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Risk factors for pathological gambling
Addictive Behaviors 29 (2004) 323 – 335
Risk factors for pathological gambling John W. Weltea,*, Grace M. Barnesa, William F. Wieczorekb, Marie-Cecile O. Tidwella, John C. Parkera a Research Institute on Addictions, 1021 Main Street, Buffalo, NY 14203, USA State University College at Buffalo, Center for Health and Social Research, HA-205E, 1300 Elmwood Avenue, Buffalo, NY 14222, USA
b
Abstract To better understand pathological gambling, potential risk factors were assessed within three domains—gambling behaviors, substance abuse and other problem behaviors, and sociodemographic factors. A random-digit-dial telephone survey was conducted in 1999– 2000 with a representative sample of the U.S. population aged 18 or older. The current analyses uses data from the 2168 respondents who gambled in the year before the interview. Gambling measures included the Diagnostic Interview Schedule (DIS)-IV for pathological gambling, frequency of 15 types of gambling, and size of win or loss on the last occasion. Other measures included the quantity and frequency of alcohol consumption, frequency of illicit drug use and criminal offending, and the DISIV for alcohol and drug abuse and dependence. Results showed that casino gambling is associated with a high risk of gambling pathology. Lottery, cards, and bingo are associated with a moderately high risk of gambling pathology. Participation in a greater number of types of gambling is strongly predictive of gambling pathology, even after frequency of gambling and size of win or loss are taken into account. Alcohol abuse is strongly predictive of gambling pathology, even with gambling behaviors held constant. Minority and low socioeconomic status (SES) group members have higher levels of gambling pathology than other groups after all other factors are considered. D 2003 Elsevier Ltd. All rights reserved. Keywords: Pathological gambling; Substance use; Adults
1. Introduction Risk for pathological gambling has been correlated with selected aspects of gambling activity, substance use and criminal offending, and sociodemographic characteristics. The * Corresponding author. Tel.: +1-716-887-2503; fax: +1-716-887-2510. E-mail address: [email protected] (J.W. Welte). 0306-4603/$ – see front matter D 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.addbeh.2003.08.007
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gambling factors studied have generally been the amount of money spent on gambling and types of gambling activity. The National Research Council (1999, p.79) reviewed eight surveys of various U.S. states that measured the respondents’ ‘‘expenditures’’ on gambling. In these studies, the expenditures of nonproblem gamblers ranged from US$24 to 131 per month, while the monthly expenditures of problem or pathological gamblers ranged from US$121 to 660. However, researchers have seldom made a distinction between a large number of small bets and a small number of large bets, implicitly taking the position that money lost is money lost, regardless of betting strategy. There have also been suggestions in the literature that certain types of gambling are more likely to produce gambling addiction than other types. It has been proposed that types of gambling which produce a highly variable reinforcement schedule, such as slot machines, may be associated with hard-toextinguish habitual gambling (Griffiths, 1995, p.11). It has also been suggested that types of gambling that provide instant feedback are more addictive than other forms of gambling. For example, the Illinois Institute for Addiction Recovery (2001) refers to video poker as the ‘‘crack cocaine of gambling,’’ and states that the immediate gratification available with video poker shortens the length of time necessary for chronic gambling addiction to develop. Griffiths (1999) identified ‘‘event frequency’’ as possibly related to the addictive properties of different forms of gambling. Event frequency is defined in terms of the time interval between gambling outcomes. Slot machines, for example, have a high event frequency because there is only a few seconds between outcomes, whereas sports betting has a low event frequency. Reasoning from principles of operant conditioning, Griffiths hypothesized that high event frequency is associated with greater addictive properties, and cited research from several countries that suggests that high-frequency gambling machines are highly pathological. However, other research studies have not verified these conjectures about highly addictive forms of gambling. For example, Volberg (1993a) found that instant scratchoff forms of lottery had a low association with pathological gambling. The National Research Council found that problem/pathological gamblers were disproportionately involved in bingo, lotteries, racetrack betting, and sports betting as compared with nonproblem gamblers (p. 78). Many studies have shown positive correlations between gambling and substance abuse among adults. In a general population survey in Texas, Wallisch (1993) found that pathological gamblers drank alcohol and used illicit drugs at several times the average rates. Even more strikingly, an Alberta general population survey (Smith, Volberg, & Wynne, 1994) reported that 100% of the respondents who were pathological gamblers were also smokers. These pathological gamblers also had higher rates of heavy drinking and drug use than the general population. Smart and Ferris (Addiction Research Foundation, 1994), in an Ontario general population telephone survey, found that the same respondents tended to report negative consequences from both gambling and drinking. The gambling–drug relationship has also been found in student populations. Frank (1992), analyzing data from a five-state college survey, found that illegal drug use and heavy drinking positively predicted scores on the South Oaks Gambling Screen. In a clinical study, Lesieur, Blume, and Zoppa (1986) examined patients in treatment for alcohol or drug abuse and found that 19% were pathological or problem gamblers, a rate much higher than found in the general population.
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The national adult survey conducted by our research group showed that being alcohol dependent increased the odds of being a pathological gambler by a factor of 23 (Welte, Barnes, Wieczorek, Tidwell, & Parker, 2001). In addition to substance abuse, pathological gambling has also been linked to criminal offending. Another study by our own research group (Barnes, Welte, Hoffman, & Dintcheff, 1999) found that in two longitudinal studies, various types of delinquency and criminal offending, were strongly correlated with frequency of gambling. Blaszczynski and McConaughy (1994) found pathological gamblers to have a much higher than average risk of committing criminal offenses, exhibiting aggressive behaviors, and having a diagnosis of antisocial personality disorder. Pathological gambling was found by Lesieur et al. (1991) to be positively correlated with arrests for nontraffic offenses in college students. Pathological gambling has shown fairly consistent demographic patterns in the United States. The national telephone survey conducted by the National Gambling Impact Study Commission (NORC, 1999) found that pathological gambling was significantly more prevalent among males than females and more prevalent among African Americans than European Americans. Lower than average prevalence was found among the affluent and the elderly. Our own national survey (Welte et al., 2001) found similar trends. While pathological gambling was not more prevalent among males than females, it was distinctly more prevalent among minority respondents than among whites. Pathological gambling was also less prevalent than average among the elderly and affluent. Numerous state and regional surveys conducted in the last 15 years have shown similar gender, race, and age patterns, with pathological gamblers tending to be disproportionately male, young, and minority group members (e.g., Volberg, 1993b; Volberg & Steufen, 1991; Wallisch, 1993). In the current study, we will extend previous research related to risk factors for pathological gambling by including more detailed information on gambling behaviors and by considering the contribution of multiple factors from the three domains described above. The data on 15 different types of gambling will enable us to examine the strength of the relationship of each type of gambling with pathological symptoms. We will also examine distinct effects of gambling frequency, size of win or loss, and number of types of gambling engaged in on gambling pathology. The existing literature has shown consistent demographic trends of pathological gambling. It is possible that the relationships between demographic factors and gambling pathology are mediated by gambling behavior, such that the trends would disappear if gambling behavior were held constant. For example, if the elderly have lower rates of gambling pathology, it may be only because they bet less frequently and make smaller bets. However, it is also possible that protective factors allow for individuals with the same gambling behavior to have varying risks of pathology. We will address this issue below. A similar question arises concerning the relationship between gambling, substance abuse, and other problem behaviors. It may be that pathological gambling and alcohol dependence, for example, are correlated only because alcoholics bet more frequently and bet larger amounts. However, it is likewise possible that at a given level of gambling behavior, the alcoholdependent person is more vulnerable to gambling compulsion. This question will also be addressed below.
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2. Method 2.1. Survey design We conducted a national random-digit-dial telephone survey with a representative sample of U.S. residents aged 18 or older. The random-digit-dial sample was purchased from Survey Sampling of Fairfield, CT. The telephone sample was selected randomly from a sampling frame of all working telephone blocks in the United States. The sample was stratified by county and by telephone block within county. This resulted in a sample that was spread evenly across the United States, and not clustered by geographic area. This type of sample does not create any adverse design effect, and the sampling error is likely to be somewhat smaller than that of a simple random sample of the United States. The interviews were conducted by trained interviewers at the Research Institute on Addiction’s Computer-Assisted Telephone Interviewing (CATI) facility. A total of 14,700 telephone numbers were contacted. A total of 4338 of these numbers were determined to be households containing an eligible potential respondent, with the remainder being nonhousehold numbers (e.g., businesses, data lines, nonassigned numbers) or else numbers whose eligibility could not be determined (e.g., numbers that were never answered). In those 4338 eligible households, 302 of the selected respondents were physically or mentally unable to participate in the interview, leaving 4036 eligible households in which we selected a respondent who was able to be interviewed, if willing. A total of 2631 interviews were conducted; therefore the response rate was 2631/ 4036 = 65.2%. The remaining 1404 uninterviewed respondents either refused both the original interviewer and the refusal converter, or else they were rescheduled and pursued unsuccessfully until the study ended. Each telephone number was called at least seven times to determine if that number was assigned to a household containing an eligible respondent. Once a household was designated as eligible, the number was called until an interview was obtained or refusal conversion had failed. The respondents were recruited by selecting randomly from the adults in each household by taking the adult with the most recent birthday. The 2631 telephone interviews were conducted from August 1999 through October 2000. The survey was in the field for approximately a year, which was according to plan. This was done to make sure that the study captured a representative sample of seasonal effects, and to allow the use of a smaller but highly trained and carefully supervised crew of interviewers. Interviews were conducted in all 50 states plus the District of Columbia. 2.2. Questionnaire Gambling participation was measured with 15 sets of questions, corresponding to the 15 types of gambling listed in Table 1. For each type, the respondents were asked if they had participated in that type of gambling in the past year. If they responded affirmatively, they were asked how frequently they had participated in that type of gambling in the past year. There were eight response options for the frequency questions, ranging from ‘‘every day’’ to ‘‘never in the past 12 months.’’ For three types of gambling (pulltabs, lottery and raffles/office pools/charity gambling), the respondents were then asked: (1) how much they spent (on
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Table 1 Negative binomial regression predicting count of pathological symptoms from volume of various types of gambling, last year gamblers, N = 2168
Raffles, charity Lottery Pulltabs Internet gambling Casino Horse or dog track Off-track Gambling machines Cards Games of skill Video keno Bingo Dice Sports betting Other
Percent who played past year
Mean volume for those who played (US$/year)
IRR for US$1000/year volume unitsa
59 80 12 0.4 32 8 2 21 25 17 9 15 5 24 2
291 327 397 665 1188 929 2004 619 641 630 328 634 1137 605 1343
1.07 1.26*** 1.75* 1.29 1.40*** 1.10 0.89 1.05 1.32*** 0.98 0.91 1.34* 0.91 1.14** 0.99
a
The IRR is the Factor by which the dependent variable is multiplied for one unit increase in the independent variable. * Significant at .05 value. ** Significant at .01 value. *** Significant at .001 value.
lottery tickets, pulltabs, etc.) the last time they played that particular game, (2) whether they won, and (3) how much they won. These questions allow for a calculation of the respondent’s net result, whether negative or positive. For the other 12 types of gambling, the respondents were asked: (1) whether they won or lost the last time they played, and (2) how much they won or lost. For all these gambling participation questions except the frequency question, the respondent was not forced to choose from a set of responses. Rather, the respondent gave an unguided answer (e.g., ‘‘I won 100 dollars’’), and that quantity was recorded by the interviewer. Table 1 uses brief names for the types of gambling which were more extensively described in the questionnaire. For example, (a) lottery included scratch tickets, (b) casino gambling included all gambling done in a casino, river boat, or cruise ship, (c) racetrack included any gambling at a racetrack, and was not limited to betting on the races, machines included slots, poker machines, and video terminals, (d) the specific questions about cards, dice, gambling machines, sports betting all excluded gambling done in a casino or track— these were covered in the casino and track questions, (e) games of skill were exemplified by pool, golf, and backgammon. Our measure of pathological gambling was from the Diagnostic Interview Schedule (DIS)-IV. The DIS for pathological gambling contains 13 items that map into 10 criteria, such as preoccupation with gambling and needing to gamble with increasing amounts of money to get the same excitement. Endorsement of five criteria is considered pathological gambling, and for our purposes we considered endorsement of three criteria to
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be problem gambling. Respondents who endorsed the requisite number of items for the past year were considered to be current pathological or problem gamblers; respondents who endorsed the requisite number of items for their entire lives were considered to be lifetime pathological or problem gamblers. Our measure of alcohol consumption comes from pairs of quantity and frequency questions for each of five types of alcoholic beverage: beer/malt liquor, fortified wine, wine, liquor, and wine cooler. Respondents were asked how frequently they drank the beverage in the past year, and how much they drank on a typical occasion. The drug frequency questions asked how many times the respondent used four different types of drugs in the past year: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The measures of alcohol abuse and dependence and drug abuse and dependence were from the DIS for DSM-IV (Robins, Marcus, Reich, Cunningham, & Gallagher, 1996). Alcohol or drug abuse is signified by the occurrence of one or more serious negative consequences, such as repeated driving while under the influence. Alcohol or drug dependence is signified by the occurrence of three or more signs of dependence, such as tolerance or withdrawal. Occurrence of the requisite number of consequences or signs in the past year constituted current abuse or dependence. In addition to alcohol, the abuse and dependence questions were asked with respect to four different types of drugs in the past year: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The number of self-reported criminal offenses in the past year is derived from 12 items taken from the work of Elliott, Huizinga, and Ageton (1985). The respondents were asked how many times in the past year they committed 12 common crimes, such as larceny and writing a bad check. 2.3. Independent variables The measure of gambling volume was calculated by multiplying gambling frequency (the number of times a type of gambling was engaged in during the past year) by the gambling quantity (absolute value of the most recent win or loss). For all gambling, the value of this variable was computed by summing the volumes of the games that each respondent had played in the past year. The gambling volume variable is a measure of the total amount that an individual gambles. It takes into account both the frequency of play and the size of bets. Average daily alcohol consumption was computed for each beverage consumed by multiplying the frequency of drinking occasions in the past year by the number of ounces of pure ethanol consumed on a typical occasion. Total average consumption was computed by summing across five alcoholic beverages: beer/malt liquor, fortified wine, wine coolers, wine, and liquor. Drug use was the total number of times that the four drugs described above were used in the past year. Alcohol abuse and dependence as well as drug abuse and dependence were determined as described above. Our collapsed drug abuse or dependence variables are based on DIS questions in four drug categories: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The measure of socioeconomic status (SES) was based on the mean of three equally weighted factors: family income, years of education, and occupational prestige. When one of these factors was absent (as with a nonworking respondent), the mean of the two existing
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factors was used. Occupational prestige was coded from census occupation categories using the method of Duncan updated (Stricker, 1988). 2.4. Dependent variable Gambling pathology was derived from the DIS-IV (Robins et al., 1996) The DIS for pathological gambling contains 13 items that map into 10 criteria, such as preoccupation with gambling and needing to gamble with increasing amounts of money to get the same excitement. The conventional use of these items is to form a dichotomous pathological gambling variable with a cutpoint of five or more criteria. However, the current study uses this information differently. The dependent variable for the current study is the sum of the DIS items (out of a possible 13) that were endorsed for the 12 months previous to the interview. This is an efficient use of the all the available information. 2.5. Analyses Results were statistically weighted to compensate for the number of potential respondents in the household. Weighting adjustments were also used to align the sample with gender, age, and race distributions shown in the United States census estimates for the year 2000. Males, Hispanics, Asians, and older respondents were ‘‘weighted up.’’ The weighted distributions of the sample, according to region, gender, race, and age, closely match the U.S. population. Analyses for the current study were conducted with the 2168 respondents who gambled in the 12 months before the interview. Statistical modeling of gambling pathology was performed by negative binomial regression. The dependent variable for the current study is a count of symptoms that could possibly range from 0 to 13. Its actual distribution was as follows. Eighty-three percent (83%) of the 2168 cases were zero. The other values declined in frequency from 1 (8% of the cases) to 13 (two occurrences). The mean of this variable was 0.45, and its variance was 2.0. Linear regression was not appropriate, as it assumes that the dependent variable ranges from negative to positive infinity, can take on any real value, and is normally distributed. This count variable is ‘‘over-dispersed’’; that is, its variance is much greater than its mean. Therefore, the appropriate regression model is negative binomial rather than Poisson regression (see Cameron & Trivedi, 1998, for a thorough discussion of regression using count data).
3. Results Table 1 shows the relationship between degree of participation in various types of gambling and the extent of gambling pathology. All gambling done at casinos or the track are included in those categories; other categories such as ‘‘dice,’’ ‘‘cards,’’ or ‘‘gambling machines’’ refer to gambling not done at a casino or track. The first column shows the percentage of respondents, among the 2168 who gambled in the past year, who engaged in a particular type of gambling. For example, 32% of the 2168 representative gambling adults
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interviewed in our study engaged in casino gambling in the year before the interview. The second column shows that the average volume for those casino gamblers was US$1188 per year. For all the other respondents included in the analyses, the casino gambling volume was zero. The third column contains regression parameters from a negative binomial regression. A negative binomial regression was performed in which the volumes of all 15 types of gambling were used as independent variables, and the count of pathological symptoms was used as a dependent variable. The parameters in this negative binomial regression are expressed as incidence rate ratios (IRR). The IRR is the factor by which the dependent variable (DV) is multiplied for every unit increase in the independent variable (IV). For example, every US$1000 per year of casino gambling volume engaged in by an individual gambler leads to a multiplication of the number of gambling pathology symptoms by 1.40, or a 40% increase. Only casino, lottery, pulltabs, cards, bingo, and sports betting have statistically significant IRRs. The IRRs show that pulltabs had the greatest impact on individual gambling pathology and casino gambling has the second greatest impact, while sports betting had a relatively smaller impact. Pulltabs seems to be a highly pathological form of gambling for those who do buy them, although confidence in this result must be qualified because the relevant regression parameter is significant only at the .05 level. The other significant results ranged somewhere in between these extremes. Internet gambling was done by only nine respondents, so it is not surprising that its adjusted IRR is not statistically significant. It is important to make the distinction between the impact of a type of gambling on the individual who engages in it (reflected by the IRR) and the impact of a particular type of gambling on gambling pathology in the United States as a whole. For example, bingo had an IRR of 1.34, and casino gambling had an IRR of 1.40, suggesting similar effects on an individual’s gambling pathology for every additional US$1000 of volume. However, casino gambling had both a higher percentage who played and a higher average volume than bingo, so it must have a much greater impact on the gambling problem in the United States as a whole. Another analysis, not shown in the tables, ascertained the reduction in the Cragg and Uhler’s pseudo-R-squared was associated with the removal of each independent variable from the model. This analysis showed that casino gambling had by far the largest contribution to gambling pathology in the entire sample, and that lottery had the second highest contribution to overall gambling pathology. Casino gambling was engaged in by 32% of the current gamblers, and it led to a robust 40% increase in pathological gambling symptoms for every US$1000 in volume. Because lottery play is the most common type of gambling (reported in the past year by 80% of our 2168 gambling respondents), lotteries also make a very important contribution to the total gambling pathology in the United States. Table 2 shows how various aspects of gambling behavior, along with substance use and other characteristics of the respondent, are related to gambling pathology. The IRRs are shown from three different negative binomial regressions, all of which use the count of DIS pathological symptoms as the dependent variable. These three regressions are organized into ‘‘blocks.’’ The first block shows the parameters from a model with only the first block of independent variables entered into the model. The second block shows the parameters from a model with both the first and second block independent variables, and the third block shows parameters from a model with all three blocks included in the analysis.
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Table 2 Negative binomial regression predicting count of pathological symptoms; predictors entered by block, last year gamblers, N = 2168 Block 1
Block 2
Block 3
Independent variable
IRRa
times gambled per week average win/loss in US$100 units number of types of gambling in year ethanol consumption in ounces per day number of times used drugs in past year number of crimes committed in past year current alcohol abuse or dependence current drug abuse or dependence male gender African American Hispanicb Asianb American Indianb Otherb Standardized age Standardized SES
1.26*** 1.65*** 1.34*** 0.98 1.00 1.00 3.06*** 1.94 0.88 2.70*** 1.96** 4.71*** 2.19 2.02* 0.93 0.66***
a
The IRR is the factor by which the dependent variable is multiplied for one unit increase in the independent variable. b Whites are the reference group * Significant at .05 value. ** Significant at .01 value. *** Significant at .001 value.
The first block of Table 2 shows the aspects of gambling behavior that influence gambling pathology. As would be expected, gambling frequency is significantly related to gambling pathology. Every instance of weekly gambling (52 occasions of gambling in the past year) increases pathological symptoms by 26%. Likewise, the average win or loss is highly related to gambling pathology. Every US$100 increase in the average win/loss is associated with a 65% increase in number of pathological gambling symptoms. This analysis also shows that gambling pathology is very strongly related to the number of types of gambling games that the respondent played in the past year. Every additional type of gambling produces a 34% increase in pathological symptoms while holding constant both the frequency of gambling and the average size of the respondent’s last win or loss. Fig. 1 shows the simple bivariate relationship between the number of types of gambling and average number of pathological symptoms. As shown, there are sudden increases in gambling pathology after five types in the past year and after eight types in the past year. Respondents who engaged in nine or more types of gambling in the year before the interview had strikingly high average pathology. The second block of Table 2 shows predictors related to alcohol use, abuse and dependence as well as drug use, drug abuse and drug dependence, and number of crimes committed by the respondent in the past year. These variables are all estimated after controlling for the gambling variables in the first block. When these variables are considered jointly, only
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Fig. 1. Gambling pathology by number of types of gambling.
alcohol abuse/dependence predicts gambling pathology after the influence of gambling frequency, quantity, and number of types of gambling are accounted for. The third block of Table 2 shows the sociodemographic risk factors associated with pathological gambling, after controlling for gambling behaviors, substance use and criminal offending. The age and SES variables are standardized, so the IRRs represent the percentage change in pathological symptoms for one standard deviation in age or SES. Gender is not significantly related to gambling pathology when the other independent variables are adjusted for. These results also show that being African American, Hispanic, or Asian and having low SES are significant risk factors for pathological gambling, even after taking into account gambling frequency, size of wins and losses, number of types of gambling, substance use, and criminal offending. It is also noteworthy that the Block 1 variables—gambling frequency, size of win or loss, and gambling versatility—retain their significance in predicting gambling pathology after all of the problem behavior and demographic variables are inserted into the model.
4. Discussion Several interesting results have emerged from these analyses. One such result is that while engaging in any one type of gambling can be associated with pathological gambling, some types of gambling are associated with a much higher risk of pathological gambling than other types of gambling. Bivariate analyses (not shown) revealed that, considered in isolation, all
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types of gambling are correlated strongly with pathological gambling symptoms. However, when all forms of gambling are considered simultaneously, only casino gambling, lottery, pulltabs, cards (outside a casino), bingo, and sports betting significantly predict gambling pathology. The correlation between the other types of gambling and pathological gambling is spurious; the source of the pathology lies elsewhere. The IRRs show that the most potent sources of gambling pathology for individuals are, in descending order of risk: pulltabs, casino gambling, bingo, cards (played outside a casino), lottery, and sports betting. These six types of gambling have little in common by way of event frequency, immediate feedback, variability of reinforcement schedule, or gambling venue. However, it is worth noting that the two riskiest types among those that significantly predict pathology, pulltabs, and casino gambling have high event frequencies. The least risky of the statistically significant types of gambling, lottery, and sports betting have low event frequencies. Bingo and the lottery are sometimes thought of as innocuous forms of gaming. However, this is clearly not the case based on the present findings. While only nine internet gamblers were interviewed, their volume of internet gambling as a stand-alone predictor was powerfully correlated with gambling pathology (analysis not shown in tables). Because it is likely that internet gambling will increase in the coming years, future research studies should track the trends and correlates of this form of gambling. While it is not surprising that the respondent’s extent of gambling pathology would be related to his or her frequency of gambling and the size of wins and losses, it is far from selfevident that gambling versatility (number of different types of gambling) would predict the extent of gambling pathology. Given two gamblers who play with equal frequency and have the same size of average win or loss, why would the one with the greatest gambling versatility have the most serious addiction to gambling? Engaging in a large number of forms of gambling could be an indication of an attachment to the essence of the gambling experience. A sports bettor may gamble partly as an extension of his/her interest in sports; a bingo player may be betting partly because of the social contacts that he/she enjoys at the bingo hall. But what about the respondents who engaged in six or more types of gambling in a 12-month period? These individuals might be devoted to the core experience of gambling—risking money in an attempt to win money. Another noteworthy finding is that alcohol abuse and dependence have a particularly important association with gambling pathology. This is true because when variables measuring drug use, alcohol use, criminal offending, drug abuse/dependence, and alcohol abuse/dependence are used to jointly predict gambling pathology, alcohol abuse /dependence carries all the predictive weight and the other variables lose their significance. Undoubtedly, some individuals with low self-control tend to both drink and gamble excessively. Furthermore, it is likely that the acute effects of alcohol may lead to poor judgement in gambling. However, the correlation between alcohol pathology and gambling pathology is not due solely to these factors, because if it were, the correlation would vanish when gambling and drinking behavior are held constant. The chronic effects of alcohol that are reflected by a designation of abuse or dependence may contribute directly to worsen gambling pathology, without increasing gambling behavior. For example, the reduced income that results from alcohol pathology could increase financial pressure and lead directly to an exacerbation of
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pathological gambling symptoms such as borrowing money to gamble, forging checks, etc. This could also be true of drug dependence or abuse, but so many drug-dependent persons also have heavy alcohol involvement that alcohol pathology emerges as the key predictor. Alcohol can be linked with gambling much more openly than drugs. Casinos serve free drinks, gamblers drink at the track and the card table; thus, heavy drinking is often part of the gambling experience. This study clearly shows that minority status and low SES are significantly linked to gambling pathology, even after adjusting for gambling behavior, substance use, and other criminal behaviors, as well as the other sociodemographic factors, gender, and age. Lower SES persons might have more gambling pathology than higher SES persons who gamble the same amount due to the fact that higher SES persons have more income and more financial resources to buffer the effects of gambling losses. It is less clear why African American, Hispanic, or Asian respondents might be at greater risk for pathological gambling after controlling for gambling behavior and SES. One possible straightforward explanation has to do with net worth. Minorities in the United States have a much lower net worth than whites, even at the same income levels. The U.S. Department of Commerce (2001) reports that African American and Hispanic households have only 1/7 of the net worth of white households, a far greater disparity than can be explained by differences in income. A further explanation of this phenomenon may be that some social groups have more financial sophistication than others. Schissel (2001) documents and discusses how lower SES persons may see gambling as a form of investment, and a possible escape from poverty. An individual who believes that gambling has these positive qualities may be more prone to gambling addiction. These findings show that diagnoses of pathological and problem gambling may have complex causes beyond mere frequent gambling or making large bets. Risk for pathological gambling is related to gambling versatility, alcohol pathology, and membership in at-risk sociodemographic groups.
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Illinois Institute for Addiction Recovery (2001). http://www.addictionrecov.org/quandagam.htm. Lesieur, H. R., Blume, S. B., & Zoppa, R. M. (1986). Alcoholism, drug abuse, and gambling. Alcoholism, Clinical and Experimental Research, 10, 33 – 38. Lesieur, H. R., Cross, J., Frank, M., Welch, N., White, C., Rubenstein, G., Moseley, K., & Mark, M. (1991). Gambling and pathological gambling among university students. Addictive Behaviors, 16, 517 – 529. National Opinion Research Center (1999). Gambling impact and behavior study. Chicago: Author. National Research Council (1999). Pathological gambling: A critical review. Washington, DC: National Academy Press. Robins, L., Marcus, L., Reich, W., Cunningham, R., & Gallagher, T. (1996). NIMH Diagnostic Interview Schedule—Version IV (DIS-IV). St. Louis: Deparment of Psychiatry, Washington University School of Medicine. Schissel, B. (2001). Betting against youth: The effects of socioeconomic marginality on gambling among young people. Youth and Society, 32(4), 473 – 491. Smith, G. J., Volberg, R. A., & Wynne, H. J. (1994, January). Gambling and problem gambling in Alberta. Edmonton, Alberta: Wynne Resources. Stricker, L. J. (1988). Measuring social status with occupational information: A simple method. Journal of Applied Social Psychology, 18(5), 423 – 437. U.S. Department of Commerce (2001). http://www.census.gov/Press-Release/www/2001/cb01-33.html. Volberg, R. A. (1993a). Gambling and problem gambling among adolescents in Washington state. Albany, NY: Gemini Research. Volberg, R. A. (1993b). Gambling and problem gambling among Native Americans in North Dakota. Albany, NY: Gemini Research. Volberg, R. A., & Steufen, R. M. (1991, November). Gambling and problem gambling in South Dakota. Delmar, NY: Policy Research Associates. Wallisch, L. S. (1993, February). Gambling in Texas: 1992 Texas survey of adult gambling behavior. Austin, TX: Texas Commission on Alcohol and Drug Abuse. Welte, J. W., Barnes, G. M., Wieczorek, W. F., Tidwell, M., & Parker, J. (2001). Alcohol and gambling pathology among U.S. adults: Prevalence, demographic patterns and co-morbidity. Journal of Studies on Alcohol, 62(5), 706 – 712.
Risk factors for pathological gambling John W. Weltea,*, Grace M. Barnesa, William F. Wieczorekb, Marie-Cecile O. Tidwella, John C. Parkera a Research Institute on Addictions, 1021 Main Street, Buffalo, NY 14203, USA State University College at Buffalo, Center for Health and Social Research, HA-205E, 1300 Elmwood Avenue, Buffalo, NY 14222, USA
b
Abstract To better understand pathological gambling, potential risk factors were assessed within three domains—gambling behaviors, substance abuse and other problem behaviors, and sociodemographic factors. A random-digit-dial telephone survey was conducted in 1999– 2000 with a representative sample of the U.S. population aged 18 or older. The current analyses uses data from the 2168 respondents who gambled in the year before the interview. Gambling measures included the Diagnostic Interview Schedule (DIS)-IV for pathological gambling, frequency of 15 types of gambling, and size of win or loss on the last occasion. Other measures included the quantity and frequency of alcohol consumption, frequency of illicit drug use and criminal offending, and the DISIV for alcohol and drug abuse and dependence. Results showed that casino gambling is associated with a high risk of gambling pathology. Lottery, cards, and bingo are associated with a moderately high risk of gambling pathology. Participation in a greater number of types of gambling is strongly predictive of gambling pathology, even after frequency of gambling and size of win or loss are taken into account. Alcohol abuse is strongly predictive of gambling pathology, even with gambling behaviors held constant. Minority and low socioeconomic status (SES) group members have higher levels of gambling pathology than other groups after all other factors are considered. D 2003 Elsevier Ltd. All rights reserved. Keywords: Pathological gambling; Substance use; Adults
1. Introduction Risk for pathological gambling has been correlated with selected aspects of gambling activity, substance use and criminal offending, and sociodemographic characteristics. The * Corresponding author. Tel.: +1-716-887-2503; fax: +1-716-887-2510. E-mail address: [email protected] (J.W. Welte). 0306-4603/$ – see front matter D 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.addbeh.2003.08.007
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gambling factors studied have generally been the amount of money spent on gambling and types of gambling activity. The National Research Council (1999, p.79) reviewed eight surveys of various U.S. states that measured the respondents’ ‘‘expenditures’’ on gambling. In these studies, the expenditures of nonproblem gamblers ranged from US$24 to 131 per month, while the monthly expenditures of problem or pathological gamblers ranged from US$121 to 660. However, researchers have seldom made a distinction between a large number of small bets and a small number of large bets, implicitly taking the position that money lost is money lost, regardless of betting strategy. There have also been suggestions in the literature that certain types of gambling are more likely to produce gambling addiction than other types. It has been proposed that types of gambling which produce a highly variable reinforcement schedule, such as slot machines, may be associated with hard-toextinguish habitual gambling (Griffiths, 1995, p.11). It has also been suggested that types of gambling that provide instant feedback are more addictive than other forms of gambling. For example, the Illinois Institute for Addiction Recovery (2001) refers to video poker as the ‘‘crack cocaine of gambling,’’ and states that the immediate gratification available with video poker shortens the length of time necessary for chronic gambling addiction to develop. Griffiths (1999) identified ‘‘event frequency’’ as possibly related to the addictive properties of different forms of gambling. Event frequency is defined in terms of the time interval between gambling outcomes. Slot machines, for example, have a high event frequency because there is only a few seconds between outcomes, whereas sports betting has a low event frequency. Reasoning from principles of operant conditioning, Griffiths hypothesized that high event frequency is associated with greater addictive properties, and cited research from several countries that suggests that high-frequency gambling machines are highly pathological. However, other research studies have not verified these conjectures about highly addictive forms of gambling. For example, Volberg (1993a) found that instant scratchoff forms of lottery had a low association with pathological gambling. The National Research Council found that problem/pathological gamblers were disproportionately involved in bingo, lotteries, racetrack betting, and sports betting as compared with nonproblem gamblers (p. 78). Many studies have shown positive correlations between gambling and substance abuse among adults. In a general population survey in Texas, Wallisch (1993) found that pathological gamblers drank alcohol and used illicit drugs at several times the average rates. Even more strikingly, an Alberta general population survey (Smith, Volberg, & Wynne, 1994) reported that 100% of the respondents who were pathological gamblers were also smokers. These pathological gamblers also had higher rates of heavy drinking and drug use than the general population. Smart and Ferris (Addiction Research Foundation, 1994), in an Ontario general population telephone survey, found that the same respondents tended to report negative consequences from both gambling and drinking. The gambling–drug relationship has also been found in student populations. Frank (1992), analyzing data from a five-state college survey, found that illegal drug use and heavy drinking positively predicted scores on the South Oaks Gambling Screen. In a clinical study, Lesieur, Blume, and Zoppa (1986) examined patients in treatment for alcohol or drug abuse and found that 19% were pathological or problem gamblers, a rate much higher than found in the general population.
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The national adult survey conducted by our research group showed that being alcohol dependent increased the odds of being a pathological gambler by a factor of 23 (Welte, Barnes, Wieczorek, Tidwell, & Parker, 2001). In addition to substance abuse, pathological gambling has also been linked to criminal offending. Another study by our own research group (Barnes, Welte, Hoffman, & Dintcheff, 1999) found that in two longitudinal studies, various types of delinquency and criminal offending, were strongly correlated with frequency of gambling. Blaszczynski and McConaughy (1994) found pathological gamblers to have a much higher than average risk of committing criminal offenses, exhibiting aggressive behaviors, and having a diagnosis of antisocial personality disorder. Pathological gambling was found by Lesieur et al. (1991) to be positively correlated with arrests for nontraffic offenses in college students. Pathological gambling has shown fairly consistent demographic patterns in the United States. The national telephone survey conducted by the National Gambling Impact Study Commission (NORC, 1999) found that pathological gambling was significantly more prevalent among males than females and more prevalent among African Americans than European Americans. Lower than average prevalence was found among the affluent and the elderly. Our own national survey (Welte et al., 2001) found similar trends. While pathological gambling was not more prevalent among males than females, it was distinctly more prevalent among minority respondents than among whites. Pathological gambling was also less prevalent than average among the elderly and affluent. Numerous state and regional surveys conducted in the last 15 years have shown similar gender, race, and age patterns, with pathological gamblers tending to be disproportionately male, young, and minority group members (e.g., Volberg, 1993b; Volberg & Steufen, 1991; Wallisch, 1993). In the current study, we will extend previous research related to risk factors for pathological gambling by including more detailed information on gambling behaviors and by considering the contribution of multiple factors from the three domains described above. The data on 15 different types of gambling will enable us to examine the strength of the relationship of each type of gambling with pathological symptoms. We will also examine distinct effects of gambling frequency, size of win or loss, and number of types of gambling engaged in on gambling pathology. The existing literature has shown consistent demographic trends of pathological gambling. It is possible that the relationships between demographic factors and gambling pathology are mediated by gambling behavior, such that the trends would disappear if gambling behavior were held constant. For example, if the elderly have lower rates of gambling pathology, it may be only because they bet less frequently and make smaller bets. However, it is also possible that protective factors allow for individuals with the same gambling behavior to have varying risks of pathology. We will address this issue below. A similar question arises concerning the relationship between gambling, substance abuse, and other problem behaviors. It may be that pathological gambling and alcohol dependence, for example, are correlated only because alcoholics bet more frequently and bet larger amounts. However, it is likewise possible that at a given level of gambling behavior, the alcoholdependent person is more vulnerable to gambling compulsion. This question will also be addressed below.
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2. Method 2.1. Survey design We conducted a national random-digit-dial telephone survey with a representative sample of U.S. residents aged 18 or older. The random-digit-dial sample was purchased from Survey Sampling of Fairfield, CT. The telephone sample was selected randomly from a sampling frame of all working telephone blocks in the United States. The sample was stratified by county and by telephone block within county. This resulted in a sample that was spread evenly across the United States, and not clustered by geographic area. This type of sample does not create any adverse design effect, and the sampling error is likely to be somewhat smaller than that of a simple random sample of the United States. The interviews were conducted by trained interviewers at the Research Institute on Addiction’s Computer-Assisted Telephone Interviewing (CATI) facility. A total of 14,700 telephone numbers were contacted. A total of 4338 of these numbers were determined to be households containing an eligible potential respondent, with the remainder being nonhousehold numbers (e.g., businesses, data lines, nonassigned numbers) or else numbers whose eligibility could not be determined (e.g., numbers that were never answered). In those 4338 eligible households, 302 of the selected respondents were physically or mentally unable to participate in the interview, leaving 4036 eligible households in which we selected a respondent who was able to be interviewed, if willing. A total of 2631 interviews were conducted; therefore the response rate was 2631/ 4036 = 65.2%. The remaining 1404 uninterviewed respondents either refused both the original interviewer and the refusal converter, or else they were rescheduled and pursued unsuccessfully until the study ended. Each telephone number was called at least seven times to determine if that number was assigned to a household containing an eligible respondent. Once a household was designated as eligible, the number was called until an interview was obtained or refusal conversion had failed. The respondents were recruited by selecting randomly from the adults in each household by taking the adult with the most recent birthday. The 2631 telephone interviews were conducted from August 1999 through October 2000. The survey was in the field for approximately a year, which was according to plan. This was done to make sure that the study captured a representative sample of seasonal effects, and to allow the use of a smaller but highly trained and carefully supervised crew of interviewers. Interviews were conducted in all 50 states plus the District of Columbia. 2.2. Questionnaire Gambling participation was measured with 15 sets of questions, corresponding to the 15 types of gambling listed in Table 1. For each type, the respondents were asked if they had participated in that type of gambling in the past year. If they responded affirmatively, they were asked how frequently they had participated in that type of gambling in the past year. There were eight response options for the frequency questions, ranging from ‘‘every day’’ to ‘‘never in the past 12 months.’’ For three types of gambling (pulltabs, lottery and raffles/office pools/charity gambling), the respondents were then asked: (1) how much they spent (on
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Table 1 Negative binomial regression predicting count of pathological symptoms from volume of various types of gambling, last year gamblers, N = 2168
Raffles, charity Lottery Pulltabs Internet gambling Casino Horse or dog track Off-track Gambling machines Cards Games of skill Video keno Bingo Dice Sports betting Other
Percent who played past year
Mean volume for those who played (US$/year)
IRR for US$1000/year volume unitsa
59 80 12 0.4 32 8 2 21 25 17 9 15 5 24 2
291 327 397 665 1188 929 2004 619 641 630 328 634 1137 605 1343
1.07 1.26*** 1.75* 1.29 1.40*** 1.10 0.89 1.05 1.32*** 0.98 0.91 1.34* 0.91 1.14** 0.99
a
The IRR is the Factor by which the dependent variable is multiplied for one unit increase in the independent variable. * Significant at .05 value. ** Significant at .01 value. *** Significant at .001 value.
lottery tickets, pulltabs, etc.) the last time they played that particular game, (2) whether they won, and (3) how much they won. These questions allow for a calculation of the respondent’s net result, whether negative or positive. For the other 12 types of gambling, the respondents were asked: (1) whether they won or lost the last time they played, and (2) how much they won or lost. For all these gambling participation questions except the frequency question, the respondent was not forced to choose from a set of responses. Rather, the respondent gave an unguided answer (e.g., ‘‘I won 100 dollars’’), and that quantity was recorded by the interviewer. Table 1 uses brief names for the types of gambling which were more extensively described in the questionnaire. For example, (a) lottery included scratch tickets, (b) casino gambling included all gambling done in a casino, river boat, or cruise ship, (c) racetrack included any gambling at a racetrack, and was not limited to betting on the races, machines included slots, poker machines, and video terminals, (d) the specific questions about cards, dice, gambling machines, sports betting all excluded gambling done in a casino or track— these were covered in the casino and track questions, (e) games of skill were exemplified by pool, golf, and backgammon. Our measure of pathological gambling was from the Diagnostic Interview Schedule (DIS)-IV. The DIS for pathological gambling contains 13 items that map into 10 criteria, such as preoccupation with gambling and needing to gamble with increasing amounts of money to get the same excitement. Endorsement of five criteria is considered pathological gambling, and for our purposes we considered endorsement of three criteria to
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be problem gambling. Respondents who endorsed the requisite number of items for the past year were considered to be current pathological or problem gamblers; respondents who endorsed the requisite number of items for their entire lives were considered to be lifetime pathological or problem gamblers. Our measure of alcohol consumption comes from pairs of quantity and frequency questions for each of five types of alcoholic beverage: beer/malt liquor, fortified wine, wine, liquor, and wine cooler. Respondents were asked how frequently they drank the beverage in the past year, and how much they drank on a typical occasion. The drug frequency questions asked how many times the respondent used four different types of drugs in the past year: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The measures of alcohol abuse and dependence and drug abuse and dependence were from the DIS for DSM-IV (Robins, Marcus, Reich, Cunningham, & Gallagher, 1996). Alcohol or drug abuse is signified by the occurrence of one or more serious negative consequences, such as repeated driving while under the influence. Alcohol or drug dependence is signified by the occurrence of three or more signs of dependence, such as tolerance or withdrawal. Occurrence of the requisite number of consequences or signs in the past year constituted current abuse or dependence. In addition to alcohol, the abuse and dependence questions were asked with respect to four different types of drugs in the past year: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The number of self-reported criminal offenses in the past year is derived from 12 items taken from the work of Elliott, Huizinga, and Ageton (1985). The respondents were asked how many times in the past year they committed 12 common crimes, such as larceny and writing a bad check. 2.3. Independent variables The measure of gambling volume was calculated by multiplying gambling frequency (the number of times a type of gambling was engaged in during the past year) by the gambling quantity (absolute value of the most recent win or loss). For all gambling, the value of this variable was computed by summing the volumes of the games that each respondent had played in the past year. The gambling volume variable is a measure of the total amount that an individual gambles. It takes into account both the frequency of play and the size of bets. Average daily alcohol consumption was computed for each beverage consumed by multiplying the frequency of drinking occasions in the past year by the number of ounces of pure ethanol consumed on a typical occasion. Total average consumption was computed by summing across five alcoholic beverages: beer/malt liquor, fortified wine, wine coolers, wine, and liquor. Drug use was the total number of times that the four drugs described above were used in the past year. Alcohol abuse and dependence as well as drug abuse and dependence were determined as described above. Our collapsed drug abuse or dependence variables are based on DIS questions in four drug categories: marijuana, cocaine, barbiturates/sedatives, narcotics/analgesics. The measure of socioeconomic status (SES) was based on the mean of three equally weighted factors: family income, years of education, and occupational prestige. When one of these factors was absent (as with a nonworking respondent), the mean of the two existing
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factors was used. Occupational prestige was coded from census occupation categories using the method of Duncan updated (Stricker, 1988). 2.4. Dependent variable Gambling pathology was derived from the DIS-IV (Robins et al., 1996) The DIS for pathological gambling contains 13 items that map into 10 criteria, such as preoccupation with gambling and needing to gamble with increasing amounts of money to get the same excitement. The conventional use of these items is to form a dichotomous pathological gambling variable with a cutpoint of five or more criteria. However, the current study uses this information differently. The dependent variable for the current study is the sum of the DIS items (out of a possible 13) that were endorsed for the 12 months previous to the interview. This is an efficient use of the all the available information. 2.5. Analyses Results were statistically weighted to compensate for the number of potential respondents in the household. Weighting adjustments were also used to align the sample with gender, age, and race distributions shown in the United States census estimates for the year 2000. Males, Hispanics, Asians, and older respondents were ‘‘weighted up.’’ The weighted distributions of the sample, according to region, gender, race, and age, closely match the U.S. population. Analyses for the current study were conducted with the 2168 respondents who gambled in the 12 months before the interview. Statistical modeling of gambling pathology was performed by negative binomial regression. The dependent variable for the current study is a count of symptoms that could possibly range from 0 to 13. Its actual distribution was as follows. Eighty-three percent (83%) of the 2168 cases were zero. The other values declined in frequency from 1 (8% of the cases) to 13 (two occurrences). The mean of this variable was 0.45, and its variance was 2.0. Linear regression was not appropriate, as it assumes that the dependent variable ranges from negative to positive infinity, can take on any real value, and is normally distributed. This count variable is ‘‘over-dispersed’’; that is, its variance is much greater than its mean. Therefore, the appropriate regression model is negative binomial rather than Poisson regression (see Cameron & Trivedi, 1998, for a thorough discussion of regression using count data).
3. Results Table 1 shows the relationship between degree of participation in various types of gambling and the extent of gambling pathology. All gambling done at casinos or the track are included in those categories; other categories such as ‘‘dice,’’ ‘‘cards,’’ or ‘‘gambling machines’’ refer to gambling not done at a casino or track. The first column shows the percentage of respondents, among the 2168 who gambled in the past year, who engaged in a particular type of gambling. For example, 32% of the 2168 representative gambling adults
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interviewed in our study engaged in casino gambling in the year before the interview. The second column shows that the average volume for those casino gamblers was US$1188 per year. For all the other respondents included in the analyses, the casino gambling volume was zero. The third column contains regression parameters from a negative binomial regression. A negative binomial regression was performed in which the volumes of all 15 types of gambling were used as independent variables, and the count of pathological symptoms was used as a dependent variable. The parameters in this negative binomial regression are expressed as incidence rate ratios (IRR). The IRR is the factor by which the dependent variable (DV) is multiplied for every unit increase in the independent variable (IV). For example, every US$1000 per year of casino gambling volume engaged in by an individual gambler leads to a multiplication of the number of gambling pathology symptoms by 1.40, or a 40% increase. Only casino, lottery, pulltabs, cards, bingo, and sports betting have statistically significant IRRs. The IRRs show that pulltabs had the greatest impact on individual gambling pathology and casino gambling has the second greatest impact, while sports betting had a relatively smaller impact. Pulltabs seems to be a highly pathological form of gambling for those who do buy them, although confidence in this result must be qualified because the relevant regression parameter is significant only at the .05 level. The other significant results ranged somewhere in between these extremes. Internet gambling was done by only nine respondents, so it is not surprising that its adjusted IRR is not statistically significant. It is important to make the distinction between the impact of a type of gambling on the individual who engages in it (reflected by the IRR) and the impact of a particular type of gambling on gambling pathology in the United States as a whole. For example, bingo had an IRR of 1.34, and casino gambling had an IRR of 1.40, suggesting similar effects on an individual’s gambling pathology for every additional US$1000 of volume. However, casino gambling had both a higher percentage who played and a higher average volume than bingo, so it must have a much greater impact on the gambling problem in the United States as a whole. Another analysis, not shown in the tables, ascertained the reduction in the Cragg and Uhler’s pseudo-R-squared was associated with the removal of each independent variable from the model. This analysis showed that casino gambling had by far the largest contribution to gambling pathology in the entire sample, and that lottery had the second highest contribution to overall gambling pathology. Casino gambling was engaged in by 32% of the current gamblers, and it led to a robust 40% increase in pathological gambling symptoms for every US$1000 in volume. Because lottery play is the most common type of gambling (reported in the past year by 80% of our 2168 gambling respondents), lotteries also make a very important contribution to the total gambling pathology in the United States. Table 2 shows how various aspects of gambling behavior, along with substance use and other characteristics of the respondent, are related to gambling pathology. The IRRs are shown from three different negative binomial regressions, all of which use the count of DIS pathological symptoms as the dependent variable. These three regressions are organized into ‘‘blocks.’’ The first block shows the parameters from a model with only the first block of independent variables entered into the model. The second block shows the parameters from a model with both the first and second block independent variables, and the third block shows parameters from a model with all three blocks included in the analysis.
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Table 2 Negative binomial regression predicting count of pathological symptoms; predictors entered by block, last year gamblers, N = 2168 Block 1
Block 2
Block 3
Independent variable
IRRa
times gambled per week average win/loss in US$100 units number of types of gambling in year ethanol consumption in ounces per day number of times used drugs in past year number of crimes committed in past year current alcohol abuse or dependence current drug abuse or dependence male gender African American Hispanicb Asianb American Indianb Otherb Standardized age Standardized SES
1.26*** 1.65*** 1.34*** 0.98 1.00 1.00 3.06*** 1.94 0.88 2.70*** 1.96** 4.71*** 2.19 2.02* 0.93 0.66***
a
The IRR is the factor by which the dependent variable is multiplied for one unit increase in the independent variable. b Whites are the reference group * Significant at .05 value. ** Significant at .01 value. *** Significant at .001 value.
The first block of Table 2 shows the aspects of gambling behavior that influence gambling pathology. As would be expected, gambling frequency is significantly related to gambling pathology. Every instance of weekly gambling (52 occasions of gambling in the past year) increases pathological symptoms by 26%. Likewise, the average win or loss is highly related to gambling pathology. Every US$100 increase in the average win/loss is associated with a 65% increase in number of pathological gambling symptoms. This analysis also shows that gambling pathology is very strongly related to the number of types of gambling games that the respondent played in the past year. Every additional type of gambling produces a 34% increase in pathological symptoms while holding constant both the frequency of gambling and the average size of the respondent’s last win or loss. Fig. 1 shows the simple bivariate relationship between the number of types of gambling and average number of pathological symptoms. As shown, there are sudden increases in gambling pathology after five types in the past year and after eight types in the past year. Respondents who engaged in nine or more types of gambling in the year before the interview had strikingly high average pathology. The second block of Table 2 shows predictors related to alcohol use, abuse and dependence as well as drug use, drug abuse and drug dependence, and number of crimes committed by the respondent in the past year. These variables are all estimated after controlling for the gambling variables in the first block. When these variables are considered jointly, only
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Fig. 1. Gambling pathology by number of types of gambling.
alcohol abuse/dependence predicts gambling pathology after the influence of gambling frequency, quantity, and number of types of gambling are accounted for. The third block of Table 2 shows the sociodemographic risk factors associated with pathological gambling, after controlling for gambling behaviors, substance use and criminal offending. The age and SES variables are standardized, so the IRRs represent the percentage change in pathological symptoms for one standard deviation in age or SES. Gender is not significantly related to gambling pathology when the other independent variables are adjusted for. These results also show that being African American, Hispanic, or Asian and having low SES are significant risk factors for pathological gambling, even after taking into account gambling frequency, size of wins and losses, number of types of gambling, substance use, and criminal offending. It is also noteworthy that the Block 1 variables—gambling frequency, size of win or loss, and gambling versatility—retain their significance in predicting gambling pathology after all of the problem behavior and demographic variables are inserted into the model.
4. Discussion Several interesting results have emerged from these analyses. One such result is that while engaging in any one type of gambling can be associated with pathological gambling, some types of gambling are associated with a much higher risk of pathological gambling than other types of gambling. Bivariate analyses (not shown) revealed that, considered in isolation, all
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types of gambling are correlated strongly with pathological gambling symptoms. However, when all forms of gambling are considered simultaneously, only casino gambling, lottery, pulltabs, cards (outside a casino), bingo, and sports betting significantly predict gambling pathology. The correlation between the other types of gambling and pathological gambling is spurious; the source of the pathology lies elsewhere. The IRRs show that the most potent sources of gambling pathology for individuals are, in descending order of risk: pulltabs, casino gambling, bingo, cards (played outside a casino), lottery, and sports betting. These six types of gambling have little in common by way of event frequency, immediate feedback, variability of reinforcement schedule, or gambling venue. However, it is worth noting that the two riskiest types among those that significantly predict pathology, pulltabs, and casino gambling have high event frequencies. The least risky of the statistically significant types of gambling, lottery, and sports betting have low event frequencies. Bingo and the lottery are sometimes thought of as innocuous forms of gaming. However, this is clearly not the case based on the present findings. While only nine internet gamblers were interviewed, their volume of internet gambling as a stand-alone predictor was powerfully correlated with gambling pathology (analysis not shown in tables). Because it is likely that internet gambling will increase in the coming years, future research studies should track the trends and correlates of this form of gambling. While it is not surprising that the respondent’s extent of gambling pathology would be related to his or her frequency of gambling and the size of wins and losses, it is far from selfevident that gambling versatility (number of different types of gambling) would predict the extent of gambling pathology. Given two gamblers who play with equal frequency and have the same size of average win or loss, why would the one with the greatest gambling versatility have the most serious addiction to gambling? Engaging in a large number of forms of gambling could be an indication of an attachment to the essence of the gambling experience. A sports bettor may gamble partly as an extension of his/her interest in sports; a bingo player may be betting partly because of the social contacts that he/she enjoys at the bingo hall. But what about the respondents who engaged in six or more types of gambling in a 12-month period? These individuals might be devoted to the core experience of gambling—risking money in an attempt to win money. Another noteworthy finding is that alcohol abuse and dependence have a particularly important association with gambling pathology. This is true because when variables measuring drug use, alcohol use, criminal offending, drug abuse/dependence, and alcohol abuse/dependence are used to jointly predict gambling pathology, alcohol abuse /dependence carries all the predictive weight and the other variables lose their significance. Undoubtedly, some individuals with low self-control tend to both drink and gamble excessively. Furthermore, it is likely that the acute effects of alcohol may lead to poor judgement in gambling. However, the correlation between alcohol pathology and gambling pathology is not due solely to these factors, because if it were, the correlation would vanish when gambling and drinking behavior are held constant. The chronic effects of alcohol that are reflected by a designation of abuse or dependence may contribute directly to worsen gambling pathology, without increasing gambling behavior. For example, the reduced income that results from alcohol pathology could increase financial pressure and lead directly to an exacerbation of
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pathological gambling symptoms such as borrowing money to gamble, forging checks, etc. This could also be true of drug dependence or abuse, but so many drug-dependent persons also have heavy alcohol involvement that alcohol pathology emerges as the key predictor. Alcohol can be linked with gambling much more openly than drugs. Casinos serve free drinks, gamblers drink at the track and the card table; thus, heavy drinking is often part of the gambling experience. This study clearly shows that minority status and low SES are significantly linked to gambling pathology, even after adjusting for gambling behavior, substance use, and other criminal behaviors, as well as the other sociodemographic factors, gender, and age. Lower SES persons might have more gambling pathology than higher SES persons who gamble the same amount due to the fact that higher SES persons have more income and more financial resources to buffer the effects of gambling losses. It is less clear why African American, Hispanic, or Asian respondents might be at greater risk for pathological gambling after controlling for gambling behavior and SES. One possible straightforward explanation has to do with net worth. Minorities in the United States have a much lower net worth than whites, even at the same income levels. The U.S. Department of Commerce (2001) reports that African American and Hispanic households have only 1/7 of the net worth of white households, a far greater disparity than can be explained by differences in income. A further explanation of this phenomenon may be that some social groups have more financial sophistication than others. Schissel (2001) documents and discusses how lower SES persons may see gambling as a form of investment, and a possible escape from poverty. An individual who believes that gambling has these positive qualities may be more prone to gambling addiction. These findings show that diagnoses of pathological and problem gambling may have complex causes beyond mere frequent gambling or making large bets. Risk for pathological gambling is related to gambling versatility, alcohol pathology, and membership in at-risk sociodemographic groups.
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