Mycobacterium tuberculosis transmission dynamics within prisons: a population-based genomic study
Bibliographic record
Abstract
Background One barrier to intervening in the global tuberculosis pandemic is that it is unknown whether Mycobacterium 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 like prisons with high incidence of infection. Our aim was to identify the type of contacts associated with M. tuberculosis transmission risk within prisons. Methods We conducted genomic surveillance in a prison system in Central West Brazil. We whole genome sequenced M. tuberculosis isolates and collected detailed incarceration histories. We modeled transmission linkages as a function of different types of prison exposures, using genomic clustering as a proxy for transmission and controlling for multiple pairwise comparisons. Findings We collected detailed incarceration histories for 595 individuals, mean age 31 (ST—standard deviation 8.5) and 99% men, from whom we sequenced 550 high quality M. tuberculosis genomes. In a binomial model, a month-long increase in exposure to an individual with tuberculosis within a prison cell increased the odds of pairwise genomic clustering by 14% (odds ratio—OR: 1.14, 95% CI: 1.09–1.19) and a six-month increase in exposure doubled the odds of genomic clustering (OR: 2.24, 95% CI: 1.73–2.91). Most (83%; 494 of 595) individuals with tuberculosis had at least one potential day-long exposure in a prison cell to another individual with tuberculosis, and frequently many, with a median of 8 (interquartile range—IQR: 4–12) potential unique exposures to individuals in prison cells. Frequent movements by the prison system create a highly connected contact network: individuals with tuberculosis were transferred a median of 8 (IQR: 4–13) times in the 2 years before diagnosis. Interpretation While documented cell-level exposures can explain a significant proportion of M. tuberculosis transmission, most transmission links occur outside prison cells, either from other contacts in the same prison or from unreported or unsampled exposures. Our results support the urgent expansion of prison-wide mass screenings, tuberculosis 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).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".