Comparing Active Case-Finding Strategies and Passive Case-Finding for Tuberculosis: An Umbrella Review of Current Evidence
Bibliographic record
Abstract
Abstract Background Tuberculosis (TB) remains a global health challenge. Recent studies have compared active case-finding (ACF) with passive case-finding (PCF) and various ACF strategies, assessing diagnostic yield, treatment outcomes, cost-effectiveness, and overall TB control impact. However, evidence remains fragmented due to methodological heterogeneity. Methods To assess how ACF compares to PCF and to different ACF strategies in improving TB detection, treatment outcomes, and overall T control, we conducted an umbrella review of systematic reviews and meta-analyses published through March 30, 2025, evaluating ACF versus PCF and different ACF strategies across diverse settings. Key outcomes included diagnostic yield, treatment outcomes, cost-effectiveness, and epidemiological impact. Methodological quality was assessed using the AMSTAR 2 tool. Results Twelve systematic reviews and meta-analyses met the inclusion criteria, with methodological quality ranging from low to moderate. ACF may be more effective than PCF in increasing TB detection rates, especially in high-risk populations such as migrants, people living with HIV, drug users, and close contacts of TB patients. Treatment outcomes among ACF-identified cases were mixed, with concerns about pre-treatment loss to follow-up. ACF interventions were cost-effective in high-prevalence settings, though their cost-effectiveness varied depending on implementation coverage, intensity, and the screening tools. However, evidence linking ACF to sustained increases in national routine TB notifications was limited. Conclusion While ACF is effective for targeted detection in high-risk groups and often cost-effective in high-burden settings, its broader epidemiological impact remains uncertain. Future TB control strategies should prioritize integrated, context-sensitive approaches that combine active and passive case detection, and future research should focus on evaluating ACF interventions based on their ability to achieve sustained reductions in TB prevalence and transmission over time. Take home message: Despite extensive research on ACF versus PCF in TB control, the fragmented and methodologically diverse evidence necessitates a comprehensive synthesis to inform global policies and practices. What this study adds - ACF improves TB detection in high-risk groups, but its effectiveness and cost-effectiveness are context-dependent. Cost-effective screening may enable early detection and treatment, reducing the TB burden on healthcare systems. ACF may be effective and cost effective in high-risk populations and at observational level but its epidemiological impact at national level is unclear How this study might affect research, practice or policy - This study highlights the need for targeted TB screening in high-risk groups to reduce TB incidence, support for continued investment in cost-effective strategies, and the enhancement of health outcomes through integrated approaches and supportive measures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.040 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.018 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".