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Published on in Vol 10 (2024)

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Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/46137, first published .
Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review

Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review

Drug-Resistant Tuberculosis Case-Finding Strategies: Scoping Review

Authors of this article:

Susanna S van Wyk1 Author Orcid Image ;   Marriott Nliwasa2 Author Orcid Image ;   Fang-Wen Lu3 Author Orcid Image ;   Chih-Chan Lan3 Author Orcid Image ;   James A Seddon4, 5 Author Orcid Image ;   Graeme Hoddinott4, 6 Author Orcid Image ;   Lario Viljoen4 Author Orcid Image ;   Gunar Günther7, 8 Author Orcid Image ;   Nunurai Ruswa9 Author Orcid Image ;   N Sarita Shah10 Author Orcid Image ;   Mareli Claassens4, 5, 8 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Review

    1Centre for Evidence Based Health Care, Division of Epidemiology and Biostatistics, Department of Global Health Stellenbosch University, Cape Town, South Africa

    2Helse Nord Tuberculosis Initiative, Kamuzu University of Health Sciences, Blantyre, Malawi

    3Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan

    4Desmond Tutu TB Centre, Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa

    5Department of Infectious Disease, Imperial College London, London, United Kingdom

    6School of Public Health, Faculty of Medicine and Health, University of Sydney, Sydney, Australia

    7Department of Pulmonary Medicine and Allergology, Inselspital, Bern University Hospital, Bern, Switzerland

    8Department of Human, Biological & Translational Medical Science, School of Medicine, University of Namibia, Windhoek, Namibia

    9National TB and Leprosy Programme, Ministry of Health and Social Services, Windhoek, Namibia

    10Departments of Epidemiology and Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States

    Corresponding Author:

    Mareli Claassens, MD, PhD

    Department of Human, Biological & Translational Medical Science

    School of Medicine

    University of Namibia

    Bach Street

    Windhoek, 10005

    Namibia

    Phone: 264 612063111

    Email: mcla@sun.ac.za


    Background: Finding individuals with drug-resistant tuberculosis (DR-TB) is important to control the pandemic and improve patient clinical outcomes. To our knowledge, systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies to inform research, policy, and practice, have not been conducted and the scope of primary research is unknown.

    Objective: We therefore assessed the available literature on DR-TB case-finding strategies.

    Methods: We looked at systematic reviews, trials, qualitative studies, diagnostic test accuracy studies, and other primary research that sought to improve DR-TB case detection specifically. We excluded studies that included patients seeking care for tuberculosis (TB) symptoms, patients already diagnosed with TB, or were laboratory-based. We searched the academic databases of MEDLINE, Embase, The Cochrane Library, Africa-Wide Information, CINAHL (Cumulated Index to Nursing and Allied Health Literature), Epistemonikos, and PROSPERO (The International Prospective Register of Systematic Reviews) using no language or date restrictions. We screened titles, abstracts, and full-text articles in duplicate. Data extraction and analyses were carried out in Excel (Microsoft Corp).

    Results: We screened 3646 titles and abstracts and 236 full-text articles. We identified 6 systematic reviews and 61 primary studies. Five reviews described the yield of contact investigation and focused on household contacts, airline contacts, comparison between drug-susceptible tuberculosis and DR-TB contacts, and concordance of DR-TB profiles between index cases and contacts. One review compared universal versus selective drug resistance testing. Primary studies described (1) 34 contact investigations, (2) 17 outbreak investigations, (3) 3 airline contact investigations, (4) 5 epidemiological analyses, (5) 1 public-private partnership program, and (6) an e-registry program. Primary studies were all descriptive and included cross-sectional and retrospective reviews of program data. No trials were identified. Data extraction from contact investigations was difficult due to incomplete reporting of relevant information.

    Conclusions: Existing descriptive reviews can be updated, but there is a dearth of knowledge on the effectiveness, cost-effectiveness, acceptability, and feasibility of DR-TB case-finding strategies to inform policy and practice. There is also a need for standardization of terminology, design, and reporting of DR-TB case-finding studies.

    JMIR Public Health Surveill 2024;10:e46137

    doi:10.2196/46137

    Keywords

    tuberculosis; drug-resistant tuberculosis; drug-resistant tuberculosis case finding; drug-resistant tuberculosis case detection; drug-resistant tuberculosis screening; drug-resistant tuberculosis contact investigation; scoping review; TB symptom; anti-tuberculosis drug; strategies; multidrug-resistant; systematic review; drug resistant; drug resistance; medication; tuberculosis; diagnosis; screening 


    With the emergence of Mycobacterium tuberculosis strains resistant to first-line antituberculosis drugs, strategies to control tuberculosis (TB) have become even more challenging [1]. It is estimated that almost half a million people developed rifampicin-resistant TB, of which 78% had multidrug-resistant tuberculosis (MDR-TB) in 2019 [2]. Although drug-resistant tuberculosis (DR-TB) is not as prevalent as drug-susceptible tuberculosis (DS-TB), it is more difficult to diagnose, treatment is longer and more toxic, outcomes are worse, and costs are higher.

    Finding individuals with DR-TB and initiating treatment as early as possible is important to improve patient clinical outcomes and to break the chain of transmission to help control the pandemic. Despite new diagnostic technologies, only a third of the estimated number of people who developed DR-TB initiated treatment in 2020 [3].

    TB can be detected after the patient presents passively to health services or follows one of several different screening pathways depending on the case-finding strategy of a TB program [4]. Pathways can also be enhanced through several activities such as health promotion in the community, improved access to TB diagnostic services, or training of health workers to identify presumptive TB at general health services. Multiple activities often result in complex interventions and heterogeneous trials that are difficult to meta-analyze in systematic reviews [5,6].

    To our knowledge, systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies to inform research, policy, and practice, have not been conducted and it is unknown whether enough research exists to conduct such reviews. It is also unknown whether case-finding strategies are similar for DR-TB and DS-TB and whether we can draw on findings from DS-TB reviews to inform decisions on DR-TB case-finding strategies.

    Scoping reviews are useful for scoping the literature and to clarify concepts [7,8]. We therefore conducted a scoping review to assess whether enough research exists for a systematic review, to identify priority questions for such a review, and to clarify which case-finding strategies exist for DR-TB specifically.


    Reporting Guidelines and Protocol

    The Arksey and O’Malley framework [9], Levac et al [10], and the Joanna Briggs Institute scoping review methodology [8] guided methods for this scoping review. The review is reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) [11]. See Multimedia Appendix 1 for the completed PRISMA-ScR checklist. The protocol for this review was published in JMIR Research Protocols [12].

    Defining the Research Question

    The question for our review was, what literature is available on DR-TB case finding and which case-finding strategies are described? We looked at studies that had sought to improve DR-TB case detection.

    Eligibility Criteria

    Textbox 1 lists the inclusion and exclusion criteria for participants, concept, outcome, context, and study design.

    Textbox 1. Eligibility criteria.

    Inclusion criteria

    • Participants
    • Participate regardless of symptoms, for example, contacts, people living with HIV attending HIV care, whole communities
    • Concept/Intervention
    • Strategies aiming to improve or enhance participants’ pathways to drug-resistant tuberculosis (DR-TB) case detection specifically
    • Outcome
    • Patients diagnosed with tuberculosis (TB)
    • Context
    • Community
    • Primary, secondary, or tertiary care
    • Study design
    • Primary studies
    • Systematic reviews
    • Qualitative studies, where the experiences of individuals who receive the intervention or those who provide the intervention are investigated
    • Studies of diagnostic test accuracy if the study describes a DR-TB screening strategy
    • Trials comparing different screening or diagnostic tools within a DR-TB case-finding intervention

    Exclusion criteria

    • Participants
    • Patients with TB symptoms seeking care
    • Patients diagnosed with TB
    • Laboratory samples/isolates
    • Concept/Intervention
    • Intervention strategies aiming to improve TB case finding in general, even if they do report the yield of people with DR-TB
    • Outcome
    • No report of patients diagnosed with TB
    • Context
    • Laboratory based
    • Study design
    • Meta-reviews (review of reviews)
    • Narrative reviews
    • Editorials
    • Opinion articles
    • Meeting summaries
    • Guidelines
    • Prevalence surveys, except if the survey includes an intervention strategy to find individuals with DR-TB specifically
    • Conference abstracts

    Identifying Relevant Studies

    With assistance from an information specialist, we searched the academic databases of MEDLINE (PubMed), Embase (Ovid), The Cochrane Library, Africa-Wide Information (EBSCOhost), CINAHL (EBSCOhost), Epistemonikos, and PROSPERO (The International Prospective Register of Systematic Reviews) using no language or date restrictions. These searches were conducted on August 31, 2021, and after an initial peer review of this article, an updated search was conducted on January 11, 2024.

    The search string included combinations of the following 3 domains, that are (1) terms related to “TB”; (2) terms related to “drug resistance”; and (3) terms related to “case finding,” “case detection,” “screening,” “contact investigation,” and “contact tracing.”

    Search strategies from each electronic database are detailed per search date in Multimedia Appendix 2.

    Study Selection

    We used Rayyan systematic review software [13] to screen titles, abstracts, and full-text articles. Decisions were blinded, except when reviewing conflicts. Reviewers screened abstracts in duplicate for inclusion. Conflicts were resolved through discussion. Full-text articles were also screened in duplicate. Disagreements were resolved through discussion to determine final inclusion.

    Charting the Data

    We developed a data extraction form in Excel (Microsoft Corp). The data extraction form was applied to all primary research reports to collect standard information on each study. Textbox 2 lists the information that was collected.

    One reviewer extracted data from included papers and a second reviewer (SSvW) checked the extracted data. Reviewers met regularly to determine whether their approach was consistent and in line with the research question.

    Textbox 2. Information collected on each study.
    • Authors, journal, year of publication
    • Aim or purpose of the research
    • Study design
    • Country
    • Income
    • Tuberculosis prevalence
    • HIV prevalence
    • Urban or rural setting
    • Participants
    • Age
    • Sex
    • HIV status
    • Other reported risk factors
    • Target group and how the group was identified if applicable
    • Interventions
    • All components (activities) of the intervention
    • Types of providers
    • Screening and diagnostic tools used
    • Treatment support, including preventive therapy
    • Outcomes assessed

    Collating, Summarizing, and Reporting the Results

    We provide a narrative report with supporting tables to summarize the data. Table 1 contains definitions we used in charting, collating, summarizing, and reporting our results.

    A systems-based logic model developed from a synthesis of DS-TB case-finding strategies (Figure 1) was used as a framework to describe different strategies and resulting pathways (care-seeking pathways or screening pathways).

    Quality appraisal was not conducted, because this is a scoping review and our interest is in the existing evidence base, regardless of study design and quality.

    Table 1. Definitions.
    TermsDefinitions
    DR-TBaAll types of DR-TB that include DR-TB with resistance to one first-line drug, MDR-TBb, XDR-TBc, and any other DR-TB reported by the authors.
    Systematic screening for TBd disease “The systematic identification of people with suspected (presumptive) TB disease, in a predetermined target group, using tests, examinations, or other procedures that can be applied rapidly. Among those screened positive, the diagnosis needs to be established by one or several diagnostic tests and additional clinical assessments, which together have high accuracy.” [14]
    A screening toolTests, examinations, or other procedures used for systematic screening for TB disease. Examples of TB screening tools include a structured symptom-based questionnaire, CXRe, or an algorithm [4]. Algorithms may include sequential or parallel tests. With sequential tests, only those who screen positive with the initial test receive a second test. With parallel tests, those who screen positive on any of the tests are regarded as screen positives.
    A diagnostic toolTests, examinations, or other procedures used to establish a diagnosis of TB disease in people identified with presumptive TB. Examples of TB diagnostic tools include a clinical algorithm, sputum smear microscopy, Xpert MTB/RIF (Cepheid Inc), or culture [4].
    TB symptomAny TB symptom, for example, cough, fever, night sweats, weight loss, or combination of TB symptoms as defined by the study authors.
    Care seekingPeople seeking care for a perceived health problem.
    TB care seekingPeople seeking care for TB symptoms specifically.
    A risk groupAny group of people in whom the prevalence or incidence of TB is significantly higher than in the general population. Examples of risk groups include a whole population within a geographical area or TB contacts [15].
    A clinical risk groupIndividuals diagnosed with a specific disease or condition that increases their risk for TB, for example, people living with HIV (PLHIV).
    Presumptive TBPresumptive TB is identified when a provider identifies a patient with suspected TB disease. In the context of screening, a person who screens positive is a patient with presumptive TB.
    Passive case finding Care-seeking pathway without TB screening, that is, the green and black dashed pathways in Figure 1 [16].
    Passive case finding with an element of systematic screening or triageTB screening at general health services, that is, the green pathway in Figure 1.
    Enhanced case findingTB health promotion with or without TB screening.
    Active case findingTB screening at TB screening services or at home, work, or school, that is, the blue and orange pathways in Figure 1.
    If the target group is TB contacts, this can also be referred to contact tracing or contact investigation.
    Intensified case findingTB screening of a clinical risk group, for example, people living with HIV (ie, the gray pathway in Figure 1).

    aDR-TB: drug-resistant tuberculosis.

    bMDR-TB: multidrug-resistant tuberculosis.

    cXDR-TB: extensively drug-resistant tuberculosis.

    dTB: tuberculosis.

    eCXR: chest radiography.

    ‎
    Figure 1. A systems-based logic model depicting types of services and associated pathways to tuberculosis (TB) case detection [16].

    Overview of the Available Literature

    We screened 3646 titles and abstracts and 236 full-text articles. We identified 6 systematic reviews and 61 primary studies (Figure 2) for inclusion. We divided primary studies into 6 different categories (themes) and described each category in more detail below. Table 2 gives an overview of the categories and references to further detail.

    ‎
    Figure 2. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram.
    Table 2. Overview of categories into which included studies were divided.
    Type of studyArticlesFurther detail
    Systematic reviewsn=6Table 3
    Primary studies (N=61)

    		Close- or household-contact investigationsn=34 (56%)Multimedia Appendix 3 and Table 4

    		Outbreak investigationsn=17 (28%)Table 5

    		Airline contact investigationsn=3 (5%)

    		Epidemiological analysesn=5 (8%)

    		Public-private partnership programn=1 (2%)

    		E-registry programn=1 (2%)

    Systematic Reviews

    We identified 6 systematic reviews. Outcomes were descriptive and none of the reviews identified any randomized controlled trials. In 5 of the 6 reviews, the date of the last search was more than 5 years ago (Table 3). In reviews with the yield of TB disease as an outcome, the denominator was reported as the number of contacts evaluated or screened; however, specific definitions for “evaluated” or “screened” were not reported and the yield for a specific screening or diagnostic strategy is unknown.

    Table 3. Overview of the included systematic reviews.
    ReviewPrimary outcomeDate of last search
    Abubakar [17]Number of contacts screened, and number of individuals with TBa infection and TB disease identifiedNovember 2009
    Fox et al [18]Yield of TB disease and TB infection for both DS-TBb and DR-TBc source casesOctober 2011
    Shah et al [19]Yield of TB infection and TB disease in contacts of DR-TB source casesDecember 2011
    Kodama et al [20]Relative risk ratio of TB disease in DS-TB contacts compared with DR-TB contactsNot reported
    Svadzian et al [21]Proportion of cases from those evaluated through universal testing (all individuals in the study received DSTd) and those evaluated through selective testing (only the high-risk group received DST)June 2019
    Chiang et al [22]Percentage of secondary cases whose Mycobacterium tuberculosis strains were resistant to the same drugs as strains from the index casesJuly 2018

    aTB: tuberculosis.

    bDS-TB: drug-susceptible tuberculosis.

    cDR-TB: drug-resistant tuberculosis.

    dDST: drug-susceptibility testing.

    Primary Studies

    We identified 61 primary studies that were not included in any of the above reviews. Primary studies were descriptive and included cross-sectional studies, prospective studies, and retrospective reviews of program data. No trials were identified. Thirty-four studies were close-contact or household-contact investigations, 17 were outbreak investigations, 3 were airline contact investigations, 5 were epidemiological analyses, 1 described a private-public partnership program, and 1 assessed the feasibility and acceptability of an e-registry program (Table 2). Case-finding pathways were seldom described clearly, for example, whether contacts were invited for screening regardless of symptoms (Figure 1, blue pathway), whether all contacts were screened for TB at home (Figure 1, orange pathway), or whether those who experienced TB symptoms were invited for further tests (Figure 1, black dashed pathway).

    Close-Contact or Household-Contact Investigations

    Countries where contact investigations were conducted included South Africa (n=6), India (n=5), Pakistan (n=4), Australia (n=2), the United States (n=1), Ethiopia (n=2), Myanmar (n=2), Thailand (n=1), France (n=1), Vietnam (n=1), Papua New Guinea (n=1), Armenia (n=1), the United Kingdom (n=1), Spain (n=1), South Korea (n=1), Oman (n=1), and Tajikistan (n=1). Two multicountry studies were conducted in Botswana, Brazil, Haiti, Kenya, Peru, South Africa, and Thailand. Data extraction from contact investigations was difficult due to incomplete reporting of relevant information, such as the total number of source cases or the number of cases tested for drug susceptibility (Table 4). Screening and diagnostic tools were not well reported and often lacked consistent or standardized use. Although investigations focused on contacts who had been exposed to DR-TB, drug-susceptibility testing (DST) was seldom reported. Case-finding pathways were also not clearly described. Some contacts were followed up over 1-2 years and some were only evaluated at baseline. Lack of or inconsistent reporting of this relevant data results in an unknown or inconsistent denominator when calculating the yield of screening the contacts of individuals with TB and makes it challenging to pool results or compare different case-finding strategies. There was also little consistency in the use of definitions. Source cases were often defined as “registered MDR-TB or extensively drug-resistant tuberculosis (XDR-TB) cases” without knowledge of how they were diagnosed. Several different definitions for “close contact” or “household contact” were reported. Some definitions were broad, for example, “people living with or having regular daily interaction with the MDR-TB source case” [23], while other definitions were more specific, for example, “a person who had shared the same enclosed living space for one or more nights a week, or for frequent or extended periods of time during the day, with the index patient during the 3 months before the current treatment episode began” [24-26]. The latter definition was used more often. See Multimedia Appendix 3 for more details.

    Table 4. Data from DR-TBa contact investigation studies.
    StudySource/Index casesContactsScreeningTBb diagnosisDS-TBc diagnosisDR-TB diagnosis

    		Total identifiedStudiedTotal identifiedScreenedPositive screenEvaluatedTotal TBDiagnosedEvaluatedDiagnosed
    Mohammadi et al [27]Not reported13Not reported140Not reportedNot reported00Not reported0
    Tuberculosis Research Centre, Indian Council of Medical Research [28]Not reported209 INHd resistant at intake779 at intake and 8358 over 15 yearsNot reportedNot reportedNot reported22 over 15 years of f/ue and 260 per 100,000 person-years in INH-resistant HHf contacts18224 INH resistant
    Denholm et al [29]474757049 LTBIgNot reportedNot reported2Not reportedNot reported2
    Seddon et al [23]Not reportedNot reported281228Not reported (102 LTBI)Not reported15Not reportedNot reportedNot reported
    Adler-Shohet et al [30]11Not reported11831 TSTh posi (21 initially and 10 at repeat)31 on LTBI treatment had 2-year f/u0000
    Garcia-Prats et al [31]113834NoneNot reported0000
    Titiyos et al [32]508508155 family members in households of 29 symptomatic contacts155Unclear (29 symptomatic contacts were initially identified and evaluated)Not reported16Not reportedNot reported16 confirmed MDR-TBj
    Arnold et al [33]113533Not reportedNot reported2 at baseline (2 in 2-year f/u)0Not reported2 XDRk (1 confirmed and 1 probable)
    García et al [34]11393919 Mantoux pos of which 1 had CXRl changesNot reported5 (within 2-year f/u)Not reported44 INH resistant (same strain as index case)
    Javaid et al [35]200154Not reported610218 symptoms, 51 AFBm-positive, and Nrn with abnormal CXR not reportedNot reported5110Not reported41
    Fournier et al [36]683284Not reportedNot reportedNot reportedNot reported2Not reported3 MDR-TB
    Golla et al [37]Not reportedNot reportedNot reported229Not reported22615 (7 bacteriological)Not reportedNot reportedNot reported
    Lee et al [38]11762 asymptomatic with minimal nodules in baseline chest CTo scan 2 followed up with chest CT scan1011
    Chatla et al [39]160216024858477179378134193415
    Dayal et al [40]Not reported43Not reported100Not reported10041Not reportedNot reportedNot reported
    Hiruy et al [24]111111340331202091208
    Huerga et al [41]265111198150 at baseline and 138 at f/uNot reportedNot reported3 at baseline (none with f/u)Not reportedNot reportedNot reported
    Boonthanapat et al [42]914317470 had screening records3 abnormal CXRsNot reported1 identified, but no screening records for this oneNot reportedNot reportedNot reported
    Hoang et al [43]11299496325 at baseline and 160 at f/u36 symptoms, 12 abnormal CXR, and 27 at f/u48 at baseline and 27 at f/u1 (no TB at f/u)1480
    Honjepari et al [44]6767 total (only 25 DR-TB)697635 total and 23 DR-TB contacts15611495Not reported4 (2 bacteriologically and 2 clinically)
    Kigozi et al [45]Not reported92297 (only 6 contacts of MDR-TB cases)259 (6)102 (1)48 (1)17 (0)Not reportedNot reportedNot reported
    Phyo et al [26]55655619081134344 presumptive TB186 presumptive TB and 213 others (399 in total)27 (6 bacteriologically and 21 clinically)15205
    Gupta et al [46]30828410161007228 signs and symptoms and 169/969 abnormal CXR1007121 (17 bacteriological)11165
    Kyaw et al [25]Not reported210Not reported620240 symptoms and Nr with abnormal CXR not reported169 AFB/Xpert (71 symptomatic contacts did not receive AFB/Xpert)24 (7 bacteriologically and 17 clinically)22432
    Malik et al [47]Not reported100800 (8 on TB treatment)737402 symptoms or <18 years or DMp or HIV or low BMIq3263Not reportedNot reportedNot reported
    Paryani et al [48]129109518 (22 with TB already on treatment and 2 diagnosed at baseline)495 (400 entered f/u)Not reportedNot reported22 already on treatment (excluded), 2 diagnosed at baseline (excluded), and 14 at f/u6Not reported6 pre-XDR, 1 XDR, and 1 missing DSTr
    Shadrach et al [49]Not reported87Not reportedNot reported2852719735Not reported62
    van de Water et al [50]284284959336Not reportedNot reported30Not reportedNot reportedNot reported
    Chang et al [51]Not reported552472158 abnormal CXRNot reported11Not reportedNot reported
    Kim et al [52]305152 RR-TB1324303 children <15 years69 symptoms93 smear microscopy, 55 CXR, and 93 culture or molecular test4 bacteriologically and 49 clinicallyNot reportedNot reportedNot reported
    Ahmed et al [53]32932419111734 symptom screen, 281 Xpert, and 123 CXR onlyNot reportedAll contacts were eligible for Xpert regardless of symptoms200202 clinically, 7 MDR/RRs TB, and 11 pre-XDR TB
    Ahmed and Dadlani [54]470100 MDR-TB and 370 DS-TB830830218 symptoms102 GeneXpert, 76 Smear microscopy, and 11 CXR10698Not reported8 MDR-TB
    Apolisi et al [55]Not reported4814611219 symptoms55 CXR only, 19 CXR and sputum, and 1 sputum only11 (2 bacteriologically and 9 clinically)1 (bacteriologically)Not reported10 (1 indeterminate for rifampicin susceptibility and 9 clinically)
    Rekart et al [56]Not reported830 RR-TBNot reported6654269 symptoms154949128 had DST47 RR (16 MDR, 12 XDR) and 1 INH resistant

    aDR-TB: drug-resistant tuberculosis.

    bTB: tuberculosis.

    cDS-TB: drug-susceptible tuberculosis.

    dINH: isoniazid.

    ef/u: follow-up.

    fHH: household.

    gLTBI: latent TB infection.

    hTST: tuberculin skin test.

    ipos: positive.

    jMDR-TB: multidrug-resistant tuberculosis.

    kXDR: extensively drug-resistant.

    lCXR: chest radiography.

    mAFB: acid fast bacilli.

    nnr: number.

    oCT: computed tomography.

    pDM: diabetes mellitus.

    qBMI: body mass index.

    rDST: drug susceptibility testing.

    sRR: rifampicin resistant.

    Outbreak Investigations

    The Dictionary of Epidemiology defines an outbreak as “an epidemic limited to localized increase in the incidence of disease, e.g., village, town, or closed institution” [57]. In the included studies, it was not always reported whether the number of identified patients was more than expected over a particular period. It was not always clear if a study was indeed an investigation of an outbreak. Studies are summarized in Table 5. These were mostly in lower TB burden countries. They were all descriptive and focused on different aspects of an outbreak, for example, contact investigation and follow-up, preventive measures, and transmission chains.

    Table 5. Overview of outbreak investigations.
    StudyCountryDiseasePopulationCases that triggered a responseFocus of the paper
    Valway et al [58]United States (New York)MDR-TBaInmates from a prison in Upstate New York4 inmates from one prison were diagnosed in the summer of 1991Transmission patterns and contact investigation results
    Ridzon et al [59]United States (California)DR-TBbCalifornia high-school students4 students were diagnosed in spring 1993Findings from an outbreak investigation
    Breathnach et al [60]United Kingdom (London)MDR-TBPatients who were HIV positive at St Thomas’ Hospital in London8 patients were identified between 1995 and 1997Epidemiology and control of the hospital outbreak
    Holdsworth et al [61]United Kingdom (London)MDR-TBNosocomial outbreak at Guy’s and St Thomas’ NHSc Trust8 patients were identified in the summer of 1996Management of public relations following the outbreak
    Moro et al [62]ItalyMDR-TBPatients infected by HIV and hospitalized in an HIV ward in Milan, Italy33 patients were diagnosed between October 1992 and February 1994Risk factors for transmission and the effectiveness of infection control measures
    Schmid et al [63]AustriaMDR-TBRefugees in AustriaIn 2005-2006 the Austrian laboratory for TBd identified 4 MDR-TB cases with similar genotypesThe chain of transmission
    Asghar et al [64]United States (Florida)Mycobacterium tuberculosis resistant to isoniazidHIV-positive, rock cocaine (crack) users who lived in low-income neighborhoods in Miami18 cases with matching spoligotypes were identified between January 2004 and May 2005Transmission patterns and recommendations for TB control in this population
    Fred et al [65]Federated States of MicronesiaMDR-TBCluster of patients with MDR-TB in Chuuk StateA cluster of 5 patients were identified in May 2008Contact tracing and control measures
    Chee et al [66]SingaporeMDR-TBLANe gaming centersIn 2012, 5 men who attended 2 LAN gaming centers were diagnosed with MDR-TBHighlights gaming centers as potential hotspots for TB transmission and notes challenges when conducting contact-tracing investigations
    Norheim et al [67]NorwayStreptomycin resistant TBStudents attending training sessions at an educational institution in Oslo, Norway3 students were identified within one week in April 2013Transmission patterns linking data from contact tracing to data from WGSf
    Ho et al [68]SingaporeMDR-TBResidents of an 11-storey apartment block6 residents were identified between February 2012 and May 2016The cluster investigation and results from mass screening
    Popovici et al [69]RomaniaXDR-TBgForeign medical students at a Romanian universityA cluster of 3 patients was identified in October 2015Results from contact investigation and the efforts to identify the source case
    Zhang et al [70]ChinaMDR-TBSchool in Zhejiang ProvinceA student was diagnosed in May 2014Results from classmate contact investigation
    Li et al [71]ChinaMDR-TBSenior high schoolA female student diagnosed in March 2020Results from household, classmate, and faculty investigations
    Kobayashi et al [72]JapanMDR-TBJapanese language school in TokyoA student was diagnosed in September 2019Results from analysis of outbreak cases
    Wu et al [73]ChinaRR-TBhMiddle school in Jiangsu ProvinceUnclear. 12 patients were diagnosed with TB of whom 6 were RR-TB.Describe characteristics and epidemiology of outbreak and suggestions for prevention and control of school TB
    Groenweghe et al [74]United States (Kansas)MDR-TBHouseholds from the same apartment complex and their contactsThe first person identified was an infant hospitalized in November 2021. An investigation identified 4 additional household members with MDR-TB.Public Health Response and results from contact investigations

    aMDR-TB: multidrug-resistant tuberculosis.

    bDR-TB: drug-resistant tuberculosis.

    cNHS: National Health Services.

    dTB: tuberculosis.

    eLAN: local area network.

    fWGS: whole-genome sequencing.

    gXDR-TB: extensively drug-resistant tuberculosis.

    hRR-TB: rifampicin-resistant tuberculosis.

    Airline Contact Investigations

    Three studies described airline DR-TB contact investigation. An der Heiden et al [75] investigated passengers and crew members after exposure to an individual with XDR-TB. The response rate was 83%. No secondary TB cases were reported and 1 individual with TB infection, probably newly acquired, was identified. Kornylo-Duong et al [76] evaluated passenger contacts of individuals with MDR-TB. More than 65% were lost to follow-up. No secondary TB cases were reported. Eight contacts tested positive for latent tuberculosis infection (LTBI); however, it was unknown if these contacts were recent converters as they might have acquired LTBI from their countries of residence. Glasauer et al [77] analyzed international contact tracing notifications received by Germany from other countries, with a focus on air travel. The high variability in the completeness of contact tracing information made analyses a challenge.

    Epidemiological Analyses

    Nitta et al [78], Anderson et al [79], de Vries et al [80], and Suppli et al [81] described transmission patterns of DR-TB in Los Angeles County (1993-1998), the United Kingdom (2004-2007), and the Netherlands (2010-2019 and 2018-2019), respectively. Intervention strategies to find those with TB disease are mentioned, but not described in detail. Villa et al [82] described a cluster of 16 pre-XDR and XDR-TB cases in Italy between 2016 and 2020 as well as the role of whole-genome sequencing in TB surveillance.

    Public-Private Partnership Program

    Joloba et al [83] described a program to improve MDR-TB detection by improving access to rapid and reliable DST, redesigning the TB specimen transport network, and training health care workers in Uganda. This study enhanced the care-seeking pathway (Figure 1, green dashed pathway) to specifically improve MDR-TB diagnosis and no screening took place.

    E-registry Program

    Naker et al [84] described a qualitative study in Mongolia to assess the feasibility and acceptability of an e-registry tool to simplify the systematic screening of MDR-TB contacts. Of 42 index cases invited to take part in the pilot study, 10 declined participation due to concerns about data security.


    Key Findings

    This scoping review charts the existing literature on DR-TB case finding. More than 60% of identified studies described DR-TB contact investigations. Included studies were all descriptive and no trials were identified. There is a lack of primary studies for inclusion in systematic reviews assessing the effectiveness, cost-effectiveness, acceptability, and feasibility of different DR-TB case-finding strategies. Case-finding strategies were not always reported in enough detail to deduce the specific pathways in our systems-based logic model (Figure 1), for example, whether symptomatic contacts were invited to a TB diagnostic service or whether contacts were screened at home or invited for screening at a health facility; however, except for target group differences, for example, contacts of DR-TB source cases compared with DS-TB source cases, and DST when presumptive DR-TB is identified, we did not note any differences between DR-TB case-finding and DS-TB case-finding strategies. Information on factors that may influence the yield of TB disease, like the number of contacts identified, screened, and evaluated, when these contacts were evaluated (baseline or follow-up), and which screening and diagnostic tools used [85] were seldom reported in detail.

    Previous Work

    Although conclusions about the most effective DR-TB case-finding strategy cannot be drawn, several reviews looked at the possible effects of active TB case finding (screening pathways in Figure 1) compared with passive TB case finding (care-seeking pathways in Figure 1) in general. Two Cochrane reviews failed to identify any studies for inclusion. Fox et al [86] aimed to compare the diagnostic yield of TB disease between active case finding and passive case finding in TB contacts but did not identify any trials for inclusion in the review. Braganza Menezes et al [87] also could not identify trials for inclusion in a review aiming to assess the effectiveness of novel methods, for example, social network analysis, of contact tracing versus the current standard of care to identify individuals with TB infection or TB disease. A review by Kranzer et al [88] included observational studies and concluded that screening compared with standard care increases the number of patients with TB disease found in the short term, but that it is unknown whether it impacts TB epidemiology. This finding was underpinned by another Cochrane review. Mhimbira et al [5] found that active case-finding strategies may result in increased case finding in the short term, but long-term outcomes were lacking. Except for the unknown effect of TB screening on TB epidemiology, the effect of screening on individual outcomes had been studied by Telisinghe et al [89] and Kranzer et al [88]. These reviews found limited patient outcome data and no difference in treatment outcomes between active and passive case findings. While it is known that screening may increase the number of identified cases, it is unclear if TB screening makes a difference to TB epidemiology and individual outcomes compared with passive case finding.

    Implications for Research

    There is a need for standardization of terminology, design, and reporting of DR-TB case-finding studies, especially contact investigation studies. Future research should focus on clear definitions, methodology, and detailed descriptions of all intervention components. There is also a need for well-conducted randomized controlled trials assessing the effect of active case finding on individual outcomes and long-term TB epidemiological outcomes.

    Strengths and Limitations

    The strengths of our review included a thorough search strategy and the use of a systems-based logic model. Nevertheless, there were some limitations. We searched several databases with no date or language restriction, but we did not translate all full-text articles. Studies that were not translated (n=4) are reported in the list of excluded studies (Multimedia Appendix 4) and from the translated abstracts it seems that similar study designs were found to those of included contact investigation studies. Our review excluded patients with TB symptoms seeking care, patients diagnosed with TB, and laboratory samples or laboratory isolates because the focus of this review was on active case finding. It should therefore be noted that studies that investigated strategies to improve identification of DR-TB after clinical identification of presumptive DR-TB cases, or studies that screened laboratory samples were not part of this review. Furthermore, for collating, summarizing, and reporting the results, we initially envisaged using our systems-based logic model as a framework to describe different case-finding strategies and resulting pathways. However, reporting was incomplete and inconsistent, and we were not able to describe pathways in detail. Nevertheless, the logic model guided our interpretation of whether a case-finding study involved screening or not. Finally, for contact investigation studies, we included studies that reported on the number of individuals with TB disease diagnosed, even if the study focused on LTBI testing and treatment. This might be a reason why the screening and diagnostic pathways were not always reported in detail. However, it is important to note that in contact investigation studies, the active case-finding component and the LTBI treatment component are both important aspects of early case finding and prevention.

    Conclusions

    Existing descriptive reviews can be updated, but there is a dearth of knowledge on the effectiveness, cost-effectiveness, acceptability, and feasibility of DR-TB case-finding strategies to inform policy and practice. There is also a need for standardization of terminology, design, and reporting of DR-TB case-finding studies, especially contact investigation studies, to decrease a large amount of research waste and increase the number of studies that could be synthesized and meta-analyzed in high-impact systematic reviews in the future [90].

    Acknowledgments

    We would like to thank Anel Schoonees and Vittoria Lutje, our information specialists, for their assistance with the search strategy. SSvW is supported by the Research, Evidence and Development Initiative (READ-It). READ-It (project number 300342-104) is funded by UK aid from the UK government; however, the views expressed do not necessarily reflect the UK government’s official policies. MC is supported jointly by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO) under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 program supported by the European Union. JAS is supported by a Clinician Scientist Fellowship jointly funded by the UK MRC and the UK Department for International Development (DFID) under the MRC/DFID Concordat agreement (MR/R007942/1). GH received financial assistance from the European Union (grant DCI-PANAF/2020/420-028), through the African Research Initiative for Scientific Excellence (ARISE), pilot program. ARISE is implemented by the African Academy of Sciences with support from the European Commission and the African Union Commission.

    Data Availability

    All data analyzed during this study are included in this published article and its Multimedia Appendices.

    Authors' Contributions

    MC and SSvW conceived the study idea. SSvW, MN, LV, FWL, CCL, and MC performed title, abstract, and full-text screening. All authors assisted with data extraction and analyses. SSvW drafted the paper. All authors read and approved the final paper.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.

    DOC File , 119 KB
    Multimedia Appendix 2

    Detailed search strategy.

    DOC File , 89 KB
    Multimedia Appendix 3

    Characteristics of drug-resistant TB contact investigation studies.

    DOC File , 92 KB
    Multimedia Appendix 4

    List of excluded studies with reasons.

    DOC File , 97 KB
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    ‎
    DR-TB: drug-resistant tuberculosis
    DS-TB: drug-susceptible tuberculosis
    DST: drug-susceptibility testing
    LTBI: latent tuberculosis infection
    MDR-TB: multidrug-resistant tuberculosis
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews
    TB: tuberculosis
    XDR-TB: extensively drug-resistant tuberculosis

    Edited by A Mavragani; submitted 31.01.23; peer-reviewed by M Anderson, J Jiang, V Dhikav, R Mpofu, J Wang, S Mukhida; comments to author 01.12.23; revised version received 12.03.24; accepted 19.04.24; published 26.06.24.

    Copyright

    ©Susanna S van Wyk, Marriott Nliwasa, Fang-Wen Lu, Chih-Chan Lan, James A Seddon, Graeme Hoddinott, Lario Viljoen, Gunar Günther, Nunurai Ruswa, N Sarita Shah, Mareli Claassens. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 26.06.2024.

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