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Record 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 on 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

Bibliographic record

VenuemedRxiv · 2024
Typepreprint
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTuberculosisTransmission (telecommunications)EpidemiologyVirologyGeographyEnvironmental healthBiologyMedicineComputer scienceTelecommunicationsPathology

Abstract

fetched live from 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.

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)0.004
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 score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.038
GPT teacher head0.358
Teacher spread0.320 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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