Addiction最新文献

{"title":"Parental alcohol use and the level of child protection response in Australia (2012-21).","authors":"Koen Smit, Jade Rintala, Benjamin Riordan, Kylie Lee, Anne-Marie Laslett","doi":"10.1111/add.16677","DOIUrl":"https://doi.org/10.1111/add.16677","url":null,"abstract":"<p><strong>Aims: </strong>To measure the association of harmful alcohol use by parents and primary caregivers with the level of child protection response.</p><p><strong>Design, setting and participants: </strong>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.</p><p><strong>Measurements: </strong>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'.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142666763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prevalence of pharmacotherapy for attention-deficit/hyperactivity disorder and prescription stimulant misuse: A national study of US college students.","authors":"Alynna G Summit, Madison C Moseley, Natasha Chaku, Kit K Elam, Wura Jacobs, Alyssa M Lederer, Ellen L Vaughan, Patrick D Quinn","doi":"10.1111/add.16716","DOIUrl":"https://doi.org/10.1111/add.16716","url":null,"abstract":"<p><strong>Background and aims: </strong>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.</p><p><strong>Design, setting and participants: </strong>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).</p><p><strong>Measurements: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-based drug repurposing with electronic health record clinical corroboration: A case for ketamine as a potential treatment for amphetamine-type stimulant use disorder.","authors":"Zhenxiang Gao, T John Winhusen, Maria P Gorenflo, Ian Dorney, Udi E Ghitza, David C Kaelber, Rong Xu","doi":"10.1111/add.16715","DOIUrl":"https://doi.org/10.1111/add.16715","url":null,"abstract":"<p><strong>Background and aims: </strong>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.</p><p><strong>Design and setting: </strong>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.</p><p><strong>Participants and measurements: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>There appears to be an association between clinician-prescribed ketamine and higher remission rates in patients with amphetamine-type stimulant use disorders.</p>","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142646191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anxiety and depression among a community-recruited cohort of people who use methamphetamine: A longitudinal analysis.","authors":"Zoe Duncan, Rebecca Kippen, Keith Sutton, Bernadette Ward, Kasun Rathnayake, Brendan Quinn, Paul Dietze","doi":"10.1111/add.16714","DOIUrl":"https://doi.org/10.1111/add.16714","url":null,"abstract":"<p><strong>Aims: </strong>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.</p><p><strong>Design, setting and participants: </strong>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.</p><p><strong>Measurements: </strong>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.</p><p><strong>Findings: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142637950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to \"The impact of introducing alcohol-free beer options in bars and public houses on alcohol sales and revenue: A randomised crossover field trial\".","authors":"","doi":"10.1111/add.16717","DOIUrl":"10.1111/add.16717","url":null,"abstract":"","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142612932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commentary on Ter Laak et al.: The importance of drug market information and differentiating drug use patterns.","authors":"Rory Verhagen, Phong K Thai","doi":"10.1111/add.16712","DOIUrl":"https://doi.org/10.1111/add.16712","url":null,"abstract":"","PeriodicalId":109,"journal":{"name":"Addiction","volume":" ","pages":""},"PeriodicalIF":5.2,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142612928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain structural covariance network features are robust markers of early heavy alcohol use","authors":"Jonatan Ottino-González,&nbsp;Renata B. Cupertino,&nbsp;Zhipeng Cao,&nbsp;Sage Hahn,&nbsp;Devarshi Pancholi,&nbsp;Matthew D. Albaugh,&nbsp;Ty Brumback,&nbsp;Fiona C. Baker,&nbsp;Sandra A. Brown,&nbsp;Duncan B. Clark,&nbsp;Massimiliano de Zambotti,&nbsp;David B. Goldston,&nbsp;Beatriz Luna,&nbsp;Bonnie J. Nagel,&nbsp;Kate B. Nooner,&nbsp;Kilian M. Pohl,&nbsp;Susan F. Tapert,&nbsp;Wesley K. Thompson,&nbsp;Terry L. Jernigan,&nbsp;Patricia Conrod,&nbsp;Scott Mackey,&nbsp;Hugh Garavan","doi":"10.1111/add.16330","DOIUrl":"10.1111/add.16330","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aims</h3>\u0000 \u0000 <p>Recently, we demonstrated that a distinct pattern of structural covariance networks (SCN) from magnetic resonance imaging (MRI)-derived measurements of brain cortical thickness characterized young adults with alcohol use disorder (AUD) and predicted current and future problematic drinking in adolescents relative to controls. Here, we establish the robustness and value of SCN for identifying heavy alcohol users in three additional independent studies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design and Setting</h3>\u0000 \u0000 <p>Cross-sectional and longitudinal studies using data from the Pediatric Imaging, Neurocognition and Genetics (PING) study (<i>n</i> = 400, age range = 14–22 years), the National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) (<i>n</i> = 272, age range = 17–22 years) and the Human Connectome Project (HCP) (<i>n</i> = 375, age range = 22–37 years).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Cases</h3>\u0000 \u0000 <p>Cases were defined based on heavy alcohol use patterns or former alcohol use disorder (AUD) diagnoses: 50, 68 and 61 cases were identified. Controls had none or low alcohol use or absence of AUD: 350, 204 and 314 controls were selected.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Measurements</h3>\u0000 \u0000 <p>Graph theory metrics of segregation and integration were used to summarize SCN.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>Mirroring our prior findings, and across the three data sets, cases had a lower clustering coefficient [area under the curve (AUC) = −0.029, <i>P</i> = 0.002], lower modularity (AUC = −0.14, <i>P</i> = 0.004), lower average shortest path length (AUC = −0.078, <i>P</i> = 0.017) and higher global efficiency (AUC = 0.007, <i>P</i> = 0.010). Local efficiency differences were marginal (AUC = −0.017, <i>P</i> = 0.052). That is, cases exhibited lower network segregation and higher integration, suggesting that adjacent nodes (i.e. brain regions) were less similar in thickness whereas spatially distant nodes were more similar.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Structural covariance network (SCN) differences in the brain appear to constitute an early marker of heavy alcohol use in three new data sets and, more generally, demonstrate the utility of SCN-derived metrics to detect brain-related psychopathology.</p>\u0000 </section>\u0000 </div>","PeriodicalId":109,"journal":{"name":"Addiction","volume":"119 1","pages":"113-124"},"PeriodicalIF":6.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.16330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10314678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tailoring CONSORT-SPI to improve the reporting of smoking cessation intervention trials: An expert consensus study","authors":"Zoe Swithenbank,&nbsp;Alessio Bricca,&nbsp;Nicola Black,&nbsp;Jamie Hartmann Boyce,&nbsp;Marie Johnston,&nbsp;Neil Scott,&nbsp;Robert West,&nbsp;Ryan J. Courtney,&nbsp;Shaun Treweek,&nbsp;Susan Michie,&nbsp;Marijn de Bruin","doi":"10.1111/add.16340","DOIUrl":"10.1111/add.16340","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aims</h3>\u0000 \u0000 <p>Inadequate reporting of smoking cessation intervention trials is common and leads to significant challenges for researchers. The aim of this study was to tailor CONSORT (Consolidated Standards of Reporting Trials)-SPI (Social and Psychological Interventions) guidelines to improve reporting of trials of behavioural interventions to promote smoking cessation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>Informed by missing data from the IC-SMOKE (Intervention and Comparison group support provided in SMOKing cEssation) systematic review project, this study used a multi-stage Delphi process to examine which items could be added or modified to improve the reporting of smoking cessation trials. The first stage involved an on-line survey of 17 international experts in smoking cessation and trial methodology voting on the importance of items for inclusion in the updated guidelines. This was followed by a face-to-face expert consensus meeting attended by 15 of these experts, where the final inclusion and exclusion of new items and modifications were agreed upon. A nine-point Likert scale was used to establish consensus, with suggested modifications requiring agreement of 75% or more. Disagreements in the first stage were presented again at the second stage for discussion and a second round of voting. Only items which reached the threshold for agreement were included.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The experts agreed on the inclusion of 10 new items and the specification of 12 existing items. This included modifications that could apply to trials more widely (e.g. the rationale for the comparator), but also modifications that were very specific to smoking cessation trials (e.g. the reporting of smoking cessation outcomes).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>A Delphi study has developed a modified CONSORT-SPI guideline (CONSORT-SPI-SMOKE) to improve the reporting of trials of behavioural interventions to promote smoking cessation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":109,"journal":{"name":"Addiction","volume":"119 2","pages":"225-235"},"PeriodicalIF":6.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/add.16340","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10674617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between stimulant use and return to illicit opioid use following initiation onto medication for opioid use disorder","authors":"Canyon Foot,&nbsp;Philip T. Korthuis,&nbsp;Judith I. Tsui,&nbsp;Sean X. Luo,&nbsp;Brian Chan,&nbsp;Ryan R. Cook","doi":"10.1111/add.16334","DOIUrl":"10.1111/add.16334","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Aim&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The aim of this study was to estimate how ongoing stimulant use affects return to illicit opioid use after initiation onto medication for opioid use disorder (MOUD).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Design&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This was a secondary analysis of pooled data from two clinical trials comparing buprenorphine (BUP-NX) and extended-release naltrexone (XR-NTX).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Setting&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Thirteen opioid treatment programs and HIV clinics across 10 states in the United States from 2014 to 2019 took part in this study.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Participants&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 528 participants who initiated MOUD as part of trial participation were included. Nearly half (49%) were between 30 and 49 years of age, 69% were male and 66% were non-Hispanic White.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Measurements&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The primary outcome was first self-reported day of non-prescribed opioid use following MOUD initiation, and the exposure of interest was daily stimulant use (methamphetamine, amphetamines or cocaine). Both were defined using time-line follow-back. Among participants reporting at least 1 day of illicit opioid use, we also examined relapse to ongoing use, defined as (1) 7 days of continuous opioid use or (2) 4 consecutive weeks with self-reported opioid use, one or more positive urine drug screens (UDS) for opioids or one or more missing UDS.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Findings&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Forty-seven per cent of participants reported stimulant use following MOUD initiation, 58% returned to illicit opioid use and 66% of those relapsed to ongoing use. Stimulant use was strongly associated with increased risk of misusing opioids after MOUD initiation when measured daily [adjusted hazard ratio (aHR) = 9.23, 95% confidence interval (CI) = 6.80–12.50, &lt;i&gt;P&lt;/i&gt; &lt; 0.001] and over a 7-day period (aHR = 1.27 for each additional day, CI = 1.18–1.37, &lt;i&gt;P&lt;/i&gt; &lt; 0.001). Using stimulants weekly or more often was associated with increased likelihood of relapse to ongoing opioid use compared with less than weekly or no stimulant use (adjusted odds ratio = 2.30, CI = 1.05–5.39, &lt;i&gt;P&lt;/i&gt; = 0.044).&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;People initiated on medication for opioid use disorder who subsequently use stimulants appear to be more likely to return to and continue using non-prescribed opioids compared with those without stimulant use. The as","PeriodicalId":109,"journal":{"name":"Addiction","volume":"119 1","pages":"149-157"},"PeriodicalIF":6.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10245594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying patients with opioid use disorder using International Classification of Diseases (ICD) codes: Challenges and opportunities","authors":"Katie P. Osterhage,&nbsp;Yih-Ing Hser,&nbsp;Larissa J. Mooney,&nbsp;Seth Sherman,&nbsp;Andrew J. Saxon,&nbsp;Maja Ledgerwood,&nbsp;Caleb C. Holtzer,&nbsp;Margaret A. Gehring,&nbsp;Sarah E. Clingan,&nbsp;Megan E. Curtis,&nbsp;Laura-Mae Baldwin","doi":"10.1111/add.16338","DOIUrl":"10.1111/add.16338","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and Aims</h3>\u0000 \u0000 <p>International Classification of Diseases (ICD) diagnosis codes are often used in research to identify patients with opioid use disorder (OUD), but their accuracy for this purpose is not fully evaluated. This study describes application of ICD-10 diagnosis codes for opioid use, dependence and abuse from an electronic health record (EHR) data extraction using data from the clinics' OUD patient registries and clinician/staff EHR entries.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Design</h3>\u0000 \u0000 <p>Cross-sectional observational study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Setting</h3>\u0000 \u0000 <p>Four rural primary care clinics in Washington and Idaho, USA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Participants</h3>\u0000 \u0000 <p>307 patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Measurements</h3>\u0000 \u0000 <p>This study used three data sources from each clinic: (1) a limited dataset extracted from the EHR, (2) a clinic-based registry of patients with OUD and (3) the clinician/staff interface of the EHR (e.g. progress notes, problem list). Data source one included records with six commonly applied ICD-10 codes for opioid use, dependence and abuse: F11.10 (opioid abuse, uncomplicated), F11.20 (opioid dependence, uncomplicated), F11.21 (opioid dependence, in remission), F11.23 (opioid dependence with withdrawal), F11.90 (opioid use, unspecified, uncomplicated) and F11.99 (opioid use, unspecified with unspecified opioid-induced disorder). Care coordinators used data sources two and three to categorize each patient identified in data source one: (1) confirmed OUD diagnosis, (2) may have OUD but no confirmed OUD diagnosis, (3) chronic pain with no evidence of OUD and (4) no evidence for OUD or chronic pain.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Findings</h3>\u0000 \u0000 <p>F11.10, F11.21 and F11.99 were applied most frequently to patients who had clinical diagnoses of OUD (64%, 89% and 79%, respectively). F11.20, F11.23 and F11.90 were applied to patients who had a diagnostic mix of OUD and chronic pain without OUD. The four clinics applied codes inconsistently.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Lack of uniform application of ICD diagnosis codes make it challenging to use diagnosis code data from EHR to identify a research population of persons with opioid use disorder.</p>\u0000 </section>\u0000 </div>","PeriodicalId":109,"journal":{"name":"Addiction","volume":"119 1","pages":"160-168"},"PeriodicalIF":6.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10261247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}