Tuberculosis outcomes among international migrants living in Europe compared with the nonmigrant population: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: Migration status refers to socioeconomic factors that challenge access to the health care system and increase the risk of developing tuberculosis (TB) with worse outcomes. This systematic review and meta-analysis aimed to investigate the outcomes of TB among international migrants arriving in Europe compared with the nonmigrant population. Methods: A systematic review and meta-analysis were conducted to identify studies investigating TB-related outcomes among migrants and nonmigrants in Europe. Six investigators searched PubMed, Scopus, and Web of Science from inception to March 2024 and screened the abstracts of potentially eligible articles. Studies reporting TB-related outcomes in both migrants and nonmigrants were also included. Studies with migrant definitions other than the one from the inclusion criteria, with no control group, and with no discernible data, including nonhuman samples or written in a non-English language, were excluded. Data were reported as relative risks (RRs) or odds ratios with their 95% confidence intervals (CIs). The risk of bias was assessed using the Newcastle-Ottawa Scale (PROSPERO Registration number: CRD42024529629). Results: = 55.8%). Treatment success, cure, not evaluated, and sustained treatment success showed no significant differences between migrants and nonmigrants. No adjusted analyses could be performed for cure, not evaluated, and sustained treatment success. Only three studies had a high risk of bias. Conclusions: Migrants living in Europe have lower mortality rates; however, TB management is affected by a higher risk of loss to follow-up and discontinuation. Therefore, migrant-targeted TB care is necessary to improve the fight against TB in Europe.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".