Prevalence and associated factors of post-tuberculosis lung disease in Sub-Saharan Africa: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Post-tuberculosis lung disease (PTLD) is a major public health challenge in sub-Saharan Africa (SSA), where the burden of tuberculosis (TB) remains high. Only a few studies have reported the global burden of PTLD, and the associated factors of PTLD have been understudied. This systematic review and meta-analysis aimed to estimate the pooled prevalence and associated factors of PTLD in SSA. METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for systematic review and meta-analysis. We included studies reporting the prevalence and associated factors of PTLD among individuals with a history of pulmonary TB in SSA. A comprehensive literature search was conducted via PubMed, Embase, Google Scholar, and African Journal Online databases from February 25, 2025, to March 20, 2025. The pooled prevalence of PTLD was estimated using a random-effects model. Due to the lack of reports on adjusted odds ratios (aORs), the associated factors were analyzed using crude odds ratios (ORs). RESULTS: A total of 21 studies, consisting of 4,463 participants, were included. The overall pooled prevalence of PTLD in SSA was 43.26% (95% CI: 34.17%-52.34%). The key Factors significantly associated with PTLD included: female sex (OR: 1.57, 95% CI: 1.16, 2.11), smoking (OR: 1.64, 95% CI: 1.09, 2.46), Presence of cough (OR: 1.73, 95% CI: 1.03, 2.9) and fibrotic pattern (OR:3.94 (95% CI: 1.96, 7.92). CONCLUSION: Nearly half of prior TB patients in SSA develop PTLD. Being female, smoking, fibrosis, and post-treatment cough were key factors associated with PTLD. To effectively manage PTLD in SSA, it is important to implement targeted interventions for high-risk groups, strengthen screening and chronic care services, enhance healthcare system capacity, ensure equity in health resources and integrate PTLD management into national TB control programs.
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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 | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".