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Social and behavioral problems among five gambling severity groups
Author’s Accepted Manuscript Social and behavioral problems among five gambling severity groups Jacquelene F. Moghaddam, Gihyun Yoon, Michael D. Campos, Timothy W. Fong www.elsevier.com/locate/psychres
PII: DOI: Reference:
S0165-1781(15)00542-9 http://dx.doi.org/10.1016/j.psychres.2015.07.082 PSY9134
To appear in: Psychiatry Research Received date: 15 October 2014 Revised date: 7 July 2015 Accepted date: 22 July 2015 Cite this article as: Jacquelene F. Moghaddam, Gihyun Yoon, Michael D. Campos and Timothy W. Fong, Social and behavioral problems among five gambling severity groups, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2015.07.082 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Social and behavioral problems among five gambling severity groups Jacquelene F. Moghaddama*, Gihyun Yoonb,c, Michael D. Camposa, Timothy W. Fonga a
University of California Los Angeles (UCLA) Gambling Studies Program, UCLA Department of Psychiatry and Biobehavioral Sciences, 760 Westwood Plaza, Suite 38-153. Los Angeles, CA, USA. 90095-1759. b
Minneapolis VA Health Care System. One Veterans Drive. Minneapolis, MN, USA. 55417.
c
Department of Psychiatry, University of Minnesota. F282/2A West. 2450 Riverside Avenue South Minneapolis, MN, USA. 55454. *Corresponding author. Phone: (714) 872-1847. Fax: (310) 825-4845. Email: [email protected]
Abstract Gambling has been associated with various social and behavioral problems, but previous analyses have been limited by sample bias regarding gambling symptom severity range and the role of antisocial personality disorder (ASPD). This study utilized a nationally representative data set and examined various characteristics of behavioral problems and ASPD among five gambling severity groups. Participants were 42,038 individuals who took part in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and provided information on social and behavioral problems, ASPD, and gambling. Using DSM-IV criteria, we derived five gambling groups from the total sample: non-gambling, low-risk, at-risk, problem, and pathological gambling. Associations between all problematic behaviors and nearly every gambling severity level were significant prior to adjustment for sociodemographic variables and ASPD. Following the adjustment, all significant associations persisted, with the exception of sexual coercion. In the adjusted model, the financially oriented behaviors had the strongest associations with gambling. All gambling severity levels were associated with an increased risk for a number of problematic behaviors and social problems in comparison to non-gamblers. Further examination of gambling problems in financial and criminal justice settings is recommended.
Keywords: Pathological gambling; problem gambling; social problem; behavioral problem; antisocial personality disorder; financial problem; criminal justice I. Introduction Previous research utilizing the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) has found an increased risk for pathological gambling1 among individuals reporting problematic behaviors such as fire setting, shoplifting, and cruelty to animals [Blanco, et al., 2010;
1
As defined by the DSM-IV.
Blanco, et al, 2008; Vaughn et al., 2009]. Although these problematic behaviors were strongly associated with other antisocial behaviors and with a diagnosis of antisocial personality disorder (ASPD) itself, controlling for ASPD did not eliminate the relationship between these behaviors and pathological gambling in previous analyses. The existing NESARC studies have considered select problematic behaviors among individuals meeting three or more DSM-IV criteria for gambling, but have not considered the full range of gambling behavior. Furthermore, previous studies using the NESARC have not simultaneously examined multiple domains of problematic behaviors, which may be interrelated as relates to gambling pathology. While former NESARC studies focusing on gambling and problematic behaviors are telling of the difficulties which individuals with gambling problems may face, previous research in this area has particular caveats: (1) prior analyses have not focused on the occurrence of problematic behaviors among samples comprised entirely of gamblers; (2) previous studies which have examined gambling and problematic behaviors have not necessarily controlled for antisocial personality disorder, and therefore have been unable to establish the role of gambling in these behaviors specifically; (3) sample populations from which NESARC analyses have been derived have rarely stratified gambling severity categories beyond pathological gambling. The study presented here attempts to address the limitations of the current literature; it is derived from a large, nationally representative data set and examines social and behavioral problems among gamblers. The current study divides NESARC respondents into non-gamblers, low-risk, at-risk, problem, and pathological gamblers based upon DSM-IV criteria, and examines the relative risk for 10 problematic behaviors across each of these five severity groups. Such an approach may have implications for the screening, referral, and treatment of gambling problems in criminal justice, credit counseling, social service, and other relevant settings. 2. Methods 2.1 NESARC survey
The NESARC is a nationally representative survey directed by the National Institute on Alcohol Abuse and Alcoholism. The “Wave 1 NESARC” was completed from 2001-2002 (Petry et al., 2005) (hereafter referred to as “NESARC”). The NESARC is derived from 2000/2001 Census data and captures the civilian public of all 50 United States and Washington D.C., including those who were residing in a number of community living settings. Following the selection of a housing unit for participation, one individual per residence was randomly selected to complete an in- person interview. The in-person completion of the NESARC permitted trained interviewers to use a reliable instrument and make formal diagnoses, as opposed to survey approaches (see “Measures”) (Grant, et al., 2005). In total, 43,093 individuals aged 18 years and older participated in the NESARC for a response rate of 81 percent (NIH, 2006). 2.2 Measures 2.2.1. Problematic behaviors. The presence of 10 lifetime problematic behaviors were assessed in NESARC participants with the following questions: (1) shoplifting: “In your entire life, did you ever shoplift?”; (2) stealing: “In your entire life, did you ever steal anything from someone or someplace when no one was around?”; (3) failure to address debt: “In your entire life, did you ever fail to pay off your debts - like moving to avoid paying rent, not making payments on a loan or mortgage, failing to make alimony or child support payments or filing for bankruptcy?”; (4) scamming for money: “In your entire life, did you ever scam or con someone for money, to avoid responsibility or just for fun?”; (5) homelessness: “In your entire life, did you ever have a time that lasted at least one month when you had no regular place to live – like living on the street or in a car?” or “In your entire life, did you ever have a time that lasted at least one month when you lived with friends, acquaintances or relatives because you didn’t really have your own place to live?”; (6) quitting a job: “In your entire life, did you ever more than once quit a job without knowing where you would find another one?”; (7) sexual coercion: “In your entire life, did you ever force someone to have sex with you against their will?”; (8) fighting: “In your entire life, did you ever get into a lot of fights that you started?” or “In your entire life, did you ever get into a
fight that came to swapping blows with someone like a husband, wife, girlfriend or boyfriend?” or “In your entire life, did you ever use a weapon like a stick, knife, or gun in a fight?”; (9) arrest possibility: “In your entire life, did you ever do anything that you could have been arrested for, regardless of whether or not you were caught?”; and (10) vehicular endangerment: “In your entire life, did you ever do things that could have easily hurt you or someone else – like speeding or driving after having too much to drink?” or “In your entire life, did you ever get more than three traffic tickets for reckless or careless driving, speeding, or causing an accident?” or “In your entire life, did you ever have your driver’s license suspended or revoked for moving violations?” 2.2.2. Gambling. Gambling was considered casino gambling, playing cards, sports betting, purchasing lottery tickets, and placing wagers on the stock market (NIH, 2006). The NESARC employed a separate section of the Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM-IV Version (AUDADIS-IV) to assess lifetime pathological gambling; current pathological gambling was not evaluated because of low prevalence and power in statistical analyses (Petry et al., 2005) . The AUDADIS-IV has demonstrated high internal consistent reliability for lifetime pathological gambling (alpha = 0.80) (Petry et al., 2005) and reliability (kappa = 0.76) (Grant, et al., 2003). Consistent with previous studies utilizing the number of DSM-IV criteria to assess gambling severity (Morasco, et al., 2006; Toce-Gerstein, et al., 2003; Desai and Potenza, 2008; Pilver, et al., 2013), a lifetime pathological gambling diagnosis required at least five of the 10 DSM-IV criteria. In order to capture the full range of gambling-related problems, our sample was classified into five gambling severity groups: (1) non-gambling (never gambled 5 or more times in any one year in lifetime); (2) low-risk gambling (gambled 5 or more times in any one year, but had no DSM-IV criteria for pathological gambling); (3) at-risk gambling (one or two DSM-IV criteria for pathological gambling); (4) problem gambling (three or four DSM-IV criteria for pathological gambling); and (5) pathological gambling (5 to 10 DSM-IV criteria for gambling). 2.2.3. Antisocial Personality Disorder
In the NESARC, ASPD criteria was derived from DSM-IV guidelines and was concerned with the one’s long term functioning (not accounting for depressed, manic, or anxious states, periods of heavy drinking, drug use, or episodes of illicit substance withdrawals) (Grant, Stinson, Dawson, et al., 2004). In all, 30 separate symptoms operationalized the presence of the following behaviors during adulthood: refusal to abide by social norms, engaging in fraud, impulsivity, exhibiting a threatening disposition, lack of concern for others’ safety, and social recklessness (Howard, et al., 2009). 2.3 Statistical analyses We utilized cross tabulations to compare the prevalence rates of the demographic indicators (gender, age, race/ethnicity, education, personal income, and marital status), stratified by the five gambling groups (Table 1). Then the p-values from chi-square analysis were used to evaluate the relationship between the demographic indicators and the five gambling groups (Table 1). We employed cross-tabulations to compare the prevalence rates of the five gambling groups for the occurrence of 10 problematic behaviors (Table 2). The 95% confidence intervals (CI) and odds ratios (OR) from logistic regression analysis were applied to evaluate the relationship between the respective gambling groups and presence of problematic behaviors. We referred to the adjusted odds ratios (AOR) and 95% confidence intervals (CI) from logistic regression analysis to further evaluate this relationship following adjustment for notable demographic variables (gender, age, race, education, personal income, and marital status) and antisocial personality disorder (Table 2). Statistical analyses were implemented using SPSS Version 19 (IBM SPSS; Armonk, New York). 3. Results 3.1 Sample characteristics Please refer to Table 1 for specific information regarding the demographic background of this sample2. The five gambling groups differ in six demographic variables (gender, age, race/ethnicity, education, personal income, and marital status). Chi square analysis indicated significant differences in 2
Further analyses of the demographic indicators in the NESARC and gambling behaviors have been published in previous behaviors, e.g., [9,11,7]. We modeled the demographic analyses of this sample after the aforementioned publications. Recommendations for secondary analyses of these five gambling groups and demographic indicators are detailed in the discussion section.
the five gambling groups as relates to all considered demographic indicators (Table 1), including gender (X2 (4) = 799, p <.001), age, (X2 (12) = 452, p <.001), race/ethnicity (X2 (16) = 394, p <.001), education (X2 (12) = 187, p <.001), personal income (X2 (12) = 386, p <.001), and marital status (X2 (12) = 197, p <.001). Regarding gender, men had at least twice as many gambling problems across all symptomatic categories (at-risk, problem, and pathological gambling) compared to women. In terms of age and the two most severe gambling categories (problem and pathological), those who were 40-64 had the most gambling problems, while those ages 65+ had the fewest gambling problems. Asian Americans/ Pacific Islanders and African Americans endorsed the most gambling problems while Hispanics had fewer gambling problems. Those with the least education (0-11 years) endorsed the most gambling problems while those with the most education (16+ years) had the fewest gambling problems. In previous analyses utilizing the NESARC, the presence of lifetime pathological gambling among individuals with ASPD was 3.3% (Goldstein et al., 2007); for individuals without ASPD, the lifetime pathological gambling prevalence nearly eight times less, 0.42% (Petry, Stinson, & Grant, 2005). Furthermore, in analyses of the NESARC examining a number of substance use disorders, results indicated a national prevalence rate of 3.8% for ASPD, with ASPD being more common among men than women [(OR = 3.04 (2.85-3.25); female: 1.9%; male: 5.5%)] (Trull, et al., 2010). 3.2 Prevalence of problematic behaviors and gambling category 3.2.1. Shoplifting When examined by the presence of shoplifting, low-risk gambling had the lowest prevalence rate (14.2%; AOR = 1.76), followed by at-risk gambling, (23.0%; AOR = 2.63), then problem gambling (29.2%; AOR = 2.79). Pathological gambling had the highest prevalence rate (34.6%; AOR = 3.85). All gambling categories met significance at the 95% level and were associated with shoplifting. 3.2.2. Stealing
Low-risk gambling had the lowest prevalence rate for stealing (11.2%; AOR = 1.70), then at-risk gambling (19.5%; AOR = 2.68), followed by problem gambling (27.4%; AOR = 3.28). Pathological gambling was associated with the highest prevalence rate for stealing (35.1%; AOR = 5.23). All gambling categories met significance at the 95% level and were associated with associated with stealing. 3.2.3. Delinquent debts Based on the incidence of delinquent debts, low-risk gambling had the lowest prevalence rate (5.4%; AOR = 1.69) followed by at-risk and problem gambling, which had comparable prevalence rates, (11.1%; AOR = 2.98), (14.8%; AOR = 3.13), respectively. Pathological gambling had the highest prevalence rate, nearly twice of that of problem gambling, (28.1%; AOR = 8.10). All gambling categories met significance at the 95% level and were associated with delinquent debts. 3.2.4. Scamming for money Low-risk gambling had the lowest prevalence for scamming for money (1.7%; AOR = 1.54), followed by at-risk gambling (4.9%; AOR = 2.79). In the next most severe gambling category, problem gambling, the prevalence was nearly twice as high (9.3%; AOR = 3.97). The prevalence was nearly twice as high moving into the most severe gambling category, pathological gambling (18.4%; AOR = 11.34). All gambling categories met significance at the 95% level and were associated with scamming for money.
3.2.5. Homelessness Regarding the occurrence of homelessness, low-risk gambling had the lowest prevalence (14.5%; AOR = 1.52). At-risk and problem gambling had comparable prevalence rates (23.7%; AOR = 2.37), (29.8%; AOR = 2.64), respectively. Pathological gambling had the highest prevalence rate (37.3%; AOR = 3.83). All gambling categories met significance at the 95% level and were associated with homelessness. 3.2.6 Quitting a job
Low-risk gambling had the lowest prevalence (13.0%; AOR = 1.47). At-risk gambling had a prevalence of 23.6% (AOR = 2.55) followed by problem gambling (31.9%; AOR = 3.18) and pathological gambling (39.5%; AOR = 4.73). All gambling categories met significance at the 95% level and were associated with quitting a job. 3.2.7. Sexual coercion All gambling categories were associated with low prevalence levels and were relatively comparable to one another. Low-risk gambling had the lowest prevalence level (0.1%), followed by atrisk (0.4%), problem (0.9%) and pathological gambling (1.1%). Only at-risk, problem, and pathological levels met significance at the 95% significance level and were associated with sexual coercion in the unadjusted model. Sexual coercion was not associated with any gambling level once socio-demographic variables and ASPD were included in the adjusted model. 3.2.8. Fighting Based on the incidence of fighting, low-risk gambling was associated with the lowest prevalence (13.3%; AOR = 1.81), followed by at-risk gambling (24.1%; AOR = 3.13). Problem and pathological gambling had comparable prevalence rates (32.8%; AOR = 3.88), (38.9%; AOR = 5.22), respectively. All gambling categories met significance at the 95% level and were associated with fighting. 3.2.9. Arrest possibility Low-risk gambling was associated with the lowest prevalence (19.5%; AOR = 1.92). At- risk and problem gambling were associated with comparable prevalence rates (33.5%; AOR = 3.52), (37.3%; AOR = 3.15), respectively. Pathological gambling was associated with the highest prevalence rate (48.6%; AOR = 6.03). All gambling categories met significance at the 95% level and were associated with arrest possibility. 3.2.10. Vehicular endangerment
When examined by the presence of vehicular endangerment, low-risk gambling was associated with the lowest prevalence (28.7%; AOR = 1.88), followed at at-risk gambling (39.9%; AOR = 2.74). At risk gambling was associated with a 47.3% prevalence rate (AOR = 3.21), followed by pathological gambling (56.2%; AOR = 4.96). All gambling categories met significance at the 95% level and were associated with arrest possibility. 4. Discussion In this nationally representative, non-clinical sample of adult men and women, all problematic behaviors included in the analyses, with the exception of sexual coercion, were associated with every level of gambling behavior, including low-risk, at-risk, problem, and pathological gambling in both the unadjusted and adjusted models. With the exception of shoplifting, sexual coercion, and fighting, there was the most significant rise in problematic behaviors (change in AOR’s) in the shift from problem to pathological gambling. (For sexual coercion and fighting, the largest change occurred in the transition from the at-risk to the problem gambling category; for shoplifting, it was from the low-risk to the at-risk gambling category). 4.1. Shoplifting Previous NESARC studies identified an increased risk for pathological gambling among shoplifters (Blanco, et al., 2008). Following bi-directional analyses and the addition of four gambling groups, we found an increased risk for shoplifting amongst all gambling categories. AOR values for shoplifting and the pathological gambling category were higher than the previous study (Blanco, et al., 2008). This may be related related to the defined reference group2 in the respective studies as well as the adjustment for family history of ASPD. Our prevalence rates were also higher compared to a study by Blaszczynski et al. (1989)
4.2. Stealing Data from a Canadian youth prevalence study (N = 9112) indicated youth with gambling problems were five times more likely to acknowledge stealing something valued at $50 or less and 14
times more likely to steal something worth more than $50 when compared to non- gamblers after controlling for sex and grade (Turner et al., 2010). Furthermore, a case study of 105 Swedish pathological gamblers indicated comparable prevalence rates of stealing to our sample (40% vs. 35.1%) (Bergh, et al., 1994). 4.3. Delinquent debts Blaszczynski, et al. (1989) explored pathological gambling and inability to meet financial obligations among treatment-seeking pathological gamblers (n = 77) and Gamblers Anonymous (GA) Members (n = 32). The prevalence rate from that study was more than twice ours (60% vs. 28%). These differences may be related to characteristics of respective study samples (treatment seeking vs. general population) and/or to the generalization of the outcome variable. Our outcome variable included failure to pay off debts, whereas the outcome variable analyzed by Blaszczynski, et al. (1989) included failure to meet financial obligations, a more general category which included debt. Failure to address financial obligations had one of the highest AORs for the pathological category in our sample. Gambling debts and financial issues have been frequently associated with motivation to initiate recovery among treatment-seeking gamblers (Suurvali et al., 2010; Evans and Delfabbro, 2005; Pulford et al., 2009; Grant, et al., 2010). 4.4. Scamming for money In prior studies, general indices of scamming for money largely exceed our prevalence rate for the pathological gambling category of 18.4%. In a sample of 437 German GA members, over 50% admitted to illegal acts in order to gamble, including embezzlement (31.1%), fraud (26.7%), and forgery/tax evasion/ rigging a gambling machine (13.3%) (Meyer and Fabian, 1992). Bergh, & Kühlhorn (1994) found of 105 Swedish pathological gamblers the following possible bases for scamming for money [fraud (72%), embezzlement (33%), and forgery (28%)]. Differences in prevalence rates may be related to help-seeking statuses of the respective samples and/or the ways in which the outcome variable was operationalized. Discrepancies may also be related to cultural beliefs regarding illegal acts and gambling in Swedish and German communities.
4.5. Homelessness Our study was one of the first to examine the explicit risk of homelessness among a sample of gamblers. Among 105 Swedish pathological gamblers, the prevalence rate for loss of home was comparable to that in our sample (30% vs. 37%) (Bergh, & Kühlhorn,1994). A U.S. study [20] examined the prevalence of gambling among a homeless, treatment-seeking population (N = 171). Our bidirectional analyses indicated comparable homelessness prevalence rates for the at-risk, problem, and pathological categories in our study. 4.6. Quitting a job
Bergh, & Kühlhorn (1994) indicated a lower prevalence rate of quitting a job among 105 Swedish pathological gamblers compared to our sample (29% vs. 40%). Furthermore, among a sample of 1520 treatment seeking Australians with gambling problems, men and women had comparable rates of employment. However, men reported more employment problems related to their gambling with this discrepancy persisting post treatment (Crisp, et al. 2000). Yet, studies exploring employment difficulty as it relates to gambling severity are rare, particularly those with sufficient sample sizes to indicate explicit measures of risk. Our study is one of the first to do so and demonstrates a positive association between employment difficulty and gambling pathology. Previous research indicates there may be underlying sex differences in treatment seeking as relates to employment difficulties for gamblers; we encourage further research in this area. 4.7. Sexual coercion Previous studies of sexual coercion and gambling have been explored as relates to intimate partner violence (IPV). However, in studies that have operationalized sexual coercion as part of IPV, indices of gambling problems, including gambling severity have been shown to have no relationship with IPV, consistent with our results. Korman, et al. (2008) utilized data from 248 individuals with gambling problems. Participants reported data regarding gambling severity, IPV gravity, and anger. Gambling severity was not associated with the presence of IPV; however, significant associations were found
between IPV and anger, suggesting anger may be a mediating factor for problem gamblers involved in IPV, including sexual coercion. 4.8. Fighting Data from 1128 American youth indicated measures of severe violence, including involvement with physical altercations or weapon use, were associated with past year gambling (OR = 2.53) (Goldstein et al., 2009). Despite the dichotomous nature of the gambling category in the study (lack of or presence of past year gambling), the range of ORs from our at-risk, problem, and pathological categories are consistent (1.75-7.26) with the previous study. Fighting in the NESARC was also operationalized by intimate partner violence (IPV). The US National Comorbidity Survey Replication (N = 3334) found significant associations between problem and pathological gambling and the perpetration of severe violence, including physical assaults (Afifi, et al., 2010). 4.9. Arrest possibility While our study evaluated arrest possibility and gambling, a U.S.-based telephone study by Momper, et al. (Momper, et al., 2010) (N = 3007) examined arrest history and gambling pathology; arrest history in that study was also associated with at-risk gambling (AOR = 1.56 vs. 3.52) and pathological gambling (AOR = 3.12 vs. 6.03). These combined values indicate that both arrest possibility and history are concerns for several classes of gamblers. A meta-analysis indicated one-third of criminal offenders meet criteria for problem or pathological gambling and once incarcerated, 50% of crime committed by problem or pathological gamblers sustains further gambling activities (Williams, et al., 2005). In response to high levels of crime among gamblers, problem gambling diversion programs may be proposed as an alternative to incarceration on a case-by-case basis. Diversion programs aim to rehabilitate individuals with gambling problems outside of jails and prisons. Individual treatment may include financial restitution, individual and group therapy, including GA. 4.10. Vehicular endangerment Blaszczynski, et al. [12] found the prevalence of reckless driving to be roughly half of that in our sample (26.7%, 56.2%, respectively). Recently, data from 1079 American college students indicated
specific aspects of problem gambling (e.g., gambling debt) were associated with vehicular endangerment, including driving under the influence of alcohol and failure to regularly wear a seatbelt (Stuhldreher et al., 2007). Our results reinforce earlier findings and raise concerns about risky driving practices among individuals who may not meet formal criteria for gambling problems, as all gambling categories in our study were associated with vehicular engenderment. 4.11. Antisocial personality disorder, gambling, and problematic behaviors As seen in the shoplifting study by Blanco, et al. (Blanco, et al., 2008), controlling for ASPD in our sample did not significantly modify the direction or power of the relationship between a given problematic behavior and gambling severity level.
This suggests the association between a problematic behavior
and gambling level may “not be solely driven by features associated with ASPD” (Blanco, et al., 2008). Our results are consistent with Blaszczynski & McConaghy (Blaszczynski and McConaghy, 1994) who queried 152 treatment seeking pathological gamblers and 154 GA members about their offense history, including those specified in this study (e.g. stealing, fighting, reckless driving, failure to meet financial obligations). Offenses were analyzed in relation to their gambling i.e., if one stole to finance their gambling. Analyses indicated offenses were not necessarily driven by an “antisocial personality spectrum but rather emerged in response to gambling induced difficulties. In the majority of cases, it was found that subjects limited themselves to only gambling-related offenses” (Blaszczynski and McConaghy, 1994). Limitations This study is subject to various limitations. The results presented here are generalizable only to the extent that the NESARC is representative of United States population in regards to race, ethnicity, gender, age, military status, etc. The gambling and problematic behavior data presented in this study were derived from participants using a self-report format, which are accurate to the degree that NESARC participants were forthcoming during data collection. Since current pathological gambling was not utilized in the preceding analyses and discussion, our ability to causally link the problematic behaviors considered in this paper and the different gambling levels was also limited. In addition to the
demographic variables and ASPD symptoms considered in our analyses, we encourage further investigation of variables which are suspected to play a role in gambling problems, such as aspects of personality (including levels of constraint and control) (Bagby et al., 2007; Slutske et al., 2005), cooccuring substance use disorders (Abdollahnejad et al., 2014), differential premorbid psychopathology and psyhosocial histories (Blaszczynski and Nower, 2002), and moderating effects of demographic variables, such as age, on clinical outcomes for gambling problems (Granero, et al., 2014). We also encourage further analyses which include a more robust sample size of current gambling symptomology. Despite these limitations, the results presented in this paper provide valuable implications for screening, treatment, and policy regarding varying levels of gambling behaviors and a number of problematic behaviors. 5. Conclusions The results of this nationally representative study indicate all lifetime gambling categories analyzed in this study are at a significantly increased risk for a number of problematic behaviors and social problems. Intuitively, there was a positive relationship between increased lifetime gambling pathology and associated risk for behavioral and social problems. In comparison to non-gamblers, pathological gamblers in particular are subject to financially oriented problems, including failure to address debt and scamming for money. Future analyses focusing on the nature of these financially oriented acts (whether they were enacted in order to enable gambling activities) are encouraged. Several of the other problematic behaviors in this study may have legal implications e.g., theft, arrest possibility, and vehicular endangerment, and are particularly salient for pathological gamblers in comparison to non-gamblers. Regarding ASPD, the persistence of significant associations for all problematic behaviors following adjustment for ASPD in this study suggests aspects of gambling pathology, and not ASPD may underlie the problematic behaviors analyzed in this study. Furthermore, our findings which suggest a possible link between sexual coercion and ASPD should be further investigated in prospective studies. Future analyses focusing on the chronological onset of ASPD as relates to gambling pathology are also recommended. Lastly, the results garnered from our analyses suggest possible screening and treatment opportunities
where gamblers may present for a variety of social services, financial services, and criminal justice settings Contributions JFM designed the study, managed the literature searches, interpreted the data, and wrote the results and discussion sections. GY analyzed the data and wrote the methods and tables sections. MDC managed the literature searches and wrote the introduction. TWF contributed to the interpretation of the data and results. Acknowledgements Gihyun Yoon is supported by a Career Development Award (CDA-2) from the Department of Veterans Affairs. On behalf of all authors, the corresponding author states that there is no conflict of interest. References Abdollahnejad, R., Delfabbro, P., & Denson, L. (2014). Psychiatric co-morbidity in problem and pathological gamblers: investigating the confounding influence of alcohol use disorder. Addictive Behaviors 39(3), 566-572. Afifi, T. O., Brownridge, D. A., MacMillan, H., & Sareen, J. (2010). The relationship of gambling to intimate partner violence and child maltreatment in a nationally representative sample. Journal of Psychiatric Research 44: 331-37. Bagby, R. M., Vachon, D. D., Bulmash, E. L., Toneatto, T., Quilty, L. C., & Costa, P. T. (2007). Pathological gambling and the five-factor model of personality. Personality and Individual Differences 43(4), 873-880. Bergh, C., & Kühlhorn, E. (1994). Social, psychological and physical consequences of pathological gambling in Sweden. Journal of Gambling Studies 10: 275-85. Blanco, C., Alegria, A. A., Petry, N. M., Grant, J., Simpson, H. B., Liu, S. M., Grant, B., & Hasin, D. (2010). Prevalence and Correlates of Firesetting in the US: Results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Journal of Clinical Psychology 71: 1218-25. Blanco, C., Grant, J., Petry, N., Simpson, H., Alegria, A., Liu, S. M., & Hasin, D. (2008). Prevalence and correlates of shoplifting in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). American Journal of Psychiatry 165: 905-13.
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Desai R. A., Potenza M. N. (2008). Gender differences in the associations between past-year gambling problems and psychiatric disorders. Social Psychiatry and Psychiatric Epidemiology 43; 173-83. Evans, L., & Delfabbro, P. H. (2005). Motivators for change and barriers to help-seeking in Australian problem gamblers. Journal of Gambling Studies 21: 133-55. Goldstein, A. L., Walton, M. A., Cunningham, R. M., Resko, S. M., & Duan, L. (2009). Correlates of gambling among youth in an inner-city emergency department. Psychology of Addictive Behaviors 23: 113-21. Granero, R., Penelo, E., Stinchfield, R., Fernandez-Aranda, F., Savvidou, L. G., Fröberg, F., ... & JiménezMurcia, S. (2014). Is pathological gambling moderated by age?. Journal of Gambling Studies 30(2), 475-492. Grant, J. E., Schreiber, L., Odlaug, B. L., & Kim, S. W. (2010). Pathologic gambling and bankruptcy. Comprehensive Psychiatry 51: 115-20. Grant, B.F., Dawson, D.A., Stinson, F.S., Chou, P.S., Kay, W., Pickering, R., 2003. The Alcohol Use Disorder and Associated Disabilities Interview Schedule-IV (AUDADIS- IV): reliability of alcohol consumption, tobacco use, family history of depression and psychiatric diagnostic modules in a general population sample. Drug and Alcohol Dependence 71:7-16. Korman, L. M., Collins, J., Dutton, D., Dhayananthan, B., Littman-Sharp, N., & Skinner, W. (2008). Problem gambling and intimate partner violence. Journal of Gambling Studies 24: 13-23. Meyer, G., & Fabian, T. (1992). Delinquency among pathological gamblers: A causal approach. Journal of Gambling Studies 8: 61-77. Momper, S. L., Delva, J., Grogan-Kaylor, A., Sanchez, N., & Volberg, R. A. (2010). The association of at-risk, problem, and pathological gambling with substance use, depression, and arrest history. Journal of Gambling Issues 7-32.
Morasco B. J., Pietrzak R. H., Blanco C., Grant B. F., Hasin D., Petry N. M. (2006). Health problems and medical utilization associated with gambling disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Psychosomatic Medecine 68:976-84. National Institutes of Health. 2006. Alcohol use and alcohol use disorders in the United States: Main findings from the 2001-2002 National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). National Institute Alcohol Abuse and Alcoholism. 8, v-26. Petry N.M., Stinson F.S., Grant B.F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: results from the National Epidemiologic Survey on Alcohol and Related Conditions. Journal Clinical Psychiatry 66: 564-74. Pilver C. E., Libby D. J., Hoff R. A., Potenza M. N. (2013). Problem gambling severity and the incidence of Axis I psychopathology among older adults in the general population. Psychiatry Research 47: 534-41. Pulford, J., Bellringer, M., Abbott, M., Clarke, D., Hodgins, D., & Williams, J. (2009). Reasons for seeking help for a gambling problem: The experiences of gamblers who have sought specialist assistance and the perceptions of those who have not. Journal of Gambling Stud 25:19-32. Shaffer, H. J., Freed, C. R., & Healea, D. (2002). Gambling disorders among homeless persons with substance use disorders seeking treatment at a community center. Psychiatric Services 53: 1112-17. Slutske, W. S., Caspi, A., Moffitt, T. E., & Poulton, R. (2005). Personality and problem gambling: A prospective study of a birth cohort of young adults. Archives of General Psychiatry 62(7), 769-775. Stuhldreher, W. L., Stuhldreher, T. J., & Forrest, K. Y. (2007). Gambling as an emerging health problem on campus. The Journal of American College Health 56: 75-88. Suurvali, H., Hodgins, D. C., & Cunningham, J. A. (2010). Motivators for resolving or seeking help for gambling problems: A review of the empirical literature. Journal of Gambling Studies 26: 1-33. Toce-Gerstein M., Gerstein D. R., Volberg R. A. (2003). A hierarchy of gambling disorders in the community. Addiction 98; 1661-72. Turner, N., Paglia-Boak, A., Adlaf, E. M., & Mann, R. E. (2010). Ontario youth gambling report: Data from the 2009 Ontario student drug use and health survey. Probllem Gambling Institute of Ontario. Vaughn, M. G., Fu, Q., DeLisi, M., Beaver, K. M., Perron, B. E., Terrell, K., & Howard, M. O. (2009). Correlates of cruelty to animals in the United States: Results from the national epidemiologic survey on alcohol and related conditions. Journal of Psychiatric Research 43: 1213-18. Williams, R. J., Royston, J., & Hagen, B. F. (2005). Gambling and problem gambling within forensic populations A review of the literature. Criminal Justice and Behavior 32: 665-89. Table 1. Demographic characteristics of five gambling groups (n = 42038) 5 Gambling Groups
Non-gambling (n = 30885) Characteristics Gender
Low-risk
At-risk
Problem
Pathological
gambling
gambling
gambling
gambling
(n = 8967)
(n = 1669)
(n = 332)
(n = 185)
Male (n =
12002 (38.9%)
4691 (52.3%)
992 (59.4%)
18009)
18883 (61.1%)
4276 (47.7%)
677 (40.6%)
AgeFemale (years)(n = 24029)(n = 8487) 18-29
6741 (21.8%)
1286 (14.3%)
344 (20.6%)
30-44 (n = 13041)
9807 (31.8%)
2596 (29.0%)
478 (28.6%)
45-64 (n = 12491)
8502 (27.5%)
3225 (36.0%)
574 (34.4%)
65 or older (n = 8019)
5835 (18.9%)
1860 (20.7%)
273 (16.4%)
Race/Ethnicity White (n =
17022 (55.1%)
5705 (63.6%)
949 (56.9%)
23928)
5844 (18.9%)
1614 (18.0%)
383 (22.9%)
Black (n =
461 (1.5%)
163 (1.8%)
41 (2.5%)
7999)
1010 (3.3%)
223 (2.5%)
47 (2.8%)
Native American (n
6548 (21.2%)
1262 (14.1%)
249 (14.9%)
= 679) Asian/P. Education (years) Islander = 1302) 0-11 (n =(n7625)
5918 (19.2%)
1279 (14.3%)
308 (18.5%)
Hispanic (n = 8130) 12 (n = 12231)
8812 (28.5%)
2741 (30.6%)
524 (31.4%)
13-15 (n = 12368)
8777 (28.4%)
2902 (32.4%)
537 (32.2%)
16 or more (n = 9814)
7378 (23.9%)
2045 (22.8%)
300 (18.0%)
Personal income ($) 0-19999 (n = 20578) 20000-34999 (n = 9727) 35000-59999 (n = 7523) Marital status 60000 or more (n = 4210) Married/Cohab (n =21669)
15937 (51.6%)
3631 (40.5%)
751 (45.0%)
6937 (22.5%)
2269 (25.3%)
400 (24.0%)
5121 (16.6%)
1993 (22.2%)
322 (19.3%)
2890 (9.4%)
1074 (12.0%)
196 (11.7%)
15727 (50.9%)
4902 (54.7%)
821 (49.2%)
4675 (15.1%)
1557 (17.4%)
291 (17.4%)
Divorced/Sep (n =
7327 (23.7%)
1656 (18.5%)
435 (26.1%)
6644) Never
3156 (10.2%)
852 (9.5%)
122 (7.3%)
married (n = 9561) Widowed = Table 2.(nPrevalence
210 (63.3%) 122 (36.7%) 82 (24.7%) 100 (30.1%) 114 (34.3%)
< 0.001
187.2 12
< 0.001
386.3 12
< 0.001
197.0 12
< 0.001
76 (41.1%) 15 (8.1%)
63 (34.1%) 8 (4.3%) 24 (13.0%)
44 (23.8%) 56 (30.3%) 55 (29.7%) 30 (16.2%)
61166 (18.4%) (50.0%)
93 (50.3%)
81 (24.4%)
30 (16.2%)
40 (21.6%) 22 (11.9%)
28 144 (8.4%) (43.4%)
75 (40.5%)
72 (21.7%)
47 (25.4%)
96 (28.9%)
393.9 16
60 (32.4%)
4 (2.2%)
57 (17.2%)
<0.001
34 (18.4%)
95 (28.6%)
97 (29.2%)
451.5 12
71 (38.4%)
86 (46.5%)
14 (4.2%) 76 47 (22.9%) (14.2%) 98 (29.5%)
P < 0.001
114 (61.6%)
36166 (10.8%) (50.0%)
10 (3.0%)
χ2 df 799.3 4
49 (26.5%) 14 (7.6%)
20 rates and adjusted odds ratios of lifetime problematic behaviors among five (6.0%)
4164)
gambling groups (n = 42038) 5 Gambling Groups
Problematic behaviors
Non-
Low-risk
At-risk
Problem
Pathological
gambling
gambling
gambling
gambling
gambling
(n = 30885)
(n = 8967)
(n = 1669)
(n = 332)
(n = 185)
Shoplifting Prevalence OR
2595 (8.4%)
1273 (14.2%)
384 (23.0%)
97 (29.2%)
64 (34.6%)
(95% CI) AOR
1 (Reference)
1.80 (1.68-1.94)
3.26 (2.89-3.68)
4.50 (3.54-5.72)
5.77 (4.25-7.83)
(95% CI)
1 (Reference)
1.76 (1.63-1.90)
2.63 (2.30-3.02)
2.79 (2.10-3.70)
3.85 (2.68-5.53)
1989 (6.4%)
1007 (11.2%)
326 (19.5%)
91 (27.4%)
65 (35.1%)
(95% CI) AOR
1 (Reference)
1.84 (1.70-1.99)
3.53 (3.10-4.01)
5.49 (4.29-7.01)
7.87 (5.80-10.68)
(95% CI)
1 (Reference)
1.70 (1.56-1.86)
2.68 (2.32-3.10)
3.28 (2.45-4.39)
5.23 (3.65-7.50)
Prevalence OR
981 (3.2%)
487 (5.4%)
186 (11.1%)
49 (14.8%)
52 (28.1%)
(95% CI) AORa
1 (Reference)
1.75 (1.57-1.96)
3.82 (3.24-4.51)
5.28 (3.87-7.20)
11.92 (8.60-16.53)
(95% CI)
1 (Reference)
1.69 (1.51-1.90)
2.98 (2.50-3.56)
3.13 (2.22-4.39)
8.10 (5.62-11.67)
323 (1.0%)
152 (1.7%)
81 (4.9%)
31 (9.3%)
34 (18.4%)
(95% CI) AOR
1 (Reference)
1.63 (1.34-1.98)
4.83 (3.76-6.19)
9.75 (6.63-14.33)
21.31 (14.46-31.40)
(95% CI)
1 (Reference)
1.54 (1.25-1.89)
2.79 (2.11-3.70)
3.97 (2.54-6.20)
11.34 (6.99-18.40)
Prevalence OR
3140 (10.2%)
1297 (14.5%)
396 (23.7%)
99 (29.8%)
69 (37.3%)
(95% CI) AORa
1 (Reference)
1.49 (1.39-1.60)
2.75 (2.44-3.10)
3.75 (2.96-4.76)
5.26 (3.89-7.10)
(95% CI)
1 (Reference)
1.52 (1.41-1.64)
2.37 (2.09-2.69)
2.64 (2.03-3.42)
3.83 (2.75-5.32)
2888 (9.4%)
1169 (13.0%)
394 (23.6%)
106 (31.9%)
73 (39.5%)
(95% CI) AOR
1 (Reference)
1.45 (1.35-1.56)
3.00 (2.66-3.38)
4.55 (3.60-5.75)
6.32 (4.69-8.51)
(95% CI)
1 (Reference)
1.47 (1.36-1.58)
2.55 (2.25-2.90)
3.18 (2.47-4.11)
4.73 (3.42-6.55)
44 (0.1%)
7 (0.1%)
7 (0.4%)
3 (0.9%)
2 (1.1%)
(95% CI) AOR
1 (Reference)
0.55 (0.25-1.22)
2.95 (1.33-6.56)
6.39 (1.98-20.69)
7.66 (1.84-31.84)
(95% CI)
1 (Reference)
0.45 (0.20-1.01)
1.51 (0.66-3.49)
2.34 (0.69-7.94)
2.52 (0.58-10.97)
a
Stealing Prevalence OR a
Not paying off debts
Scam for money Prevalence OR a
Homelessness
Quit a job Prevalence OR a
Sexual coercion Prevalence OR a
Fighting Prevalence OR
2493 (8.1%)
1197 (13.3%)
402 (24.1%)
109 (32.8%)
72 (38.9%)
(95% CI) AORa
1 (Reference)
1.75 (1.63-1.89)
3.61 (3.21-4.07)
5.57 (4.41-7.03)
7.26 (5.39-9.78)
(95% CI)
1 (Reference)
1.81 (1.67-1.96)
3.13 (2.74-3.58)
3.88 (2.96-5.08)
5.22 (3.69-7.37)
3272 (10.6%)
1747 (19.5%)
559 (33.5%)
124 (37.3%)
90 (48.6%)
(95% CI) AOR
1 (Reference)
2.04 (1.92-2.18)
4.25 (3.82-4.73)
5.03 (4.02-6.30)
8.00 (5.98-10.69)
(95% CI)
1 (Reference)
1.92 (1.79-2.06)
3.52 (3.12-3.98)
3.15 (2.42-4.12)
6.03 (4.31-8.45)
4939 (16.0%)
2572 (28.7%)
666 (39.9%)
157 (47.3%)
104 (56.2%)
(95% CI) AOR
1 (Reference)
2.11 (2.00-2.23)
3.49 (3.15-3.87)
4.71 (3.79-5.86)
6.75 (5.04-9.03)
(95% CI)
1 (Reference)
1.88 (1.77-1.99)
2.74 (2.45-3.07)
3.21 (2.52-4.09)
4.96 (3.60-6.84)
Arrest possibility Prevalence OR a
Vehicular endangerment Prevalence OR a
Abbreviations: OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval a
Adjusted for demographic variables (gender, age, race, education, personal income, and marital status) and
antisocial personality disorder
Highlights All gambling severity categories are at increased risk for various problematic behaviors. Pathological gamblers (compared to non-gamblers) especially are subject to financially oriented problems. Several of the examined problematic behaviors may have legal implications. Aspects of gambling pathology, not ASPD, may underlie problematic behaviors.
PII: DOI: Reference:
S0165-1781(15)00542-9 http://dx.doi.org/10.1016/j.psychres.2015.07.082 PSY9134
To appear in: Psychiatry Research Received date: 15 October 2014 Revised date: 7 July 2015 Accepted date: 22 July 2015 Cite this article as: Jacquelene F. Moghaddam, Gihyun Yoon, Michael D. Campos and Timothy W. Fong, Social and behavioral problems among five gambling severity groups, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2015.07.082 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Social and behavioral problems among five gambling severity groups Jacquelene F. Moghaddama*, Gihyun Yoonb,c, Michael D. Camposa, Timothy W. Fonga a
University of California Los Angeles (UCLA) Gambling Studies Program, UCLA Department of Psychiatry and Biobehavioral Sciences, 760 Westwood Plaza, Suite 38-153. Los Angeles, CA, USA. 90095-1759. b
Minneapolis VA Health Care System. One Veterans Drive. Minneapolis, MN, USA. 55417.
c
Department of Psychiatry, University of Minnesota. F282/2A West. 2450 Riverside Avenue South Minneapolis, MN, USA. 55454. *Corresponding author. Phone: (714) 872-1847. Fax: (310) 825-4845. Email: [email protected]
Abstract Gambling has been associated with various social and behavioral problems, but previous analyses have been limited by sample bias regarding gambling symptom severity range and the role of antisocial personality disorder (ASPD). This study utilized a nationally representative data set and examined various characteristics of behavioral problems and ASPD among five gambling severity groups. Participants were 42,038 individuals who took part in the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) and provided information on social and behavioral problems, ASPD, and gambling. Using DSM-IV criteria, we derived five gambling groups from the total sample: non-gambling, low-risk, at-risk, problem, and pathological gambling. Associations between all problematic behaviors and nearly every gambling severity level were significant prior to adjustment for sociodemographic variables and ASPD. Following the adjustment, all significant associations persisted, with the exception of sexual coercion. In the adjusted model, the financially oriented behaviors had the strongest associations with gambling. All gambling severity levels were associated with an increased risk for a number of problematic behaviors and social problems in comparison to non-gamblers. Further examination of gambling problems in financial and criminal justice settings is recommended.
Keywords: Pathological gambling; problem gambling; social problem; behavioral problem; antisocial personality disorder; financial problem; criminal justice I. Introduction Previous research utilizing the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) has found an increased risk for pathological gambling1 among individuals reporting problematic behaviors such as fire setting, shoplifting, and cruelty to animals [Blanco, et al., 2010;
1
As defined by the DSM-IV.
Blanco, et al, 2008; Vaughn et al., 2009]. Although these problematic behaviors were strongly associated with other antisocial behaviors and with a diagnosis of antisocial personality disorder (ASPD) itself, controlling for ASPD did not eliminate the relationship between these behaviors and pathological gambling in previous analyses. The existing NESARC studies have considered select problematic behaviors among individuals meeting three or more DSM-IV criteria for gambling, but have not considered the full range of gambling behavior. Furthermore, previous studies using the NESARC have not simultaneously examined multiple domains of problematic behaviors, which may be interrelated as relates to gambling pathology. While former NESARC studies focusing on gambling and problematic behaviors are telling of the difficulties which individuals with gambling problems may face, previous research in this area has particular caveats: (1) prior analyses have not focused on the occurrence of problematic behaviors among samples comprised entirely of gamblers; (2) previous studies which have examined gambling and problematic behaviors have not necessarily controlled for antisocial personality disorder, and therefore have been unable to establish the role of gambling in these behaviors specifically; (3) sample populations from which NESARC analyses have been derived have rarely stratified gambling severity categories beyond pathological gambling. The study presented here attempts to address the limitations of the current literature; it is derived from a large, nationally representative data set and examines social and behavioral problems among gamblers. The current study divides NESARC respondents into non-gamblers, low-risk, at-risk, problem, and pathological gamblers based upon DSM-IV criteria, and examines the relative risk for 10 problematic behaviors across each of these five severity groups. Such an approach may have implications for the screening, referral, and treatment of gambling problems in criminal justice, credit counseling, social service, and other relevant settings. 2. Methods 2.1 NESARC survey
The NESARC is a nationally representative survey directed by the National Institute on Alcohol Abuse and Alcoholism. The “Wave 1 NESARC” was completed from 2001-2002 (Petry et al., 2005) (hereafter referred to as “NESARC”). The NESARC is derived from 2000/2001 Census data and captures the civilian public of all 50 United States and Washington D.C., including those who were residing in a number of community living settings. Following the selection of a housing unit for participation, one individual per residence was randomly selected to complete an in- person interview. The in-person completion of the NESARC permitted trained interviewers to use a reliable instrument and make formal diagnoses, as opposed to survey approaches (see “Measures”) (Grant, et al., 2005). In total, 43,093 individuals aged 18 years and older participated in the NESARC for a response rate of 81 percent (NIH, 2006). 2.2 Measures 2.2.1. Problematic behaviors. The presence of 10 lifetime problematic behaviors were assessed in NESARC participants with the following questions: (1) shoplifting: “In your entire life, did you ever shoplift?”; (2) stealing: “In your entire life, did you ever steal anything from someone or someplace when no one was around?”; (3) failure to address debt: “In your entire life, did you ever fail to pay off your debts - like moving to avoid paying rent, not making payments on a loan or mortgage, failing to make alimony or child support payments or filing for bankruptcy?”; (4) scamming for money: “In your entire life, did you ever scam or con someone for money, to avoid responsibility or just for fun?”; (5) homelessness: “In your entire life, did you ever have a time that lasted at least one month when you had no regular place to live – like living on the street or in a car?” or “In your entire life, did you ever have a time that lasted at least one month when you lived with friends, acquaintances or relatives because you didn’t really have your own place to live?”; (6) quitting a job: “In your entire life, did you ever more than once quit a job without knowing where you would find another one?”; (7) sexual coercion: “In your entire life, did you ever force someone to have sex with you against their will?”; (8) fighting: “In your entire life, did you ever get into a lot of fights that you started?” or “In your entire life, did you ever get into a
fight that came to swapping blows with someone like a husband, wife, girlfriend or boyfriend?” or “In your entire life, did you ever use a weapon like a stick, knife, or gun in a fight?”; (9) arrest possibility: “In your entire life, did you ever do anything that you could have been arrested for, regardless of whether or not you were caught?”; and (10) vehicular endangerment: “In your entire life, did you ever do things that could have easily hurt you or someone else – like speeding or driving after having too much to drink?” or “In your entire life, did you ever get more than three traffic tickets for reckless or careless driving, speeding, or causing an accident?” or “In your entire life, did you ever have your driver’s license suspended or revoked for moving violations?” 2.2.2. Gambling. Gambling was considered casino gambling, playing cards, sports betting, purchasing lottery tickets, and placing wagers on the stock market (NIH, 2006). The NESARC employed a separate section of the Alcohol Use Disorder and Associated Disabilities Interview Schedule–DSM-IV Version (AUDADIS-IV) to assess lifetime pathological gambling; current pathological gambling was not evaluated because of low prevalence and power in statistical analyses (Petry et al., 2005) . The AUDADIS-IV has demonstrated high internal consistent reliability for lifetime pathological gambling (alpha = 0.80) (Petry et al., 2005) and reliability (kappa = 0.76) (Grant, et al., 2003). Consistent with previous studies utilizing the number of DSM-IV criteria to assess gambling severity (Morasco, et al., 2006; Toce-Gerstein, et al., 2003; Desai and Potenza, 2008; Pilver, et al., 2013), a lifetime pathological gambling diagnosis required at least five of the 10 DSM-IV criteria. In order to capture the full range of gambling-related problems, our sample was classified into five gambling severity groups: (1) non-gambling (never gambled 5 or more times in any one year in lifetime); (2) low-risk gambling (gambled 5 or more times in any one year, but had no DSM-IV criteria for pathological gambling); (3) at-risk gambling (one or two DSM-IV criteria for pathological gambling); (4) problem gambling (three or four DSM-IV criteria for pathological gambling); and (5) pathological gambling (5 to 10 DSM-IV criteria for gambling). 2.2.3. Antisocial Personality Disorder
In the NESARC, ASPD criteria was derived from DSM-IV guidelines and was concerned with the one’s long term functioning (not accounting for depressed, manic, or anxious states, periods of heavy drinking, drug use, or episodes of illicit substance withdrawals) (Grant, Stinson, Dawson, et al., 2004). In all, 30 separate symptoms operationalized the presence of the following behaviors during adulthood: refusal to abide by social norms, engaging in fraud, impulsivity, exhibiting a threatening disposition, lack of concern for others’ safety, and social recklessness (Howard, et al., 2009). 2.3 Statistical analyses We utilized cross tabulations to compare the prevalence rates of the demographic indicators (gender, age, race/ethnicity, education, personal income, and marital status), stratified by the five gambling groups (Table 1). Then the p-values from chi-square analysis were used to evaluate the relationship between the demographic indicators and the five gambling groups (Table 1). We employed cross-tabulations to compare the prevalence rates of the five gambling groups for the occurrence of 10 problematic behaviors (Table 2). The 95% confidence intervals (CI) and odds ratios (OR) from logistic regression analysis were applied to evaluate the relationship between the respective gambling groups and presence of problematic behaviors. We referred to the adjusted odds ratios (AOR) and 95% confidence intervals (CI) from logistic regression analysis to further evaluate this relationship following adjustment for notable demographic variables (gender, age, race, education, personal income, and marital status) and antisocial personality disorder (Table 2). Statistical analyses were implemented using SPSS Version 19 (IBM SPSS; Armonk, New York). 3. Results 3.1 Sample characteristics Please refer to Table 1 for specific information regarding the demographic background of this sample2. The five gambling groups differ in six demographic variables (gender, age, race/ethnicity, education, personal income, and marital status). Chi square analysis indicated significant differences in 2
Further analyses of the demographic indicators in the NESARC and gambling behaviors have been published in previous behaviors, e.g., [9,11,7]. We modeled the demographic analyses of this sample after the aforementioned publications. Recommendations for secondary analyses of these five gambling groups and demographic indicators are detailed in the discussion section.
the five gambling groups as relates to all considered demographic indicators (Table 1), including gender (X2 (4) = 799, p <.001), age, (X2 (12) = 452, p <.001), race/ethnicity (X2 (16) = 394, p <.001), education (X2 (12) = 187, p <.001), personal income (X2 (12) = 386, p <.001), and marital status (X2 (12) = 197, p <.001). Regarding gender, men had at least twice as many gambling problems across all symptomatic categories (at-risk, problem, and pathological gambling) compared to women. In terms of age and the two most severe gambling categories (problem and pathological), those who were 40-64 had the most gambling problems, while those ages 65+ had the fewest gambling problems. Asian Americans/ Pacific Islanders and African Americans endorsed the most gambling problems while Hispanics had fewer gambling problems. Those with the least education (0-11 years) endorsed the most gambling problems while those with the most education (16+ years) had the fewest gambling problems. In previous analyses utilizing the NESARC, the presence of lifetime pathological gambling among individuals with ASPD was 3.3% (Goldstein et al., 2007); for individuals without ASPD, the lifetime pathological gambling prevalence nearly eight times less, 0.42% (Petry, Stinson, & Grant, 2005). Furthermore, in analyses of the NESARC examining a number of substance use disorders, results indicated a national prevalence rate of 3.8% for ASPD, with ASPD being more common among men than women [(OR = 3.04 (2.85-3.25); female: 1.9%; male: 5.5%)] (Trull, et al., 2010). 3.2 Prevalence of problematic behaviors and gambling category 3.2.1. Shoplifting When examined by the presence of shoplifting, low-risk gambling had the lowest prevalence rate (14.2%; AOR = 1.76), followed by at-risk gambling, (23.0%; AOR = 2.63), then problem gambling (29.2%; AOR = 2.79). Pathological gambling had the highest prevalence rate (34.6%; AOR = 3.85). All gambling categories met significance at the 95% level and were associated with shoplifting. 3.2.2. Stealing
Low-risk gambling had the lowest prevalence rate for stealing (11.2%; AOR = 1.70), then at-risk gambling (19.5%; AOR = 2.68), followed by problem gambling (27.4%; AOR = 3.28). Pathological gambling was associated with the highest prevalence rate for stealing (35.1%; AOR = 5.23). All gambling categories met significance at the 95% level and were associated with associated with stealing. 3.2.3. Delinquent debts Based on the incidence of delinquent debts, low-risk gambling had the lowest prevalence rate (5.4%; AOR = 1.69) followed by at-risk and problem gambling, which had comparable prevalence rates, (11.1%; AOR = 2.98), (14.8%; AOR = 3.13), respectively. Pathological gambling had the highest prevalence rate, nearly twice of that of problem gambling, (28.1%; AOR = 8.10). All gambling categories met significance at the 95% level and were associated with delinquent debts. 3.2.4. Scamming for money Low-risk gambling had the lowest prevalence for scamming for money (1.7%; AOR = 1.54), followed by at-risk gambling (4.9%; AOR = 2.79). In the next most severe gambling category, problem gambling, the prevalence was nearly twice as high (9.3%; AOR = 3.97). The prevalence was nearly twice as high moving into the most severe gambling category, pathological gambling (18.4%; AOR = 11.34). All gambling categories met significance at the 95% level and were associated with scamming for money.
3.2.5. Homelessness Regarding the occurrence of homelessness, low-risk gambling had the lowest prevalence (14.5%; AOR = 1.52). At-risk and problem gambling had comparable prevalence rates (23.7%; AOR = 2.37), (29.8%; AOR = 2.64), respectively. Pathological gambling had the highest prevalence rate (37.3%; AOR = 3.83). All gambling categories met significance at the 95% level and were associated with homelessness. 3.2.6 Quitting a job
Low-risk gambling had the lowest prevalence (13.0%; AOR = 1.47). At-risk gambling had a prevalence of 23.6% (AOR = 2.55) followed by problem gambling (31.9%; AOR = 3.18) and pathological gambling (39.5%; AOR = 4.73). All gambling categories met significance at the 95% level and were associated with quitting a job. 3.2.7. Sexual coercion All gambling categories were associated with low prevalence levels and were relatively comparable to one another. Low-risk gambling had the lowest prevalence level (0.1%), followed by atrisk (0.4%), problem (0.9%) and pathological gambling (1.1%). Only at-risk, problem, and pathological levels met significance at the 95% significance level and were associated with sexual coercion in the unadjusted model. Sexual coercion was not associated with any gambling level once socio-demographic variables and ASPD were included in the adjusted model. 3.2.8. Fighting Based on the incidence of fighting, low-risk gambling was associated with the lowest prevalence (13.3%; AOR = 1.81), followed by at-risk gambling (24.1%; AOR = 3.13). Problem and pathological gambling had comparable prevalence rates (32.8%; AOR = 3.88), (38.9%; AOR = 5.22), respectively. All gambling categories met significance at the 95% level and were associated with fighting. 3.2.9. Arrest possibility Low-risk gambling was associated with the lowest prevalence (19.5%; AOR = 1.92). At- risk and problem gambling were associated with comparable prevalence rates (33.5%; AOR = 3.52), (37.3%; AOR = 3.15), respectively. Pathological gambling was associated with the highest prevalence rate (48.6%; AOR = 6.03). All gambling categories met significance at the 95% level and were associated with arrest possibility. 3.2.10. Vehicular endangerment
When examined by the presence of vehicular endangerment, low-risk gambling was associated with the lowest prevalence (28.7%; AOR = 1.88), followed at at-risk gambling (39.9%; AOR = 2.74). At risk gambling was associated with a 47.3% prevalence rate (AOR = 3.21), followed by pathological gambling (56.2%; AOR = 4.96). All gambling categories met significance at the 95% level and were associated with arrest possibility. 4. Discussion In this nationally representative, non-clinical sample of adult men and women, all problematic behaviors included in the analyses, with the exception of sexual coercion, were associated with every level of gambling behavior, including low-risk, at-risk, problem, and pathological gambling in both the unadjusted and adjusted models. With the exception of shoplifting, sexual coercion, and fighting, there was the most significant rise in problematic behaviors (change in AOR’s) in the shift from problem to pathological gambling. (For sexual coercion and fighting, the largest change occurred in the transition from the at-risk to the problem gambling category; for shoplifting, it was from the low-risk to the at-risk gambling category). 4.1. Shoplifting Previous NESARC studies identified an increased risk for pathological gambling among shoplifters (Blanco, et al., 2008). Following bi-directional analyses and the addition of four gambling groups, we found an increased risk for shoplifting amongst all gambling categories. AOR values for shoplifting and the pathological gambling category were higher than the previous study (Blanco, et al., 2008). This may be related related to the defined reference group2 in the respective studies as well as the adjustment for family history of ASPD. Our prevalence rates were also higher compared to a study by Blaszczynski et al. (1989)
4.2. Stealing Data from a Canadian youth prevalence study (N = 9112) indicated youth with gambling problems were five times more likely to acknowledge stealing something valued at $50 or less and 14
times more likely to steal something worth more than $50 when compared to non- gamblers after controlling for sex and grade (Turner et al., 2010). Furthermore, a case study of 105 Swedish pathological gamblers indicated comparable prevalence rates of stealing to our sample (40% vs. 35.1%) (Bergh, et al., 1994). 4.3. Delinquent debts Blaszczynski, et al. (1989) explored pathological gambling and inability to meet financial obligations among treatment-seeking pathological gamblers (n = 77) and Gamblers Anonymous (GA) Members (n = 32). The prevalence rate from that study was more than twice ours (60% vs. 28%). These differences may be related to characteristics of respective study samples (treatment seeking vs. general population) and/or to the generalization of the outcome variable. Our outcome variable included failure to pay off debts, whereas the outcome variable analyzed by Blaszczynski, et al. (1989) included failure to meet financial obligations, a more general category which included debt. Failure to address financial obligations had one of the highest AORs for the pathological category in our sample. Gambling debts and financial issues have been frequently associated with motivation to initiate recovery among treatment-seeking gamblers (Suurvali et al., 2010; Evans and Delfabbro, 2005; Pulford et al., 2009; Grant, et al., 2010). 4.4. Scamming for money In prior studies, general indices of scamming for money largely exceed our prevalence rate for the pathological gambling category of 18.4%. In a sample of 437 German GA members, over 50% admitted to illegal acts in order to gamble, including embezzlement (31.1%), fraud (26.7%), and forgery/tax evasion/ rigging a gambling machine (13.3%) (Meyer and Fabian, 1992). Bergh, & Kühlhorn (1994) found of 105 Swedish pathological gamblers the following possible bases for scamming for money [fraud (72%), embezzlement (33%), and forgery (28%)]. Differences in prevalence rates may be related to help-seeking statuses of the respective samples and/or the ways in which the outcome variable was operationalized. Discrepancies may also be related to cultural beliefs regarding illegal acts and gambling in Swedish and German communities.
4.5. Homelessness Our study was one of the first to examine the explicit risk of homelessness among a sample of gamblers. Among 105 Swedish pathological gamblers, the prevalence rate for loss of home was comparable to that in our sample (30% vs. 37%) (Bergh, & Kühlhorn,1994). A U.S. study [20] examined the prevalence of gambling among a homeless, treatment-seeking population (N = 171). Our bidirectional analyses indicated comparable homelessness prevalence rates for the at-risk, problem, and pathological categories in our study. 4.6. Quitting a job
Bergh, & Kühlhorn (1994) indicated a lower prevalence rate of quitting a job among 105 Swedish pathological gamblers compared to our sample (29% vs. 40%). Furthermore, among a sample of 1520 treatment seeking Australians with gambling problems, men and women had comparable rates of employment. However, men reported more employment problems related to their gambling with this discrepancy persisting post treatment (Crisp, et al. 2000). Yet, studies exploring employment difficulty as it relates to gambling severity are rare, particularly those with sufficient sample sizes to indicate explicit measures of risk. Our study is one of the first to do so and demonstrates a positive association between employment difficulty and gambling pathology. Previous research indicates there may be underlying sex differences in treatment seeking as relates to employment difficulties for gamblers; we encourage further research in this area. 4.7. Sexual coercion Previous studies of sexual coercion and gambling have been explored as relates to intimate partner violence (IPV). However, in studies that have operationalized sexual coercion as part of IPV, indices of gambling problems, including gambling severity have been shown to have no relationship with IPV, consistent with our results. Korman, et al. (2008) utilized data from 248 individuals with gambling problems. Participants reported data regarding gambling severity, IPV gravity, and anger. Gambling severity was not associated with the presence of IPV; however, significant associations were found
between IPV and anger, suggesting anger may be a mediating factor for problem gamblers involved in IPV, including sexual coercion. 4.8. Fighting Data from 1128 American youth indicated measures of severe violence, including involvement with physical altercations or weapon use, were associated with past year gambling (OR = 2.53) (Goldstein et al., 2009). Despite the dichotomous nature of the gambling category in the study (lack of or presence of past year gambling), the range of ORs from our at-risk, problem, and pathological categories are consistent (1.75-7.26) with the previous study. Fighting in the NESARC was also operationalized by intimate partner violence (IPV). The US National Comorbidity Survey Replication (N = 3334) found significant associations between problem and pathological gambling and the perpetration of severe violence, including physical assaults (Afifi, et al., 2010). 4.9. Arrest possibility While our study evaluated arrest possibility and gambling, a U.S.-based telephone study by Momper, et al. (Momper, et al., 2010) (N = 3007) examined arrest history and gambling pathology; arrest history in that study was also associated with at-risk gambling (AOR = 1.56 vs. 3.52) and pathological gambling (AOR = 3.12 vs. 6.03). These combined values indicate that both arrest possibility and history are concerns for several classes of gamblers. A meta-analysis indicated one-third of criminal offenders meet criteria for problem or pathological gambling and once incarcerated, 50% of crime committed by problem or pathological gamblers sustains further gambling activities (Williams, et al., 2005). In response to high levels of crime among gamblers, problem gambling diversion programs may be proposed as an alternative to incarceration on a case-by-case basis. Diversion programs aim to rehabilitate individuals with gambling problems outside of jails and prisons. Individual treatment may include financial restitution, individual and group therapy, including GA. 4.10. Vehicular endangerment Blaszczynski, et al. [12] found the prevalence of reckless driving to be roughly half of that in our sample (26.7%, 56.2%, respectively). Recently, data from 1079 American college students indicated
specific aspects of problem gambling (e.g., gambling debt) were associated with vehicular endangerment, including driving under the influence of alcohol and failure to regularly wear a seatbelt (Stuhldreher et al., 2007). Our results reinforce earlier findings and raise concerns about risky driving practices among individuals who may not meet formal criteria for gambling problems, as all gambling categories in our study were associated with vehicular engenderment. 4.11. Antisocial personality disorder, gambling, and problematic behaviors As seen in the shoplifting study by Blanco, et al. (Blanco, et al., 2008), controlling for ASPD in our sample did not significantly modify the direction or power of the relationship between a given problematic behavior and gambling severity level.
This suggests the association between a problematic behavior
and gambling level may “not be solely driven by features associated with ASPD” (Blanco, et al., 2008). Our results are consistent with Blaszczynski & McConaghy (Blaszczynski and McConaghy, 1994) who queried 152 treatment seeking pathological gamblers and 154 GA members about their offense history, including those specified in this study (e.g. stealing, fighting, reckless driving, failure to meet financial obligations). Offenses were analyzed in relation to their gambling i.e., if one stole to finance their gambling. Analyses indicated offenses were not necessarily driven by an “antisocial personality spectrum but rather emerged in response to gambling induced difficulties. In the majority of cases, it was found that subjects limited themselves to only gambling-related offenses” (Blaszczynski and McConaghy, 1994). Limitations This study is subject to various limitations. The results presented here are generalizable only to the extent that the NESARC is representative of United States population in regards to race, ethnicity, gender, age, military status, etc. The gambling and problematic behavior data presented in this study were derived from participants using a self-report format, which are accurate to the degree that NESARC participants were forthcoming during data collection. Since current pathological gambling was not utilized in the preceding analyses and discussion, our ability to causally link the problematic behaviors considered in this paper and the different gambling levels was also limited. In addition to the
demographic variables and ASPD symptoms considered in our analyses, we encourage further investigation of variables which are suspected to play a role in gambling problems, such as aspects of personality (including levels of constraint and control) (Bagby et al., 2007; Slutske et al., 2005), cooccuring substance use disorders (Abdollahnejad et al., 2014), differential premorbid psychopathology and psyhosocial histories (Blaszczynski and Nower, 2002), and moderating effects of demographic variables, such as age, on clinical outcomes for gambling problems (Granero, et al., 2014). We also encourage further analyses which include a more robust sample size of current gambling symptomology. Despite these limitations, the results presented in this paper provide valuable implications for screening, treatment, and policy regarding varying levels of gambling behaviors and a number of problematic behaviors. 5. Conclusions The results of this nationally representative study indicate all lifetime gambling categories analyzed in this study are at a significantly increased risk for a number of problematic behaviors and social problems. Intuitively, there was a positive relationship between increased lifetime gambling pathology and associated risk for behavioral and social problems. In comparison to non-gamblers, pathological gamblers in particular are subject to financially oriented problems, including failure to address debt and scamming for money. Future analyses focusing on the nature of these financially oriented acts (whether they were enacted in order to enable gambling activities) are encouraged. Several of the other problematic behaviors in this study may have legal implications e.g., theft, arrest possibility, and vehicular endangerment, and are particularly salient for pathological gamblers in comparison to non-gamblers. Regarding ASPD, the persistence of significant associations for all problematic behaviors following adjustment for ASPD in this study suggests aspects of gambling pathology, and not ASPD may underlie the problematic behaviors analyzed in this study. Furthermore, our findings which suggest a possible link between sexual coercion and ASPD should be further investigated in prospective studies. Future analyses focusing on the chronological onset of ASPD as relates to gambling pathology are also recommended. Lastly, the results garnered from our analyses suggest possible screening and treatment opportunities
where gamblers may present for a variety of social services, financial services, and criminal justice settings Contributions JFM designed the study, managed the literature searches, interpreted the data, and wrote the results and discussion sections. GY analyzed the data and wrote the methods and tables sections. MDC managed the literature searches and wrote the introduction. TWF contributed to the interpretation of the data and results. Acknowledgements Gihyun Yoon is supported by a Career Development Award (CDA-2) from the Department of Veterans Affairs. On behalf of all authors, the corresponding author states that there is no conflict of interest. References Abdollahnejad, R., Delfabbro, P., & Denson, L. (2014). Psychiatric co-morbidity in problem and pathological gamblers: investigating the confounding influence of alcohol use disorder. Addictive Behaviors 39(3), 566-572. Afifi, T. O., Brownridge, D. A., MacMillan, H., & Sareen, J. (2010). The relationship of gambling to intimate partner violence and child maltreatment in a nationally representative sample. Journal of Psychiatric Research 44: 331-37. Bagby, R. M., Vachon, D. D., Bulmash, E. L., Toneatto, T., Quilty, L. C., & Costa, P. T. (2007). Pathological gambling and the five-factor model of personality. Personality and Individual Differences 43(4), 873-880. Bergh, C., & Kühlhorn, E. (1994). Social, psychological and physical consequences of pathological gambling in Sweden. Journal of Gambling Studies 10: 275-85. Blanco, C., Alegria, A. A., Petry, N. M., Grant, J., Simpson, H. B., Liu, S. M., Grant, B., & Hasin, D. (2010). Prevalence and Correlates of Firesetting in the US: Results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Journal of Clinical Psychology 71: 1218-25. Blanco, C., Grant, J., Petry, N., Simpson, H., Alegria, A., Liu, S. M., & Hasin, D. (2008). Prevalence and correlates of shoplifting in the United States: results from the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). American Journal of Psychiatry 165: 905-13.
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Non-gambling (n = 30885) Characteristics Gender
Low-risk
At-risk
Problem
Pathological
gambling
gambling
gambling
gambling
(n = 8967)
(n = 1669)
(n = 332)
(n = 185)
Male (n =
12002 (38.9%)
4691 (52.3%)
992 (59.4%)
18009)
18883 (61.1%)
4276 (47.7%)
677 (40.6%)
AgeFemale (years)(n = 24029)(n = 8487) 18-29
6741 (21.8%)
1286 (14.3%)
344 (20.6%)
30-44 (n = 13041)
9807 (31.8%)
2596 (29.0%)
478 (28.6%)
45-64 (n = 12491)
8502 (27.5%)
3225 (36.0%)
574 (34.4%)
65 or older (n = 8019)
5835 (18.9%)
1860 (20.7%)
273 (16.4%)
Race/Ethnicity White (n =
17022 (55.1%)
5705 (63.6%)
949 (56.9%)
23928)
5844 (18.9%)
1614 (18.0%)
383 (22.9%)
Black (n =
461 (1.5%)
163 (1.8%)
41 (2.5%)
7999)
1010 (3.3%)
223 (2.5%)
47 (2.8%)
Native American (n
6548 (21.2%)
1262 (14.1%)
249 (14.9%)
= 679) Asian/P. Education (years) Islander = 1302) 0-11 (n =(n7625)
5918 (19.2%)
1279 (14.3%)
308 (18.5%)
Hispanic (n = 8130) 12 (n = 12231)
8812 (28.5%)
2741 (30.6%)
524 (31.4%)
13-15 (n = 12368)
8777 (28.4%)
2902 (32.4%)
537 (32.2%)
16 or more (n = 9814)
7378 (23.9%)
2045 (22.8%)
300 (18.0%)
Personal income ($) 0-19999 (n = 20578) 20000-34999 (n = 9727) 35000-59999 (n = 7523) Marital status 60000 or more (n = 4210) Married/Cohab (n =21669)
15937 (51.6%)
3631 (40.5%)
751 (45.0%)
6937 (22.5%)
2269 (25.3%)
400 (24.0%)
5121 (16.6%)
1993 (22.2%)
322 (19.3%)
2890 (9.4%)
1074 (12.0%)
196 (11.7%)
15727 (50.9%)
4902 (54.7%)
821 (49.2%)
4675 (15.1%)
1557 (17.4%)
291 (17.4%)
Divorced/Sep (n =
7327 (23.7%)
1656 (18.5%)
435 (26.1%)
6644) Never
3156 (10.2%)
852 (9.5%)
122 (7.3%)
married (n = 9561) Widowed = Table 2.(nPrevalence
210 (63.3%) 122 (36.7%) 82 (24.7%) 100 (30.1%) 114 (34.3%)
< 0.001
187.2 12
< 0.001
386.3 12
< 0.001
197.0 12
< 0.001
76 (41.1%) 15 (8.1%)
63 (34.1%) 8 (4.3%) 24 (13.0%)
44 (23.8%) 56 (30.3%) 55 (29.7%) 30 (16.2%)
61166 (18.4%) (50.0%)
93 (50.3%)
81 (24.4%)
30 (16.2%)
40 (21.6%) 22 (11.9%)
28 144 (8.4%) (43.4%)
75 (40.5%)
72 (21.7%)
47 (25.4%)
96 (28.9%)
393.9 16
60 (32.4%)
4 (2.2%)
57 (17.2%)
<0.001
34 (18.4%)
95 (28.6%)
97 (29.2%)
451.5 12
71 (38.4%)
86 (46.5%)
14 (4.2%) 76 47 (22.9%) (14.2%) 98 (29.5%)
P < 0.001
114 (61.6%)
36166 (10.8%) (50.0%)
10 (3.0%)
χ2 df 799.3 4
49 (26.5%) 14 (7.6%)
20 rates and adjusted odds ratios of lifetime problematic behaviors among five (6.0%)
4164)
gambling groups (n = 42038) 5 Gambling Groups
Problematic behaviors
Non-
Low-risk
At-risk
Problem
Pathological
gambling
gambling
gambling
gambling
gambling
(n = 30885)
(n = 8967)
(n = 1669)
(n = 332)
(n = 185)
Shoplifting Prevalence OR
2595 (8.4%)
1273 (14.2%)
384 (23.0%)
97 (29.2%)
64 (34.6%)
(95% CI) AOR
1 (Reference)
1.80 (1.68-1.94)
3.26 (2.89-3.68)
4.50 (3.54-5.72)
5.77 (4.25-7.83)
(95% CI)
1 (Reference)
1.76 (1.63-1.90)
2.63 (2.30-3.02)
2.79 (2.10-3.70)
3.85 (2.68-5.53)
1989 (6.4%)
1007 (11.2%)
326 (19.5%)
91 (27.4%)
65 (35.1%)
(95% CI) AOR
1 (Reference)
1.84 (1.70-1.99)
3.53 (3.10-4.01)
5.49 (4.29-7.01)
7.87 (5.80-10.68)
(95% CI)
1 (Reference)
1.70 (1.56-1.86)
2.68 (2.32-3.10)
3.28 (2.45-4.39)
5.23 (3.65-7.50)
Prevalence OR
981 (3.2%)
487 (5.4%)
186 (11.1%)
49 (14.8%)
52 (28.1%)
(95% CI) AORa
1 (Reference)
1.75 (1.57-1.96)
3.82 (3.24-4.51)
5.28 (3.87-7.20)
11.92 (8.60-16.53)
(95% CI)
1 (Reference)
1.69 (1.51-1.90)
2.98 (2.50-3.56)
3.13 (2.22-4.39)
8.10 (5.62-11.67)
323 (1.0%)
152 (1.7%)
81 (4.9%)
31 (9.3%)
34 (18.4%)
(95% CI) AOR
1 (Reference)
1.63 (1.34-1.98)
4.83 (3.76-6.19)
9.75 (6.63-14.33)
21.31 (14.46-31.40)
(95% CI)
1 (Reference)
1.54 (1.25-1.89)
2.79 (2.11-3.70)
3.97 (2.54-6.20)
11.34 (6.99-18.40)
Prevalence OR
3140 (10.2%)
1297 (14.5%)
396 (23.7%)
99 (29.8%)
69 (37.3%)
(95% CI) AORa
1 (Reference)
1.49 (1.39-1.60)
2.75 (2.44-3.10)
3.75 (2.96-4.76)
5.26 (3.89-7.10)
(95% CI)
1 (Reference)
1.52 (1.41-1.64)
2.37 (2.09-2.69)
2.64 (2.03-3.42)
3.83 (2.75-5.32)
2888 (9.4%)
1169 (13.0%)
394 (23.6%)
106 (31.9%)
73 (39.5%)
(95% CI) AOR
1 (Reference)
1.45 (1.35-1.56)
3.00 (2.66-3.38)
4.55 (3.60-5.75)
6.32 (4.69-8.51)
(95% CI)
1 (Reference)
1.47 (1.36-1.58)
2.55 (2.25-2.90)
3.18 (2.47-4.11)
4.73 (3.42-6.55)
44 (0.1%)
7 (0.1%)
7 (0.4%)
3 (0.9%)
2 (1.1%)
(95% CI) AOR
1 (Reference)
0.55 (0.25-1.22)
2.95 (1.33-6.56)
6.39 (1.98-20.69)
7.66 (1.84-31.84)
(95% CI)
1 (Reference)
0.45 (0.20-1.01)
1.51 (0.66-3.49)
2.34 (0.69-7.94)
2.52 (0.58-10.97)
a
Stealing Prevalence OR a
Not paying off debts
Scam for money Prevalence OR a
Homelessness
Quit a job Prevalence OR a
Sexual coercion Prevalence OR a
Fighting Prevalence OR
2493 (8.1%)
1197 (13.3%)
402 (24.1%)
109 (32.8%)
72 (38.9%)
(95% CI) AORa
1 (Reference)
1.75 (1.63-1.89)
3.61 (3.21-4.07)
5.57 (4.41-7.03)
7.26 (5.39-9.78)
(95% CI)
1 (Reference)
1.81 (1.67-1.96)
3.13 (2.74-3.58)
3.88 (2.96-5.08)
5.22 (3.69-7.37)
3272 (10.6%)
1747 (19.5%)
559 (33.5%)
124 (37.3%)
90 (48.6%)
(95% CI) AOR
1 (Reference)
2.04 (1.92-2.18)
4.25 (3.82-4.73)
5.03 (4.02-6.30)
8.00 (5.98-10.69)
(95% CI)
1 (Reference)
1.92 (1.79-2.06)
3.52 (3.12-3.98)
3.15 (2.42-4.12)
6.03 (4.31-8.45)
4939 (16.0%)
2572 (28.7%)
666 (39.9%)
157 (47.3%)
104 (56.2%)
(95% CI) AOR
1 (Reference)
2.11 (2.00-2.23)
3.49 (3.15-3.87)
4.71 (3.79-5.86)
6.75 (5.04-9.03)
(95% CI)
1 (Reference)
1.88 (1.77-1.99)
2.74 (2.45-3.07)
3.21 (2.52-4.09)
4.96 (3.60-6.84)
Arrest possibility Prevalence OR a
Vehicular endangerment Prevalence OR a
Abbreviations: OR, odds ratio; AOR, adjusted odds ratio; CI, confidence interval a
Adjusted for demographic variables (gender, age, race, education, personal income, and marital status) and
antisocial personality disorder
Highlights All gambling severity categories are at increased risk for various problematic behaviors. Pathological gamblers (compared to non-gamblers) especially are subject to financially oriented problems. Several of the examined problematic behaviors may have legal implications. Aspects of gambling pathology, not ASPD, may underlie problematic behaviors.