Tuberculosis outcomes among international migrants living in Europe compared with the nonmigrant population: A systematic review and meta-analysis
Bibliographic record
Abstract
• Being migrant predisposes to different tuberculosis (TB) outcomes compared with nonmigrants in Europe. • Lower TB mortality risk and higher TB mortality risk characterize migrants in Europe. • Migrants in Europe showed a higher risk of loss to follow-up during TB treatment. • No differences were found in treatment success rates between migrants and nonmigrants. 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. 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). Of the 1,109 papers screened, 34 were included, consisting of 601,293 participants (459,670 nonmigrants and 141,623 migrants). The meta-analysis, adjusted for potential confounders, showed that migrants presented a lower mortality risk (RR = 0.391, 95% CI: 0.276-0.554; P <0.0001; I 2 = 71.6%), a lower rate of treatment completion (RR = 0.313; 95% CI: 0.163-0.600; P <0.0001), and a higher rate of loss to follow-up (RR = 4.331, 95% CI: 1.542-12.163, P = 0.005; I 2 = 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. 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".