Self-injecting non-prescribed substances into vascular access devices: a case study of one health system’s ongoing journey from clinical concern to practice and policy response
Bibliographic record
Abstract
BACKGROUND: Overdose-associated deaths and morbidity related to substance use is a global public health emergency with devastating social and economic costs. Complications of substance use are most pronounced among people who inject drugs (PWID), particularly infections, resulting in increased risk of hospitalization. PWID often require intravenous access for medical treatments such as antibiotics; however, vascular access may be limited due to the impacts of long-term self-venipuncture. While vascular access devices including peripherally inserted central catheters (PICCs) allow reliable and sustained routes of administration for indicated therapies, the use of PICCs among PWID presents unique challenges. The incidence and risks associated with self-injecting non-prescribed substances into vascular access devices (SIVAD) is one such concern for which there is limited evidence and absence of formal practice guidance. CASE PRESENTATION: We report the experience of a multidisciplinary team at a health organization in Vancouver, Canada, working to characterize the incidence, patient and healthcare provider perspectives, and overall impact of SIVAD. The case study of SIVAD begins with a patient's perspective, including patient rationale for SIVAD, understanding of risks and the varying responses given by healthcare providers following disclosure of SIVAD. Using the limited literature available on the subject, we summarize the intersection of SIVAD and substance use and outline known and anticipated health risks. The case study is further contextualized by experience from a Vancouver in-hospital Overdose Prevention Site (OPS), where 37% of all individual visits involve SIVAD. The case study concludes by describing the systematic process by which local clinical guidance for SIVAD harm reduction was developed with stakeholder engagement, medical ethics consultation, expert consensus guideline development and implementation with staff education and planned research evaluation. CONCLUSION: SIVAD is encountered with enough frequency in an urban healthcare setting in Vancouver, Canada, to warrant an organizational approach. This case study aims to enhance appreciation of SIVAD as a common and complex clinical issue with anticipated health risks. The authors conclude that using a harm reduction lens for SIVAD policy and research can provide benefit to clinicians and patients by offering a clear and a consistent healthcare response to this common issue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".