Latent tuberculosis infection in healthcare workers in low- and middle-income countries: an updated systematic review
Bibliographic record
Abstract
Healthcare workers (HCWs) are at increased risk of latent tuberculosis (TB) infection (LTBI) and TB disease.We conducted an updated systematic review of the prevalence and incidence of LTBI in HCWs in low- and middle-income countries (LMICs), associated factors, and infection control practices. We searched MEDLINE, Embase and Web of Science (January 1, 2005-June 20, 2017) for studies published in any language. We obtained pooled estimates using random effects methods and investigated heterogeneity using meta-regression.85 studies (32 630 subjects) were included from 26 LMICs. Prevalence of a positive tuberculin skin test (TST) was 14-98% (mean 49%); prevalence of a positive interferon-γ release assay (IGRA) was 9-86% (mean 39%). Countries with TB incidence ≥300 per 100 000 had the highest prevalence (TST: pooled estimate 55%, 95% CI 41-69%; IGRA: pooled estimate 56%, 95% CI 39-73%). Annual incidence estimated from the TST was 1-38% (mean 17%); annual incidence estimated from the IGRA was 10-30% (mean 18%). The prevalence and incidence of a positive test was associated with years of work, work location, TB contact and job category. Only 15 studies reported on infection control measures in healthcare facilities, with limited implementation.HCWs in LMICs in high TB incidence settings remain at increased risk of acquiring LTBI. There is an urgent need for robust implementation of infection control measures.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".