Effect of BMI on Mortality in Patients With Tuberculosis and HIV Coinfection in Asia and Africa: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Tuberculosis (TB) continues to pose a major global health threat, particularly in low- and middle-income countries. An estimated 10.8 million people developed TB in 2023, corresponding to 134 cases per 100,000 population. The Southeast Asia region accounted for 45% of global TB incidence, while the African region contributed 24%. Nutritional status, particularly low BMI, is a key modifiable determinant of adverse clinical outcomes. However, its overall impact on mortality among TB-HIV coinfected populations in Asia and Africa remains poorly quantified. Objective: This study aimed to systematically assess the association between BMI and mortality among patients with TB-HIV coinfection in Asia and Africa. Methods: A systematic review and meta-analysis were conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines. Cohort studies published between 2000 and 2024 were identified through PubMed, Scopus, and ProQuest. Data extraction and risk of bias assessment were performed independently by 3 reviewers using the Risk of Bias in Non-Randomized Studies - of Exposure tool. A random effects model with the DerSimonian-Laird method was applied using RevMan 7.2 (Cochrane) to estimate pooled risk ratios with 95% CI. Heterogeneity was quantified using the I² statistic, and subgroup analyses were conducted by geographic region. Results: Seven cohort studies met the inclusion criteria. The pooled estimate indicated that patients with BMI less than 18.5 kg/m² had approximately twofold higher mortality risk compared to those with normal or higher BMI (risk ratio=2.01; 95% CI 1.63-2.48; P<.001). Despite moderate heterogeneity (I²=64%), the association remained consistent across subgroups. Although most studies were rated as having high or moderate risk of bias, sensitivity analyses confirmed the robustness of the results. Conclusions: Low BMI significantly increases the risk of mortality among patients with TB and HIV coinfection in Asia and Africa, underscoring its prognostic and modifiable role in clinical outcomes. Routine nutritional screening, BMI monitoring, and targeted supplementation should be integrated into national TB-HIV management programs, particularly in resource-limited settings. Strengthening TB-HIV nutrition service integration is essential to improve survival and achieve global End TB and United Nations Programme on HIV/AIDS 95-95-95 targets.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".