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 Table of Contents  
Year : 2016  |  Volume : 5  |  Issue : 4  |  Page : 374-378

Passive case finding for tuberculosis is not enough

1 Woolcock Institute of Medical Research, University of Sydney; South Western Sydney Clinical School, University of New South Wales; Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney, Sydney, Australia
2 Woolcock Institute of Medical Research, University of Sydney; Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney; Sydney Medical School, University of Sydney, Sydney, Australia
3 Centre for Research Excellence in Tuberculosis (TB-CRE) and the Marie Bashir Institute for Infectious Diseases and Biosecurity (MBI), University of Sydney; Sydney Medical School, University of Sydney; The Children's Hospital at Westmead, University of Sydney, Sydney, Australia

Date of Web Publication14-Feb-2017

Correspondence Address:
Jennifer Ho
Woolcock Institute of Medical Research, 431 Glebe Point Road, Glebe, NSW 2037
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Source of Support: None, Conflict of Interest: None

DOI: 10.1016/j.ijmyco.2016.09.023

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Current World Health Organisation targets calling for an end to the global tuberculosis (TB) epidemic by 2035 require a dramatic improvement in current case-detection strategies. A reliance on passive case finding (PCF) has resulted consistently, in over three million infectious TB cases per year, being missed by the health system, leading to ongoing transmission of infection within families and communities. Active case finding (ACF) for TB has been recognized as an important complementary strategy to PCF, in order to diagnose and treat patients earlier, reducing the period of infectiousness and therefore transmission. ACF may also achieve substantial population-level TB control. Local TB epidemiology and the resources available in each setting will influence which populations should be screened, and the types of ACF interventions to use for maximal impact. TB control programs should begin with the highest risk groups and broaden their activities as resources allow. Mathematical models can help to predict the population-level effects and the cost-effectiveness of a variety of ACF strategies on different risk populations.

Keywords: Active case finding, Screening, Diagnosis, TB elimination, End TB strategy

How to cite this article:
Ho J, Fox GJ, Marais BJ. Passive case finding for tuberculosis is not enough. Int J Mycobacteriol 2016;5:374-8

How to cite this URL:
Ho J, Fox GJ, Marais BJ. Passive case finding for tuberculosis is not enough. Int J Mycobacteriol [serial online] 2016 [cited 2022 Jan 26];5:374-8. Available from: https://www.ijmyco.org/text.asp?2016/5/4/374/200118

  Introduction Top

In the past two decades, the Directly Observed Treatment Short-Course (DOTS) strategy and the subsequent Stop TB (DOTS expansion) strategy, recommended by the World Health Organisation (WHO), saved more than five million lives [1]. However, total case numbers continue to rise and tuberculosis (TB) remains the leading infectious cause of death worldwide [2]. The WHO launched the End TB Strategy in 2015 with the ambitious goal of ending the global TB epidemic [3]. Targets include a 90% reduction in TB incidence and 95% reduction in TB deaths by 2035, compared to 2015. “Bending the epidemiological curve” of TB incidence and mortality to meet these targets, will require improved case detection to ensure early disease diagnosis, improve individual patient outcomes, and limit ongoing transmission.

In the majority of TB endemic settings worldwide, the status quo for TB case finding is based on “passive case finding” (PCF). This relies upon a patient with active TB experiencing symptoms serious enough to seek health care and a health-care system capable of correctly diagnosing the patient's condition. [4] However, this strategy, as shown consistently in prevalence surveys [5],[6], is grossly inadequate to detect the substantial burden of undiagnosed TB in the community. It is estimated that in 2014, more than 3.5 million people who developed TB (one-third of all cases) were “missed” by the health system [2]. This massive case detection gap culminates in late disease presentation, with poor disease outcomes, and undiagnosed infectious cases continuing to spread infection within families and communities.

The term “active case finding” (ACF) includes any methods for TB identification that does not rely on patients presenting to the healthcare system of their own accord [7]. The objectives of ACF are to diagnose and treat patients earlier, thereby reducing negative treatment outcomes, sequelae, and socioeconomic consequences, as well as reducing the period of infectiousness and therefore transmission [4]. ACF has been increasingly recognised as an important complementary strategy to PCF in high-prevalence settings in order to overcome the gaps in TB detection and treatment. This need has also been recognized by international donors, with initiatives such as TB REACH established to support innovative approaches to increasing TB case detection [8]. Although two WHO guidelines, systematic screening [4] and household contact investigations [9], provide some guidance to ACF in resource limited settings, designing and implementing interventions that target the most appropriate populations, and utilise feasible and cost-effective strategies, may be difficult for national TB control programs (NTP) already struggling to manage the existing burden of known disease.

In this review, we compare the additional individual and population-level benefits of ACF with those of PCF, consider pragmatic and economic factors relevant to ACF implementation in resource-limited settings, and highlight future research needs and priorities.

  Active case finding versus passive case finding Top

The principle objective of ACF is to find and treat cases of active TB that would otherwise not have been diagnosed at this time, using strategies that are in keeping with available resources. An important distinction between ACF and PCF is that the former is a screening intervention initiated by health services, as opposed to the latter, which is initiated by symptomatic individuals presenting to health-care. In general, ACF activities are additive to PCF. Consequently, the diagnostic algorithms and measures of success used in ACF may differ from those used for PCF. The population targeted for ACF is typically larger, and the prevalence of disease (or pretest probability) is lower. This results in a higher number needed to screen, to diagnose one TB patient, compared to the PCF context.

ACF for TB generally begins with an initial screening step followed by confirmatory testing. Initial screening may comprise of one or a combination of symptom reporting or chest radiography, and if either are positive, a confirmatory microbiological test, such as smear microscopy, or a molecular test e.g. Xpert MTB/RIF [4]. Ideally, the confirmatory test should be rapid, hence Mycobacterium tuberculosis (MTB) culture is a less feasible option, unless the health system in place has sufficient capacity to follow-up screened patients [4].

However, the use of symptoms or chest X-ray as the initial screening step has important limitations. Prevalence surveys have shown consistently that the majority of undiagnosed TB patients in the community lack typical symptoms of TB and a large proportion have no symptoms at all [10],[11],[12]. Furthermore, while chest radiography is more sensitive than using a symptom-based approach alone, this can be logistically difficult in many rural and remote settings. Xpert MTB/RIF used up-front as a primary screening tool (i.e., regardless of symptoms reported or chest X-ray findings), has been shown to be feasible and improve case detection in certain high risk populations, such as people living with HIV (PLHIV) [13],[14], and also in ACF conducted in the general community [15]. While this approach may overcome some limitations of traditional TB screening, the feasibility and cost-effectiveness of this strategy in a programmatic setting is yet to be determined.

One argument against ACF it that it merely detects disease earlier, but does not substantially alter individual patient outcomes. However, diagnosing and treating TB disease earlier is likely to have a substantial impact on TB transmission, decreasing the long term trajectory of TB in a population, and subsequently reducing the cost of TB control overall [16]. It is important however, when evaluating the population-level effects and the cost-effectiveness of ACF, to consider its impact over a longer time frame (e.g., a 20-year time horizon), as short-term assessments can dramatically underestimate longer- term gains of ACF [16]. [Table 1] lists outcome measures that should be considered when evaluating the benefit of ACF, as well as the other key characteristics of ACF compared to PCF for TB.
Table 1: Characteristics of active compared to passive case finding for TB.

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  Selecting a suitable population for screening: high yield target populations versus community-wide interventions Top

Factors that influence the choice of which populations should be targeted for ACF include: TB prevalence, TB risk (e.g., comorbidities and socioeconomic factors), accessibility, acceptability (amongst individuals in the population and health care workers), and the infrastructure and resources available.

In the “systematic screening for active TB” guidelines, the WHO strongly recommend ACF for PLHIV, household contacts of active TB patients, and workers exposed to silica [4]. Conditional recommendations (based on expert opinion and dependent on local TB epidemiology and resource factors), are given for other risk groups such as prisoners, those with untreated fibrotic lesions on chest X-ray; those seeking health care and selected risk groups where the TB prevalence is ≥0.1%; populations where the TB prevalence is ≥1%; or those with poor access to health-care [4]. Clinical groups with a higher risk of developing TB (and poorer treatment outcomes) that may benefit from ACF include those who are underweight, malnourished, diabetic, have alcohol dependence, smoke, have chronic renal failure, or immunosuppression [4],[17]. Homeless people, those living in slums, and other deprived communities generally have poorer access to health-care, a higher risk of TB transmission in some settings, and may also benefit greatly from ACF interventions [18],[19],[20].

Before the 1960 s, mass chest radiography was the primary form of ACF in many industrialized countries [21]. However, in their ninth report in 1974, the WHO expert committee on tuberculosis recommended that “the policy of indiscriminate TB case-finding by mobile mass radiography should now be abandoned” , citing evidence of the inefficiency, low yield, and high cost of mass radiography [22]. Since this recommendation, screening of populations for TB generally has been avoided, other than for selected high-risk groups. Instead, high prevalence countries have been encouraged to focus their resources on optimizing existing diagnostic and treatment services. However, this narrow focus on selected high-risk groups has been unable to reach the majority of undiagnosed cases in the community and, despite substantial investment in PCF, has only achieved a global decline in TB incidence of 2% per year [2]. This figure needs to increase to 10% per year by 2025, to achieve TB elimination targets [3]. Therefore, it is imperative that broader screening strategies that will achieve the desired epidemiological impact are considered.

Few randomized trials that have assessed the effectiveness of ACF in a community-wide setting. Four randomized controlled trials (RCTs) have been performed in Africa, with mixed results. A cluster RCT conducted in Zimbabwe (the DETECTB study) [23] evaluated an intervention of six rounds of six monthly screening for adults with ≥2 weeks of cough, to over 100,000 participants using either mobile vans or door-to-door visits. The study found a decline in disease prevalence from 6.5 to 3.7 per 1000 adults (risk ratio 0.59, 95% CI 0.40–0.89, p = 0.0112) at the end of, compared to before, the intervention. In contrast, a large community randomized trial of active case-finding among almost 45,000 participants in Zambia and the Western Cape of South Africa (the ZAMSTAR study), did not demonstrate a population-wide benefit [24]. The study compared two enhanced case finding interventions: community mobilization and promotion of sputum smear examination; and combined TB-HIV household-level activities. Neither intervention led to a reduction in the community prevalence or incidence of TB [24]. While these two trials yielded different effects on disease prevalence, the actual interventions used in these two studies differed considerably.

Two additional RCTs of community-wide ACF studies in rural southern Ethiopia [25],[26] implemented periodic education about TB symptoms, by community workers in peripheral health facilities, and encouraged symptomatic individuals to seek health-care. One study found the intervention improved case detection rates, compared to passive case finding [25], whereas the other found a reduction in time to diagnosis, but not the extent of case finding for smear positive TB [26].

  What active case finding approach is optimal? Top

ACF for TB is likely to be feasible to some degree in all settings; however, determining the most appropriate strategy for each setting requires careful planning and consideration. The most suitable approach will depend on the resources available and the local TB epidemiology. NTPs should start with a narrow focus (e.g., highest risk groups) and broaden as resources allow. Baseline information required includes high quality surveillance data such as prevalence rates, incidence rates, and transmission ‘hot spots’. Mathematical modelling can help predict the population level impact, cost-effectiveness and optimal duration and frequency for specific ACF interventions [16],[27],[28],[29]. A web-based modeling tool, developed by Nishikori [30], can be used to compare cost-effectiveness parameters for different diagnostic algorithms when applied to different risk populations [30].

Novel strategies involving community workers, volunteers, mobile phone technology, or the private sector to implement ACF activities [25],[31], may improve the efficiency and feasibility of ACF on a broader scale. Integrating ACF into existing health-care services is also important to optimize resources and achieve sustainability [32]. Adequate diagnostic and treatment facilities, with the capacity for scale-up, are crucial to avoid treatment delays and loss to follow-up. Lastly, additional treatment support should be considered for patients diagnosed via ACF, given that they may be minimally symptomatic and their failure to initiate health care contact may indicate reduced “readiness” for treatment. These patients may also be burdened with socioeconomic factors that make them more likely to fail to start, or be noncompliant with treatment [33],[34].

  Research gaps and priorities Top

While there is consensus on the effectiveness of ACF in certain high-risk groups such as PLHIV and household contacts [4], major research gaps remain. Whether ACF leads to better treatment outcomes, substantially reduces transmission, or has a beneficial impact on the longer-term epidemiology of TB, remains unknown. We also do not know what strategies are most effective in different settings, or, how frequently and for how long ACF interventions need to be performed. As the prevalence of disease decreases, ACF is likely to appear less cost-effective (per case diagnosed) but will be important to sustain long-term declines in incidence to achieve TB elimination targets.

Further operational and public health research with appropriate impact evaluation, together with mathematical and economic modeling, will be greatly beneficial in answering some of these questions. Careful monitoring and evaluation of all current and future ACF activities is also essential. Better screening tools will also facilitate up-scaling of ACF strategies. A sensitive, portable, low-cost, point-of-care test will revolutionize TB screening, improving the efficiency, feasibility, and cost-effectiveness of all screening interventions.

  Conclusion Top

Current passive TB case finding approaches are insufficient to meet ambitious TB elimination targets, especially in endemic areas where the bulk of the “missing 3.5 million cases” reside [2]. Until new breakthroughs in disease prevention occur, significant improvements in case detection will be essential if the global TB epidemic is to be overcome. ACF interventions are likely to be feasible in all settings, but the scale and focus of these interventions will need to be contextualized and will inevitably be limited by available resources. When designing an ACF strategy, TB control programs should begin with easily identifiable high-risk target groups and then widen their scope of activities as resources allow. Further research is imperative to determine the most feasible and cost-effective ACF approaches in different settings.

  Conflicts of interest Top

None to declare.

  References Top

P. Glaziou et al, Lives saved by tuberculosis control and prospects for achieving the 2015 global target for reducing tuberculosis mortality, Bull. World Health Organ. 89 (2011) 573–582, http://dx.doi.org/10.2471/BLT.11.087510.  Back to cited text no. 1
World Health Organization, Global Tuberculosis Report 2015, 20th Edition.  Back to cited text no. 2
WHO End TB Strategy, <http://www.who.int/tb/post2015_ strategy/en/> (2015).  Back to cited text no. 3
WHO, Systematic Screening for Active Tuberculosis. (2013).  Back to cited text no. 4
I. Onozaki et al, National tuberculosis prevalence surveys Asia, 1990–2012: an overview of results and lessons learned, Trop. Med. Int. Health 20 (2015) 1128–1145, http://dx.doi.org/10.1111/tmi.12534.  Back to cited text no. 5
Report FIRST National TB Prevalence Survey 2012, Nigeria.  Back to cited text no. 6
G.H. Bothamley, L, Ditiu, G.B. Migliori, C, Lange, contributors. Active case finding of tuberculosis in Europe: a Tuberculosis Network European Trials Group (TBNET) survey, Eur. Respir. (32) (2008), 1023–1030. doi:10.1183/09031936.00011708.  Back to cited text no. 7
TB REACH, Stop TB Partnership, <http://www.stoptb. org/global/awards/tbreach/about.asp>.  Back to cited text no. 8
World Health Organization. Recommendations for investigating contacts of persons with infectious tuberculosis in low- and middle-income countries. (2012).  Back to cited text no. 9
Ministry of Health Cambodia, National Tuberculosis Prevalence Survey Cambodia, 2002, Ministry of Health Cambodia, Phnom Penh, 2005.  Back to cited text no. 10
S. den Boon et al, An evaluation of symptom and chest radiographic screening in tuberculosis prevalence surveys, Int. J. Tuberc. Lung Dis. 10 (2006) 876–882.  Back to cited text no. 11
N.B. Hoa et al, National survey of tuberculosis prevalence in Viet Nam, Bull. World Health Organ. 88 (2010) 273–280, http:// dx.doi.org/10.2471/BLT.09.067801.  Back to cited text no. 12
T.T. Balcha et al, Intensified tuberculosis case-finding in HIVpositive adults managed at Ethiopian health centers: diagnostic yield of Xpert MTB/RIF compared with smear microscopy and liquid culture, PLoS One 9 (2014) e85478, http://dx.doi.org/10.1371/journal.pone.0085478.  Back to cited text no. 13
H.A. Al-Darraji, H. Abd Razak, K.P. Ng, F.L. Altice, A. Kamarulzaman, The diagnostic performance of a single GeneXpert MTB/RIF assay in an intensified tuberculosis case finding survey among HIV-infected prisoners in Malaysia, PLoS One 8 (2013) e73717, http://dx.doi.org/10.1371/journal. pone.0073717.  Back to cited text no. 14
J. Ho et al, Reassessment of the positive predictive value and specificity of Xpert MTB/RIF: a diagnostic accuracy study in the context of community-wide screening for tuberculosis, Lancet Infect. Dis. (2016), http://dx.doi.org/10.1016/S1473- 3099(16)30067-6.  Back to cited text no. 15
A.S. Azman, J.E. Golub, D.W. Dowdy, How much is tuberculosis screening worth? Estimating the value of active case finding for tuberculosis in South Africa, China, and India, BMC Med. 12 (2014) 216, http://dx.doi.org/10.1186/ s12916-014-0216-0.  Back to cited text no. 16
B.J. Marais et al, Tuberculosis comorbidity with communicable and non-communicable diseases: integrating health services and control efforts, Lancet Infect. Dis. 13 (2013) 436–448, http://dx.doi.org/10.1016/S1473-3099(13) 70015-X.  Back to cited text no. 17
C.L. Ogbudebe et al, Reaching the underserved: active tuberculosis case finding in urban slums in southeastern Nigeria, Int. J. Mycobacteriol. 4 (2015) 18–24, http://dx.doi.org/10.1016/j.ijmyco.2014.12.007.  Back to cited text no. 18
K. Kranzer et al, Feasibility, yield, and cost of active tuberculosis case finding linked to a mobile HIV service in Cape Town, South Africa: a cross-sectional study, PLoS Med. 9 (2012) e1001281, http://dx.doi.org/10.1371/journal. pmed.1001281.  Back to cited text no. 19
N. Lorent et al, Community-based active tuberculosis case finding in poor urban settlements of Phnom Penh, Cambodia: a feasible and effective strategy, PLoS One 9 (2014) e92754, http://dx.doi.org/10.1371/journal.pone.0092754.  Back to cited text no. 20
J.E. Golub, C.I. Mohan, G.W. Comstock, R.E. Chaisson, Active case finding of tuberculosis: historical perspective and future prospects, Int. J. Tuberc. Lung Dis. 9 (2005) 1183–1203.  Back to cited text no. 21
WHO Expert Committee on Tuberculosis: ninth report,. Geneva, World Health Organization (16) 1974. (WHO Technical Report Series, No. 552).  Back to cited text no. 22
E.L. Corbett et al, Comparison of two active case-finding strategies for community-based diagnosis of symptomatic smear-positive tuberculosis and control of infectious tuberculosis in Harare, Zimbabwe (DETECTB): a clusterrandomised trial, Lancet 376 (2010) 1244–1253, http://dx.doi. org/10.1016/S0140-6736(10)61425-0.  Back to cited text no. 23
H. Ayles et al, Effect of household and community interventions on the burden of tuberculosis in southern Africa: the ZAMSTAR community-randomised trial, Lancet 382 (2013) 1183–1194, http://dx.doi.org/10.1016/s0140-6736(13) 61131-9.  Back to cited text no. 24
D.G. Datiko, B. Lindtjorn, Health extension workers improve tuberculosis case detection and treatment success in southern Ethiopia: a community randomized trial, PLoS One 4 (2009) e5443, http://dx.doi.org/10.1371/journal. pone.0005443.  Back to cited text no. 25
E.B. Shargie, O. Morkve, B. Lindtjorn, Tuberculosis casefinding through a village outreach programme in a rural setting in southern Ethiopia: community randomized trial, Bull. World Health Organ. 84 (2006) 112–119. doi:/S0042-96862006000200011.  Back to cited text no. 26
P. Kasaie, J.R. Andrews, W.D. Kelton, D.W. Dowdy, Timing of tuberculosis transmission and the impact of household contact tracing. An agent-based simulation model, Am. J. Respir. Crit. Care Med. 189 (2014) 845–852, http://dx.doi.org/10.1164/rccm.201310-1846OC.  Back to cited text no. 27
P.J. Dodd, R.G. White, E.L. Corbett, Periodic active case finding for TB: when to look?, PLoS ONE 6 (2011) e29130, http://dxdoi. org/10.1371/journal.pone.0029130.  Back to cited text no. 28
D.W. Dowdy et al, Population-level impact of active tuberculosis case finding in an Asian megacity, PLoS One 8 (2013) e77517, http://dx.doi.org/10.1371/journal. pone.0077517.  Back to cited text no. 29
N. Nishikiori, C. Van Weezenbeek, Target prioritization and strategy selection for active case-finding of pulmonary tuberculosis: a tool to support country-level project planning, BMC Public Health 13 (2013) 97, http://dx.doi.org/10.1186/1471-2458-13-97.  Back to cited text no. 30
A.J. Khan et al, Engaging the private sector to increase tuberculosis case detection: an impact evaluation study, Lancet Infect. Dis 12 (2012) 608–616, http://dx.doi.org/10.1016/ s1473-3099(12)70116-0.  Back to cited text no. 31
C.M. Yuen et al, Turning off the tap: stopping tuberculosis transmission through active case-finding and prompt effective treatment, Lancet 386 (2015) 2334–2343, http://dx. doi.org/10.1016/s0140-6736(15)00322-0.  Back to cited text no. 32
P.G. Gopi, V. Chandrasekaran, R. Subramani, P.R. Narayanan, Failure to initiate treatment for tuberculosis patients diagnosed in a community survey and at health facilities under a DOTS programme in a district of South India, Indian J. Tuberc. 52 (2005) 153–156.  Back to cited text no. 33
J.E. Golub, D.W. Dowdy, Screening for active tuberculosis: methodological challenges in implementation and evaluation, Int. J. Tuberc. Lung Dis. 17856–65 (2013) 2013, http://dx.doi.org/10.5588/ijtld.13.0059.  Back to cited text no. 34


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[Pubmed] | [DOI]
21 Tuberculosis in migrants – screening, surveillance and ethics
Gabriella Scandurra,Chris Degeling,Paul Douglas,Claudia C. Dobler,Ben Marais
Pneumonia. 2020; 12(1)
[Pubmed] | [DOI]
22 Mobilising community networks for early identification of tuberculosis and treatment initiation in Cambodia: an evaluation of a seed-and-recruit model
Alvin Kuo Jing Teo,Kiesha Prem,Sovannary Tuot,Chetra Ork,Sothearith Eng,Tripti Pande,Monyrath Chry,Li Yang Hsu,Siyan Yi
ERJ Open Research. 2020; 6(2): 00368-2019
[Pubmed] | [DOI]
23 Estimating the yield of tuberculosis from key populations to inform targeted interventions in South Africa: a scoping review
Lucy Andere Chimoyi,Christian Lienhardt,Nishila Moodley,Priya Shete,Gavin J Churchyard,Salome Charalambous
BMJ Global Health. 2020; 5(7): e002355
[Pubmed] | [DOI]
24 Prediagnostic loss to follow-up in an active case finding tuberculosis programme: a mixed-methods study from rural Bihar, India
Tushar Garg,Vivek Gupta,Dyuti Sen,Madhur Verma,Miranda Brouwer,Rajeshwar Mishra,Manish Bhardwaj
BMJ Open. 2020; 10(5): e033706
[Pubmed] | [DOI]
25 Excess Risk of Tuberculosis Infection Among Extra-household Contacts of Tuberculosis Cases in an African City
Robert Kakaire,Noah Kiwanuka,Sarah Zalwango,Juliet N Sekandi,Trang Ho Thu Quach,Maria Eugenia Castellanos,Frederick Quinn,Christopher C Whalen
Clinical Infectious Diseases. 2020;
[Pubmed] | [DOI]
26 Tuberculosis in children
Annaleise R. Howard-Jones,Ben J. Marais
Current Opinion in Pediatrics. 2020; 32(3): 395
[Pubmed] | [DOI]
27 A pragmatic stepped-wedge cluster randomized trial to evaluate the effectiveness and cost-effectiveness of active case finding for household contacts within a routine tuberculosis program, San Juan de Lurigancho, Lima, Peru
Lena Shah,Marlene Rojas Peña,Oscar Mori,Carlos Zamudio,Jay S. Kaufman,Larissa Otero,Eduardo Gotuzzo,Carlos Seas,Timothy F. Brewer
International Journal of Infectious Diseases. 2020;
[Pubmed] | [DOI]
28 Household contact investigation for the detection of tuberculosis in Vietnam: economic evaluation of a cluster-randomised trial
Thomas Lung,Guy B Marks,Nguyen Viet Nhung,Nguyen Thu Anh,Nghiem Le Phuong Hoa,Le Thi Ngoc Anh,Nguyen Binh Hoa,Warwick John Britton,Jessica Bestrashniy,Stephen Jan,Gregory J Fox
The Lancet Global Health. 2019; 7(3): e376
[Pubmed] | [DOI]
29 Active case finding in tuberculosis-affected households: time to scale up
Tom Wingfield,Stéphane Verguet
The Lancet Global Health. 2019; 7(3): e296
[Pubmed] | [DOI]
30 Performance of algorithms for tuberculosis active case finding in underserved high-prevalence settings in Cambodia: a cross-sectional study
Kimcheng Choun,Tom Decroo,Tan Eang Mao,Natalie Lorent,Lisanne Gerstel,Jacob Creswell,Andrew J. Codlin,Lutgarde Lynen,Sopheak Thai
Global Health Action. 2019; 12(1): 1646024
[Pubmed] | [DOI]
31 Treatment for latent tuberculosis infection in low- and middle-income countries: progress and challenges with implementation and scale-up
Anthony D. Harries,Ajay M. V. Kumar,Srinath Satyanarayana,Kudakwashe C. Takarinda,Collins Timire,Riitta A. Dlodlo
Expert Review of Respiratory Medicine. 2019; : 1
[Pubmed] | [DOI]
32 Screening and testing for tuberculosis among the HIV-infected: outcomes from a large HIV programme in western Kenya
Philip Owiti,Dickens Onyango,Robina Momanyi,Anthony D. Harries
BMC Public Health. 2019; 19(1)
[Pubmed] | [DOI]
33 Delay in diagnosis of pulmonary tuberculosis increases the risk of pulmonary cavitation in pastoralist setting of Ethiopia
Fentabil Getnet,Meaza Demissie,Alemayehu Worku,Tesfaye Gobena,Rea Tschopp,Michael Girmachew,Gebeyehu Assefa,Berhanu Seyoum
BMC Pulmonary Medicine. 2019; 19(1)
[Pubmed] | [DOI]
34 Active Case Finding for Tuberculosis through TOUCH Agents in Selected High TB Burden Wards of Kolkata, India: A Mixed Methods Study on Outcomes and Implementation Challenges
Abhijit Dey,Pruthu Thekkur,Ayan Ghosh,Tanusree Dasgupta,Soumyajyoti Bandopadhyay,Arista Lahiri,Chidananda Sanju S V,Milan K. Dinda,Vivek Sharma,Namita Dimari,Dibyendu Chatterjee,Isita Roy,Anuradha Choudhury,Parthiban Shanmugam,Brojo Kishore Saha,Sanghamitra Ghosh,Sharath Burugina Nagaraja
Tropical Medicine and Infectious Disease. 2019; 4(4): 134
[Pubmed] | [DOI]
35 Management of Children with Tuberculosis
Ameneh Khatami,Philip N. Britton,Ben J. Marais
Clinics in Chest Medicine. 2019; 40(4): 797
[Pubmed] | [DOI]
36 Risk factors for unsuccessful tuberculosis treatment outcomes in children
Meherunissa Hamid,Meredith B. Brooks,Falak Madhani,Hassan Ali,Mohammad Junaid Naseer,Mercedes Becerra,Farhana Amanullah,HASNAIN SEYED EHTESHAM
PLOS ONE. 2019; 14(9): e0222776
[Pubmed] | [DOI]
37 The implementation of Xpert MTB/RIF assay for diagnosis of tuberculosis in Nepal: A mixed-methods analysis
Basant Joshi,Trisasi Lestari,Stephen Michael Graham,Sushil Chandra Baral,Sharat Chandra Verma,Gokarna Ghimire,Bandana Bhatta,Shyam Prakash Dumre,Adi Utarini,Seyed Ehtesham Hasnain
PLOS ONE. 2018; 13(8): e0201731
[Pubmed] | [DOI]
38 Challenges and Progress with Diagnosing Pulmonary Tuberculosis in Low- and Middle-Income Countries
Anthony Harries,Ajay Kumar
Diagnostics. 2018; 8(4): 78
[Pubmed] | [DOI]
39 Investigating the impact of TB case-detection strategies and the consequences of false positive diagnosis through mathematical modelling
Marek Lalli,Matthew Hamilton,Carel Pretorius,Debora Pedrazzoli,Richard G. White,Rein M. G. J. Houben
BMC Infectious Diseases. 2018; 18(1)
[Pubmed] | [DOI]
40 What can National TB Control Programmes in low- and middle-income countries do to end tuberculosis by 2030?
Anthony D. Harries,Yan Lin,Ajay M.V. Kumar,Srinath Satyanarayana,Kudakwashe C. Takarinda,Riitta A. Dlodlo,Rony Zachariah,Piero Olliaro
F1000Research. 2018; 7: 1011
[Pubmed] | [DOI]
41 Efficacy of district tuberculosis co-ordinating team on health service performance for suspected TB patient in district hospital
Sasithorn Tangsawad,Surasak Taneepanichskul
Journal of Health Research. 2018; 32(3): 251
[Pubmed] | [DOI]
42 Household-Contact Investigation for Detection of Tuberculosis in Vietnam
Greg J. Fox,Nguyen V. Nhung,Dinh N. Sy,Nghiem L.P. Hoa,Le T.N. Anh,Nguyen T. Anh,Nguyen B. Hoa,Nguyen H. Dung,Tran N. Buu,Nguyen T. Loi,Le T. Nhung,Nguyen V. Hung,Phan T. Lieu,Nguyen K. Cuong,Pham D. Cuong,Jessica Bestrashniy,Warwick J. Britton,Guy B. Marks
New England Journal of Medicine. 2018; 378(3): 221
[Pubmed] | [DOI]
43 Identification of a plasma microRNA profile in untreated pulmonary tuberculosis patients that is modulated by anti-mycobacterial therapy
Barry Simone E,Ellis Magda,Yang YuRong,Guan Guangyu,Wang Xiaolin,Britton Warwick J,Saunders Bernadette M
Journal of Infection. 2018;
[Pubmed] | [DOI]
44 Drug-resistant tuberculosis – primary transmission and management
Alexander C. Outhred,Philip N. Britton,Ben J. Marais
Journal of Infection. 2017; 74: S128
[Pubmed] | [DOI]


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