Tuberculosis incidence in foreign-born people residing in European countries in 2020
Bibliographic record
Abstract
BackgroundEuropean-specific policies for tuberculosis (TB) elimination require identification of key populations that benefit from TB screening.AimWe aimed to identify groups of foreign-born individuals residing in European countries that benefit most from targeted TB prevention screening.MethodsThe Tuberculosis Network European Trials group collected, by cross-sectional survey, numbers of foreign-born TB patients residing in European Union (EU) countries, Iceland, Norway, Switzerland and the United Kingdom (UK) in 2020 from the 10 highest ranked countries of origin in terms of TB cases in each country of residence. Tuberculosis incidence rates (IRs) in countries of residence were compared with countries of origin.ResultsData on 9,116 foreign-born TB patients in 30 countries of residence were collected. Main countries of origin were Eritrea, India, Pakistan, Morocco, Romania and Somalia. Tuberculosis IRs were highest in patients of Eritrean and Somali origin in Greece and Malta (both > 1,000/100,000) and lowest among Ukrainian patients in Poland (3.6/100,000). They were mainly lower in countries of residence than countries of origin. However, IRs among Eritreans and Somalis in Greece and Malta were five times higher than in Eritrea and Somalia. Similarly, IRs among Eritreans in Germany, the Netherlands and the UK were four times higher than in Eritrea.ConclusionsCountry of origin TB IR is an insufficient indicator when targeting foreign-born populations for active case finding or TB prevention policies in the countries covered here. Elimination strategies should be informed by regularly collected country-specific data to address rapidly changing epidemiology and associated risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".