The aims of this study were to examine the distribution of alcohol use and to define ‘harm density functions’ representing distributions of alcohol-caused health harm in Canada, by sex, towards better understanding which groups of drinkers experience the highest aggregate harms.
This was an epidemiological modeling study using survey and administrative data on alcohol exposure, death and disability and risk relationships from epidemiological meta-analyses.
This work took place in Canada, 2019.
Canadians aged 15 years or older participated.
Measures included modeled life-time mean daily alcohol use in grams of pure alcohol (ethanol) per day, alcohol-caused deaths and alcohol-caused disability-adjusted life-years.
As a life-time average, more than half of Canadians aged 15+ (62.8% females, 46.9% males) use fewer than 10 g of pure alcohol per day (g/day). By volume, the top 10% of the population consume 45.9% of the total ethanol among males and 47.1% of the total ethanol among females. The remaining 90% of the population experience a slim majority of alcohol-caused deaths (males 55.3%, females 46.9%). Alcohol harm density functions compose the size of the using population and the risk experienced at each volume level to show that the population-level harm experienced is highest for males at 25 g/day and females at 13 g/day.
Almost 50% of alcohol use in Canada is concentrated among the highest 10% of drinkers, but more than half of the alcohol-caused deaths in Canada in 2019 were experienced by the bottom 90% of the population by average volume, providing evidence for the prevention paradox. New alcohol harm density functions provide insight into the aggregate health harm experienced across the mean alcohol use spectrum and may therefore be used to help determine where alcohol policies should be targeted for highest efficacy.
Hallucinogens encompass a diverse range of compounds of increasing scientific and public interest. Risks associated with hallucinogen use are under-researched and poorly understood. We aimed to compare the trends in hallucinogen-associated health-care use with alcohol- and cannabis-associated health-care use.
We conducted an ecological study with publicly available data on International Classification of Diseases, 10th Revision (ICD-10) diagnosis codes associated with emergency department (ED) visits and hospitalizations from the California Department of Healthcare Access and Information (HCAI). HCAI includes primary and secondary ICD-10 codes reported with ED or hospital discharge from every non-federal health-care facility licensed in California, United States, from 2016 to 2022.
ICD-10 codes were classified as hallucinogen-, cannabis- or alcohol-associated if they were from the corresponding category in the ICD-10 block ‘mental and behavioral disorders due to psychoactive substance use’.
Observed hallucinogen-associated ED visits increased by 54% between 2016 and 2022, from 2260 visits to 3476 visits, compared with a 20% decrease in alcohol-associated ED visits and a 15% increase in cannabis-associated ED visits. The observed hallucinogen-associated hospitalizations increased by 55% during the same period, from 2556 to 3965 hospitalizations, compared with a 1% increase in alcohol-associated hospitalizations and a 1% increase in cannabis-associated hospitalizations. This rise in hallucinogenic ED visits was significantly different from the trend in cannabis-associated (P < 0.001) and alcohol-associated (P = 0.005) ED visits. The hallucinogen-associated hospitalizations trend also significantly differed when compared with cannabis- (P < 0.001) and alcohol-associated (P < 0.001) hospitalizations.
Hallucinogen-associated emergency department visits and hospitalizations in California, USA, showed a large relative but small absolute increase between 2016 and 2022.
A vast amount of media-related text data is generated daily in the form of social media posts, news stories or academic articles. These text data provide opportunities for researchers to analyse and understand how substance-related issues are being discussed. The main methods to analyse large text data (content analyses or specifically trained deep-learning models) require substantial manual annotation and resources. A machine-learning approach called ‘zero-shot learning’ may be quicker, more flexible and require fewer resources. Zero-shot learning uses models trained on large, unlabelled (or weakly labelled) data sets to classify previously unseen data into categories on which the model has not been specifically trained. This means that a pre-existing zero-shot learning model can be used to analyse media-related text data without the need for task-specific annotation or model training. This approach may be particularly important for analysing data that is time critical. This article describes the relatively new concept of zero-shot learning and how it can be applied to text data in substance use research, including a brief practical tutorial.
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