Prevalence of Tuberculosis in Central Asia and Southern Caucasus: A Systematic Literature Review
Bibliographic record
Abstract
Background: In 2023, tuberculosis (TB) caused 1.25 million deaths globally, remaining a leading infectious killer. Central Asia and Southern Caucasus face high TB burdens, particularly Mongolia. This review synthesizes TB prevalence data and diagnostic capabilities in these regions to support public health strategies. Methods: This systematic review aimed to synthesize current data on TB prevalence in Central Asia, Southern Caucasus, and Mongolia to support public health strategies and research priorities. A comprehensive search of PubMed and Google Scholar was conducted for English-language articles published up to 2023. Studies were assessed using a modified Newcastle–Ottawa Scale. Nine studies met the inclusion criteria, covering Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan, Turkmenistan, Mongolia, Georgia, Armenia, and Azerbaijan. Results: TB incidence ranged from 67 per 100,000 in Kazakhstan to 190 per 100,000 in Kyrgyzstan, with the highest prevalence of 68.5% in Mongolia. TB affected men more frequently (65.3%), and the key risk factors included HIV (30.5%), comorbidities, and undernutrition. Diagnostic performance varied significantly (microscopy sensitivity, 45–65%; GeneXpert MTB/RIF, 89–96% sensitivity and 98% specificity for rifampicin resistance). Diagnostic turnaround times ranged from hours (molecular) to weeks (conventional). Only 58% of TB facilities had GeneXpert technology, with urban–rural disparities in diagnostic access. Drug-resistant TB imposed a significant economic burden, with treatment costs ranging from USD 106 to USD 3125. Conclusions: Strengthening surveillance, improving data collection, and conducting longitudinal studies are essential for designing effective TB control strategies in these regions. Significant diagnostic gaps persist across these regions, especially with regard to drug-resistant strains. Point-of-care molecular diagnostics, improved algorithms, and expanded laboratory training show promise. Future research should focus on rapid biomarker-based diagnostics, field-deployable technologies for settings with limited resources, and AI integration to enhance diagnostic accuracy and efficiency.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".