POPPY II Cohort Profile– a population-based linked cohort examining the patterns and outcomes of prescription opioid use in NSW, Australia, 2003-2018.
Bibliographic record
Abstract
ObjectivesAlthough opioid prescribing and harms have increased in Australia, there is a lack of population-level evidence about the drivers of long-term opioid use, dependence and other harms. This study aims to profile the POPPY II cohort, with respect to sociodemographic and clinical health characteristics and patterns of opioid initiation. ApproachThe POPPY II cohort includes adult residents (≥18 years) in NSW who were initiated on prescribed opioids subsidised through Australia’s Pharmaceutical Benefits Scheme for any period between 1st July 2003 and 31st December 2018. The cohort has been linked to nine other datasets containing information on socio-demographic and clinical characteristics, health service use, and adverse outcomes. ResultsThere were 3,569,433 people in the cohort. One in four people were aged ≥65 years at the time of opioid initiation (26.8%) and half were female (52.7%). About 71% resided in a major city. Approximately 6% had evidence of being treated for cancer in the year prior to opioid initiation (5.8%). In the 3 months prior to cohort entry, 27% used an analgesic medicine and 21% used a psychotropic medicine. Less than a third initiated on a strong opioid (22.2%) and the most commonly initiated opioid was paracetamol/codeine (61.3%). ConclusionThe POPPY II study is the largest post-marketing surveillance study of prescribed opioids in Australia, and one of the largest studies worldwide. Understanding the characteristics of the cohort will inform future work aimed at generating robust evidence of the long-terms patterns and outcomes of prescribed opioid use in the Australian community.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".