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Record W2763075252 · doi:10.1016/s2468-2667(17)30161-5

Effectiveness of Canada's tuberculosis surveillance strategy in identifying immigrants at risk of developing and transmitting tuberculosis: a population-based retrospective cohort study

2017· article· en· W2763075252 on OpenAlexafffundabout
Leyla Asadi, Courtney Heffernan, Dick Menzies, Richard Long

Bibliographic record

VenueThe Lancet Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill UniversityUniversity of Alberta
FundersCanadian Institutes of Health ResearchAlberta Innovates
KeywordsTuberculosisMedicineTuberculinPopulationEpidemiologyIncidence (geometry)ReferralChest radiographTransmission (telecommunications)Retrospective cohort studyCohortContact tracingPediatricsDemographyFamily medicineSurgeryEnvironmental healthInternal medicineDiseasePathologyInfectious disease (medical specialty)

Abstract

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BACKGROUND: In Canada, tuberculosis disproportionately affects the foreign-born population. The national tuberculosis medical surveillance programme aims to prevent these cases. Individuals referred for further in-country surveillance (referrals) have a history of active tuberculosis or have features of old, healed tuberculosis on chest radiograph; those not referred (non-referrals) do not undergo surveillance. We aimed to examine the risk of transmission arising from referrals versus non-referrals. METHODS: We did this population-based retrospective cohort study of foreign-born migrants (aged 15-64 years) to Alberta, Canada, between Jan 1, 2002, and Dec 31, 2013. We obtained information about year of arrival and country of citizenship from Immigration, Refugees and Citizenship Canada, and data for tuberculosis cases and their contacts from the Alberta Tuberculosis Registry. The outcome of interest was culture-positive pulmonary tuberculosis. We compared the incidence of pulmonary tuberculosis and the odds of transmission among referrals versus non-referrals. By use of conventional and molecular epidemiological techniques, we defined transmission as either a secondary case or a tuberculin skin-test (TST) conversion among close contacts. We used multivariate logistic regression to determine the independent association between referral for tuberculosis surveillance and transmission. FINDINGS: Between 2002 and 2013, there were 223 225 foreign-born migrants to Alberta, of whom 5500 (2%) were referrals and 217 657 (98%) were non-referrals. 3805 (69%) referrals and 115 226 (53%) non-referrals were from countries with a tuberculosis incidence of more than 150 per 100 000 populations, or sub-Saharan Africa. 234 foreign-born individuals were diagnosed with culture-positive pulmonary tuberculosis between Jan 1, 2004, and Dec 31, 2013. The incidence of culture-positive pulmonary disease was nine times higher in referrals (n=50) than all non-referrals (n=184; incidence rate ratio 9·1, 95% CI 6·7-12·5) and five times higher in referrals than non-referrals from high-risk countries (n=167; 5·0, 3·6-6·8). 71 total transmission events arose from the individuals with culture-positive pulmonary tuberculosis-three (4%) from referrals and 68 (96%) from non-referrals. No secondary cases were attributable to a referral source case, whereas 18 secondary cases were attributable to 11 different non-referral source cases. Three TST conversions were attributable to three different referral source cases compared with 50 conversions from 31 different non-referral source cases. That is, three (6%) referrals transmitted tuberculosis compared with 42 (22%) non-referrals (adjusted odds ratio of 0·19, 95% CI 0·054-0·66; p=0·009). INTERPRETATION: Despite a much higher incidence of pulmonary tuberculosis in referrals than non-referrals, referrals were 80% less likely to transmit tuberculosis. Rather than a focus on referrals, Canada could consider screening and treatment of latent tuberculosis in all migrants from high-risk countries-a group that accounted for 100% of secondary cases. FUNDING: Canadian Institutes of Health Research.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0150.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.361
Teacher spread0.309 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations30
Published2017
Admission routes3
Has abstractyes

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