135 Trends and variation in the sales of over-the-counter analgesics: a protocol for a retrospective database study and policy review
Bibliographic record
Abstract
Objectives Analgesics are among the most commonly used and accessible drugs in the world. They are generally well tolerated and effective if consumed appropriately. Yet, not all medicines available over-the-counter (OTC) are low risk. Products containing codeine have been associated with dependence, addiction, overdose-related deaths and collateral toxicity from combinations with paracetamol or ibuprofen. Policy surrounding public access to OTC codeine varies across the world. Currently, 15 of the 28 European Union member states do not permit the sale of OTC codeine. More recently, Australia and Manitoba, Canada changed the status of codeine-containing medicines to prescription-only. Access to data and monitoring of OTC medicines is limited. Without access to this data, it is unclear whether policies that restrict OTC medicines are effective in reducing use and associated harms. Codeine-containing products are available OTC in the United Kingdom (UK). Thus, we will explore trends and variation in OTC analgesics in the UK and review current policy across the world. Method We will use a national retail database from a global data analytics company. The data will include value sales (cost), unit sales (number of packs sold) and volume sales (number of tablets sold) of oral analgesics for adults at the national and regional level. The data will be adjusted for population growth using data from the Office of National Statistics. Trends will be plotted over time, aggregated and stacked by class of analgesic (i.e. opioids, paracetamol and non-steroidal anti-inflammatory drugs). Choropleth maps will be created to depict geographical variation aggregated to regions. For each region, we will calculate the value, unit and volume sold per 1000 of the population for each class of analgesic. Descriptive statistics will be used to compare regions and analgesic classes over time. Restrictive review methods will be used to compare and contrast current policy on OTC codeine-containing products globally. Results Over the last five years, sales of OTC medicines increased by 13% (£2.3 billion in 2012 to £2.6 billion in 2017, IRI, 2017). Implementation of this protocol is required to determine what proportion of this increase was for analgesics and codeine-containing medicines. The restricted review with synthesise current policies and restrictions on OTC codeine containing medicines and the evidence-based used for these decisions. Conclusions Examining trends, variation and legislation of OTC codeine sales is important with the current push to promote self-care and change policy on the access of codeine-containing medicines. Previous studies have focused on analysing trends in the prescribed medicines. This focus may be attributed to the difficulty and lack of available data on sales of OTC medicines. Our team is interested in investigating international trends so if your country or region has access to OTC analgesic data please get in contact. Conflicts of interest GCR is receiving funding from the National Health Service (NHS) National Institute of Health Research (NIHR) School of Primary Care Research, Naji Foundation and Rotary Foundation to study for a Doctor of Philosophy at the University of Oxford with no other relevant conflicts of interests.
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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 | 0.069 | 0.083 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.014 |
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".