Elsevier

Drug and Alcohol Dependence

Volume 201, 1 August 2019, Pages 212-219
Drug and Alcohol Dependence

Full length article
Shared and specific associations of substance use disorders on adverse outcomes: A national prospective study

https://doi.org/10.1016/j.drugalcdep.2019.03.003Get rights and content

Highlights

  • Substance use disorders (SUDs) are associated with increased risk of adverse social outcomes.

  • Associations occur through general predisposition rather than being drug-specific.

  • General predisposition represents mechanisms shared across SUDs.

  • This dimension should be a therapeutic target for adverse outcome prevention.

Abstract

Background and aims

Substance use disorders (SUD) frequently co-occur and are associated with numerous adverse outcomes and lower quality of life. The goal of this study was to examine whether the associations of SUD with adverse outcomes occur through a shared liability or are disorder-specific even after taking into account their frequent co-occurrence.

Basic procedures

Data were drawn from the National Epidemiological Survey on Alcohol and Related Conditions. The association between nine SUDs assessed at Wave 1 (2001–2002) and a broad range of outcomes (divorce/separation, violence, unemployment, financial crisis, legal problems, problems with a neighbor, friend, or relative, and quality of life) at Wave 2 (2005−2005) were estimated separately and simultaneously using a latent variable model to account for their co-occurrence and identify potential disorder-specific effects.

Main findings

SUD at Wave 1 were associated with increased prevalence of all adverse outcomes at Wave 2 (p < .05). With the exception of nicotine dependence and tranquilizer use disorder, we found no specific associations of any SUD with any adverse outcome. Rather, associations occurred primarily through the latent variable representing the shared effects of the different SUDs.

Conclusions and relevance

Our findings underscore the importance of adopting dimensional approaches to model the co-occurrence of SUD. Because SUD increases the risk of adverse outcomes mainly through a general predisposition representing mechanisms shared across SUD rather than through drug-specific mechanisms, this dimension should be considered as a therapeutic target to substantially advance prevention of adverse outcomes caused by SUD.

Introduction

The shifting landscape of cannabis legalization (Pacula and Smart, 2017) and increases in the prevalence of use of alcohol (Grant et al., 2017), cannabis (Hasin et al., 2015), and other drug use disorders (SUD) (Grant et al., 2016) have brought to the fore the question of whether certain SUDs are more strongly associated with adverse outcomes and could therefore be considered more dangerous. The answer to this important question has been hampered by two important methodological limitations. First, most published studies examining the adverse outcomes related to SUDs have focused on a single substance (e.g., alcohol or nicotine dependence) rather than comparing SUDs with one another (Hall and Degenhardt, 2009; Sommers et al., 2006). Second, SUDs often co-occur in the same individual (Blanco et al., 2015, 2013a; Grant et al., 2016; Hoertel et al., 2018; Pascal et al., 2018), making it difficult to differentiate whether adverse outcomes are associated with a specific SUD or are shared across different disorders.

Multiple lines of evidence suggest the existence of drug-specific and shared mechanisms that may contribute to the development of SUDs and consequently their adverse outcomes. Shared mechanisms include shared genetic and epigenetic vulnerabilities (Cerda et al., 2010; Nestler, 2014; Tsuang et al., 1998), gene-environment interactions (Agrawal and Lynskey, 2008; Koob and Volkow, 2016), deficits in the brain reward systems (Fontenelle et al., 2011; Ross and Peselow, 2012) or HPA axis activity (Sinha, 2008), and environmental influences such as deficits in social support and early exposure to stress or trauma (Kelly and Daley, 2013). Drug-specific mechanisms also play a role in the development of individual SUD. For example, convergent results using genome-wide association support that a nicotinic receptor subunit gene cluster (CHRNA5/CHRNA3/CHRNB4) influences heavy smoking behavior (Berrettini et al., 2008). Genetic variation in nicotine metabolism and variations in the CYP2a6 region of chromosome 19 also play an important role in the development of nicotine use disorder (Audrain-McGovern et al., 2007). Polymorphisms in alcohol metabolizing enzymes strongly influence alcohol consumption and alcohol dependence (Bierut, 2011), with aldehyde dehydrogenase 2 deficiency exerting a protective role in the risk of alcohol use disorder (Harada et al., 1982). Variants in the alcohol metabolizing genes contribute to differences in alcohol use patterns but not to other drug use patterns. Similarly, variations of nicotine metabolizing genes increase smoking behavior but not other drug behavior (Bierut, 2011). Like their mechanisms, some adverse outcomes may be specific or more associated with certain SUDs, which would help stratify different SUDs risks; however, given their frequent co-occurrence, this can be an arduous task.

In this report, we sought to address whether adverse outcomes are associated with specific SUDs or are shared across different disorders by using a latent variable approach in which each SUD is conceptualized as a manifestation of a general latent predisposition for SUDs. The approach of representing this predisposition or liability as a latent factor is uniquely equipped to disentangle which associations are shared by all SUDs and which (if any) are unique to specific SUDs without making a priori assumptions about the reasons for these associations. We drew on data from a large, prospective general population study, the National Epidemiological Survey on Alcohol and Related Conditions (NESARC), to maximize the generalizability of our results. Prior studies (Blanco et al., 2015, 2013a; Blanco et al., 2013b; Eaton et al., 2012, 2015; Hoertel et al., 2015b; Pascal et al., 2018) have found that a single factor represents an underlying predisposition to psychiatric disorders. Based on this prior literature, we sought to examine whether the associations of SUDs with adverse outcomes occurred through a shared liability or were disorder-specific even after taking comorbidity into account. We hypothesized that: 1) the latent structure of SUD would be well-described by a single liability factor for SUDs, and 2) this factor would explain the association of SUDs with adverse consequences.

Prior research has documented that SUDs are strongly associated with a variety of adverse outcomes that may co-occur, including financial (Robbins and Nugent, 1975), social (Robbins and Nugent, 1975), marital (Lander et al., 2013), employment (Platt, 1995), violence (Goldstein, 1985), and legal (Robbins and Nugent, 1975) problems. Furthermore, individuals with SUDs have an increased risk of general medical conditions (Lieber, 1998; Stein, 1999) and psychiatric disorders (Kessler et al., 1996; Regier et al., 1990), which can affect health-related quality of life (Schaar and Ojehagen, 2003). Because of the lack of prior prospective national studies comparing the adverse consequences of different SUDs, we did not make any assumption regarding the specific contribution of each disorder to each adverse consequence. One application of factor analysis is the potential to better understand SUDs mechanisms leading to adverse outcomes and to develop management strategies targeted at multiple SUDs (Kim et al., 2012).

Section snippets

Sample

Data were drawn from the Wave 1 and Wave 2 of the NESARC, a nationally representative face-to-face survey of the US adult population conducted in 2001–2002 (Wave 1) and 2004–2005 (Wave 2) by the National Institute on Alcoholism and Alcohol Abuse (NIAAA) (Grant et al., 2009, 2016). The target population included the civilian non-institutionalized population, aged 18 years and older, residing in the United States. Face-to-face personal interviews were conducted with 43,093 respondents at Wave 1.

Associations between SUD at wave 1 and adverse outcomes at wave 2

In Wave 1, 12.66% of individuals had 1 SUD, 1.76%, had 2 SUDs, and 0.67% had 3 or more SUDs. In Wave 2, the most common adverse outcome was financial crisis, ranging from 22.8% (for alcohol dependence) to 42.6% (for amphetamine use disorder), whereas the least common was legal problems, ranging from 3.0% (for nicotine dependence) to 24.4% (for cocaine use disorder). After adjusting for sex, age, race/ethnicity, urbanicity, family history of SUDs and other psychiatric disorders, and Wave 1

Discussion

The use of a latent variable model allowed us to examine the underlying structure of SUDs and to address for the first time in a large, nationally representative sample the question of whether certain SUDs are more often associated with adverse outcomes. We had three main findings. First, we found that a one-factor model provided a good fit to the latent structure of SUD. Second, with the exception of nicotine dependence and tranquilizer use disorder, we found no specific associations of any

Conflict of interest

Dr. Blanco owns stock in Eli Lilly, Sanofi and General Electric.

Role of funding source

This study was supported by NIH grants DA019606 (Dr. Olfson), MH076051 and MH082773 (Drs. Blanco, Olfson and Wall) and the New York State Psychiatric Institute (Drs. Blanco, Olfson and Wall). Dr. Blanco’s work was conducted as part of previous work for New York State Psychiatric Institute/Columbia University. The views and opinions expressed in this report are those of the authors and should not be construed to represent the views of any of the sponsoring organizations, agencies, or the US

Contributors

Dr. Franco conceptualized the study, drafted the initial manuscript, and approved the final manuscript as submitted. Dr. Olfson, critically reviewed and revised the manuscript, and approved the final manuscript as submitted. Dr. Wall helped design the study, reviewed the manuscript and approved the final manuscript as submitted. Dr. Wang carried out the initial analyses and approved the final manuscript as submitted. Dr. Hoertel designed the study, drafted the initial manuscript, and approved

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