J Gambl Stud DOI 10.1007/s10899-015-9534-1 ORIGINAL PAPER

Sociodemographic Correlates and Morbidity in Lottery Gamblers: Results from a Population Survey Mythily Subramaniam1,2 • Benjamin Tang1 • Edimansyah Abdin1 • Janhavi Ajit Vaingankar1 Louisa Picco1 • Siow Ann Chong1

•

Ó Springer Science+Business Media New York 2015

Abstract The aim of the current study was to examine the socio-demographic correlates, the association of mental and physical illness, and the prevalence of pathological gambling among three groups (1) those with lottery gambling only (2) those with lottery and other types of gambling and (3) those with other types of gambling only—such as playing cards, sports betting, horse racing, casino gambling etc. Data was used from a nationwide crosssectional epidemiological nationally representative survey of the resident (Singapore Citizens and Permanent Residents) population in Singapore of 6616 Singaporean adults aged 18 years and older. All respondents were administered the South Oaks Gambling Screen to screen for pathological gambling. The diagnoses of mental disorders were established using the Composite International Diagnostic Interview and relevant socio-demographic data was collected using a structured questionnaire. Lottery gambling was by far the most popular form of gambling in Singapore, with 83.5 % of those who had ever gambled indicating that they had participated in lottery gambling. Those who participated in lottery gambling alone were more likely to belong to the older age group (as compared to the 18–35 years age group), be of Indian ethnicity, have a secondary or vocational education, and earn a lower income as compared to the other two groups. Our findings that those with pure lottery gambling were significantly less likely to be pathological gamblers and had significantly lower odds of psychiatric and physical morbidity as compared to the other two groups are unique and need further research. Keywords

Lottery gambling
Pathological gambling
Comorbidity
Singapore
Survey

& Mythily Subramaniam [email protected] 1

Research Division, Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747, Singapore

2

Saw Swee Hock School of Public Health, Singapore, Singapore

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Introduction Gambling is broadly defined as any wagering of assets on an unpredictable outcome which has long been a facet of many societies. Gambling exists on a continuum ranging from social or recreational gambling where there are no adverse impacts to disordered gambling where gambling leads to adverse consequences for the individual, their families, friends and colleagues, or for the community. Recreational gamblers gamble within their means and with no significant problems; gambling usually does not result in any negative consequences for the person in terms of time and money spent. Pathological gamblers on the other hand are believed to have ‘‘diminished cognitive control over the urge to engage in addictive behaviors,’’ which results in their inability to control the behavior despite negative consequences (Singer et al. 2014). Pathological gamblers are associated with cognitive dysfunctions, (increased impulsivity, cognitive interference etc.) (Singer et al. 2014) and comorbid psychiatric disorders (e.g. nicotine dependence, major depression, anxiety disorders and substance use disorders) (Lorains et al. 2011). Additionally, rates of suicide attempts are high among pathological gamblers seeking help (Maccallum and Blaszczynski 2003). While there are numerous forms of gambling, modern gambling can be divided into five broad classes: sports, social, lottery/scratch, slot machines and casino table gambling. Lotteries are a big business, involving at least 100 countries and 200 jurisdictions worldwide. Lotteries are characterized by an extremely low probability of winning and a low pay-out ratio i.e. the total amount of money returned to the gamblers (Clotfelter and Cook 1990). Yet large numbers of people continue to buy lottery tickets. Lottery gambling is one of the simplest forms of gambling, and one that requires minimal skill or thought: 67 % of participants in UK’s National Lottery reported that they chose the same number every week (Crosbie 1996). Lottery gambling too exists on a continuum and can become habitual and even addictive. An economic paper examining the Texas State Lottery sale of lottery tickets uncovered a peculiar demand trend in the winning zip code. Initially, the study discovered an abnormal increase in the demand for lottery tickets: 38 % for the winning lottery ticket store and 14.1 % in stores in its zip code (Guryan and Kearny 2009). This unusual spike was ascribed to an ‘‘erroneous belief that the winning store is lucky’’, subsequently increasing the lottery gambling participants in the area (Guryan and Kearny 2009). Even after 18 months from when the store sold its winning lottery ticket, almost 40 % of this irregular spike in demand for lottery tickets was maintained. This preservation of demand reflected habit formation in consumers attests to the addictive quality of lottery gambling. Petry (2003) showed that lottery gamblers, compared to slot machine, horse/dog track, sports gamblers and card players, gamble more frequently and have high rates of life-time as well as current substance use and psychiatric problems while also representing the lowest median income across all the other gambling populations, arguably reflecting a lower functionality—on average—in their socio-economic environment. Singapore is an island city–nation off the southern tip of the Malay Peninsula. In 2013, the population of Singapore was just under 5.4 million of which 3.85 million were Singapore residents. Of these residents, 74.2 % are of Chinese descent, 13.3 % are Malays, and 9.2 % are of Indian descent while those of ‘Other’ ethnicity comprise 3.3 % of the resident population (Department of Statistics Singapore 2013). The legal right to operate a lottery in Singapore is held by the Singapore Totalisator Board, through its wholly-owned subsidiary, Singapore Pools (Private) Limited. There are three main forms of lottery

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gambling: 4D, TOTO and Singapore Sweep. 4D is a four digit lottery that is drawn thrice a week, while TOTO requires participants to choose six numbers from 49 given options and is drawn twice a week. Finally, Singapore Sweep is a typical raffle-style lottery that is drawn monthly. In Singapore, lottery gambling, which is responsible for a significant proportion of all gambling, has managed to remain a socially acceptable, daily expenditure with 49 % of the Singaporean population considering Singapore Sweep a leisure activity (National Council On Problem Gambling 2011), and 81.6 % of all gamblers admitting that lottery gambling was their first regular gambling participation (National Council on Problem Gambling 2011). Given the wide availability of lotteries, the prevalence of lottery gambling and its perception as a leisure activity despite the risks highlighted by other studies (Gru¨sser et al. 2007; Petry 2003; Ariyabuddhiphongs 2011), the aim of the current study was to understand the phenomenon of lottery gambling in this multi-ethnic population. Using data from the Singapore Mental Health Study (SMHS) we examined the socio-demographic correlates, the association of mental and physical illness as well as the prevalence of pathological gambling among three groups classified on the basis of the type of gambling (1) those with lottery gambling only (LG) (2) those with lottery and other types of gambling (LOG) and (3) those with other types of gambling only (OG)—such as playing cards, sports betting, horse racing, casino gambling etc.

Methodology Data for the current study was extracted from the SMHS; a population-based, crosssectional, epidemiological study that was conducted in Singapore from December 2009 to December 2010. The ethics committee (National Healthcare Group, Domain Specific Review Board) approved the study and all participants, along with the parents/guardians of those aged below 21 years, gave written informed consent for participating in the study. The SMHS involved a single-stage design, without geographic clustering, that surveyed Singapore Residents (including Singapore Citizens and Permanent Residents) aged 18 years and above. In order to determine the necessary sample sizes—both overall and for sub-groups (i.e. age and ethnicity)—to produce an estimate with a margin of error of 0.05 for different disorders, statistical power calculations for single and two proportions were conducted. The sampling frame was based on a regularly updated administrative database of all citizens and Permanent Residents in Singapore. Residents who were younger than 18 years, without any address or residing outside the country since July 2009, were excluded from the frame to ensure that data was confined to a local pool of participants. A probability sample was randomly selected using a disproportionate stratified sampling design with 16 strata, defined according to ethnicity and age groups. This study has been described previously in greater detail (Subramaniam et al. 2012a).

Data Collection and Instruments The South Oaks Gambling Screen (SOGS) was used to identify pathological gamblers. SOGS is based on the Diagnostic and Statistical Manual of Mental Disorders Third Edition (DSM-III) criteria for pathological gambling (APA 1980; Lesieur and Blume 1987). Internal consistency and test–retest reliability of this questionnaire has been established, and it shows good agreement with the DSM-IV criteria for pathological gambling (Stinchfield

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2002). The English version of SOGS has been validated in the Singapore population as part of an earlier study on the same sample. The SOGS demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.84 (Abdin et al. 2012). There are 20 scoring items in SOGS, all equally weighted, requiring a ‘yes’ or ‘no’ answer. Each ‘yes’ answer attains one point, and a score of five or more indicates ‘probable’ pathological gambling. The non-scoring items identify type of gambling, amount of money gambled in a day, and relatives and friends with a gambling problem. For the purpose of this study, those with scores of 0–2 were classified as ‘non-gamblers and non- problem gamblers’, scores of 3–4 as ‘Problem Gamblers’ and respondents scoring 5 or more were categorized as ‘Pathological Gamblers’. Those who indicated that they had— ‘‘Played the numbers or bet on lotteries’’ were classified as lottery gamblers. Those who had specified ‘other’ forms of gambling like ‘4D’ or ‘Singapore Sweep’ were also reclassified as lottery gamblers. The assessment of mental disorders was established using version 3.0 of the World Health Organisation Composite International Diagnostic Interview (CIDI) (Kessler and Ustun 2004). CIDI has been widely used in many countries and validated by comparing it with clinician-administered non-patient edition of the Structured Clinical Interview for DSM-IV (SCID) in probability subsamples of the World Mental Health surveys in France, Italy, Spain, and the US. Moderate to good individual-level CIDI–SCID concordance was found for lifetime prevalence estimates of most disorders (Haro et al. 2006). Diagnostic modules for lifetime and 12-month prevalence of affective disorders [including major depressive disorder (MDD) and bipolar disorder], anxiety disorders [including generalised anxiety disorder (GAD) and obsessive–compulsive disorder (OCD)], and alcohol use disorder (i.e. alcohol abuse and alcohol dependence) were incorporated in the survey. CIDI organic exclusion rules as well as diagnostic hierarchy rules were applied to generate the final diagnoses. Nicotine dependence was established using the Modified Fagerstrom test for Nicotine Dependence (FTND) (Heatherton et al. 1991). The FTND had a good internal consistency as demonstrated by a Cronbach’s alpha of 0.73 in the Singapore population (Abdin et al. 2011). We used a modified version of the CIDI checklist of chronic medical conditions during the interview in order to gather information on a range of chronic physical conditions. Respondents were asked to report any of the conditions listed in the checklist that comprised of 15 chronic physical conditions, each of which was re-classified into eight broad types of physical disorders (Chong et al. 2012). Height and weight were self-reported by all respondents. Body mass index (BMI) was defined as the weight in kilograms divided by the square of the height in meters (kg/m2), and for comparisons, standard World Health Organization (WHO) cut-offs were used (WHO 1995; Subramaniam et al. 2013). Participants’ health related quality of life was measured using the Euro-Quality of Life Scale (EQ-5D) (EuroQol Group 1990). EQ-5D, a standardized measure of health status, comprises a descriptive index system and a visual analogue scale (VAS); both were used for the study. Population norms have been established for EQ-5D in Singapore (Abdin et al. 2013). The EQ-5D has been used widely in Singapore to examine HRQOL among different disease groups (Subramaniam et al. 2014; Zhang et al. 2009). The test–retest reliability of each domain was found to be good to very good (Cohen’s kappa = 0.4–1.00) (Luo et al. 2003), while the known-groups and convergent validity of the scale was found to be satisfactory (Wee et al. 2007). Data on EQ5D index was available for 1912 participants who had engaged in gambling activities at least once in their lifetime.

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Statistical Analyses All estimates were weighted to adjust for over sampling and post-stratified for age and ethnicity distributions between the survey sample and the Singapore resident population in 2007. Weighted mean and standard error were calculated for continuous variables, and frequencies and percentages for categorical variables. The demographic and clinical characteristics were compared among the groups (LG, LOG and OG) and tested for significant differences using Chi square tests followed by multinomial logistic regression. Multiple logistic regression models were used to generate odd ratios (ORs) and 95 % confidence intervals for relationship between outcome variables and predictor variables. All regression analyses assessing the relationship between type of gambling and outcomes were adjusted for socio-demographic characteristics including age group, gender, ethnicity, marital status, education, employment and income to control for confounders. Mean EQ5D index scores were compared between three groups using ANOVA test followed by multiple linear regression to adjust for differences in demographics variables. Statistical significance was evaluated at the \0.05 level using two-sided tests. All statistical analyses were carried out using the Statistical Analysis Software (SAS) System version 9.

Results 2252 respondents who had engaged in gambling activities at least once in their lifetime were included in this study. Slightly more than half of the sample (57.5 %) was male, with a mean age of 43.9 years, ranging from 19 to 89 years. Majority of the respondents (90.1 %) were of Chinese descent, 2.7 % were Malays, 5 % were of Indian descent and 2.2 % belonged to Other ethnic group. The proportion of respondents who were involved in lottery gambling was 83.5 %. The proportion of respondents who had participated in LG, LOG and OG were 41.3, 42.2 and 16.5 %, respectively. Figure 1 shows the amount spent per day on gambling by the three groups. There were significant differences in the maximum amount spent per day between the three groups. Gamblers with LG had lower rates of spending per day than other groups. The mean number of types of gambling endorsed by those who participated in LOG was significantly higher than those who endorsed OG (2.6 vs. 1.6, p value\0.001) (LG was excluded from this analysis). We also found that there were significant difference in endorsement of items ‘go back another day to win back the money’ and ‘ever lost time from work due to betting’ between the three groups (Fig. 2). Table 1 shows the demographic characteristics of respondents among those who participated in LG, LOG and OG. Multinomial logistic regression analyses indicate that those who participated in LG (as compared to those with LOG or OG) were more likely to be older (35–49, 50–64 and 65 years and above vs. 18–34 years) of Indian ethnicity (vs. Chinese), with secondary and vocational education (vs. university) and earning an annual income of SGD 20,000-49,000 (vs. SGD 50,000 and above). Table 2 shows the prevalence of and OR for mental disorders between the three groups. After adjusting for demographic variables in multiple logistic regression analyses, gamblers who had participated in LG had significantly lower odds of MDD, alcohol use disorder and any one of the mental disorders assessed by CIDI in the study as compared to those who with LOG. Those with LG also had lower odds of having alcohol dependence than those who participated in OG. Those with LG had significantly lower odds of problem

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Fig. 1 Amount spent of gamblers who played lottery only (LG), lottery and other type of gambling (LOG) and other gambling only (OG)

gambling as compared to those with LOG and lower odds of pathological gambling as compared to those with LOG and OG. Table 3 shows the prevalence of and OR for chronic physical conditions by three groups. After adjusting for demographic variables in multiple logistic regression analyses, Those with LG had a lower risk for respiratory disorders, hypertension, pain conditions and any physical illness as compared to those with LOG. Those who participated in LG had lower odds of having respiratory conditions than those who participated in OG. There were no significant differences in BMI (calculated using self-reported weight and height) and quality of life as measured using the EQ-5D index between the three groups (data available on request).

Discussion Lottery gambling is by-far the most popular form of gambling in Singapore with 83.5 % of those who had ever gambled indicating that they had participated in lottery gambling. Those who participated in LG were more likely to belong to the older age group (as compared to the 18–35 years age group), be of Indian ethnicity, have secondary or vocational education, and earn a lower income as compared to the other two groups. Our results are similar to that reported by Welte et al. (2002) who conducted a representative household survey of adults aged 18 years and over in the US, and observed that

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Fig. 2 Endorsement of selected items of the SOGS among gamblers who played lottery only (LG), lottery and other type of gambling (LOG) and other gambling only (OG)

66 % of respondents had engaged in lottery gambling during the past year, making it the most common type of gambling. Congruent with other studies (Welte et al. 2002; Barnes et al. 2011) the lowest income bracket in our sample displayed the highest prevalence of LG. Some researchers have speculated that the link between gambling pathology and lower socioeconomic status is due in part to individuals with lower income viewing gambling as a type of investment providing a possible escape from poverty (Welte et al. 2004). This may be especially true for lottery gamblers where the ‘investment’ in lottery gambling may be ‘affordable’ as 90.4 % of LGs indicated that the largest amount of money they had ever gambled with on any one-day was between S$1–S$100, the lowest two ranges of monetary bets placed in a day. The profile of LGs in this study is distinct from the general gamblers (any person who partook in gambling activities regardless of its medium or form) as reported by the National Council of Problem Gambling (NCPG), Singapore (NCPG 2011). The NCPG (2011) study found relatively higher gambling participation rates among Chinese, males, residents aged 40–59 years, those with primary school and below educational qualifications and a higher average monthly personal income. Our results thus suggest that profiles of gamblers may vary according to their preferred gambling activities. Holtgraves (2009) identified two groups based on factor analysis of type and frequency of gambling activity—one group comprised Internet, sports and horse race gamblers and the other consisted of slot/video lottery terminal (VLT), raffle, lottery, and bingo gamblers. These groups varied on a number of dimensions including gender differences and amount wagered, attesting to the distinctness of gambling activities and their appeal to individuals with different characteristics.

123

123

419

351

64

50–64 years

65 years and above

384

22

Malay

Indian

Other

386

Female

37

Widowed

50

194

384

166

Pre primary

Primary

Secondary

Pre–u/junior college/diploma

Education

53

772

Married

Divorced/separated

186

Single

Marital status

662

Male

Gender

488

154

Chinese

Ethnicity

214

35–49 years

17.5

33.8

19.2

7.9

5.1

5.3

69.3

20.3

47.1

52.9

0.8

7.3

3.7

88.2

11.0

33.2

37.4

18.4

202

218

79

17

14

40

513

264

280

551

61

164

71

535

39

166

293

333

24.9

25.1

12.6

3.5

3.2

4.4

62.7

29.6

38.5

61.5

2.7

3.2

1.7

92.5

9.4

20.6

35.7

34.4

%

N

N

%

LOG (N = 831)

LG (N = 1048)

18–34 years

Age group

Variables

105

69

27

7

1

5

191

175

135

238

40

81

43

209

12

61

103

197

N

29.2

19.0

10.9

2.6

0.4

1.1

49.0

49.5

41.5

58.5

4.5

3.9

2.6

89.0

6.4

16.8

24.8

52.0

%

OG (N = 373)

1.2

1.6

1.5

1.9

1.1

1.0

1.1

Ref.

Ref

0.7

0.6

3.0

2.2

Ref.

2.1

2.5

1.9

Ref.

OR

0.8–1.8

1.0–2.4

0.9–2.5

0.9–4.1

0.5–2.8

0.5–1.9

0.8–1.5

0.5–0.9

0.3–1.0

2.4–3.9

1.6–3.1

1.1–4.5

1.6–3.7

1.3–2.8

95 % CI lower–upper

LG versus LOG

0.466

0.033

0.140

0.102

0.789

0.970

0.689

0.006

1.3

2.3

1.5

3.2

7.4

5.7

1.9

Ref.

Ref

0.7

0.3

2.2

\.0001 0.068

1.3

\.0001

Ref.

4.3

3.3

\.0001 0.040

3.1

Ref.

OR

\0.001

p value

0.8–2.2

1.3–4.0

0.7–3.3

0.8–12.2

0.8–70.7

1.6–21.0

1.2–2.9

0.5–1.1

0.2–0.6

1.5–3.0

0.8–2.0

1.4–13.2

1.9–6.0

1.9–5.0

95 % CI lower–upper

LG versus OG

0.241

0.005

0.296

0.092

0.081

0.008

0.004

0.111

0.001

\.0001

0.351

0.01

\.0001

\.0001

p value

1.1

1.4

1.0

1.7

6.6

5.7

1.7

Ref.

Ref

1.1

0.5

0.7

0.6

Ref.

2.0

1.4

1.6

Ref.

OR

0.7–1.8

0.8–2.5

0.5–2.2

0.4–6.7

0.7–63.5

1.6–20.0

1.2–2.6

0.8–1.5

0.3–0.9

0.5–1.0

0.3–0.9

0.7–6.2

0.8–2.4

1.1–2.5

95 % CI lower–upper

LOG versus OG

Table 1 Demographic characteristics of respondents among those who played lottery only, lottery and other type of gambling and other gambling only

0.527

0.201

0.965

0.464

0.104

0.007

0.005

0.606

0.023

0.060

0.024

0.226

0.285

0.029

p value

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140

149

47

Economically inactive

Unemployed

136

$SGD 20,000–49,999

$SGD 50,000 and above

8.4

13.1

34.9

52.1

3.9

19.5

76.6

13.2

71

225

281

298

27

110

665

244

6.7

25.4

35.6

39.0

3.0

16.8

80.2

27.2

93

94

164

15

81

262

141

24

N

22.5

28.3

49.1

3.7

26.3

70.0

32.6

5.6

%

OG (N = 373)

Ref.

1.8

2.1

1.0

0.7

Ref.

Ref.

2.1

OR

1.2–2.6

1.4–3.3

0.5–2.1

0.5–1.1

1.2–3.6

95 % CI lower–upper

LG versus LOG

0.005

0.001

0.916

0.146

0.009

p value

Ref.

2.2

1.7

1.0

0.5

Ref.

Ref.

3.0

OR

1.3–3.8

0.9–3.2

0.4–2.1

0.3–0.8

1.4–6.5

95 % CI lower–upper

LG versus OG

0.005

0.106

0.910

0.004

0.004

p value

Ref.

1.2

0.8

0.9

0.6

Ref.

Ref.

1.5

OR

0.8–2.0

0.4–1.5

0.4–2.0

0.4–1.1

0.7–3.1

95 % CI lower–upper

LOG versus OG

0.402

0.464

0.834

0.079

0.310

p value

LG lottery gambling only, LOG lottery and other gambling, OG other gambling only, OR odds ratio was estimated by multinomial logistic regression analyses adjusted for socio-demographic characteristics including age, gender, ethnicity, marital status, education, employment and income

499

372

Below $SGD 20,000

Personal Annual income

818

Employed

Employment

114

University

%

N

N

%

LOG (N = 831)

LG (N = 1048)

Vocational

Variables

Table 1 continued

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123

3.6

8.8

6

142

87

Alcohol dependence

Any mental disorder

Nicotine dependence

36

18

3–4 (problem gamblers)

5? (pathological gamblers)

1.9

2.6

95.5

6.2

2.9

73

68

70

683

58

178

13

66

45

13

20

7.5

8.2

7.7

84.1

5.4

17.5

0.8

6.3

4.6

1.1

2.0

18

12

343

21

62

7

23

18

2

4

25

n

5.7

3.2

91

5.4

12.5

1.3

3.5

3.5

0.1

0.2

5.3

%

\0.001

0.2

0.2

Ref.

1.1

0.5

\.0001 0.821

0.2

0.3

0.6

0.7

0.4

0.5

OR

0.6 0.2

Ref.

0.4

0.7

0.2

0.6

0.6

6.9

2.9

0.8

OR

0.1–0.5 \0.001

0.682

\.0001

0.004

0.001

0.108

0.598

0.175

0.024

p value

0.1–0.5

0.2–1.6

1.9–0.822

0.4–1.1

0.04–0.6

0.3–1.5

0.2–1.6

0.6–78.6

0.7–11.6

0.4–1.9

95 % CI

LG versus OG

0.1–0.3 \0.001

0.7–1.9

0.3–0.7

0.1–0.6

0.2–0.6

0.3–1.1

0.1–3.1

0.1–1.4

0.3–0.9

95 % CI

LG versus LOG

0.007

0.004

0.072

0.039

0.005

0.008

p value

OG (N = 373)

\0.001

0.326

0.9

0.123

0.004

0.324

0.321

0.120

0.132

0.632

p value

1.2

2.6

Ref.

0.8

1.5

0.8

1.9

1.1

10.1

6.9

1.7

OR

0.6–2.4

1.2–5.9

0.4–1.7

1.0–2.4

0.2–2.7

0.9–4.1

0.5–2.4

2.0–51.4

2.1–22.2

0.8–3.4

95 % CI

LOG versus OG

0.587

0.021

0.629

0.067

0.667

0.086

0.841

0.005

0.001

0.163

p value

Significance was set at p value \0.05

GAD generalised anxiety disorder, LG lottery gambling only, LOG lottery and other gambling, MDD major depressive disorder, OCD obsessive compulsive disorder, OG other gambling only, SOGS South Oaks Gambling Screen, OR odds ratio was estimated by multiple logistic regression analyses adjusted for socio-demographic characteristics including age, gender, ethnicity, marital status, education, employment and income

994

0–2 (non–gamblers and nonproblem gamblers)

SOGS cut-off

0.1

45

Alcohol abuse

0.5

2.3

9

31

0.9

OCD

Bipolar disorder

GAD

64

14

MDD

%

n

n

%

LOG (N = 831)

LG (N = 1048)

Table 2 Prevalence of and odds ratio for lifetime mental disorders among gamblers who played lottery only, lottery and other type of gambling and other gambling only

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51

19

464

Cardiovascular disease

Ulcer

Any chronic physical condition

5.5

42.5

2.4

4.3

5.6

0.5

14.7

23.5

9.6

393

33

29

26

7

156

154

70

116

48.3

3.5

3.8

3.8

1.0

18.0

22.9

8.8

11.5

149

5

9

19

3

57

39

17

52

n

40.4

1.2

1.7

3.8

1.0

14.8

11.7

0.047

0.141

0.199

0.287

0.666

0.249

0.001

0.6

0.8

0.7

1.3

0.2

0.6

0.7

0.7

0.5

0.068

13.3 \0.001 4.2

OR

0.4–0.7

0.4–1.6

0.3–1.5

0.7–2.5

0.0–4.2

0.5–0.9

0.5–0.9

0.4–1.2

0.3–0.8

95 % CI

2.3 0.7

0.493

0.9

1.2

1.4

0.7

1.3

1.5

0.4

OR

0.5–1.0

0.5–9.6

0.3–3.0

0.5–2.7

0.1–32.3

0.5–1.2

0.8–2.1

0.6–3.7

0.2–0.7

95 % CI

0.054

0.256

0.891

0.703

0.833

0.215

0.347

0.342

0.002

p value

Lottery only versus other gambling only

\0.001

0.340

0.432

0.301

0.012

0.019

0.177

0.004

p value

Lottery only versus lottery and other gambling

p value

%

Other gambling only

1.2

2.7

1.2

0.9

5.5

1.1

1.7

1.9

0.9

OR

0.8–1.6

0.7–10.3

0.4–3.5

0.4–2.0

0.9–32.9

0.7–1.7

1.0–2.8

0.8–4.6

0.6–1.4

95 % CI

0.383

0.134

0.767

0.708

0.064

0.650

0.045

0.158

0.676

p value

Lottery and other gambling versus other gambling only

Significance was set at p value \0.05

LG lottery gambling only, LOG lottery and other gambling, OG other gambling only, OR odds ratio was estimated by multiple logistic regression analyses adjusted for sociodemographic characteristics including age, gender, ethnicity, marital status, education, employment and income

41

Neurological conditions

166

Chronic pain

3

219

High blood pressure

Cancer

79

113

Respiratory conditions

Diabetes

%

n

n

%

Lottery and other gambling

Lottery only

Table 3 Prevalence of and odds ratio for lifetime chronic medical conditions among gamblers who played lottery only, lottery and other type of gambling and other gambling only

J Gambl Stud

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J Gambl Stud

Interestingly, the odds of pathological gambling were significantly lower among LGs as compared to the other two groups. The prevalence of pathological gambling in LGs was 1.8 %; similar to those reported by Holtgraves (2009) who found that rates of non-problematic gambling were relatively high (78 %) among lottery gamblers, with only 1 % of them meeting criteria for ‘problem gambling’. The authors also suggested that there was an increase in the number of gambling activities that individuals engage in as problem gambling severity increases. Our study similarly shows that the group with LOG endorsed the most number of gambling activities and in fact had the highest prevalence and odds of pathological gambling. The study also found that LGs were less likely to have lost time from school or work due to gambling and chase losses, with only 5.2 % indicating that they returned another day to win back losses. Studies have demonstrated the importance of the immediacy of the reinforcement in maintaining and increasing the gambling behavior. When the result appears immediately, more games are played than when the response is delayed regardless of the results obtained (Lieberman et al. 1979; Cho´liz 2010). Similarly, when the reward is paid out immediately both in terms of contingency and materially in the form of coins—as is the case for slot machines—there is immediate reinforcement. However, there is little immediacy of the reinforcement in traditional lottery gambling, which may in turn lower the risk of disordered gambling in this group. The study found that those who participated in LG had lower odds of having alcohol dependence as compared to the other two groups. Those with LG also had a lower risk of MDD, alcohol abuse and any one of the mental illness assessed by CIDI as compared to those with LOG. Petry (2003) suggested that the nature of the gambling activity may influence the typography of the gambling (frequency and intensity) and may be differentially associated with substance use and psychiatric difficulties. As this is a cross-sectional study, we are unable to determine the causality of this association. While it is possible that, due to their ability to limit financial losses, those with LG have fewer stressors and thus a lowered risk of mental illnesses, it is also possible that those with psychiatric illnesses gamble on more types of activities. Our findings differ from that of Petry (2003) who reported that scratch/lottery gamblers had high rates of life-time as well as current substance use and psychiatric problems. However, their sample comprised treatment- seeking gamblers with half of the participants meeting the criteria of DSM-IV pathological gambling. The authors determined the group membership based on the question—‘What is your most problematic form of gambling?’ and psychiatric diagnoses were not determined using structured instruments. Our results indicate that those who participate in LOG suffer the most from psychiatric illnesses as they also had higher odds of bipolar disorder, GAD and problem gambling as compared to those with OG. Those who participated in LG had lower odds of physical illnesses as compared to those with LOG. These differences are difficult to explain, as we did not collect information on dietary habits or exercise related behavior in our study. It is possible that these chronic physical illnesses may be related to the higher prevalence of alcohol use disorder among the LOG group (Subramaniam et al. 2012b). However, it is important to note that there were no significant differences in the association with BMI, nicotine dependence or in terms of quality of life in the three groups. The study has several limitations. This was a household survey that excluded those in institutional settings including the prison population. Since the design of the survey was cross-sectional it was not possible to establish a causal relationship between gambling and other comorbid disorders. However, the strengths of the study are that it is the first study examining the correlates and morbidity associated with lottery gamblers in a multi-ethnic community sample. Diagnoses of mental disorders were established using a structured

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interview based on DSM-IV criteria using face to face interview techniques. It is also among one of the few community surveys that examined the association of gambling with chronic physical conditions and quality of life. Our high response rate of about 76 % ensures the generalizability of our findings in this population. Gambling still remains a worrying issue in Singapore where, the average monthly betting amount in 2011 was SGD 212, a 20 % increase from its 2008 value (NCPG 2011). While lottery gambling’s economic cost may appear relatively low, 83.5 % of those who had ever gambled indicated in our study that they had participated in lottery gambling and 86.6 % of all gamblers admitted that it was their first regular gambling participation (NCPG 2011). Our findings that those with LG were significantly less likely to be pathological gamblers and had significantly lower odds of psychiatric and physical morbidity as compared to the other two groups are unique and interesting. Although LG alone might be considered as a relatively harmless gambling activity, one can argue that it is a potential gateway to other gambling activities and hazardous habits. In fact, the LOG group played an average of 2.6 types of gambling while those with OG played an average of 1.6 types of gambling; both excluding lottery gambling. The former group also exhibited the highest average spending and chasing losses. They also had a higher risk of hypertension and mental disorders. However, our results need to be interpreted cautiously given the cross-sectional nature of our study; more research is needed to understand these groups better along with the temporal relationship of gambling activities as well as mental disorders and gambling practices. While this study has uncovered certain behavioral trends and correlations with gamblers who partake in LG, much more needs to be done to understand the behavior, its progress and the outcomes for the individual and society. Acknowledgments This study was supported by funding from the Singapore Millennium Foundation and the Ministry of Health, Singapore.

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