Prevalence of the addictions: a problem of the majority or the minority? (2011)

Search Strategy

Four search engines were examined: PsycINFO, OVID Medline (1950 through first 2 weeks of April 2010), PubMed, and Google Scholar. The terms used to apply to the 11 addictive behaviors were “tobacco addiction,” “nicotine addiction,” “alcoholism,” “alcohol dependence,” “marijuana abuse,” “illicit drug abuse,” “drug abuse,” “drug dependence,” “drug addiction,” “overeating addiction,” “food addiction,” “eating addiction,” “binge eating disorder,” “overeating dependence,” “gambling addiction,” “compulsive gambling,” “pathological gambling,” “Internet addiction,” “web addiction,” “pathological Internet use,” “video game addiction” (only searched for Internet-related activity), “love addiction,” “pathological love,” “sexual dependency,” “sex addiction,” “sexual compulsivity,” “physical activity addiction,” “exercise addiction,” “workaholic,” “work addiction,” “shopping addiction,” and “compulsive shopping.” At least two terms were used to apply to each addictive behavior (e.g., “sex addiction” and “sexual compulsivity”), all crossed with five terms that indicated prevalence (“prevalence” and “incidence”) or co-occurrence (“co-occurring addictions,” “co-occurring disorders,” and “comorbidity”). Titles were examined first to screen potential articles, followed by abstracts, and then manuscripts were downloaded. The reference sections of downloaded manuscripts were examined for additional references not located in the searches. If few relevant articles were found, other terms were attempted or only the addictive behavior was searched for without pairing it with a prevalence or co-addiction term. A total of 640 initial searches were completed (32 addictive behavior search terms × 5 prevalence/co-occurrence terms × 4 search engines).

Analysis

We established a set of inclusion and exclusion criteria that might permit retention of a variety of studies but also permit some type of comparability across studies. First, we attempted to include only studies that reported a sample size of at least 500 participants (e.g., as in Stucki & Rihs-Middel, 2007). By doing so, we would avoid the most selective, convenient samples. However, if there were few studies available for a particular addictive behavior, we included data-based studies with a smaller sample size in the text. Because so few co-occurrence studies were located, all data-based studies of addiction co-occurrence located in the search were included in the text of this review, regardless of sample size. Second, we limited our age range of inclusion from 16 to 65 years of age, to capture developmental periods from older adolescence at the start of emerging adulthood to middle/older adulthood. In articles that included a wider age range, we attempted to use the data within the 16- to 65-year age range. Third, we included only prevalence studies that examined both males and females, to rule out any particularly selective samples (e.g., single gender chemical dependence inpatients). Fourth, we included only studies that specified use of some measure of addiction and provided any psychometrics indicating reliability or validity (e.g., see Albrecht, Kirschner, & Grusser, 2007). There were some articles that were very influential but did not permit relatively clear estimates of prevalence, and these were excluded from compilation (e.g., Brenner, 1997, regarding Internet addiction). Finally, we attempted to include only data on recent participation in the addictive behavior, primarily participation in the last year, and we screened out studies that reported only lifetime prevalence of an addictive behavior (exceptions are stated in the text).

Data that met the inclusion criteria are shown in Table 2 (n = 83 studies). These data summarize relatively extreme levels of the behavior as being “addicted.” Both drug “abuse” and “dependence” were considered as “addicted” in the table and in our calculations. In studies that measured “at-risk,” “problem,” and “pathological” gambling, generally only pathological gambling was considered in the table (if provided and presented separately from the other categories). Unless otherwise specified, data were on last 12 month use (see Table 2). Calculation of the overall prevalence of the 11 addictive behaviors across studies was completed by considering (to the extent possible) a sample size weighted average of all values provided by empirical data reported. Estimates were calculated across age groups and gender.

 

Table 2

Prevalence and Co-Occurrence of 11 Addictions of Studies With Ns at Least 500

Prevalence and Co-Occurrence

We located a total of 83 studies with sample sizes of at least 500 subjects (Table 2). Across these 83 studies, 12 examined cigarette dependence/nicotine addiction (2 of these looked at some type of co-occurrence with at least one other of the 11 addictive behaviors), 22 examined alcohol abuse/dependence (5 of these looked at co-occurrence), 20 examined drug abuse/dependence (3 of these looked at co-occurrence), 9 examined food disorders/addiction (2 of these looked at co-occurrence), 26 examined pathological gambling (7 of these looked at co-occurrence), 16 examined Internet addiction (1 of these looked at co-occurrence), 2 examined love addiction (none of these looked at co-occurrence), 4 examined sex addiction (1 of these looked at co-occurrence), 3 examined exercise addiction (none of these looked at co-occurrence), 3 examined workaholism (none of these looked at co-occurrence), and 4 examined shopping/spending/buying addiction (none of these looked at co-occurrence). Results are presented as a function of age group (older teens, college age youth, and then adults).

Marijuana and other illicit drug abuse/dependence

In the MTF survey, daily (20 or more days in last 30 days) marijuana smoking varied from 5.4% among 18-year-olds to 2% among 50-year-olds (Johnston et al., 2009a, 2009b). Cohen et al. (1993) found a prevalence of marijuana abuse among 17- to 20-year-olds of 2.9%. Other data on marijuana abuse/dependence among population samples indicated a prevalence among older teenagers and adults of approximately 7% (Agrawal, Neale, Prescott, & Kendler, 2004; Barnes et al., 2009; Coffey et al., 2002; Hall, Degenhardt, & Patton, 2008; Kandel et al., 1997; Kilpatrick et al., 2000; MacLaren & Best, 2010; Young et al., 2002), though Chen, Sheth, Elliott, and Yeager (2004) found a prevalence of 13.4% among teenagers, and Agrawal, Neale, Prescott, and Kendler (2004) found that the marijuana abuse/dependence rate among U.S. college males and females was 18% and 7.5%, respectively. Alternatively, Compton, Grant, Colliver, Glantz, and Stinson (2004), Grant et al. (2004), and Stinson, Ruan, Picering, and Grant (2006) assessed a very large sample and found a 1.5% prevalence of marijuana abuse/dependence among the U.S. general adult population (between 4% and 5% prevalence among those 18 to 29 years of age), and Farrell et al. (2003) found a prevalence of 1.8% in Great Britain. Kandel, Chen, Warner, Kessler, and Grant (1997) found 0.1% prevalence among U.S. adults (9.0% prevalence among those who used in the last year).

Prevalence of other illicit drug abuse disorders, which includes marijuana use in the calculation in some studies, varied between 2% and 5% among teenagers, college age youth, and adults (Alexander & Schweighofer, 1989; Chen et al., 2004; Cohen et al., 1993 [1.1% among 17- to 20-year-olds, clearly excluding marijuana use in the calculation]; Cook, 1987; Grant et al., 2004; Kandel et al., 1997 [lower, at 0.3%; 11.6% among last year baseline users]; Kilpatrick et al., 2000; Regier et al., 1990; Sussman & Ames, 2008; Young et al., 2002). However, Agrawal et al. (2004) found a rate of 9% and 19.2% other illicit drug dependence (excluding marijuana) among 2,125 adult female and male twin pairs, respectively. On the other hand, Compton, Thomas, Stinson, and Grant (2007), and Warner, Kessler, Hughes, Anthony, and Nelson (1995), reported a U.S. general adult 12-month prevalence of illicit drug abuse/dependence (marijuana and other illicit drugs) of 2.0% and 1.8%, respectively. In Great Britain, this prevalence within a general adult population was found to be 2.1% (Farrell et al., 2003). The adult last 12-month illicit drug dependence in Canada was found to be about 1% (Gadalla & Piran, 2007). Based on this pool of studies, we estimate last 12-month illicit drug abuse/dependence (marijuana and/or other drugs) prevalence in the general adult population of the United States as approximately 5% (any drug dependence is 1% to 3%; e.g., Regier et al., 1990; Teesson et al., 2006).

Co-occurrence of tobacco, alcohol, and illicit drugs with each other or other addictive behaviors

Several studies (only 4 of which involved samples of 500 or more subjects) have found 30–60% co-occurrence of cigarette, alcohol, and other drug use disorders with each other among youth and adults in the United States or elsewhere (Essau & Hutchinson, 2008; Falk et al., 2006; Ford et al., 2009; Kaufman, 1982; Miller, Gold, & Klahr, 1990; Palmer et al., 2009 [lifetime use-based]; Regier et al., 1990 [lifetime use-based]; Stinson, Ruan, Picering, & Grant, 2006; Sussman & Ames, 2008). However, in a large German adult sample, a last 12-month prevalence of only 18.4% alcohol hazardous use/abuse/dependence was found among general population daily smokers (Ulrich, Hill, Rumpf, Hapke, & Meyer, 2003).

In a review by Holderness, Brooks-Gunn, and Warren (1994), there were three small studies (ns = 20, 27, and 138) that indicated that approximately 20% of drug abusers also exhibited an eating disorder (bulimia or bulimic behaviors), though Freimuth et al. (2008) suggested this co-occurrence is higher at 35%, and Lesieur and Blume (1993) suggested that the co-occurrence varies by age and is higher among older adults. Lesieur and Blume (1993) reported one study of female alcoholics (n = 31) that showed 36% with symptoms of binge eating and 21% with a clinical eating disorder.

Only five studies with a sample size of at least 500 were located that examined co-occurrence of the 8 other addictive behaviors among those suffering from tobacco, alcohol, or illicit drug use disorders, and all of these pertained to gambling addiction (Cunningham-Williams, Cottler, Compton, Spitznagel, & Ben-Abdallah, 2000 [lifetime use]; French et al., 2008; Griffiths, Wardle, Orford, Sproston, & Erens, 2010; Toneatto & Brennan, 2002; Welte, Barnes, Wieczorek, Tidwell, & Parker, 2001). Among a large sample of adults in Great Britain that reported having been “smokers” in the past year, 1.1% reported problem gambling (Griffiths et al., 2010). We could locate no other such studies pertaining to tobacco use. Among mostly large samples of weekly or greater drinkers, and among adult alcoholic inpatients, 3–5% reported a gambling problem (French et al., 2008; Griffiths et al., 2010; Lesieur, Blume, & Zoppa, 1986; Toneatto & Brennan, 2002), although in a representative sample of U.S. adults, 24% of alcohol dependent persons also reported a gambling problem (Welte et al., 2001). In a convenience sample of 97 13- to 18-year-old outpatient substance abusers (alcohol or marijuana abuse/dependence), only 1% (n = 1) met criteria for gambling addiction (Kaminer, Burleson, & Jadamec, 2002). Among a sample of 990 drug abusers (ranging from 28% being sedative dependent, to 77% being stimulant dependent), 11% reported also being pathological gamblers (Cunningham-Williams et al., 2000 [lifetime use]), and other mostly small-scale research also indicated that 5–25% of illicit substance abusers were also gambling addicts (Freimuth et al., 2008; Lesieur & Blume, 1993; Lesieur, Blume, & Zoppa, 1986; Petry, 2007; Spunt, Dupont, Lesieur, Liberty, & Hunt, 1998; Steinberg, Kosten, & Rounsaville, 1992; Toneatto & Brennan, 2002).

Freimuth et al. (2008) estimated that about a third of substance abusers also exhibit sexual addition; however, that one report is speculative. Griffin-Shelley (1995) speculated that only 10% of those with drug dependence also suffer from love or sex addiction. We found no other studies indicating co-occurrence of other addictions among cigarette smokers, alcohol abusers, or illicit substance abusers.

Estimating the prevalence of cigarette, alcohol, and illicit drug use (marijuana or other drugs) addictions in U.S. adults at 15%, 10%, and 5%, respectively, if one infers a 50% overlap among any two of the three drug use disorders, then one might sum half of each of these addictive behaviors and find that 15% of the adult population in the United States is addicted to either cigarettes, alcohol, or other drugs, controlling for overlap (30% total if there was no overlap). Tentatively, we estimate a 50% overlap among tobacco, alcohol, or illicit drug addictions. In addition, we tentatively estimate, based on the few gambling and binge eating reports (also see review by Lacey & Evans, 1986), that 20% of cigarette smokers, alcohol abusers, or illicit substance abusers may also experience any of the other eight addictions. This is depicted in Table 3.

 

Table 3

Estimated Prevalence and Co-Occurrence of 11 Different Addictive Behaviors

Co-Addictive Behavior Assumptions and Overall Prevalence

Based on the above review, we estimated the overall last 12-month prevalence of the 11 addictive behaviors among U.S. adults as follows: cigarettes—15%, alcohol—10%, illicit drug use—5%, eating—2%, gambling—2%, Internet—2%, love—3%, sex—3%, exercise—3%, work—10%, and shopping—6%. Only 20 of the large-scale studies reported in Table 2 provided any information on specific co-addictions. Thus, we made liberal use of small-scale studies in the text. A matrix of addiction co-occurrence was created estimated from these data, as shown in Table 3. Considering all 11 of these addictions, one could make any of the 3 assumptions regarding their co-occurrence. First, one might assume that there was no overlap among the addictions. If so, one would simply add up individual addictive behavior prevalence, and the total percentage of addictions would add to 61%. Clearly, because co-addictions were widely reported, this assumption is untenable.

Second, one might assume that there was “total overlap” among the addictions; that is, that the same people suffered from all of these 11 addictions. If this was true, then the sufferers of addictive behaviors would report experiencing all 11 addictions with 100% overlap. If there was 100% overlap, one could consider overall prevalence as being the single highest prevalence addictive behavior, which would be cigarette smoking at approximately 15% of the population. However, for any two addictions, co-occurrence averages 23.42% (range = 10–50%), across 110 pairs of estimates provide in Table 3 (Also see discussion by Essau, 2008).

Third, one might assume that there are people who are addicted to single behaviors and people who are addicted to two or more behaviors. This assumption is consistent with the available data. Ascertaining the percentage of the population with nonoverlapping and overlapping addictive behavior has not been well addressed as of yet. However, one may make a range of estimates. One estimate is derived by removing from each addictive behavior’s prevalence the average co-occurrence (23.42%), and then summing up the remaining “unique variance.” That would equal 46.71%. That estimate is the same as subtracting from the total prevalence assuming nonoverlap (61%), 23.42% of that total (i.e., 61% − [61% × 23.42%]). In that case, approximately 47% of the U.S. adult population would be estimated to be suffering from an addictive behavior over the last 12 months. If one used an algorithm suggesting that a certain percentage (say 25%) of people suffered from a co-addiction and, if they do, then a certain percentage of them (say 50%) are likely to have suffered from a third addiction, the additional co-addiction variance would be culled from within the initially reported co-occurrence (because the third addictive behavior is a conditional probability; e.g., see figure in Regier et al., 1990). Therefore, no additional decrease in overall prevalence of an addictive behavior would be observed.

A second estimate derived from the third assumption is based on the argument that most people do not experience multiple addictions except for one shared addictive behavior, most popularly cigarette smoking. If one simply removes cigarette smoking prevalence from the list, assuming that it co-occurs with all other addictive behaviors, then approximately 46% of the population would be estimated to be suffering from an addictive behavior (i.e., 61% − 15%).

A third estimate derived from the third assumption is that, perhaps, one should remove the prevalence of the addiction most strongly associated with all others, and then remove 25% of the remainder from each addictive behavior, assuming some residue overlap, to derive a more conservative percentage. By doing so, approximately 34.5% (i.e., 46% − [46% × 25%]) of the population would be estimated to have an addictive behavior. The second and third estimates derived from the third assumption are likely to be overly conservative because no one addictive behavior is differentially and more strongly associated with all other addictions. For example, cigarette smoking clearly is strongly associated with alcohol use, other drug use, and gambling, but not the other behaviors.

In summary, one might assume total nonoverlap, total overlap, or partial overlap among the addictive behaviors. Partial overlap appears to fit the data the best. Within this model, one may remove the average two-addiction overlap, the highest prevalence addiction assuming it overlaps with all over addictions, or the highest prevalence addiction plus an additional 25% assuming a relatively conservative model. There are many other estimates that could be made. For example, one other estimate would derive from the assertion that the highest prevalence of co-occurrence (approximately 50%) should be removed from each other addictive behavior, simply to be very conservative, or perhaps, stemming from the belief that persons who are addicts are “hard wired” as addicts, and they must suffer from multiple addictions. Only 30.5% of the population would be estimated to be suffering from at least one addictive disorder if that 50% were removed (i.e., 61% − [61% × 50%]).

Considering these different algorithms, we believe that taking the total prevalence summed across all behaviors and subtracting from that sum the average co-occurrence of any two addictions best reflects the pattern depicted in Table 3. Thus, we estimate that approximately 47% of the adult U.S. population suffers from 1 of these 11 addictions in a 12-month period.

Addiction as a Disease or Problem in Living

At present, most professionals consider addiction to be a chronic, relapsing disease (e.g., Leshner, 1997; Sussman & Ames, 2008), similar to other chronic disorders such as hypertension or asthma (O’Brien & McLellan, 1996). Therefore, it becomes imperative to distinguish whether addiction is a very common disease-like phenomenon (perhaps, as common as some types of flu), or whether, with such apparently high prevalence, addiction reflects some other or additional (multiple) phenomena. For example, perhaps, addiction (also) is a condition of lifestyle modeled by social–environmental conditions (Sussman & Ames, 2008), possibly with critical periods of development in which one is most vulnerable to imprint this lifestyle (Volkow & Wise, 2005). If addiction is popularly learned by victims, differing in objects of addiction by accessibility to the objects, social circumstances, time demands, and other social environmental–developmental forces, then arguably social policy changes are needed, which could inhibit or redirect such excessive behavior away from self-destructive outcomes throughout the lifespan, and channel behavior in more constructive directions (see Griffiths, 2009b; Marks, 1990; Orford, 2001; Schaef, 1987).

Taken further, Larkin, Wood, and Griffiths (2006) argue that health professionals must be careful not to attach the pejorative label of “addiction” to people who, while they are heavily involved in, or strongly identified with, a particular activity, are not suffering unpleasant consequences. As Larkin and colleagues (2006) note, unless people are at obvious risk for experiencing physical, emotional, relational, and financial difficulties as a result of their activity, it is both pointless and unfair to describe them as suffering from a disease-like condition.

Unfortunately, regardless of one’s perception of “addiction,” consistent engagement in any of the 11 behaviors presented in this article may cause significant changes in the brain pathways, in particular the mesolimbic dopaminergic (reward) system (Leshner, 1997; Sussman & Ames, 2008). These changes, in turn, may be involved in difficulty with cessation of addictive behavior. At some point, addictions often do lead to an accumulation of various negative consequences (Sussman & Ames, 2008). Even so-called positive addictions (Glasser, 1976; Griffiths, 1996) might have negative consequences for the addict (see Brown, 1993, on “mixed blessings”). This may include burnout from workaholism, for example. What is perhaps most important to target by health professionals is the compulsion to seek repeatedly certain behaviors even with knowledge of potentially dire health and social consequences. This compulsion may be a function of neurobiological changes, associated psychological states (e.g., subjective sense of restlessness, irritability, or discontent), and social facilitation of the behavior. Future research is needed to better understand why some people quit easily and others do not, and whether differences in ease of quitting might be a function of the relative influence of physiology versus social context. Certainly, the extent that a disease label facilitates compassion in treatment, it may continue to serve a heuristic function (Sussman & Ames, 2008)

Limitations

There are numerous limitations with the analysis attempted in this study. First, there was a paucity of data on the prevalence and co-occurrence of some of the addictive behaviors (i.e., love, sex, exercise, workaholism, and shopping). More studies on these behaviors with large samples are needed. Second, very few studies examined multiple addictions in the same sample. Further work of this type might be enlightening. For example, in a factor analytic study, some legal addictive behaviors were found to load on the same factor (work, eating, love, exercise, and shopping), whereas gambling was found to load on a separate factor (perhaps, less socially approved of but generally legal), and drug use (tobacco, alcohol, and illicit drug use) and sex addiction was found to load on a third factor, perhaps reflecting relatively extreme behaviors (MacLaren & Best, 2010). Additional research that examines patterns of covariation of addictive behaviors in the same sample might enable various stakeholder communities (including researchers and practitioners in the addictive behaviors field) to learn more about the underlying etiology and co-occurrence of addictive behaviors and, consequently, how to best treat these behaviors.

Third, prevalence of an addictive behavior considered in our analysis depended on the inclusion criteria. For example, we attempted to consider only those who reported relatively extreme levels of gambling (“pathological gambling”) as being gambling addicts. Had we consistently included less extreme “problem gambling” (Lesieur et al., 1991), the prevalence of gambling would have been doubled and the overlap with other addictive behaviors might have been greater. As another example within the realm of Internet addiction, we tried to only include general population samples in our research. However, much of the research on Internet addiction has been carried out on self-selected samples of Internet users and not on general population studies. Thus, rates of Internet addiction would tend to be inflated. As a third example, some researchers have addressed sexual addiction as number of orgasms per week, as opposed to consequences criteria, and the relation between sexual frequency and adverse consequences is not clear (Kaplan & Krueger, 2010). If we had defined sexual addiction as experiencing orgasms at least 7 days a week, the prevalence would be relatively higher at 5–7%. As a fourth example, while a majority of studies focused on a last 12-month time frame and used a DSM model, there was some variation in time frame and scoring criteria (see Table 2). This adds yet more noise when thinking about inclusion criteria compared across addictions. As a fifth example, we attempted to focus on U.S. adults. Including other countries or age groups in the calculations may have altered some results (e.g., prevalence of cigarette smoking tends to be higher in some European countries than in the United States).

Fourth, this study did not examine the concept of “substitute addictions.” The notion of substitute addiction has been discussed for many years by members of the recovery movement and by recovery specialists (Chiauzzi, 1991; Gorski & Miller, 1986; Horvath, 1999, 2006; Murphy & Hoffman, 1993; Sussman & Black, 2008). This concept refers to any addictive behavior that serves at least one key function previously achieved by another addictive behavior, and it is plausible that any of these addictive behaviors may serve as substitutes for each other. Unless both addictions were engaged in the same 12-month period, they would not have been addressed as co-occurring addictions in the current study.

Fifth, only identifying the “object” of an addiction may not provide knowledge of its specifics, which may vary across individuals. For example, Griffiths (2000a) argued that there is a need to distinguish between addictions to the Internet and addictions on the Internet (e.g., gambling, computer gaming, love, or sex). Of course, some behaviors engaged on the Internet (e.g., cybersex, cyberstalking, and romance chat) may be behaviors that the person would only carry out on the Internet because the medium may be perceived as anonymous, non-face-to-face, and disinhibiting (Griffiths, 2000a, 2000b; Young, 1996).

Sixth, simply identifying co-occurrence of addictions does not identify causation of “unique” and “shared” components. Future research needs to assess possible reasons why some people might suffer from one addictive behavior but not another, or why some people tend to suffer particular multiple addictions (e.g., such as may be the case among sufferers of Borderline Personality Disorder; Bagby, Vachon, Bulmash, & Quilty, 2008). Furthermore, one must still account for the fact that, for example, a significant minority of people become addicted to heroin or gambling, but very few are thought to become addicted to, say, gardening.

Finally, the list of addictions selected in this article could be accused of convenience sampling. There are other potentially addictive behaviors that one might also include in such an analysis that may or may not be experienced by the same people. Other behaviors that have been considered addictive include caffeine (Cook, 1987), religion (Sussman & Black, 2008; Taylor, 2002), offline video games (Fisher, 1994 [6% of 467 11- to 16-year-old U.K. youth were found to be video game addicts]; Griffiths & Meredith, 2009), compulsive skin picking or hair pulling (i.e., trichotillomania; Brewer & Potenza, 2008; Marks, 1990), kleptomania (Cook, 1987; Marks, 1990), mobile telephone use (Bianchi & Phillips, 2005), pica (Lacey, 1990), tanning (Poorsattar & Hornung, 2010), and even violent behavior (Sussman & Ames, 2008) or stealing cars and going joy riding (Kellett & Gross, 2006). More research on the breadth of addictive behaviors may lead to a better understanding of addictive processes underlying specific objects of addiction.

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: the National Institute on Drug Abuse (#sDA020138).

Declaration of Conflicting Interests

The author(s) declared no conflicts of interest with respect to the authorship and/or publication of this article.

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