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Enregistrement W4405967095 · doi:10.1101/2024.12.18.24318332

<i>M. tuberculosis</i> transmission dynamics in congregate settings: a genomic epidemiology study

2024· preprint· en· W4405967095 sur OpenAlexaff
Katharine S. Walter, Everton Ferreira Lemos, Ana Paula Cavalcante Aires Alves, Gabriela Felix Chaves Ferreira, Vanessa Maruyama Martins Coutinho, Barun Mathema, Joshua L. Warren, Caroline Colijn, Ted Cohen, Júlio Croda, Jason R. Andrews

Notice bibliographique

RevuemedRxiv · 2024
Typepreprint
Langueen
DomaineMedicine
ThématiqueTuberculosis Research and Epidemiology
Établissements canadiensSimon Fraser University
Organismes subventionnairesnon disponible
Mots-clésTuberculosisTransmission (telecommunications)EpidemiologyVirologyGeographyEnvironmental healthBiologyMedicineComputer scienceTelecommunicationsPathology

Résumé

récupéré en direct d'OpenAlex

Abstract Background One barrier to intervening in the global tuberculosis (TB) pandemic is that it is unknown whether M. tuberculosis transmission largely occurs through repeated close exposures among few contacts or many shorter-term contacts. Identifying sources of transmission is particularly urgent in congregate settings with high incidence of infection. Methods To identify drivers of M. tuberculosis transmission within a congregate setting with extremely high incidence of TB, we conducted genomic surveillance in a prison system in Central West Brazil. We whole genome sequenced M. tuberculosis isolates and collected detailed incarceration histories, including movements between and within prisons. We integrated incarceration histories with M. tuberculosis genomes to investigate the relationship between exposures of differing proximity (cell, cell block, prison) and transmission risk, using genomic clustering as a proxy for transmission. Findings We collected detailed incarceration histories for 595 individuals from whom we sequenced 561 high quality M. tuberculosis genomes. A month-long increase in exposure to an individual with TB within a prison cell increased the odds of pairwise genomic clustering by 7.4% (95% CI: 4.4-10.4%) and a six-month increase in exposure, by 54% (95% CI: 29.9%-82.5%). Most (89%; 528 of 595) individuals with TB had at least one potential week-long exposure in a prison cell to another individual with TB, and frequently many, with a median of 12 (IQR: 5-21) potential unique exposures to individuals in prison cells. Frequent movements by the prison system create a highly connected contact network: individuals with TB were transferred a median of 5 (IQR: 1-17) times in the 12 months before diagnosis. Interpretation While close exposures within a prison were related to pairwise genomic clustering, most individuals with TB had multiple exposures to other individuals with TB due to frequent movements by the prison system. Our results support the urgent expansion of prison-wide mass screenings, TB preventive therapy, and structural interventions to reduce transmission risk in prisons and other congregate settings. Funding National Institutes of Health (NIAID: 5K01AI173385, R01AI100358, and R01AI149620) Research in context Evidence before this study We searched PubMed for relevant articles published in English from database inception to November 26, 2024, using the terms “ Mycobacterium tuberculosis ”, “transmission,” “genom*,” and “congregate setting” or “prison.” We found several genomic epidemiology articles describing close genetic relatedness of M. tuberculosis sampled from prisons and the community. These earlier genomic epidemiology studies did not include individual-level exposure or movement information. We additionally identified two studies that conducted environmental sampling in congregate settings: one that identified M. tuberculosis DNA in bioaerosols in a primary care clinic and one from environmental swabs collected in a prison. Previous studies did not investigate the types of contacts driving transmission in high-incidence congregate settings. Added value of this study We conducted a genomic epidemiology study of M. tuberculosis transmission in a congregate setting with extremely high incidence of tuberculosis (TB): a state prison system in Central West, Brazil. We integrated M. tuberculosis genomes with detailed individual movement data to reconstruct transmission linkages and infer the types of contacts associated with transmission in a congregate setting. We found that close contacts within a prison—incarceration within the same prison cell—increase the likelihood of transmission. Further, the frequent movement of individuals within and between prisons creates large, highly connected large contact networks. The result is that individuals have many close contacts with other individuals with tuberculosis, such that any single potential exposure may not result in genetically linked cases. Implications of all the available evidence Together, our results suggest that close exposures to other individuals with TB increase transmission risk in congregate settings with high incidence of TB. Due to frequent transfers within prison systems, people may have close exposures to many individuals with TB, with the result that contact tracing investigations may not be effective in such settings. Our results support the urgent expansion of mass screenings, TB preventive therapy, and structural interventions to reduce transmission risk in prisons and other congregate settings.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,029
Score d'incertitude au seuil0,058

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,038
Tête enseignante GPT0,358
Écart entre enseignants0,320 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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