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Drug abuse epidemiologists have made great progress in developing methodologies to measure an elusive public health problem over the past three decades. As we learn more about the biology of dependence, particularly the process of moving from use to addiction, we in the field may be able to refine our own measurements. In addition, as was pointed out by the IOM (1996), there is a great need for more research to examine co-occurring drug use, physical and psychiatric problems, and to continue to refine our methods of data collection and data analyses.

In addition to the research on the association between substance use disorders and antisociality, there is a large body of literature reporting connections with behavior disinhibition and affect dysregulation. It is not possible in this short chapter to review these and other findings on risk factors for substance use disorders. However, the above discussion illustrates the convergences in the research findings on risk factors that point to possible clusters having implications for further understanding the heterogeneity of substance abuse and the identification of drug abuse subtypes.

It is also not possible in this chapter to discuss important related issues, such as the relationship of heterogeneity to substance use disorder phenotypes and endophenotypes, the role of protective factors in the divergence of subtypes, the relationship of co-morbid psychiatric conditions and developmental psychopathology to heterogeneity, developmental influences, and individual and group diversity, and the implications of heterogeneity and subtypes for prevention and treatment. Despite the unanswered questions, however, it is clear that investigation of the heterogeneity of substance use disorders and their underlying processes can advance our ability to effectively understand, prevent and treat substance abuse.

It is clearly important to go beyond the recognition of the heterogeneity of drug abuse and to look for systematic variations in the etiology and manifest patterns of substance abuse. There may be critical variations in the underlying processes of substance abuse as well as significant systematic differences in the observable behavior patterns. Distinguishing major divergences in the differing patterns may lead to the identification of clinically significant subtypes, help determine the underlying processes of substance abuse, and facilitate the study of the ways in which environmental factors interact with individuals’ characteristics (and the underlying processes of substance abuse) to result in different subtypes. While the available research does not answer the question of whether there are drug abuse subtypes, it does provide encouragement to continue the search.

The “classical” approach to represent Petri nets by graph transformation systems is to translate each transition of a specific Petri net to a graph rule (behavior rule). This translation depends on a concrete model and may yield large graph transformation systems as the number of rules depends directly on the number of transitions in the net. Hence, the aim of this paper is to define the behavior of Algebraic High-Level nets, a high-level Petri net variant, by a parallel, typed, attributed graph transformation system. Such a general parallel transformation system for AHL nets replaces the translation of transitions of specific AHL nets. After reviewing the formal definitions of AHL nets and parallel attributed graph transformation, we formalize the classical translation from AHL nets to graph transformation systems and prove the correctness of the translation. The translation approach then is contrasted to a definition for AHL net behavior based on parallel graph transformation. We show that the resulting amalgamated rules correspond to the behavior rules from the classical translation approach.

In this chapter a number of uses of treatment data to support epidemiological research, analysis, and interpretation were reviewed. It is clear that treatment data alone or integrated with other sources of information can provide important insights into the epidemiology of drug abuse. The major contributions appear to be in estimating trends and comparing these across geographic or demographic groups. The utility of treatment data to accurately estimate prevalence is limited by the proportionately few persons who enter treatment.

Despite these limitations, much more can and should be done to better utilize the rich information from treatment data bases. The first is to reach consensus on key questions on usage patterns, institutional contact (e.g. jails, social service, health care, etc), and treatment program admission that will enable cross study comparison and the potential aggregation of data. The second approach requires a systematic investigation of the influences on treatment admissions, particularly the substance abuse patterns and the ecology of treatment services. With the accumulation of data over the past decades such investigation should be feasible. Finally, we need strong theoretical models and heuristic hypotheses to guide future analyses and interpretations involving treatment data. The increased use of treatment data in a sound framework should advance not only our scientific knowledge about drug use epidemiology, but also help guide policy and practice to better address the needs of the millions suffering from drug abuse and dependence.

Drug abuse has become recognized as a public health problem around the globe by both the United Nations and the World Health Organization. But even in the most accepting of countries the nature of drug abuse poses a barrier to the use of traditional public health epidemiologic approaches. Part of that nature is how it can and has changed over time, presenting public health workers with new drugs of abuse, new and sometimes very dangerous methods for drug administration, and involving more vulnerable populations. At times these changes are contained and short-lived, but many times they spread across population groups and become endemic over years. The Community Epidemiology Work Group has become an important tool to be used with others from the more traditional epidemiologic armatarium to assess drug abuse at the local, regional, national, and international levels. The information gathered describes current drug use patterns and can suggest potential future issues. It can also generate questions or issues that can be further researched. Finally, it serves as a resource for public health planners and policy makers to plan for services and the allocation of resources. Clearly, the rapid diffusion of the CEWG model to other countries and regions of the world support the efficacy of this approach.

In this chapter a number of uses of treatment data to support epidemiological research, analysis, and interpretation were reviewed. It is clear that treatment data alone or integrated with other sources of information can provide important insights into the epidemiology of drug abuse. The major contributions appear to be in estimating trends and comparing these across geographic or demographic groups. The utility of treatment data to accurately estimate prevalence is limited by the proportionately few persons who enter treatment.

Despite these limitations, much more can and should be done to better utilize the rich information from treatment data bases. The first is to reach consensus on key questions on usage patterns, institutional contact (e.g. jails, social service, health care, etc), and treatment program admission that will enable cross study comparison and the potential aggregation of data. The second approach requires a systematic investigation of the influences on treatment admissions, particularly the substance abuse patterns and the ecology of treatment services. With the accumulation of data over the past decades such investigation should be feasible. Finally, we need strong theoretical models and heuristic hypotheses to guide future analyses and interpretations involving treatment data. The increased use of treatment data in a sound framework should advance not only our scientific knowledge about drug use epidemiology, but also help guide policy and practice to better address the needs of the millions suffering from drug abuse and dependence.

Drug abuse has become recognized as a public health problem around the globe by both the United Nations and the World Health Organization. But even in the most accepting of countries the nature of drug abuse poses a barrier to the use of traditional public health epidemiologic approaches. Part of that nature is how it can and has changed over time, presenting public health workers with new drugs of abuse, new and sometimes very dangerous methods for drug administration, and involving more vulnerable populations. At times these changes are contained and short-lived, but many times they spread across population groups and become endemic over years. The Community Epidemiology Work Group has become an important tool to be used with others from the more traditional epidemiologic armatarium to assess drug abuse at the local, regional, national, and international levels. The information gathered describes current drug use patterns and can suggest potential future issues. It can also generate questions or issues that can be further researched. Finally, it serves as a resource for public health planners and policy makers to plan for services and the allocation of resources. Clearly, the rapid diffusion of the CEWG model to other countries and regions of the world support the efficacy of this approach.

This chapter presented the indirect methods currently being used to estimate the prevalence of drug abuse in various populations. We have been critical of these methods pointing out their limitations and that the estimates derived from them need to be interpreted with caution. However, we believe that they are the answer to the problem of estimating the prevalence of rare problem drug using behaviors such as the use of heroin, drug injecting, or crack-cocaine use as population surveys are inefficient and not cost effective. Data sources for capture-recapture and multplier estimates should be carefully chosen to minimize both dependence and heterogeneity. Most communities have data that are available and these can be assessed for their utility for prevalence estimation. For example, do the data sources collect data on drug profile (injecting status, and problem drugs), collect identifiers to allow matching with other data sources /or/ if anonymised suffer little under-reporting; would problem drug users remember or recognise being captured by the data source, are only a sub-set of problem drug users captured by the data source; is it known how the data sources relate to other potential data sources. If the available data sources are poor—recommend steps to policy-makers (and data owners) to improve them for future estimation work. Collecting the data is the most time consuming part of prevalence estimation work. This work could be dramatically reduced if contributing to prevalence estimates was one of the objectives of routine data on problem drug use—such that a “public health surveillance” system of problem drug use was developed that specifically linked and integrated multiple data sources to allow prevalence estimation.

Finally, multiplier and capture-recapture estimation methods tend to be more reliable within discrete geographical locations in part to avoid heterogeneity (i.e., the relationship between the population of problem drug users and data sources is likely to vary from city to city), which has implications for public health surveillance and the design of studies.

In this chapter a number of uses of treatment data to support epidemiological research, analysis, and interpretation were reviewed. It is clear that treatment data alone or integrated with other sources of information can provide important insights into the epidemiology of drug abuse. The major contributions appear to be in estimating trends and comparing these across geographic or demographic groups. The utility of treatment data to accurately estimate prevalence is limited by the proportionately few persons who enter treatment.

Despite these limitations, much more can and should be done to better utilize the rich information from treatment data bases. The first is to reach consensus on key questions on usage patterns, institutional contact (e.g. jails, social service, health care, etc), and treatment program admission that will enable cross study comparison and the potential aggregation of data. The second approach requires a systematic investigation of the influences on treatment admissions, particularly the substance abuse patterns and the ecology of treatment services. With the accumulation of data over the past decades such investigation should be feasible. Finally, we need strong theoretical models and heuristic hypotheses to guide future analyses and interpretations involving treatment data. The increased use of treatment data in a sound framework should advance not only our scientific knowledge about drug use epidemiology, but also help guide policy and practice to better address the needs of the millions suffering from drug abuse and dependence.

The “classical” approach to represent Petri nets by graph transformation systems is to translate each transition of a specific Petri net to a graph rule (behavior rule). This translation depends on a concrete model and may yield large graph transformation systems as the number of rules depends directly on the number of transitions in the net. Hence, the aim of this paper is to define the behavior of Algebraic High-Level nets, a high-level Petri net variant, by a parallel, typed, attributed graph transformation system. Such a general parallel transformation system for AHL nets replaces the translation of transitions of specific AHL nets. After reviewing the formal definitions of AHL nets and parallel attributed graph transformation, we formalize the classical translation from AHL nets to graph transformation systems and prove the correctness of the translation. The translation approach then is contrasted to a definition for AHL net behavior based on parallel graph transformation. We show that the resulting amalgamated rules correspond to the behavior rules from the classical translation approach.