Skip to main content
Advertisement
  • Loading metrics

Improving cascade outcomes for active TB: A global systematic review and meta-analysis of TB interventions

  • Gifty Marley ,

    Contributed equally to this work with: Gifty Marley, Xia Zou, Juan Nie, Weibin Cheng

    Roles Data curation, Methodology, Writing – original draft, Writing – review & editing

    Affiliations Dermatology Hospital of Southern Medical University, Guangzhou, China, University of North Carolina Project-China, Guangzhou, China

  • Xia Zou ,

    Contributed equally to this work with: Gifty Marley, Xia Zou, Juan Nie, Weibin Cheng

    Roles Formal analysis, Methodology, Writing – review & editing

    Affiliation Global Health Research Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China

  • Juan Nie ,

    Contributed equally to this work with: Gifty Marley, Xia Zou, Juan Nie, Weibin Cheng

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    Affiliation Department of Research and Education, Guangzhou Concord Cancer Center, Guangzhou, China

  • Weibin Cheng ,

    Contributed equally to this work with: Gifty Marley, Xia Zou, Juan Nie, Weibin Cheng

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    Affiliations Institute for Healthcare Artificial Intelligence Application, Guangdong Second Provincial General Hospital, Guangzhou, China, School of Data Science, City University of Hong Kong, Hong Kong, China

  • Yewei Xie,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation University of North Carolina Project-China, Guangzhou, China

  • Huipeng Liao,

    Roles Data curation, Methodology, Validation, Writing – review & editing

    Affiliation University of North Carolina Project-China, Guangzhou, China

  • Yehua Wang,

    Roles Methodology, Validation, Writing – review & editing

    Affiliation University of North Carolina Project-China, Guangzhou, China

  • Yusha Tao,

    Roles Validation, Writing – review & editing

    Affiliation University of North Carolina Project-China, Guangzhou, China

  • Joseph D. Tucker,

    Roles Conceptualization, Supervision, Validation, Writing – review & editing

    Affiliations University of North Carolina Project-China, Guangzhou, China, Faculty of Infectious and Tropical Diseases, London School of Health and Tropical Medicine, London, United Kingdom

  • Sean Sylvia,

    Roles Supervision, Validation, Writing – review & editing

    Affiliations University of North Carolina Project-China, Guangzhou, China, Department of Health Policy and Management, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, United States of America

  • Roger Chou,

    Roles Supervision, Validation, Writing – review & editing

    Affiliation Oregon Health & Science University, Portland, Oregon, United States of America

  • Dan Wu,

    Roles Supervision, Validation, Writing – review & editing

    Affiliations University of North Carolina Project-China, Guangzhou, China, Faculty of Infectious and Tropical Diseases, London School of Health and Tropical Medicine, London, United Kingdom

  • Jason Ong,

    Roles Supervision, Validation, Writing – review & editing

    Affiliation School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

  • Weiming Tang

    Roles Conceptualization, Investigation, Resources, Supervision, Validation, Writing – original draft, Writing – review & editing

    weimingtangscience@gmail.com

    Affiliations Dermatology Hospital of Southern Medical University, Guangzhou, China, University of North Carolina Project-China, Guangzhou, China

Abstract

Background

To inform policy and implementation that can enhance prevention and improve tuberculosis (TB) care cascade outcomes, this review aimed to summarize the impact of various interventions on care cascade outcomes for active TB.

Methods and findings

In this systematic review and meta-analysis, we retrieved English articles with comparator arms (like randomized controlled trials (RCTs) and before and after intervention studies) that evaluated TB interventions published from January 1970 to September 30, 2022, from Embase, CINAHL, PubMed, and the Cochrane library. Commentaries, qualitative studies, conference abstracts, studies without standard of care comparator arms, and studies that did not report quantitative results for TB care cascade outcomes were excluded. Data from studies with similar comparator arms were pooled in a random effects model, and outcomes were reported as odds ratio (OR) with 95% confidence interval (CI) and number of studies (k). The quality of evidence was appraised using GRADE, and the study was registered on PROSPERO (CRD42018103331). Of 21,548 deduplicated studies, 144 eligible studies were included. Of 144 studies, 128 were from low/middle-income countries, 84 were RCTs, and 25 integrated TB and HIV care. Counselling and education was significantly associated with testing (OR = 8.82, 95% CI:1.71 to 45.43; I2 = 99.9%, k = 7), diagnosis (OR = 1.44, 95% CI:1.08 to 1.92; I2 = 97.6%, k = 9), linkage to care (OR = 3.10, 95% CI = 1.97 to 4.86; I2 = 0%, k = 1), cure (OR = 2.08, 95% CI:1.11 to 3.88; I2 = 76.7%, k = 4), treatment completion (OR = 1.48, 95% CI: 1.07 to 2.03; I2 = 73.1%, k = 8), and treatment success (OR = 3.24, 95% CI: 1.88 to 5.55; I2 = 75.9%, k = 5) outcomes compared to standard-of-care. Incentives, multisector collaborations, and community-based interventions were associated with at least three TB care cascade outcomes; digital interventions and mixed interventions were associated with an increased likelihood of two cascade outcomes each. These findings remained salient when studies were limited to RCTs only. Also, our study does not cover the entire care cascade as we did not measure gaps in pre-testing, pretreatment, and post-treatment outcomes (like loss to follow-up and TB recurrence).

Conclusions

Among TB interventions, education and counseling, incentives, community-based interventions, and mixed interventions were associated with multiple active TB care cascade outcomes. However, cost-effectiveness and local-setting contexts should be considered when choosing such strategies due to their high heterogeneity.

Author summary

Why was this study done?

  • Developing new and innovative interventions to improve tuberculosis (TB) care services use and successful treatment are essential to the global efforts to end TB.
  • There is a limited scope on the overall impact of these interventions because most studies focus on interventions’ capacity to enhance specific TB care outcomes.
  • Evaluating existing evidence to ascertain the effect TB interventions on overall care cascade outcomes is paramount to informing holistic TB control strategies

What did the researchers do and find?

  • We systematically reviewed and meta-analyzed evidence on TB interventions and their effects on the TB care cascade for active TB from 144 peer-reviewed studies.
  • In this study, the 5 out of 12 identified TB interventions associated with multiple care cascade outcomes were education and counseling, incentives, digital interventions, community-based, multisector collaborations, and mixed interventions.
  • Among LMIC studies, education and counseling, incentives, community-based interventions, and multisector collaborations were the interventions associated with at least three TB care cascade outcomes.

What do these findings mean?

  • A wide range of relatively simple interventions could substantially improve TB care outcomes.
  • Multistep efficient interventions like education and counseling, incentives, and mixed interventions should be keenly considered in expanding active TB control programs.
  • Researchers should revise multistage effective interventions to incorporate local context needs due to their high heterogeneity.

Introduction

Tuberculosis (TB) affected an estimated 10.6 million people and caused 1.6 million deaths in 2021 [1]. The United Nations Sustainable Development Goals and World Health Organization (WHO)’s End TB Strategy set ambitious global targets for significant reductions in the global TB burden by 2030 [2]. Summarizing the existing evidence is essential in planning future TB control programs, as additional efforts are strongly needed to attain the goal.

The TB care cascade comprises six fundamental steps: testing, diagnosis, linkage-to-care, cure, treatment completion, and treatment success [3,4]. Programmatic intervention refers to any public health intervention that seeks to prevent, promote health, or reduce the TB disease burden within a given population [5]. Many interventions like public education, staff training, mobile testing, and point-of-care testing have proven effective in enhancing TB services across the care cascade [6,7]. However, most intervention evaluations have focused on single TB care cascade outcomes, despite some affecting multiple care cascade outcomes.

Moreover, previous reviews have mainly focused on synthesizing evidence of interventions on single care cascade outcomes—per our knowledge [8,9]. This limited scope is likely due to most studies focusing on interventions’ capacity to enhance specific care outcomes [1013]. Recent studies have sought to assess intervention effects on multiple care cascade outcomes [1416]. Yet, no current review has assessed the impacts of interventions across the whole TB care cascade. Evaluating existing evidence to ascertain the multistep effects capacity of TB care interventions across the care cascade is paramount to inform holistic prevention and control strategies for achieving the global End TB targets.

This global systematic review and meta-analysis aimed to synthesize evidence on TB interventions and their effects on the TB care cascade for active TB.

Method

The study protocol was registered in PROSPERO (registration number CRD42018103331) (Protocol A in S1 File), and our report writing followed the PRISMA checklist [17].

Search strategy and selection criteria

Four databases, including PubMed, Embase, CINAHL, and Cochrane trials registry, were searched using free text and controlled vocabulary terms (MeSH) for studies published from January 1970 till September 2022. The PICO framework informed search terms (Table 1).

thumbnail
Table 1. Detail of PICO components that informed search strategy.

https://doi.org/10.1371/journal.pmed.1004091.t001

The final PubMed search strategy included the following: (“tuberculosis, meningeal”[MeSH Terms] OR (“Tuberculosis”[Text Word] OR “TB”[Text Word])) AND 1848/01/01:2022/12/31[Date—Publication] AND (“Uptake”[Title/Abstract] OR “Adherence”[Title/Abstract] OR “adhere”[Title/Abstract] OR “Compliance”[Title/Abstract] OR “comply”[Title/Abstract] OR “compliant”[Title/Abstract] OR “retain”[Title/Abstract] OR “retained”[Title/Abstract] OR “Retention”[Title/Abstract] OR “outcome”[Title/Abstract] OR “outcomes”[Title/Abstract] OR “Testing”[Title/Abstract] OR “Diagnosis”[Title/Abstract] OR “Diagnostics”[Title/Abstract] OR “linkage-to-care”[Title/Abstract] OR “linkage-to-care”[Title/Abstract]) AND ((“intervention”[Title/Abstract] OR “interventions”[Title/Abstract] OR “interventional”[Title/Abstract] OR “cohort*”[Title/Abstract] OR “trial”[Title/Abstract] OR “trials”[Title/Abstract] OR “RCT”[Title/Abstract]).

We also searched through the reference lists of similar published systematic reviews to identify studies not captured by our database search outcomes. Details of search outcomes are in Table B in S1 File.

Eligibility

Peer-reviewed articles, abstracts, and clinical trials that met the search criteria were screened for eligibility. Randomized control trials (RCTs) and non-RCTs with comparator arms that implemented nonpharmaceutical interventions were eligible. Studies that reported outcomes on at least one care cascade outcome were eligible (Fig 1). Studies reporting the use of pharmaceutical interventions (studies that reported change in TB drug regimens or introduced new lines of TB drugs as interventions) were excluded, as recent reviews have evaluated DOTS efficiency in improving TB care outcomes [8,18]. We also excluded dissertations, systematic reviews, studies on latent TB, qualitative studies, mathematical modeling/simulation studies, quantitative studies without a standard-of-care control group, studies that did not report quantitative data on care cascade outcomes, and short reports.

Screening

WT and YH independently screened the title and abstracts for eligibility using Covidence. YW and YS, JN and YS, and GM and CH screened the eligible full texts for inclusion, and WT resolved full-text discrepancies.

Data extraction and quality assessment

WC and WT and HL and JN double-extracted data from included studies using a designed spreadsheet, and XZ resolved discrepancies. Data extracted included first author, publication year, country, target population, study settings, designs, type of interventions, TB care cascade outcomes, and sample size. We assessed the risk of bias using the Cochrane Risk of Bias tool and the quality of observational studies with the Effective Public Health Practice Project (EPHPP) Quality Assessment Tool [1921]. The EPHPP tool assessed each study in seven main domains (selection bias, study design, confounders, blinding, data collection, methods, and withdrawals and dropouts of patients) and rated studies as strong, moderate, or weak quality.

Definitions

TB testing in this review refers to the initial screening tests that are administered to persons suspected of TB after evaluation of risk and symptoms [22]. It aims to sort persons who probably have a disease from those who do not and is not intended to be a diagnostic test. Screening tests use a simpler testing process (usually a sputum smear test for bacteriologically testing, chest X-ray for pulmonary TB and low complexity nucleic acid amplification tests (NAATs) for simultaneous initial resistance testing) [23,24], after which persons with positive or clinically suspicious results (like inconclusive results) are referred for diagnostic testing [25]. Diagnosis refers to administering comprehensive clinical evaluation and laboratory tests to confirm a screen-tested person as having TB by using at least one approved diagnostic test approach [26]. Diagnostic tests for active TB include Xpert MTB/RIF (for multidrug-resistant and rifampicin-resistant TB), cartridge-based NAAT methods (like TrueNat), biopsy tissue culture (for extrapulmonary TB), Xpert Ultra assay, and X-ray [25]. Linkage-to-care in this review refers to the stage in care starting from registering newly diagnosed TB patients at designated facilities to initiate treatment or successfully initiating TB patients on treatment. Treatment completion refers to TB patients who finished the required treatment course but without evidence of failure or cure [27]. Cured was defined as patients’ bacteriologically confirmed TB positive at the beginning of treatment but with a smear- or culture-negative result in the last month of treatment and three or more consecutive cultures taken at least 30 days apart are negative after the intensive phase [27]. Treatment success describes the total number of persons diagnosed with TB who completed treatment and were cured [27].

Mixed interventions refer to strategies that merge or concurrently implement two or more interventions. For example, adopting a digital intervention and using incentives to facilitate linkage-to-care (Table 2).

thumbnail
Table 2. Definitions of care cascade outcomes, interventions, and strategies discussed in this review.

https://doi.org/10.1371/journal.pmed.1004091.t002

Data analysis

We reviewed the outcomes individually and included studies that reported mixed interventions in the data synthesis and meta-analyses.

Data pooling

Data from RCTs and non-RCTs with similar comparator arms and intervention strategies compared to standard-of-care were pooled in a meta-analysis using the Review Manager (The Cochrane Collaboration, 2014; Version 5.3). We utilized a random-effect model in pooling the data, funnel plots to assess publication bias and Egger’s test to assess small sample size effects as a potential marker of publication bias (p < 0.05). The results are reported as odds ratios (OR) with corresponding 95% confidence intervals (CI), the number of studies (k), heterogeneity (I2), and certainty of evidence quality.

Subgroup analysis and risk assessment

Causes of heterogeneity were exploited in subgroup analyses stratified by country designation according to the 2020 World Bank ranking (LMICs versus HICs), study designs (RCTs versus non-RCTs), and HIV services integration. The certainty of the evidence quality for each outcome was appraised using the Cochrane Grading Recommendations, Assessment, Development, and Evaluation (GRADE).

Quality assessment

In an analysis of quality assessment, studies were stratified based on study design and level of evidence. Bias among randomized controlled studies was assessed using the Cochrane Collaboration “Risk of Bias” tool, using six criteria in four sources of bias: selection bias, performance and detection bias, attrition bias, and reporting bias. Bias in other quantitative studies was assessed using the Newcastle–Ottawa Quality Assessment (EPHPP) Scale, which assessed selection bias, patient-level barrier, and measurement bias. The EPHPP tool assessed each study in seven main domains (selection bias, study design, confounders, blinding, data collection, methods, and withdrawals and dropouts of patients) and rated each aspect as strong, moderate, or weak quality. Results of the quality assessment were used in estimating the quality of evidence as part of the GRADE assessment for each intervention. The quality of evidence was assessed according to the methodology described by the GRADE working group. A GRADE table was generated for each meta-analysis outcome and sub-analysis.

This study is reported as per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline (S1 PRISMA Checklist in S1 File).

Results

We identified 21,548 deduplicated articles from the database search and 36 studies from searching reference lists of similar systematic reviews, and finally, 144 studies were included in this review (Fig 1). Thirty-two studies (64.4%) were published before 2010, and 25 (14.8%) targeted persons living with HIV (PLWHs). There were 84 (58.3%) RCTs and 31 (21.5%) observational, and 126 (87.5%) from LMICs (Table C in S1 File). By care cascade outcomes, 92 (63.9%) studies reported intervention effects on single care cascade outcomes, and 26 (20.1%) studies appraised the effects of more than one intervention strategy. We identified a total of 12 major TB interventions across the six care cascade outcomes of interest (Fig 2). Among single interventions, most studies assessed incentives (11.8%) and digital interventions (11.8%), while staff training and multisector collaborations were the least appraised singular interventions (4.2%) each. See Table 3 for further details.

thumbnail
Fig 2. Summary showing the various programmatic interventions associated with outcomes at each stage of the TB care cascade.

https://doi.org/10.1371/journal.pmed.1004091.g002

thumbnail
Table 3. Summary showing characteristics of all eligible studies included in this review (N = 144).

https://doi.org/10.1371/journal.pmed.1004091.t003

On single outcomes by cascade, 11 (12.2%) studies reported TB testing outcomes [2838], 22 (23.9%) reported TB diagnosis [3957], 15 (16.3%) reported on linkage to care [5874], 21 (22.8%) studies reported treatment completion [11,12,7594], 9 (9.8%) reported on cure [14,80,95102], and 14 (15.2%) studies reported on treatment success [80,103116].

Among the 84 RCTs, 34 (36.9%) had a low risk of selection bias, 48 (41.7.0%) had a high risk of performance bias, 47 (56.0%) had a low risk of attrition bias, and 45 (53.6%) had an unclear risk of reporting bias (Table E in S1 File). Among the 60 non-RCT studies, 12 (20.0%) were rated high quality, 32 (53.3%) were moderate, and 16 (26.7%) had poor quality (Table F in S1 File). GRADE assessment score showed 19/54 outcomes (35.2%) were of high evidence certainty, 24 (44.4%) were moderate, eight (14.8%) were low, and four (7.1%) were very low (Table G in S1 File). Funnel plots and Egger’s test suggested potential publication bias for studies reporting TB diagnosis (p_Egger = 0.029) and treatment completion (p_Egger = 0.0052). The plots were adjusted to correct for publication bias using and the trim and fill method (S1 Fig).

The interventions were highly heterogeneous (I2 > 50%) due to variations in components, intensity, training/resources required, and other factors. For example, mixed interventions varied in the number and types of interventions combined. Meta-regression results showed that study design, year of publication, and region of the study were the main sources of heterogeneity among interventions with effects on multiple TB care cascade outcomes. The main source of heterogeneity in mixed interventions reporting linkage to care was study design (OR = 9.44, 95% CI: 1.04 to 85.59, p = 0.046). Similarly, the source of heterogeneity in studies appraising the use of community interventions (OR = 1.46, 95% CI: 1.00 to 2.14, p = 0.051) and digital interventions (OR = 1.67, 95% CI: 1.11 to 2.51, p = 0.013) in TB cure was study design; and the source of heterogeneity in the use of incentives (OR = 1.03, 95% CI: 1.00 to 1.05, p = 0.039) and alcohol and tobacco use control (OR = 0.88, 95% CI: 0.81 to 0.96, p < 0.01) among studies reporting treatment success outcomes was year of publication. Additionally, the source of heterogeneity among studies assessing community-based and education and counseling interventions in treatment completion were year of publication (OR = 0.96, 95% CI: 0.93 to 0.99, p = 0.014) as well as study design (OR = 0.25, 95% CI: 0.12 to 0.52, p < 0.01) and region (OR = 0.76, 95% CI: 0.63 to 0.92, p < 0.01), respectively (Table D in S1 File).

Care cascade outcomes

Among the interventions identified, seven interventions (education and counseling, incentives, digital interventions, community-based interventions, multisector collaborations, mixed interventions, and reminders and tracers) were significantly associated with outcomes at multiple stages of the care cascade (Table 4).

thumbnail
Table 4. Interventions significantly associated with TB care outcomes at multiple stages of the TB care cascade.

https://doi.org/10.1371/journal.pmed.1004091.t004

TB testing

Among interventions affecting outcomes at multiple stages of the care cascade, education and counseling (OR = 8.82, 95% CI: 1.71 to 45.43; I2 = 99.9%, k = 7; high certainty), incentives (OR = 1.74, 95% CI: 1.63 to 1.85]; I2 = 37.2%, k = 5; low certainty), multisector collaborations (OR = 4.14, 95% CI: 3.42 to 5.01; I2 = 99.2%, k = 2; low certainty), and digital interventions (OR = 1.97, 95% CI: 1.28 to 3.04; I2 = 75.1%, k = 2; high certainty) were significantly associated with an increased likelihood of testing (Fig 3).

thumbnail
Fig 3. Forest plots showing the effects of various interventions on TB testing outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g003

TB diagnosis

Only education and counseling (OR = 1.44, 95% CI: 1.08 to 1.92; I2 = 97.6, k = 9; high certainty) and multisector collaborations (OR = 8.00, 95% CI: 1.53 to 41.84; I2 = 99.8, k = 5; low certainty) were each associated with an increased likelihood of TB diagnosis among interventions that affected outcomes at multiple stages of the care cascade (Fig 4).

thumbnail
Fig 4. Forest plots showing the effects of various interventions on TB diagnosis outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g004

Linkage-to-care

Among interventions that affected multiple care cascade outcomes, education and counseling (OR = 3.10, 95% CI: 1.97 to 4.86; I2 = 0, k = 1; high certainty), incentives (OR = 2.86, 95% CI: 1.25 to 6.50; I2 = 86.2% k = 4, moderate certainty), community-based interventions (OR = 9.91, 95% CI: 1.86 to 52.74; I2 = 99.6, k = 4; moderate certainty), multisector collaborations (OR = 3.25, 95% CI: 2.05 to 5.14; I2 = 86.5, k = 3; moderate certainty), and digital interventions (OR = 1.10, 95% CI: 1.04 to 1.17; I2 = 0.1%, k = 4; moderate certainty) were each associated with an increased likelihood of linkage-to-care (Fig 5).

thumbnail
Fig 5. Forest plots showing the effects of various interventions on TB linkage to care outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g005

TB cure

Education and counseling (OR = 2.08, 95% CI: 1.11 to 3.88; I2 = 76.7%, k = 4, moderate certainty), incentives (OR = 1.62, 95% CI: 1.06 to 2.48; I2 = 98.5%, k = 5; high certainty), community-based interventions (OR = 2.53, 95% CI: 1.92 to 3.35; I2 = 97.4%, k = 9; moderate certainty), and mixed interventions (OR = 1.19, 95% CI: 1.13 to 1.26; I2 = 0% k = 3, high certainty) were each significantly associated with an increased likelihood of TB cure among the interventions that affected outcomes at multiple stages (Fig 6).

thumbnail
Fig 6. Forest plots showing the effects of various interventions on TB cure outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g006

Treatment completion

Among interventions affecting multiple care cascade outcomes, education and counseling (OR = 1.48, 95% CI: 1.07 to 2.03; I2 = 73.1%, k = 8; high certainty), incentives (OR = 1.37, 95% CI: 1.10 to 1.71; I2 = 88.3%, k = 12; moderate certainty), and reminder and tracers (OR = 1.03, 95% CI: 1.00 to 1.07; I2 = 15.2%, k = 6; high certainty) were each significantly associated with an increased likelihood of TB treatment completion (Fig 7).

thumbnail
Fig 7. Forest plots showing the effects of various interventions on TB treatment completion outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g007

Treatment success

Among the interventions with multistage effects on care cascade outcomes, education and counseling (OR = 3.24, 95% CI: 1.88 to 5.55; I2 = 75.9%; k = 5, moderate certainty), incentives (OR = 1.08, 95% CI: 1.05 to 1.11; I2 = 0% k = 5; high certainty), community-based interventions (OR = 2.91, 95% CI: 2.01 to 4.21; I2 = 97.6, k = 10; moderate certainty), reminders and tracers (OR = 1.09, 95% CI: 1.01 to 1.16; I2 = 33.4, k = 2; moderate certainty), and mixed interventions (OR = 1.14, 95% CI: 1.09 to 1.19; I2 = 44.7%, k = 3; moderate certainty) were each significantly associated with increased likelihood of treatment success (Fig 8).

Supplementary Table G shows the generated GRADE tables for each outcome of interest (Table G in S1 File).

thumbnail
Fig 8. Forest plots showing the effects of various interventions on TB treatment success outcomes for active TB.

https://doi.org/10.1371/journal.pmed.1004091.g008

Subgroup analysis

RCTs and non-RCTs.

Among RCT and non-RCT studies, seven interventions (education and counseling, incentives, community-based intervention, digital interventions, active case finding, mixed interventions, and multisector collaboration) were identified to be associated with multiple TB care cascade outcomes (Table 5).

thumbnail
Table 5. Interventions significantly associated with TB care outcomes at multiple stages of the TB care cascade stratified by subgroups.

https://doi.org/10.1371/journal.pmed.1004091.t005

TB testing.

According to RCT studies only, digital interventions (OR = 1.97, 95% CI: 1.28 to 3.04; I2 = 83.7%, k = 2) and multisector collaborations (OR = 4.00, 95% CI: 3.37 to 4.75; I2 = 0%, k = 1) were the interventions with effects on multiple care outcomes associated with increased likelihood of testing. Whereas education and counseling (OR = 6.63, 95% CI: 1.11 to 39.55; I2 = 98.4%, k = 4), incentives (OR = 1.74, 95% CI: 1.63 to 1.85; I2 = 45.3%, k = 5) and multisector collaborations (OR = 1.22, 95% CI: 1.08 to 1.38; I2 = 0, k = 1) were interventions with effects on multiple outcomes associated with an increased likelihood of testing among non-RCT studies (S2 Fig).

TB diagnosis.

Education and counseling (OR = 1.58, 95% CI: 1.09 to 2.29; I2 = 93.5%, k = 7), and active case finding (OR = 1.44, 95% CI: 1.21 to 1.70; I2 = 40.9%, k = 6) and multisector collaborations (OR = 2.33, 95% CI: 1.65 to 3.30; I2 = 0%, k = 1) were associated with increased likelihood of TB diagnosis among RCTs. Comparatively, only multisector collaborations (OR = 10.70, 95% CI: 1.42 to 80.49; I2 = 99.3%, k = 4) was associated with an increased likelihood of diagnosis among non-RCT studies (S2 Fig).

Linkage-to-care.

Education and counseling (OR = 3.10, 95% CI: 1.97 to 4.86; I2 = 0%, k = 1), digital interventions (OR = 1.10, 95% CI: 1.04 to 1.17; I2 = 26.0%, k = 4), and multisector collaborations (OR = 1.39, 95% CI: 1.04 to 1.86; I2 = 0%, k = 1) were associated with an increased likelihood of linkage-to-care among RCTs only. Among non-RCT studies, incentives (OR = 4.79, 95% CI: 1.28 to 17.94; I2 = 78.4%, k = 2), community-based interventions (OR = 26.93, 95% CI: 16.21 to 44.75; I2 = 19.3%, k = 2), and multisector collaboration (OR = 3.80, 95% CI: 2.73 to 5.28; I2 = 34.49%, k = 2) were each associated with an increased likelihood of linkage-to-care (S2 Fig).

Cure.

Community-based interventions (OR = 2.15, 95% CI: 1.41 to 3.27; I2 = 63.5%, k = 5), mixed interventions (OR = 1.19, 95% CI: 1.13 to 1.26; I2 = 38.8%, k = 3), and education and counseling (OR = 3.45, 95% CI: 1.92 to 6.18; I2 = 0%, k = 2) were associated with increased likelihood of TB cure in RCTs only. Among non-RCT studies, only community-based intervention (OR = 2.92, 95% CI: 2.08 to 4.10; I2 = 95.4, k = 4) and incentives (OR = 1.97, 95% CI: 1.21 to 3.19; I2 = 96.3, k = 3) were each associated with an increased likelihood of TB cure (S2 Fig).

Treatment completion.

Education and counseling (OR = 1.47, 95% CI: 1.00 to 2.16; I2 = 70.2%, k = 5), and digital health (OR = 1.05, 95% CI: 1.01 to 1.10; I2 = 49.8%, k = 6) in RCT-only studies and incentives (OR = 1.76, 95% CI: 1.19 to 2.62; I2 = 59.5%, k = 5) in non-RCT were each associated with an increased likelihood of treatment completion (S2 Fig).

Treatment success.

Education and counseling (OR = 4.85, 95% CI: 2.75 to 8.58; I2 = 0%, k = 2), incentives (OR = 1.08, 95% CI: 1.05 to 1.12; I2 = 0%, k = 3), community-based interventions (OR = 2.69, 95% CI: 1.14 to 6.33; I2 = 87.8%, k = 5), and mixed interventions (OR = 1.14, 95% CI: 1.09 to 1.19; I2 = 38.8%, k = 3) were associated with an increased likelihood of treatment success among RCT-only studies. At the same time, education and counseling (OR = 1.28, 95% CI: 1.08 to 1.51; I2 = 0%, k = 3), incentives (OR = 1.83, 95% CI: 1.11 to 3.03; I2 = 45.6%, k = 2), and community-based interventions (OR = 3.38, 95% CI: 2.48 to 4.61; I2 = 93.3%, k = 5) were each associated with an increased likelihood of treatment success in non-RCTs (S2 Fig).

LMICs vs. HICs

Between LMIC and HIC studies, only five interventions (education and counseling, incentives, community-based intervention, digital interventions, and multisector collaborations) were associated with multiple TB care cascade outcomes (Table 5).

Testing

In LMIC studies, education and counseling (OR = 16.48, 95% CI: 4.40 to 61.70; I2 = 97.4%, k = 3), digital interventions (OR = 2.17, 95% CI: 1.67 to 2.82; I2 = 0%, k = 1), and multisector collaborations (OR = 4.00, 95% CI: 3.37 to 4.75; I2 = 0%, k = 1) were associated with an increased likelihood of TB testing. At the same time, only incentives (OR = 1.74, 95% CI: 1.63 to 1.85; I2 = 45.3%, k = 5) as an intervention with multistage effect was associated with an increased likelihood of TB testing in HIC studies (S3 Fig).

Diagnosis.

Only multisector collaboration (OR = 8.00, 95% CI: 1.53 to 41.84; I2 = 99.3%, k = 5) and education and counseling (OR = 1.44, 95% CI: 1.08 to 1.92; I2 = 89.9%, k = 9) were associated with increased likelihood of diagnosis in LMIC-only studies. But none of the interventions with effect on multiple care cascade outcomes was associated with diagnosis in HIC studies (S3 Fig).

Linkage-to-care.

Similar to TB testing, education and counseling (OR = 3.10, 95% CI: 1.97 to 4.86; I2 = 0%, k = 1), incentives (OR = 2.27, 95% CI: 1.55 to 3.33; I2 = 0%, k = 2), community-based interventions (OR = 9.91, 95% CI: 1.86 to 52.74; I2 = 93.8%, k = 4), digital interventions (OR = 1.10, 95% CI: 1.04 to 1.17; I2 = 26.0%, k = 4), and multisector collaborations (OR = 3.25, 95% CI: 2.05 to 5.14; I2 = 68.9%, k = 3) were associated with increased likelihood of linkage-to-care in LMIC studies. But none of the interventions with effect on multiple care cascade outcomes was associated with linkage to care in HIC studies (S3 Fig).

Cure.

In LMICs, education and counseling (OR = 2.08, 95% CI: 1.11 to 3.881.86–3.58; k = 4), incentives (OR = 1.62, 95% CI: 1.06 to 2.48; I2 = 97.2%, k = 5), mixed interventions (OR = 1.19, 95% CI: 1.13 to 1.26; I2 = 38.8%, k = 3), and community-based interventions (OR = 2.53, 95% CI: 1.92 to 3.35; I2 = 91.8%, k = 9) were associated with increased likelihood of TB cure. However, not interventions were found to be associated with cure in HIC studies (S3 Fig).

Treatment completion.

Education and counseling (OR = 1.17; k = 5, 95% CI: 1.04, 1.32; I2 = 0%, k = 5) and incentives (OR = 1.531.44, 95% CI: 1.04, 2.00; I2 = 77.8, k = 9) were each associated with an increased likelihood of treatment completion in LMIC studies. While only incentives (OR = 1.04, 95% CI: 1.02 to 1.07; I2 = 18.6, k = 3) was associated with an increased likelihood of treatment completion in HIC studies (S3 Fig).

Treatment success.

In LMIC studies, community-based interventions (OR = 2.91, 95% CI: 2.01 to 4.21; I2 = 94.7, k = 10) and mixed interventions (OR = 1.14, 95% CI: 1.09 to 1.19; I2 = 38.8, k = 3) were associated with an increased likelihood of treatment success. However, none of the interventions with effects on multiple TB care cascade outcomes were associated with an increased likelihood of treatment success in HIC studies (S3 Fig).

HIV integration vs. non-HIV integration

Among studies appraising TB interventions integrated with HIV services and non-HIV integrated interventions, education and counseling, incentives, and multisector collaborations were associated with at least three TB care cascade outcomes (Table 5).

Testing.

Among HIV-integrated TB interventions, education and counseling (OR = 10.06, 95% CI: 1.69 to 59.78; I2 = 98.9, k = 3) was the only intervention with multistage effect associated with and increased likelihood of testing. But among non-HIV integrated studies, digital health (OR = 1.58, 95% CI: 1.35 to 1.58; I2 = 0%, k = 1), multisector collaborations (OR = 4.00, 95% CI: 3.37 to 4.75; I2 = 0%, k = 4), and incentives (OR = 1.78, 95% CI: 1.66 to 1.90; k = 4) were associated with an increased likelihood of testing (S4 Fig).

Diagnosis.

Only education and counseling (OR = 1.25, 95% CI: 1.09 to 1.44; I2 = 0%, k = 2) was associated with an increased likelihood of TB diagnosis among HIV-integrated interventions. At the same time, multisector collaboration (OR = 8.00, 95% CI: 1.53 to 41.84; I2 = 99.3%, k = 4) and education and counseling (OR = 1.56, 95% CI: 1.03 to 2.35; I2 = 89.3, k = 7) were the non-HIV-integrated interventions associated with an increased likelihood of TB diagnosis (S4 Fig).

Linkage-to-care.

Similarly, only education and counseling (OR = 3.10, 95% CI: 1.97 to 4.86; I2 = 0%, k = 1) as a multistep effective intervention was associated with an increased likelihood of linkage to care among HIV-integrated interventions, while multisector collaboration (OR = 3.25, 95% CI: 2.05 to 5.14; I2 = 68.9, k = 3), digital health (OR = 1.12, 95% CI: 1.04 to 1.20; I2 = 0%, k = 2), and incentives (OR = 2.86, 95% CI: 1.25 to 6.50; I2 = 77.7%, k = 4) were associated with an increased likelihood of linkage-to-care among nonintegrated interventions (S4 Fig).

Cured.

Although none of the interventions with multistage effects were associated with cure outcomes among HIV-integrated studies, community-based interventions (OR = 2.53, 95% CI: 1.92 to 3.35; I2 = 91.8, k = 9), mixed interventions (OR = 1.19, 95% CI: 1.13 to 1.26; I2 = 38.8%, k = 3), counseling and education (OR = 2.08, 95% CI: 1.11 to 3.88; I2 = 41.8%, k = 4), and incentives (OR = 1.62, 95% CI: 1.06 to 2.48; I2 = 97.2%, k = 5) were associated with increased likelihood of TB cure among nonintegrated TB interventions (S4 Fig).

Treatment completion.

Like cure, no interventions with multistage effects were associated with treatment completion among HIV-integrated studies. However, counseling and education (OR = 1.48, 95% CI: 1.07 to 2.03; I2 = 57.5%, k = 8), home-based care (OR = 1.17, 95% CI: 1.11 to 1.24; I2 = 10%, k = 2), and incentives (OR = 1.37, 95% CI: 1.10 to 1.71; I2 = 68.5%, k = 12) were associated with increased likelihood of treatment completion in nonintegrated studies (S4 Fig).

Treatment success.

Similarly, no interventions with multistage effects were associated with treatment success among HIV-integrated studies. At the same time, home-based care (OR = 1.24, 95% CI: 1.15 to 1.33; I2 = 37.3%, k = 2), incentives (OR = 1.08, 95% CI: 1.05 to 1.11; I2 = 31.5%, k = 5), education and counseling (OR = 3.24, 95% CI: 1.88 to 5.55; I2 = 37.8%, k = 5), community-based interventions (OR = 2.91, 95% CI: 2.01 to 4.21; I2 = 94.7%, k = 10), and mixed interventions (OR = 1.14, 95% CI: 1.09 to 1.19; I2 = 38.8%, k = 3) were associated with an increased likelihood of treatment success in nonintegrated studies (S4 Fig).

Discussion

Ensuring the delivery of quality person-centered service to all people living with TB is a global TB control priority and crucial to ending the TB pandemic [117,118]. This review synthesized existing evidence on the effects of various TB interventions in optimizing care cascade outcomes from a global perspective. Our findings extend the literature by summarizing evidence on how the intervention impacts TB care cascade outcomes to inform holistic TB control strategies. Among TB interventions, education and counseling were associated with an increased likelihood of TB testing, diagnosis, cure, treatment completion, and treatment success compared to standard-of-care. Mixed interventions, community-based interventions, and incentives were each associated with multiple care cascade outcomes, and digital interventions were significantly associated with two care cascade outcomes.

Per our findings, community-based interventions, incentives, and multisector collaborations were the interventions associated an increased likelihood of outcomes for at least three care cased stages in LMICs. The evidence quality ranged from low to moderate certainty GRADE assessment. This is consistent with results in the literature on community-based interventions associated with testing, linkage-to-care, and treatment adherence [119123]. Our sub-analysis findings showed that community-based interventions increased the likelihood of testing, especially in LMICs, where TB is estimated to be more prevalent. Community-based interventions and multisector collaborations may have worked well in LMICs due to the fragile and fragmented healthcare systems and limited resources of many LMICs [124,125]. More research is needed on how best to implement and fund community-based TB care to improve overall outcomes in the settings.

The overall and RCTs-only sub-analysis showed that education and counseling increased the likelihood of all TB care cascade outcomes with an average moderate certainty. Mitigating public misconceptions and stigma that hinder TB services utilization through education and counseling may explain this observation. Similar to our findings, a previous review found that patient counseling at diagnosis improved linkage-to-care and treatment completion among TB patients [126]. Another review and Ethiopian study found that education and counseling engaged TB patients in the active self-management of their TB infection, which was essential to cure, treatment success, and the reduction of self-stigma [127,128]. We, therefore, emphasize that education and counseling should be valued and incorporated as a necessary component of ending TB strategies, especially for newly diagnosed TB patients.

Mixed interventions and incentives were associated with an increased likelihood of multiple care cascade outcomes with low to moderate evidence quality. Some studies combined two or more interventions and reported the effect of this tailored intervention on TB care. However, it is worth noting that various factors (including where binding constraints are on the cascade, the overall effectiveness of interventions mixed at each point, and the relative costs of different interventions) influence the impact of mixed interventions. Therefore, researchers should consider mixing interventions with multiple outcome effects or efficient single-step strategies that span all six steps of the care cascade when designing mixed interventions. Also, resource availability, existing structures, local settings, and long-term sustainability should be well thought-out when adopting methods like incentives in limited resources settings.

Overall, digital intervention only increased the likelihood of testing and linkage to care. This finding conforms with a previous review’s results that evidence of digital technologies improving TB care is contradictory and limited [129]. However, digital interventions have significantly improved healthcare services delivery and uptake for other infectious diseases like HIV [130]. New digital tools like smartphone-based diagnostics are cost-effective for rapid diagnostics in point-of-care testing and could enable real-time remote patient monitoring [131,132]. Thus, the roles of digital interventions in decentralizing and expanding healthcare could be tailored for efficient use in TB care and should be further researched.

To be noted, the interventions identified in this review were highly diverse. The pooled effects of these interventions on TB care cascade were highly heterogenous. Results from our meta-regression analysis showed that the study design, year of publication, and region of study were the major sources of heterogeneity. This was not surprising as we observed wide diversification in how interventions were designed, the duration and intensity of implementation and variations in how the interventions were implemented. The variations in settings (rural communities versus urban slums) and target populations (general populations versus prisoners or ex-convicts) and approach to implementation for each intervention contributed to heterogeneity. For example, a study adapted “staff training + recruited and trained lay workers + active case finding” as a comprehensive care approach, while another study implemented “peer training + patient counseling and education +onsite sputum collection + expediated treatment initiation” to improve case detection and treatment outcomes [133,134]. Therefore, attention should be paid to local setting needs and cultural context when choosing to adopt any of the interventions with multistage effects to improve TB care cascade outcomes.

We observed some evidence of publication bias per the funnel plots among the studies assessing interventions in TB diagnosis and treatment completion. This suggests that the effect size of certain interventions found in under this outcome may have been affected by missing small-size or negative finding studies. The reluctance of academic journals to publish studies with negative findings and our exclusion of case reports and short research reports may have contributed to this bias. Therefore, our reported effects of interventions on multiple care cascade outcomes should be interpreted with caution and within context. However, we corrected this bias by adjusting the plot using the trim-and-fill method. Therefore, our drawn conclusions based on the meta-analysis results remain salient.

Our study has implementation, policy, and research implications. First, our findings reiterate the WHO recommendation that education and counseling should form part of comprehensive TB care strategies [135]. Thus, countries without patient counseling guidelines should consider establishing policies to incorporate counseling into routine TB care. Secondly, merging or concurrently implementing intervention with multiple outcomes effect could improve global TB control significantly. Therefore, researchers should consider revising mixed TB interventions to incorporate more such interventions or effective single-step strategies that span the entire care cascade. Also, programs to upscale evidence-based approaches should consider local context variations and adjust strategies to reach national TB goals. Finally, digital health is the cornerstone of modern healthcare but have unclear impact on TB care cascade outcomes. Therefore, future research should further explore potential roles of digital intervention in optimizing TB care.

Our study has some limitations. First, the interventions were highly heterogeneous due to many factors, and the differences in implementation approaches like intensity, coverage, local context settings, and resource availability may have contributed to their effectiveness. Second, we did not evaluate the cost of implementing these interventions. Hence, our findings should be interpreted with attention to cost and feasibility. We also excluded non-English studies, which may have impacted the capture of literature from bibliographic databases of non-anglophone countries and biased our findings. However, our findings can be generalized because many of the interventions reviewed targeted diverse populations like PLWH, children, and migrants in both HIC and LMIC settings. Third, our study does not cover the entire TB care cascade as gaps in the early stages (focusing on testing or pretreatment loss to follow-up) and post-treatment outcomes (like TB recurrence and death) were not outcomes of interest and were not assessed in this review. Future reviews should consider examining these gaps and other key distinctions (like drug susceptibility or different forms of TB) and their effects on TB care outcomes to help inform strategies and policy adoption. Fourth, our funnel plots and meta-regression results showed the existence of publication bias. Nonetheless, our findings remain relevant to informing TB intervention programming if interpreted with caution and within context.

Conclusions

Our study shows the existence of a wide range of relatively simple interventions that could substantially improve TB care outcomes. Nonetheless, high fidelity along the care cascade would become increasingly important as the rate of TB drug resistance increases. Therefore, multistep efficient interventions like education and counseling, incentives, and mixed interventions should be keenly considered in expanding active TB control programs. But factors like differences in implementation intensity, resource availability, and local setting contexts should be well thought-out when choosing strategies to strengthen holistic TB care as the interventions were sufficiently heterogeneous.

Supporting information

S1 File. Supporting information file.

Protocol A in S1 File. Study protocol as published on PROSPERO. Table B in S1 File. Search strategy and results. Table C in S1 File. Characteristics of included studies grouped by intervention type. Table D in S1 File. Results of meta-regression analysis. Table E in S1 File. Outcome of risk of bias assessment for 84 RCTs studies using the Cochrane Risk of Bias Assessment Tool. Table F in S1 File. Quality assessment of included studies using the EPHPP quality assessment tool. Table G in S1 File. GRADE Outcomes. Table H in S1 File. PRISMA checklist for protocol.

https://doi.org/10.1371/journal.pmed.1004091.s001

(DOCX)

S2 Fig. Forest plots of subgroup analysis by study design (RCTs vs. non-RCTs).

https://doi.org/10.1371/journal.pmed.1004091.s003

(TIFF)

S3 Fig. Forest plots of subgroup analysis by region (LIMCs vs. HICs).

https://doi.org/10.1371/journal.pmed.1004091.s004

(TIFF)

S4 Fig. Forest plots of subgroup analysis by services integration (LIMCs vs. HICs).

https://doi.org/10.1371/journal.pmed.1004091.s005

(TIFF)

S1 Data. Data sets used in this review stratified by cascade outcomes.

https://doi.org/10.1371/journal.pmed.1004091.s006

(XLSX)

S2 Data. Spreadsheet used in data extraction with details of all data extracted for each study.

https://doi.org/10.1371/journal.pmed.1004091.s007

(XLSX)

Acknowledgments

We appreciate Dr. Madhu Pai, Dr. Liu Wei, and Miss. Jiayu He for serving as scientific advisors during this study. We also thank Miss Shamen Susan Chauma for assisting with screening articles and extracting data.

References

  1. 1. World Health Organization. Tuberculosis: Key Facts. Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis. 2022. [cited 2022 Nov 5].
  2. 2. World Health Organization. The End TB Strategy. Available from: https://www.who.int/tb/strategy/end-tb/en/. [cited 2022 Feb 22].
  3. 3. Subbaraman R, Nathavitharana RR, Mayer KH, Satyanarayana S, Chadha VK, Arinaminpathy N, et al. Constructing care cascades for active tuberculosis: A strategy for program monitoring and identifying gaps in quality of care. PLoS Med. 2019;16(2):e1002754. pmid:30811385
  4. 4. Yasobant S, Bhavsar P, Kalpana P, Memon F, Trivedi P, Saxena D. Contributing Factors in the Tuberculosis Care Cascade in India: A Systematic Literature Review. Risk Manag Healthc Policy. 2021;14:3275–3286. pmid:34408513
  5. 5. The National Center on Safe Supportive Learning Environments. Implementation: Definition of Programmatic Interventions. The U.S. Department of Education, Office of Safe and Supportive Schools to the American Institutes for Research (AIR). Available from: https://safesupportivelearning.ed.gov/topic-research/program-implementation. [cited 2021 Dec 27].
  6. 6. Wei W, Sha W, Sun W, Sun Q. Impact of programmatic intervention on the treatment compliance of patients with HIV/TB dual infection. Int J Clin Exp Med. 2016;9(2):4173–4177.
  7. 7. Zwarenstein M, Fairall LR, Lombard C, Mayers P, Bheekie A, English RG, et al. Outreach education for integration of HIV/AIDS care, antiretroviral treatment, and tuberculosis care in primary care clinics in South Africa: PALSA PLUS pragmatic cluster randomised trial. BMJ. 2011;342:d2022. pmid:21511783
  8. 8. Alipanah N, Jarlsberg L, Miller C, Linh NN, Falzon D, Jaramillo E, et al. Adherence interventions and outcomes of tuberculosis treatment: A systematic review and meta-analysis of trials and observational studies. PLoS Med. 2018;15(7):e1002595. pmid:29969463
  9. 9. Gashu KD, Gelaye KA, Mekonnen ZA, Lester R, Tilahun B. Does phone messaging improves tuberculosis treatment success? A systematic review and meta-analysis. BMC Infect Dis. 2020;20(1):42. pmid:31937260
  10. 10. Ciobanu A, Domente L, Soltan V, Bivol S, Severin L, Plesca V, et al. Do incentives improve tuberculosis treatment outcomes in the Republic of Moldova? Public Health Action. 2014;4(Suppl 2):S59–S63. pmid:26393100
  11. 11. Martins N, Morris P, Kelly PM. Food incentives to improve completion of tuberculosis treatment: randomised controlled trial in Dili, Timor-Leste. BMJ. 2009;339(7730):b4248–b. pmid:19858174
  12. 12. Hermans SM, Elbireer S, Tibakabikoba H, Hoefman BJ, Manabe YC. Text messaging to decrease tuberculosis treatment attrition in TB-HIV coinfection in Uganda. Patient Prefer Adherence. 2017;11:1479–1487. pmid:28919720
  13. 13. Bai LQ, Yang HL, Jian XW, He XG, Chen YF, Tang Y, et al. Increasing tuberculosis case detection through intensive referral and tracing in Hunan, China. Int J Tuberc Lung Dis. 2008;12(12):1431–1435. pmid:19017453
  14. 14. Bediang G, Stoll B, Elia N, Abena JL, Geissbuhler A. SMS reminders to improve adherence and cure of tuberculosis patients in Cameroon (TB-SMS Cameroon): a randomised controlled trial. BMC Public Health. 2018;18(1):583. pmid:29720146
  15. 15. Datiko DG, Lindtjorn B. Health extension workers improve tuberculosis case detection and treatment success in southern Ethiopia: a community randomized trial. PLoS ONE. 2009;4(5):e5443. pmid:19424460
  16. 16. 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):270–280. pmid:28479622
  17. 17. Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. pmid:19621072
  18. 18. Pradipta IS, Houtsma D, van Boven JFM, Alffenaar JC, Hak E. Interventions to improve medication adherence in tuberculosis patients: a systematic review of randomized controlled studies. NPJ Prim Care Respir Med. 2020;30(1):21. pmid:32393736
  19. 19. Higgins JP, Altman DG, Gøtzsche PC, Jüni P, Moher D, Oxman AD, et al. The Cochrane Collaboration’s tool for assessing risk of bias in randomised trials. BMJ. 2011;343:d5928. pmid:22008217
  20. 20. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ. 2019;366:l4898. pmid:31462531
  21. 21. Armijo-Olivo S, Stiles CR, Hagen NA, Biondo PD, Cummings GG. Assessment of study quality for systematic reviews: a comparison of the Cochrane Collaboration Risk of Bias Tool and the Effective Public Health Practice Project Quality Assessment Tool: methodological research. J Eval Clin Pract. 2012;18(1):12–18. pmid:20698919
  22. 22. Lönnroth K, Corbett E, Golub J, Godfrey-Faussett P, Uplekar M, Weil D, et al. Systematic screening for active tuberculosis: rationale, definitions and key considerations. Int J Tuberc Lung Dis. 2013;17(3):289–298. pmid:23407219
  23. 23. Assefa Y, Woldeyohannes S, Gelaw YA, Hamada Y, Getahun H. Screening tools to exclude active pulmonary TB in high TB burden countries: systematic review and meta-analysis. Int J Tuberc Lung Dis. 2019;23(6):728–734. pmid:31315706
  24. 24. van’t Hoog A, editor. Sensitivity and specificity of different TB screening tools and approaches: a systematic review. 43rd Union World Conference on Lung Health, Kuala Lumpur, Malaysia; 2012.
  25. 25. Coulter C. Tuberculosis testing. Aust J Gen Pract. 2012;41:489–492. pmid:22762067
  26. 26. World Health Organization. Diagnostic testing for TB, HIV-associated TB and drug-resistant TB. Available from: https://www.who.int/publications/digital/global-tuberculosis-report-2021/tb-diagnosis-treatment/diagnostic-testing. [cited 2022 Oct 30].
  27. 27. World Health Organization. Definitions and reporting framework for tuberculosis– 2013 revision (updated December 2014 and January 2020). Available from: https://apps.who.int/iris/bitstream/handle/10665/79199/9789241505345_eng.pdf;jsessionid=763D9DA03D3591356F8823AEF9A85061?sequence=1. [cited 2021 Nov 25].
  28. 28. Aldridge RW, Hayward AC, Hemming S, Possas L, Ferenando G, Garber E, et al. Effectiveness of peer educators on the uptake of mobile X-ray tuberculosis screening at homeless hostels: a cluster randomised controlled trial. BMJ Open. 2015;5(9):e008050. pmid:26391630
  29. 29. Bello G, Faragher B, Sanudi L, Namakhoma I, Banda H, Malmborg R, et al. The effect of engaging unpaid informal providers on case detection and treatment initiation rates for TB and HIV in rural Malawi (Triage Plus): A cluster randomised health system intervention trial. PLoS ONE. 2017;12(9):e0183312. pmid:28877245
  30. 30. Chaisson RE, Keruly JC, McAvinue S, Gallant JE, Moore RD. Effects of an incentive and education program on return rates for PPD test reading in patients with HIV infection. J Acquir Immune Defic Syndr Hum Retrovirol. 1996;11(5):455–459. pmid:8605590
  31. 31. Ekwueme OE, Omotowo BI, Agwuna KK. Strengthening contact tracing capacity of pulmonary tuberculosis patients in Enugu, southeast Nigeria: a targeted and focused health education intervention study. BMC Public Health. 2014;14:1175. pmid:25407379
  32. 32. FitzGerald JM, Patrick DM, Strathdee S, Rekart M, Elwood RK, Schecter MT, et al. Use of incentives to increase compliance for TB screening in a population of intravenous drug users. Vancouver Injection Drug Use Study Group. Int J Tuberc Lung Dis. 1999;3(2):153–155. pmid:10091882
  33. 33. Griffiths C, Sturdy P, Brewin P, Bothamley G, Eldridge S, Martineau A, et al. Educational outreach to promote screening for tuberculosis in primary care: a cluster randomised controlled trial. Lancet (London, England). 2007;369(9572):1528–1534. pmid:17482983
  34. 34. Harstad I, Henriksen AH, Sagvik E. Collaboration between municipal and specialist public health care in tuberculosis screening in Norway. BMC Health Serv Res. 2014;14:238. pmid:24885211
  35. 35. Malotte CK, Hollingshead JR, Rhodes F. Monetary versus nonmonetary incentives for TB skin test reading among drug users. Am J Prev Med. 1999;16(3):182–188. pmid:10198656
  36. 36. Sequeira-Aymar E, Cruz A, Serra-Burriel M, di Lollo X, Gonçalves AQ, Camps-Vilà L, et al. Improving the detection of infectious diseases in at-risk migrants with an innovative integrated multi-infection screening digital decision support tool (IS-MiHealth) in primary care: A pilot cluster-randomized controlled trial. J Travel Med. 2021.
  37. 37. Shah L, Rojas Peña M, Mori O, Zamudio C, Kaufman JS, Otero L, et al. 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. Int J Infect Dis. 2020;100:95–103.
  38. 38. Vo LNQ, Forse RJ, Codlin AJ, Vu TN, Le GT, Do GC, et al. A comparative impact evaluation of two human resource models for community-based active tuberculosis case finding in Ho Chi Minh City, Viet Nam. BMC Public Health. 2020;20(1):934. pmid:32539700
  39. 39. Alisjahbana B, van Crevel R, Danusantoso H, Gartinah T, Soemantri ES, Nelwan RH, et al. Better patient instruction for sputum sampling can improve microscopic tuberculosis diagnosis. Int J Tuberc Lung Dis. 2005;9(7):814–817. pmid:16013780
  40. 40. Balakrishnan S, Manikantan J, Sreenivas A, Jayasankar S, Sunilkumar M, Rakesh PS, et al. Social inclusion: An effort to end loss-to-treatment follow-up in tuberculosis. Indian J Tuberc. 2015;62(4):230–234. pmid:26970465
  41. 41. Bjerrum S, Bonsu F, Hanson-Nortey NN, Kenu E, Johansen IS, Andersen AB, et al. Tuberculosis screening in patients with HIV: use of audit and feedback to improve quality of care in Ghana. Glob Health Action. 2016;9:32390. pmid:27569593
  42. 42. Calligaro GL, Zijenah LS, Peter JG, Theron G, Buser V, McNerney R, et al. Effect of new tuberculosis diagnostic technologies on community-based intensified case finding: a multicentre randomised controlled trial. Lancet Infect Dis. 2017;17(4):441–450. pmid:28063795
  43. 43. Corbett EL, Bandason T, Duong T, Dauya E, Makamure B, Churchyard GJ, 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 cluster-randomised trial. Lancet (London, England). 2010;376(9748):1244–1253. pmid:20923715
  44. 44. Durovni B, Saraceni V, van den Hof S, Trajman A, Cordeiro-Santos M, Cavalcante S, et al. Impact of replacing smear microscopy with Xpert MTB/RIF for diagnosing tuberculosis in Brazil: a stepped-wedge cluster-randomized trial. PLoS Med. 2014;11(12):e1001766. pmid:25490549
  45. 45. Eom JS, Park S, Jang H, Kim S, Yoo WH, Kim SH, et al. Bronchial washing using a thin versus a thick bronchoscope to diagnose pulmonary tuberculosis: A randomized trial. Clin Infect Dis. 2022. pmid:36151949
  46. 46. Fatima R, Qadeer E, Yaqoob A, Haq MU, Majumdar SS, Shewade HD, et al. Extending ’Contact Tracing’ into the Community within a 50-Metre Radius of an Index Tuberculosis Patient Using Xpert MTB/RIF in Urban, Pakistan: Did It Increase Case Detection? PLoS ONE. 2016;11(11):e0165813. pmid:27898665
  47. 47. Geldenhuys HD, Whitelaw A, Tameris MD, Van As D, Luabeya KK, Mahomed H, et al. A controlled trial of sputum induction and routine collection methods for TB diagnosis in a South African community. Eur J Clin Microbiol Infect Dis. 2014;33(12):2259–2266. pmid:25022447
  48. 48. Khan MS, Dar O, Sismanidis C, Shah K, Godfrey-Faussett P. Improvement of tuberculosis case detection and reduction of discrepancies between men and women by simple sputum-submission instructions: a pragmatic randomised controlled trial. Lancet (London, England). 2007;369(9577):1955–1960. pmid:17560448
  49. 49. Khan MA, Munir MA, Anil S, Ahmad M, Walley J, Qadeer E, et al. Structured performance monitoring of TB-care at facility, district and province levels—Pakistan experience. J Pak Med Assoc. 2016;66(4):418–424. pmid:27122268
  50. 50. Khan MA, Anil S, Ahmed M, Athar A, Ghafoor A, Brouwer M. Active Case Finding of Tuberculosis: Randomized Evaluation of Simple and Infotainment Chest Camps. Ann Glob Health. 2016;82(5):813–818.
  51. 51. Martinson NA, Lebina L, Webb EL, Ratsela A, Varavia E, Kinghorn A, et al. Household Contact Tracing With Intensified Tuberculosis and Human Immunodeficiency Virus Screening in South Africa: A Cluster-Randomized Trial. Clin Infect Dis. 2022;75(5):849–856. pmid:34950944
  52. 52. Mhalu G, Hella J, Doulla B, Mhimbira F, Mtutu H, Hiza H, et al. Do Instructional Videos on Sputum Submission Result in Increased Tuberculosis Case Detection? A Randomized Controlled Trial. PLoS ONE. 2015;10(9):e0138413. pmid:26418678
  53. 53. Parija D, Patra TK, Kumar AM, Swain BK, Satyanarayana S, Sreenivas A, et al. Impact of awareness drives and community-based active tuberculosis case finding in Odisha India. Int J Tuberc Lung Dis. 2014;18(9):1105–1107. pmid:25189560
  54. 54. Qureshi H, Arif A, Alam E, Qadir N. Integration of informal medical practitioners in DOTS implementation to improve case detection rate. J Pak Med Assoc. 2010;60(1):33–37. pmid:20055277
  55. 55. Rudolf F, Abate E, Moges B, Mendes AM, Mengistu MY, Sifna A, et al. Increasing smear positive tuberculosis detection using a clinical score—A stepped wedge multicenter trial from Africa. Int J Infect Dis. 2021;113(Suppl 1):S55–s62. pmid:33757875
  56. 56. Ruutel K, Loit HM, Sepp T, Kliiman K, McNutt LA, Uuskula A. Enhanced tuberculosis case detection among substitution treatment patients: a randomized controlled trial. BMC Res Notes. 2011;4:192. pmid:21676222
  57. 57. Sah R, Singh UK, Mainali R, Sanaie A, Pande T, Vasquez NA, et al. Engaging Private Health Care Providers to Identify Individuals with TB in Nepal. Int J Environ Res Public Health. 2021;18:11762. pmid:34831519
  58. 58. Adane K, Spigt M, Winkens B, Dinant GJ. Tuberculosis case detection by trained inmate peer educators in a resource-limited prison setting in Ethiopia: a cluster-randomised trial. Lancet Glob Health. 2019;7(4):e482–e491. pmid:30824364
  59. 59. Bassett IV, Coleman SM, Giddy J, Bogart LM, Chaisson CE, Ross D, et al. Sizanani: A Randomized Trial of Health System Navigators to Improve Linkage to HIV and TB Care in South Africa. J Acquir Immune Defic Syndr. 2016;73(2):154–60. pmid:27632145
  60. 60. Davis J, Katamba A, Vasquez J, Crawford E, Sserwanga A, Kakeeto S, et al. Evaluating tuberculosis case detection via real-time monitoring of tuberculosis diagnostic services. Am J Respir Crit Care Med. 2011;184(3):362–367. pmid:21471088
  61. 61. Fairall LR, Zwarenstein M, Bateman ED, Bachmann M, Lombard C, Majara BP, et al. Effect of educational outreach to nurses on tuberculosis case detection and primary care of respiratory illness: pragmatic cluster randomised controlled trial. BMJ. 2005;331(7519):750–754. pmid:16195293
  62. 62. Jenum S, Selvam S, Jesuraj N, Ritz C, Hesseling AC, Cardenas V, et al. Incidence of tuberculosis and the influence of surveillance strategy on tuberculosis case-finding and all-cause mortality: a cluster randomised trial in Indian neonates vaccinated with BCG. BMJ Open Respir Res. 2018;5(1):e000304. pmid:30397482
  63. 63. Khatana GH, Haq I, Khan SMS. Effectiveness, acceptance and feasibility of home-based intervention model for tuberculosis contact tracing in Kashmir. J Clin Tuberc Other Mycobact Dis. 2019;14:19–25. pmid:31720414
  64. 64. Lambert ML, Delgado R, Michaux G, Vols A, Speybroeck N, Van der Stuyft P. Collaboration between private pharmacies and national tuberculosis programme: an intervention in Bolivia. Trop Med Int Health. 2005;10(3):246–250. pmid:15730509
  65. 65. Law S, Seepamore B, Oxlade O, Sikhakhane N, Dawood H, Chetty S, et al. Acceptability, feasibility, and impact of a pilot tuberculosis literacy and treatment counselling intervention: a mixed methods study. BMC Infect Dis. 2021;21(1):449. pmid:34006254
  66. 66. Lee JE, Kim YK, Kim TH, Kim KH, Lee EJ, Uh ST, et al. What strategy can be applied to the patients with culture positive tuberculosis to reduce treatment delay in a private tertiary healthcare center? Infect Chemother. 2011;43(1):42–47.
  67. 67. Majella M, Thekkur P, Kumar A, Chinnakali P, Saka V, Roy G. Effect of mobile voice calls on treatment initiation among patients diagnosed with tuberculosis in a tertiary care hospital of Puducherry: A randomized controlled trial. J Postgrad Med. 2021;67(4):205–212. pmid:34169923
  68. 68. Mukoka M, Twabi HH, Msefula C, Semphere R, Ndhlovu G, Lipenga T, et al. Utility of Xpert MTB/RIF Ultra and digital chest radiography for the diagnosis and treatment of TB in people living with HIV: a randomised controlled trial (XACT-TB). Trans R Soc Trop Med Hyg. 2022. pmid:35963826
  69. 69. Mwansa-Kambafwile JRM, Chasela C, Levin J, Ismail N, Menezes C. Treatment initiation among tuberculosis patients: the role of short message service (SMS) technology and Ward-based outreach teams (WBOTs). BMC Public Health. 2022;22(1):318. pmid:35168581
  70. 70. Perlman DC, Friedmann P, Horn L, Nugent A, Schoeb V, Carey J, et al. Impact of monetary incentives on adherence to referral for screening chest x-rays after syringe exchange-based tuberculin skin testing. J Urban Health. 2003;80(3):428–437. pmid:12930881
  71. 71. Wagstaff A, van Doorslaer E, Burger R, journal.pone. SMS nudges as a tool to reduce tuberculosis treatment delay and pretreatment loss to follow-up. A randomized controlled trial. PLoS ONE. 2019;14(6):e0218527.
  72. 72. Wang L, Cheng S, Xu M, Huang F, Xu W, Li R, et al. Model collaboration between hospitals and public health system to improve tuberculosis control in China. Int J Tuberc Lung Dis. 2009;13(12):1486–1492. pmid:19919765
  73. 73. White MC, Tulsky JP, Reilly P, McIntosh HW, Hoynes TM, Goldenson J. A clinical trial of a financial incentive to go to the tuberculosis clinic for isoniazid after release from jail. Int J Tuberc Lung Dis. 1998;2(6):506–512. pmid:9626609
  74. 74. Zhu M, Wang J, Dib HH, Wang Z. Enhancing the management of cross-regional transfer of floating tuberculosis cases by active follow-up and communication. Eur J Public Health. 2012;22(4):577–582. pmid:22117052
  75. 75. Burzynski J, Mangan JM, Lam CK, Macaraig M, Salerno MM, deCastro BR, et al. In-Person vs Electronic Directly Observed Therapy for Tuberculosis Treatment Adherence: A Randomized Noninferiority Trial. JAMA Netw Open. 2022;5(1):e2144210. pmid:35050357
  76. 76. Chaisson RE, Barnes GL, Hackman J, Watkinson L, Kimbrough L, Metha S, et al. A randomized, controlled trial of interventions to improve adherence to isoniazid therapy to prevent tuberculosis in injection drug users. Am J Med. 2001;110(8):610–615. pmid:11382368
  77. 77. Chua AP, Lim LK, Ng H, Chee CB, Wang YT. Outcome of a grocery voucher incentive scheme for low-income tuberculosis patients on directly observed therapy in Singapore. Singapore Med J. 2015;56(5):274–279. pmid:25788246
  78. 78. Chuck C, Robinson E, Macaraig M, Alexander M, Burzynski J. Enhancing management of tuberculosis treatment with video directly observed therapy in New York City. Int J Tuberc Lung Dis. 2016;20(5):588–593. pmid:27084810
  79. 79. Demissie M, Getahun H, Lindtjørn B. Community tuberculosis care through “TB clubs” in rural North Ethiopia. Soc Sci Med. 2003;56(10):2009–2018. pmid:12697193
  80. 80. Diaw MM, Ndiaye M, Riccardi N, Ungaro R, Alagna R, Cirillo DM, et al. Implementing TB control in a rural, resource-limited setting: the stop-TB Italia project in Senegal. Multidiscip Respir Med. 2018;13:41. pmid:30455883
  81. 81. Fang XH, Guan SY, Tang L, Tao FB, Zou Z, Wang JX, et al. Effect of Short Message Service on Management of Pulmonary Tuberculosis Patients in Anhui Province, China: A Prospective, Randomized, Controlled Study. Med Sci Monit. 2017;23:2465–2469. pmid:28534476
  82. 82. Jahnavi G, Sudha CH. Randomised controlled trial of food supplements in patients with newly diagnosed tuberculosis and wasting. Singapore Med J. 2010;51(12):957–962. pmid:21221502
  83. 83. Kufa T, Fielding KL, Hippner P, Kielmann K, Vassall A, Churchyard GJ, et al. An intervention to optimise the delivery of integrated tuberculosis and HIV services at primary care clinics: results of the MERGE cluster randomised trial. Contemp Clin Trials. 2018;72:43–52. pmid:30053431
  84. 84. Liefooghe R, Suetens C, Meulemans H, Moran MB, De Muynck A. A randomised trial of the impact of counselling on treatment adherence of tuberculosis patients in Sialkot, Pakistan. Int J Tuberc Lung Dis. 1999;3(12):1073–1080. pmid:10599010
  85. 85. Miller AC, Golub JE, Cavalcante SC, Durovni B, Moulton LH, Fonseca Z, et al. Controlled trial of active tuberculosis case finding in a Brazilian favela. Int J Tuberc Lung Dis. 2010;14(6):720–726. pmid:20487610
  86. 86. Morisky DE, Malotte CK, Ebin V, Davidson P, Cabrera D, Trout PT, et al. Behavioral interventions for the control of tuberculosis among adolescents. Public Health Rep. 2001;116(6):568–574. pmid:12196616
  87. 87. Moulding TS, Caymittes M. Managing medication compliance of tuberculosis patients in Haiti with medication monitors. Int J Tuberc Lung Dis. 2002;6(4):313–319. pmid:11936740
  88. 88. Nyamathi AM, Christiani A, Nahid P, Gregerson P, Leake B. A randomized controlled trial of two treatment programs for homeless adults with latent tuberculosis infection. Int J Tuberc Lung Dis. 2006;10(7):775–782. pmid:16848340
  89. 89. Rajasekaran S. Short course chemotherapy: A controlled study of indirect defaulter retrieval method. Indian J Tuberc. 1993:40.
  90. 90. Rocha C, Montoya R, Zevallos K, Curatola A, Ynga W, Franco J, et al. The Innovative Socio-economic Interventions Against Tuberculosis (ISIAT) project: an operational assessment. Int J Tuberc Lung Dis. 2011;15(Suppl 2):50–57. pmid:21740659
  91. 91. Turnbull L, Bell C, Davies S, Child F. Delivering tertiary tuberculosis care virtually. Arch Dis Child. 2021;106(12):1226–1228. pmid:34158279
  92. 92. Wei X, Zou G, Yin J, Walley J, Yang H, Kliner M, et al. Providing financial incentives to rural-to-urban tuberculosis migrants in Shanghai: an intervention study. Infect Dis Poverty. 2012;1(1):9. pmid:23849348
  93. 93. Yao H, Wei X, Liu J, Zhao J, Hu D, Walley JD. Evaluating the effects of providing financial incentives to tuberculosis patients and health providers in China. Int J Tuberc Lung Dis. 2008;12(10):1166–1172. pmid:18812047
  94. 94. Zou G, Wei X, Witter S, Yin J, Walley J, Liu S, et al. Incremental cost-effectiveness of improving treatment results among migrant tuberculosis patients in Shanghai. Int J Tuberc Lung Dis. 2013;17(8):1056–1064. pmid:23827030
  95. 95. Ali AOA, Prins MH. Mobile health to improve adherence to tuberculosis treatment in Khartoum state, Sudan. J Public Health Afr. 2019;10(2):1101. pmid:32257081
  96. 96. Müller AM, Osório CS, de Figueiredo RV, Silva DR, Dalcin PTR. Educational strategy intervention and remote supervision on the post-discharge management of tuberculosis diagnosed in the hospital: Randomized clinical trial. Clin Respir J. 2019;13(8):505–512. pmid:31207148
  97. 97. Broomhead S, Mars M. Retrospective return on investment analysis of an electronic treatment adherence device piloted in the Northern Cape Province. Telemed J E Health. 2012;18(1):24–31. pmid:22150713
  98. 98. Lee CY, Chi MJ, Yang SL, Lo HY, Cheng SH. Using financial incentives to improve the care of tuberculosis patients. Am J Manag Care. 2015;21(1):e35–e42. pmid:25880266
  99. 99. Manyazewal T, Woldeamanuel Y, Holland DP, Fekadu A, Marconi VC. Effectiveness of a digital medication event reminder and monitor device for patients with tuberculosis (SELFTB): a multicenter randomized controlled trial. BMC Med. 2022;20(1):310. pmid:36167528
  100. 100. Niazi AD, Al-Delaimi AM. Impact of community participation on treatment outcomes and compliance of DOTS patients in Iraq. East Mediterr Health J. 2003;9(4):709–717. pmid:15748068
  101. 101. Torrens AW, Rasella D, Boccia D, Maciel EL, Nery JS, Olson ZD, et al. Effectiveness of a conditional cash transfer programme on TB cure rate: a retrospective cohort study in Brazil. Trans R Soc Trop Med Hyg. 2016;110(3):199–206. pmid:26884501
  102. 102. Venkatapraveen A, Rampure MV, Patil N, Shivanand Hinchageri SS, Lakshmi DP. Assessment of clinical pharmacist intervention to improve compliance and health care outcomes of tuberculosis patients. Der Pharmacia Lettre. 2012;4(3):931–937.
  103. 103. Acosta J, Flores P, Alarcón M, Grande-Ortiz M, Moreno-Exebio L, Puyen ZM. A randomised controlled trial to evaluate a medication monitoring system for TB treatment. Int J Tuberc Lung Dis. 2022;26(1):44–49. pmid:34969428
  104. 104. Cattamanchi A, Crowder R, Kityamuwesi A, Kiwanuka N, Lamunu M, Namale C, et al. Digital adherence technology for tuberculosis treatment supervision: A stepped-wedge cluster-randomized trial in Uganda. PLoS Med. 2021;18(5):1–15.
  105. 105. Gashu KD, Gelaye KA, Lester R, Tilahun B. Effect of a phone reminder system on patient-centered tuberculosis treatment adherence among adults in Northwest Ethiopia: a randomised controlled trial. BMJ Health Care Inform. 2021;28(1).
  106. 106. Iribarren S, Beck S, Pearce PF, Chirico C, Etchevarria M, Cardinale D, et al. TextTB: A Mixed Method Pilot Study Evaluating Acceptance, Feasibility, and Exploring Initial Efficacy of a Text Messaging Intervention to Support TB Treatment Adherence. Tuberc Res Treat. 2013;2013:349394. pmid:24455238
  107. 107. Lee S, Khan OF, Seo JH, Kim DY, Park KH, Jung SI, et al. Impact of Physician’s Education on Adherence to Tuberculosis Treatment for Patients of Low Socioeconomic Status in Bangladesh. Chonnam Med J. 2013;49(1):27–30. pmid:23678474
  108. 108. Lu H, Yan F, Wang W, Wu L, Ma W, Chen J, et al. Do transportation subsidies and living allowances improve tuberculosis control outcomes among internal migrants in urban Shanghai, China? Western Pac Surveill Response J. 2013;4(1):19–24. pmid:23908951
  109. 109. Parwati NM, Bakta IM, Januraga PP, Wirawan IMA. A Health Belief Model-Based Motivational Interviewing for Medication Adherence and Treatment Success in Pulmonary Tuberculosis Patients. Int J Environ Res Public Health. 2021;18(24). pmid:34948846
  110. 110. Ravenscroft L, Kettle S, Persian R, Ruda S, Severin L, Doltu S, et al. Video-observed therapy and medication adherence for tuberculosis patients: randomised controlled trial in Moldova. Eur Respir J. 2020;56(2). pmid:32381495
  111. 111. Shargie EB, Morkve O, Lindtjorn B. Tuberculosis case-finding through a village outreach programme in a rural setting in southern Ethiopia: community randomized trial. Bull World Health Organ. 2006;84(2):112–119. pmid:16501728
  112. 112. Shin S, Livchits V, Connery HS, Shields A, Yanov S, Yanova G, et al. Effectiveness of alcohol treatment interventions integrated into routine tuberculosis care in Tomsk, Russia. Addiction. 2013;108(8):1387–1396. pmid:23490304
  113. 113. Soares EC, Vollmer WM, Cavalcante SC, Pacheco AG, Saraceni V, Silva JS, et al. Tuberculosis control in a socially vulnerable area: a community intervention beyond DOT in a Brazilian favela. Int J Tuberc Lung Dis. 2013;17(12):1581–1586. pmid:24200272
  114. 114. Sudarsanam TD, John J, Kang G, Mahendri V, Gerrior J, Franciosa M, et al. Pilot randomized trial of nutritional supplementation in patients with tuberculosis and HIV-tuberculosis coinfection receiving directly observed short-course chemotherapy for tuberculosis. Trop Med Int Health. 2011;16(6):699–706. pmid:21418447
  115. 115. Thomas B, Watson B, Senthil EK, Deepalakshmi A, Balaji G, Chandra S, et al. Alcohol intervention strategy among tuberculosis patients: a pilot study from South India. Int J Tuberc Lung Dis. 2017;21(8):947–952. pmid:28786805
  116. 116. Ukwaja KN, Alobu I, Gidado M, Onazi O, Oshi DC. Economic support intervention improves tuberculosis treatment outcomes in rural Nigeria. Int J Tuberc Lung Dis. 2017;21(5):564–570. pmid:28399972
  117. 117. Alsdurf H, Hill PC, Matteelli A, Getahun H, Menzies D. The cascade of care in diagnosis and treatment of latent tuberculosis infection: a systematic review and meta-analysis. Lancet Infect Dis. 2016;16(11):1269–1278. pmid:27522233
  118. 118. Reid MJ, Arinaminpathy N, Bloom A, Bloom BR, Boehme C, Chaisson R, et al. Building a tuberculosis-free world: The Lancet Commission on tuberculosis. Lancet. 2019;393(10178):1331–1384. pmid:30904263
  119. 119. Simwaka BN, Theobald S, Willets A, Salaniponi FM, Nkhonjera P, Bello G, et al. Acceptability and effectiveness of the storekeeper-based TB referral system for TB suspects in sub-districts of Lilongwe in Malawi. PLoS ONE. 2012;7(9):e39746. pmid:22962575
  120. 120. Jacobson KB, Moll AP, Friedland GH, Shenoi SV. Successful Tuberculosis Treatment Outcomes among HIV/TB Coinfected Patients Down-Referred from a District Hospital to Primary Health Clinics in Rural South Africa. PLoS ONE. 2015;10(5):e0127024. pmid:25993636
  121. 121. Uwimana J, Zarowsky C, Hausler H, Swanevelder S, Tabana H, Jackson D. Community-based intervention to enhance provision of integrated TB-HIV and PMTCT services in South Africa. Int J Tuberc Lung Dis. 2013;17(10 Suppl 1):48–55. pmid:24020602
  122. 122. Kigozi G, Heunis C, Engelbrecht M. Community health worker motivation to perform systematic household contact tuberculosis investigation in a high burden metropolitan district in South Africa. BMC. 2020;20(1):882.
  123. 123. Musa BM, Iliyasu Z, Yusuf SM, Uloko AE. Systematic review and metanalysis on community based interventions in tuberculosis care in developing countries. Niger J Med. 2014;23(2):103–117. pmid:24956684
  124. 124. National Academies of Sciences, Engineering and Medicine, Health and Medicine Division, Board on Health Care Services, Board on Global Health, Committee on Improving the Quality of Health Care Globally. Crossing the Global Quality Chasm: Improving Health Care Worldwide. Washington (DC): National Academies Press (US); 2018 Aug 28. 4, The Current State of Global Health Care Quality. Available from: https://www.ncbi.nlm.nih.gov/books/NBK535654/?report=classic. [cited 2021 Dec 27].
  125. 125. Salifu RS, Hlongwana KW. Frontline healthcare workers’ experiences in implementing the TB-DM collaborative framework in Northern Ghana. BMC Health Serv Res. 2021;21(1):861. pmid:34425809
  126. 126. Müller AM, Osório CS, Silva DR, Sbruzzi G, de Tarso P, Dalcin R. Interventions to improve adherence to tuberculosis treatment: systematic review and meta-analysis. Int J Tuberc Lung Dis. 2018;22(7):731–740. pmid:29914598
  127. 127. Macq J, Torfoss T, Getahun H. Patient empowerment in tuberculosis control: reflecting on past documented experiences. Trop Med Int Health. 2007;12(7):873–885. pmid:17596255
  128. 128. Zaeh S, Kempker R, Stenehjem E, Blumberg HM, Temesgen O, Ofotokun I, et al. Improving tuberculosis screening and isoniazid preventive therapy in an HIV clinic in Addis Ababa, Ethiopia. Int J Tuberc Lung Dis. 2013;17(11):1396–1401. pmid:24125440
  129. 129. Ngwatu BK, Nsengiyumva NP, Oxlade O, Mappin-Kasirer B, Nguyen NL, Jaramillo E, et al. The impact of digital health technologies on tuberculosis treatment: a systematic review. Eur Respir J. 2018;51(1). pmid:29326332
  130. 130. Daher J, Vijh R, Linthwaite B, Dave S, Kim J, Dheda K, et al. Do digital innovations for HIV and sexually transmitted infections work? Results from a systematic review (1996–2017). BMJ Open. 2017;7(11):e017604. pmid:29101138
  131. 131. Osei E, Mashamba-Thompson TP. Mobile health applications for disease screening and treatment support in low-and middle-income countries: A narrative review. Heliyon. 2021;7(3):e06639. pmid:33869857
  132. 132. Osei E, Nkambule SJ, Vezi PN, Mashamba-Thompson TP. Systematic Review and Meta-Analysis of the Diagnostic Accuracy of Mobile-Linked Point-of-Care Diagnostics in Sub-Saharan Africa. Diagnostics. 2021;11(6). pmid:34204848
  133. 133. Puchalski Ritchie LM, Schull MJ, Martiniuk AL, Barnsley J, Arenovich T, van Lettow M, et al. A knowledge translation intervention to improve tuberculosis care and outcomes in Malawi: a pragmatic cluster randomized controlled trial. Implement Sci. 2015;10:38. pmid:25890186
  134. 134. Lisboa M, Fronteira I, Mason PH, Martins M. Using hospital auxiliary worker and 24-h TB services as potential tools to overcome in-hospital TB delays: a quasi-experimental study. Hum Resour Health. 2020;18(1):28. pmid:32245488
  135. 135. World Health Organization. WHO Consolidated Guidelines on Tuberculosis. Module 4: treatment—drug-resistant tuberculosis treatment. Available from: https://www.aidsdatahub.org/sites/default/files/resource/who-consolidated-guidelines-tuberculosis-module-4-treatment-2020.pdf. 2020. [cited 2021 Dec 12].