Data-driven identification of potentially successful intervention implementations: a proof of concept using five years of opioid prescribing data from over 7000 practices in England
Bibliographic record
Abstract
Abstract Background We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies and there is some evidence that progress is being made. We sought to extend our previous work and develop an unbiased, data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented. Methods We analysed five years of prescribing data for three opioid prescribing measures: one capturing total opioid prescribing and two capturing regular prescribing of high dose opioids. Using a data-driven approach, we applied a modified version of our change detection Python library to identify changes in these measures over time, consistent with the successful implementation of an intervention. This analysis was carried out for general practices and CCGs, and organisations were ranked according to the change in prescribing rate. Results We present data for the three CCGs and practices demonstrating the biggest reduction in opioid prescribing across the three opioid prescribing measures. We observed a 40% drop in the regular prescribing of high dose opioids (measured as a percentage of regular opioids) in the highest ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices. Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over two years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time. Conclusions By applying one of our existing analysis tools to a national dataset, we were able to rank NHS organisations by reduction in opioid prescribing rates. Highly ranked organisations are candidates for further qualitative research into intervention design and implementation. Contributions to the literature Demonstrating that a data-driven approach can identify and quantify changes in important clinical measures in publicly available NHS data Identifying changes in this way allows the unbiased identification of candidates for further qualitative research into intervention design and implementation Large reductions observed at the CCG level (which are more robust to local circumstances) demonstrate that it is possible to reduce opioid prescribing and that continued and wider success in reducing opioid prescribing is dependent, at least in part, to closing an implementation gap
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".