A rapid systematic scoping review of the levels of bacterial antimicrobial resistance and antibiotic use amongst people in contact with the criminal justice system
Bibliographic record
Abstract
Background Antimicrobial resistance (AMR) poses a significant public health threat. Individuals in contact with the criminal justice system may be particularly vulnerable due to living conditions, behaviours, and pre-existing health issues. This review assesses bacterial AMR and antibiotic use in this population. Methods A rapid systematic scoping review was conducted (OSF: https://doi.org/10.17605/OSF.IO/XHCFJ ). Embase, Medline, and Scopus were searched for studies published between 1 January 2010 and 28 September 2023. One author screened all records, with 10% dual-screened. Included studies examined AMR bacteria or antibiotic use among people in contact with the criminal justice system. Study quality was assessed using the Newcastle-Ottawa Scale and STROBE AMS checklist. Findings were synthesized narratively. Results Sixteen papers met inclusion criteria; eight were at lower risk of bias. Three studies examined antibiotic use, reporting common inappropriate prescribing (n=1) and associations between recent antibiotic use and resistant infections (n=2). Fourteen papers reported AMR findings, most with a focus on Mycobacterium tuberculosis and Staphylococcus aureus . Drug-resistant TB prevalence in prison populations ranged from 5.2% to 37% (n=4). MRSA colonization ranged from 8.1% to 8.8% (n=4). Other bacteria examined included Salmonella spp ., Acinetobacter spp ., Group A Streptococcus , and Mycoplasma genitalium . Conclusions People in contact with the criminal justice system face heightened risks of resistant bacterial infections. However, with only three studies addressing antibiotic use, evidence is limited. Addressing AMR in this group requires collaborative and targeted public health interventions.
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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 | 0.035 | 0.143 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.037 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".