Understanding and preventing injecting-related bacterial and fungal infections among people who inject drugs
Bibliographic record
Abstract
Background: Injection drug use-associated bacterial and fungal infections (e.g., skin and soft tissue infections, endocarditis, osteomyelitis, septic arthritis, epidural abscess, etc.) are increasingly common. Risk factors include subcutaneous/intramuscular injecting and lack of skin cleaning, but individual-level educational interventions on safer injecting practices have shown limited effectiveness. There may be value in looking beyond individual injecting behaviours to understand risk and prevention opportunities. \n \nAims: (1) identify social-structural factors that influence risk for injecting-related infections; (2) estimate the effect of opioid agonist treatment on all-cause mortality or infection-related rehospitalization, after hospital admissions with injecting-related infections; (3) assess how risk for injecting-related infections changes within-individuals over time, in relation to social (i.e., incarceration) and clinical (i.e., opioid agonist treatment) exposures. \n \nMethods: Qualitative systematic review with thematic synthesis; quantitative systematic review with meta-analysis; survival analysis and self-controlled case series using data from a cohort of people with opioid use disorder in New South Wales, Australia. \n \nResults: Injecting-related bacterial and fungal infections are shaped by modifiable social-structural factors, including poor quality unregulated drugs, criminalization and policing enforcement, insufficient housing, limited harm reduction services, and harmful health care practices. People who inject drugs navigate these barriers while attempting to protect themselves and their community. After a hospital admission, opioid agonist treatment is associated with a large reduction in mortality but a modest reduction in risk of infection-related rehospitalization. Risk of injecting-related infections changes substantially within-individuals over time; high-risk moments include release from incarceration and around initiation and discontinuation of opioid agonist treatment. \n \nConclusions: Risk for injecting-related bacterial and fungal infections, and associated treatment outcomes, are shaped by social-structural factors beyond individuals’ control. Offering individual-level education and addiction treatment may be helpful, but is likely insufficient. Prevention and treatment strategies should engage more broadly with the social and material conditions within which people prepare and consume drugs, and access health care
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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 | 0.012 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".