Addiction最新文献

Aims: To measure the association of harmful alcohol use by parents and primary caregivers with the level of child protection response.

Design, setting and participants: This study was a multivariable logistic regression analysis using data drawn from the Victorian child protection database (2012-21) in Victoria, Australia. Focusing upon the most recent case per child, we analysed whether harmful parental alcohol use was probably associated with children's progression throughout the child protection system (from investigation phase, to substantiation, through to protective intervention, protection application and protection orders), while adjusting for socio-demographic variables. The participants comprised 352 800 children [48.5% female, 50.0% male, 1.6% other/unknown; mean age = 8.1 (0-18 years)] with one or more reports (mean = 1.4) in the child protection system.

Measurements: Child protection workers reported on two risk factor variables indicating parental alcohol use during an intake risk assessment: 'alcohol abuse' and 'alcohol use compromises child's safety'.

Findings: Of the 95 592 child cases investigated between 2012 and 2021, 50 476 were substantiated. Probable parental alcohol use was reported as a risk factor in 5.1% of children investigated and substantiated in 9.1% of children. The odds of progressing to investigation [odds ratio (OR) = 1.64, 95% confidence interval (CI) = 1.59, 1.69, P < 0.001], substantiation (OR = 2.02, 95% CI = 1.91, 2.13, P < 0.001), protective intervention (OR = 1.40, 95% CI = 1.23, 1.59, P < 0.001), protection application (OR = 1.16, 95% CI = 1.08, 1.25, P < 0.001) and protection order (OR = 1.17, 95% CI = 1.02, 1.34, P = 0.028) were statistically significantly higher for children experiencing probable parental harmful alcohol use. However, the associations for protection application and protection order were not statistically significant after accounting for variables related to family accommodation, income and composition.

Conclusions: In Victoria, Australia, in cases where child protection workers document parental alcohol use, those children are more likely to progress through the Victorian child protection system than children whose parents have no documented alcohol use.

Background and aims: Increasing rates of attention-deficit/hyperactivity disorder (ADHD) pharmacotherapy may simultaneously benefit patients and increase the availability of stimulants for misuse. We measured the association between university-level prevalence of ADHD medication treatment and prevalence of prescription stimulant misuse (PSM) among college students.

Design, setting and participants: This was an observational study using cross-sectional data from the American College Health Association-National College Health Assessment III. Data included 395 participating universities between Fall 2019 and Fall 2022. Our sample included 224 469 undergraduates aged 18-25 years (65.2% cisgender female; 58.7% White).

Measurements: Students self-reported any life-time clinical ADHD diagnosis, past-year ADHD medication treatment and past-3-month PSM. We defined university-level ADHD medication prevalence as the proportion of included students endorsing past-year ADHD medication treatment. Secondary outcomes included life-time PSM and moderate- to high-risk alcohol and cannabis use. We also measured university-level depression medication prevalence as a negative control exposure.

Findings: Among the included students, 9.6% reported a life-time clinical ADHD diagnosis, 5.1% reported past-year medication treatment and 2.4% reported past-3-month PSM. The prevalence of ADHD medication treatment varied among universities [mean = 5.3%, standard deviation (SD) = 2.8%]. In adjusted models, prevalence of PSM was 7% relatively greater for every 1% increase in university-level medication prevalence [adjusted prevalence ratio (aPR) = 1.07; 95% confidence interval (CI) = 1.04-1.09]. Further, individuals with non-medication-treated ADHD were 40% more likely to report PSM than those without ADHD (aPR = 1.40; 95% CI = 1.25-1.56). There was no statistically significant difference in PSM among individuals with ADHD who did or did not receive medication (aPR = 0.90; 95% CI = 0.78-1.04). Results for secondary outcomes and the negative control partially supported the specificity of the findings.

Conclusions: Among university students in the United States, there appears to be a positive association between attending universities with a greater prevalence of attention deficit/hyperactivity disorder (ADHD) medication treatment and risk of prescription stimulant misuse (PSM). This study provides further support for the possibility that ADHD medication treatment prevalence is a risk factor for PSM.

Background and aims: Amphetamine-type stimulants are the second-most used illicit drugs globally, yet there are no US Food and Drug Administration (FDA)-approved treatments for amphetamine-type stimulant use disorders (ATSUD). The aim of this study was to utilize a drug discovery framework that integrates artificial intelligence (AI)-based drug prediction, clinical corroboration and mechanism of action analysis to identify FDA-approved drugs that can be repurposed for treating ATSUD.

Design and setting: An AI-based knowledge graph model was first utilized to prioritize FDA-approved drugs in their potential efficacy for treating ATSUD. Among the top 10 ranked candidate drugs, ketamine represented a novel candidate with few studies examining its effects on ATSUD. We therefore conducted a retrospective cohort study to assess the association between ketamine and ATSUD remission using US electronic health record (EHR) data. Finally, we analyzed the potential mechanisms of action of ketamine in the context of ATSUD.

Participants and measurements: ATSUD patients who received anesthesia (n = 3663) or were diagnosed with depression (n = 4328) between January 2019 and June 2022. The outcome measure was the diagnosis of ATSUD remission within one year of the drug prescription.

Findings: Ketamine for anesthesia in ATSUD patients was associated with greater ATSUD remission compared with other anesthetics: hazard ratio (HR) = 1.58, 95% confidence interval (CI) = 1.15-2.17. Similar results were found for ATSUD patients with depression when comparing ketamine with antidepressants and bupropion/mirtazapine with HRs of 1.51 (95% CI = 1.14-2.01) and 1.68 (95% CI = 1.18-2.38), respectively. Functional analyses demonstrated that ketamine targets several ATSUD-associated pathways including neuroactive ligand-receptor interaction and amphetamine addiction.

Conclusions: There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.

Aims: This study (1) estimated changes in anxiety and depression throughout 3 years in a community-recruited cohort who use methamphetamine and (2) modelled whether these changes were associated with patterns of methamphetamine use or other time-varying or fixed covariates.

Design, setting and participants: We used a longitudinal analysis using data derived from surveys conducted between August 2016 and March 2020, set in metropolitan and rural locations in Victoria, Australia. Participants comprised a total of 849 adults with regular methamphetamine use history at baseline, recruited for the prospective VMAX study via snowball and respondent-driven sampling.

Measurements: Anxiety and depression symptoms were measured using the Generalized Anxiety Disorder (GAD)-7 and the Patient Health Questionnaire (PHQ)-9 instruments. Frequency of methamphetamine use was measured by self-reported number of days per week participants used any form of methamphetamine in the past month.

Findings: Changes in anxiety and depression symptom scores were associated with change in route of administration from non-injecting to injecting [adjusted coefficient (adj. coeff.) = 1.44, 95% confidence intervals (CI) = 0.39, 2.48, adj. coeff. = 1.49, 95% CI = 0.39, 2.58], change in severity of dependence for methamphetamine (adj. coeff. = 0.29, 95% CI = 0.21, 0.37, adj. coeff. = 0.34, 95% CI = 0.26, 0.42), starting treatment for drugs other than methamphetamine (adj. coeff. = -2.21, 95% CI = -3.70, -0.73, adj. coeff. = -2.09, 95% CI = -3.60, -0.58) and other covariates.

Conclusions: Among regular methamphetamine users in Australia, changes in anxiety or depression scores are associated with changes in route of administration, dependence severity and starting treatment for other drugs, but do not appear to be associated with frequency of methamphetamine use.