Addiction最新文献

Background and aims: Workplaces offer a practical setting for alcohol and other drug interventions, especially in industries where impairment introduces substantial risk. Screening, brief intervention and referral to treatment has demonstrated effectiveness in health care settings and shows promise in workplace settings. However, low participation and high attrition in previous workplace studies indicate a need for deeper understanding of feasibility and acceptability. This exploratory qualitative study aimed to identify likely determinants for implementing alcohol and other drug screening, brief intervention and referral to treatment in two safety-sensitive industries in Australia.

Methods: Qualitative research design based on semi-structured online interviews, focussed on the construction and manufacturing industries. Participants included 23 professionals working in health and safety roles representing 21 organisations located across six Australian jurisdictions. Interview transcripts were coded against the five domains of the updated Consolidated Framework for Implementation Research.

Findings: Sixteen determinants were identified that were expected to act as barriers (n = 10) or enablers (n = 5) or have bidirectional impacts (n = 1) on the implementation of screening, brief intervention and referral to treatment in construction and manufacturing. Enabling factors included freely available tools, flexible delivery methods and delivery by trusted, external, peer-based organisations. Pervasive barriers included workers' mistrust of management, concerns about confidentiality and fear of consequences for disclosing substance use.

Conclusions: Successful workplace implementation of screening, brief intervention and referral to treatment appears to depend on organisational cultures where workers trust management, are assured of confidentiality and are not afraid of retribution for disclosure.

Background and aims: There is significant concern about potential rising harms from gamma-hydroxybutyrate (GHB) but an absence of studies internationally synthesising data across indicators to identify changes in harms and broader patterns of use. This paper contributes to addressing this gap by measuring national trends in GHB use, harms and treatment in Australia.

Design, setting, and cases: Triangulation of indicators (2013-2024) from Australian triennial population surveys; annual interviews with cross-sectional non-representative samples of people who use illicit stimulants or who inject drugs; and administrative data on GHB-related hospitalisations, GHB-related deaths, and treatment episodes where GHB was the principal drug of concern.

Measurements: Annual trend data were analysed using Joinpoint regression. Survey data were modelled as the annual percent change in the proportion reporting lifetime, past 12-month, and past 6-month use, depending on the survey. Administrative data were modelled as the annual percent change in crude rates per 100 000 population.

Findings: Lifetime and past 12-month GHB use in the general population remained below 1.2% and 0.2% respectively, but the latter increased from 0.07% in 2013 to 0.19% in 2022-2023 (annual percent change [APC] 9.3; 95% confidence interval [CI]: 5.2, 13.2). The percentage of people who use illicit stimulants reporting past 6-month use increased from 5.7% in 2013 to 7.3% in 2017 (APC 11.6; 95%CI: 0.2, 52.9) and from 5.4% in 2019 to 11.5% in 2024 (APC 17.8; 95%CI: 5.9, 41.1). The proportion of people who inject drugs reporting use varied between 7.2% and 17.5% over the short period studied (2020-2024). There were statistically significant increases in GHB-related hospitalisations from 5.3 in 2012-13 to 19.1 per 100 000 people in 2022-23 (APC 19.0; 95%CI: 11.9, 31.1) and GHB-related deaths from 0.02 in 2013 to 0.24 per 100 000 people in 2022 (APC 36.5; 95%CI: 27.2, 58.1). Treatment episodes also increased across the period, from 0.07 in 2012-13 to 6.0 episodes per 100 000 people in 2020-21 (APC 97.3; 95%CI: 83.5, 830.9), with no subsequent statistically significant change (8.4 per 100 000 people in 2022-23).

Conclusions: Gamma-hydroxybutyrate use, harms and treatment engagement increased in Australia from 2013 to 2024. These findings highlight a need to implement acceptable, tailored prevention and harm reduction strategies for key populations, and implement stronger monitoring efforts nationally and internationally.

Aims: Industry arguments against public health policies that reduce the consumption of unhealthy commodities often include the assertion that the policy will harm the economy by reducing production and costing jobs. However, this argument does not consider that consumers may spend money previously used for unhealthy commodity consumption on other products, benefiting other sectors and potentially offsetting those negative economic consequences. In this study we aimed to estimate the macroeconomic impacts of reducing consumption of alcohol, tobacco, confectionary and gambling, accounting for reallocation of spending from these commodities to alternatives.

Method: We developed the open-source Commercial Determinants of Health Input-Output (CDOHIO) model version 1.1.0. CDOHIO models inter-sectoral linkages in the United Kingdom (UK) economy using published input-output tables to estimate the macroeconomic outcomes of changes in the total national consumer expenditure on selected unhealthy commodities and the reallocation of this expenditure to other consumption. We modelled a 10% decrease in total consumer expenditure on (1) alcohol, (2) tobacco, (3) confectionary and (4) gambling, assuming that the reduced expenditure was reallocated entirely to other products. The comparator in each case was no change in expenditure. We analysed six economic outcomes: (i) output (the total value of production in the economy), (ii) tax receipts from employees, (iii) tax receipts from employers, (iv) full-time equivalent employment, (v) total net earnings to individuals, and (vi) Gross Value Added (GVA), which is the primary outcome measure used as a proxy for national Gross Domestic Product.

Results: For tobacco, confectionary and gambling, reduced spending was estimated to yield positive effects across all six measures. The total effect of a 10% reduction in confectionary spending was an increase in GVA of £0.389 billion (0.02%), for reduced spending on tobacco, +£1.859 billion GVA (+0.09%) and for gambling +£1.250 billion GVA (+0.06%). For alcohol, a 10% reduction in spending led to a small negative effect on GVA (-£0.134 billion, -0.01%), which is the net effect of positive effects of reduced spending on off-trade alcohol (+£2.543 billion) and negative effects of reduced spending on on-trade alcohol (-£2.677 billion).

Conclusions: The potential negative macroeconomic impacts of reducing spending on tobacco, confectionary and gambling in the United Kingdom could be more than mitigated when consumers reallocate money spent on these products to other consumption. This is also the case for off-trade alcohol consumption, but not for on-trade alcohol consumption.

Background and aims: Tobacco smoking is a major risk factor for cardiovascular and lung diseases. A better understanding of its neurobiological underpinnings will benefit the prevention of smoking-related illnesses and mortality. Previous studies link smoking to increased iron concentration in the striatum, a central component of the brain's reward system, and to reduced cognitive performance. This study aimed to investigate whether smoking and striatal iron share common biological pathways and to assess potential causal relationships between the two.

Methods: Using data from the UK Biobank, we investigated phenotypic and genetic correlations, and causal relationships between smoking initiation and magnetic resonance imaging (MRI)-derived markers of iron content (T2* and quantitative susceptibility mapping) in the bilateral putamen, caudate and accumbens nuclei.

Results: We found positive correlations between smoking and striatal iron (β ∈ [0.03, 0.40], P < 0.001), particularly when comparing current smokers with never smokers. Striatal iron was positively associated with pack-years (β ∈ [0.11, 0.13], P < 0.001) and inversely related to years since smoking cessation (β ∈ [0.06, 0.10], P < 0.001), suggesting iron levels may decrease after quitting. Genetic analysis confirmed phenotypic correlations, with shared genetic associations (P < 2.73 × 10^-6, or 0.01 for candidate genes) in genes related to dopaminergic, glutamatergic and synaptic systems (DRD2, PPP1R1B, NCAM1, DLX5, GGACT, NAT16, PLEKHM1). Causality analysis revealed a relationship from smoking to striatal iron via genes involved in synaptogenesis and plasticity (BAI3, SEMA6D, TENM2), with evidence of reverse causality from iron to smoking through inflammatory and immune system-related genes (ING5, NLRP7).

Conclusions: There appear to be links between smoking and striatal iron with complex causal mechanisms involving synaptic transmission and inflammatory circuits. Striatal iron content could serve as a biomarker for smoking-related neurobiological changes and a potential target for interventions aimed at mitigating cognitive decline related to striatal iron accumulation.

Background and aims: Thanks to smart devices, social media and streaming platforms, watching videos, like movies or short social media clips, has become extremely popular. Alcohol portrayals are frequent in videos, yet their prevalence is difficult to quantify using traditional methods such as manual coding. Artificial intelligence (AI) offers a scalable solution to analyse large volumes of video images. This study aimed to compare the accuracy of three AI models in detecting alcohol presence in video images.

Method: Experimental evaluation of three models: one supervised deep learning model (ABIDLA2) and two zero-shot learning models (ZSL-CLIP and ZSL-LLaVA). The models were tested on datasets of video frames that had been annotated by researchers for whether they included alcohol or not. Three datasets of increasing complexity were used: (1) a Google/Bing image set of clearly visible alcohol and non-alcohol images; (2) a set of movie frames manually annotated as containing or not containing alcohol; and (3) a contextually challenging set of movie frames from alcohol-related settings (e.g. bars, parties) that may or may not include visible alcohol. Model performance was assessed using accuracy, unweighted average recall (UAR) and F1 score, representing the balance between precision and recall. Execution time per frame was also measured to evaluate computational efficiency.

Results: Across the three datasets, ABIDLA2, ZSL-CLIP and ZSL-LLaVA achieved percentage accuracies of 90%, 91% and 92% on the Google/Bing images; 70%, 65% and 95% on the diverse movie-scene dataset; and 67%, 63% and 94% on the most complex alcohol-related dataset, respectively. In terms of execution time, ABIDLA2 processed a single frame the fastest (0.21 seconds), followed by ZSL-LLaVA (0.45 seconds), while ZSL-CLIP was the slowest (0.58 seconds).

Conclusion: Automated artificial intelligence (AI) models appear to be able to detect alcohol imagery in videos at large scale with high accuracy and in near real time. Of the three AI models tested, ZSL-LLaVA achieved the best balance between accuracy and speed. Offering a cost- and time-efficient alternative to labour-intensive manual coding, ZSL-LLaVA could be used to monitor alcohol-related visual content in videos across diverse media platforms.