The epidemiology of cutaneous injection-related infection among injection drug users at a supervised injection facility
Bibliographic record
Abstract
Background: To date, there is limited understanding of the epidemiology of cutaneous injection-related infections (CIRI), including abscesses and cellulitis, among persons who inject drugs (IDU). The objectives of this thesis were to: describe the microbiology of wounds among IDU; examine the correlates of developing a CIRI; investigate determinants of CIRI care at a supervised injection facility (SIF); model predictors of Emergency Department (ED) visits for CIRI; and explore predictors of hospitalization for CIRI or related infectious complications. Methods: Quantitative data was derived from a prospective cohort study established to evaluate a SIF in Vancouver, Canada. A random sample of 1083 IDU attending the SIF were asked to complete an interview-administered questionnaire, undergo semi-annual HIV and hepatitis C virus testing and to consent to hospital database linkage. Results: Nearly 25% participants with a wound had a culture positive for community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA). Female sex, living in unstable housing, borrowing a used syringe, requiring assistance with injection and cocaine injection at least daily were associated with developing a CIRI. Female sex, living in unstable housing and heroin injection daily or more frequently were associated with receiving CIRI care by nurses at the SIF. Being referred to hospital by a nurse at the SIF was predictive of ED use for CIRI. Among females, residing in Vancouver’s Downtown Eastside was associated with an increased likelihood of ED use for CIRI, whereas among men, requiring assistance with injecting and being HIV positive were associated this outcome. Participants were more likely to be hospitalized for a CIRI or related infectious complication if they were HIV positive and referred to hospital by a nurse at the SIF. Importantly, length of stay in hospital was significantly shorter and less expensive among participants referred to hospital by a nurse at the SIF. Conclusions: CIRI are common among IDU in our setting and are associated with a number of individual harm and environmental factors. Our findings support the need for prevention and treatment efforts to reduce the burden of CIRI. Additional improvements in treatment protocols, such as wound management clinics within harm reduction services, are urgently needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".