Dennis Michael L, Wechsberg Wendee M, McDermeit Melissa, Campbell R Supatra, Rasch Randolph R
Chestnut Health Systems, 720 West Chestnut, Bloomington, IL 61701, USA.
Research Triangle Institute, Research Triangle Park, NC, USA.
Eval Program Plann. 2001 May;24(2):187-206. doi: 10.1016/s0149-7189(01)00014-3. Epub 2001 Apr 9.
Outreach and intervention with out-of-treatment drug users in their natural communities has been a major part of our national HIV-prevention strategy for over a decade. Intervention design and evaluation is complicated because this population has heterogeneous patterns of HIV risk behaviors. The objectives of this paper are to: (a) empirically identify the major HIV risk groups; (b) examine how these risk groups are related to demographics, interactions with others, risk behaviors, and community (site); and (c) evaluate the predictive validity of these risk groups in terms of future risk behaviors. Exploratory cluster analysis of a sample of 4445 out-of-treatment drug users from the national data set identified eight main risk subgroups that could explain over 99% of the variance in the 20 baseline indices of HIV risk. We labeled these risk groups: Primary Crack Users (29.2%), Cocaine and Sexual Risk (12.8%), High Poly Risk Type 2 (0.3%), Poly Drug and Sex Risk (10.9%), Primary Needle Users (24.1%), High Poly Risk Type 1 (1.4%), High Frequency Needle Users (19.8%), and High Risk Needle Users (1.6%). Risk group membership was highly related to HIV characteristics (testing, sero-status), demographics (gender, race, age, education), status (marital, housing, employment, and criminal justice), prior target populations (needle users, crack users, pattern of sexual partners), and geography (site). Risk group membership explained 63% of the joint distribution of the original 20 HIV risk behaviors 6 months later (ranging from 0.03 to 37.2% of the variance individual indices). These analyses were replicated with both another 25% sample from the national data set and an independent sample collected from a new site. These findings suggest HIV interventions could probably be more effective if they targeted specific subgroups and that evaluations would be more sensitive if they consider community and sub-populations when evaluating these interventions.
十多年来，在吸毒者所在的自然社区开展外展服务和干预措施一直是我国国家艾滋病预防战略的重要组成部分。由于这一人群的艾滋病风险行为模式各异，干预措施的设计和评估变得复杂。本文的目的是：(a) 通过实证确定主要的艾滋病风险群体；(b) 研究这些风险群体与人口统计学特征、与他人的互动、风险行为以及社区（地点）之间的关系；(c) 评估这些风险群体在未来风险行为方面的预测效度。对来自国家数据集的4445名戒毒者样本进行探索性聚类分析，确定了八个主要风险亚组，这些亚组可以解释艾滋病风险20个基线指标中超过99%的方差。我们将这些风险群体标记为：主要快克使用者（29.2%）、可卡因与性风险群体（12.8%）、高多重风险类型2（0.3%）、多种药物与性风险群体（10.9%）、主要针具使用者（24.1%）、高多重风险类型1（1.4%）、高频针具使用者（19.8%）和高风险针具使用者（1.6%）。风险群体成员身份与艾滋病特征（检测、血清学状态）、人口统计学特征（性别、种族、年龄、教育程度）、状况（婚姻、住房、就业和刑事司法）、先前的目标人群（针具使用者、快克使用者、性伴侣模式）以及地理位置（地点）高度相关。风险群体成员身份解释了6个月后最初20种艾滋病风险行为联合分布情况的63%（各个指标方差的范围为0.03%至37.2%）。使用国家数据集另外25%的样本以及从一个新地点收集到的独立样本重复了这些分析。这些研究结果表明，如果针对特定亚组开展艾滋病干预措施可能会更有效，并且在评估这些干预措施时，如果考虑到社区和亚人群体，评估将更加敏感。
