Risk of latent and active tuberculosis infection in travellers: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Achieving tuberculosis (TB) elimination in low TB incidence countries requires identification and treatment of individuals at risk for latent TB infection (LTBI). Persons travelling to high TB incidence countries are potentially at risk for TB exposure. This systematic review and meta-analysis estimates incident LTBI and active TB among individuals travelling from low to higher TB incidence countries. METHODS: Five electronic databases were searched from inception to 18 February 2020. We identified incident LTBI and active TB among individuals travelling from low (<10 cases/100 000 population) to intermediate (10-100/100 000) or high (>100/100 000) TB incidence countries. We conducted a meta-analysis and meta-regression using a random effects model of log-transformed proportions (cumulative incidence). Subgroup analyses investigated the impact of travel duration, travel purpose and TB incidence in the destination country. RESULTS: Our search identified 799 studies, 120 underwent full-text review, and 10 studies were included. These studies included 1 154 673 travellers observed between 1994 and 2013, comprising 443 health care workers (HCW), 1 068 636 military personnel and 85 594 general travellers/volunteers. We did not identify any studies that estimated incidence of LTBI or active TB among people travelling to visit friends and relatives (VFRs). The overall cumulative incidence of LTBI was 2.3%, with considerable heterogeneity. Among individuals travelling for a mean/median of up to 6 months, HCWs had the highest cumulative incidence of LTBI (4.3%), whereas the risk was lower for military (2.5%) and general travellers/volunteers (1.6%). Meta-regression did not identify a difference in incident LTBI based on travel duration and TB incidence in the destination country. Five studies reported cases of active TB, with an overall pooled estimate of 120.7 cases per 100 000 travellers. CONCLUSIONS: We found that travelling HCWs were at highest risk of developing LTBI. Individual risk activities and travel purpose were most associated with risk of TB infection acquired during travel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".