The Wiley-Blackwell Handbook of Addiction Psychopharmacology

James MacKillop, Harriet de Wit, James MacKillop, Harriet de Wit

• Publisher: Wiley-Blackwell
• Year: 2013
• Print ISBN: 9781119978268
• eBook ISBN: 9781118384367
• Edition: 1
• Topic: Psychologie
• Subtopic: Addiction en psychologie

"Much of our scientific effort in tackling the multifactorial nature of addiction has taken place within individual disciplines. However, it has become increasingly clear that the complexity of addiction requires an integrated approach. This Handbook is timely and exceptional, intelligently combining the latest research approaches and applying them to understanding and tackling the prodigious public health burden of addiction. An authoritative resource, it establishes a comprehensive framework that will guide the field in the next era of addiction research."

John F. Kelly, PhD, President Elect, Society of Addiction Psychology, American Psychological Association; Associate Professor in Psychiatry, Harvard Medical School; Director, Addiction Recovery Management Service, Massachusetts General Hospital

The Wiley-Blackwell Handbook of Addiction Psychopharmacology presents a comprehensive guide to contemporary research approaches to the study of drug addiction in adults. With a focus on empirically relevant research methods and nuanced methodologies, it provides practical tools to enable strong psychopharmacological practices. Contributions from experts in diverse domains offer reviews of the most current experimental methodologies, make recommendations for "best practices," and identify future directions for the field. Topics covered include core methods for assessing drug effects, distal and proximal determinants of drug use, and insights from cognitive neuroscience.

Compiled by a team of widely published researchers in substance addiction, The Wiley-Blackwell Handbook of Addiction Psychopharmacology is an authoritative, state-of-the-art collection of modern research approaches to the scientific study of drug addiction. Its multidisciplinary approach makes it a comprehensive and invaluable resource for all those in this field.

Part 1

Distal Determinants of Drug Use

1

Developmental Factors in Addiction: Methodological Considerations1

Lauriex Chassin, Clark Presson, Young Il-Cho, Matthew Lee, Jonathan Macy

1 Introduction

Epidemiological data show that substance use and substance use disorders follow characteristic age-related trajectories, such that the onset of substance use typically occurs in adolescence, peaks in rates of substance use (and in rates of clinical substance use disorders) occur during emerging adulthood (ages 18–25), and rates of both substance use and substance use disorders decline later in adulthood (Bachman et al., 2002; Masten, Faden, Zucker, and Spear, 2008). Moreover, adult substance use outcomes and substance use disorders are predictable from early childhood factors (Caspi, Moffitt, Newman, and Silva, 1996; Masten, Faden, Zucker, and Spear, 2008). These age-related patterns of substance use and their association with early childhood predictors suggest the value of applying a developmental perspective to the study of addiction. Accordingly, this chapter focuses on methodological issues in research on developmental factors in addiction. We focus on methodological issues in studies of substance use among children and adolescents, and particularly on longitudinal studies, which are well suited for examining developmental trajectories and prospective predictors of addiction outcomes. However, it is also important to recognize that each of the topics that are covered in the other chapters of this volume also present methodological challenges when the particular domain of interest is studied in childhood and adolescence. Thus, studies of drug administration, psychophysiology, imaging, genetics, intellectual functioning, psychiatric comorbidities, impulsive and risky behavior, distress tolerance, expectancies, social context, implicit cognition, ecological momentary assessment, etc. each present both opportunities and methodological challenges when applied to child and adolescent samples and studied in a developmental context. Clearly, no single chapter could cover the numerous methodological issues involved in studying developmental factors in each of those many different domains. Therefore, instead we focus on more general methodological and conceptual issues involved in studying substance use (and risk factors for substance use) during childhood and adolescence, and we illustrate some of the unique methodological challenges in this research.

2 Empirical Relevance of Developmental Factors for Substance Use Research

Research on developmental factors is critical to an understanding of substance use disorders for multiple reasons. First, these studies are needed to inform etiology by identifying prospective predictors of substance use outcomes and testing the multivariate and multilevel etiological mechanisms that are hypothesized to underlie addiction. Second, these studies inform the design and targeting of preventive intervention. They identify the risk and age groups who are the target audiences for preventive intervention and, to the extent that malleable risk and protective factors can be identified, these studies pinpoint the factors to be targeted for modification in prevention programs. Third, studies of developmental factors are needed to understand the impact and consequences of substance use. Cross-sectional comparisons of individuals with and without substance use disorders cannot disentangle the causes of substance use disorders from their consequences. Thus, studies of children and adolescents before the onset of substance use are needed to separate the antecedents from the consequences of substance use.

Another sense in which developmental factors are critical to addiction research is that substance use involvement itself can be conceptualized as a series of stages or developmental milestones ranging from initial exposure to experimental use, regular and/or heavy use, substance use-related problems, and diagnosable clinical substance use disorders (e.g., Jackson, 2010; Mayhew, Flay, and Mott, 2000). The time that it takes to pass through these stages varies for different individuals and substances and is predictable by factors such as gender and family history of substance use disorder (Hussong, Bauer, and Chassin, 2008; Ridenour, Lanza, Donny, and Clark, 2006; Sartor et al., 2008). Such predictable variability in the speed of transition from first exposure to addiction suggests that the speed of progression may itself be an important phenotype to study in order to understand the etiology of addiction.

Importantly, particular etiological factors may not only determine the speed of progression but may show unique prediction of specific transitions such that different factors may influence substance use initiation than influence substance use progression (e.g., Sartor et al., 2007). For example, Fowler et al. (2007) found that common environment influences were more important for initiation whereas genetic influences were more important for progression. Methodologically, this suggests the need for researchers to disaggregate predictors of different developmental milestones in the development of addiction.

Moreover, developmental progressions may be important not only within “stages” of the use of a single substance but across different substances. It has been suggested that individuals progress from involvement with “gateway” drugs such as alcohol, tobacco, and marijuana to the use of other illicit drugs (Kandel, Yamaguchi, and Chen, 1992). This progression might reflect a common propensity to use drugs, an affiliation with a drug-using social network that promotes the use of multiple substances, or a causal effect in which the use of one drug sensitizes an individual to the use of other substances (Kandel, Yamaguchi, and Klein, 2006; MacCoun, 2006). Methodologically, the notion of developmental progressions across the use of different substances implies that researchers who study the use of any one particular substance should measure and consider the co-occurring use of other substances.

Another developmental milestone that is important for the study of addiction is the age at which an individual first begins to use substances. Early onset of use is associated with a greater likelihood of developing dependence, and this has been reported for cigarette smoking (Breslau and Peterson, 1996), alcohol use (Dawson et al., 1998) and illicit drug use (Grant and Dawson, 2008). There have been multiple interpretations of these findings, including the idea that they are spurious and caused by correlated “3rd” variables that are associated both with early onset and with risk for addiction (Prescott and Kendler, 1999). Other studies that have considered various hypothesized confounding variables have still supported a relation between early onset and greater likelihood of dependence or heavy use in adulthood. This pattern was found by Buchmann et al. (2009) for alcohol use and by King and Chassin (2007) for drug dependence. It has also been suggested that age of onset is a feature that might distinguish different subtypes of substance disorder. For example, Zucker (1986) distinguished among different forms of alcoholism with early-onset forms being either antisocial or developmentally limited (compared to older-onset negative affect forms of alcoholism). Other disorders have similarly considered age of onset in formulating subtypes. For example, Moffitt (1993) distinguished between adolescent-limited and child-onset life course persistent forms of conduct disorder. Methodologically, the possibility that age of onset is a marker for a particularly high-risk group for addiction suggests that age of onset is a useful phenotype for study. For example, Schmid et al. (2009) found effects of DAT1 on tobacco and alcohol use for individuals who started daily smoking or drinking to intoxication at a young age. Finally, it is possible that the relation between early onset of use and elevated risk of developing dependence occurs not because of particular subtypes of substance disorder or particular high-risk phenotypes, but rather because the central nervous system, early in development, is particularly vulnerable to substance use effects. For example, Levin et al. (2003) found that female rats who were randomly assigned to begin self-administration of nicotine in adolescence showed higher levels of later adult self-administration than did those whose self-administration began in adulthood.

These findings thus suggest that both age of onset of substance use and the speed of progression from initiation to heavy use or to clinical substance use disorder might be important developmental factors to study in order to better understand addiction. Some researchers have built on these findings by attempting to identify heterogeneity in longitudinal trajectories of substance use that consider multiple features, including age of onset, steepness of acceleration in use, peaks of use, and stability of use over time. These studies have often used mixture modeling techniques to identify clusters of trajectories, and have suggested that such dynamic trajectories might be better phenotypes for the study of addiction than static features of the addictive behavior (see Chassin et al., 2009 for a review). For example, Chassin et al. (2008) reported that parents' smoking trajectories had a unique effect on their adolescents' cigarette smoking over and above parents' current smoking. Parents whose smoking showed early onset, steep acceleration, high levels, and greater persistence were more likely to have adolescent children who smoked. That is, over and above parents' current smoking, their different smoking trajectories showed different levels of intergenerational transmission.

The potential value of developmental trajectories as phenotypes for addiction research raises important methodological issues. Measuring these trajectories is challenging because it requires either a reliance on retrospective data, which are limited by recall biases, or longitudinal studies, which are expensive and difficult to implement. Moreover, statistical methods for identifying and clustering trajectories (such as mixture modeling) have limitations (Bauer and Curran, 2003; Chassin et al., 2009; Jackson and Sher, 2006; Sher et al., 2011; Sterba and Bauer, 2010), requiring that researchers interpret their findings cautiously and follow recommended practices for establishing the validity of the findings (see Ialongo, 2010), including making decisions about competing models based on theoretical considerations in addition to empirical means of comparison (Sher et al., 2011) .

Finally, given the evidence reviewed to this point concerning the etiological significance of age of onset, speed of progression, and developmental milestones or “stages” of substance use both within and across substances, it is not surprising that different findings are produced by studying addiction among participants of different ages and stages of use. For example, behavioral genetic studies often report that the heritability of substance use phenotypes is lower in adolescence than in adulthood (Dick et al., 2007; Kendler, Schmitt, Aggen, and Prescott, 2008). One interpretation of this finding is that developmentally limited, peer-driven forms of substance use in adolescence may mask the effects of genetic risk, which are then more clearly detected in adulthood when developmentally limited forms of use have remitted. In addition, adults probably have greater control to select their own social environments than do adolescents. Thus, there is probably greater gene–environment covariation in adult peer social environments than adolescent peer social environments because of greater adult “niche picking.” Methodologically, this suggests that researchers should carefully consider the effects of age and “stage” of substance use in sample selection and data analysis.

3 Methodological Issues in Sampling Child and Adolescent Populations

Many studies of child and adolescent populations use school-based samples because of their relative ease of access, cost-effectiveness, and ability to accrue large sample sizes. However, although school-based samples contain quite diverse samples of children and adolescents, there are also limits to their representativeness. School-based samples may under-represent pathology, because truant, homeless, runaway, and institutionalized children are unlikely to be accessed. Moreover, because of school drop-out, the representativeness of school-based samples in terms of including high-risk individuals is likely to diminish with the age of the participants, particularly after the age of legal school drop-out. The need for active parent consent also limits sample representativeness in school-based settings (e.g., Anderman et al., 1995; Esbensen, Miller, Taylor, and Freng, 1999) as well as other settings (Rojas, Sherrit, Harris, and Knight, 2008), and active parental consent has been found to under-represent higher-risk and lower-socioeconomic-status participants.

Recruiting community-based samples of children and families using techniques like random digit dialing or birth records has the potential to achieve greater representativeness, but is expensive and labor intensive. Moreover, recruitment using telephone screening has become more difficult with changes in telecommunications and declining participation rates. Recruiting community samples may require mixed methods including using address-based sampling frames to mail surveys or to send advance invitation letters followed up by phone contacts (Mokdad, 2009).

Methods for improving recruitment rates (and parent consent rates) include mailing parent consent forms directly to parents (with telephone follow-up for non-responders) rather than attempting to obtain parental consent by going through the adolescent, and also stressing that participants include both users and ...

Table of contents

• Cover Page
• Title Page
• Copyright
• Dedication
• About the Editors
• About the Contributors
• Introduction: The Science of Addiction Psychopharmacology
• Part 1: Distal Determinants of Drug Use
• Part II: Proximal Determinants of Drug Use
• Part III: Insights from Cognitive Science
• Index