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Journal of substance use and addiction treatment最新文献

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IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2026-01-01
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引用次数: 0
Justice system experiences and health risks among Medicaid and uninsured populations receiving treatment for substance use disorder 司法系统经验和医疗补助和未投保人群接受药物使用障碍治疗的健康风险。
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2025-12-26 DOI: 10.1016/j.josat.2025.209863
Meghan M. O'Neil , Jenna Pryor , Rebecca A. Johnson , MinAh Kim , Charlene Nguyen , Debra A. Pinals

Introduction

We explored the relationship between justice system experiences and health risks using a sample of Medicaid and uninsured patients in addiction treatment, focusing on four health behaviors correlated with premature mortality: (1) opioid misuse, (2) overdose, (3) multi-substance use, (4) recurrent treatment admissions.

Methods

From 2021 to 2023, electronic in-depth life-course history surveys were administered in-person with adult patients selected from a community-based public health case management system while undergoing addiction treatment in Michigan, USA. This study utilized a series of logistic regression models to examine the impact of civil and criminal justice predictors on risk for each of the four outcomes of interest.

Results

Participants were highly vulnerable: seventy percent of participants reported prior homelessness, modal income was $10,000 or less, and nine out of ten reported histories of having been arrested. Participants were more likely to identify as Black or American Indian Alaska Native relative to the broader community, in line with the disproportionate justice system involvement among these communities in Michigan, USA. In logistic regressions, participants with a history of homelessness were 2.16 times as likely to have misused opioids, 2.4 times as likely to have overdosed, and 2.41 times as likely to report multi-substance use relative to peers who had not experienced homelessness (p < 0.01); those with child support obligations were 1.78 times more likely to have misused opioids relative to their peers who did not have a child support obligation (p < 0.05); those who had experienced eviction were 1.8 times as likely to have overdosed and 2.09 times as likely to report multi-substance use compared to peers who did not report eviction history (p < 0.01); and those reporting legal financial obligations (LFOs) were 1.85 times as likely to report multi-substance use (p < 0.05) and 2.28 times as likely to report recurrent treatment admissions (p < 0.05) relative to peers who did not have LFOs.

Conclusions

Absent intervention, these systemic risk factors present barriers to successful recovery and reentry, with ramifications for public health and community-based crime prevention as well as potential to contribute to growing premature mortality among Black and American Indian Alaska Native persons and broader racial health disparities.
前言:我们以医疗补助和未参保的成瘾治疗患者为样本,探讨了系统体验与健康风险之间的关系,重点关注与过早死亡相关的四种健康行为:(1)阿片类药物滥用,(2)过量,(3)多种物质使用,(4)反复接受治疗。方法:从2021年至2023年,对美国密歇根州社区公共卫生病例管理系统中选择的正在接受成瘾治疗的成年患者进行深入的生活史调查。本研究利用一系列逻辑回归模型来检验民事和刑事司法预测因子对四种结果的风险影响。结果:参与者非常脆弱:70% %的参与者报告先前无家可归,模式收入为10,000美元或更少,十分之九的人报告被逮捕的历史。相对于更广泛的社区,参与者更有可能认为自己是黑人或美国印第安人阿拉斯加原住民,这与密歇根州这些社区中不成比例的司法系统参与一致。在logistic回归中,报告无家可归者中阿片类药物的使用增加了15 %,过量使用的可能性增加了19 %,多物质使用的可能性增加了13.5 % (p )。如果不采取干预措施,这些风险因素对成功康复和重返社会构成障碍,对公共卫生和以社区为基础的预防犯罪产生影响,并有可能导致黑人和美洲印第安人阿拉斯加土著人的过早死亡率不断上升,并造成更大的种族健康差距。
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引用次数: 0
Predicting and differentiating opioid and non-opioid drug poisonings using health records data 使用健康记录数据预测和区分阿片类药物和非阿片类药物中毒。
IF 1.9 0 PSYCHOLOGY, CLINICAL Pub Date : 2025-12-22 DOI: 10.1016/j.josat.2025.209861
Gregory E. Simon , Robert Wellman , Susan M. Shortreed , Eric Johnson , Stacy A. Sterling , Karen J. Coleman , Brian K. Ahmedani , Zimri S. Yaseen , Andrew D. Mosholder

Introduction

To facilitate effective targeting of overdose prevention programs, this research developed and evaluated prediction models to identify people at highest risk of specific types of drug overdose or poisoning. Using samples of mental health specialty visits and general medical visits with mental health or substance use diagnoses, this research examined how well prediction models using health records data perform in predicting either any drug poisoning or opioid-involved poisoning and how the specific predictors of opioid-involved poisoning differ from general predictors of any drug poisoning.

Methods

Records data regarding mental health and general medical visits between 2015 and 2019 in four large health systems were used to develop two-step models predicting any poisoning or overdose and differentiating opioid-involved poisonings from other poisonings. Random forest models were developed in random samples of 70 % of visits and validated in held out 30 % samples.

Results

Among 19,130,028 visits, 114,911 were followed by a poisoning event and 12,758 by a poisoning involving opioids. A first-step model predicting any poisoning had moderate accuracy, with AUCs of 0.778 among mental health specialty visits and 0.767 among general medical visits. The two-step model to specifically predict opioid involved poisoning had superior performance (AUCs = 0.895 among mental health specialty visits and 0.915 among general medical visits). Predictors of any poisoning included prior self-harm, accidental poisoning, and mental health service use. Specific predictors of opioid-involved poisoning included prior substance use disorder diagnoses and opioid dispensings.

Limitations

Health records data would not identify overdoses or poisonings that do not present for health care and would not identify use of non-prescribed opioids. Finding may not generalize to settings with different patterns of diagnosis or service use.

Conclusions

Models to predict any overdose or poisoning from health records data in four large health systems had only moderate accuracy. Patients at highest risk for opioid poisoning can be more accurately identified by prediction models specifically focused on opioid poisoning, most strongly influenced by indicators of substance use disorder.
导论:为了促进药物过量预防计划的有效目标,本研究开发并评估了预测模型,以确定特定类型药物过量或中毒的最高风险人群。本研究利用精神健康专科就诊和精神健康或物质使用诊断的普通医疗就诊样本,检查了使用健康记录数据的预测模型在预测任何药物中毒或阿片类药物中毒方面的表现,以及阿片类药物中毒的具体预测因素与任何药物中毒的一般预测因素有何不同。方法:利用2015年至2019年四个大型卫生系统的心理健康和一般医疗就诊记录数据,建立两步模型,预测任何中毒或过量,并将阿片类药物中毒与其他中毒区分开来。随机森林模型在70% %的随机样本中建立,并在30% %的样本中得到验证。结果:在19130,028例就诊中,114,911例发生了阿片类药物中毒事件,12,758例发生了阿片类药物中毒。预测任何中毒的第一步模型具有中等准确性,心理健康专科就诊的auc为0.778,普通医疗就诊的auc为0.767。专门预测阿片类药物中毒的两步模型表现更优(心理健康专科就诊的auc = 0.895,普通医疗就诊的auc为0.915)。任何中毒的预测因素包括先前的自残、意外中毒和使用心理健康服务。阿片类药物中毒的具体预测因素包括先前的物质使用障碍诊断和阿片类药物分配。局限性:健康记录数据不能确定非保健用药过量或中毒,也不能确定非处方类阿片的使用情况。发现可能不能推广到具有不同诊断模式或服务使用的设置。结论:从四个大型卫生系统的健康记录数据中预测任何过量或中毒的模型只有中等准确性。阿片类药物中毒风险最高的患者可以通过专门关注阿片类药物中毒的预测模型更准确地识别出来,这些模型受物质使用障碍指标的影响最大。
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引用次数: 0
期刊
Journal of substance use and addiction treatment
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