Particular attention should be given to address the methodological issues when conducting a HRQL assessment study in TB patients.

Guyatt GH, Feeny DH, Patrich DL: Measuring health-related quality of life. Ann Intern Med 1993, 118: 622–629.

Article   CAS   Google Scholar  

Drummond MF, Sculpher MJ, Torrance GW, O'Brien BJ, Stoddart GL, (Eds): Methods for the economic evaluation of health care programmes. 3rd edition. Oxford University Press; 2005.

Google Scholar  

Sherbourne CD, Sturm R, Wells KB: What outcomes matter to patients. J Gen Intern Med 1999, 14: 357–363. 10.1046/j.1525-1497.1999.00354.x

Patrick DL, Deyo RA: Generic and disease-specific measures in assessing health status and quality of life. Med Care 1989, 27 (Suppl 3):217–232. 10.1097/00005650-198903001-00018

Article   Google Scholar  

Dye C, Scheele S, Dolin P, Pathania V, Raviglione MC: Consensus statement. Global burden of tuberculosis: estimated incidence, prevalence, and mortality by country. WHO Global Surveillance and Monitoring Project. JAMA 1999, 282: 677–686. 10.1001/jama.282.7.677

Blumberg HM, Burman WJ, Chaisson RE, Daley CL, Etkind SC, Friedman LN, Fujiwara P, Grzemska M, Hopewell PC, Iseman MD, Jasmer RM, Koppaka V, Menzies RI, O'Brien RJ, Reves RR, Reichman LB, Simone PM, Starke JR, Vernon AA: American Thoracic Society/Centers for Disease Control and Prevention/Infectious Diseases Society of America: treatment of tuberculosis. Am J Respir Crit Care Med 2003, 167: 603–662. 10.1164/rccm.167.4.603

Forget EJ, Menzies D: Adverse reactions to first-line antituberculosis drugs. Expert Opin Drug Saf 2006, 5: 231–249. 10.1517/14740338.5.2.231

Marra F, Marra CA, Bruchet N, Richardson K, Moadebi S, Elwood RK, FitzGerald JM: Adverse drug reactions associated with first-line anti-tuberculosis drug regimens. Int J Tuberc Lung Dis 2007, 11: 868–875.

CAS   Google Scholar  

Liefooghe R, Michiels N, Habib S, Moran MB, De Muynck A: Perception and social consequences of tuberculosis: a focus group study of tuberculosis patients in Sialkot, Pakistan. Soc Sci Med 1995, 41: 1685–1692. 10.1016/0277-9536(95)00129-U

Kelly P: Isolation and stigma: the experience of patients with active tuberculosis. J Community Health Nurs 1999, 16: 233–241. 10.1207/S15327655JCHN1604_3

Long NH, Johansson E, Diwan VK, Winkvist A: Fear and social isolation as consequences of tuberculosis in VietNam: a gender analysis. Health Policy 2001, 58: 69–81. 10.1016/S0168-8510(01)00143-9

Macq J, Solis A, Martinez G, Martiny P, Dujardin B: An exploration of the social stigma of tuberculosis in five 'municipios' of Nicaragua to reflect on local interventions. Health Policy 2005, 74: 205–217. 10.1016/j.healthpol.2005.01.003

Hansel NN, Wu AW, Chang B, Diette GB: Quality of life in tuberculosis: Patient and provider perspectives. Qual Life Res 2004, 13: 639–652. 10.1023/B:QURE.0000021317.12945.f0

Marra CA, Marra F, Cox VC, Palepu A, Fitzgerald JM: Factors influencing quality of life in patients with active tuberculosis. Health Qual Life Outcomes 2004, 20: 58. 10.1186/1477-7525-2-58

Chang B, Wu AW, Hansel NN, Diette GB: Quality of life in tuberculosis: a review of the English language literature. Qual Life Res 2004, 13: 1633–1642. 10.1007/s11136-004-0374-1

Natani GD, Jain NK, Sharma TN, Gehlot PS, Agrawal SP, Koolwal S, Gupta RB, Agnihotri SP: Depression in tuberculosis patients: correlation with duration of disease and response to anti-tuberculous chemotherapy. Indian Journal of Tuberculosis 1985, 32: 195–198.

Kelly-Rossini L, Perlman DC, Mason DJ: The experience of respiratory isolation for HIV-infected with TB. J Assoc Nurses AIDS Care 1996, 7: 29–36. 10.1016/S1055-3290(96)80035-5

Rajeswari R, Muniyandi M, Balasubramanian R, Narayanan PR: Perceptions of tuberculosis patients about their physical, mental and social well-being: A field report from south India. Soc Sci Med 2005, 60: 1845–1853. 10.1016/j.socscimed.2004.08.024

de Figueiredo AA, Lucon AM, Srougi M: Bladder augmentation for the treatment of chronic tuberculous cystitis. Clinical and urodynamic evaluation of 25 patients after long term follow-up. Neurourol Urodyn 2006, 25: 433–440. 10.1002/nau.20264

Dhingra VK, Rajpal S: Health related quality of life (HRQL) scoring in tuberculosis. Indian J Tuberc 2003, 50: 99–104.

Dhingra VK, Rajpal S: Health related quality of life (HRQL) scoring (DR-12 score) in tuberculosis – additional evaluative tool under DOTS. J Commun Dis 2005, 37: 261–268.

Ji Q, Sun Y-J, Liu Q-G, Gong F-Q, Jin Y, Wu J: Correlation of mental status and social function with quality of life in patients with tuberculosis. Zhongguo Linchuang Kangfu 2006, 10: 21–24.

Dion MJ, Tousignant P, Bourbeau J, Menzies D, Schwartzman K: Feasibility and reliability of health-related quality of life measurements among tuberculosis patients. Qual Life Res 2004, 13: 653–665. 10.1023/B:QURE.0000021320.89524.64

Dion MJ, Tousignant P, Bourbeau J, Menzies D, Schwartzman K: Measurement of health preferences among patients with tuberculous infection and disease. Med Decis Making 2002, 22 (Suppl 5):102–114. 10.1177/027298902237706

Marra CA, Marra F, Colley L, Moadebi S, Elwood RK, Fitzgerald JM: Health-Related Quality of Life Trajectories among Adults with Tuberculosis: Differences between Latent and Active Infection. Chest 2008, 133: 396–403. 10.1378/chest.07-1494

Guo N, Marra CA, Marra F, Moadebi S, Elwood RK, Fitzgerald JM: Health State Utilities in Latent and Active Tuberculosis. Value Health 2008, 11: 1154–1161. 10.1111/j.1524-4733.2008.00355.x

Wang Y, Lii J, Lu F: Measuring and assessing the quality of life of patients with pulmonary tuberculosis. Zhonghua Jie He He Hu Xi Za Zhi 1998, 21 (12):720–723.

Chamla D: The assessment of patients' health-related quality of life during tuberculosis treatment in Wuhan, China. Int J Tuberc Lung Dis 2004, 8: 1100–1106.

Yang L, Wu DL, Guo HG, Liu JW: A study of the psychological and social factors in patients with pulmonary tuberculosis. Zhonghua Jie He He Hu Xi Za Zhi 2003, 26 (11):704–707.

Duyan V, Kurt B, Aktas Z, Duyan GC, Kulkul DO: Relationship between quality of life and characteristics of patients hospitalised with tuberculosis. Int J Tuberc Lung Dis 2005, 9: 1361–1366.

Aydin IO, Ulusahin A: Depression, anxiety comorbidity, and disability in tuberculosis and chronic obstructive pulmonary disease patients: applicability of GHQ-12. Gen Hosp Psychiatry 2001, 23: 77–83. 10.1016/S0163-8343(01)00116-5

Nyamathi A, Berg J, Jones T, Leake B: Predictors of perceived health status of tuberculosis-infected homeless. West J Nurs Res 2005, 27: 896–910. 10.1177/0193945905278385

He CY, He LM, Li MH: Application of SF-36 scale on pulmonary tuberculosis patients in Yunnan province of China and southern Thailand. Zhonghua Liu Xing Bing Xue Za Zhi 2005, 26 (3):187–189.

Pasipanodya JG, Miller TL, Vecino M, Munguia G, Bae S, Drewyer G, Weis SE: Using the St. George respiratory questionnaire to ascertain health quality in persons with treated pulmonary tuberculosis. Chest 2007, 132: 1591–1598. 10.1378/chest.07-0755

Muniyandi M, Rajeswari R, Balasubramanian R, Nirupa C, Gopi PG, Jaggarajamma K, Sheela F, Narayanan PR: Evaluation of post-treatment health-related quality of life (HRQoL) among tuberculosis patients. Int J Tuberc Lung Dis 2007, 11: 887–892.

Hays RD, Morales LS: The RAND-36 measure of health-related quality of life. Ann Med 2001, 33: 350–357. 10.3109/07853890109002089

Greenley JR, Greenberg JS, Brown R: Measuring quality of life: a new and practical survey instrument. Soc Work 1997, 42: 244–254.

Hays RD, Sherbourne CD, Mazel R: User's Manual for the Medical Outcomes Study (MOS) Core Measures of Health-Related Quality of Life. [ http://www.rand.org/pubs/monograph_reports/MR162/ ]

Jones PW, Quirk FH, Baveystock CM: The St George's Respiratory Questionnaire. Respir Med 1991, 85 (Suppl B):25–31. 10.1016/S0954-6111(06)80166-6

Derogatis LR, Lipman RS, Covi L: SCL-90: an outpatient psychiatric rating scale – preliminary report. Psychopharmacol Bull 1973, 9: 13–28.

Golderberg D, Williams P: A user's guide to the General Health Questionnaire. Windsor, UK: NFER-Nelson; 1988.

Stewart AL, Hays RD, Ware JE: The MOS short form general health survey: reliability and validity in a patient population. Med Care 1988, 26: 724–732. 10.1097/00005650-198807000-00007

Beck AT, Steer RA, Brown GK: Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corporation; 1996.

Horsman J, Furlong W, Feeny D, Torrance G: The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes 2003, 16: 54. 10.1186/1477-7525-1-54

Brazier J, Roberts J, Deveril M: The estimation of a preference-based measure of health from the SF-36. J Health Econ 2002, 21: 271–292. 10.1016/S0167-6296(01)00130-8

House JS, Landis KR, Umberson D: Social relationships and health. Science 1988, 241: 540–545. 10.1126/science.3399889

Health Canada: Canadian tuberculosis standards. 6th edition. 2007. [ http://www.phac-aspc.gc.ca/tbpc-latb/pubs/pdf/tbstand07_e.pdf ]

Sloan JA, Symonds T, Vargas-Chanes D, Friedly B: Practical guidelines for assessing the clinical significance of heath related quality of life changes within clinical trials. Drug Inf J 2003, 37: 23–31.

Samsa G, Edelman D, Rothman ML, Williams GR, Lipscomb J, Matchar D: Determining clinically important differences in health status measures: a general approach with illustration to the Health Utilities Index Mark II. Pharmacoeconomics 1999, 15: 141–155. 10.2165/00019053-199915020-00003

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Guo, N., Marra, F. & Marra, C.A. Measuring health-related quality of life in tuberculosis: a systematic review. Health Qual Life Outcomes 7 , 14 (2009). https://doi.org/10.1186/1477-7525-7-14

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  • Chronic Obstructive Pulmonary Disease Patient
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literature review on tuberculosis

Tuberculosis: a survey and review of current literature

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Despite being a treatable and preventable disease, tuberculosis will kill an estimated 30 million people during the current decade. Tuberculosis is a global problem, and increases in case rates are occurring not only in the developing countries of the world but also in several industrialized nations, such as the United States. Coincident with the resurgence of tuberculosis in the United States, there has also been an alarming increase in the number and proportion of cases caused by strains of Mycobacterium tuberculosis that are resistant to multiple first-line drugs. The increase in multiple-drug resistant tuberculosis has re-taught physicians about the importance of pursuing and ensuring treatment until cure. The HIV epidemic is playing a pivotal and permissive role in the resurgence of tuberculosis morbidity and mortality in those populations where tuberculosis and HIV are prevalent and overlap. Co-infection with HIV distorts the natural history and clinical expression of tuberculosis. Molecular biology has yielded important insights into the mechanisms of drug resistance and provided powerful tools for the rapid diagnosis and epidemiologic study of this disease.

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Molecular Diagnosis of Drug-Resistant Tuberculosis; A Literature Review

Thi ngoc anh nguyen.

1 UMR MIVEGEC, Institute of Research for Development, Centre National de la Recherche Scientifique, University of Montpellier, Montpellier, France

2 Laboratory of Tuberculosis, Department of Bacteriology, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam

3 LMI Drug Resistance in South East Asia, National Institute of Hygiene and Epidemiology, Hanoi, Vietnam

Véronique Anton-Le Berre

4 LISBP, CNRS, INRA, INSA, Université de Toulouse, Toulouse, France

Anne-Laure Bañuls

Thi van anh nguyen, associated data.

Drug-resistant tuberculosis is a global health problem that hinders the progress of tuberculosis eradication programs. Accurate and early detection of drug-resistant tuberculosis is essential for effective patient care, for preventing tuberculosis spread, and for limiting the development of drug-resistant strains. Culture-based drug susceptibility tests are the gold standard method for the detection of drug-resistant tuberculosis, but they are time-consuming and technically challenging, especially in low- and middle-income countries. Nowadays, different nucleic acid-based assays that detect gene mutations associated with resistance to drugs used to treat tuberculosis are available. These tests vary in type and number of targets and in sensitivity and specificity. In this review, we will describe the available molecular tests for drug-resistant tuberculosis detection and discuss their advantages and limitations.

Introduction

Drug resistance is a major challenge for tuberculosis (TB) treatment and eradication. The number of drug-resistant TB (DR-TB) cases is increasing worldwide: the estimated number of new cases of multidrug-resistant TB (MDR-TB, defined as TB resistant at least to isoniazid and rifampicin) or rifampicin-resistant TB (RR-TB) was 600,000 in 2016 compared with 480,000 in 2014 ( WHO, 2017a , b ). The treatment of MDR-TB is complex, much more expensive than the treatment of non-MDR-TB, and is associated with important side effects for the patients. The treatment success rate decreases from 83% for patients with newly diagnosed or relapse non-DR-TB who start treatment with a first-line regimen (2015 cohort) to 54% for MDR/RR-TB (2014 cohort) and to 30% for extensively drug-resistant TB (XDR-TB, defined as MDR-TB showing resistance also to at least one fluoroquinolone and one second-line injectable agent; 2014 cohort) ( WHO, 2017b ). In 2015, only one–third of new patients with bacteriologically confirmed TB and previously treated TB patients underwent drug susceptibility testing (DST) for rifampicin (RIF). The others were treated based only on smear-positive results without DST and differentiation of Mycobacterium tuberculosis (MTB) from non-tuberculous mycobacteria (NTM). In low and middle income countries, DST accessibility is limited by its high cost and complexity and by technical constraints (maintenance and environmental conditions). Such difficulties could result in the transmission and emergence of highly DR-MTB strains due to inappropriate treatments.

Drug susceptibility testing, the reference method for DR-TB detection, is based on the proportional agar method on Lowenstein-Jensen medium ( Canetti et al., 1969 ). As this culture method is time-consuming (up to 3–8 weeks to get results), other solid culture methods, such as Middlebrook agar, have been developed to reduce the turn-around time to 10–12 days ( Brady et al., 2008 ; Nguyen et al., 2015 ). Liquid culture methods have been used for faster detection with high sensitivity ( Lawson et al., 2013 ). For instance, the BACTEC Mycobacteria Growth Indicator Tube (MGIT TM ) are barcoded for the automated processing of large numbers of samples (up to 960 tubes) 1 with a turn-around time between 10 and 30 days ( Lawson et al., 2013 ). However, liquid culture methods come with several disadvantages: invisible contamination, overgrowth of NTM, and need of expensive complex systems ( Singhal et al., 2012 ; Lawson et al., 2013 ; Nguyen et al., 2015 ). Moreover, culture-based methods generally require well-trained personnel and high biosafety-level laboratories for the protection of the technicians and the environment. Thus, the use of culture methods for DR-TB detection is limited, especially in low- or middle-income countries.

Previous studies demonstrated that in MTB strains, drug resistance is mainly acquired through spontaneous mutations, especially single nucleotide polymorphisms (SNPs), in the circular chromosome ( McGrath et al., 2014 ). For each anti-TB drug, mutations in one or several genes have been described, and each mutation relates to different levels of drug resistance. Mutation frequency also can be variable. For instance, 97% of the cases of resistance to RIF are linked to mutations in the rpo B gene, mostly in an 81 bp hotspot region (codon 507 to codon 533; Escherichia coli numbering system, used throughout the text for RIF) ( Laurenzo and Mousa, 2011 ). Mutations at codons 526–531 of rpoB show the highest frequency and confer high-level RIF resistance. Isoniazid (INH) resistance is acquired through mutations in the kat G, inh A and its promoter, ahp C, ndh , and fur A genes but mainly in kat G, inh A and its promoter ( Laurenzo and Mousa, 2011 ). The most frequent kat G mutations (50–90%) are found at codon 315 and confer high-level resistance to INH. In ethambutol (EMB)-resistant isolates, mutations at codon 306 of the emb B gene are the most frequent (47–62%). Mutations associated with resistance to second-line drugs also have been described. In streptomycin (STR)-resistant MTB isolates, the most frequent mutations are found at codons 43 and 88 of the rps L gene, and codon 514 of the rrs gene ( Laurenzo and Mousa, 2011 ; Nguyen H.Q. et al., 2017 ). Resistance to fluoroquinolones (FQs) arises through mutations in the gyr A and gyr B genes. About 60–70% of quinolone-resistant MTB isolates harbor mutations in the quinolone resistance-determining region of gyr A, with the highest frequency at codon 94, followed by codon 90, 91, and 88 ( Laurenzo and Mousa, 2011 ). Mutations in the gyr B gene are rare. Resistance to second-line injectable drugs, including amikacin (AMK), kanamycin (KAN), and capreomycin (CAP), is mainly associated with rrs gene mutations. Approximately 70–80% of CAP resistance and about 60% of KAN resistance are caused by the rrs A1401G mutation. Although the mechanisms of drug resistance is not clear in about 10–40% of DR-MTB isolates without mutations, the detection of known mutations enables to identify a high proportion of DR-MTB ( Jeanes and O’Grady, 2016 ).

Therefore, many nucleic acid-based assays that detect mutations associated with anti-TB drug resistance have been developed recently, in order to provide affordable, accurate, simple, and rapid diagnostic tests for DR-TB detection. In this review, we will describe the commercially available molecular tests for DR-TB detection and discuss their advantages and limitations (see Supplementary Table S1 for a summary of the available molecular tests).

Molecular Assays for the Detection of Drug Resistance in MTB

Dna line probe assays.

Line probe assays (LPAs) are basically DNA–DNA hybridization assays that allow the simultaneous detection of different mutations by using multiple probes 2 ( Makinen et al., 2006 ). After DNA extraction and target amplification, amplicons are hybridized to specific oligonucleotide probes that are complementary to the target sequences and are immobilized on the surface of a strip. After several post-hybridization washes to remove non-specific binding, the amplicon-probe hybrids are visualized by eye as colored bands on the strip. The turnaround time of the whole assay is 5–7 h ( Makinen et al., 2006 ; Mitarai et al., 2012 ).

Although several LPAs have been developed, most of them focus only on the hotspot regions of drug-resistance and different assays target different genes. For instance, the INNO-LiPA Rif TB LPA (Innogenetics, Zwijndrecht, Belgium) analyzes only the rpo B hotspot region (codon 509 to codon 534; Asp516Val, His526Tyr, His526Asp, and Ser531Leu mutations) for MTB identification and RIF resistance screening ( WHO, 2008 ). The AID TB Resistance LPA ( Aid Diagnostika GmbH ) includes three modules to detect first-line and second-line anti-TB drug resistance in culture and clinical specimens 3 ( Ritter et al., 2014 ). Module 1 targets rpo B, kat G, and the inhA promoter for RIF and INH resistance screening; module 2 covers rps L and rrs to detect aminoglycoside resistance (STR, AMK, CAP); and module 3 analyzes gyr A and emb B for FQ and EMB resistance detection. The three modules include wild type and mutant probes to cover the most common mutations. The AID TB Resistance LPA showed high sensitivity and specificity for the detection of RIF, INH, STR, FQs, and second-line injectable agents resistance (between 90 and 100%), but lower sensitivity for EMB resistance (72.9%) ( Molina-Moya et al., 2014 ). However, with the AID TB Resistance LPA, uninterpretable results have been reported in up to 8.3% of smear-positive and 65% of smear-negative samples ( Deggim-Messmer et al., 2016 ).

Currently, the LPAs recommended by WHO for the initial drug resistance screening of sputum smear-positive samples include GenoType MTBDR plus , GenoType MTBDR sl ( Hain LifeScience GmbH , Germany), and Nipro NTM+MDR-TB (Nipro Co., Osaka, Japan) ( WHO, 2017b ). GenoType MTBDR plus VER2.0 has the advantage of detecting both RIF and INH resistance by screening mutations in rpo B, kat G, and the inhA promoter 4 . GenoType MTBDR sl VER1.0 and VER2.0 detect the MTB complex and its resistance to FQs, EMB and aminoglycosides/cyclic peptides by analyzing the gyr A, gyr B, rrs , emb B, and eis genes 5 . It may be used as initial test for patients with confirmed RR-TB or MDR-TB ( WHO, 2017b ). Nipro NTM+MDR-TB detects MDR-TB cases by targeting rpo B, kat G, and inh A and also differentiates four important Mycobacterium species (MTB, M. avium , M. intracellulare , and M. kansasii ) that cause the human disease ( Ruesch-Gerdes and Ismail, 2015 ; Nathavitharana et al., 2016 ).

GenoType MTBDR plus VER2.0 shows good accuracy for the detection of MDR isolates in smear-positive specimens (sensitivity between 83.3 and 96.4%, and specificity between 98.6 and 100%) ( Bai et al., 2016 ; Nathavitharana et al., 2016 ; Dantas et al., 2017 ; Meaza et al., 2017 ). The Nipro NTM+MDRTB strips also show high specificity (between 97 and 100%) for INH and RIF resistance screening in cultured isolates and clinical (sputum) samples, but sensitivity varies between studies (from 50 to 95%) ( Mitarai et al., 2012 ; Ruesch-Gerdes and Ismail, 2015 ; Nathavitharana et al., 2016 ). Regarding second-line drugs, GenoType MTBDR sl VER2.0 displays high sensitivity and specificity (between 91 and 100%) for detecting FQ resistance, but variable sensitivity and specificity for the screening of resistance to second-line injectable drugs (SLIDs) ( Bang et al., 2016 ; Brossier et al., 2016 ; Gardee et al., 2017 ). Therefore, the overall GenoType MTBDR sl VER2.0 specificity (between 59 and 100%) and sensitivity (between 83 and 87%) for detecting XDR isolates differ among studies. Moreover, uninterpretable results were reported for drug resistance screening in sputum specimens, especially smear-negative samples ( Tomasicchio et al., 2016 ; Meaza et al., 2017 ). In addition, when GenoType MTBDR sl (both VER1.0 and VER2.0) is used for FQ resistance screening, some synonymous and non-synonymous mutations (i.e., that do not and that do change the encoded amino acid, respectively) in the gyr A gene prevent the hybridization of either the wild type or mutant probe, leading to false-resistance results. Although these mutations (T80A + A90G, gcG/gcA A90A, and atC/atT I92I) are not frequent, they account for around 7% of all MDR-TB strains in some studied regions ( Ajileye et al., 2017 ).

In conclusion, LPAs are rapid, simple and easy to perform. Result analysis (manually or automatically) is simple. However, LPAs require complex laboratory infrastructure and expensive equipment that is normally only available in reference laboratories ( WHO, 2017c ). The number of uninterpretable results is high, and LPA target coverage is limited to the main mutations. Thus, their sensitivity and specificity vary according to the mutation prevalence in the area under study.

Real-Time PCR Assays

Real-time PCR is now broadly applied for the development of rapid diagnostic tests. Two main approaches are commonly used in real-time PCR: (i) the use of non-specific fluorescent dyes to detect any double-stranded DNA generated by PCR amplification, and (ii) the use of sequence-specific probes tagged with a fluorescent reporter for the specific detection of the hybridization between probes and amplicons 6 . Each probe has a specific melting temperature (Tm), and a Tm change reflects the presence of mutations in the target. This feature has been used to develop real-time PCR tests for drug resistance screening.

Some types of probes can detect mutations conferring drug resistance in MTB, such as dual labeled linear probes ( Edwards et al., 2001 ; Espasa et al., 2005 ; Liu et al., 2013 ) and sloppy molecular beacon (SMB) probes ( Chakravorty et al., 2011 , 2012 , 2015 ; Roh et al., 2015 ). The dual labeled probes (DLPs) consist of a fluorescence-quencher pair that generates different melting profiles between wild-type and mutant sequences when they hybridize to amplified targets ( Roh et al., 2015 ). Besides, SMB probes consist of stem-loop structures that are thermodynamically more stable than DLPs. SMB probes have the highest sensitivity and specificity (up to 100%) for the detection of mutations in the 81 bp RIF resistance determining region. The presence of 10-fold excess of NTM DNA or 10 5 -fold excess of human DNA does not affect the results with SMB probes ( Chakravorty et al., 2012 ). SMB probes can detect correctly 100% of mutations in 2 pg DNA templates. This suggests that SMB probes can detect rpo B gene mutations in both smear-positive and smear-negative samples. SMB probes identify heteroresistance in a mixture of 10% of mutant DNA and 90% of wild type DNA, while dual labeled probes require at least 40% of mutant DNA ( Roh et al., 2015 ). Although the limit of detection depends on the mutation, overall, SMB probes show higher sensitivity than dual labeled probes.

The reduced risk of contamination (all steps after DNA extraction are performed in one tube) and the shorter turn-around time (compared with LPAs because of the absence of the hybridization step) are among the advantages of real-time PCR. However, real-time PCR has some remarkable disadvantages. First, it requires expensive, specific equipment and skilled technicians. Second, the number of probes that can be used in one reaction is limited, resulting in limited target numbers. This is a major drawback for MTB, a pathogen that can harbor many mutations. Third, the size range of the amplified targets should be limited to 75–200 bp for efficiently detecting multiple mutations (see text foot note 6).

The following sections present some examples of real-time PCR-based commercial kits and new strategies for DR-TB detection.

Xpert MTB/RIF and Xpert MTB/RIF Ultra

Xpert MTB/RIF ( Cepheid , United States), a fast molecular-based test, is endorsed by WHO for the detection of the MTB complex and RIF resistance screening in suspected cases ( WHO, 2017b ). This test was first recommended in 2010 for the diagnosis of pulmonary TB in adults from sputum specimens. Since 2013, it has been recommended also for the diagnosis of TB in children and of some specific extra-pulmonary forms.

The Xpert MTB/RIF assay uses semi-quantitative nested real-time PCR to amplify a fragment containing the 81 bp hotspot region of the rpo B gene (codons 507–533) that is then hybridized to five molecular beacon probes ( Bunsow et al., 2014 ; Steingart et al., 2014 ; Ochang et al., 2016 ). Each probe covers a separate sequence and is labeled with a fluorescent dye. The whole experiment is performed in a self-contained cartridge, like a mini-laboratory, to minimize cross-contamination between samples. Sensitivity and specificity for smear-positive samples can reach 100 and 99%, respectively, and for smear-negative samples are 67 and 99%, respectively, compared to the standard culture-based DST. It significantly decreases the detection time of RIF resistance from 4 to 8 weeks (culture and DST) to 2 h. It has immediately a good impact on patients because it allows starting rapidly the MDR-TB treatment. Moreover, Xpert MTB/RIF increases of 23% the MTB detection rate among culture-confirmed cases compared with smear microscopy, with high accuracy of TB detection and limiting the misdiagnosis between MTB and NTM ( Steingart et al., 2014 ; Sharma et al., 2015 ).

However, some studies reported false-positive results with Xpert MTB/RIF due to silent mutations [e.g., at codon 514 of rpo B ( Bunsow et al., 2014 )], and false-negative results because of the impossibility to detect RIF-resistance mutations outside the hotspot region [e.g., mutations at codon 572 ( Sanchez-Padilla et al., 2015 )]. Consequently, Xpert MTB/RIF scope might be limited, for instance, in Swaziland where more than 30% of patients with RR-TB carry the I572F mutation (I491F in the original paper, according to the MTB numbering system). In addition, this test does not detect mutations in genes associated with INH resistance, but uses RIF resistance as a proxy for MDR-TB detection ( Manson et al., 2017 ). Consequently, many INH mono-resistant TB cases are misdiagnosed. Recently, whole genome sequencing (WGS) studies discovered that INH resistance arises before RIF resistance in all lineages, geographical regions and time periods. As Xpert MTB/RIF can detect only RIF resistance, it is unable to identify MDR-TB in its earliest form (i.e., INH mono-resistance). In addition, it must always be used together with other tests, such as DST, to confirm and identify the whole resistance phenotype of each MTB isolate. Another remarkable limitation is its high cost due to the use of complex GeneXpert system and disposable cartridges (the GeneXpert apparatus costs between US$12 000 and $71 000, depending on the number of test modules, and the price of each single-use test cartridge is $9⋅98) ( WHO, 2017b ; Walzl et al., 2018 ). Therefore, its use as initial diagnostic test for all suspected TB cases is unaffordable in many high TB-burden countries. According to a WHO report (2016), only 15 of the 48 high TB-burden countries have used the Xpert tests for all suspected TB cases (i.e., 10% of all estimated TB cases globally in 2015) ( WHO, 2017b ). Finally, the GeneXpert machine requires a constant electricity source and is sensitive to heat and dust. A number of machine failures has been reported due to these problems ( Walzl et al., 2018 ).

Recently, the WHO evaluated the next-generation Xpert MTB/RIF Ultra system ( Cepheid ) that has a larger amplification chamber to increase the amount of sputum and two additional targets (IS1081 and IS6110) to identify MTB ( Chakravorty et al., 2017 ; Perez-Risco et al., 2018 ). The new Ultra cartridge can be used in the old GeneXpert machine and has the same price as the old one ( WHO, 2017d ). However, the analytical sensitivity is significantly increased, more than 10 times. Its higher MTB detection sensitivity (16 bacilli/ml compared with 131 bacilli/ml for the current Xpert MTB/RIF cartridge) facilitates MTB screening in specimens with low numbers of bacilli, such as sputum samples from children and from patients co-infected by HIV, and in difficult-to-diagnose cases, such as smear-negative pulmonary and extra-pulmonary TB. As a result of its higher sensitivity, Xpert Ultra specificity for MTB detection is lower than that of Xpert MTB/RIF 7 ( WHO, 2017b ). However, RR-TB detection accuracy is similar with both cartridge types. Xpert MTB/RIF Ultra overcomes some limitations of the current cartridge by excluding the silent rpo B mutations Q513Q and F514F ( Chakravorty et al., 2017 ). Therefore, in the latest report, WHO recommended to use the Ultra cartridge as initial diagnostic test for all adults and children with signs and symptoms of TB, and also for screening some extra-pulmonary specimens, such as cerebrospinal fluid, lymph node and tissue samples ( WHO, 2017c ). Interestingly, Xpert XDR cartridge is in development for the detection of XDR-TB.

Finally, a new device named GeneXpert Omni ( Cepheid , United States) is under development. It uses the same cartridges as the Xpert system, but will be smaller, lighter and cheaper than the current Xpert system. It will have a 4-h battery, and thus will be better adapted to overcome the requirement of a stable electric supply 8 .

Genedrive MTB/RIF ID Kit

The Genedrive MTB/RIF ID Kit ( Epistem , United Kingdm) is an innovative system developed after the Xpert test for the detection of MTB and RR-TB from raw sputum samples. It is a portable low-power thermal cycling apparatus (560 g in weight) that can be operated using a 12V DC power supply and used at TB point-of-care sites ( Niemz and Boyle, 2012 ). The system uses a simple paper-based DNA extraction method combined with asymmetric real-time PCR and a proprietary hybridization probe technology (Highlighter Probes) ( Castan et al., 2014 ). The composite paper is chemically treated to decontaminate and extract DNA from bacteria without any additional equipment, such as vortex or centrifuge. The system employs multiplex real-time PCR to target two regions: a short repetitive region (the REP13E12 family), and the 81 bp hotspot region of rpo B. The Highlighter Probes detect the most important mutations associated with RIF resistance at codons 516, 526, and 531, with an overall sensitivity for rpo B mutation detection of 72.3%. For MTB identification, Genedrive MTB/RIF ID is comparable to the Xpert assay (100% vs. 93.5% of sputum samples). The platform can detect as low as five genome copies. It is user-friendly and fast (results in 75 min). Although the Genedrive system can analyze only eight samples per working day 9 , the low price ($4000 for the system and $10–$17 for the disposable cartridge) makes the assay affordable for low-income regions ( Niemz and Boyle, 2012 ). Due to its light weight, stable power supply and capacity to function without air conditioning (up to 40°C), screening with the Genedrive system could be implemented at many acid fast bacilli (AFB) smear microscopy centers ( WHO, 2017c ).

Anyplex II MTB/MDR and MTB/XDR

The Anyplex kits ( Seegene , South Korea) have been designed for the detection of MTB, MDR-TB, and XDR-TB from sputum and bronchial wash samples, culture isolates, and fresh tissues based on a semi-automated multiplex real-time PCR method 10 . The two proprietary technologies use dual-priming oligonucleotides (DPO TM ) and tagging-oligonucleotide cleavage and extension (TOCE TM ) and the real-time PCR CFX96 TM apparatus for the highly specific detection of specific SNPs. The Anyplex kits can identify multiple targets in one reaction because the PCR CFX96 TM system allows the simultaneous detection of five different fluorescent dyes. Specifically, Anyplex II MTB/MDR detects the 34 most frequent mutations in rpo B, kat G, and inh A for MDR-TB screening, and the MTB/XDR kit detects the 13 main mutations in the gyr A and rrs genes and eis promoter associated with XDR-TB (see text foot note 10) ( Igarashi et al., 2017 ). The entire protocol requires 3.5 h from DNA extraction to result interpretation using the provided software ( Molina-Moya et al., 2015 ).

The Anyplex MTB/MDR kit accurately detects more than 83% of MTB complex bacilli in pulmonary and extra-pulmonary samples, and therefore, is much more sensitive than the AFB smear microscopy method ( Sali et al., 2016 ), and can improve the diagnosis of extra-pulmonary TB with high specificity. In evaluation studies of both kits ( Molina-Moya et al., 2015 ; Sali et al., 2016 ; Igarashi et al., 2017 ), specificity ranged between 94 and 100% for the detection of MTB resistant to all targeted drugs in clinical specimens, and sensitivity was between 50 and 100%. The lowest sensitivity was obtained for FQ resistance screening, but was similar to that of pyrosequencing and GenoType MTBDR sl ( Molina-Moya et al., 2015 ). Anyplex MTB/MDR sensitivity for detecting resistance to RIF was always higher than 90% for all sample types ( Molina-Moya et al., 2015 ; Sali et al., 2016 ; Igarashi et al., 2017 ). This real-time PCR method seems to be better than the Xpert technology because of the very low rate of false-positive results. However, the limited number of targets remains a limitation. Overall, the Anyplex II MTB/MDR and MTB/XDR kits are potential tools for the efficient and rapid detection of MTB and DR-TB in clinical specimens, especially for the diagnosis of extra-pulmonary TB.

Digital PCR

Heteroresistance is found in 9–20% of clinical isolates, and up to 25.8% of clinical samples in some high TB incidence regions ( Zhang X. et al., 2012 ). Heteroresistance is the result of the super-infection by at least two isolates, or of the evolution of a single isolate leading to various subpopulations of drug-resistant and -susceptible bacteria in the presence of antibiotics ( Pholwat et al., 2013 ). The detection of heteroresistance is still a challenge for rapid DR-TB diagnosis, even when using sequencing methods ( Folkvardsen et al., 2013 ). Pholwat et al., developed an assay based on digital PCR ( Pholwat et al., 2013 ) that can identify and quantify different resistant subpopulations in mixtures containing as little as one XDR-MTB among thousand susceptible MTB bacilli. Digital PCR is basically a quantitative PCR that relies on the partitioning of samples into thousands of individual small-volume reactions before amplification ( Morley, 2014 ). In these compartments, the reaction components and amplification process are similar to quantitative PCR, but some will contain the DNA target, whereas others will not. After amplification, the number of positive and negative reactions is determined and analyzed using the Poisson distribution. The recent development of micro- and nano-fluidic technologies makes digital PCR simpler and more practical ( Morley, 2014 ). This technique is reproducible and can detect bacilli at concentrations as low as 1000 CFU/ml ( Pholwat et al., 2013 ). With these advantages, digital PCR enables the earlier detection of new mutations emerging during treatment that could require a therapy change.

LATE-PCR With Lights-On/Lights-Off Probes

PCR-based detection of variant sequences is often carried out using probes labeled with different fluorescent dyes. However, the number of fluorescent dyes and the analysis capacity of a PCR machine are often limited to 4–6 colors. To overcome this problem, linear-after-the-exponential PCR (LATE-PCR), an asymmetric PCR method with sets of Lights-On/Lights-Off probes, has been developed to analyze nucleotide substitutions ( Rice et al., 2012 ). In each probe pair, the Lights-Off probe is labeled with a quencher moiety and the Lights-On probe with a fluorescent molecule. The Tm of the probes are at least 5°C lower than that of the primers. After amplification, the number of single-stranded DNA molecules is much higher than that of double-stranded DNA molecules, due to the asymmetric PCR. The use of low temperature at the end of the process enables the hybridization of single-stranded DNA to the probes with lower Tm. After both probes are bound to their target sequence, the quencher of the Lights-Off probe extinguishes the fluorescence signal. Although each probe pair hybridizes only to a short part of the target, each fluorescent signal can be integrated to analyze sequences of several hundred nucleotides in length. The signal of the whole set of probes during the melting curve analysis can be used to create an annealing curve that shows the time-dependent fluorescence contour. In the case of mutations in the target sequence, the Tm of the hybridization changes, leading to a shift in the annealing curve. This new analytical technology is very sensitive for the detection of each single nucleotide change in a sequence of hundreds base pairs even with the use of a single fluorescent dye. The use of different color probes allows detecting various target genes in one single tube. However, the analysis of many different targets with multiple fluorescent dyes requires a powerful system. Besides, the use of one pair of modified probes for each mutation is expensive.

Hain Lifescience has applied this technology to develop FluoroType MTBDR VER1.0 for the simultaneously detection of the MTB complex and MDR-TB by targeting rpo B, kat G, and inh A in one single tube 11 . The kit includes probe pairs to target a set of known mutations in these genes (T508A, S509T, E510H, L511P, S512K, Q513L, Q513P, Q513R, D516A, D516F, D516V, D516Y, N518I, S522L, S522Q, H526C, H526D, H526G, H526L, H526N, H526P, H526Q, H526R, H526S, H526Y, R529K, S531F, S531L, S531L, S531Q, S531W, L533E, L533P in rpo B; S315T1, S315T2, S315N, S315R in kat G; and G-17T, A-16G, C-15T, G-9A, T-8A, T-8C, and T-8G in inh A), and can also detect unknown mutations, but not identify the exact type of mutation ( Hillemann et al., 2018 ). Compared with GenoType MTBDRplus, FluoroType MTBDR is characterized by shorter hands-on time, faster results (within 3 h), no DNA contamination, and automatic result interpretation. The sensitivity and specificity are comparable to those of Genotype MTBDR plus and Xpert MTB/RIF for the detection of RIF resistance ( Hillemann et al., 2018 ). However, they are lower than those of GenoType MTBDR plus for the detection of INH resistance-associated mutations in kat G and/or inh A. Hain Lifescience stated that the test can be used with decontaminated sputum and cultured samples 12 . More evaluation studies in different TB regions are needed before its implementation at TB point-of-care sites. WHO will start to evaluate the use of FluoroType MTBDR in the period 2018–2019 ( WHO, 2017c ).

Sequencing is the best technology to rapidly analyze the genotype of an organism. Beside targeted gene sequencing (TGS), the development of Next Generation Sequencing (NGS) has been a major breakthrough in molecular biology because it can rapidly provide whole genome data in a single run ( Phelan et al., 2016 ; Dheda et al., 2017 ; Manson et al., 2017 ). This allows species identification, screening of all (known and new) mutations (synonymous and non-synonymous mutations, insertions and deletions) in a sample, detecting drug resistance, and predicting the organism evolution. NGS-based kits for targeted sequencing have appeared on the market for DR-TB screening. For instance, Life technology has developed a novel protocol for rapid (2 days) full-length Mycobacterium tuberculosis gene analysis to detect first- and second-line drug resistance using the Ion Torrent Personal Genome Machine (PGM) ( Daum et al., 2012 ). Eight genes ( rpo B, kat G, and inh A, pnc A, gyr A, eis , emb B, and rps L) are amplified (PCR amplification of full-length genes, not of full genomes like for WGS) and sequenced. The AmpliSeq for Illumina TB Research Panel (Illumina) targets the same genome regions (see text foot note 12). Besides, WGS is now only valuable for cultured strains due to its requirement in terms of quantity and quality of DNA ( WHO, 2018 ). Although some studies have performed WGS for direct sputum samples but the results were variable with a high level of human genome contamination. Currently, numbers of commercially novel NGS platforms such as Oxford Nanopore MinION (Oxford Nanopore Sequencing Technology, Oxford, United Kingdom) and PacBio RSII (Pacific Biosciences) are now available with advantage of short run time and long read. However, the massive output and high error rate are still main issues for their application ( WHO, 2018 ). The Oxford Nanopore MinION, which is a small benchtop device that can plug directly into a laptop via a USB port cable, could generate 10–20 GB of data output per sample that requires a high speed computer and large storage space adequate for data analysis. In addition, the error rate is still high, up to 20–35%, but is expected to be improved as the MinION and its associated base-calling software continue to be developed ( Senol Cali et al., 2018 ). Though, outstanding advantages of WGS including higher genome coverage, providing epidemiological information and understanding of new resistance mechanisms for both current and new drugs bring precious information to research and treatment of disease. Therefore, the use of WGS as a DR-TB detection tool could be potentially used in clinical settings in the future.

The large-scale application of sequencing, especially in middle- and low-income countries is still difficult for the following reasons: (i) robust software and database tools need to be developed for the full exploitation of this technology in this specialized area of medicine; (ii) specialized personnel and bioinformatics facilities are required for the experiments, data acquisition and data analysis; (iii) the high cost of NGS platforms; (iv) need to determine whether new mutations confer anti-TB drug resistance; and (v) high amounts of high quality DNA are required for sequencing ( Phelan et al., 2016 ; Dheda et al., 2017 ; Zignol et al., 2018 ). Moreover, the sample preparation procedure and DNA extraction method should be standardized for consistency ( Zignol et al., 2018 ).

Nevertheless, the cost of sequencing is progressively decreasing and is already lower than that of phenotypic testing for first- and second-line drug resistance in most settings ( Dheda et al., 2017 ; Zignol et al., 2018 ). The Foundation for Innovative Diagnostics (FIND) and their partners are developing a system designed to perform targeted amplicon sequencing directly from primary sputum samples ( Dolinger et al., 2016 ). Several studies have reported that WGS can be successfully performed directly using sputum samples thanks to new strategies, such as targeted DNA enrichment ( McNerney et al., 2017 ; Doyle et al., 2018 ). The procedure is progressively becoming simpler, affordable and rapid. NGS and TGS are promising tools for the surveillance of drug resistance and the WHO has published a technical guideline for use of NGS technology for the detection of mutations associated with drug resistance in MTB ( WHO, 2018 ). Two consortiums of NGS experts, the ReSeqTB Consortium and CRyPTIC, have established huge repositories of genomic and phenotypic data accumulated from thousands of MTB strains worldwide. These consortiums provide platforms that allow automatic processing raw WGS data to comprehensive mutation lists and drug resistance panels for users that are convenient for non-expert bioinformatics 13 . Currently, these platforms are only available for surveillance and research use. Ultimately, they will be interoperable and transferred to the WHO to support global DR-TB surveillance in the short term and clinical diagnosis of DR-TB in the long term ( WHO, 2018 ).

DNA Microarray

Apart from sequencing, the DNA microarray technology displays the highest capacity of multiple target detection with thousands of sequences in one reaction. In many studies, this technology has been used for detecting mutations associated with drug resistance in MTB. After DNA extraction, the targets are amplified and labeled with fluorescent dyes during the PCR step ( Noyer et al., 2015 ). Then, the labeled amplicons are complementarily hybridized to the probes immobilized on the array to form double-stranded DNA. Non-specific targets and non-specific binding are eliminated in the post-hybridization washing steps. The hybridization signal intensity is detected by a scanner at the appropriate wavelength. Different kinds of fluorescent dyes are used, such as Cy3 ( Naiser et al., 2008 ) and Cy5 ( Noyer et al., 2015 ). Often, very short targets are amplified (from 60 to 300 bp) to optimize the hybridization step. The probe length varies between 10 and 40 bp ( Naiser et al., 2008 ; Yao et al., 2010 ; Noyer et al., 2015 ). Two-round PCR amplification ( Yao et al., 2010 ) or multiplex PCR (classical or asymmetric) ( Shimizu et al., 2008 ; Zimenkov et al., 2013 ; Linger et al., 2014 ) are generally used to obtain sufficient amplicons for hybridization. However, the hybridization procedure varies between studies [55°C for 2 h ( Yao et al., 2010 ), 42°C for 60 min ( Shimizu et al., 2008 ), or 37°C for 10–16 h ( Zimenkov et al., 2013 )]. A threshold signal-to-noise ratio (often higher than 3) is defined to avoid false results ( Shimizu et al., 2008 ; Yao et al., 2010 ; Zimenkov et al., 2013 ; Linger et al., 2014 ).

To simplify the numerous steps of the microarray-based workflow, Akonni developed the TruArray MDR-TB Assay that uses a microfluidic chamber to integrate almost all steps (amplification, hybridization and target detection) in one platform 14 ( Linger et al., 2014 ; Maltseva et al., n.d. ), and to limit the risk of cross-contamination. The assay covers up to 30 rpo B mutations, 6 kat G and inh A mutations, 1 emb B mutation, and 2 rps L mutations. In addition, it targets the IS6110 and IS1245 markers for the detection of the MTB complex and M. avium complex, respectively. The results are available in few hours. VereMTB TM (Veredus Laboratories, Singapore) is another kit based on a multiplexed PCR-microarray-based method and microfluidic chamber to detect MDR-TB, MTB and nine other Mycobacterium species 15 . Interestingly, Zimenkov et al. (2016) recently developed a low-density hydrogel microarray (TB-TEST) that enables to detect simultaneously first- and second-line drug resistances in one run. This microarray was developed from two previous biochips (TB-Biochip, one for MDR and one for XDR) with an additional of targeting emb B gene and 12 probes for identification of the main lineages circulating in Russia. By using two separate asymmetrical PCRs and universal adapters, this array covers nine genes including rpo B, kat G, inh A, ahp C, gyr A, gyr B, rrs , eis , and emb B genes and six SNPs for lineage identification. The turn-around time is about 19 h (3 h for PCR and 16 h for hybridization), that is quite long compared to other molecular-based methods but detecting both MDR-TB and XDR-TB is the greatest advantage of this microarray. The sensitivity and the specificity of TB-TEST are identical between clinical samples and clinical isolates. This microarray has high potential to apply directly in clinical samples.

Currently, no microarray-based assay has been endorsed by WHO for the detection of DR-TB ( WHO, 2017c ). Most tests are still in development. The GeneChip MDR Kit (CapitalBio, China) is the only microarray-based assay on the market that has been evaluated in the framework of the NHFPC-Bill & Melinda Gates Foundation tuberculosis project, but not by WHO ( CapitalBio, n.d. ; WHO, 2017c ). It is considered to be an accurate and feasible tool with high sensitivity and specificity for the direct detection of RIF and INH resistance in clinical samples in China ( Zhang Z. et al., 2012 ). In a study using spinal tuberculosis samples, this test showed a sensitivity and specificity of 88.9 and 90.7%, respectively, for RIF resistance detection, and of 80 and 91%, respectively, for INH resistance screening ( Zhang Z. et al., 2012 ). The GeneChip MDR Kit has been widely implemented in different provinces of China 16 .

Overall, DNA microarray-based tests show high specificity and sensitivity for the detection of mutations associated with anti-TB drug resistance in clinical isolates: 100% sensitivity and specificity for INH and RIF detection ( Yao et al., 2010 ); and 90 and 95.7% sensitivity for the detection of resistance to FQ, and SLIDs, respectively, with a specificity of 90.9% for FQ resistance and 90.2% for SLIDs ( Zimenkov et al., 2016 ). The lowest sensitivity (89.9%) and specificity (57%) is for EMB resistance. For the detection of resistance to RIF, INH, STR, EMB and KAN in sputum samples from patients with TB, specificity varied from 60 to 95%, and sensitivity was higher than 90% ( Shimizu et al., 2008 ). The microarray detection limit varies among platforms and studies. The GeneChip MDR Kit ( CapitalBio ) could detect at as low as 25 genome copies ( Guo et al., 2009 ; Zhang Z. et al., 2012 ). The microarray developed by Linger et al. has an analytical sensitivity of 110 genome copies per assay ( Linger et al., 2014 ). Zimenkov et al. (2013) tested the biochip using various clinical specimens, such as sputum and bronchoalveolar lavage samples, and found that its diagnostic sensitivity varies according to the smear grade, from 67% for smear “1+” to 100% for smear “3+.”

DNA microarray could become an effective method for the rapid screening of MTB resistance. However, additional improvements, such as dedicated software to analyze and interpret the fluorescent signal intensity, are necessary to make it accessible to all TB point-of-care sites and promote its application in the clinical routine.

Challenges for the Development of Molecular Markers for the Detection of DR-TB

Many molecular tools for DR-TB diagnosis have been developed and are employed worldwide. However, there are still many challenges and issues that need to be solved to provide efficient and affordable point-of-care diagnostic tests.

Technically, most of the tests only focus on one or few genes, hotspot genomic regions, or frequent mutations ( Supplementary Table S1 ). Therefore, one sample should use several tests to get the complete drug-resistance genotype and to choose the most appropriate treatment for each patient. In Vietnam, a middle-income country, presumptive MDR-TB cases such as TB patients with treatment failure or HIV /TB co-infected patients are tested with Xpert MTB/RIF ( Vietnam’s Ministry of Health, 2018 ) (see Figure 1 ). Many steps are necessary from diagnosis to treatment, especially for the diagnosis of XDR-TB cases. The total diagnostic time might be extended up to 4 months. The application of molecular tests such as NGS, TGS, or DNA microarray would reduce the diagnostic time to only 2 days. Moreover, the mutation frequency and distribution vary among populations, and many mutations linked to DR-TB are outside the regions targeted by commercial kits. Consequently, the sensitivity and specificity of a test can vary in different populations. An ideal test should cover all mutations worldwide to provide high sensitivity and specificity. The number of fluorophores, target size, and the complexity of investigating multiple targets are the main limitations that affect the test design and efficiency. DNA microarray could overcome the multiple target limitation because it allows querying thousands of sequences with only one fluorophore in one assay. Several studies demonstrated that DNA microarray can detect low numbers of DR-TB bacilli with high sensitivity and specificity in clinical specimens ( Shimizu et al., 2008 ; Guo et al., 2009 ; Zimenkov et al., 2013 ). Several microarray-based devices are currently in development or under evaluation to be used in clinical settings ( WHO, 2017c ). However, a simpler platform and an easier assay/analysis are necessary to implement DNA microarray as a routine test.

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Diagnostic flowchart for presumptive multi-drug resistant tuberculosis in Vietnam (Vietnam’s Ministry of Health) and perspectives. MTB, Mycobacterium tuberculosis ; RIF, rifampicin; Hain 2, GenoType MTBDR sl (Hain Lifescience, Germany); MDR, multi-drug resistance; XDR, extensively drug resistance; NGS, next generation sequencing; TGS, targeted gene sequencing; DST, drug susceptibility testing; ∗ If the result of consultation is non-TB, non-tuberculosis mycobacteria identification by culture is recommended; ∗∗ Culture and culture DST are performed when the patient is still suspected of having second-line drug resistant TB.

Drug resistance emerges quickly after the introduction of new anti-TB drugs ( Bañuls et al., 2018 ). To develop accurate genetic-based diagnostic tests for DR-TB, the first crucial step is to understand the molecular mechanisms of resistance. For example, a recent review alerted about resistance of MTB to bedaquiline, a new potential drug for the treatment of DR-TB, and its cross-resistance with clofazimine ( Nguyen T.V.A. et al., 2017 ). The mechanism of resistance was linked to several genes: atp E, Rv0678 , and pep Q. However, it is only a non-exhaustive list because a standardized DST for bedaquiline does not exist yet, and not all resistance genotypes are known. Due to MTB high capacity of drug resistance acquisition, the development of a standardized DST should be started simultaneously with the evaluation of a new drug in clinical trials. Thereby, DST would be available when the new drug will be introduced in treatment regimens. However, it is a long and difficult path. For instance, although pyrazinamide has been used for many years, the standardized DST for this drug is still “work in progress” due to its current not satisfactory accuracy and reproducibility ( Chedore et al., 2010 ; Huy et al., 2017 ; Zignol et al., 2018 ). Thus, the development of quick molecular tests for drug resistance is still limited by the lack of highly accurate standard phenotypic tests, as reference.

Harsh climatic and environmental conditions are another big issue for the development of a molecular test. For instance, the Xpert machine is sensitive to dust and temperature ( Walzl et al., 2018 ). In high TB-burden countries, such as India and Vietnam, the harsh climate conditions (intense heat, high humidity, and pollution) require setting up air-conditioned laboratories and regular maintenance check-ups to ensure the shelf life of the machine and the test accuracy. This kind of working environment might cost more than the machine itself. Although the Xpert test is rapid and easy to use, such difficulties hamper its implementation in low- or middle-income countries.

Besides these issues, commercial pressure might limit the screening of optimal biomarkers ( Walzl et al., 2018 ). The numerous steps from the design/development to the scaled-up manufacture and marketing might take more than 10 years and cost more than $100 million. Thus, although many academic and private-sector research groups have been working on the development of novel diagnostic tools, only few tools were endorsed by WHO because most of them do not meet the required performance standards.

The progressive increase of DR-TB cases emphasizes the vital need for accurate and rapid diagnostic tools for their detection. The main current molecular techniques are LPA, real-time PCR, DNA microarray and sequencing. Cost, shelf life, sample throughput and accuracy are key factors for the development and application of such tests/systems. Other factors should also be considered, such as target capacity (i.e., how many drug resistance mutations can be detected) and the personnel skills required for running the test. Furthermore, to have a real impact, molecular diagnostic tests should be affordable for resource-poor countries, where TB and DR-TB are major problems. The turnaround time should be as short as possible to quickly prescribe the adapted treatment to the patients. As DR-TB is associated with mutations in different genes, an ideal test should simultaneously detect many mutations in one reaction. A simple testing procedure will increase the test accessibility at different laboratory levels.

Author Contributions

All authors contributed to the review, manuscript writing, critical review of the manuscript, and approved the final manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank the National Institute of Hygiene and Epidemiology (Vietnam), the Institute of Research for Development (France), the LMI DRISA, and the “Institut National des Sciences Appliquées,” Toulouse (France) for their support. We are grateful to Elisabetta Andermarcher for editing the English.

Funding. This work was supported by a PHC Lotus project. TNN was supported by the “Programme de Bourses d’Excellence de l’Ambassade de France au Vietnam.”

1 https://www.bd.com/en-uk/products/diagnostics-systems/identification-and-susceptibility-systems/bactec-mgit-

2 http://www.hain-lifescience.de/en/technologies/dnastrip.html

3 https://www.aid-diagnostika.com/en/kits/molecular-genetic-assay/infections-diseases/antibiotic-resistences/tb-isoniazid-rifampicin/

4 https://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/genotype-mtbdrplus.html

5 http://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/genotype-mtbdrsl.html

6 http://www.bio-rad.com/fr-fr/applications-technologies/what-real-time-pcr-qpcr

7 http://www.who.int/tb/features_archive/Xpert-Ultra/en/

8 https://www.tbfacts.org/genexpert/

9 https://www.genedrive.com/documents/Clinical-performance-evaluation-Genedrive-MTB_RIF-assay-India-July-2016.pdf

10 http://www.seegene.com/neo/en/products/tuberculosis/tuberculosis_list.php

11 http://www.hain-lifescience.de/en/products/microbiology/mycobacteria/tuberculosis/fluorotype-mtbdr.html

12 https://www.illumina.com/products/by-brand/ampliseq/community-panels/tb.html

13 https://platform.reseqtb.org/

14 https://akonni.com/products/trudiagnosis/truarray-mdr-tb-assay/

15 https://vereduslabs.com/products/clinical/veremtb/

16 http://en.thholding.com.cn/2016-08/22/c_56177.htm

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmicb.2019.00794/full#supplementary-material

  • Aid Diagnostika GmbH (n.d.). Autoimmun Diagnostika GmbH - Kits: TB Isoniazid, Rifampicin . Available at: https://www.aid-diagnostika.com/en/kits/molecular-genetic-assay/infections-diseases/antibiotic-resistences/tb-isoniazid-rifampicin/ (accessed July 13 2017). [ Google Scholar ]
  • Ajileye A., Alvarez N., Merker M., Walker T. M., Akter S., Brown K., et al. (2017). Some synonymous and nonsynonymous gyrA mutations in Mycobacterium tuberculosis lead to systematic false-positive fluoroquinolone resistance results with the Hain GenoType MTBDRsl assays. Antimicrob. Agents Chemother. 61 : e02169 -16. 10.1128/AAC.02169-16 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Akonni Biosystems (n.d.). TruArray ® MDR-TB Assay. Available at: https://akonni.com/products/trudiagnosis/truarray-mdr-tb-assay/ (accessed July 27 2017). [ Google Scholar ]
  • Bai Y., Wang Y., Shao C., Hao Y., Jin Y., Espinal M., et al. (2016). GenoType MTBDRplus assay for rapid detection of multidrug resistance in Mycobacterium tuberculosis : a meta-analysis. PLoS One 11 : e0150321 . 10.1371/journal.pone.0150321 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bang D., Andersen S. R., Vasiliauskienë E., Rasmussen E. M. (2016). Performance of the GenoType MTBDRplus assay (v2.0) and a new extended GenoType MTBDRsl assay (v2.0) for the molecular detection of multi- and extensively drug-resistant Mycobacterium tuberculosis on isolates primarily from Lithuania. Diagn. Microbiol. Infect. Dis. 86 377–381. 10.1016/j.diagmicrobio.2016.08.026 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bañuls A.-L., Nguyen T. V. A., Nguyen Q. H., Nguyen T. N. A., Tran H. H., Godreuil S. (2018). “ Antimicrobial resistance: the 70-year arms race between humans and bacteria ,” in Ecology and Evolution of Infectious Diseases , eds Roche B., Broutin H., Simard F. (Oxford: Oxford University Press; ),77–90. [ Google Scholar ]
  • Brady M. F., Coronel J., Gilman R. H., Moore D. A. (2008). The MODS method for diagnosis of tuberculosis and multidrug resistant tuberculosis. J. Vis. Exp. 18 : 845 . 10.3791/845 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Brossier F., Guindo D., Pham A., Reibel F., Sougakoff W., Veziris N., et al. (2016). Performance of the new version (v2.0) of the GenoType MTBDR sl test for detection of resistance to second-line drugs in multidrug-resistant Mycobacterium tuberculosis complex strains. J. Clin. Microbiol. 54 1573–1580. 10.1128/JCM.00051-16 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Bunsow E., Ruiz-Serrano M. J., López Roa P., Kestler M., Viedma D. G., Bouza E. (2014). Evaluation of GeneXpert MTB/RIF for the detection of Mycobacterium tuberculosis and resistance to rifampin in clinical specimens. J. Infect. 68 338–343. 10.1016/j.jinf.2013.11.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Canetti G., Fox W., Khomenko A., Mahler H. T., Menon N. K., Mitchison D. A., et al. (1969). Advances in techniques of testing mycobacterial drug sensitivity, and the use of sensitivity tests in tuberculosis control programmes. Bull. World Health Organ. 41 21–43. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • CapitalBio (n.d.). CapitalBio Tuberculosis Rapid Molecular Diagnostics Series. Available at: http://en.thholding.com.cn/2016-08/22/c_56177.htm (accessed July 28 2017). [ Google Scholar ]
  • Castan P., de Pablo A., Fernández-Romero N., Rubio J. M., Cobb B. D., Mingorance J., et al. (2014). Point-of-care system for detection of Mycobacterium tuberculosis and rifampin resistance in sputum samples. J. Clin. Microbiol. 52 502–507. 10.1128/JCM.02209-13 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Cepheid (n.d.). 2017 Launch of New TB Test Ultra Backed by WHO Recommendation. Available at: http://www.cepheid.com/us/about-us/news-events/press-releases/216-2017-launch-of-new-tb-test-ultra-backed-by-who-recommendation (accessed June 9 2017). [ Google Scholar ]
  • Cepheid (n.d.). Xpert MTB/RIF. Available at: http://www.cepheid.com/us/cepheid-solutions/clinical-ivd-tests/critical-infectious-diseases/xpert-mtb-rif (accessed June 9 2017). [ Google Scholar ]
  • Chakravorty S., Aladegbami B., Thoms K., Lee J. S., Lee E. G., Rajan V., et al. (2011). Rapid detection of fluoroquinolone-resistant and heteroresistant Mycobacterium tuberculosis by use of sloppy molecular beacons and dual melting-temperature codes in a real-time PCR assay. J. Clin. Microbiol. 49 932–940. 10.1128/JCM.02271-10 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chakravorty S., Kothari H., Aladegbami B., Cho E. J., Lee J. S., Roh S. S., et al. (2012). Rapid, high-throughput detection of rifampin resistance and heteroresistance in Mycobacterium tuberculosis by use of sloppy molecular beacon melting temperature coding. J. Clin. Microbiol. 50 2194–2202. 10.1128/JCM.00143-12 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chakravorty S., Lee J. S., Cho E. J., Roh S. S., Smith L. E., Lee J., et al. (2015). Genotypic susceptibility testing of Mycobacterium tuberculosis isolates for amikacin and kanamycin resistance by use of a rapid sloppy molecular beacon-based assay identifies more cases of low-level drug resistance than phenotypic Lowenstein-Jensen testin. J. Clin. Microbiol. 53 43–51. 10.1128/JCM.02059-14 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chakravorty S., Simmons M., Rowneki M., Parmar H., Schumacher S. G., Nabeta P., et al. (2017). The new xpert MTB/RIF ultra: improving detection of Mycobacterium tuberculosis and resistance to rifampin in an assay suitable for point-of-care testing. mBio 8 : e00812 -17. 10.1128/mBio.00812-17 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Chedore P., Bertucci L., Wolfe J., Sharma M., Jamieson F. (2010). Potential for erroneous results indicating resistance when using the bactec MGIT 960 system for testing susceptibility of Mycobacterium tuberculosis to pyrazinamide. J. Clin. Microbiol. 48 300–301. 10.1128/JCM.01775-09 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dantas N. G. T., Suffys P. N., Carvalho W. D. S., Gomes H. M., Almeida I. N., Figueiredo L. J. A., et al. (2017). Correlation between the BACTEC MGIT 960 culture system with genotype MTBDRplus and TB-SPRINT in multidrug resistant Mycobacterium tuberculosis clinical isolates from Brazil. Mem. Inst. Oswaldo Cruz 112 769–774. 10.1590/0074-02760170062 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Daum L. T., Rodriguez J. D., Worthy S. A., Ismail N. A., Omar S. V., Dreyer A. W., et al. (2012). Next-generation ion torrent sequencing of drug resistance mutations in Mycobacterium tuberculosis strains. J. Clin. Microbiol. 50 3831–3837. 10.1128/JCM.01893-12 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Deggim-Messmer V., Bloemberg G. V., Ritter C., Voit A., Hömke R., Keller P. M., et al. (2016). Diagnostic molecular mycobacteriology in regions with low tuberculosis endemicity: combining real-time PCR Assays for detection of multiple mycobacterial pathogens with line probe assays for identification of resistance mutations. EBioMedicine 9 228–237. 10.1016/j.ebiom.2016.06.016 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dheda K., McNerney R., Esmail A., Gumbo T., Pasipanodya J. G., Maartens G., et al. (2017). The epidemiology, pathogenesis, transmission, diagnosis, and management of multidrug-resistant, extensively drug-resistant, and incurable tuberculosis. Lancet Respir. Med. Comm. 5 291–360. 10.1016/S2213-2600(17)30079-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dolinger D. L., Colman R. E., Engelthaler D. M., Rodwell T. C. (2016). Next-generation sequencing-based user-friendly platforms for drug-resistant tuberculosis diagnosis: a promise for the near future. Int. J. Mycobacteriol. 5 S27–S28. 10.1016/j.ijmyco.2016.09.021 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Doyle R. M., Burgess C., Williams R., Gorton R., Booth H., Brown J., et al. (2018). Direct whole-genome sequencing of sputum accurately identifies drug-resistant Mycobacterium tuberculosis faster than MGIT culture sequencing. J. Clin. Microbiol. 56 : e00666 -18. 10.1128/JCM.00666-18 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Edwards K. J., Metherell L. A., Yates M., Saunders N. A. (2001). Detection of rpoB mutations in Mycobacterium tuberculosis by biprobe analysis. Microbiology 39 3350–3352. 10.1128/JCM.39.9.3350 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Epistem (n.d.). Performance Evaluation of the Genedrive MTB/RIF Assay on Indian Patients. Available at: https://www.genedrive.com/documents/Clinical-performance-evaluation-Genedrive-MTB_RIF-assay-India-July-2016.pdf [ Google Scholar ]
  • Espasa M., González-Martín J., Alcaide F., Aragón L. M., Lonca J., Manterola J. M., et al. (2005). Direct detection in clinical samples of multiple gene mutations causing resistance of Mycobacterium tuberculosis to isoniazid and rifampicin using fluorogenic probes. J. Antimicrob. Chemother. 55 860–865. 10.1093/jac/dki132 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Folkvardsen D. B., Thomsen V. O., Rigouts L., Rasmussen E. M., Bang D., Bernaerts G., et al. (2013). Rifampin heteroresistance in Mycobacterium tuberculosis Cultures as Detected by phenotypic and genotypic drug susceptibility test methods. J. Clin. Microbiol. 51 4220–4222. 10.1128/JCM.01602-13 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Gardee Y., Dreyer A. W., Koornhof H. J., Omar S. V., Silva P., Bhyat Z. (2017). Evaluation of the GenoType MTBDRsl version 2.0 assay for second-line drug resistance detection of Mycobacterium tuberculosis isolates in South Africa. J. Clin. Microbiol. 55 791–800. 10.1128/JCM.01865-16 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Guo Y., Zhou Y., Wang C., Zhu L., Wang S., Li Q., et al. (2009). Rapid, accurate determination of multidrug resistance in M. tuberculosis isolates and sputum using a biochip system. Int. J. Tuberc. Lung Dis. 13 914–920. [ PubMed ] [ Google Scholar ]
  • Hain LifeScience GmbH (n.d.). DNASTRIP ® Technology. Available at: http://www.hain-lifescience.de/en/technologies/dnastrip.html (accessed July 12 2017). [ Google Scholar ]
  • Hillemann D., Haasis C., Andres S., Behn T. (2018). Validation of the FluoroType MTBDR assay for detection of rifampin and isoniazid resistance in Mycobacterium tuberculosis complex isolates. J. Clin. Microbiol. 56 : e00072 -18. 10.1128/JCM.00072-18 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Huy N. Q., Lucie C., Hoa T. T. T., Van Hung N., Lan N. T. N., Son N. T., et al. (2017). Molecular analysis of pyrazinamide resistance in Mycobacterium tuberculosis in Vietnam highlights the high rate of pyrazinamide resistance-associated mutations in clinical isolates. Emerg. Microbes Infect. 6 : e86 . 10.1038/emi.2017.73 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Igarashi Y., Chikamatsu K., Aono A., Yi L., Yamada H., Takaki A., et al. (2017). Laboratory evaluation of the AnyplexTM II MTB/MDR and MTB/XDR tests based on multiplex real-time PCR and melting-temperature analysis to identify Mycobacterium tuberculosis and drug resistance. Diagn. Microbiol. Infect. Dis. 89 276–281. 10.1016/j.diagmicrobio.2017.08.016 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Jeanes C., O’Grady J. (2016). Diagnosing tuberculosis in the 21st century – Dawn of a genomics revolution? Int. J. Mycobacteriol. 5 384–391. 10.1016/j.ijmyco.2016.11.028 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Laurenzo D., Mousa S. A. (2011). Mechanisms of drug resistance in Mycobacterium tuberculosis and current status of rapid molecular diagnostic testing. Acta Trop. 119 5–10. 10.1016/j.actatropica.2011.04.008 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Lawson L., Emenyonu N., Abdurrahman S. T., Lawson J. O., Uzoewulu G. N., Sogaolu O. M., et al. (2013). Comparison of Mycobacterium tuberculosis drug susceptibility using solid and liquid culture in Nigeria. BMC Res. Notes 6 : 215 . 10.1186/1756-0500-6-215 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Linger Y., Kukhtin A., Golova J., Perov A., Lambarqui A., Bryant L., et al. (2014). Simplified microarray system for simultaneously detecting rifampin, isoniazid, ethambutol, and streptomycin resistance markers in Mycobacterium tuberculosis . J. Clin. Microbiol. 52 2100–2107. 10.1128/JCM.00238-14 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Liu Q., Luo T., Li J., Mei J., Gao Q. (2013). Triplex real-time pcr melting curve analysis for detecting Mycobacterium tuberculosis mutations associated with resistance to second-line drugs in a single reaction. J. Antimicrob. Chemother. 68 1097–1103. 10.1093/jac/dks509 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Makinen J., Marttila H. J., Marjama M., Viljanen M. K., Soini H. (2006). Comparison of two commercially available DNA line probe assays for detection of multidrug-resistant Mycobacterium tuberculosis . J. Clin. Microbiol. 44 350–352. 10.1128/JCM.44.2.350 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Maltseva E., Musralina L., Ismagulova G., Patel K., Zimmerman C., Jacobs J. S., et al. (n.d.). Comparison of the Akonni Biosystems MDR-TB Microarray Test to the Hain Lifescience GenoType MTBDRplus for Resistance to Rifampin and Isoniazid in Kazakh M. tuberculosis Isolates. Available at: http://www.akonni.com/wp-content/uploads/2016/12/AkonniASTMH-Skiba-Maltseva-et-al.pdf [ Google Scholar ]
  • Manson A. L., Cohen K. A., Abeel T., Desjardins C. A., Armstrong D. T., Barry C. E., et al. (2017). Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance. Nat. Genet. 49 395–402. 10.1038/ng.3767 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McGrath M., Gey van Pittius N. C., van Helden P. D., Warren R. M., Warner D. F. (2014). Mutation rate and the emergence of drug resistance in Mycobacterium tuberculosis . J. Antimicrob. Chemother. 69 292–302. 10.1093/jac/dkt364 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • McNerney R., Clark T. G., Campino S., Rodrigues C., Dolinger D., Smith L., et al. (2017). Removing the bottleneck in whole genome sequencing of Mycobacterium tuberculosis for rapid drug resistance analysis: a call to action. Int. J. Infect. Dis. 56 130–135. 10.1016/j.ijid.2016.11.422 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Meaza A., Kebede A., Yaregal Z., Dagne Z., Moga S., Yenew B., et al. (2017). Evaluation of genotype MTBDRplus VER 2.0 line probe assay for the detection of MDR-TB in smear positive and negative sputum samples. BMC Infect. Dis. 17 : 280 . 10.1186/s12879-017-2389-6 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Mitarai S., Kato S., Ogata H., Aono A., Chikamatsu K., Mizuno K., et al. (2012). Comprehensive multicenter evaluation of a new line probe assay kit for identification of Mycobacterium species and detection of drug-resistant Mycobacterium tuberculosis . J. Clin. Microbiol. 50 884–890. 10.1128/JCM.05638-11 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Molina-Moya B., Lacoma A., Prat C., Diaz J., Dudnyk A., Haba L., et al. (2014). AID TB resistance line probe assay for rapid detection of resistant Mycobacterium tuberculosis in clinical samples. J. Infect. 70 400–408. 10.1016/j.jinf.2014.09.010 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Molina-Moya B., Lacoma A., Prat C., Pimkina E., Diaz J., García-Sierra N., et al. (2015). Diagnostic accuracy study of multiplex PCR for detecting tuberculosis drug resistance. J. Infect. 71 220–230. 10.1016/j.jinf.2015.03.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Morley A. A. (2014). Digital PCR: a brief history. Biomol. Detect. Quantif. 1 1–2. 10.1016/j.bdq.2014.06.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Naiser T., Ehler O., Kayser J., Mai T., Michel W., Ott A. (2008). Impact of point-mutations on the hybridization affinity of surface-bound DNA/DNA and RNA/DNA oligonucleotide-duplexes: comparison of single base mismatches and base bulges. BMC Biotechnol. 8 : 48 . 10.1186/1472-6750-8-48 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nathavitharana R. R., Hillemann D., Schumacher S. G., Schlueter B., Ismail N., Omar S. V., et al. (2016). Multicenter noninferiority evaluation of hain genotype MTBDRplus version 2 and Nipro NTM+MDRTB line probe assays for detection of rifampin and isoniazid resistance. J. Clin. Microbiol. 54 1624–1630. 10.1128/JCM.00251-16 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nguyen H. Q., Nguyen N. V., Contamin L., Tran T. H. T., Vu T. T., Nguyen H. V., et al. (2017). Quadruple-first line drug resistance in Mycobacterium tuberculosis in Vietnam: what can we learn from genes? Infect. Genet. Evol. 50 55–61. 10.1016/j.meegid.2017.02.012 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nguyen T. V. A., Anthony R. M., Bañuls A.-L., Vu D. H., Alffenaar J.-W. C. (2017). Bedaquiline resistance: its emergence, mechanism, and prevention. Clin. Infect. Dis. 66 1625–1630. 10.1093/cid/cix992 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Nguyen V. A. T., Nguyen H. Q., Vu T. T., Nguyen N. A. T., Duong C. M., Tran T. H. T., et al. (2015). Reduced turn-around time for Mycobacterium tuberculosis drug susceptibility testing with a proportional agar microplate assay. Clin. Microbiol. Infect. 21 1084–1092. 10.1016/j.cmi.2015.08.024 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Niemz A., Boyle D. S. (2012). Nucleic acid testing for tuberculosis at the point-of-care in high-burden countries. Expert Rev. Mol. Diagn. 12 687–701. 10.1586/erm.12.71 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Noyer C., Abot A., Trouilh L., Leberre V. A., Dreanno C. (2015). Phytochip: development of a DNA-microarray for rapid and accurate identification of Pseudo-nitzschia spp and other harmful algal species. J. Microbiol. Methods 112 55–66. 10.1016/j.mimet.2015.03.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ochang E. A., Udoh U. A., Emanghe U. E., Tiku G. O., Offor J. B., Odo M., et al. (2016). Evaluation of rifampicin resistance and 81-bp rifampicin resistant determinant region of rpoB gene mutations of Mycobacterium tuberculosis detected with Xpert MTB/RIF in Cross River State, Nigeria. Int. J. Mycobacteriol. 5 S145–S146. 10.1016/J.IJMYCO.2016.09.007 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Perez-Risco D., Rodriguez-Temporal D., Valledor-Sanchez I., Alcaide F. (2018). Evaluation of the Xpert MTB/RIF ultra assay for direct detection of Mycobacterium tuberculosis complex in smear-negative extrapulmonary samples. J. Clin. Microbiol. 56 : e00659 -18. 10.1128/JCM.00659-18 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Phelan J., O’Sullivan D. M., Machado D., Ramos J., Whale A. S., O’Grady J., et al. (2016). The variability and reproducibility of whole genome sequencing technology for detecting resistance to anti-tuberculous drugs. Genome Med. 8 : 132 . 10.1186/s13073-016-0385-x [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Pholwat S., Stroup S., Foongladda S., Houpt E. (2013). Digital PCR to detect and quantify heteroresistance in drug resistant Mycobacterium tuberculosis . PLoS One 8 : e57238 . 10.1371/journal.pone.0057238 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Rice J. E., Reis A. H., Rice L. M., Carver-Brown R. K., Wangh L. J., Wangh L. J. (2012). Fluorescent signatures for variable DNA sequences. Nucleic Acids Res. 40 : e164 . 10.1093/nar/gks731 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ritter C., Lucke K., Sirgel F. A., Warren R. W., Van Helden P. D., Böttger E. C., et al. (2014). Evaluation of the AID TB resistance line probe assay for rapid detection of genetic alterations associated with drug resistance in Mycobacterium tuberculosis strains. J. Clin. Microbiol. 52 940–946. 10.1128/JCM.02597-13 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Roh S. S., Smith L. E., Lee J. S., Via L. E., Barry C. E., Alland D., et al. (2015). Comparative evaluation of sloppy molecular beacon and dual-labeled probe melting temperature assays to identify mutations in Mycobacterium tuberculosis resulting in rifampin, fluoroquinolone and aminoglycoside resistance. PLoS One 10 : e0126257 . 10.1371/journal.pone.0126257 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Ruesch-Gerdes S., Ismail N. (2015). REPORT FOR WHO Non-inferiority Evaluation of Nipro NTM+MDRTB and Hain GenoType MTBDRplus V2 Line Probe Assays. [ Google Scholar ]
  • Sali M., De Maio F., Caccuri F., Campilongo F., Sanguinetti M., Fiorentini S., et al. (2016). Multicenter evaluation of anyplex plus MTB / NTM MDR-TB assay for rapid detection of Mycobacterium tuberculosis complex and multidrug-resistant isolates in pulmonary and extrapulmonary. J. Clin. Microbiol. 54 59–63. 10.1128/JCM.01904-15.Editor [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sanchez-Padilla E., Merker M., Beckert P., Jochims F., Dlamini T., Kahn P., et al. (2015). Detection of drug-resistant tuberculosis by Xpert MTB/RIF in Swaziland. N. Engl. J. Med. 372 1181–1182. 10.1056/NEJMc1413930 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Seegene (n.d.). Tuberculosis MTB Detection Product. Available at: http://www.seegene.com/neo/en/products/tuberculosis/tuberculosis_list.php (accessed August 16 2018). [ Google Scholar ]
  • Senol Cali D., Kim J. S., Ghose S., Alkan C., Mutlu O. (2018). Nanopore sequencing technology and tools for genome assembly: computational analysis of the current state, bottlenecks and future directions. Brief. Bioinform. 10.1093/bib/bby017 [Epub ahead of print]. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sharma S. K., Kohli M., Yadav R. N., Chaubey J., Bhasin D., Sreenivas V., et al. (2015). Evaluating the diagnostic accuracy of xpert MTB/RIF assay in pulmonary tuberculosis. PLoS One 10 : e0141011 . 10.1371/journal.pone.0141011 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Shimizu Y., Dobashi K., Yoshikawa Y., Yabe S., Higuchi S., Koike Y., et al. (2008). Five-antituberculosis drug-resistance genes detection using array system. J. Clin. Biochem. Nutr. 42 228–234. 10.3164/jcbn.2008033 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Singhal R., Arora J., Lal P., Bhalla M., Myneeedu V. P., Behera D. (2012). Comparison of line probe assay with liquid culture for rapid detection of multi-drug resistance in Mycobacterium tuberculosis . Indian J. Med. Res. 136 1044–1047. [ PMC free article ] [ PubMed ] [ Google Scholar ]
  • Steingart K. R., Schiller I., Horne D. J., Pai M., Boehme C. C., Dendukuri N. (2014). Xpert ® MTB/RIF assay for pulmonary tuberculosis and rifampicin resistance in adults. Cochrane Database Syst. Rev. 1 : CD009593 . 10.1002/14651858.CD009593.pub3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Tomasicchio M., Theron G., Pietersen E., Streicher E., Stanley-Josephs D., van Helden P., et al. (2016). The diagnostic accuracy of the MTBDRplus and MTBDRsl assays for drug-resistant TB detection when performed on sputum and culture isolates. Sci. Rep. 6 : 17850 . 10.1038/srep17850 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Vietnam’s Ministry of Health (2018). Decision No. 3126 / QD-BYT of 2018 on Guidelines for Diagnosis, Treatment and Prevention of TB Disease Issued by the Ministry of Health. Available at: https://vanbanphapluat.co/quyet-dinh-3126-qd-byt-2018-huong-dan-chan-doan-dieu-tri-du-phong-benh-lao . [ Google Scholar ]
  • Walzl G., McNerney R., du Plessis N., Bates M., McHugh T. D., Chegou N. N., et al. (2018). Tuberculosis: advances and challenges in development of new diagnostics and biomarkers. Lancet Infect. Dis. 18 E199–E210. 10.1016/S1473-3099(18)30111-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • WHO (2008). Molecular Line Probe Assays for Rapid Screening of Patients at Risk of Multi-Drug Resistant Tuberculosis. Available at: http://www.who.int/tb/dots/laboratory/lpa_policy.pdf (accessed 18 November 2009). [ Google Scholar ]
  • WHO (2017a). Frequently Asked Questions about the WHO Technical Expert Consultation Findings on Xpert ® MTB / RIF Ultra. Available at: http://www.who.int/tb/areas-of-work/laboratory/diagnostics/XpertUltraFAQs.pdf [ Google Scholar ]
  • WHO (2017b). Global Tuberculosis Report 2016. Geneva: WHO. [ Google Scholar ]
  • WHO (2017c). Global Tuberculosis Report 2017. Geneva: WHO. [ Google Scholar ]
  • WHO (2017d). Next-Generation Xpert ® MTB/RIF Ultra Assay Recommended by WHO. Geneva: WHO. [ Google Scholar ]
  • WHO (2018). The Use of Next-Generation Sequencing Technologies for the Detection of Mutations Associated with Drug Resistance in Mycobacterium tuberculosis Complex: Technical Guide. Geneva: World Health Organization. [ Google Scholar ]
  • Yao C., Zhu T., Li Y., Zhang L., Zhang B., Huang J., et al. (2010). Detection of rpoB, katG and inhA gene mutations in Mycobacterium tuberculosis clinical isolates from Chongqing as determined by microarray. Clin. Microbiol. Infect. 16 1639–1643. 10.1111/j.1469-0691.2010.03267.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zhang X., Zhao B., Liu L., Zhu Y., Zhao Y., Jin Q. (2012). Subpopulation analysis of heteroresistance to fluoroquinolone in Mycobacterium tuberculosis isolates from Beijing, China. J. Clin. Microbiol. 50 1471–1474. 10.1128/JCM.05793-11 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zhang Z., Li L., Luo F., Cheng P., Wu F., Wu Z., et al. (2012). Rapid and accurate detection of RMP- and INH- resistant Mycobacterium tuberculosis in spinal tuberculosis specimens by CapitalBioTM DNA microarray: a prospective validation study. BMC Infect. Dis. 12 : 303 . 10.1186/1471-2334-12-303 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zignol M., Cabibbe A. M., Dean A. S., Glaziou P., Alikhanova N., Ama C., et al. (2018). Genetic sequencing for surveillance of drug resistance in tuberculosis in highly endemic countries: a multi-country population-based surveillance study. Lancet Infect. Dis. 18 675–683. 10.1016/S1473-3099(18)30073-2 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zimenkov D. V., Antonova O. V., Kuz’min A. V., Isaeva Y. D., Krylova L. Y., Popov S. A., et al. (2013). Detection of second-line drug resistance in Mycobacterium tuberculosis using oligonucleotide microarrays. BMC Infect. Dis. 13 : 240 . 10.1186/1471-2334-13-240 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Zimenkov D. V., Kulagina E. V., Antonova O. V., Zhuravlev V. Y., Gryadunov D. A. (2016). Simultaneous drug resistance detection and genotyping of Mycobacterium tuberculosis using a low-density hydrogel microarray. J. Antimicrob. Chemother. 71 1520–1531. 10.1093/jac/dkw015 [ PubMed ] [ CrossRef ] [ Google Scholar ]

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  • Published: 12 February 2024

A spotlight on the tuberculosis epidemic in South Africa

Nature Communications volume  15 , Article number:  1290 ( 2024 ) Cite this article

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  • Tuberculosis

Tuberculosis is the leading cause of death from a single infectious agent, with over 25% of these occurring in the African region. Multi-drug resistant strains which do not respond to first-line antibiotics continue to emerge, putting at risk numerous public health strategies which aim to reduce incidence and mortality. Here, we speak with Professor Valerie Mizrahi, world-leading researcher and former director of the Institute of Infectious Disease and Molecular Medicine at the University of Cape Town, regarding the tuberculosis burden in South Africa. We discuss the challenges faced by researchers, the lessons that need to be learnt and current innovations to better understand the overall response required to accelerate progress.

literature review on tuberculosis

Given there is a vaccine against tuberculosis, the BCG, why does this disease remain such a problem in South Africa and what are the factors that contribute to this?

The evidence that infant BCG vaccination offers protection against tuberculosis (TB) in early childhood, especially against disseminated forms of the disease such as miliary TB or TB meningitis, is clear 1 . However, the effectiveness of infant BCG vaccination wanes over time, and, as a result, it offers little to no protection against pulmonary TB, the major form of the disease in adolescents and adults. Although South Africa has made significant strides in the fight against TB since 2010, as evidenced by the slow but steady decline in TB incidence and mortality, the incidence of TB in South Africa, estimated by the World Health Organisation to be 468 per 100,000 of the population in 2022 2 , remains stubbornly high for various reasons. South Africa is an upper middle-income country plagued by poverty, extreme income inequality and high levels of unemployment. As the prototypical disease of poverty, the major social determinants of TB in a high-burden country such as South Africa are undernutrition and overcrowding. Compounding these drivers are other biosocial risk factors; namely, HIV co-infection, alcohol use disorders, smoking, and diabetes. It is sobering to note that while South Africa runs the world’s largest HIV treatment program with 5.4 million people out of an estimated population of 60.4 million currently receiving antiretroviral therapy, no less than 54% of the estimated incident TB cases and 57% of the 54,000 deaths attributable to TB in 2022 occurred in HIV-infected persons 2 .

What aspects of this disease remain understudied and why is this?

There are major knowledge gaps throughout the infection-disease-transmission cycle of TB. Most immunocompetent persons infected with Mycobacterium tuberculosis cure or restrict the infection, and while significant progress has been made in understanding the mechanisms controlling later-stage infection, our understanding of early events in M. tuberculosis infection, and the mechanisms that can control infection at the earliest stages, and thereby restrict the tissue damage associated with TB, remains limited 3 , 4 . More generally, a deeper understanding of what cell types, cytokines, and molecular mechanisms are necessary and sufficient for protection is required as this would enable identification of specific biomarkers that define whether an infection was sterilized or controlled, that can predict whether an infection will progress to disease, that can monitor the response to treatment and establish whether cure was achieved, and are able to determine whether a vaccine will elicit a protective immune response.

The paucibacillary nature of TB, coupled with the microbiological complexities of M. tuberculosis , such as phenotypic heterogeneity 5 , differential culturability 6 and persistence in the face of drug pressure and an effective immune response 7 , 8 present myriad challenges for the microbiological investigation of TB in humans. At the most basic level, no test is currently available to determine accurately the number of viable tubercle bacilli in a human host. Enumerating M. tuberculosis in all the anatomical sites in which this organism might reside and understanding its various physiologic and replicative states in all the environments encountered during infection and disease in humans are very challenging problems 7 . What are those environments? Can they be modelled? 9 It’s conceivable that some aspects of TB might not be amenable to microbiological investigation in humans, at least in the short to medium term. For the foreseeable future, we will therefore remain heavily reliant on the use of animal models of infection and disease, in particular nonhuman primates 10 , which mimic human disease most closely and have provided critical insights relevant to the pathogenesis of TB in humans 11 , 12 .

Another significantly understudied area of TB research, albeit one that appears poised for major advances in the future, is TB transmission 13 . To break chains of transmission, we need a far better understanding of the dynamics of TB transmission: Who is transmitting M. tuberculosis , and when, where and how does transmission to a new host occur? A major question in this regard is what the transmission potential of subclinical TB might be 14 . A recent TB prevalence survey conducted in South Africa revealed the scale of the problem of subclinical TB as evidenced by the fact that 58% of survey participants with TB disease reported no symptoms 15 . This finding is consistent with those from prevalence surveys in other countries and underlines the yawning detection gap created by the reliance on symptom screening to trigger TB testing and treatment. At a more fundamental level, we also need to understand the physiological adaptation of M. tuberculosis during transmission 16 . Which cellular function/s does M. tuberculosis engage in response to the multiple stresses imposed on the organism as it is expelled in the bioaerosol produced by an infected person, and suspended in the air before being inhaled by a new host? Could these functions be targeted to develop drugs that are highly effective at blocking transmission?

What are the biggest challenges that TB researchers are currently facing?

While TB researchers face myriad challenges 17 , the single greatest challenge, in my opinion, is the inadequate resourcing of the global TB research enterprise, which is acutely dependent on the vision, generosity and staying power of a handful of funding agencies and philanthropic organisations, most notably, the US National Institutes of Health and the Bill & Melinda Gates Foundation 18 . Decades of chronic underfunding have limited the scope of ambition of individual TB researchers and research consortia by curbing the ability to “dream big”. Resourcing impacts every facet of the TB research enterprise: the pace at which research projects can be executed; the ability to attract and retain the best and brightest talent from across the globe; and, critically, the ability to increase the TB science base by establishing and sustaining world-class laboratory and clinical research infrastructure and expertise in the places where they’re needed the most: research centres with biosafety level 3 facilities in high-burden countries. Being heavily (or even entirely) reliant on soft money, research entities within disease-endemic countries that conduct pivotal studies on TB operate in an ongoing state of precarity. While securing the core support needed to sustain the clinical research sites, expert staff and laboratory infrastructure is challenging, developing innovative ways to address this problem ought to be a top resourcing priority.

The resourcing gap reflects the woefully low prioritisation afforded to TB relative to other diseases. The magnitude of this problem was brought into sharp relief by the COVID-19 pandemic which demonstrated the level of funding that can in fact be mobilised to address a major global health challenge, when needed: US$100 billion was spent on COVID-19 research and development during the first eleven months of the pandemic. In contrast, an analysis of funding trends by the Treatment Action Group with support from the Stop TB Partnership revealed a total investment of $4.7 billion on TB research and development from 2018-2022—less than half the amount of $10 billion that world leaders had pledged at the United Nations High-Level Meeting on TB held in 2018 18 . It remains to be seen whether the resolution by the General Assembly of the United Nations adopting the “Political declaration of the high-level meeting on the fight against tuberculosis” on October 5, 2023, which commits to “mobilize adequate, predictable and sustainable financing for tuberculosis research and innovation especially to high-burden countries towards reaching 5 billion United States dollars a year by 2027”, is backed up by the political will needed to achieve this ambitious goal. In this regard, it is essential for TB to establish or maintain high visibility within global and national research agendas and strategic plans for other high-priority infectious disease challenges, including pandemic prevention, preparedness and response 19 , and antimicrobial drug resistance 20 .

In terms of the pace of research, progress in the TB field is frustrating slow; this applies to both the production of new knowledge and its application. The long duration—and hence, high cost—of clinical studies in TB is linked in part to a paucity of tools, which, if available, would have a transformative impact on the field. These include better biomarkers to predict the sterilising effect and duration of new drug regimens, and immune correlates of vaccine-mediated protection. Addressing these deficiencies is a top research priority, and one with which TB scientists in South Africa are actively engaged.

What have been the biggest advances in TB research in the last decade? Can you highlight current research that scientists in South Africa are driving forward?

Replacement of the binary paradigm in which TB was historically categorised as either “latent” or “active”, with a new paradigm in which TB is viewed instead as a spectrum or continuum of distinct disease states from latent to incipient, subclinical and active TB, stands out as one of the most important scientific advances in recent years 21 , 22 . This paradigm shift has profound implications for TB control by underscoring the urgent need for research to establish improved strategies to find, diagnose and treat people with TB at all points along the continuum, but especially those with early TB, before irreversible lung damage is sustained. In this context, it will be critically important to establish how shifting from the traditional, ‘one-size-fits-all’ strategy for the diagnosis and treatment of TB towards a more diversified or stratified approach would impact individual health and population health as well as health systems.

In another significant advance, persons with active TB were shown to aerosolise M. tuberculosis by tidal breathing 23 . Using a bespoke device known as the “Respiratory Aerosol Sampling Chamber”, scientists from the Desmond Tutu Health Foundation and the University of Cape Town captured the bioaerosol produced by TB patients and enumerated bacilli within the bioaerosol by live-cell fluorescence imaging using a solvatochromic trehalose probe, DMN-Tre 24 , which is incorporated enzymatically into the mycomembrane of metabolically active Actinobacteria, during separate respiratory manoeuvres: tidal breathing, forced vital capacity (FVC) and cough. The headline finding from this landmark study was that most bacilli aerosolised by symptomatic TB patients are contributed by tidal breathing rather than by cough – the signature symptom of TB. If one assumes that the number of live tubercle bacilli identified in patient bioaerosol correlates with patient infectiousness – a reasonable albeit untested assumption – then tidal breathing could be a dominant contributor to the transmission of M. tuberculosis from active TB cases to new hosts. Intriguing results from a follow-on study have demonstrated that, at presentation, TB clinic attendees aerosolise M. tuberculosis organisms independent of TB diagnosis, as determined by sputum-GeneXpert. This discovery suggests that unidentified individuals who aerosolise M. tuberculosis could contribute significantly to community exposure to the organism 25 .

In terms of translation, the development of new drugs and drug regimens which have revolutionised the treatment of drug-resistant TB, and the demonstration that treatment-shortening for drug-susceptible TB from six months down to four is achievable 26 , stand out as major advances which have signalled the start of an exciting new era in TB chemotherapy. An early study conducted in South Africa, and enabled by a clinical access program, demonstrated the profound impact of adding bedaquiline to an optimised background regimen on treatment outcomes of a cohort of pre-extensively drug resistant (XDR) and XDR-TB patients 27 . Subsequent trials to which South African researchers contributed significantly led the WHO to recommend bedaquiline-pretomanid-linezolid-moxifloxacin (BPaLM) as a six-month regimen for the treatment of rifampicin-resistant (RR) or multidrug resistant (MDR) TB 28 .

In the area of TB prevention, we’ve seen some positive signals from vaccine studies in the past few years, which have energised and brought a renewed sense of hope to the field of TB vaccinology. A Phase 2b trial of the M72/ASO1 E vaccine for prevention of progression from infection to disease in interferon gamma release assay (IGRA)-positive, HIV-uninfected adults demonstrated a vaccine efficacy of 49.7% at 3 years 29 and inspired the recently announced Phase 3 trial of this vaccine for which $550 m in funding has been secured from the Bill & Melinda Gates Foundation and Wellcome Trust. In another encouraging development, a Phase 2 trial of BCG revaccination in IGRA-negative adolescents in a high-transmission setting demonstrated a reduction in the rate of sustained QuantiFERON-TB Gold In-tube assay (QFT) conversion with an efficacy of 45.4% 30 , which has led to a larger multicentre study in South Africa to assess whether a second dose of BCG given at 10 and 18 years of age, can help protect against TB. Importantly, samples collected from the two Phase 2b trials described above have provided a key resource for discovering immune correlates of protection against TB. Studies in this regard are being pursued through international collaborative research programs that involve large teams of leading TB immunologists, including those from the South African Tuberculosis Vaccine Initiative at the University of Cape Town, who are bringing state-of-the-art technologies to bear on this critically important problem 31 . In another key advance, intravenous administration of BCG was shown to prevent TB in rhesus macaques 32 with subsequent work pointing to the early innate transcriptional response in peripheral blood as a potentially promising correlate of protection against TB 33 .

Over the past decade, there has also been spectacular progress on understanding the impact of genetic variation, whether natural or engineered, on the biology of M. tuberculosis . For example, genomic analyses of collections of clinical isolates from across the world are paving the way towards implementing sequencing-based resistance prediction in the clinic in the not-too-distant future 34 and have uncovered the molecular basis of an “antibiotic resilience” phenotype exhibited by M. tuberculosis strains that carry mutations in a regulator, ResR, which enable faster resumption of growth after drug exposure 35 . In an exciting new discovery, the tight, PcaA-dependent packing of tubercle bacilli growing in cords was shown to protect M. tuberculosis against antibiotic-mediated clearance by allowing the bacilli to remain transcriptionally active under drug exposure and to re-grow after drug removal 8 . High-throughput functional genomic analyses by transposon mutagenesis and inducible CRISPR interference have enabled mycobacterial gene function to be probed at scale 36 . This approach has provided a new metric—the vulnerability index—for comparing, selecting and prioritising new TB drug targets 37 .

How can we better translate scientific findings from bench to the patient?

We need a multi-pronged approach to bridge gaps, which threaten the ability to translate findings rapidly and effectively from the bench to bedside and from there, to the community. On the one hand, the TB field urgently requires greater industry (both pharma and biotech) involvement; therefore, incentives are required to ensure that developers of new tools, be they drugs, vaccines, biomarkers or diagnostics, are not hamstrung by financial risk. The TB Drug Accelerator, a multi-sectoral, multi-party, multidisciplinary collaboration provides a useful public-private-academic partnership model which has demonstrated what can be achieved by creating a virtual collaborative network which, in this case, is focused on accelerating the development of new drugs and treatment-shortening regimens 38 . One the other hand, I feel strongly that we need more forums which bring together those working on the fundamental biology of TB and discovery of new tools, with healthcare workers on the frontlines of TB control to meet, exchange ideas, and learn from one another. Bridging the gap between the intellectual spheres in which scientists operate will be important to facilitate research that is targeted at, and effective in, translation. As we grapple with some of the dogma-challenging findings in M. tuberculosis biology and disease pathogenesis such as those highlighted above, having opportunities to consider their consequences from different perspectives will increase our ability to translate those discoveries into impactful interventions.

What are you currently most hopeful about?

As a “glass half full”-type optimist (a useful attribute for one working in this field!), I’m hopeful that safe and tolerable drug regimens that effect durable cure in two months or less will be developed within the next decade. I am also convinced that much greater attention will be paid to the detection and treatment of subclinical TB, and that this will have a major impact on the epidemic. I expect to see insights emerging from research at the nexus of human TB immunology and vaccinology, which will impact significantly on prevention research. Perhaps most importantly, I am inspired by the talent, passion and commitment of TB scientists with whom I work, and from whom I learn—especially the next generation of researchers. This makes me confident that scientists based in high-burden countries such as mine are poised to play an increasingly prominent role in setting the global agenda for TB research, and in the discovery, development, and implementation of new tools for controlling this age-old disease.

Martinez, L. et al. Infant BCG vaccination and risk of pulmonary and extrapulmonary tuberculosis throughout the life course: a systematic review and individual participant data meta-analysis. Lancet Glob. Health 10 , e1307–e1316 (2022).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Global tuberculosis report 2023. Accessed at https://www.who.int/publications/i/item/9789240083851 (2023).

Bloom, B. R. A half-century of research on tuberculosis: successes and challenges. J. Exp. Med. 220 , e20230859 (2023).

Hunter, R. L. The pathogenesis of tuberculosis-the koch phenomenon reinstated. Pathogens 9 , 813 (2020).

Dhar N., McKinney J. & Manina G. Phenotypic heterogeneity in Mycobacterium tuberculosis . Microbiol. Spectr. 4 (2016).

Mishra, S. & Saito, K. Clinically encountered growth phenotypes of tuberculosis-causing bacilli and their in vitro study: a review. Front. Cell Infect. Microbiol. 12 , 1029111 (2022).

Dartois, V. A. & Rubin, E. J. Anti-tuberculosis treatment strategies and drug development: challenges and priorities. Nat. Rev. Microbiol. 20 , 685–701 (2022).

Mishra, R. et al. Mechanopathology of biofilm-like Mycobacterium tuberculosis cords. Cell 186 , 5135–5150.e5128 (2023).

Sollier, J. et al. Revitalizing antibiotic discovery and development through in vitro modelling of in-patient conditions. Nat. Microbiol. 9 , 1–3 (2024).

Article   CAS   PubMed   Google Scholar  

Scanga, C. A. & Flynn, J. L. Modeling tuberculosis in nonhuman primates. Cold Spring Harb. Perspect. Med. 4 , a018564 (2014).

Article   PubMed   PubMed Central   Google Scholar  

Gideon, H. P. et al. Multimodal profiling of lung granulomas in macaques reveals cellular correlates of tuberculosis control. Immunity 55 , 827–846.e810 (2022).

Martin, C. J. et al. Digitally barcoding Mycobacterium tuberculosis reveals in vivo infection dynamics in the macaque model of tuberculosis. mBio 8 , e00312–e00317 (2017).

Auld, S. C. et al. Research roadmap for tuberculosis transmission science: where do we go from here and how will we know when we’re there? J. Infect. Dis. 216 , S662–S668 (2017).

Kendall, E. A., Shrestha, S. & Dowdy, D. W. The epidemiological importance of subclinical tuberculosis. A critical reappraisal. Am. J. Respir. Crit. Care Med. 203 , 168–174 (2021).

Moyo, S. et al. Prevalence of bacteriologically confirmed pulmonary tuberculosis in South Africa, 2017-19: a multistage, cluster-based, cross-sectional survey. Lancet Infect. Dis. 22 , 1172–1180 (2022).

Nathan, C. Mycobacterium tuberculosis as teacher. Nat. Microbiol. 8 , 1606–1608 (2023).

Scriba T. J., Dinkele R., Warner D. F. & Mizrahi V. Challenges in TB research. J. Exp. Med. 219 (2022).

Tuberculosis research funding trends, 2005-2022. Accessed at https://www.stoptb.org/file/17281/download (2023).

Rationale and recommendations for an integrated approach to TB and pandemic prevention, preparedness and response (PPPR). Accessed at https://www.stoptb.org/file/16145/download (2023).

WHO. Global research agenda for antimicrobial resistance in human health. Accessed at https://www.who.int/publications/m/item/global-research-agenda-for-antimicrobial-resistance-in-human-health (2023).

Zaidi, S. M. A. et al. Beyond latent and active tuberculosis: a scoping review of conceptual frameworks. eClinicalMedicine 66 , 102332 (2023).

Lin, P. L. & Flynn, J. L. The end of the binary era: revisiting the spectrum of tuberculosis. J. Immunol. 201 , 2541–2548 (2018).

Dinkele, R. et al. Aerosolization of Mycobacterium tuberculosis by tidal breathing. Am. J. Respir. Crit. Care Med. 206 , 206–216 (2022).

Article   PubMed   Google Scholar  

Kamariza, M. et al. Rapid detection of Mycobacterium tuberculosis in sputum with a solvatochromic trehalose probe. Sci. Transl. Med. 10 , eaam6310 (2018).

Patterson, B. et al. Aerosolization of viable Mycobacterium tuberculosis bacilli by tuberculosis clinic attendees independent of sputum-GeneXpert status. Preprint at medRxiv https://doi.org/10.1101/2022.11.14.22282157 (2023).

Dorman, S. E. et al. Four-month rifapentine regimens with or without moxifloxacin for tuberculosis. N. Engl. J. Med. 384 , 1705–1718 (2021).

Ndjeka, N. et al. High treatment success rate for multidrug-resistant and extensively drug-resistant tuberculosis using a bedaquiline-containing treatment regimen. Eur. Respir. J. 52 , 1801528 (2018).

WHO consolidated guidelines on tuberculosis. Module 4: treatment—drug-resistant tuberculosis treatment, 2022 update. Accessed at https://www.who.int/publications/i/item/9789240063129 (2022).

Tait, D. R. et al. Final analysis of a trial of M72/AS01(E) vaccine to prevent tuberculosis. N. Engl. J. Med. 381 , 2429–2439 (2019).

Nemes, E. et al. Prevention of M. tuberculosis infection with H4:IC31 vaccine or BCG revaccination. N. Engl. J. Med. 379 , 138–149 (2018).

Nemes, E. et al. The quest for vaccine-induced immune correlates of protection against tuberculosis. Vaccine Insights 1 , 165–181 (2022).

Darrah, P. A. et al. Prevention of tuberculosis in macaques after intravenous BCG immunization. Nature 577 , 95–102 (2020).

Liu, Y. E. et al. Blood transcriptional correlates of BCG-induced protection against tuberculosis in Rhesus macaques . Cell Rep. Med. 4 , 101096 (2023).

The, C. C. Genome-wide association studies of global Mycobacterium tuberculosis resistance to 13 antimicrobials in 10,228 genomes identify new resistance mechanisms. PLoS Biol. 20 , e3001755 (2022).

Article   Google Scholar  

Liu, Q. et al. Tuberculosis treatment failure associated with evolution of antibiotic resilience. Science 378 , 1111–1118 (2022).

Winkler, K. R., Mizrahi, V., Warner, D. F. & De Wet, T. J. High-throughput functional genomics: a (myco)bacterial perspective. Mol. Microbiol. 120 , 141–158 (2023).

Bosch, B. et al. Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis . Cell 184 , 4579–4592.e4524 (2021).

Aldridge, B. B. et al. The tuberculosis drug accelerator at year 10: what have we learned? Nat. Med. 27 , 1333–1337 (2021).

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