Cohort profile: POPPY II – a population-based cohort examining the patterns and outcomes of prescription opioid use in New South Wales, Australia
Bibliographic record
Abstract
PURPOSE: The POPPY II cohort is an Australian state-based cohort linking data for a population of individuals prescribed opioid medicines, constructed to allow a robust examination of the long-term patterns and outcomes of prescription opioid use. PARTICIPANTS: The cohort includes 3 569 433 adult New South Wales residents who initiated a subsidised prescription opioid medicine between 2003 and 2018, identified through pharmacy dispensing data (Australian Pharmaceutical Benefits Scheme) and linked to 10 national and state datasets and registries including rich sociodemographic and medical services data. FINDINGS TO DATE: Of the 3.57 million individuals included in the cohort, 52.7% were female and 1 in 4 people were aged ≥65 years at the time of cohort entry. Approximately 6% had evidence of cancer in the year prior to cohort entry. In the 3 months prior to cohort entry, 26.9% used a non-opioid analgesic and 20.5% used a psychotropic medicine. Overall, 1 in 5 individuals were initiated on a strong opioid (20.9%). The most commonly initiated opioid was paracetamol/codeine (61.3%), followed by oxycodone (16.3%). FUTURE PLANS: The POPPY II cohort will be updated periodically, both extending the follow-up duration of the existing cohort, and including new individuals initiating opioids. The POPPY II cohort will allow a range of aspects of opioid utilisation to be studied, including long-term trajectories of opioid use, development of a data-informed method to assess time-varying opioid exposure, and a range of outcomes including mortality, transition to opioid dependence, suicide and falls. The duration of the study period will allow examination of population-level impacts of changes to opioid monitoring and access, while the size of the cohort will also allow examination of important subpopulations such as people with cancer, musculoskeletal conditions or opioid use disorder.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".