Population Risk of Syringe Reuse: Estimating the Probability of Transmitting Bloodborne Disease
Bibliographic record
Abstract
BACKGROUND: In 2008, the Medical Officer of Health at Alberta Health Services (Edmonton, Canada) was notified that, in some practice settings, a syringe was used to administer medication through the side port of an intravenous circuit and then the syringe, with residual drug, was used to administer medication to other patients in the same manner. This practice has been implicated in several outbreaks of bloodborne infection in hospital and clinic settings. METHODS: A risk assessment model was developed to predict the risk of a patient contracting a bloodborne viral infection from the practice. The risk of transmission was defined as the product of 5 factors: (1) the population prevalence of a specific bloodborne pathogen, (2) the probability of finding a viral bloodborne pathogen in an intravenous circuit, (3) the rate of syringe reuse, (4) the probability of causing disease given a bloodborne pathogen exposure, and (5) the susceptibility of the exposed person. RESULTS: The risk was modeled first with consistent use of the proximal port of the intravenous circuit. The risk of transmission of hepatitis B virus was approximately 12-53 transmission events per 1,000,000 exposure events for a range of practice probabilities (ie, frequency of the risk practice) from 20% to 80%, respectively. The risk of transmission of hepatitis C virus was approximately 1.0-4.3 transmission events per 1,000,000 exposure events for the same practice probability range, and the risk of transmission of human immunodeficiency virus was approximately 0.03-0.15 transmission events per 1,000,000 exposure events for the same practice probability range. The use of the distal port was associated with a 10-fold decrease in the risk. CONCLUSIONS: Practitioners must practice safe, aseptic injection techniques. The model presented here can be used to estimate the risk of disease transmission in situations where reuse has occurred and can serve as a framework for informing public health action.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".