Addiction最新文献

Background and aims: Opioid dependence is associated with an increased risk of suicide. Drug-related mortality among people with opioid dependence in Scotland has more than tripled since 2010; less is known about changes in suicide risk. We aimed to determine if opioid agonist therapy (OAT) in Scotland is protective against suicide and to measure trends in suicide rates in those with opioid dependence over time.

Design: Retrospective cohort study.

Setting: Scotland, UK.

Participants: 46 453 individuals in Scotland who received at least one prescription for OAT between 2011 and 2020 with over 304 000 person-years (pys) of follow-up.

Measurements: We calculated standardised mortality ratios (SMR) using the age- and sex-specific suicide rates in Scotland for years 2011-2020. We fitted multivariable competing-risk regression models to estimate suicide rates by OAT exposure and to estimate trends over time, adjusting for potential confounders.

Findings: There were 575 deaths classed as suicide among the cohort and the overall suicide rate was 1.89 (95% confidence interval [CI] = 1.74-2.05) per 1000 pys. Age and sex SMR for suicide was 7.05 times (95% CI = 6.50-7.65) higher than in the general population. After adjustment, OAT was shown to be highly protective against suicide, with rates more than three times greater (adjusted hazard ratio: 3.07; 95% CI = 2.60-3.62) off OAT compared with on OAT. Suicide rates decreased over time, falling from 2.57 (95% CI = 2.19-3.02) per 1000 pys in 2011-12 to 1.48 (95% CI = 1.21-1.82) in 2019-20.

Conclusion: People with opioid dependence in Scotland appear to have a greater risk of suicide than the general population. Treatment is protective, with rates of suicide lower among those on opioid agonist therapy. Suicide rates have decreased over time, during a period in which drug-related death rates in Scotland have risen to globally high levels.

Background and aims: Patient initiated transport refusal during Emergency Medical Service (EMS) opioid overdose encounters has become an endemic problem. This study aimed to quantify circumstantial and environmental factors which predict refusal of further care.

Design: In this cross-sectional analysis, a case definition for opioid overdose was applied retrospectively to EMS encounters. Selected cases had sociodemographic and situational/incident variables extracted using patient information and free text searches of case narratives. 50 unique binary variables were used to build a logistic model.

Setting: Prehospital EMS overdose encounters in Washington, DC, USA, from July 2017 to July 2023.

Participants: Of EMS encounters in the study timeframe, 14 587 cases were selected as opioid overdoses.

Measurements: Predicted probability for covariates was the outcome variable. Model performance was assessed using Stratified K-Fold Cross-Validation and scored with positive predictive value, sensitivity and F1. Prediction accuracy and McFadden's pseudo-R squared are also determined.

Findings: The model achieved a predictive accuracy of 78% with a high positive predictive value (0.83) and moderate sensitivity (0.68). Bystander type influenced the refusal outcome, with decreased refusal probability associated with family (nondescript) (-28%) and parents (-16%), while presence of a girlfriend increased it (+28%). Negative situational factors like noted physical trauma (-62%), poor weather (-14%) and lack of housing (-14%) decreased refusal probability. Characteristics of the emergency response team, like a prior crew member encounter (+20%) or crew experience <1 year (-36%), had a variable association with transport.

Conclusions: Refusal of emergency transport for opioid overdose cases in Washington, DC, USA, has expanded by 43.8% since 2017. Several social, environmental and systematic factors can predict this refusal. Logistic regression models can be used to quantify broad categories of behavior in surveillance medical research.

Aims: To determine the contribution that variation in the unregulated drug market has on the risk of heroin overdose across individuals with different levels of personal overdose risk.

Design: A retrospective cohort study of heroin injecting episodes and overdose cases were examined over a 12-month period between 30 June 2022 and 30 June 2023.

Setting: The Medically Supervised Injecting Room in Melbourne, Australia.

Cases: 1474 witnessed heroin overdose cases were examined amongst a cohort of 337 individuals who were predominantly male (n = 276, 81.7%) with a median age of 43.5 years (interquartile range 37.25-49.00 years, range 20-75 years).

Measurements: The daily overdose rate was used to differentiate High and Low daily overdose risk categories. The number of overdose events that an individual experienced during the study period was used to differentiate people into Standard, Moderate and High personal overdose risk categories. Each overdose case was differentiated by the personal overdose risk of the individual who experienced the overdose, as well as the overdose risk of the day that overdose occurred. A stratified overdose risk profile was then derived across the nine different daily overdose risk and personal overdose risk categories.

Findings: The rate of overdose approximately doubled on High overdose risk days compared with Standard overdose risk days, increasing by a factor of 2.11, 2.41 and 2.03 times for individuals in the Standard, Moderate and High personal overdose risk groups. Conversely, the rate of overdose was also substantially reduced on Low overdose risk days to a factor of 0.17, 0.28 and 0.20, respectively.

Conclusion: Among heroin users in Melbourne, Australia, there is an approximately 10-times difference in the risk of overdose on some days compared with others, which appears to be attributable to the effects of the unregulated drug market and not the effects of variation in personal overdose risk of individuals.

Background and aim: In 2018, the country of Georgia legalized cannabis for recreational use and decriminalized limited possession. This study aimed to assess whether cannabis use increased among young adults (ages 18-29 years) in Georgia after national policy changes and to evaluate whether perceived access became easier after legalization and current risk factors of young adult cannabis use.

Methods: We used data from the Georgian nationally representative survey administered in 2015 (n = 1308) and 2022 (n = 758), before and after decriminalization. We performed appropriate bivariate analyses and multivariable linear and logistic regressions to assess the following: legalization's impact on cannabis use; perceived difficulty to obtain cannabis; age of first use; differences in use between females and males; and factors associated with current use.

Findings: Among young adults lifetime prevalence of cannabis use was similar in 2015 (17.3%) and 2022 (18.1%) [Odds Ratio (95% confidence interval) = 1.1 [0.7, 1.6], P = 0.726). Annual prevalence (7% in 2015 vs 7.7% in 2022) was also similar (1.1 [0.7, 2.0], P = 0.650). In 2022 it was less difficult to obtain cannabis than in 2015 (0.5 [0.4, 0.8], P = 0.021). The age of first use increased statistically significantly (18.1 years in 2015 vs 19.1 in 2022, P = 0.003). In 2022, annual prevalence of use was lower among females (1.9% vs 13.1%; OR = 0.1 [0.0, 0.3], P < 0.0001) and higher among those who gambled (11.7% vs 4.4%; OR = 3.2 [1.5, 6.8], P < 0.003). Males initiated cannabis use at an earlier age (19.1 years vs 20.6 for females, P = 0.03), and could obtain cannabis easier than females (P < 0.0001).

Conclusion: There was a minimal shift of cannabis use in young adults following implementation of recreational cannabis use legalization in Georgia. Males and people who gambled were at higher risk of cannabis use.

Background and aims: Research demonstrates that siblings, especially older siblings, make unique contributions to adolescents' substance use above and beyond shared genetics and shared parenting. Older siblings' influences on younger adolescent siblings' alcohol use operate through both direct and indirect pathways. Using three waves of longitudinal data, the present study tested an integrated model of sibling influence processes focused on the linkages between older adolescent siblings' earlier alcohol use and younger adolescent siblings' later alcohol use.

Design: Longitudinal study using data collected from families on three occasions: Time 1 (March 2019-February 2020), Time 2 (July 2020-February 2021) and Time 3 (November 2021-February 2022) via online surveys.

Setting: Families resided in five midwestern states in the US (Illinois, Indiana, Ohio, Pennsylvania and Wisconsin).

Participants: Participants included two adolescent-aged siblings and one parent from 682 families (n = 2046 persons).

Measurements: Alcohol use by adolescents and parents was assessed at Time 1; younger siblings' social alcohol expectancies and perceptions of modeling were measured at Time 2; and younger siblings' alcohol use was measured at Time 3.

Findings: Older siblings' earlier alcohol use predicted younger siblings' later drinking both directly [b = 0.15, standard error (SE) = 0.04, β = 0.17, P < 0.001] and indirectly through younger siblings' social alcohol expectancies [δ = 0.02, SE = 0.008, 95% confidence interval (CI) = 0.003, 0.03]. The direct (δ = -0.14, SE = 0.07, 95% CI = -0.27, -0.01) and indirect (δ = 0.03, SE = 0.02, 95% CI = 0.0001, 0.06) links were further moderated by younger siblings' reports of sibling modeling, but not by gender composition of the sibling dyad or the interaction of modeling and gender composition.

Conclusions: Older siblings' alcohol use appears to influence younger siblings' later alcohol use directly, as well as indirectly through younger siblings' expectancies about alcohol. The global context of the sibling relationship, in this case sibling modeling, may further amplify or dampen these pathways of influence.