Addiction最新文献

Background and aims: This study examined whether socio-economic status (SES) and geographic remoteness are associated with cannabis consumption across Australia by comparing wastewater-based surveillance (WBS) data and self-reported cannabis use in national household surveys.

Methods: We compared cannabis consumption estimates from 6 years of wastewater data from the National Wastewater Drug Monitoring Program (NWDMP) with cannabis use data from the National Drug Strategy Household Survey (NDSHS). We categorised the Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) into quartiles for each wastewater catchment area. Three remoteness categories (Major Cities, Inner Regional, Outer Regional/Remote) from the Australian Bureau of Statistics were used to categorise wastewater and survey data by degree of urbanisation. Trends were analysed using linear regression, analysis of variance and logistic regression.

Results: Wastewater: Estimated cannabis consumption in wastewater increased between 2018 and 2025 annually across SES quartiles from 2.9% [95% confidence interval (CI) = 2.0-3.9%] for the most disadvantaged to 5.2% (95% CI = 3.2-7.3%) for the second most disadvantaged quartile. The annual rates increased for all three categories of remoteness and ranged from 3.6% (95% CI = 2.2-5.2%) to 4.8% (95% CI = 4.2-5.5%) for Inner Regional and Major Cities, respectively. Consumption was statistically significantly higher in the most disadvantaged SES quartile (3000 mg/1000 people/day, 95% CI = 2900-3100) than in the least disadvantaged (1200 mg/1000 people/day, 95% CI = 1100-1200, P < 0.001). It was also highest in the most remote category (2400 mg/1000 people/day, 95% CI = 2300-2500), compared with the most urban category (980 mg/1000 people/day, 95% CI = 960-1000, P < 0.001).

Survey: In the NDSHS the prevalence of regular cannabis use was higher in the most disadvantaged SES quintile (6.1%, 95% CI = 5.2-7.3% in 2022/2023) than in the least disadvantaged (4.2%, 95% CI = 3.5-5.1% in 2022/2023). It was also higher in the most remote category (5.9%, 95% CI = 4.8-7.4%) than in the most urban category (4.3%, 95% CI = 3.9-4.8%), in all survey years (i.e. 2016 and 2019).

Conclusions: Wastewater-based surveillance and survey data describe similar trends in population cannabis use by socio-economic status and urban remoteness in Australia from 2018 to 2025.

Background and aims: Opioid addiction poses a significant challenge to both the health of mothers and the outcomes for their newborns. Treatments based on buprenorphine offer a proven strategy for addressing opioid dependence among pregnant women. Various studies have examined the effectiveness of buprenorphine treatments versus methadone, revealing several potential advantages of buprenorphine for the outcomes of newborns. This systematic review sought to clarify the benefits and risks associated with buprenorphine therapy.

Methods: The PubMed, Web of Science and Scopus databases were searched with keywords for qualifying papers published before February 2025. Mean differences (MD) and 95% confidence intervals (CIs) were calculated for continuous data, while pooled proportions were estimated for categorical variables. Interstudy heterogeneity and publication bias were assessed using I² and Egger's tests with Meta-Essential software analyses.

Results: The initial database search identified 2019 studies. Following a screening process based on inclusion criteria, 38 studies were selected for data extraction. The number of participants involved in all included studies was 5524. The results indicated that infants exposed to methadone had a higher incidence of neonatal abstinence syndrome (NAS) and required more pharmacologic treatment compared with those exposed to buprenorphine-naloxone (0.44, 95% CI = 0.25-0.75, P < 0.01). Additionally, infants exposed to buprenorphine had slightly higher birth weights than those exposed to methadone (0.17 kg, 95% CI = -0.14 to 1.49, P = 0.049), which might be linked to a greater need for NAS treatment.

Conclusion: Compared with methadone in treating opioid use disorder during pregnancy, buprenorphine-based therapies, buprenorphine-naloxone in particular, have demonstrated greater efficacy in enhancing neonatal outcomes.

The work by Chang and colleagues [1] is an important contribution to our understanding of drug-related death. Why does understanding death matter? The dead have much to teach the living. If we can understand why, and how, people die from various forms of drug toxicity, we will be better informed to prevent such deaths. In this work, the authors draw our attention to the important differences between opioid and psychostimulant toxicity deaths, and the role of cardiovascular and cerebrovascular disease in the latter. There are two important points here. First, that the mechanisms of opioid and psychostimulant ‘overdoses’ are distinct. Opioids lead to depressed respiration resulting from central nervous system (CNS) depression. Heart failure may occur secondary to respiratory failure, but it is respiration that is the primary mechanism. In contrast, psychostimulant toxicity relates primarily to the cardiovascular system, leading to psychostimulant-induced arrhythmia, coronary infarction and stroke. Indeed, the term overdose may not be appropriate for psychostimulant-related deaths.

The second important point raised in this article that distinguishes opioids from psychostimulants is the chronic effect of psychostimulants on the cardiovascular system. Chronic pathologies caused by psychostimulant use include cardiomegaly, ventricular hypertrophy, accelerated atherosclerotic coronary artery disease, ischaemic heart disease, hypertensive heart disease and cardiomyopathy. Indeed, it has been observed that we frequently see 70-year-old hearts in 30-year-old bodies. The use of a psychostimulant in the presence of such pathology increases the risk of acute cardiovascular effects, such as a fatal arrhythmia. In contrast, opioids have no such effects upon the cardiovascular system. Consistent with what we know about psychostimulants, Chang and colleagues found that psychostimulant-only deaths were significantly more likely than those involving opioids to have cardiovascular and cerebrovascular disease noted in the cause of death. These deaths were more likely to be a complex interaction of acute drug effects and chronic disease than an overdose per se.

A great deal of attention has been devoted over the years to psychiatric co-morbidity. The work of Chang and colleagues reminds us of the clinical importance of physical comorbidity. My colleagues and I recently drew attention to the underappreciated role of chronic obstructive pulmonary disease in heroin overdose deaths [2]. Chang and colleagues draw our attention to the importance of cardiovascular disease in psychostimulant deaths. The clinical monitoring and treatment of such comorbidities may not only improve the quality of life for people who use these drugs, but reduce their risk of death from drug toxicity.

Shane Darke: Conceptualization; writing—original draft.

None.

Background and aims: Opioid use disorder (OUD), cannabis use disorder (CUD) and cocaine use disorder have been associated with a range of adverse health outcomes, including certain ocular manifestations; however, their impact on diabetic retinopathy (DR) remains insufficiently explored. This study aimed to measure the association between OUD, CUD and cocaine use disorder and the risk of DR among patients with type 2 diabetes mellitus (T2DM).

Design: Propensity-score-matched retrospective cohort study.

Setting: This study used the TriNetX US Collaborative Network to access electronic health records (EHRs), including data on demographics, diagnoses, medication use and laboratory results.

Participants/cases: A total of 131 088 adult patients with T2DM and comorbid OUD, CUD or cocaine use disorder, and 131 088 adult patients with T2DM without these conditions, were identified following propensity score matching.

Measurements: The primary outcome was the risk of DR evaluated over a 5-year follow-up period. The risks of various DR subtypes and diabetic macular edema (DME) were also assessed. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated.

Findings: Over a 5-year follow-up period, patients with T2DM comorbid with OUD, CUD or cocaine use disorder had a statistically significantly higher risk of developing DR [HR (95% CI) = 2.90 (2.55-3.30), P < 0.00] compared with those without any drug use disorder. Drug use disorders were also associated with elevated risks of vision-threatening diabetic retinopathy (VTDR) [HR (95% CI) = 2.78 (2.24-3.46), P < 0.00], non-proliferative diabetic retinopathy (NPDR) [HR (95% CI) = 3.10 (2.61-3.68), P < 0.00], proliferative diabetic retinopathy (PDR) [HR (95% CI) = 3.17 (2.26-4.45), P < 0.00] and DME [HR (95% CI) = 2.64 (2.04-3.42), P < 0.00] among patients with T2DM.

Conclusions: Opioid use disorder, cannabis use disorder and cocaine use disorder appear to be associated with an elevated risk of diabetic retinopathy among patients with type 2 diabetes mellitus.

Background and aims: Addressing the opioid crisis requires opioid use disorder (OUD) prevalence estimates at the community level for targeted interventions. This study presents a new method that utilizes Medicaid claims data and publicly available data to estimate OUD prevalence at the United States (US) county level and compares it with other existing estimates.

Design, setting and participants: This study utilized data on OUD diagnoses among Medicaid beneficiaries in combination with national survey data, Census data and published literature to estimate the prevalence of OUD (including undiagnosed OUD) in each of the US state of Ohio's 88 counties for the years 2019 to 2021 among the population aged 12 years and older. Prevalence estimates were adjusted for misclassification in claims data and for variation in healthcare utilization among individuals with OUD.

Measurements: Counts and proportions of the population aged 12 and older with OUD at the US state and county level.

Findings: OUD prevalence for Ohioans aged 12 years and older was relatively stable at 3.6% [95% uncertainty interval (UI) = 3.5%-3.8%] in 2019 and 3.7% (95% UI = 3.5%-3.9%) in 2021. County-level prevalence estimates ranged from 0.7% to 14.2% in 2021. Southern counties generally had higher OUD prevalence than northern counties. The prevalence estimates were strongly correlated (Pearson's r = 0.88) with prevalence estimates for 19 Ohio counties from a previous study. Compared with the previous estimates, estimates from the current study tended to be lower for most communities but remained within the 95% credible intervals of previous estimates.

Conclusions: This approach for estimating opioid use disorder prevalence within United States communities by using Medicaid claims data and publicly available data is a robust alternative to methods relying on individual-level data or multiple linked datasets.