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This article examines the literature on drug treatment counselor practices and effectiveness and discusses relevant issues in a multilevel framework by considering client outcomes in relation to varying characteristics of clients, counselors, and programs. Most drug treatment evaluation studies collect data from clients of the same treatment programs, and many of these clients share the same counselors. Across multiple programs, these data often form a multilevel (or hierar chical) structure, with clients nested within counselors and counselors within programs. Most analyses of treatment outcome are based on individual clients; this violates the assumption of independent observations underlying most statistical approaches. After reviewing relevant literature, the article presents a conceptual model for understanding counselor effects on client outcomes. Then follows a description of a hierarchical linear model that assesses counselor effectiveness on client outcomes while allowing considerations of potential interactions among the three levels of influence (i.e., client, counselor, and program).

Matching clients to treatment has been a topic of great interest to clinicians and researchers in the field of alcohol and drug abuse. Currently, several nationwide efforts are attempting to establish central intakes in communities using computerized systems. This article includes a brief literature review to provide an assessment of the current knowledge about matching that can be used to support a matching system guided by expert knowledge. A conceptual framework for the development of an expert-guided system to be used in a central intake or referral agency is then described. A drug treatment referral system that matches drug users to appropriate treatment programs requires several interlinked components: (1) an accurate list of available programs with comprehensive descriptors characterizing the programs, including the nature and kinds of services provided; (2) an assessment instrument that can be easily and routinely administered is needed for determining clients' characteristics and needs; and (3) results of that assessment instrument are used to match clients with the most appropriate available programs according to a set of decisional guidelines, which is the most critical component of the referral system. Component-specific issues that need to be addressed in developing an expert-guided referral system are discussed. The paper concludes that an expert-guided system using profile matching, although constrained by the resources available in a community, is likely to improve client outcomes and program efficiency over the current haphazard utilization practices of unstructured referral. Development of a matching index that can predict client outcome is necessary to provide empirical directions for future research and improved practice.

Several nationwide data systems have been specifically designed to assemble data on prevalence of drug use. Additional information is available from other systems that are oriented to different purposes, but also collect drug-related data. National prevalence estimation efforts are variously based on one or more of these prominent data sources, which are briefly reviewed in this article. Also discussed is the utility of these existing data in terms of sampling and coverage, nature and validity of measures, methods of enumeration, and consistency of data collection over time and across systems. To determine the nature of drug use at the local level, local data should be examined. Regardless of their geographic scope, all data systems should improve sampling and coverage of high-risk groups and results should be assessed by occasional small-scale validity studies. Most important, comparability among data systems using comparable measures is needed to enable integration of information, leading to more comprehensive and accurate estimates of the prevalence of drug use.

The author describes the application of several methods to obtain population estimates of illicit drug users in Los Angeles County. The study applied several multiple-capture models to drug treatment data and the synthetic estimation method to arrestee data to provide separate estimates for cocaine, heroin, amphetamine, and intravenous drug users. The author demonstrates the advantages of employing complementary methods and data sources.

Policymakers and researchers interested in prevalence estimation often seek practical guidance on how to obtain and interpret quality estimates. Although the preceding chapters have addressed aspects of these issues, it is helpful to summarize some of the common problems and to provide practical solutions. This chapter assembles a set of questions and abbreviated answers in areas of policy needs, prevalence definitions, data characteristics and availability, and estimation techniques. Readers are directed to extensive discussion in other relevant chapters.

The relationship between women's narcotics use and crime is examined among Anglo and Chicana methadone maintenance clients. Three types of analyses are employed: (1) the temporal ordering between narcotics involvement and criminal activities: (2) comparisons of crime levels before and after critical events in the addiction career including narcotic initiation, addiction, last daily use, first treatment entry, and first treatment discharge; and (3) crime levels as a function of increasing narcotics use. Women in this study demonstrate extensive criminal involvement and some also engage in dealing and/or prostitution. Property crime activities precede the addiction career for many women but, once addicted, the amount of property crime committed appears to be generally regulated by narcotics use levels. After the addiction career, property crime decreases substantially. Chicanas, in general, display higher baseline pretreatment criminal activity and show fewer changes in crime levels than Anglo women in reaction to events such as treatment or termination of addiction career.

In this article, we applied a marginal structural model (MSM) to estimate the effect on later drug use of drug treatments occurring over 10 years following first use of the primary drug. The study was based on the longitudinal data that were collected in three projects among 421 subjects and covered 15 years since first use of their primary drug. The cumulative treatment effect was estimated by the inverse-probability of treatment weighted estimators of MSM as well as the traditional regression analysis. Contrary to the traditional regression analysis, results of the MSM showed that the cumulative treatment occurring over the 10 years significantly increased the likelihood of drug use abstinence in the subsequent 5-year period. From both the statistical and empirical point of view, MSM is a better approach to assessing cumulative treatment effects, considering its advantage of controlling for self-selection bias over time.