J Gambl Stud DOI 10.1007/s10899-015-9536-z ORIGINAL PAPER
Problem Gambling Associated with Violent and Criminal Behaviour: A Danish Population-Based Survey and Register Study Bjarne Laursen1 • Rikke Plauborg1 • Ola Ekholm1 Christina Viskum Lytken Larsen1 • Knud Juel1
•
Ó Springer Science+Business Media New York 2015
Abstract This study compares the number of criminal charges among problem gamblers (N = 384) and non-problem gamblers including non-gamblers (N = 18,241) and examines whether problem gambling is more strongly associated with income-generating crimes like theft, fraud and forgery than other types of crimes such as violent crimes. A cohort study was carried out, based on data from the Danish Health and Morbidity Surveys in 2005 and 2010, which were linked at the individual level with data from The Danish National Criminal Register. Multiple logistic regression analyses were used to determine the association between problem gambling and charges for different categories of crime. We found that problem gamblers had significantly higher odds of being charged than nonproblem gamblers (adjusted odds ratio 1.5; 95 % confidence interval 1.1–1.9). The odds ratio for economic crime charges was 2.6 (1.5–4.5), for violence charges 2.2 (1.1–4.5), and for drug charges 3.7 (1.7–8.0). For road traffic charges the odds ratio was 1.3 (1.0–1.8). Hence, there was a strong association between problem gambling and being charged except for road traffic charges; however the association was not stronger for economic charges than for violence and drug charges. Keywords
Gambling Problem gambling Crime Violence Income-generating crime
Introduction In Denmark, most people (91 %) have spent money on gambling at some point in their life (Bonke and Borregaard 2009). To most of these people, gambling is a harmless pastime, but for some people it results in a number of serious negative consequences such as job
& Bjarne Laursen [email protected] 1
National Institute of Public Health, University of Southern Denmark, Oster Farimagsgade 5 A, 2. Floor, 1353 Copenhagen K, Denmark
123
J Gambl Stud
loss, financial problems, relationship breakdown, emotional distress and depression. Furthermore, extensive gambling is expensive and people who develop gambling problems may be at risk of committing fraud, theft, robbery and similar crimes in order to finance their gambling activities or to pay gambling debts. In a previous study, the past year prevalence of problem gaming in Danish adults 16 years or above was found to be 0.8 % in 2010, young men having the highest prevalence of 4.1 % (Ekholm et al. 2014). The prevalence is comparable to other countries taking differences in the definition of problem gambling and differences in age groups into account. Previous studies have investigated the association between gambling and crime by analyzing aggregated community data on crime rates before and after the introduction of casinos in the US (Hakim and Friedman 1985; Albanese 1985; Hakim and Buck 1989; Curran and Scarpitti 1991; Gazel et al. 2001; Stitt et al. 2003; Park and Stokowski 2011). These studies have been inconsistent with some studies showing an association between gambling and crime (Hakim and Friedman 1985; Hakim and Buck 1989; Gazel et al. 2001), and other studies showing no association (Albanese 1985; Curran and Scarpitti 1991; Stitt et al. 2003; Park and Stokowski 2011). Other studies have examined the relationship between pathological gambling and criminal behaviour in Germany (Meyer and Stadler 1999; Zurhold et al. 2014), in Australia (Blaszczynski and McConaghy 1994; Lahn 2005), in Canada (Turner et al. 2009), and in Finland (Kuoppama¨ki et al. 2014). The majority of these studies find a significant relationship between pathological gambling and criminal behaviour (Meyer and Stadler 1999; Blaszczynski and McConaghy 1994; Lahn 2005; Turner et al. 2009). However, most of the studies are based on self-reported data and have focused on relatively small and selected groups of problem or pathological gamblers recruited through treatment centres, self-help groups or prisons and within a defined geographic area. Some studies have suggested that pathological gambling is most often related to income-generating crimes such as theft, fraud and forgery (Lesieur 1984; Brown 1987; Blaszczynski et al. 1989; Turner et al. 2009; Wheeler et al. 2010; Pastwa-Wojciechowska 2011). According to some of these studies, gamblers rarely commit crimes of violence except under extreme pressure and as part of a more desperate property crime such as armed robbery (Lesieur 1984; Brown 1987). The majority of the studies on the gambling–crime relationship have been conducted in the US followed by the UK, Canada, Australia and Germany. According to a Swedish review of studies dealing with gambling and crime there is a lack of European research on the subject (Skagero¨ and Westfelt 2006). The mixed results and the methodological limitations of previous studies indicate a need for more research to determine the association between problem gambling and criminal activity. Denmark has exceptional opportunities to perform register-based research, because of the unique personal identification number assigned to all persons with a permanent residence in the country (Thygesen and Ersbøll 2011). This number makes it possible to link information at the individual level from several registers for investigation of various research questions. To our knowledge, no studies have examined this association using survey data linked to relevant register information at the individual level. Thus, the purpose of the present study is to test whether there is an association at the individual level between problem gambling and being charged and further, whether gambling is more strongly associated with income-generating crimes like theft, fraud and forgery than other types of crimes such as violent crimes.
123
J Gambl Stud
Materials and Methods The study was designed as a retrospective cohort study. Data were derived from The Danish Health and Morbidity Surveys in 2005 and 2010. Furthermore, data were derived from the Danish National Criminal Register during the period 2000–2010. The data used from the 2005 survey were based on a region-stratified random sample of 10,916 Danish citizens aged 16 or above. Data were collected via personal interviews and self-administrated questionnaires. The self-administered questionnaire included two questions about gambling behaviour used to classify respondents as either problem gamblers or nonproblem gamblers. In total, 5686 individuals (52.1 % of the original sample) completed both the personal interview and returned the self-administered questionnaire. Data obtained from the 2010 survey were based on a national random sample of 23,405 Danish citizens aged 16 or above. Data were collected via a self-administered questionnaire that could be completed either on paper or on the Internet. In total, 14,670 individuals (62.7 %) answered the questionnaire. Only participants aged 20 and above were included in the present study because we wanted to trace their criminal record in the 5 years before the survey due to the fact that the age of criminal responsibility in Denmark is 15.
Measurement of Problem Gambling To determine whether a person was a problem gambler the lie/bet questionnaire originally suggested by Johnson et al. (1997) and Johnson and Hamer (1998) using the definition by the American Psychiatric Association (American Psychiatric Association 1994) as background was used. The questions were: ‘Have you ever lied (to family, friends, co-workers or teachers) about how much you gamble, lost or the size of your debt due to gambling?’ and ‘Have you ever felt the need to bet more and more money (in order to get the same level of excitement)?’ For both questions respondents were given the following four answer categories: Yes, in the past 12 months; Yes, previously; No; I never gamble. If a person answered ‘Yes, in the last 12 months’ or ‘Yes, previously’ to either of the two questions, he or she was classified as a problem gambler, and if not, as a non-problem gambler including those who never gambled. When these two questions are combined they represent a measure for lifetime problem gambling, i.e. problem gambling, which has occurred at some point in life whether or not it is a current and/or earlier condition. The term problem gambler covers lifetime problem gambling in the present paper. The same method of classification was used in two recent Danish studies by Ekholm et al. (2014), and Algren et al. (2014). Only respondents that answered both questions were included, leaving 5233 persons in the 2005 study and 13,392 in the 2010 study. Information on alcohol consumption was obtained from survey questions on the weekly alcohol consumption in units of alcohol (beer/wine/spirits); one unit corresponds to 12 grams of alcohol. Smoking was determined based on questions on smoking frequency, dichotomised into whether the person was daily smoker or not.
Measurement of Criminal Behaviour In this study we linked the survey participants in 2005 and 2010 to the Danish National Criminal Register (Gosden et al. 2005) by using their personal identification numbers. The National Criminal Register is administered by the National Commissioner of the Danish Police and contains data on all persons in Denmark over the age of 15 recorded with
123
J Gambl Stud
charges. The register is updated to and includes 2010. This gives us an opportunity to analyze the risk of being charged during the 6-year period before participating in the surveys in 2005 and in 2010, including the survey year, and to compare the risk of being charged during a 5-year period before and after participating in the survey in 2005. After linking the data all person identification was removed and the personal identification number was replaced by an encrypted key thereby ensuring privacy of the participants.
Types of Charges The charges were divided into the following categories: drug charges, charges of violent or sexual offences, weapon-related charges, economic crime charges, road traffic related charges and other charges. Drug charges include charges for smuggling or selling drugs. Charges of violent and/or sexual offences cover a broad range of offences involving potential or actual physical harm to the alleged victim, ranging from threats, simple assaults and indecent exposure to homicide, attempted homicide, aggravated assault and sexual abuse. Weapon-related charges cover any crime that involves the possession or use of a gun or certain types of knives. Economic crime charges include a wide range of activities from bicycle and car thefts to burglary, forgery, fraud, robbery etc. Road traffic Table 1 Prevalence of problem gambling stratified by gender, age, survey year, and education Respondents number
Problem gamblers number
18,625
384
2.1 (1.9–2.3)
2005
5233
136
2.6 (2.1–3.1)
2010
13,392
248
1.9 (1.6–2.1)
Total
Problem gamblers percent (95 % confidence interval)
Survey year
Men 20–24 years
424
45
10.6 (7.9–13.9)
25–34 years
1052
86
8.2 (6.6–10.0)
35–49 years
2370
94
4.0 (3.2–4.8)
50?
4822
79
1.6 (1.3–2.0)
Total
8668
304
3.5 (3.1–3.9)
Women 20–24 years
574
8
1.4 (0.6–2.7)
25–34 years
1303
11
0.8 (0.4–1.5)
35–49 years
2839
29
1.0 (0.7–1.5)
50?
5241
32
0.6 (0.4–0.9)
Total
9957
80
0.8 (0.6–1.0)
Education 10 years or less
2943
78
2.7 (2.1–3.3)
11–12 years
4054
79
1.9 (1.6–2.4)
13–14 years
5658
135
2.4 (2.0–2.8)
15 years or more
5495
65
1.2 (0.9–1.5)
Student or other education
295
19
6.4 (3.9–9.9)
Missing information
180
8
4.4 (1.9–8.6)
123
J Gambl Stud
charges include offences that may result in fines larger than 1500 DKK (1000 DKK before 2001).
Statistical Analysis Multiple logistic regression models were used to determine the association between problem gambling and criminal behaviour. The statistical analyses were all carried out by means of SAS 9.3 (SAS Institute, Inc., Cary, NC). The explanatory factor was problem gambling versus non-problem gambling. The model was adjusted for age, gender, survey year and highest level of completed educational attainment. Further, we adjusted for alcohol consumption and smoking indicating risk-taking behaviour. The respondent´s gender and age was identified via their personal identification numbers and survey year. Age was grouped as follows: 20–24, 25–34, 35–49 and 50 years or above. Respondents’ educational attainment was classified according to the International Standard Classification of Education (ISCED), which combines school and vocational education. Alcohol consumption was classified as above or below the national recommended maximum levels (21 weekly units for men and 14 weekly units for women), and smoking was classified as daily smoker or not. The Danish Health and Morbidity Surveys in 2005 and 2010 were both approved by the Danish Data Protection Agency.
Results The study population consists of 18,625 individuals. Of these, 384 (2.1 %) were identified as problem gamblers. Most of the problem gamblers were men (304). In all, 136 problem gamblers were identified by the survey in 2005 and 248 by the survey in 2010. The 18,241 non-problem gamblers formed the reference group. As shown in Table 1, men aged 20–24 years had the highest prevalence of problem gambling, 10.6 %. The prevalence of problem gambling significantly decreased with increasing age (p \ 0.0001). Table 2 Association between different types of charges and problem gambling Type of charge
Problem gamblers (no. of charges)
Non-problem gamblers (no. of charges)
Problem gamblers (no. of persons) N = 384
Non-problem gamblers (no. of persons) N = 18,241
Drug charges
51
50
11 (2.9 %)
37 (0.2 %)
Violence or sexual offence charges
13
109
11 (2.9 %)
75 (0.4 %)
Weapon-related charges
7
15
7 (1.8 %)
15 (0.1 %)
Economic crime charges
106
219
19 (4.9 %)
144 (0.8 %)
Traffic related charges
119
1972
68 (17.7 %)
1537 (8.4 %)
Other charges
21
179
13 (3.4 %)
113 (0.6 %)
Any of the above
317
2544
87 (22.7 %)
1785 (9.8 %)
Any of the four first
177
393
32 (8.3 %)
246 (1.3 %)
123
J Gambl Stud
Table 2 shows the number of charges filed against problem gamblers and non-problem gamblers, respectively, for the different charge categories during the 6-year period, and the number of people who have been charged in each group. A total of 317 charges were filed against 87 individuals from the problem gambling group, whereas 2544 charges were filed against 1785 individuals from the non-problem gambling group. Thus, the average number of charges against the problem gamblers was 3.6, while the average number of charges against the non-problem gamblers was 1.4. A significantly higher percentage of problem gamblers had been charged compared to non-problem gamblers, 22.7 and 9.8 % respectively (p \ 0.001). Of the 87 charged problem gamblers 82 were men and five were women. More than 80 % of the charges filed against the non-problem gamblers where related to road traffic offences, while this type of charge only represented 38 % of the charges filed against problem gamblers. Economic crime charges accounted for 33 % of the charges against problem gamblers but only for 9 % against the non-problem gamblers. Drug charges accounted for 16 % of the charges against problem gamblers but only for 2 % against the non-problem gamblers. Table 3 shows the adjusted and unadjusted odds ratios of being charged for different types of offences. Problem gamblers had significantly higher odds than non-problem gamblers of being charged for all types of offences. When adjusted for age, gender and education the odds of being charged was lower, but still significantly higher for problem gamblers compared to non-problem gamblers. The results in Table 3 are based on data during the 6-year periods prior to and including the year of the two surveys. Table 4 describes charges filed against participants from the survey in 2005 in two 5-year periods before and after they answered the survey, i.e. the periods 2000–2004 and 2006–2010. The table shows no significant difference in OR for the different charges before and after the survey year, but there was a tendency to an increase for drug charges and violence related charges. The results show that problem gamblers identified via the 2005 survey had higher odds of being charged both before and after their response to the questionnaire, compared to non-problem gamblers with the exception of traffic related charges.
Table 3 Odds ratio for being charged (problem gamblers compared to non-problem gamblers) Type of charge
OR (95 % CI) Unadjusted
OR (95 % CI) Adjusteda
OR (95 % CI) Adjustedb
Drug charges
14.5 (7.3–28.7)
4.4 (2.1–9.1)
3.7 (1.7–8.0)
7.1 (3.8–13.6)
3.3 (1.7–6.4)
2.2 (1.1–4.5)
Weapon-related charges
22.6 (9.1–55.7)
9.4 (3.5–25.0)
9.2 (3.3–26.0)
Economic crime charges
6.5 (4.0–10.7)
3.6 (2.1–6.0)
2.6 (1.5–4.5)
Violent or sexual offences
Traffic related charges
2.3 (1.8–3.1)
1.5 (1.1–1.9)
1.3 (1.0–1.8)
Other charges
5.6 (3.1–10.1)
3.0 (1.6–5.4)
2.4 (1.2–4.5)
Any of the above
2.7 (2.1–3.4)
1.7 (1.3–2.1)
1.5 (1.1–1.9)
Any of the four first
6.7 (4.5–9.8)
3.3 (2.2–4.9)
2.4 (1.6–3.7)
a
Adjusted for age, gender, year and education. A total of 180 observations were excluded due to missing values
b
Adjusted for age, gender, year, education, alcohol consumption and smoking behaviour. A total of 665 observations were excluded due to missing values
123
J Gambl Stud
Discussion The aim of the present study was to test two hypotheses: (1) Problem gamblers are more likely than non-problem gamblers to be charged; (2) Problem gamblers are more likely to commit income generating crimes than other types of crimes. The study is based on crosssectional data, which means that inferences regarding causation between gambling and crime cannot be made. The results demonstrate a significant association between problem gambling and criminal activity measured as charges. However, we did not find a higher risk of being charged for economic crimes compared to violence and drug related crime. Further the comparison between charges before and after the survey did not indicate an increase in economic crime for the problem gamblers, but rather a tendency to a decrease. The observation that problem gambling is a risk factor for criminal behaviours has been noted by Blaszczynski and McConaghy (1994), Blaszczynski et al. (1989), Meyer and Stadler (1999) and Turner et al. (2009). The studies concerned were all based on selfreported data on criminal activity. Blaszczynski and McConaghy (1994), for example, conducted semi-structured interviews with 306 problem gamblers in New South Wales who were attending Gamblers Anonymous or had been admitted to hospital for inpatient treatment. Interviewees were asked to describe the frequency and nature of any offences they had committed, whether directly or indirectly related or completely unrelated to gambling, and irrespective of whether or not the offences were detected by others. A large share (59 %) admitted having committed criminal offences that were motivated by a need to obtain funds for gambling, while 6 % admitted having committed criminal offences for reasons not directly or indirectly related to gambling or problems caused by gambling behaviour. An important limitation of the study is that due to the survey method problem gambling was defined based on answers to two questions and therefore no clinical validation has been conducted. This information may be unreliable and may lead to misclassification, probably resulting in underestimation of the relationship between problem gambling and criminal behaviour. Further, no information was available on mental disorders and other personality traits that may confound the relation between problem gambling and criminal behaviour.
Table 4 Number of persons charged among problem and non-problem gamblers during a 5-year period before and after the survey year, for the 2005 survey (N = 5233) Charge category
Before survey Problem gambler
Nonproblem gambler
After survey a
OR
Problem gambler
Nonproblem gambler
ORa
Drug, weapon, violence and economic crime charges
8
63
2.4 (1.1–5.4)
7
42
4.0 (1.7–9.3)
Traffic related charges
19
299
1.5 (0.9–2.6)
24
359
1.6 (1.0–2.6)
Any charge
25
359
1.7 (1.1–2.7)
30
408
1.9 (1.2–2.9)
a
OR were adjusted for age, gender and education
123
J Gambl Stud
In this study we used a mix of self-reported data on gambling problems and register data on criminal charges, and we did not have the opportunity to examine the motivation behind crimes committed by problem gamblers. Consequently, it is not possible to determine to what extent the problem gamblers were driven to commit crimes by a desire, need or compulsion to gamble or pay off gambling debts. It is possible, that psychological characteristics like risk-seeking behaviour may be a confounder causing both problem gambling and criminal behaviour. This is supported by the fact that problem gambling is associated with other risk behaviours like smoking, alcohol and drug use (Algren et al. 2014). The questionnaire could be completed either online or on paper. It is well known that survey modes might influence responses, but differences exist primarily between interviewer modes and self-administrated modes and not within these modes (Hoebel et al. 2014). Thus, there is no reason to expect that the survey modes used in the present study would affect responses differently. In some ways our findings support the recent changes in the Diagnostic Statistical Manual of Mental Disorders. In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders the criteria of illegal acts has been removed and the perception that if a person’s borrowing resources are strained, the person may resort to criminal behaviour in order to obtain money, no longer persists (American Psychiatric Association 2013). Our results supplement this understanding by illustrating that although the prevalence of criminal behaviour is higher among problem gamblers, this behaviour might not be directly related to chasing funds for gambling but rather reflects the complexity of risk-seeking behaviour among problem gamblers. Despite these limitations, the present study has several major strengths. The study is based on randomly selected participants and representative of the general population, which provides sufficient statistical power and makes our findings generalizable to broader populations of problem gamblers. Furthermore, it has been argued, that one of the difficulties of determining the extent of gambling-related crime is the lack of objective data concerning the criminal offences reported, as many studies rely on self-reported data rather than official data, and that the accuracy of these data is sometimes questionable (Sakurai and Smith 2003). This study relies on register data on charges, which are not only more reliable compared to self-reported data but also unaffected by survey response bias and recall bias. In addition, by linking survey data with registry data it is possible to follow individuals over time and use a comparison group of non-problem gamblers. It should, of course, be noted that being charged does not mean you are sentenced or even guilty. On the other hand, a person might have committed a crime and not be charged for it. This is particularly the case for burglary, where only a low fraction of crimes are solved. As mentioned above, previous studies have suggested that problem gamblers do not commit crimes of violence except under extreme pressure but rather specialize in income producing crimes (Brown 1987; Lesieur 1984). In the present study, we found that problem gamblers were more likely than non-problem gamblers to commit both violent and incomegenerating crimes, drug crimes and weapon-related crimes. However, income-generating crime charges accounted for a large number of charges in both the problem gambling and the non-problem gambling group. A possible explanation for the high proportion of violence, weapon and drug charges in our study compared to other studies might be that these crimes are underreported in the previous studies based on self-reported data on criminal careers. Either because the persons holding these charges are less likely to participate in a survey or because they may be too
123
J Gambl Stud
embarrassed or unwilling to report their criminal activities. According to several studies problem gamblers do not always perceive their illegal acts as criminal and therefore they are prone to under-report these acts when asked about their criminal history (Bergh and Ku¨hlhorn 1994; Lesieur 1984). Acknowledgments This work was funded by the Danish Agency for Science, Technology and Innovation. Conflict of interest The authors declare that they have no conflict of interest.
References Albanese, J. S. (1985). The effect of casino gambling on crime. Federal Probation ,49(2), 39–44. Algren, M. H., Ekholm, O., Davidsen, M., Larsen, C. V. L., & Juel, K. (2014). Health behaviour and body mass index among problem gamblers: Results from a nationwide survey. Journal of Gambling Studies. doi:10.1007/s10899-013-9437-y. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Bergh, C., & Ku¨hlhorn, E. (1994). Social, psychological and physical consequences of pathological gambling in Sweden. Journal of Gambling Studies, 10(3), 275–285. Blaszczynski, A., & McConaghy, N. (1994). Antisocial personality disorder and pathological gambling. Journal of Gambling Studies, 10(2), 129–145. Blaszczynski, A., McConaghy, N., & Frankova, A. (1989). Crime, antisocial personality and pathological gambling. Journal of Gambling Behavior, 5(2), 137–152. Bonke, J., & Borregaard, K. (2009). The prevalence of problematic gambling behaviour: A Scandinavian comparison. Scandinavian Journal of Public Health, 37(6), 654–660. Brown, R. I. F. (1987). Pathological gambling and associated patterns of crime: Comparisons with alcohol and other drug addictions. Journal of Gambling Behavior, 3(2), 98–114. Curran, D., & Scarpitti, F. (1991). Crime in Atlantic City: Do casinos make a difference? Deviant Behavior, 12(4), 431–449. Ekholm, O., Eiberg, S., Davidsen, M., Holst, M., Larsen, C. V. L., & Juel, K. (2014). The prevalence of problem gambling in Denmark in 2005 and 2010: A sociodemographic and socioeconomic characterization. Journal of Gambling Studies, 30, 1–10. Gazel, R. C., Rickman, D. S., & Thompson, W. N. (2001). Casino gambling and crime: a panel study of Wisconsin Counties managerial and decision economics. Managerial and Decision Economics, 22(1–3), 65–75. Gosden, N. P., Kramp, P., Gabrielsen, G., Andersen, T. F., & Sestoft, D. (2005). Violence of young criminals predicts schizophrenia: A 9-year register-based followup of 15- to 19-year-old criminals. Schizophrenia Bulletin, 31(3), 759–768. Hakim, S., & Buck, A. J. (1989). Do casinos enhance crime? Journal of Criminal Justice, 17, 409–416. Hakim, S., & Friedman, J. (1985). The impact of casino gambling on crime in Atlantic City and its region. Research Report. National Institute of Justice, USA. https://www.ncjrs.gov/App/Publications/abstract. aspx?ID=108233. Accessed August 15, 2014. Hoebel, J., von der Lippe, E., Lange, C., & Ziese, T. (2014). Mode differences in a mixed-mode health interview survey among adults. Archives of Public Health, 72, 46. Johnson, E. E., & Hamer, R. M. (1998). The Lie/Bet Questionnaire for screening pathological gamblers: A follow-up study. Psychological Reports, 83(3 Pt 2), 1219–1224. Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80(1), 83–88. Kuoppama¨ki, S. M., Ka¨a¨ria¨inen, J., & Lind, K. (2014). Examining gambling-related crime reports in the National Finnish Police Register. Journal of Gambling Studies, 30(4), 967–983. Lahn, J. (2005). Gambling among offenders: Results from an Australian survey. International Journal of Offender Therapy and Comparative Criminology, 49(3), 343–355. Lesieur, H. R. (1984). The chase: career of the compulsive gambler. Cambridge, MA: Schenkman Publishing Co.
123
J Gambl Stud Meyer, G., & Stadler, M. A. (1999). Criminal behavior associated with pathological gambling. Journal of Gambling Studies, 15(1), 29–43. Park, M., & Stokowski, P. A. (2011). Casino gaming and crime: Comparisons among gaming counties and other tourism places. Journal of Travel Research, 50, 289–302. Pastwa-Wojciechowska, B. (2011). The relationship of pathological gambling to criminality behavior in a sample of Polish male offenders. Medical Science Monitor, 17(11), CR669–CR675. Sakurai Y., & Smith R. G. (2003). Gambling as a motivation for the commission of financial crime. Trends & issues in crime and criminal justice, no. 256. Canberra: Australian Institute of Criminology. Skagero¨, A., & Westfelt, L. (2006). En litteraturstudie om spel och kriminalitet [A literature study of gaming and crime]. SoRad Rapportserie, no. 33. Stockholm: Stockholms Universitet, SoRAD. Stitt, B. G., Nichols, M., & Giacopassi, D. (2003). Does the presence of casinos increase crime? An examination of casino and control communities. Crime & Delinquency, 49(2), 253–284. Thygesen, L. C., & Ersbøll, A. K. (2011). Danish population-based registers for public health and healthrelated welfare research: Introduction to the supplement. Scandinavian Journal of Public Health, 39(suppl. 7), 8–10. Turner, N. E., Preston, D. L., Saunders, C., McAvoy, S., & Jain, U. (2009). The relationship of problem gambling to criminal behavior in a sample of Canadian male federal offenders. Journal of Gambling Studies, 25(2), 153–169. Wheeler, S. A., Round, D. K., & Wilson, J. K. (2010). The relationship between crime and electronic gaming expenditure: Evidence from Victoria, Australia. Journal of Quantitative Criminology, 27(3), 315–338. Zurhold, H., Verthein, U., & Kalke, J. (2014). Prevalence of problem gambling among the prison population in Hamburg, Germany. Journal of Gambling Studies, 30(2), 309–319.
123
Problem Gambling Associated with Violent and Criminal Behaviour: A Danish Population-Based Survey and Register Study Bjarne Laursen1 • Rikke Plauborg1 • Ola Ekholm1 Christina Viskum Lytken Larsen1 • Knud Juel1
•
Ó Springer Science+Business Media New York 2015
Abstract This study compares the number of criminal charges among problem gamblers (N = 384) and non-problem gamblers including non-gamblers (N = 18,241) and examines whether problem gambling is more strongly associated with income-generating crimes like theft, fraud and forgery than other types of crimes such as violent crimes. A cohort study was carried out, based on data from the Danish Health and Morbidity Surveys in 2005 and 2010, which were linked at the individual level with data from The Danish National Criminal Register. Multiple logistic regression analyses were used to determine the association between problem gambling and charges for different categories of crime. We found that problem gamblers had significantly higher odds of being charged than nonproblem gamblers (adjusted odds ratio 1.5; 95 % confidence interval 1.1–1.9). The odds ratio for economic crime charges was 2.6 (1.5–4.5), for violence charges 2.2 (1.1–4.5), and for drug charges 3.7 (1.7–8.0). For road traffic charges the odds ratio was 1.3 (1.0–1.8). Hence, there was a strong association between problem gambling and being charged except for road traffic charges; however the association was not stronger for economic charges than for violence and drug charges. Keywords
Gambling Problem gambling Crime Violence Income-generating crime
Introduction In Denmark, most people (91 %) have spent money on gambling at some point in their life (Bonke and Borregaard 2009). To most of these people, gambling is a harmless pastime, but for some people it results in a number of serious negative consequences such as job
& Bjarne Laursen [email protected] 1
National Institute of Public Health, University of Southern Denmark, Oster Farimagsgade 5 A, 2. Floor, 1353 Copenhagen K, Denmark
123
J Gambl Stud
loss, financial problems, relationship breakdown, emotional distress and depression. Furthermore, extensive gambling is expensive and people who develop gambling problems may be at risk of committing fraud, theft, robbery and similar crimes in order to finance their gambling activities or to pay gambling debts. In a previous study, the past year prevalence of problem gaming in Danish adults 16 years or above was found to be 0.8 % in 2010, young men having the highest prevalence of 4.1 % (Ekholm et al. 2014). The prevalence is comparable to other countries taking differences in the definition of problem gambling and differences in age groups into account. Previous studies have investigated the association between gambling and crime by analyzing aggregated community data on crime rates before and after the introduction of casinos in the US (Hakim and Friedman 1985; Albanese 1985; Hakim and Buck 1989; Curran and Scarpitti 1991; Gazel et al. 2001; Stitt et al. 2003; Park and Stokowski 2011). These studies have been inconsistent with some studies showing an association between gambling and crime (Hakim and Friedman 1985; Hakim and Buck 1989; Gazel et al. 2001), and other studies showing no association (Albanese 1985; Curran and Scarpitti 1991; Stitt et al. 2003; Park and Stokowski 2011). Other studies have examined the relationship between pathological gambling and criminal behaviour in Germany (Meyer and Stadler 1999; Zurhold et al. 2014), in Australia (Blaszczynski and McConaghy 1994; Lahn 2005), in Canada (Turner et al. 2009), and in Finland (Kuoppama¨ki et al. 2014). The majority of these studies find a significant relationship between pathological gambling and criminal behaviour (Meyer and Stadler 1999; Blaszczynski and McConaghy 1994; Lahn 2005; Turner et al. 2009). However, most of the studies are based on self-reported data and have focused on relatively small and selected groups of problem or pathological gamblers recruited through treatment centres, self-help groups or prisons and within a defined geographic area. Some studies have suggested that pathological gambling is most often related to income-generating crimes such as theft, fraud and forgery (Lesieur 1984; Brown 1987; Blaszczynski et al. 1989; Turner et al. 2009; Wheeler et al. 2010; Pastwa-Wojciechowska 2011). According to some of these studies, gamblers rarely commit crimes of violence except under extreme pressure and as part of a more desperate property crime such as armed robbery (Lesieur 1984; Brown 1987). The majority of the studies on the gambling–crime relationship have been conducted in the US followed by the UK, Canada, Australia and Germany. According to a Swedish review of studies dealing with gambling and crime there is a lack of European research on the subject (Skagero¨ and Westfelt 2006). The mixed results and the methodological limitations of previous studies indicate a need for more research to determine the association between problem gambling and criminal activity. Denmark has exceptional opportunities to perform register-based research, because of the unique personal identification number assigned to all persons with a permanent residence in the country (Thygesen and Ersbøll 2011). This number makes it possible to link information at the individual level from several registers for investigation of various research questions. To our knowledge, no studies have examined this association using survey data linked to relevant register information at the individual level. Thus, the purpose of the present study is to test whether there is an association at the individual level between problem gambling and being charged and further, whether gambling is more strongly associated with income-generating crimes like theft, fraud and forgery than other types of crimes such as violent crimes.
123
J Gambl Stud
Materials and Methods The study was designed as a retrospective cohort study. Data were derived from The Danish Health and Morbidity Surveys in 2005 and 2010. Furthermore, data were derived from the Danish National Criminal Register during the period 2000–2010. The data used from the 2005 survey were based on a region-stratified random sample of 10,916 Danish citizens aged 16 or above. Data were collected via personal interviews and self-administrated questionnaires. The self-administered questionnaire included two questions about gambling behaviour used to classify respondents as either problem gamblers or nonproblem gamblers. In total, 5686 individuals (52.1 % of the original sample) completed both the personal interview and returned the self-administered questionnaire. Data obtained from the 2010 survey were based on a national random sample of 23,405 Danish citizens aged 16 or above. Data were collected via a self-administered questionnaire that could be completed either on paper or on the Internet. In total, 14,670 individuals (62.7 %) answered the questionnaire. Only participants aged 20 and above were included in the present study because we wanted to trace their criminal record in the 5 years before the survey due to the fact that the age of criminal responsibility in Denmark is 15.
Measurement of Problem Gambling To determine whether a person was a problem gambler the lie/bet questionnaire originally suggested by Johnson et al. (1997) and Johnson and Hamer (1998) using the definition by the American Psychiatric Association (American Psychiatric Association 1994) as background was used. The questions were: ‘Have you ever lied (to family, friends, co-workers or teachers) about how much you gamble, lost or the size of your debt due to gambling?’ and ‘Have you ever felt the need to bet more and more money (in order to get the same level of excitement)?’ For both questions respondents were given the following four answer categories: Yes, in the past 12 months; Yes, previously; No; I never gamble. If a person answered ‘Yes, in the last 12 months’ or ‘Yes, previously’ to either of the two questions, he or she was classified as a problem gambler, and if not, as a non-problem gambler including those who never gambled. When these two questions are combined they represent a measure for lifetime problem gambling, i.e. problem gambling, which has occurred at some point in life whether or not it is a current and/or earlier condition. The term problem gambler covers lifetime problem gambling in the present paper. The same method of classification was used in two recent Danish studies by Ekholm et al. (2014), and Algren et al. (2014). Only respondents that answered both questions were included, leaving 5233 persons in the 2005 study and 13,392 in the 2010 study. Information on alcohol consumption was obtained from survey questions on the weekly alcohol consumption in units of alcohol (beer/wine/spirits); one unit corresponds to 12 grams of alcohol. Smoking was determined based on questions on smoking frequency, dichotomised into whether the person was daily smoker or not.
Measurement of Criminal Behaviour In this study we linked the survey participants in 2005 and 2010 to the Danish National Criminal Register (Gosden et al. 2005) by using their personal identification numbers. The National Criminal Register is administered by the National Commissioner of the Danish Police and contains data on all persons in Denmark over the age of 15 recorded with
123
J Gambl Stud
charges. The register is updated to and includes 2010. This gives us an opportunity to analyze the risk of being charged during the 6-year period before participating in the surveys in 2005 and in 2010, including the survey year, and to compare the risk of being charged during a 5-year period before and after participating in the survey in 2005. After linking the data all person identification was removed and the personal identification number was replaced by an encrypted key thereby ensuring privacy of the participants.
Types of Charges The charges were divided into the following categories: drug charges, charges of violent or sexual offences, weapon-related charges, economic crime charges, road traffic related charges and other charges. Drug charges include charges for smuggling or selling drugs. Charges of violent and/or sexual offences cover a broad range of offences involving potential or actual physical harm to the alleged victim, ranging from threats, simple assaults and indecent exposure to homicide, attempted homicide, aggravated assault and sexual abuse. Weapon-related charges cover any crime that involves the possession or use of a gun or certain types of knives. Economic crime charges include a wide range of activities from bicycle and car thefts to burglary, forgery, fraud, robbery etc. Road traffic Table 1 Prevalence of problem gambling stratified by gender, age, survey year, and education Respondents number
Problem gamblers number
18,625
384
2.1 (1.9–2.3)
2005
5233
136
2.6 (2.1–3.1)
2010
13,392
248
1.9 (1.6–2.1)
Total
Problem gamblers percent (95 % confidence interval)
Survey year
Men 20–24 years
424
45
10.6 (7.9–13.9)
25–34 years
1052
86
8.2 (6.6–10.0)
35–49 years
2370
94
4.0 (3.2–4.8)
50?
4822
79
1.6 (1.3–2.0)
Total
8668
304
3.5 (3.1–3.9)
Women 20–24 years
574
8
1.4 (0.6–2.7)
25–34 years
1303
11
0.8 (0.4–1.5)
35–49 years
2839
29
1.0 (0.7–1.5)
50?
5241
32
0.6 (0.4–0.9)
Total
9957
80
0.8 (0.6–1.0)
Education 10 years or less
2943
78
2.7 (2.1–3.3)
11–12 years
4054
79
1.9 (1.6–2.4)
13–14 years
5658
135
2.4 (2.0–2.8)
15 years or more
5495
65
1.2 (0.9–1.5)
Student or other education
295
19
6.4 (3.9–9.9)
Missing information
180
8
4.4 (1.9–8.6)
123
J Gambl Stud
charges include offences that may result in fines larger than 1500 DKK (1000 DKK before 2001).
Statistical Analysis Multiple logistic regression models were used to determine the association between problem gambling and criminal behaviour. The statistical analyses were all carried out by means of SAS 9.3 (SAS Institute, Inc., Cary, NC). The explanatory factor was problem gambling versus non-problem gambling. The model was adjusted for age, gender, survey year and highest level of completed educational attainment. Further, we adjusted for alcohol consumption and smoking indicating risk-taking behaviour. The respondent´s gender and age was identified via their personal identification numbers and survey year. Age was grouped as follows: 20–24, 25–34, 35–49 and 50 years or above. Respondents’ educational attainment was classified according to the International Standard Classification of Education (ISCED), which combines school and vocational education. Alcohol consumption was classified as above or below the national recommended maximum levels (21 weekly units for men and 14 weekly units for women), and smoking was classified as daily smoker or not. The Danish Health and Morbidity Surveys in 2005 and 2010 were both approved by the Danish Data Protection Agency.
Results The study population consists of 18,625 individuals. Of these, 384 (2.1 %) were identified as problem gamblers. Most of the problem gamblers were men (304). In all, 136 problem gamblers were identified by the survey in 2005 and 248 by the survey in 2010. The 18,241 non-problem gamblers formed the reference group. As shown in Table 1, men aged 20–24 years had the highest prevalence of problem gambling, 10.6 %. The prevalence of problem gambling significantly decreased with increasing age (p \ 0.0001). Table 2 Association between different types of charges and problem gambling Type of charge
Problem gamblers (no. of charges)
Non-problem gamblers (no. of charges)
Problem gamblers (no. of persons) N = 384
Non-problem gamblers (no. of persons) N = 18,241
Drug charges
51
50
11 (2.9 %)
37 (0.2 %)
Violence or sexual offence charges
13
109
11 (2.9 %)
75 (0.4 %)
Weapon-related charges
7
15
7 (1.8 %)
15 (0.1 %)
Economic crime charges
106
219
19 (4.9 %)
144 (0.8 %)
Traffic related charges
119
1972
68 (17.7 %)
1537 (8.4 %)
Other charges
21
179
13 (3.4 %)
113 (0.6 %)
Any of the above
317
2544
87 (22.7 %)
1785 (9.8 %)
Any of the four first
177
393
32 (8.3 %)
246 (1.3 %)
123
J Gambl Stud
Table 2 shows the number of charges filed against problem gamblers and non-problem gamblers, respectively, for the different charge categories during the 6-year period, and the number of people who have been charged in each group. A total of 317 charges were filed against 87 individuals from the problem gambling group, whereas 2544 charges were filed against 1785 individuals from the non-problem gambling group. Thus, the average number of charges against the problem gamblers was 3.6, while the average number of charges against the non-problem gamblers was 1.4. A significantly higher percentage of problem gamblers had been charged compared to non-problem gamblers, 22.7 and 9.8 % respectively (p \ 0.001). Of the 87 charged problem gamblers 82 were men and five were women. More than 80 % of the charges filed against the non-problem gamblers where related to road traffic offences, while this type of charge only represented 38 % of the charges filed against problem gamblers. Economic crime charges accounted for 33 % of the charges against problem gamblers but only for 9 % against the non-problem gamblers. Drug charges accounted for 16 % of the charges against problem gamblers but only for 2 % against the non-problem gamblers. Table 3 shows the adjusted and unadjusted odds ratios of being charged for different types of offences. Problem gamblers had significantly higher odds than non-problem gamblers of being charged for all types of offences. When adjusted for age, gender and education the odds of being charged was lower, but still significantly higher for problem gamblers compared to non-problem gamblers. The results in Table 3 are based on data during the 6-year periods prior to and including the year of the two surveys. Table 4 describes charges filed against participants from the survey in 2005 in two 5-year periods before and after they answered the survey, i.e. the periods 2000–2004 and 2006–2010. The table shows no significant difference in OR for the different charges before and after the survey year, but there was a tendency to an increase for drug charges and violence related charges. The results show that problem gamblers identified via the 2005 survey had higher odds of being charged both before and after their response to the questionnaire, compared to non-problem gamblers with the exception of traffic related charges.
Table 3 Odds ratio for being charged (problem gamblers compared to non-problem gamblers) Type of charge
OR (95 % CI) Unadjusted
OR (95 % CI) Adjusteda
OR (95 % CI) Adjustedb
Drug charges
14.5 (7.3–28.7)
4.4 (2.1–9.1)
3.7 (1.7–8.0)
7.1 (3.8–13.6)
3.3 (1.7–6.4)
2.2 (1.1–4.5)
Weapon-related charges
22.6 (9.1–55.7)
9.4 (3.5–25.0)
9.2 (3.3–26.0)
Economic crime charges
6.5 (4.0–10.7)
3.6 (2.1–6.0)
2.6 (1.5–4.5)
Violent or sexual offences
Traffic related charges
2.3 (1.8–3.1)
1.5 (1.1–1.9)
1.3 (1.0–1.8)
Other charges
5.6 (3.1–10.1)
3.0 (1.6–5.4)
2.4 (1.2–4.5)
Any of the above
2.7 (2.1–3.4)
1.7 (1.3–2.1)
1.5 (1.1–1.9)
Any of the four first
6.7 (4.5–9.8)
3.3 (2.2–4.9)
2.4 (1.6–3.7)
a
Adjusted for age, gender, year and education. A total of 180 observations were excluded due to missing values
b
Adjusted for age, gender, year, education, alcohol consumption and smoking behaviour. A total of 665 observations were excluded due to missing values
123
J Gambl Stud
Discussion The aim of the present study was to test two hypotheses: (1) Problem gamblers are more likely than non-problem gamblers to be charged; (2) Problem gamblers are more likely to commit income generating crimes than other types of crimes. The study is based on crosssectional data, which means that inferences regarding causation between gambling and crime cannot be made. The results demonstrate a significant association between problem gambling and criminal activity measured as charges. However, we did not find a higher risk of being charged for economic crimes compared to violence and drug related crime. Further the comparison between charges before and after the survey did not indicate an increase in economic crime for the problem gamblers, but rather a tendency to a decrease. The observation that problem gambling is a risk factor for criminal behaviours has been noted by Blaszczynski and McConaghy (1994), Blaszczynski et al. (1989), Meyer and Stadler (1999) and Turner et al. (2009). The studies concerned were all based on selfreported data on criminal activity. Blaszczynski and McConaghy (1994), for example, conducted semi-structured interviews with 306 problem gamblers in New South Wales who were attending Gamblers Anonymous or had been admitted to hospital for inpatient treatment. Interviewees were asked to describe the frequency and nature of any offences they had committed, whether directly or indirectly related or completely unrelated to gambling, and irrespective of whether or not the offences were detected by others. A large share (59 %) admitted having committed criminal offences that were motivated by a need to obtain funds for gambling, while 6 % admitted having committed criminal offences for reasons not directly or indirectly related to gambling or problems caused by gambling behaviour. An important limitation of the study is that due to the survey method problem gambling was defined based on answers to two questions and therefore no clinical validation has been conducted. This information may be unreliable and may lead to misclassification, probably resulting in underestimation of the relationship between problem gambling and criminal behaviour. Further, no information was available on mental disorders and other personality traits that may confound the relation between problem gambling and criminal behaviour.
Table 4 Number of persons charged among problem and non-problem gamblers during a 5-year period before and after the survey year, for the 2005 survey (N = 5233) Charge category
Before survey Problem gambler
Nonproblem gambler
After survey a
OR
Problem gambler
Nonproblem gambler
ORa
Drug, weapon, violence and economic crime charges
8
63
2.4 (1.1–5.4)
7
42
4.0 (1.7–9.3)
Traffic related charges
19
299
1.5 (0.9–2.6)
24
359
1.6 (1.0–2.6)
Any charge
25
359
1.7 (1.1–2.7)
30
408
1.9 (1.2–2.9)
a
OR were adjusted for age, gender and education
123
J Gambl Stud
In this study we used a mix of self-reported data on gambling problems and register data on criminal charges, and we did not have the opportunity to examine the motivation behind crimes committed by problem gamblers. Consequently, it is not possible to determine to what extent the problem gamblers were driven to commit crimes by a desire, need or compulsion to gamble or pay off gambling debts. It is possible, that psychological characteristics like risk-seeking behaviour may be a confounder causing both problem gambling and criminal behaviour. This is supported by the fact that problem gambling is associated with other risk behaviours like smoking, alcohol and drug use (Algren et al. 2014). The questionnaire could be completed either online or on paper. It is well known that survey modes might influence responses, but differences exist primarily between interviewer modes and self-administrated modes and not within these modes (Hoebel et al. 2014). Thus, there is no reason to expect that the survey modes used in the present study would affect responses differently. In some ways our findings support the recent changes in the Diagnostic Statistical Manual of Mental Disorders. In the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders the criteria of illegal acts has been removed and the perception that if a person’s borrowing resources are strained, the person may resort to criminal behaviour in order to obtain money, no longer persists (American Psychiatric Association 2013). Our results supplement this understanding by illustrating that although the prevalence of criminal behaviour is higher among problem gamblers, this behaviour might not be directly related to chasing funds for gambling but rather reflects the complexity of risk-seeking behaviour among problem gamblers. Despite these limitations, the present study has several major strengths. The study is based on randomly selected participants and representative of the general population, which provides sufficient statistical power and makes our findings generalizable to broader populations of problem gamblers. Furthermore, it has been argued, that one of the difficulties of determining the extent of gambling-related crime is the lack of objective data concerning the criminal offences reported, as many studies rely on self-reported data rather than official data, and that the accuracy of these data is sometimes questionable (Sakurai and Smith 2003). This study relies on register data on charges, which are not only more reliable compared to self-reported data but also unaffected by survey response bias and recall bias. In addition, by linking survey data with registry data it is possible to follow individuals over time and use a comparison group of non-problem gamblers. It should, of course, be noted that being charged does not mean you are sentenced or even guilty. On the other hand, a person might have committed a crime and not be charged for it. This is particularly the case for burglary, where only a low fraction of crimes are solved. As mentioned above, previous studies have suggested that problem gamblers do not commit crimes of violence except under extreme pressure but rather specialize in income producing crimes (Brown 1987; Lesieur 1984). In the present study, we found that problem gamblers were more likely than non-problem gamblers to commit both violent and incomegenerating crimes, drug crimes and weapon-related crimes. However, income-generating crime charges accounted for a large number of charges in both the problem gambling and the non-problem gambling group. A possible explanation for the high proportion of violence, weapon and drug charges in our study compared to other studies might be that these crimes are underreported in the previous studies based on self-reported data on criminal careers. Either because the persons holding these charges are less likely to participate in a survey or because they may be too
123
J Gambl Stud
embarrassed or unwilling to report their criminal activities. According to several studies problem gamblers do not always perceive their illegal acts as criminal and therefore they are prone to under-report these acts when asked about their criminal history (Bergh and Ku¨hlhorn 1994; Lesieur 1984). Acknowledgments This work was funded by the Danish Agency for Science, Technology and Innovation. Conflict of interest The authors declare that they have no conflict of interest.
References Albanese, J. S. (1985). The effect of casino gambling on crime. Federal Probation ,49(2), 39–44. Algren, M. H., Ekholm, O., Davidsen, M., Larsen, C. V. L., & Juel, K. (2014). Health behaviour and body mass index among problem gamblers: Results from a nationwide survey. Journal of Gambling Studies. doi:10.1007/s10899-013-9437-y. American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: American Psychiatric Association. American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Washington, DC: American Psychiatric Association. Bergh, C., & Ku¨hlhorn, E. (1994). Social, psychological and physical consequences of pathological gambling in Sweden. Journal of Gambling Studies, 10(3), 275–285. Blaszczynski, A., & McConaghy, N. (1994). Antisocial personality disorder and pathological gambling. Journal of Gambling Studies, 10(2), 129–145. Blaszczynski, A., McConaghy, N., & Frankova, A. (1989). Crime, antisocial personality and pathological gambling. Journal of Gambling Behavior, 5(2), 137–152. Bonke, J., & Borregaard, K. (2009). The prevalence of problematic gambling behaviour: A Scandinavian comparison. Scandinavian Journal of Public Health, 37(6), 654–660. Brown, R. I. F. (1987). Pathological gambling and associated patterns of crime: Comparisons with alcohol and other drug addictions. Journal of Gambling Behavior, 3(2), 98–114. Curran, D., & Scarpitti, F. (1991). Crime in Atlantic City: Do casinos make a difference? Deviant Behavior, 12(4), 431–449. Ekholm, O., Eiberg, S., Davidsen, M., Holst, M., Larsen, C. V. L., & Juel, K. (2014). The prevalence of problem gambling in Denmark in 2005 and 2010: A sociodemographic and socioeconomic characterization. Journal of Gambling Studies, 30, 1–10. Gazel, R. C., Rickman, D. S., & Thompson, W. N. (2001). Casino gambling and crime: a panel study of Wisconsin Counties managerial and decision economics. Managerial and Decision Economics, 22(1–3), 65–75. Gosden, N. P., Kramp, P., Gabrielsen, G., Andersen, T. F., & Sestoft, D. (2005). Violence of young criminals predicts schizophrenia: A 9-year register-based followup of 15- to 19-year-old criminals. Schizophrenia Bulletin, 31(3), 759–768. Hakim, S., & Buck, A. J. (1989). Do casinos enhance crime? Journal of Criminal Justice, 17, 409–416. Hakim, S., & Friedman, J. (1985). The impact of casino gambling on crime in Atlantic City and its region. Research Report. National Institute of Justice, USA. https://www.ncjrs.gov/App/Publications/abstract. aspx?ID=108233. Accessed August 15, 2014. Hoebel, J., von der Lippe, E., Lange, C., & Ziese, T. (2014). Mode differences in a mixed-mode health interview survey among adults. Archives of Public Health, 72, 46. Johnson, E. E., & Hamer, R. M. (1998). The Lie/Bet Questionnaire for screening pathological gamblers: A follow-up study. Psychological Reports, 83(3 Pt 2), 1219–1224. Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80(1), 83–88. Kuoppama¨ki, S. M., Ka¨a¨ria¨inen, J., & Lind, K. (2014). Examining gambling-related crime reports in the National Finnish Police Register. Journal of Gambling Studies, 30(4), 967–983. Lahn, J. (2005). Gambling among offenders: Results from an Australian survey. International Journal of Offender Therapy and Comparative Criminology, 49(3), 343–355. Lesieur, H. R. (1984). The chase: career of the compulsive gambler. Cambridge, MA: Schenkman Publishing Co.
123
J Gambl Stud Meyer, G., & Stadler, M. A. (1999). Criminal behavior associated with pathological gambling. Journal of Gambling Studies, 15(1), 29–43. Park, M., & Stokowski, P. A. (2011). Casino gaming and crime: Comparisons among gaming counties and other tourism places. Journal of Travel Research, 50, 289–302. Pastwa-Wojciechowska, B. (2011). The relationship of pathological gambling to criminality behavior in a sample of Polish male offenders. Medical Science Monitor, 17(11), CR669–CR675. Sakurai Y., & Smith R. G. (2003). Gambling as a motivation for the commission of financial crime. Trends & issues in crime and criminal justice, no. 256. Canberra: Australian Institute of Criminology. Skagero¨, A., & Westfelt, L. (2006). En litteraturstudie om spel och kriminalitet [A literature study of gaming and crime]. SoRad Rapportserie, no. 33. Stockholm: Stockholms Universitet, SoRAD. Stitt, B. G., Nichols, M., & Giacopassi, D. (2003). Does the presence of casinos increase crime? An examination of casino and control communities. Crime & Delinquency, 49(2), 253–284. Thygesen, L. C., & Ersbøll, A. K. (2011). Danish population-based registers for public health and healthrelated welfare research: Introduction to the supplement. Scandinavian Journal of Public Health, 39(suppl. 7), 8–10. Turner, N. E., Preston, D. L., Saunders, C., McAvoy, S., & Jain, U. (2009). The relationship of problem gambling to criminal behavior in a sample of Canadian male federal offenders. Journal of Gambling Studies, 25(2), 153–169. Wheeler, S. A., Round, D. K., & Wilson, J. K. (2010). The relationship between crime and electronic gaming expenditure: Evidence from Victoria, Australia. Journal of Quantitative Criminology, 27(3), 315–338. Zurhold, H., Verthein, U., & Kalke, J. (2014). Prevalence of problem gambling among the prison population in Hamburg, Germany. Journal of Gambling Studies, 30(2), 309–319.
123
