Systematic review of barriers to and enablers of tuberculosis diagnosis, notification, and intervention for designing customised intervention package to minimise ‘missing millions’ in tribal communities of India
Bibliographic record
Abstract
Background: Tribal communities in India experience a very high burden of tuberculosis (TB), estimated at 7030 per million. The diagnosis and notification gaps are substantial, partly due to the geographical remoteness of these populations. Within an overarching study to design an intervention for finding the 'missing millions' among tribal communities, we conducted a systematic review to identify the barriers and enablers of tuberculosis diagnosis and notification, with the aim of developing a contextually relevant intervention. Methods: We searched PubMed, Embase and Web of Science using terms related to TB, diagnosis, notification, barriers, enablers, and interventions. Studies from lower- and lower-middle-income countries (LICs and LMICs) published between 2000-2023 were included. Qualitative and quantitative studies were assessed using the Critical Appraisal Skills Programme tool and Newcastle Ottawa scale, respectively. Narrative and thematic analyses were performed, applying the socio-ecological model (SEM) to categorise barriers and enablers of diagnosis and notification, and the consolidated framework for implementation research (CFIR) to assess intervention implementation. Results: Thirty-four eligible studies from 15 LICs and LMICs were included in the review. At community level, limited knowledge, illiteracy, stigma, geographical inaccessibility, and financial constraints were key barriers of diagnosis. At health system level, active case finding was the major intervention; however, inadequate diagnostic facilities, shortage of trained staff, insufficient incentives, weak counselling, and inadequate budget were the major barriers. Reported enablers were: increasing awareness about TB in the community to reduce stigma, encouragement from family members and TB survivors, mobilising human resources, regular capacity-building and monetary incentives to health workers. Conclusions: This systematic review identified barriers and enablers at multiple levels of the SEM and CFIR frameworks. To addressed the interconnected challenges, multifaceted and context-specific strategies are essential. Approaches that combine community engagement along with health system strengthening are essential for reducing the diagnosis and notification gaps among tribal populations. Registration: PROSPERO: CRD42023439841.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".