The extent and distribution of gambling harm in Finland as assessed by the Problem Gambling Severity Index (original) (raw)

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1 1 Department of Alcohol, Drugs and Addiction, National Institute for Health and Welfare, Helsinki, Finland

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1 1 Department of Alcohol, Drugs and Addiction, National Institute for Health and Welfare, Helsinki, Finland

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2 2 Department of Mental Health and Substance Abuse Services, National Institute for Health and Welfare, Helsinki, Finland

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3 3 The Finnish Foundation for Alcohol Studies, Helsinki, Finland

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10 December 2014

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Susanna U. Raisamo, Pia Mäkelä, Anne H. Salonen, Tomi P. Lintonen, The extent and distribution of gambling harm in Finland as assessed by the Problem Gambling Severity Index, European Journal of Public Health, Volume 25, Issue 4, August 2015, Pages 716–722, https://doi.org/10.1093/eurpub/cku210
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Abstract

Background : Preventing gambling harm has become a policy priority in many European countries. Adverse consequences related to problem gambling are well known, but few studies have analyzed gambling-related harm in detail in general population samples. We determined the extent and distribution of gambling harm in Finland, as assessed by the Problem Gambling Severity Index (PGSI), and analyzed gambling involvement, demographics and their association with various types of harm. Methods: A nationwide telephone survey was conducted among 4484 Finns aged 15–74 years in 2011–12. Gambling-related harms were based on the nine-item PGSI. Gambling involvement was measured by gambling frequency and weekly average gambling expenditure. Associations among harms, demographics and gambling involvement were examined in logistic regression. Results: During the previous year, 13% of respondents experienced at least one gambling-related harm (males 18.1%, females 7.2%). The four commonest harms were ‘chasing losses’ (8.6%), ‘escalating gambling to maintain excitement’ (3.1%), ‘betting more than could afford to lose’ (2.8%), and ‘feeling guilty’ (2.6%). The harm profile in descending order was the same for both genders but differed in prevalence. Young age (<25 years) was associated with increased likelihood of reporting harms. Both monthly and weekly gambling and spending over €21 per week on gambling were related to the harms. Conclusions: Our results provide support for the public health approach to gambling: harms were reported even at low gambling frequency-expenditure levels. In addition to the high-risk approach, adopting a population-level approach to preventing gambling harm could shift the population distribution of harm in a lower direction.

Introduction

The wide variety of adverse psychological, physical, social and economic consequences related to problem gambling has long received consideration; it has been demonstrated that excessive gambling may lead to, e.g., breakdown of relationships, loss of job, legal problems and a reduction in the quality of life. 1–4 Moreover, a range of comorbidities often accompany problem gambling including substance use disorders, anxiety and mood disorder. 3 Much less attention has been given to the harms gambling might cause among non-problem gamblers in the general population. 5,6

In the field of alcohol research, analyses determining the overall burden of alcohol-related harms to the whole population have a long history showing that harm exists at all consumption levels and is not limited to alcohol abusers. The total number of many types of harm in the non-abuser population exceeds the number among abusers, simply because this group is much larger. 7 This notion of ‘prevention paradox’ is important from the public health perspective, as it implies that preventative strategies targeted to the whole population are essential. ‘Prevention paradox’ may logically be relevant for gambling. Gambling occurs along a continuum, ranging from no gambling through occasional gambling to problem gambling. Accordingly, gambling harms exist within a severity continuum, which ranges from no harm through mild, substantial and severe harm. 8–10 Therefore, it seems necessary to consider the harms experienced at any gambling involvement level and among individuals who do not meet the criteria for problem gambling.

The prevention of gambling harm has become a priority for gambling policy in many European countries, including Finland. 2,11 The concept of gambling harm, however, seems rather vague, and it has generally been approached in terms of problem gambling. 1,12 Adverse consequences related to gambling affect not only gamblers, but also their families and the society as a whole. 1 There exist, however, no single valid instrument measuring potential health, social and economic harm dimensions of gambling. While no such broad measure is yet proposed, standardized problem gambling measurement tools can be used more effectively to examine the individual harm items rather than reporting results as a dichotomy only (problem gambler vs. non-problem gambler).

For the purpose of this study, we utilize the Problem Gambling Severity Index (PGSI) 13 which is a nine-item scale for measuring the severity of gambling problems in population surveys. We pay particular attention to the extent and distribution of independent items included in the index. We use the term gambling harm throughout this article to indicate all of the nine items described in the PGSI. Four of the nine items are related to difficulties in controlling gambling and are characterized as early-warning signs of problem gambling (chasing losses, escalating gambling to maintain excitement, betting more money than could afford to lose, borrowing money for gambling). The other five items include the following harm features: health problems, financial difficulties, feelings of guilty, criticism by others and self-perceived gambling problem.

When examining gambling, and its related harm a range of individual characteristics and broader contextual factors deserve attention. 14 Studies in Canada 5,6 have established positive associations among gambling frequency, expenditure on gambling and gambling harm. More precisely, the chances of at least two harms due to gambling increased steadily with greater gambling frequency and higher sums of money spent on gambling. Though the link between individual gambling involvement and the gambling harm has been documented, there is a lack of elaborate analysis as to the type of harm. In this study, our intention is to analyze how gambling involvement patterns, as described by the gambling frequency and weekly average gambling expenditure, are related to the various types of gambling harm.

National variations in legal policies (e.g. availability of games) and societal norms surrounding gambling may lead to differences in the prevalence of gambling and gambling-related harms. 1,4,10,14 Research has further suggested that some types of gambling, e.g. slot machines, are more strongly associated with gambling harm than others. 4 Although it is beyond the scope of this article to provide evidence of these factors, a short summary of the key features of the Finnish gaming market is in place. Gambling plays a significant and visible role in the everyday lives of Finns. The most common gambling activities are the national lottery, scratch cards and slot machines, which are widely available in nearly all corner stones. Gambling is organized as a state-owned monopoly, which has been consistently argued in Finland as the best way to minimize gambling harm. 15

To summarize the aim of this study, our purpose is (i) to describe the extent and distribution of harms through gambling among the Finnish population, as measured by the items included in the PGSI and (ii) to analyze the associated demographic characteristics, gambling involvement patterns, and their relationships to gambling harms. This study is the first of its kind detailed empirical analysis of gambling harms in Finland.

Methods

Sample and data collection

We used data from the nationwide Finnish Gambling 2011 survey, 16 which was a computer-assisted telephone interview carried out between October 2011 and January 2012 among Finns aged 15–74. The survey was designed by the National Institute for Health and Welfare, and an independent Finnish market research company was responsible for data collection. The ethical approval for the study was obtained from The Institutional Review Board of the National Institute for Health and Welfare, Helsinki, Finland.

The sample was randomly selected from the population register. In all, 11 129 individuals were approached before the telephone interview, and they were invited to participate by a letter. A total of 4484 participated in the survey. The response rate was 40%, which was the number of completed interviews divided by the number of persons with an available telephone number. To ensure representativeness of the general Finnish population, data were weighted based on age, gender and region. Table 1 summarizes characteristics of the final study sample.

Table 1

Characteristics of the study sample aged 15–74 years

a Unweighted number of respondents, weighted percentage (in parentheses).

b Pearson’s chi-square test (two-tailed) for gender difference.

c Frequent gamblers included weekly and daily gamblers, occasional gamblers had gambled monthly or less often.

Table 1

Characteristics of the study sample aged 15–74 years

a Unweighted number of respondents, weighted percentage (in parentheses).

b Pearson’s chi-square test (two-tailed) for gender difference.

c Frequent gamblers included weekly and daily gamblers, occasional gamblers had gambled monthly or less often.

Measures

Problem Gambling Severity Index

The previous year’s prevalence of gambling problems was examined for the total sample using the PGSI. 13 PGSI is a self-report instrument comprising nine scored questions. Strengths and limitations of PGSI have been evaluated in numerous articles, and it is a broadly preferred measure for determining problem gambling in population-based studies. 13,17–19 The PGSI items are responded to on four-point scale: (0 = never, 1 = sometimes, 2 = most of the time, 3 = almost always). Total scores (range 0–27) were calculated (Cronbach’s alpha = 0.76) and the following four categories were classified: non-problem gambler (PGSI score = 0), low-risk gambler (PGSI score = 1–4), moderate-risk gambler (PGSI score = 5–7) and problem gambler (PGSI > 7).

Gambling-related harms experienced within the past 12 months as included in the PGSI

Respondents were asked on a four-point scale (never/sometimes/most of the time/almost always) whether they had experienced any of the following due to gambling over the past 12 months: (i) gone back another day to try to win back money lost (‘chasing losses’); (ii) gambling causing financial problems; (iii) gambling causing health problems; (iv) borrowing money or selling something to finance gambling; (v) criticism from others; (vi) feelings of guilt; (vii) need to gamble with larger amounts of money to maintain excitement; (viii) betting more than can afford to lose; (ix) feeling that gambling may be a problem. For the purposes of this study, we refer to all these nine items as gambling-related harms. The responses we re-coded to indicate either the presence (Yes = sometimes/most of the time/almost always) or absence of harm (No = never). Only a minority of respondents (0.1%) had experienced harms most of the time/almost always. Two alternative criteria for having experienced gambling harm over the past 12 months were used: (i) reporting one or more harms on the PGSI and (ii) reporting two or more harms. The similar definitions were used elsewhere. 5

Gambling frequency

The questionnaire outlined 18 gambling activities, and respondents were asked whether they had participated in any of them (in their lifetime; during the past 12 months; never). Subjects reporting that they had not gambled during the past 12 months are referred to as non-gamblers . Respondents were further asked to indicate the frequency of involvement: (i) daily or almost daily; (ii) several times a week; (iii) once a week; (iv) two to three times a month; (v) once a month and (vi) less often. To take into account the total sample (including non-gamblers), we created a variable describing the gambling frequency within the following five categories: daily, weekly, monthly, less often and non-gambler.

Gambling expenditure

Average weekly expenditure on gambling (in euros) was assessed with the question ‘How much money on average do you typically spend on gambling in a week?’ Based on the distribution of expenditure, the following categories were formed: €0.01–5.99, €6.00–10.99, €11–20.99, >€21, and none/non-gambler.

Combined gambling frequency-expenditure measure

We constructed a new variable to investigate the combined effect of gambling frequency–gambling expenditure on self-reported harms. The following categories were created: (i) frequent gamblers with low/medium expenditure; (ii) frequent gamblers with high expenditure; (iii) occasional gamblers with low/medium or high expenditure and (iv) none/non-gamblers. Owing to the small number of respondents reporting occasional gambling with high expenditure (see table 1 , n = 11), we did not differentiate between expenditure in category 3. Occasional gamblers gambled monthly or less often; frequent gamblers gambled on a weekly or daily basis. The expenditure categories were defined as follows: gamblers with high expenditure spent over €21 a week; gamblers with medium expenditure spent €6–20.99; those in the lowest category spent €5.99 or less a week on gambling.

Two demographic variables (age, gender) were included. Age was grouped into five categories: 15–24, 25–34, 35–49, 50–64 and 65–74 years.

Data analysis

First, bivariate analyses were conducted to describe sample characteristics as well as the prevalence of each of the nine PGSI items—overall and by age, gender, gambling frequency and weekly average gambling expenditure. Association between variables was tested using Pearson’s chi-square test (Exact Sig. two-sided). Since we aimed to estimate population prevalence, we performed all analyses on the entire sample, not gamblers only.

Second, to examine whether the ‘prevention paradox’ applies to the harms, we calculated the distribution of harms among different segments of problem gambling severity continuum.

Third, the relationships among the four commonest harms and the explanatory variables were further examined in logistic regression analyses. In each logistic model, the outcome variable (specific harm item) was dichotomized based on whether or not respondents had experienced that harm in the previous year. Model 0 (unadjusted) included each of the explanatory variables of interest, one at a time. Model 1 was adjusted for age, gender, gambling frequency and gambling expenditure. After taking into account demographic variables, we further investigated the effect of the combined gambling involvement measure (frequency–expenditure) on the four commonest harms in Model 1. We also tested the interaction of gambling frequency and expenditure on harms. However, no statistically significant interactions were found.

As the final phase of analysis, logistic regression analysis was performed to determine the relationships between the explanatory study variables and the odds of reporting harms (two or more harms reported for the previous 12 months vs. one harm or none). Results were expressed as odds ratios with 95% confidence intervals. All analyses were performed using PASW Statistics 19.0.

Results

Sample characteristics

Table 1 presents descriptive sample statistics–overall and by gender. The sample comprised 4484 respondents with a mean age of 48.2 years (SD = 16.8 years). The majority (78%) had participated in gambling within the past 12 months (males 83%, females 73%, P < 0.001). The highest past-year’s gambling prevalence rate was found among subjects aged 25–34 years (79%); the lowest rate (59%) was among those aged 15–24 years (results not shown). Males gambled significantly ( _P_ < 0.001) more frequently and spent larger amounts of money on gambling than females. The overall problem gambling prevalence rate for males and females was 0.6% (PGSI > 7). Gambling problems were most prevalent among males in the younger age-groups (results not shown).

Prevalence of PGSI harm items

The prevalence of each harm—overall and by gender, age, gambling frequency and weekly average gambling expenditure—is shown in table 2 . The four most prevalent harms were chasing losses (8.6%), a need to gamble with more money to maintain excitement (3.1%), betting more than could afford to lose (2.8%) and feeling guilty about gambling (2.6%). The prevalence of harms was higher in males than females. The proportion of respondents reporting the four commonest harm types varied across age-groups, but chasing losses remained the commonest harm type in all age groups. The extent to which respondents were subjected to any of the nine harms significantly differed according to gambling involvement patterns; greater gambling frequency and higher sums of money spent on gambling were related to the harms.

Table 2

Prevalence (%) of self-reported gambling harm among 15- to 74-year-old Finns–overall and by gender, age, gambling frequency and gambling expenditure

ns , not statistically significant.

a Pearson’s chi-square test (Exact Sig. two-sided).

b Weighted %, unweighted n .

Table 2

Prevalence (%) of self-reported gambling harm among 15- to 74-year-old Finns–overall and by gender, age, gambling frequency and gambling expenditure

ns , not statistically significant.

a Pearson’s chi-square test (Exact Sig. two-sided).

b Weighted %, unweighted n .

Overall, 13% ( n = 519) of respondents experienced at least one type of gambling harm in the previous year (males 18.1%, females 7.2%, P < 0.001).With the criterion of two or more harms, the corresponding figure was 5% in total (7.1% for males, 2.0% for females, P < 0.001; results not shown).

Table 3 shows the distribution of harms for different segments of the problem gambling continuum. The results clearly demonstrated that the harms distributed across low to moderate-risk gamblers, and were not limited to problem gamblers.

Table 3

Distribution of self-reported gambling harm among 15–74-year-old Finns, by different segment of the problem gambling continuum

a Weighted %, unweighted n.

Table 3

Distribution of self-reported gambling harm among 15–74-year-old Finns, by different segment of the problem gambling continuum

a Weighted %, unweighted n.

Associations of harms with demographic factors and gambling involvement patterns

Table 4 shows the results of multiple logistic regressions. In the unadjusted models, males and those in younger age-groups were significantly more likely to experience the four commonest harms than females and those in older age groups. The only exception was the harm item ‘feeling guilty about gambling’, which was not related to age. The odds of reporting harms increased substantially with greater involvement in gambling. Similarly, a high expenditure per week on gambling (>€21) was strongly associated with the harms. In the adjusted models male gender was no longer statistically significant for studied harm items. The effect of the combined gambling involvement measure (frequency–expenditure) on the four commonest harms showed that frequent gambling with low/medium and high expenditure were significantly related to all harm items. Finally, separate logistic regression analysis produced similar results for the relationships among the explanatory variables for subjects reporting at least two or more types of harm compared with those who reported one or none harms (see last column in table 4 ).

Table 4

Odds ratios (OR) with 95% confidence intervals (CI) for the associations among gambling harms, demographics, and different gambling involvement patterns, and for the associations of explanatory variables and two or more harms reported on PGSI

Model 0, unadjusted model, variables entered in the model one at a time; Model 1, adjusted for age, gender, gambling frequency and gambling expenditure. Age and gender were adjusted for with the variable representing frequency–expenditure-based gambling pattern.

a Definition of harms (2+) was drawn from the complete list of nine harm items in the PGSI, the comparison was with those who reported none or one gambling harm.

b Weekly gamblers were those who had gambled once a week of more often, including daily gamblers.

Bold figures indicates statistical significance at p level < 0.001.

Table 4

Odds ratios (OR) with 95% confidence intervals (CI) for the associations among gambling harms, demographics, and different gambling involvement patterns, and for the associations of explanatory variables and two or more harms reported on PGSI

Model 0, unadjusted model, variables entered in the model one at a time; Model 1, adjusted for age, gender, gambling frequency and gambling expenditure. Age and gender were adjusted for with the variable representing frequency–expenditure-based gambling pattern.

a Definition of harms (2+) was drawn from the complete list of nine harm items in the PGSI, the comparison was with those who reported none or one gambling harm.

b Weekly gamblers were those who had gambled once a week of more often, including daily gamblers.

Bold figures indicates statistical significance at p level < 0.001.

Discussion

Findings of this study showed that, altogether, 13% of the Finns had experienced at least one gambling-related harm in the previous year, as measured by PGSI. Subjects in younger age-groups (<25 years) were more likely to report harms than those in older age-groups. Both weekly and monthly gambling, as well as spending over €21 per week on gambling were strongly associated with harms. Moreover, our findings lend support to the so-called ‘prevention paradox’; even though the individual risk of harm is highest among problem gamblers, most gambling harms can be found among the majority of low-risk gamblers. This would suggest that in addition to the traditional high-risk approach, the population approach to gambling harm prevention is well justified.

There are limited possibilities for comparisons with other studies. Some research reports from Britain reported the prevalence of each PGSI item for population aged 16 years and over. 20,21 Our results correspond well with the findings from those surveys. In the British Gambling Prevalence Survey (BGPS), 20 the prevalence of single harm items was 1%–5%, and in the Scottish Health Survey (SHS) 21 1%–3%. We found similar prevalence patterns of the different types of harm with the exception ‘chasing losses’, which was the most common item in all data sets but much more prevalent in Finland (9%) than in Scotland (3%) or Britain (5%). The reasons for this remain unclear and warrant attention in further studies.

In males and females, the commonest harm type was ‘chasing losses’ followed by ‘the need to gamble more money to maintain the feeling of excitement’, ‘betting more than they could afford to lose’ and ‘feeling guilty’. In this descending order, males and females shared exactly the same harm profile, although the prevalence of harms differed. The three most endorsed harm items reflect loss of control over one’s own gambling behavior, and thus, are characterized as key features of problem gambling, too. 22 Guilty feelings and external reactions to gambling (criticism from others), however, could be the harm types that are likely to vary across different populations depending on the cultural and moral acceptance of gambling. In our study, the prevalence of ‘feeling guilty’ was the same (2.6%) as that found in the BGPS, but for Scotland this rate was somewhat lower (1.7%). A previous Finnish study among adolescents aged 12–18 years found that feeling guilty about gambling was the most often reported harm. 23 One should, therefore, consider the developmental phase of individuals when implementing interventions for gambling harm prevention.

Gender and age are salient factors for harm experience. Our initial bivariate analyses showed that the kinds of harm were more prevalent among males than females and among those in younger (<25 years) than older age groups. Being male was, however, no longer significant in the adjusted models. Some support for this finding can be found from Swedish population-based study 24 : when examined the PGSI items separately to look at gender differences, few differences were observed among those men and women who gambled.

Our findings on individual gambling involvement patterns are consistent with Canadian studies 5,6 , indicating that gambling frequency and money spent per week on gambling are linked to harms, even after controlling for demographic factors. Detailed analyses of the four commonest harms also revealed that the more frequent the gambling, the higher the likelihood of experiencing harm. A similar association was found in terms of money spent on gambling. In our study, the risk of being harmed by gambling particularly increased when weekly expenditure exceeded €21.

Limitations

The basic limitations of cross-sectional survey studies apply: unknown accuracy of self-reports and uncertainty about the direction of causality. The overall response rate of this study was fairly low (40%). Whether the low response rate result in deflated prevalence rates in this study is not known. Information was not available about non-respondents. If the substantial response bias exists, it limits generalizability of the results. To minimize non-response bias, however, the results were weighted to reflect the age, gender and regional distribution of the Finnish population. Furthermore, we recognize the limitations of the PGSI. The picture of gambling harm is insufficient as the PGSI items cover only a small proportion of the domains where harm can occur. In addition, it is important to note that certain PGSI items are more severe in nature than others (e.g. health problems, financial problems), and can thus be seen as better indications of harm. However, to provide a scientific basis for successful prevention, considerable value appears to be in conducting studies which aim to capture the different harm dimensions across entire population.

Conclusions

Our findings provide support for a population approach: gambling-related harm was not limited to people who gambled at the highest frequency-expenditure levels. In addition to the high-risk approach, the population approach could shift the population level distribution of gambling harm in a lower direction. Primary prevention efforts could include increasing the general awareness of the nature of various types of gambling harm, enhancing consciousness of the role of expenditure patterns on harms through public messaging, and providing public guidance about means to control individual gambling occasions. Finally, given that gambling harms are likely to vary somewhat culturally, contextually and over time, more research is needed about the extent and distribution of gambling harms and their costs in different jurisdictions. Such information would also allow us to better compare and interpret the role of different gambling regulatory frameworks in minimizing gambling harm.

Funding

This study was financially supported by the Ministry of Social Affairs and Health, Helsinki, Finland. The 2011 Gambling survey was funded by the Ministry of Social Affairs and Health, Helsinki, Finland (the Lotteries Act, section 52).

Conflicts of interest : None declared.

• The harm caused by problem gambling has long received consideration. Much less attention has been given to the extent, type and distribution of harm gambling cause in population samples, including non-problem gamblers.
• Overall, 13% of the Finnish population aged 15–74 years had experienced harm due to gambling in the previous year, as measured by the items described in the PGSI.
• Younger age groups and frequent gamblers were subject to most harm. The risk of being harmed by gambling particularly increased when weekly average expenditure on gambling exceeded €21.
• Harms were reported even at low gambling frequency and expenditure levels. In addition to the high-risk approach, also a population approach to preventing gambling harm is crucial and could shift the population distribution of gambling harm in a lower direction.

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