135 Trends and variation in the sales of over-the-counter analgesics: a protocol for a retrospective database study and policy review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,069 | 0,083 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,003 |
| Méta-épidémiologie (sens large) | 0,006 | 0,008 |
| Bibliométrie | 0,010 | 0,010 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,005 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,072 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».