Impact of medicines regulatory risk communications in the UK on prescribing and clinical outcomes: Systematic review, time series analysis and meta‐analysis
Bibliographic record
Abstract
AIMS: Regulatory risk communications are important to ensure medication safety, but their impact is poorly understood. The aim was to quantify the impact of UK risk communications on medication use and other outcomes. METHODS: We conducted a systematic review of studies reporting prescribing/health outcome data relevant to UK regulatory risk communication. Data were reanalysed using interrupted time series regression 12 months after each regulatory intervention. Mean changes were pooled using random-effects generic inverse variance examining the following subgroups: drug withdrawals; restrictions/changes in indications; be aware messages without specific recommendations for action; communication via direct healthcare practitioner communications; communication via drug bulletins. RESULTS: Of 11 466 articles screened, 40 studies examining 25 UK regulatory risk communications were included. Product withdrawals, restriction in indications and be aware communications were associated with relative mean changes of -78% (95% confidence interval [CI] -60 to -96%), -34% (95% confidence interval [CI] -12 to -55%) and -11% (95%CI -8 to -15%) in targeted drug prescribing respectively. Direct healthcare professional communications were associated with relative mean changes of -47% (95%CI -27 to -68%) compared to -13% (95%CI -6 to -20%) for drug bulletins. Of 7 studies examining unique health outcomes related to the safety concern, risk communications were associated with a mean -10% (95%CI -3 to -16%) decrease in intended and a 7% (95%CI 4 to 10%) increase in unintended health outcomes. DISCUSSION: UK regulatory risk communications were associated with significant changes in targeted prescribing and potential changes in clinical outcomes. Further research is needed to systematically study the impact of regulatory interventions.
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 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 | 0.020 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.014 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 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".