HIV infection and multidrug resistant tuberculosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Multidrug-resistant tuberculosis (MDR-TB) in HIV infected individuals is a serious threat to global efforts to combat tuberculosis. Inconsistent findings on the association between HIV infection and MDR-TB were present in many studies. We aimed to review existing data on the relationship between HIV infection and MDR-TB systematically to assess the contribution of HIV on MDR-TB worldwide. We also investigated the patterns of MDR-TB by age, country-wise income, study designs, and global regions. Methods We utilized PubMed, Google Scholar, and ScienceDirect databases to select eligible studies for meta-analysis that were published between January 1, 2010, and July 30, 2020. The random-effects model was used to obtain the pooled odds ratio of the crude association between HIV and MDR-TB with a 95% confidence interval. We investigated the potential publication-bias by checking funnel plot asymmetry and using the Egger’s test. Moreover, we assessed the heterogeneity using the I 2 statistic. Sensitivity analysis was performed based on sample size and adjustment factors. The protocol was registered with PROSPERO-CRD42019132752. Results We identified 1603 studies through a database search, and after subsequent eliminations we selected 54 studies including 430,534 TB patients. The pooled odds of MDR-TB was 1.42 times higher in HIV-positive patients than HIV-negative patients (OR=1.42,CI=1.17–1.71, I 2 =75.8%). Subgroup analysis revealed that the estimated pooled odds for South-East Asian countries was 1.86, which is the highest in WHO regions (OR=1.86,CI=1.30–2.67, I 2 =0.00%), followed by Europe and Africa. The effect estimate was found to be higher for primary MDR-TB (OR=2.76,CI=1.70–4.46, I 2 =0.00%). There was also a trend towards increased odds of MDR-TB for HIV patients older than 40 years (OR=1.56,CI=1.17–2.06). The association was found to be significant in high-burden TB countries (OR=1.75, CI=1.39–2.19) and in high-income countries (OR=1.55, CI=1.06–2.27). Conclusion Such findings indicate that HIV infection raises the risk of MDR-TB, and after contrasting it with the results of the earlier pooled study, it appeared to be an upward risk trend. Moreover, we found that the risk is the highest in the South-East Asian region. A balanced allocation of resources is needed to halt both primary and secondary MDR-TB, particularly in HIV infected people with 40 years of age and older.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.038 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".