Effect of incarceration and opioid agonist treatment transitions on risk of hospitalisation with injection drug use-associated bacterial infections: A self-controlled case series in New South Wales, Australia
Bibliographic record
Abstract
BACKGROUND: Transitional times in opioid use, such as release from prison and discontinuation of opioid agonist treatment (OAT), are associated with health harms due to changing drug consumption practices and limited access to health and social supports. Using a self-controlled (within-person) study design, we aimed to understand if these transitions increase risks of injection drug use-associated bacterial infections. METHODS: We performed a self-controlled case series among a cohort of people with opioid use disorder (who had all previously accessed OAT) in New South Wales, Australia, 2001-2018. The outcome was hospitalisation with injecting-related bacterial infections. We divided participants' observed days into time windows related to incarceration and OAT receipt. We compared hospitalization rates during focal (exposure) windows and referent (control) windows (i.e., 5-52 weeks continuously not incarcerated or continuously receiving OAT). We estimated adjusted incidence rate ratios (aIRR) using conditional logistic regression, adjusted for time-varying confounders. RESULTS: There were 7590 participants who experienced hospitalisation with injecting-related bacterial infections (35% female; median age 38 years; 78% hospitalised with skin and soft-tissue infections). Risk for injecting-related bacterial infections was elevated for two weeks following release from prison (aIRR 1.45; 95%CI 1.22-1.72). Risk was increased during two weeks before (aIRR 1.89; 95%CI 1.59-2.25) and after (aIRR 1.91; 95%CI 1.54-2.36) discontinuation of OAT, and during two weeks before (aIRR 3.63; 95%CI 3.13-4.22) and after (aIRR 2.52; 95%CI 2.09-3.04) OAT initiation. CONCLUSION: Risk of injecting-related bacterial infections varies greatly within-individuals over time. Risk is raised immediately after prison release, and around initiation and discontinuation of OAT. Social contextual factors likely contribute to excess risks at transitions in incarceration and OAT exposure.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".