J Gambl Stud DOI 10.1007/s10899-013-9437-y ORIGINAL PAPER
Health Behaviour and Body Mass Index Among Problem Gamblers: Results from a Nationwide Survey Maria H. Algren • Ola Ekholm • Michael Davidsen Christina V. L. Larsen • Knud Juel
•
Springer Science+Business Media New York 2014
Abstract Problem gambling is a serious public health issue. The objective of this study was to investigate whether past year problem gamblers differed from non-problem gamblers with regard to health behaviour and body mass index (BMI) among Danes aged 16 years or older. Data were derived from the Danish Health and Morbidity Surveys in 2005 and 2010. Past year problem gambling was defined using the lie/bet questionnaire. Logistic regression analyses were used to examine the association between past year problem gambling and health behaviour and BMI. Problem gambling was associated with unhealthy behaviour and obesity. The odds of smoking was significantly higher among problem gamblers than among non-problem gamblers. Further, the odds of high-risk alcohol drinking and illicit drug use were significantly higher among problem gamblers. The prevalence of sedentary leisure activity, unhealthy diet pattern and obesity was higher among problem gamblers than among non-problem gamblers. The associations found in this study remained significant after adjustment for sex, age, educational and cohabiting status as well as other risk factors. Our findings highlight the presence of a potential, public health challenge and elucidate the need for health promotion initiatives targeted at problem gamblers. Furthermore, more research is needed in order to understand the underlying social mechanism of the association between problem gamblers and unhealthy behaviour. Keywords index
Gambling Health surveys Health behaviour Body mass
Introduction During the past three decades, the visibility, availability and acceptability of legalized gambling have grown widely, and so has the extent of gambling-related problems
M. H. Algren (&) O. Ekholm M. Davidsen C. V. L. Larsen K. Juel National Institute of Public Health, University of Southern Denmark, Øster Farimagsgade 5A, 2nd Floor, 1353 Copenhagen K, Denmark e-mail: [email protected]
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(Lyk-Jensen 2010; Shaffer et al. 2004). In Denmark, past year and lifetime prevalences of problem gambling were 0.8 and 2.0 % in 2010, respectively (Ekholm et al. 2012). Problem gambling is nowadays a serious public health issue, with implications for individuals, families and communities (Shaffer and Hall 2001). In order to prevent illness and premature death it is important to gain knowledge on their health behaviour such as smoking, alcohol consumption, illicit drug use, physical activity, dietary habits and body mass index (BMI) (Juel et al. 2008). Previous epidemiological research on the health behaviour of problem gamblers has focused mostly on smoking behaviour, alcohol consumption and illicit drug use (Cunningham-Williams et al. 1998; French et al. 2008; Lorains et al. 2011; Morasco et al. 2006; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996), whereas research on physical activity, dietary habits, and BMI is very limited (Morasco et al. 2006; Black et al. 2013; Griffiths 2004). General population surveys provide evidence that the rate of smoking, alcohol consumption and illicit drug use is higher among problem gamblers than among non-problem gamblers (Cunningham-Williams et al. 1998; French et al. 2008; Lorains et al. 2011; Morasco et al. 2006; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996; Gerstein et al. 1999; Bland et al. 1993). Regarding physical activity and BMI, previous studies have shown that problem gamblers were more likely to avoid regular exercise and to be obese (Morasco et al. 2006; Black et al. 2013). Due to the scarce literature, more research is needed in order to investigate the association between problem gambling and physical activity, dietary habits and BMI. Ultimately, the awareness on the association between problem gambling and health behaviour and BMI will increase the attention on the health needs of problem gamblers, and future interventions and preventive national health policies can be more targeted at problem gamblers. The objective of the present study was to investigate whether past year problem gamblers differed from non-problem gamblers with regard to health behaviour and BMI among Danes aged 16 years or older. This study will contribute to the knowledge on health behaviour among problem gamblers in both a Danish and Scandinavian context.
Methods Sample Data were derived from the Danish Health and Morbidity Survey 2005 (Ekholm et al. 2009) and the Danish Health and Morbidity Survey 2010 (the national subsample in the Danish Health Survey 2010) (Christensen et al. 2012) carried out by the National Institute of Public Health, University of Southern Denmark. The surveys included a range of validated questions on health behaviour, morbidity, illness behaviour, social relations, working conditions and socio-demographic factors. The survey in 2005 was based on a region-stratified random sample of 10,916 Danish citizens aged 16 years or older. Data were collected via face-to-face in the respondents’ home, and following the interview all respondents were asked to complete a self-administered questionnaire. The survey in 2010 was based on a random sample of 23,405 Danish citizens aged 16 years or older. All selected individuals were mailed a survey questionnaire and a letter of introduction that briefly described the aims and content of the survey, and it was further emphasized that participation was voluntary. A mixed-mode approach was used to collect the survey data. The letter of introduction invited the selected individuals
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either to fill in a web questionnaire or to complete the postal questionnaire. The invited individuals received a unique username and password to access the web questionnaire. The survey samples are nationally representative for characteristics like sex, age and socioeconomic factors. The surveys are described in detail elsewhere (Ekholm et al. 2009; Christensen et al. 2012; Ekholm et al. 2012). Measures The lie/bet questionnaire, originally suggested by Johnson et al. (1997) and later validated in both treatment (Johnson and Hamer 1998) and community samples (Go¨testam et al. 2004) was used to define whether a respondent was a past year problem gambler. The questions were: ‘Have you ever lied to people important to you about how much you gambled?’ and ‘Have you ever felt the need to bet more and more money?’ The questions had the following four answer categories: Yes, in the past 12 months; Yes, previously; No; I never gamble. In the present study, the answers have been dichotomized. Respondents answering ‘‘Yes, in the past 12 months’’ to any of the two questions were defined as past year problem gamblers, otherwise as non-problem gamblers. The two questions combined represent a short screen version of the 10 DSM-IV criteria for screening pathological gambling. The lie/bet screen has been found valid to identify lifetime problem gamblers in a community sample, defined as those who responded positively to five or more of the 10 DSM-IV criteria combined with those who only responded positively to three or four of these criteria (Go¨testam et al. 2004). The short screen does not qualify to distinguish between pathological and problem gambling. The lie/bet questionnaire was included in the self-administered questionnaire in both surveys. Self-reported health behaviour included smoking behaviour, alcohol consumption, illicit drug use, physical activity and dietary habits. Respondents who reported that they smoked daily were also asked to specify how many cigarettes they smoked daily. From this, the prevalence of heavy smokers (15 or more cigarettes daily) was calculated. The weekly alcohol consumption was assessed by asking a beverage-specific question for each day in a week. The Danish National Board of Health’s sensible drinking limits (21 drinks per week for men and 14 drinks for women) were used to define a high alcohol consumption (Gronbaek et al. 1997). Furthermore, the respondents were asked how often they drink more than five drinks at the same occasion (binge drinking). The CAGE-C test was used to detect problem drinking (Zierau et al. 2005). Binge drinking and problem drinking were only measured in 2010. The use of selected illicit drugs (cannabis, amphetamines, cocaine, LSD, ecstasy, heroin, magic mushrooms and other substances) was assessed based on the recommendations by the European Monitoring Center for Drugs and Drug Addiction (EMCDDA) (European Monitoring Centre for Drugs and Drug Addiction 2002). Hence, the following question was asked to all respondents: ‘Have you ever tried one of more of the following drugs?’ with the four possible answer categories: No; Yes, during the past month; Yes, during the past year (but not during the past month; Yes, previously (but not during the past year). Measures of physical activity included the respondents’ typical level of physical activity in leisure time during the past 12 months in one of four predefined categories: vigorous physical activity (strenuous activities usually involving competition or endurance training performed regularly or several times a week); moderate physical activity (exercise, endurance training); low physical activity (walking, bicycling, or other light activities for a minimum of 4 h a week); sedentary activities (reading, TV watching, or other sedentary activities).
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Dietary habits were examined using a dietary score. The dietary score was calculated based on four components of diet (fruit, vegetables, fish and fat) that have been shown to be a good predictor of overall healthy or unhealthy dietary habits (Toft et al. 2007). The dietary score was only included in 2010. BMI was calculated based on self-reported weight and height. In accordance with World Health Organization’s criteria, BMI was categorized as underweight or normal weight (BMI \ 25), overweight (25 B BMI \ 30) and obesity (BMI C 30) (WHO 2000). Underweight and normal weight individuals were handled together because of too few underweight individuals in the study population. It was therefore not possible to analyze the underweight individuals separately. Accumulation of risk factors was calculated using the following risk factors: daily smoking, exceeding sensible drinking limits, illicit drug use within past year, sedentary leisure activity and obesity. Study Population Only respondents who answered both questions were included in the analyses, leaving 5,448 respondents (response rate 52.1 %) in the 2005 study (2,539 men and 2,909 women) and 14,225 (response rate 62.7 %) in the 2010 study (6,588 men and 7,637 women) eligible for analysis. The analyses in the present study were based on pooled data from 2005 to 2010, comprising 19,673 respondents. Statistical Analysis Multiple logistic regressions were used to investigate the association between past year problem gambling and health behaviour and BMI. The results are presented as odds ratio (OR) with 95 % confidence intervals (CI). The regression analyses were adjusted for sex, age and survey year, highest completed education and cohabiting status. Since relatively few persons at age 25 years or below have completed their education or are cohabiting analyses adjusting for these two factors were only carried out for respondents aged 25 or above. Furthermore, multiple logistic regression models were used to examine the independent association of each health behaviour and BMI with past year problem gambling. The regression models were adjusted for all other variables for health behaviour and BMI besides the sociodemographic and socioeconomic factors. All estimates presented in this study that derives from the Danish Health and Morbidity Survey in 2005 were weighted to take into account the complex sampling design. Due to the low number of female past year problem gamblers no sex-specific analyses are presented. Statistical analyses were performed using SAS version 9.2.
Results In total, 0.8 % of the study population is identified as past year problem gamblers (Table 1), corresponding to 164 respondents (136 males and 28 females). Table 2 shows the prevalence and adjusted OR of various health behaviour and obesity according to past year problem gambling. The prevalence of daily smoking was 45.8 % among past year problem gamblers and 21.8 % among non-problem gamblers. Past year problem gamblers had 3.2 (95 % CI 2.3–43) times higher odds of being daily smokers than non-problems gamblers. The analyses also revealed a strong association between problem
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J Gambl Stud Table 1 Past year prevalence of problem gambling stratified by sex and age in 2005 and 2010 (pooled data)
%
Number of respondents
Men 16–24 years
4.8
883
25–34 years
2.5
1,052
35–49 years
1.4
2,370
C50 years
0.7
4,822
All men
1.5
9,127
16–24 years
0.6
1,163
25–34 years
0.2
1,303
35–49 years
0.3
2,839
C50 years
0.2
5,241
All women
0.3
10,546
16–24 years
2.4
2,046
25–34 years
1.2
2,355
35–49 years
0.8
5,209
C50 years
0.4
10,063
All
0.8
19,673
Women
All
gambling and heavy smoking. The odds of exceeding the sensible drinking limits and problem drinking were significantly higher among past year problem gamblers than among non-problem gamblers. Further, the past year prevalence of illicit drug use was substantially higher among past year problem gamblers than non-problem gamblers. The odds of sedentary leisure activity was significantly higher among past year problem gamblers than among non-problem gamblers, and past year problem gamblers were more likely to have an unhealthy dietary pattern and to be obese compared to non-problem gamblers. In all, 17.5 % of the current problems gamblers had at least three of the examined risk factors (daily smoking, high alcohol intake, illicit drug use, sedentary lifestyle and obesity). The corresponding prevalence among non-problem gamblers was 2.6 %. Table 3 shows the results from the multiple logistic regression of past year problem gambling on selected risk factors. The results show that even though the analyses were adjusted, the associations still persisted. For example, the odds of problem gambling in the past year were 2.7 (95 % CI 1.7–4.3) times higher among heavy smokers than among never smokers, when controlling for other potential risk factors.
Discussion The present study shows marked differences between past year problem gamblers and nonproblem gamblers regarding their health behaviour and BMI, as past year problem gamblers have an unhealthier behaviour than non-problem gamblers. These patterns persisted when adjusting for sociodemographic factors, socioeconomic factors and all other variables for health behaviour and BMI. The strong association between problem gambling and smoking is consistent with international findings from population-based surveys (Lorains et al. 2011; CunninghamWilliams et al. 1998; Smart and Ferris 1996). Our results regarding alcohol consumption
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J Gambl Stud Table 2 Prevalence of health behaviours and obesity according to past year problem gambling Problem gambling Yes
No
ORa
95 % CI
ORb
95 % CI
Daily smoker (\15 cigarettes/day)
45.8
21.8
3.2
(2.3–4.3)
2.9
(2.0–4.3)
Heavy smoker (15 or more cigarettes/day)
28.0
10.7
3.1
(2.2–4.5)
3.0
(2.0–4.6)
Smoking behaviour
Alcohol consumption Exceed sensible drinking limits
35.3
11.9
3.2
(2.3–4.5)
3.9
(2.5–5.9)
Binge drinkingc
30.3
19.4
1.2
(0.8–1.8)
1.2
(0.7–2.1)
Problem drinkingc
31.0
16.8
2.1
(1.4–3.1)
2.4
(1.5–4.0)
22.1
4.1
3.0
(1.9–4.5)
3.9
(2.2–7.0)
8.4
1.1
3.2
(1.7–5.9)
5.5
(2.5–12.1)
20.6
11.0
2.3
(1.5–3.3)
1.9
(1.2–3.0)
21.8
11.7
1.6
(1.0–2.5)
1.3
(0.7–2.3)
15.2
12.6
1.5
(1.0–2.4)
1.5
(0.9–2.4)
Illicit drugs within the last year Cannabis Other illicit drugs than cannabis Physical activity Sedentary leisure activity Diet Unhealthy dietary patternc BMI Obesity
Odds ratios (OR) with 95 % confidence intervals (CI) a
Adjusted for sex, age and survey year
b
Adjusted for sex, age, survey year, highest completed education and cohabiting status. Only respondents aged 25 or above were included in the analyses
c
Data only available in 2010
among past year problem gamblers also support previous research (Cunningham-Williams et al. 1998; French et al. 2008; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996; Gerstein et al. 1999). Furthermore, the association between problem gambling and illicit drug use in the present study is in accordance with results from other general population surveys (Lorains et al. 2011; Cunningham-Williams et al. 1998; Petry et al. 2005). The prevalence of sedentary leisure activity was significantly higher among past year problem gamblers than among non-problem gamblers. Previous studies have also showed that problem gamblers were more likely to avoid regular exercise (Black et al. 2013). Past year problem gamblers had a higher prevalence of having an unhealthy dietary pattern than non-problem gamblers. As far as we know, no previous studies have investigated the association between problem gambling and an unhealthy dietary pattern. The present study confirms results from previous studies that show that increased gambling severity is associated with current obesity status (Morasco et al. 2006; Black et al. 2013). It is not unexpected that the prevalence of obesity is higher among past year problem gamblers than among non-problem gamblers with the prevalence of sedentary leisure activity and unhealthy diet in mind. Even though the odds of being a past year problem gambler on selected risk factors were adjusted for all risk factors the effect of each risk factor still persisted. In addition, past year problem gamblers clearly experience a greater accumulation of unhealthy behaviour than non-problem gamblers. All in all, our results are consistent with international findings, though they are not directly comparable as they are based on different measures of gambling problems. Given
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J Gambl Stud Table 3 Odds of being a past year problem gambler with regard to health behaviour and BMI ORa
95 % CI
P value
ORb
95 % CI
P value
0.002
Smoking behaviour Heavy smoker (15 or more cigarettes/day)
2.7
(1.7–4.3) \0.0001
2.7
(1.5–4.8)
Daily smoker (\15 cigarettes/day)
1.7
(1.1–2.7)
1.9
(1.1–3.5)
Previous smoker
1.0
(0.6–1.7)
1.1
(0.6–1.9)
Never-smoker
1
1
Alcohol consumption (1.5–3.2) \0.0001
Exceed sensible drinking limits
2.2
Do not exceed sensible drinking limits
1
Zero standard drinks per week or do not drink
0.8
(0.5–1.4)
Yes
1.7
(1.1–2.7)
No
1
2.6
(1.6–4.1) \0.0001
1 0.7
(0.4–1.4)
2.4
(1.3–4.3)
Illicit drugs within the last year 0.02
0.005
1
Physical activity Moderate/vigorous physical activity
1.2
Low physical activity
1
Sedentary leisure activity
2.0
(1.8–1.8)
0.008
1.3
(0.8–2.0)
C0.10
1 (1.3–3.2)
1.8
(1.0–3.0)
BMI Underweight and normal weight
1
Overweight
1.4
(1.0–2.1)
0.05
1.3
1 (0.8–2.1)
C0.10
Obesity
1.7
(1.1–2.9)
1.7
(1.0–3.1)
Odds ratios (OR) with 95 % confidence intervals (CI) a
Variables in the model: sex, age, survey year, and all other variables listed in the table
b
Variables in the model: sex, age, survey year, highest completed education, cohabiting status, and all other variables listed in the table. Only respondents aged 25 or above were included in the analyses
its brief character, the lie/bet screen is likely to cover both milder gambling problems along with severe problems compared to a more comprehensive assessment such as the NORC DSM Screen for gambling problems (NODS) (Gerstein et al. 1999) used in several studies. This is considered as a weakness of the screen. The purpose of screening for problem gambling in a large health survey is to investigate an overall prevalence of how widely gambling is affecting a population, which is by no means comparable to a thorough clinical assessment of pathological gambling based on the DSM-IV-R criteria. However, short screens are an important tool if gambling is to be investigated in larger population based health surveys because these surveys usually include several topics, which challenges the limit for duration, as to how much time people can be expected to spend on participating in a survey. It is possible that some of these associations occur because drinking or illicit drug use cause gambling, similarly to the instances in which alcohol and illicit drug use promote other risky and short sighted behaviours such as unsafe sex and violence (Petry 2000). For some gamblers, alcohol and illicit drugs can also be a motive for maintaining the need for gambling to pay the consumption of for instance illicit drugs. It is also possible that unsuccessful gamblers occasionally try to forget their gambling losses by drinking alcohol or by using illicit drugs. However, it is likely that much of the behaviour in relation to drinking, illicit drugs, unhealthy diet or gambling arises from underlying mechanisms that
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give rise to these behaviours. As problem gambling is a disorder of impulse control where a person becomes unable to resist the impulse to gamble, maybe it is the same loss of control in relation to smoking, alcohol, illicit drugs and unhealthy diet. It may be assumed that problem gamblers do not think of the long-term negative consequences of their behaviour and that they are influenced by their immediate desire and needs. Maybe it is possible that problem gamblers and individuals with problem drinking and an illicit drug use have similar predispositions (genetic, environmental and social) and personality profiles (Lorains et al. 2011; Slutske et al. 2005). It may influence the development and maintenance of their unhealthy behaviour as well as problem gambling. This study suggests that it is important to deal with the risk factors associated with problem gambling in a public health perspective, and therefore, problem gambling should be incorporated in public health strategies against addictive behaviour. By understanding gambling and its potential impact on public health, policy makers, health practitioners and community leaders can minimize the negative impacts of gambling on the individuals, their families and society. By understanding the distribution and determinants of gambling related health problems, it is possible to develop health promotion strategies to protect the problem gamblers from the risk factors associated with gambling. The present study suggest screening for alcohol and illicit drug use disorders while treating for gambling problems, since the present study found that problem gambling is strongly associated with high alcohol consumption and illicit drug use. As with any empirical study, there are limitations of the data in relation to proper interpretation of the findings. Since the data derives from cross-sectional surveys it is not possible to identify causality, and therefore, focus was on associations in this study. Another limitation in this study is the fact, that data from 2005 to 2010 have been collected by slightly different methods. Face-to-face interview combined with self-administered questionnaire was used in 2005 while a self-administered questionnaire was used in 2010. However, the gambling questions were only included in the self-administered questionnaire. Furthermore, the overall response rates were adequate (52 % in 2005 and 63 % in 2010, respectively). However, the response rate was somewhat lower among men (58 %) than among women (67 %) in 2010. The lowest response rates were found among young men (16–24 years: 44 % and 25–34 years: 49 %). The same patterns were found in 2005. Since all Danes have a unique 10-digit civil registration number, it was possible to link both respondents and non-respondents in the survey in 2010 on an individual level to different administrative registers (e.g. the Danish Civil Registration System, the Population Education Register and the Income Statistics Register). Calibration was used to adjust for non-response as proposed by Sa¨rndal and Lundstro¨m (2005). The analyses indicated that non-response do not significantly alter the estimates or the conclusions of the present study (data not shown). A major strength of the present study is that it is based on two large nationally representative samples. Further, it is a strength that the study is based on key topics of health behaviour included in the general health survey, which has made it possible to analyze problem gambling in a broader health perspective. Gambling problems have typically been a topic of the psychiatric discipline with focus on addictive behaviour among gamblers in treatment. It is a strength that the multiple logistic regression models include adjustments for both sociodemographic factors, socioeconomic factors and all other variables for health behaviour and BMI, thus it is possible to isolate the effect of problem gambling. In summary, this study demonstrated marked differences between past year problem gamblers and non-problem gamblers regarding their health behaviour and BMI. Behavioural risk factors such as smoking, high alcohol consumption, illicit drug use, sedentary
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leisure activity, unhealthy diet pattern and obesity were more prevalent among past year problem gamblers than among non-problem gamblers. Our findings highlight the presence of a potential, public health challenge and elucidate the need for health promotion initiatives targeted at problem gamblers. Furthermore, more research is needed in order to understand the underlying social mechanism of the association between problem gamblers and unhealthy behaviour.
Key-points This study • shows that problem gamblers are more likely to smoke, have a high alcohol intake and use illicit drugs and, furthermore, more likely to have a sedentary lifestyle, unhealthy diet pattern and to be obese compared to non-problem gamblers. • shows that problem gamblers clearly experience a greater accumulation of risk factors than non-problem gamblers. • elucidate the need for health promotion initiatives targeted at problem gamblers. Acknowledgments The study 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 Black, D. W., Shaw, M., McCormick, B., & Allen, J. (2013). Pathological gambling: Relationship to obesity, self-reported chronic medical conditions, poor lifestyle choices, and impaired quality of life. Comprehensive Psychiatry, 54(2), 97–104. Bland, R. C., Newman, S. C., Orn, H., & Stebelsky, G. (1993). Epidemiology of pathological gambling in Edmonton. Canadian Journal of Psychiatry, 38(2), 108–112. Christensen, A. I., Ekholm, O., Glu¨mer, C., Andreasen, A. H., Hvidberg, M. F., Kristensen, P. L., et al. (2012). The Danish National Health Survey 2010. Study design and respondent characteristics. Scandinavian Journal of Public Health, 40(4), 391–397. Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M, I. I. I., & Spitznagel, E. L. (1998). Taking chances: Problem gamblers and mental health disorders—Results from the St. Louis Epidemiologic Catchment Area Study. American Journal of Public Health, 88(7), 1093–1096. Ekholm, O., Eiberg, S., Davidsen, M., Holst, M., Larsen, C. V., & Juel, K. (2012). The prevalence of problem gambling in Denmark in 2005 and 2010: A sociodemographic and socioeconomic characterization. Journal of Gambling Studies. doi:10.1007/s10899-012-9347-4. Ekholm, O., Hesse, U., Davidsen, M., & Kjøller, M. (2009). The study design and characteristics of the Danish national health interview surveys. Scandinavian Journal of Public Health, 37(7), 758–765. European Monitoring Centre for Drugs and Drug Addiction. (2002). Handbook for surveys on drug use among a project CT.99.EP.08 B. Lisbon: EMCDDA. French, M., Maclean, J., & Ettner, S. (2008). Drinkers and bettors: Investigating the complementarity of alcohol consumption and problem gambling. Drug and Alcohol Dependence, 96(1–2), 155–164. Gerstein, D. R., Hoffmann, J. P., Larison, C., Engelman, L., Murphy, S., Palmer, A., et al. (1999). Gambling impact and behavior study: Report to the national gambling impact study commission. Chicago: National Opinion Research Center. Go¨testam, K., Johansson, A., Wenzel, H., & Simonsen, I. (2004). Validation of the lie/bet screen for pathological gambling on two normal population data sets. Psychological Reports, 95(3 Pt 1), 1009–1013. Griffiths, M. (2004). Betting your life on it. British Medical Journal, 329(7474), 1055–1056.
123
J Gambl Stud Gronbaek, M. N., Iversen, L., Olsen, J., Becker, P. U., Hardt, F., & Sorensen, T. I. (1997). Sensible drinking limits. Ugeskrift for Laeger, 159(40), 5939–5945. 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. Juel, K., Sorensen, J., & Bronnum-Hansen, H. (2008). Risk factors and public health in Denmark. Scandinavian Journal of Public Health, 36(Suppl 1), 11–227. Lorains, F. K., Cowlishaw, S., & Thomas, S. A. (2011). Prevalence of comorbid disorders in problem and pathological gambling: Systematic review and meta-analysis of population surveys. Addiction, 106(3), 490–498. Lyk-Jensen, S. V. (2010). New evidence from the grey area: Danish results for at-risk gambling. Journal of Gambling Studies, 26(3), 455–467. Morasco, B. J., Pietrzak, R. H., Blanco, C., Grant, B. F., Hasin, D., & Petry, N. M. (2006). Health problems and medical utilization associated with gambling disorders: Results from the national epidemiologic survey on alcohol and related conditions. Psychosomatic Medicine, 68(6), 976–984. Petry, N. M. (2000). Gambling problems in substance abusers are associated with increased sexual risk behaviors. Addiction, 95(7), 1089–1100. Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the national epidemiologic survey on alcohol and related conditions. Journal of Clinical Psychiatry, 66(5), 564–574. Sa¨rndal, C.-E., & Lundstro¨m, S. (2005). Estimation in surveys with nonresponse. New York: Wiley. Shaffer, H. J., & Hall, M. N. (2001). Updating and refining prevalence estimates of disordered gambling behaviour in the United States and Canada. Canadian Journal of Public Health, 92(3), 168–172. Shaffer, H. J., LaBrie, R. A., LaPlante, D. A., Nelson, S. E., & Stanton, M. V. (2004). The road less travelled: Moving from distribution to determinants in the study of gambling epidemiology. The Canadian Journal of Psychiatry, 49(8), 504–516. Slutske, W. S., Caspi, A., Moffitt, T. E., & Poulton, R. (2005). Personality and problem gambling: A prospective study of a birth cohort of young adults. Archives of General Psychiatry, 62(7), 769–775. Smart, R. G., & Ferris, J. (1996). Alcohol, drugs and gambling in the Ontario adult population, 1994. Canadian Journal of Psychiatry, 41(1), 36–45. Stewart, S. H., & Kushner, M. G. (2005). Introduction to the special issue on ‘‘relations between gambling and alcohol use’’. Journal of Gambling Studies, 21(3), 223–231. Toft, U., Kristoffersen, L. H., Lau, C., Borch-Johnsen, K., & Jorgensen, T. (2007). The dietary quality score: Validation and association with cardiovascular risk factors: The Inter99 study. European Journal of Clinical Nutrition, 61(2), 270–278. Welte, J., Barnes, G., Wieczorek, W., Tidwell, M. C., & Parker, J. (2001). Alcohol and gambling pathology among US adults: Prevalence, demographic patterns and comorbidity. Journal of Studies on Alcohol, 62(5), 706–712. WHO. (2000). Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organization Technical Report Series (2001/03/10 Ed., Vol. 894, pp. i–xii, 1–253). Zierau, F., Hardt, F., Henriksen, J. H., Holm, S. S., Jørring, S., Melsen, T., et al. (2005). Validation of a selfadministered modified CAGE test (CAGE-C) in a somatic hospital ward: Comparison with biochemical markers. Scandinavian Journal of Clinical and Laboratory Investigation, 65(7), 615–622.
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Health Behaviour and Body Mass Index Among Problem Gamblers: Results from a Nationwide Survey Maria H. Algren • Ola Ekholm • Michael Davidsen Christina V. L. Larsen • Knud Juel
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Springer Science+Business Media New York 2014
Abstract Problem gambling is a serious public health issue. The objective of this study was to investigate whether past year problem gamblers differed from non-problem gamblers with regard to health behaviour and body mass index (BMI) among Danes aged 16 years or older. Data were derived from the Danish Health and Morbidity Surveys in 2005 and 2010. Past year problem gambling was defined using the lie/bet questionnaire. Logistic regression analyses were used to examine the association between past year problem gambling and health behaviour and BMI. Problem gambling was associated with unhealthy behaviour and obesity. The odds of smoking was significantly higher among problem gamblers than among non-problem gamblers. Further, the odds of high-risk alcohol drinking and illicit drug use were significantly higher among problem gamblers. The prevalence of sedentary leisure activity, unhealthy diet pattern and obesity was higher among problem gamblers than among non-problem gamblers. The associations found in this study remained significant after adjustment for sex, age, educational and cohabiting status as well as other risk factors. Our findings highlight the presence of a potential, public health challenge and elucidate the need for health promotion initiatives targeted at problem gamblers. Furthermore, more research is needed in order to understand the underlying social mechanism of the association between problem gamblers and unhealthy behaviour. Keywords index
Gambling Health surveys Health behaviour Body mass
Introduction During the past three decades, the visibility, availability and acceptability of legalized gambling have grown widely, and so has the extent of gambling-related problems
M. H. Algren (&) O. Ekholm M. Davidsen C. V. L. Larsen K. Juel National Institute of Public Health, University of Southern Denmark, Øster Farimagsgade 5A, 2nd Floor, 1353 Copenhagen K, Denmark e-mail: [email protected]
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(Lyk-Jensen 2010; Shaffer et al. 2004). In Denmark, past year and lifetime prevalences of problem gambling were 0.8 and 2.0 % in 2010, respectively (Ekholm et al. 2012). Problem gambling is nowadays a serious public health issue, with implications for individuals, families and communities (Shaffer and Hall 2001). In order to prevent illness and premature death it is important to gain knowledge on their health behaviour such as smoking, alcohol consumption, illicit drug use, physical activity, dietary habits and body mass index (BMI) (Juel et al. 2008). Previous epidemiological research on the health behaviour of problem gamblers has focused mostly on smoking behaviour, alcohol consumption and illicit drug use (Cunningham-Williams et al. 1998; French et al. 2008; Lorains et al. 2011; Morasco et al. 2006; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996), whereas research on physical activity, dietary habits, and BMI is very limited (Morasco et al. 2006; Black et al. 2013; Griffiths 2004). General population surveys provide evidence that the rate of smoking, alcohol consumption and illicit drug use is higher among problem gamblers than among non-problem gamblers (Cunningham-Williams et al. 1998; French et al. 2008; Lorains et al. 2011; Morasco et al. 2006; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996; Gerstein et al. 1999; Bland et al. 1993). Regarding physical activity and BMI, previous studies have shown that problem gamblers were more likely to avoid regular exercise and to be obese (Morasco et al. 2006; Black et al. 2013). Due to the scarce literature, more research is needed in order to investigate the association between problem gambling and physical activity, dietary habits and BMI. Ultimately, the awareness on the association between problem gambling and health behaviour and BMI will increase the attention on the health needs of problem gamblers, and future interventions and preventive national health policies can be more targeted at problem gamblers. The objective of the present study was to investigate whether past year problem gamblers differed from non-problem gamblers with regard to health behaviour and BMI among Danes aged 16 years or older. This study will contribute to the knowledge on health behaviour among problem gamblers in both a Danish and Scandinavian context.
Methods Sample Data were derived from the Danish Health and Morbidity Survey 2005 (Ekholm et al. 2009) and the Danish Health and Morbidity Survey 2010 (the national subsample in the Danish Health Survey 2010) (Christensen et al. 2012) carried out by the National Institute of Public Health, University of Southern Denmark. The surveys included a range of validated questions on health behaviour, morbidity, illness behaviour, social relations, working conditions and socio-demographic factors. The survey in 2005 was based on a region-stratified random sample of 10,916 Danish citizens aged 16 years or older. Data were collected via face-to-face in the respondents’ home, and following the interview all respondents were asked to complete a self-administered questionnaire. The survey in 2010 was based on a random sample of 23,405 Danish citizens aged 16 years or older. All selected individuals were mailed a survey questionnaire and a letter of introduction that briefly described the aims and content of the survey, and it was further emphasized that participation was voluntary. A mixed-mode approach was used to collect the survey data. The letter of introduction invited the selected individuals
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either to fill in a web questionnaire or to complete the postal questionnaire. The invited individuals received a unique username and password to access the web questionnaire. The survey samples are nationally representative for characteristics like sex, age and socioeconomic factors. The surveys are described in detail elsewhere (Ekholm et al. 2009; Christensen et al. 2012; Ekholm et al. 2012). Measures The lie/bet questionnaire, originally suggested by Johnson et al. (1997) and later validated in both treatment (Johnson and Hamer 1998) and community samples (Go¨testam et al. 2004) was used to define whether a respondent was a past year problem gambler. The questions were: ‘Have you ever lied to people important to you about how much you gambled?’ and ‘Have you ever felt the need to bet more and more money?’ The questions had the following four answer categories: Yes, in the past 12 months; Yes, previously; No; I never gamble. In the present study, the answers have been dichotomized. Respondents answering ‘‘Yes, in the past 12 months’’ to any of the two questions were defined as past year problem gamblers, otherwise as non-problem gamblers. The two questions combined represent a short screen version of the 10 DSM-IV criteria for screening pathological gambling. The lie/bet screen has been found valid to identify lifetime problem gamblers in a community sample, defined as those who responded positively to five or more of the 10 DSM-IV criteria combined with those who only responded positively to three or four of these criteria (Go¨testam et al. 2004). The short screen does not qualify to distinguish between pathological and problem gambling. The lie/bet questionnaire was included in the self-administered questionnaire in both surveys. Self-reported health behaviour included smoking behaviour, alcohol consumption, illicit drug use, physical activity and dietary habits. Respondents who reported that they smoked daily were also asked to specify how many cigarettes they smoked daily. From this, the prevalence of heavy smokers (15 or more cigarettes daily) was calculated. The weekly alcohol consumption was assessed by asking a beverage-specific question for each day in a week. The Danish National Board of Health’s sensible drinking limits (21 drinks per week for men and 14 drinks for women) were used to define a high alcohol consumption (Gronbaek et al. 1997). Furthermore, the respondents were asked how often they drink more than five drinks at the same occasion (binge drinking). The CAGE-C test was used to detect problem drinking (Zierau et al. 2005). Binge drinking and problem drinking were only measured in 2010. The use of selected illicit drugs (cannabis, amphetamines, cocaine, LSD, ecstasy, heroin, magic mushrooms and other substances) was assessed based on the recommendations by the European Monitoring Center for Drugs and Drug Addiction (EMCDDA) (European Monitoring Centre for Drugs and Drug Addiction 2002). Hence, the following question was asked to all respondents: ‘Have you ever tried one of more of the following drugs?’ with the four possible answer categories: No; Yes, during the past month; Yes, during the past year (but not during the past month; Yes, previously (but not during the past year). Measures of physical activity included the respondents’ typical level of physical activity in leisure time during the past 12 months in one of four predefined categories: vigorous physical activity (strenuous activities usually involving competition or endurance training performed regularly or several times a week); moderate physical activity (exercise, endurance training); low physical activity (walking, bicycling, or other light activities for a minimum of 4 h a week); sedentary activities (reading, TV watching, or other sedentary activities).
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Dietary habits were examined using a dietary score. The dietary score was calculated based on four components of diet (fruit, vegetables, fish and fat) that have been shown to be a good predictor of overall healthy or unhealthy dietary habits (Toft et al. 2007). The dietary score was only included in 2010. BMI was calculated based on self-reported weight and height. In accordance with World Health Organization’s criteria, BMI was categorized as underweight or normal weight (BMI \ 25), overweight (25 B BMI \ 30) and obesity (BMI C 30) (WHO 2000). Underweight and normal weight individuals were handled together because of too few underweight individuals in the study population. It was therefore not possible to analyze the underweight individuals separately. Accumulation of risk factors was calculated using the following risk factors: daily smoking, exceeding sensible drinking limits, illicit drug use within past year, sedentary leisure activity and obesity. Study Population Only respondents who answered both questions were included in the analyses, leaving 5,448 respondents (response rate 52.1 %) in the 2005 study (2,539 men and 2,909 women) and 14,225 (response rate 62.7 %) in the 2010 study (6,588 men and 7,637 women) eligible for analysis. The analyses in the present study were based on pooled data from 2005 to 2010, comprising 19,673 respondents. Statistical Analysis Multiple logistic regressions were used to investigate the association between past year problem gambling and health behaviour and BMI. The results are presented as odds ratio (OR) with 95 % confidence intervals (CI). The regression analyses were adjusted for sex, age and survey year, highest completed education and cohabiting status. Since relatively few persons at age 25 years or below have completed their education or are cohabiting analyses adjusting for these two factors were only carried out for respondents aged 25 or above. Furthermore, multiple logistic regression models were used to examine the independent association of each health behaviour and BMI with past year problem gambling. The regression models were adjusted for all other variables for health behaviour and BMI besides the sociodemographic and socioeconomic factors. All estimates presented in this study that derives from the Danish Health and Morbidity Survey in 2005 were weighted to take into account the complex sampling design. Due to the low number of female past year problem gamblers no sex-specific analyses are presented. Statistical analyses were performed using SAS version 9.2.
Results In total, 0.8 % of the study population is identified as past year problem gamblers (Table 1), corresponding to 164 respondents (136 males and 28 females). Table 2 shows the prevalence and adjusted OR of various health behaviour and obesity according to past year problem gambling. The prevalence of daily smoking was 45.8 % among past year problem gamblers and 21.8 % among non-problem gamblers. Past year problem gamblers had 3.2 (95 % CI 2.3–43) times higher odds of being daily smokers than non-problems gamblers. The analyses also revealed a strong association between problem
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J Gambl Stud Table 1 Past year prevalence of problem gambling stratified by sex and age in 2005 and 2010 (pooled data)
%
Number of respondents
Men 16–24 years
4.8
883
25–34 years
2.5
1,052
35–49 years
1.4
2,370
C50 years
0.7
4,822
All men
1.5
9,127
16–24 years
0.6
1,163
25–34 years
0.2
1,303
35–49 years
0.3
2,839
C50 years
0.2
5,241
All women
0.3
10,546
16–24 years
2.4
2,046
25–34 years
1.2
2,355
35–49 years
0.8
5,209
C50 years
0.4
10,063
All
0.8
19,673
Women
All
gambling and heavy smoking. The odds of exceeding the sensible drinking limits and problem drinking were significantly higher among past year problem gamblers than among non-problem gamblers. Further, the past year prevalence of illicit drug use was substantially higher among past year problem gamblers than non-problem gamblers. The odds of sedentary leisure activity was significantly higher among past year problem gamblers than among non-problem gamblers, and past year problem gamblers were more likely to have an unhealthy dietary pattern and to be obese compared to non-problem gamblers. In all, 17.5 % of the current problems gamblers had at least three of the examined risk factors (daily smoking, high alcohol intake, illicit drug use, sedentary lifestyle and obesity). The corresponding prevalence among non-problem gamblers was 2.6 %. Table 3 shows the results from the multiple logistic regression of past year problem gambling on selected risk factors. The results show that even though the analyses were adjusted, the associations still persisted. For example, the odds of problem gambling in the past year were 2.7 (95 % CI 1.7–4.3) times higher among heavy smokers than among never smokers, when controlling for other potential risk factors.
Discussion The present study shows marked differences between past year problem gamblers and nonproblem gamblers regarding their health behaviour and BMI, as past year problem gamblers have an unhealthier behaviour than non-problem gamblers. These patterns persisted when adjusting for sociodemographic factors, socioeconomic factors and all other variables for health behaviour and BMI. The strong association between problem gambling and smoking is consistent with international findings from population-based surveys (Lorains et al. 2011; CunninghamWilliams et al. 1998; Smart and Ferris 1996). Our results regarding alcohol consumption
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J Gambl Stud Table 2 Prevalence of health behaviours and obesity according to past year problem gambling Problem gambling Yes
No
ORa
95 % CI
ORb
95 % CI
Daily smoker (\15 cigarettes/day)
45.8
21.8
3.2
(2.3–4.3)
2.9
(2.0–4.3)
Heavy smoker (15 or more cigarettes/day)
28.0
10.7
3.1
(2.2–4.5)
3.0
(2.0–4.6)
Smoking behaviour
Alcohol consumption Exceed sensible drinking limits
35.3
11.9
3.2
(2.3–4.5)
3.9
(2.5–5.9)
Binge drinkingc
30.3
19.4
1.2
(0.8–1.8)
1.2
(0.7–2.1)
Problem drinkingc
31.0
16.8
2.1
(1.4–3.1)
2.4
(1.5–4.0)
22.1
4.1
3.0
(1.9–4.5)
3.9
(2.2–7.0)
8.4
1.1
3.2
(1.7–5.9)
5.5
(2.5–12.1)
20.6
11.0
2.3
(1.5–3.3)
1.9
(1.2–3.0)
21.8
11.7
1.6
(1.0–2.5)
1.3
(0.7–2.3)
15.2
12.6
1.5
(1.0–2.4)
1.5
(0.9–2.4)
Illicit drugs within the last year Cannabis Other illicit drugs than cannabis Physical activity Sedentary leisure activity Diet Unhealthy dietary patternc BMI Obesity
Odds ratios (OR) with 95 % confidence intervals (CI) a
Adjusted for sex, age and survey year
b
Adjusted for sex, age, survey year, highest completed education and cohabiting status. Only respondents aged 25 or above were included in the analyses
c
Data only available in 2010
among past year problem gamblers also support previous research (Cunningham-Williams et al. 1998; French et al. 2008; Petry et al. 2005; Stewart and Kushner 2005; Welte et al. 2001; Smart and Ferris 1996; Gerstein et al. 1999). Furthermore, the association between problem gambling and illicit drug use in the present study is in accordance with results from other general population surveys (Lorains et al. 2011; Cunningham-Williams et al. 1998; Petry et al. 2005). The prevalence of sedentary leisure activity was significantly higher among past year problem gamblers than among non-problem gamblers. Previous studies have also showed that problem gamblers were more likely to avoid regular exercise (Black et al. 2013). Past year problem gamblers had a higher prevalence of having an unhealthy dietary pattern than non-problem gamblers. As far as we know, no previous studies have investigated the association between problem gambling and an unhealthy dietary pattern. The present study confirms results from previous studies that show that increased gambling severity is associated with current obesity status (Morasco et al. 2006; Black et al. 2013). It is not unexpected that the prevalence of obesity is higher among past year problem gamblers than among non-problem gamblers with the prevalence of sedentary leisure activity and unhealthy diet in mind. Even though the odds of being a past year problem gambler on selected risk factors were adjusted for all risk factors the effect of each risk factor still persisted. In addition, past year problem gamblers clearly experience a greater accumulation of unhealthy behaviour than non-problem gamblers. All in all, our results are consistent with international findings, though they are not directly comparable as they are based on different measures of gambling problems. Given
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J Gambl Stud Table 3 Odds of being a past year problem gambler with regard to health behaviour and BMI ORa
95 % CI
P value
ORb
95 % CI
P value
0.002
Smoking behaviour Heavy smoker (15 or more cigarettes/day)
2.7
(1.7–4.3) \0.0001
2.7
(1.5–4.8)
Daily smoker (\15 cigarettes/day)
1.7
(1.1–2.7)
1.9
(1.1–3.5)
Previous smoker
1.0
(0.6–1.7)
1.1
(0.6–1.9)
Never-smoker
1
1
Alcohol consumption (1.5–3.2) \0.0001
Exceed sensible drinking limits
2.2
Do not exceed sensible drinking limits
1
Zero standard drinks per week or do not drink
0.8
(0.5–1.4)
Yes
1.7
(1.1–2.7)
No
1
2.6
(1.6–4.1) \0.0001
1 0.7
(0.4–1.4)
2.4
(1.3–4.3)
Illicit drugs within the last year 0.02
0.005
1
Physical activity Moderate/vigorous physical activity
1.2
Low physical activity
1
Sedentary leisure activity
2.0
(1.8–1.8)
0.008
1.3
(0.8–2.0)
C0.10
1 (1.3–3.2)
1.8
(1.0–3.0)
BMI Underweight and normal weight
1
Overweight
1.4
(1.0–2.1)
0.05
1.3
1 (0.8–2.1)
C0.10
Obesity
1.7
(1.1–2.9)
1.7
(1.0–3.1)
Odds ratios (OR) with 95 % confidence intervals (CI) a
Variables in the model: sex, age, survey year, and all other variables listed in the table
b
Variables in the model: sex, age, survey year, highest completed education, cohabiting status, and all other variables listed in the table. Only respondents aged 25 or above were included in the analyses
its brief character, the lie/bet screen is likely to cover both milder gambling problems along with severe problems compared to a more comprehensive assessment such as the NORC DSM Screen for gambling problems (NODS) (Gerstein et al. 1999) used in several studies. This is considered as a weakness of the screen. The purpose of screening for problem gambling in a large health survey is to investigate an overall prevalence of how widely gambling is affecting a population, which is by no means comparable to a thorough clinical assessment of pathological gambling based on the DSM-IV-R criteria. However, short screens are an important tool if gambling is to be investigated in larger population based health surveys because these surveys usually include several topics, which challenges the limit for duration, as to how much time people can be expected to spend on participating in a survey. It is possible that some of these associations occur because drinking or illicit drug use cause gambling, similarly to the instances in which alcohol and illicit drug use promote other risky and short sighted behaviours such as unsafe sex and violence (Petry 2000). For some gamblers, alcohol and illicit drugs can also be a motive for maintaining the need for gambling to pay the consumption of for instance illicit drugs. It is also possible that unsuccessful gamblers occasionally try to forget their gambling losses by drinking alcohol or by using illicit drugs. However, it is likely that much of the behaviour in relation to drinking, illicit drugs, unhealthy diet or gambling arises from underlying mechanisms that
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give rise to these behaviours. As problem gambling is a disorder of impulse control where a person becomes unable to resist the impulse to gamble, maybe it is the same loss of control in relation to smoking, alcohol, illicit drugs and unhealthy diet. It may be assumed that problem gamblers do not think of the long-term negative consequences of their behaviour and that they are influenced by their immediate desire and needs. Maybe it is possible that problem gamblers and individuals with problem drinking and an illicit drug use have similar predispositions (genetic, environmental and social) and personality profiles (Lorains et al. 2011; Slutske et al. 2005). It may influence the development and maintenance of their unhealthy behaviour as well as problem gambling. This study suggests that it is important to deal with the risk factors associated with problem gambling in a public health perspective, and therefore, problem gambling should be incorporated in public health strategies against addictive behaviour. By understanding gambling and its potential impact on public health, policy makers, health practitioners and community leaders can minimize the negative impacts of gambling on the individuals, their families and society. By understanding the distribution and determinants of gambling related health problems, it is possible to develop health promotion strategies to protect the problem gamblers from the risk factors associated with gambling. The present study suggest screening for alcohol and illicit drug use disorders while treating for gambling problems, since the present study found that problem gambling is strongly associated with high alcohol consumption and illicit drug use. As with any empirical study, there are limitations of the data in relation to proper interpretation of the findings. Since the data derives from cross-sectional surveys it is not possible to identify causality, and therefore, focus was on associations in this study. Another limitation in this study is the fact, that data from 2005 to 2010 have been collected by slightly different methods. Face-to-face interview combined with self-administered questionnaire was used in 2005 while a self-administered questionnaire was used in 2010. However, the gambling questions were only included in the self-administered questionnaire. Furthermore, the overall response rates were adequate (52 % in 2005 and 63 % in 2010, respectively). However, the response rate was somewhat lower among men (58 %) than among women (67 %) in 2010. The lowest response rates were found among young men (16–24 years: 44 % and 25–34 years: 49 %). The same patterns were found in 2005. Since all Danes have a unique 10-digit civil registration number, it was possible to link both respondents and non-respondents in the survey in 2010 on an individual level to different administrative registers (e.g. the Danish Civil Registration System, the Population Education Register and the Income Statistics Register). Calibration was used to adjust for non-response as proposed by Sa¨rndal and Lundstro¨m (2005). The analyses indicated that non-response do not significantly alter the estimates or the conclusions of the present study (data not shown). A major strength of the present study is that it is based on two large nationally representative samples. Further, it is a strength that the study is based on key topics of health behaviour included in the general health survey, which has made it possible to analyze problem gambling in a broader health perspective. Gambling problems have typically been a topic of the psychiatric discipline with focus on addictive behaviour among gamblers in treatment. It is a strength that the multiple logistic regression models include adjustments for both sociodemographic factors, socioeconomic factors and all other variables for health behaviour and BMI, thus it is possible to isolate the effect of problem gambling. In summary, this study demonstrated marked differences between past year problem gamblers and non-problem gamblers regarding their health behaviour and BMI. Behavioural risk factors such as smoking, high alcohol consumption, illicit drug use, sedentary
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leisure activity, unhealthy diet pattern and obesity were more prevalent among past year problem gamblers than among non-problem gamblers. Our findings highlight the presence of a potential, public health challenge and elucidate the need for health promotion initiatives targeted at problem gamblers. Furthermore, more research is needed in order to understand the underlying social mechanism of the association between problem gamblers and unhealthy behaviour.
Key-points This study • shows that problem gamblers are more likely to smoke, have a high alcohol intake and use illicit drugs and, furthermore, more likely to have a sedentary lifestyle, unhealthy diet pattern and to be obese compared to non-problem gamblers. • shows that problem gamblers clearly experience a greater accumulation of risk factors than non-problem gamblers. • elucidate the need for health promotion initiatives targeted at problem gamblers. Acknowledgments The study 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 Black, D. W., Shaw, M., McCormick, B., & Allen, J. (2013). Pathological gambling: Relationship to obesity, self-reported chronic medical conditions, poor lifestyle choices, and impaired quality of life. Comprehensive Psychiatry, 54(2), 97–104. Bland, R. C., Newman, S. C., Orn, H., & Stebelsky, G. (1993). Epidemiology of pathological gambling in Edmonton. Canadian Journal of Psychiatry, 38(2), 108–112. Christensen, A. I., Ekholm, O., Glu¨mer, C., Andreasen, A. H., Hvidberg, M. F., Kristensen, P. L., et al. (2012). The Danish National Health Survey 2010. Study design and respondent characteristics. Scandinavian Journal of Public Health, 40(4), 391–397. Cunningham-Williams, R. M., Cottler, L. B., Compton, W. M, I. I. I., & Spitznagel, E. L. (1998). Taking chances: Problem gamblers and mental health disorders—Results from the St. Louis Epidemiologic Catchment Area Study. American Journal of Public Health, 88(7), 1093–1096. Ekholm, O., Eiberg, S., Davidsen, M., Holst, M., Larsen, C. V., & Juel, K. (2012). The prevalence of problem gambling in Denmark in 2005 and 2010: A sociodemographic and socioeconomic characterization. Journal of Gambling Studies. doi:10.1007/s10899-012-9347-4. Ekholm, O., Hesse, U., Davidsen, M., & Kjøller, M. (2009). The study design and characteristics of the Danish national health interview surveys. Scandinavian Journal of Public Health, 37(7), 758–765. European Monitoring Centre for Drugs and Drug Addiction. (2002). Handbook for surveys on drug use among a project CT.99.EP.08 B. Lisbon: EMCDDA. French, M., Maclean, J., & Ettner, S. (2008). Drinkers and bettors: Investigating the complementarity of alcohol consumption and problem gambling. Drug and Alcohol Dependence, 96(1–2), 155–164. Gerstein, D. R., Hoffmann, J. P., Larison, C., Engelman, L., Murphy, S., Palmer, A., et al. (1999). Gambling impact and behavior study: Report to the national gambling impact study commission. Chicago: National Opinion Research Center. Go¨testam, K., Johansson, A., Wenzel, H., & Simonsen, I. (2004). Validation of the lie/bet screen for pathological gambling on two normal population data sets. Psychological Reports, 95(3 Pt 1), 1009–1013. Griffiths, M. (2004). Betting your life on it. British Medical Journal, 329(7474), 1055–1056.
123
J Gambl Stud Gronbaek, M. N., Iversen, L., Olsen, J., Becker, P. U., Hardt, F., & Sorensen, T. I. (1997). Sensible drinking limits. Ugeskrift for Laeger, 159(40), 5939–5945. 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. Juel, K., Sorensen, J., & Bronnum-Hansen, H. (2008). Risk factors and public health in Denmark. Scandinavian Journal of Public Health, 36(Suppl 1), 11–227. Lorains, F. K., Cowlishaw, S., & Thomas, S. A. (2011). Prevalence of comorbid disorders in problem and pathological gambling: Systematic review and meta-analysis of population surveys. Addiction, 106(3), 490–498. Lyk-Jensen, S. V. (2010). New evidence from the grey area: Danish results for at-risk gambling. Journal of Gambling Studies, 26(3), 455–467. Morasco, B. J., Pietrzak, R. H., Blanco, C., Grant, B. F., Hasin, D., & Petry, N. M. (2006). Health problems and medical utilization associated with gambling disorders: Results from the national epidemiologic survey on alcohol and related conditions. Psychosomatic Medicine, 68(6), 976–984. Petry, N. M. (2000). Gambling problems in substance abusers are associated with increased sexual risk behaviors. Addiction, 95(7), 1089–1100. Petry, N. M., Stinson, F. S., & Grant, B. F. (2005). Comorbidity of DSM-IV pathological gambling and other psychiatric disorders: Results from the national epidemiologic survey on alcohol and related conditions. Journal of Clinical Psychiatry, 66(5), 564–574. Sa¨rndal, C.-E., & Lundstro¨m, S. (2005). Estimation in surveys with nonresponse. New York: Wiley. Shaffer, H. J., & Hall, M. N. (2001). Updating and refining prevalence estimates of disordered gambling behaviour in the United States and Canada. Canadian Journal of Public Health, 92(3), 168–172. Shaffer, H. J., LaBrie, R. A., LaPlante, D. A., Nelson, S. E., & Stanton, M. V. (2004). The road less travelled: Moving from distribution to determinants in the study of gambling epidemiology. The Canadian Journal of Psychiatry, 49(8), 504–516. Slutske, W. S., Caspi, A., Moffitt, T. E., & Poulton, R. (2005). Personality and problem gambling: A prospective study of a birth cohort of young adults. Archives of General Psychiatry, 62(7), 769–775. Smart, R. G., & Ferris, J. (1996). Alcohol, drugs and gambling in the Ontario adult population, 1994. Canadian Journal of Psychiatry, 41(1), 36–45. Stewart, S. H., & Kushner, M. G. (2005). Introduction to the special issue on ‘‘relations between gambling and alcohol use’’. Journal of Gambling Studies, 21(3), 223–231. Toft, U., Kristoffersen, L. H., Lau, C., Borch-Johnsen, K., & Jorgensen, T. (2007). The dietary quality score: Validation and association with cardiovascular risk factors: The Inter99 study. European Journal of Clinical Nutrition, 61(2), 270–278. Welte, J., Barnes, G., Wieczorek, W., Tidwell, M. C., & Parker, J. (2001). Alcohol and gambling pathology among US adults: Prevalence, demographic patterns and comorbidity. Journal of Studies on Alcohol, 62(5), 706–712. WHO. (2000). Obesity: Preventing and managing the global epidemic. Report of a WHO consultation. World Health Organization Technical Report Series (2001/03/10 Ed., Vol. 894, pp. i–xii, 1–253). Zierau, F., Hardt, F., Henriksen, J. H., Holm, S. S., Jørring, S., Melsen, T., et al. (2005). Validation of a selfadministered modified CAGE test (CAGE-C) in a somatic hospital ward: Comparison with biochemical markers. Scandinavian Journal of Clinical and Laboratory Investigation, 65(7), 615–622.
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