The effect of minimum unit pricing for alcohol on prescriptions for treatment of alcohol dependence: a controlled interrupted time series analysis

Francesco Manca*, Lisong Zhang, Niamh Fitzgerald, Daniel Mackay, Andrew McAuley, Clare Sharp, Jim Lewsey

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
55 Downloads (Pure)

Abstract

In 2018, Scotland introduced a minimum unit price (MUP) for alcohol to reduce alcohol-related harms. We aimed to study the association between MUP introduction and the volume of prescriptions to treat alcohol dependence, and volume of new patients receiving such prescriptions. We also examined whether effects varied across different socio-economic groups. A controlled interrupted time series was used to examine variations of our two outcomes. The same prescriptions in England and prescriptions for methadone in Scotland were used as controls. There was no evidence of an association between MUP implementation and the volume of prescriptions for alcohol dependence (immediate change: 2.74%, 95% CI: -0.068 0.014; slope change: 0% 95%CI: -0.001 0.000). A small, significant increase in slope in number of new patients receiving prescriptions was observed (0.2% 95%CI: 0.001 0.003). However, no significant results were confirmed after robustness checks. We found also no variation across different socioeconomic groups.

Original languageEnglish
Pages (from-to)3623-3638
Number of pages16
JournalInternational Journal of Mental Health and Addiction
Volume22
Issue number6
Early online date22 May 2023
DOIs
Publication statusPublished - Dec 2024

Keywords

  • alcohol dependence prescriptions
  • alcohol use disorder
  • interrupted time series
  • Minimum Unit Price for Alcohol
  • natural experiment
  • Scotland

ASJC Scopus subject areas

  • Psychiatry and Mental health

Fingerprint

Dive into the research topics of 'The effect of minimum unit pricing for alcohol on prescriptions for treatment of alcohol dependence: a controlled interrupted time series analysis'. Together they form a unique fingerprint.

Cite this