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基于行政数据的药物滥用严重程度测量方法的前瞻性验证

Prospective validation of substance abuse severity measures from administrative data.

作者信息

McCamant Lynn E, Zani Brigid G, McFarland Bentson H, Gabriel Roy M

机构信息

Department of Psychiatry, CR-139, Oregon Health & Science University, Portland, OR 97239, USA.

出版信息

Drug Alcohol Depend. 2007 Jan 5;86(1):37-45. doi: 10.1016/j.drugalcdep.2006.04.016. Epub 2006 Jun 27.

Abstract

BACKGROUND

Severity measures for clients in substance abuse treatment programs are becoming increasingly important as funders adopt payment systems linked to agency performance. Recently, two severity measures based on administrative data have been developed. This study validated these measures using prospective data.

METHODS

Subjects were participants in the Drug Abuse Treatment Outcomes Study (adult or adolescent components) or the Substance Abuse and Mental Health Services Administration Medicaid Managed Behavioral Healthcare and Vulnerable Populations project (adult or adolescent chemical dependency components). Severity measures were calculated based on data obtained at entry into substance abuse treatment. The baseline severity measures were included along with age, gender, and race/ethnicity in logistic regression models predicting abstinence at follow-up for alcohol use, marijuana use, cocaine use, or heroin use.

RESULTS

For adults, the severity measures were highly statistically significant (p<0.001) for all models in both data sets, indicating that adults with higher severity were more likely (and much more likely in many cases) to use alcohol, marijuana, cocaine, or heroin at the follow-up interview than were those with lower severity. For adolescents, the severity measure was highly statistically significant (p<0.001) for marijuana in both data sets and for alcohol in the Medicaid data set.

CONCLUSIONS

Baseline severity measures were powerful predictors of abstinence at follow-up. These measures, derived from routinely available electronic records, appear to have noteworthy predictive validity. The severity indicators can be used for administrative purposes such as risk-adjustment when examining treatment agency performance.

摘要

背景

随着资助者采用与机构绩效挂钩的支付系统,药物滥用治疗项目中客户的严重程度衡量指标变得越来越重要。最近,基于行政数据开发了两种严重程度衡量指标。本研究使用前瞻性数据对这些指标进行了验证。

方法

研究对象为药物滥用治疗结果研究(成人或青少年部分)或药物滥用和精神健康服务管理局医疗补助管理行为医疗保健与弱势群体项目(成人或青少年化学依赖部分)的参与者。根据进入药物滥用治疗时获得的数据计算严重程度指标。在预测随访时酒精使用、大麻使用、可卡因使用或海洛因使用戒断情况的逻辑回归模型中,纳入基线严重程度指标以及年龄、性别和种族/族裔。

结果

对于成年人,在两个数据集中所有模型的严重程度指标均具有高度统计学意义(p<0.001),表明严重程度较高的成年人在随访访谈中比严重程度较低的成年人更有可能(在许多情况下可能性大得多)使用酒精、大麻、可卡因或海洛因。对于青少年,在两个数据集中大麻的严重程度指标以及在医疗补助数据集中酒精的严重程度指标均具有高度统计学意义(p<0.001)。

结论

基线严重程度指标是随访时戒断情况的有力预测指标。这些指标源自常规可得的电子记录,似乎具有显著的预测效度。严重程度指标可用于行政目的,如在评估治疗机构绩效时进行风险调整。

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