MétaCan
Menu
Back to cohort
Record W2967705718 · doi:10.1371/journal.pone.0221081

Prevalence and risk factors for latent tuberculosis infection among healthcare workers in Morocco

2019· article· en· W2967705718 on OpenAlexaff
Ayoub Sabri, Jocelyn Quistrebert, Hicham Naji Amrani, A. Abid, Adil Zegmout, Ismail Abderrhamani Ghorfi, Hicham Souhi, Abdelhalim Boucaid, Anas Benali, Rachid Abilkassem, M. Kmari, Amal Hassani, Lahcen Belyamani, Samir Siah, Erwin Schurr, Stéphanie Boisson‐Dupuis, Jean‐Laurent Casanova, Amine Idriss Lahlou, Abdelkader Laatiris, Lhoussain Louzi, A. Ouarssani, Ahmed Bourazza, A. Aouragh, Nezha Messaoudi, Aomar Agader, Aurélie Cobat, Laurent Abel, Jamila El Baghdadi

Bibliographic record

VenuePLoS ONE · 2019
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University Health Centre
FundersNational Institute of Allergy and Infectious DiseasesCHIST-ERAAgence Nationale de la RechercheSt. Giles FoundationHoward Hughes Medical Institute
KeywordsMedicineLatent tuberculosisTuberculinTuberculosisInterferon gamma release assayQuantiFERONLogistic regressionVaccinationInternal medicineSkin testOdds ratioImmunologyMycobacterium tuberculosisPathology

Abstract

fetched live from OpenAlex

Increased prevalence of latent tuberculosis infection (LTBI) has been observed among high-risk populations such as healthcare workers (HCWs). The results may depend on the method of LTBI assessment, interferon-gamma release assay (IGRA) and/or tuberculin skin test (TST). Here, we investigated the prevalence and risk factors for LTBI assessed by both IGRAs and TST in HCWs living in Morocco, a country with intermediate tuberculosis (TB) endemicity and high BCG vaccination coverage. HCWs were recruited in two Moroccan hospitals, Rabat and Meknes. All the participants underwent testing for LTBI by both IGRA (QuantiFERON-TB Gold In-Tube, QFT-GIT) and TST. Different combinations of IGRA and TST results defined the LTBI status. Risk factors associated with LTBI were investigated using a mixed-effect logistic regression model. The prevalence of LTBI among 631 HCWs (age range 18-60 years) varied from 40.7% (95%CI 36.9-44.5%) with QFT-GIT to 52% (95%CI 48.2-56.0%) with TST using a 10 mm cut-off. The highest agreement between QFT-GIT and TST (κ = 0.50; 95%CI 0.43-0.56) was observed with the 10 mm cut-off for a positive TST. For a definition of LTBI status using a double positive result for both QFT-GIT and TST, significant associations were found with the following risk factors: being male (OR = 2.21; 95%CI 1.40-3.49; p = 0.0007), belonging to age groups 35-44 years (OR = 2.43; 95%CI 1.45-4.06; p = 0.0007) and even more 45-60 years (OR = 4.81; 95%CI 2.72-8.52; p = 7.10-8), having a family history of TB (OR = 6.62; 95%CI 2.59-16.94; p = 8.10-5), and working at a pulmonology unit (OR = 3.64; 95%CI 1.44-9.23; p = 0.006). Smoking was associated with LTBI status when defined by a positive QFT-GIT result (OR = 1.89; 95%CI 1.12-3.21; p = 0.02). A high prevalence of LTBI was observed among HCWs in two Moroccan hospitals. Male gender, increased age, family history of TB, and working at a pulmonology unit were consistent risk factors associated with LTBI.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.310
Teacher spread0.253 · 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 source (direct Gemma or distilled Codex), 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

Citations33
Published2019
Admission routes1
Has abstractyes

Explore more

Same venuePLoS ONESame topicTuberculosis Research and EpidemiologyFrench-language works237,207