A typical problem gambler affects six others

A typical problem gambler affects six others Belinda Goodwin Matthew Browne Matthew Rockloff Central Queensland University Judy Rose Griffith University Preferred Citation: Goodwin, Belinda C., Matthew Browne, Matthew Rockloff, and Judy Rose. 2017. “A Typical Problem Gambler Affects Six Others.” International Gambling Studies, June. Routledge, 1–20. Abstract While the financial and psychological burden on problem gamblers can be severe, at least some of the ill effects are also passed on to family or other close social ties. The present study estimated the number of affected-others for the typical problem gambler. Australian members of an online panel with gambling problems (N = 3,076) and panel members who indicated that they had been affected by someone else’s gambling (N= 2,129) were asked to estimate the number of other people who were negatively affected by their gambling. Using robust statistics to analyse this data, we found lower estimates made by problem gamblers (four affected people) compared to estimates made by affected others (six affected people; including the respondent). We concluded that a point-estimate of six people affected is a more accurate figure since it does not suffer from self-presentation effects of problem-gamblers. Low-risk and moderate-risk gamblers, unsurprisingly, affected far fewer other people (one and three, respectively). Both gamblers and affected-others most often identified close family members, including spouses and children, as the people impacted by other’s gambling problems. These results provide an approximate measure of the number of persons affected, per problem gambler, to facilitate accurate accounting of the harms accruing from gambling problems. Keywords gambling harms; gamblers; affected others; PGSI; problem gambling; family. Introduction In evaluating the harm associated with problematic gambling behaviour, it is important to consider how ‘affected others’; including spouses, children, friends, and associates of the gambler; might be negatively impacted (Korn, Gibbons, & Azmier, 2003). Research in various international settings has revealed the extent to which close friends and family can be impacted by others’ gambling (Dowling, Rodda, Ludman, & Jackson, 2014; Ferland et al. 2008; Hing et al. 2013; Holdsworth, Nuske, Tiyce, & Hing, 2013; McComb et al. 2009; Orford, Templeton, Velleman, & Copello, 2005; Salonen Castrén, Alho, & Lahti, 2014; Wenzel, Øren, & Bakken . 2008). For example, a recent cross-sectional Norwegian study found that concerned significant others (CSOs) reported elevated conflict, financial detriment, and impaired mental and physical health as a result of a partner's gambling (Wenzel et al., 2008). Longitudinal evidence from a Swedish population suggests that CSOs of gamblers experience poor mental health, risky alcohol use, financial hardship, and strained relationships, although causality could not be established. Similar findings are true for Australian and Canadian samples, where problem gamblers’ spouses typically report decrements to financial security, social activity, emotional and physical health, and family interaction (Ferland et al. 2008; Hing et al. 2013; Holdsworth, Nuske, Tiyce, & Hing, 2013). These issues are accompanied by increased problems at work, personal debt, marital problems, as well as drug and alcohol use (Ferland et al. 2008; Hing et al. 2013; Holdsworth, Nuske, Tiyce, & Hing, 2013). Negative physical and mental-health outcomes are also reported for children of gamblers, who may experience neglect due to diminished parental care or lack of resources (Darbyshire, Oster, & Carrig, 2001; Shaw, Forbush, Schlinder, Rosenman, & Black, 2007). Furthermore, financial insecurity associated with ongoing gambling by parents can affect more than one generation (Darbyshire, Oster, & Carrig, 2001), and can also affect extended relatives, friends, and extend to the wider community (Clarke, Abbott, DeSouza, & Bellringer, 2007; Hing et al. 2013; Kalischuk, Nowatzki, Cardwell, Klein, & Solowoniuk 2006). These findings, largely derived from qualitative research, describe the experience of gambling related harm from the perspective of affected others, and highlight the way in which harm is not limited only to those in close proximity to the gambler, but also extended familial, social, and community networks. Given the impact of problem gambling on others, it is not surprising that efforts to quantify the social cost of gambling have attempted to include this aspect of harm (Centre for Social and Health Outcomes Research and Evaluation 2008; Productivity Commission 2010). For these calculations to be correct, however, it is critically important to employ a reasonable estimate of the mean number of affected others for every problem gambler. It is also helpful to know what demographic groups are most affected by the gambling of others. A 1999, Australian Productivity Commission report suggested that each gambler in Australia affects between five and 10 other individuals. This figure has been cited often, both in the literature (Banks 2007; Leung, Wong, Lau, & Yeung, 2010; Hinchliffe, 2008) and in non-academic communications (e.g., Responsible Gambling Fund Trustees 2007), however, no empirical evidence has been offered in support. A similarly non-precise estimate of ‘at least 10 people’ has been attributed to Ladouceur (1993, as cited in Ferland et al., 2008). Several population based studies have estimated incidence of others affected by gambling. For example, Scandinavian studies, using a range of different measures, suggest that between 2% and 19% of the population are gambling-related CSOs (Wenzel et al., 2008; Salonen et al., 2014; Svensson, Romild, & Sheperdson, 2013). Abbott et al. (2014), estimated that 8% of New Zealanders were affected by the gambling of someone close to them. Given the prevalence of problem gambling in New Zealand is estimated to be less than 1% (Devlin & Walton 2012), their figure might be taken to imply approximately eight CSOs per problem gambler, but this would ignore those who are affected by individuals in Problem Gambling Severity Index (PGSI) categories other than ‘high risk/problem-gambling’. The varying estimates between countries may be due to differences in each survey’s wording and the terminologies used, rather than an indication of international heterogeneity. For example, the term ‘concerned significant other’, infers a close family member (most likely a partner) that is showing concern whereas the term ‘affected other’ does not imply relationship status or level of concern. Study Aims Determining an estimate of the number of people affected by a typical problem gambler is of significant practical importance, particularly in understanding the aggregate harm caused by problem gambling. Previous research has provided estimates of the proportion of people affected by gambling (most commonly CSOs), however, to date no studies have provided a precise estimate of the amount of people affected per gambler. This paper presents findings from direct questioning of gamblers and affected others in terms of how many people are influenced by problem gambling harms, with the aim of providing a precise estimate of the number and type of people affected per each low-, moderate- and high-risk gambler according to the respective PGSI categories. Limited research has shown that relationship status and type of gambling activity might be associated with more negative impact from another’s gambling (Dowling et al., 2014), but the likelihood of being an affected-other based on these characteristics has not been directly assessed. The current study also aims to identify the most affected people (e.g., spouses, children, co-workers etc.) and the types of gambling activities that are impacting on the greatest number of affected others. This is important knowledge for targeting intervention efforts for affected-others towards the most vulnerable groups and most risky products. Method Participants and Procedure We analysed data gathered as part of a large scale survey of gambling-related harm to gamblers and affected others (Browne et al. 2016). Research participants were invited via email by a commercial online panel provider in Australia. Participants were compensated for their time by points that could be accumulated and exchanged with the agency for cash. The sample comprised 5205 participants (45.3% Male) ranging from 18 - 89 years of age (M = 46.96, SD = 15.18) that reported either: a) their own gambling had caused them some degree of problems at some point in their lives (n=3076); or b) having had a close relationship with a person whose gambling had caused them problems at some point in their lives (n=2129). Participants were recruited in two phases. In the first phase participants were first asked “Has there been a time when your gambling has caused problems in your life, no matter how minor?” Those who answered ‘yes’ were directed to a survey for gamblers. If they answered ‘no’ participants were asked “Have you had a close relationship with a person whose gambling has caused problems in your life, no matter how minor?” In the second phase of recruitment the question order was reversed and participants were preferentially directed to the affected others survey (see Appendix A for a visual representation of the process). All participants were Australian residents, with the majority (80.5%) born in Australia. Age, gender, income, education level, and country of birth are described separately for each group in Appendix B. Ethical approval for the research was granted by the university’s internal review board and participants provided informed consent before participating. Measures At the outset of the survey, participants were directed to consider a 12 month period in their life when gambling had been causing them the most problems. Questions were then phrased retrospectively with respect to this period. Thirty-three percent of participants reported this period to be in the most recent 12 months. As part of the larger research project, participants also completed a checklist of gambling related harms that had occurred to them during this time (see Appendix C). Age, gender, and relationship with gambler/affected other was recorded for all participants as well as the preferred form of gambling of the gambler. Amount of affected others Gamblers were asked to consider the checklist of gambling related harms (Appendix C) and report the number of people who they believed had been affected by their gambling. Affected others were asked to report the number of people, who they believed had been affected by one gambler In calculating total affected others, the respondent was included by adding 1 to each response.. Item wording was as follows: “Considering all the issues raised earlier, how many other people would you estimate were affected by your gambling during this period of time?” (gambler), and “Considering all the issues raised earlier, how many other people would you estimate were affected by this person’s gambling during this period of time?” (affected other). Problem gambling status Gamblers responded to nine items on the Problem Gambling Severity Index (PGSI) designed to measure problem gambling in the general population (Ferris & Wynne 2001). All items began with “At this time”, (this replaced the typical text “In the last 12 months” for some items) to reflect retrospective responding. For affected others, the PGSI was also modified for second-person responding; i.e., to describe the problem gambling status of a gambler, rather than themselves. (e.g., “At this time, did you feel that the person bet more than they could really afford to lose?”). A detailed evaluation of the psychometric validity of these modifications has been reported elsewhere (Browne et al. 2016). In brief, as described in Browne et al. (2016), the PGSI was shown to have measurement invariance for recent retrospective reporting, and for reporting by self and affected others. One exception was that self-reporters tended to provide lower mean scores than affected others - presumably due to a tendency to minimise the negative impact of their behaviour. Cronbach’s alpha values were α=.90 and α =.78 for the first and second-hand reported PGSI measures respectively. Based on summed PGSI score, participants and gamblers nominated by affected others were categorised as ‘low-risk’ (PGSI = 1–2), ‘moderate-risk’ (PGSI = 3–7), or ‘problem-gambler’ (PGSI >7) according to Ferris and Wynne 2001 Non-gamblers (PGSI =0) were categorized as low-risk as all study targets had experienced and/or caused some form of harm gambling and very few participants recorded a PGSI score of zero (n=140, ). Analysis Descriptive analyses were conducted to examine the characteristics of those affected by others’ gambling. In designing the main analysis, we assumed that variability in the number of affected others reported could be affected by several factors: PGSI category of the gambler. More severe gambling problems should be related to an increased number of affected others. First versus second-hand reporting. We expected that self-reporters may be more likely to minimise the impact of their gambling on others due to self-serving/presentation bias (Greenburg, Pyszczynski, & Solomon, 1982). Additionally, affected others are a censored sample, in that at least one person (the respondent) must have been affected in order for them to be eligible to complete the survey. Natural variation within PGSI category, including familial structure, and size of social network. Individual differences in response frame. Respondents would be expected to vary with regard to an implicit threshold of what it means to be ‘affected’. Because the response, a count of affected others, is bounded at zero; these sources of variation have the potential to create an upward bias in the calculation of a simple mean. Therefore, our estimates and uncertainty estimates are based on bootstrapped trimmed means using a stringent threshold for excluding extreme cases, with 25% of extreme values excluded. Analyses were conducted using standard function in the R statistical programming environment (R Core Team, 2014). Results The (25% trimmed) mean number of affected others was compared between PGSI status for both the self-report and reports of the affected others. As shown in Figure 1, problem gamblers reported the highest number of total affected others (self-report M =3.65; reported by others M = 5.88), followed by moderate risk (self-report M = 0.73; reported by others M = 3.20) and low risk gamblers (self-report M = 0.03; reported by others M = 1.51). Thus, there was a consistent discrepancy between reporting by affected others and gamblers, whereby affected others estimated a greater number of people affected by the gamblers’ behaviour. Figure 1. 25% trimmed means and bootstrapped 95% confidence intervals by gambler status and reporting group Table 1 details the relationship status and preferred product of gamblers who affected others. The table also shows the percentage of gamblers that reported affecting at least one spouse (or partner), close friend, parent, sibling, child, other family member, or colleague/co-worker. Almost half of the affected others’ sample were affected by someone who primarily played EGMs (47.5%), followed by race bettors (23.5%). Affected others were most often spouses (38.0%), children (19.2%), and close friends (14.8%). In support of these direct results from affected others, over half (51.7%) of gamblers who reported affecting at least one other person indicated they had affected a spouse or a partner. Children (19.2%), close friends (18.9%) and parents (19.2%) was also commonly reported to have been affected by the gamblers in their self-reports. Table 1. Relationship statuses reported by gamblers affected others. N (%) Gambler^ (n=2076) Affected Other (n=2069) The person is my… spouse, de facto or romantic partner 1589 (51.7) 809 (38.0) Son/Daughter 590 (19.2) 408 (19.2) Close friend 580 (18.9) 315 (14.8) Other family 387 (12.6) 264 (12.4) Sibling 289 (9.4) 142 (6.7) Colleague/Co-worker 289 (9.4) 73 (3.4) Parent 573 (18.6) 58 (2.7) Other N/A 60 (2.8) ^ Gamblers could select more than one relationship that is affected. Table 2. Preferred product of gamblers reported by gamblers and affected others. N (%) Gambler^ (n=2120) Affected Other (n=2129) The gambler’s preferred product is… Electronic Gaming Machines (EGM) 1094 (51.6) 1014 (47.6) Race betting 325 (15.3) 501 (23.5) Casino table games 141 (6.7) 146 (6.9) Sports betting 236 (11.1) 134 (6.3) Poker 125 (5.9) 123 (5.8) Lottery 172 (8.1) 63 (3.0) Keno 27 (1.3) 19 (0.9) Don’t know N/A 129 (6.1) ^ Only gamblers who reported affect at least one other person included. We analysed the potential impact of demographic and gambling status variables on the number of affected others reported. Given the response is a non-negative integer count, and subject to overdispersion, we applied negative-binomial count multiple regression with a log link. While this model specification is well suited to handle to a reasonable degree of overdispersion, very large outliers may still create problems with estimation. Therefore we tested the model using outlier rejection thresholds, with counts of greater than 10, 20, and 30 being excluded. Estimated beta coefficients appeared stable across these different thresholds. Table 3 presents beta coefficients and standard errors for the model for cases reporting 30 or less affected others (N = 4,520). The number of affected others reported significantly increased by problem gambler category, and when the nominating party was an affected other (rather than a gambler). Controlling for gambling risk-status, sports and racing gamblers affected significantly more people than EGM players; while Keno and Lotto players affected fewer people than EGM players. When controlling for gambling characteristics, gamblers who were older and female tended to affect fewer others. However, when the reporting was done by an affected other, those respondents who were older and female tended to report more people were affected. Table 3. Negative binomial regression predicting number of affected others reported Dependent variable: Count # Affected r (SE) Age gambler -0.010*** (0.001) Affected Other Reporting 0.222 (0.129) Female gambler -0.117*** (0.038) PGSI Low Risk -0.565*** (0.125) PGSI Moderate Risk 0.384*** (0.084) PGSI Problem Gambler 1.035*** (0.079) Preferred Gambling activity (EGM vs) Sports 0.105* (0.052) Race 0.114*** (0.036) Poker 0.081 (0.063) Casino -0.050 (0.056) Keno -0.269* (0.138) Lotto -0.176*** (0.059) Other -0.004 (0.077) Age (Affected other) 0.006*** (0.002) Female (Affected other) 0.116* (0.056) Intercept 0.704*** (0.119) Observations 4,520 Log Likelihood -10,114.200 theta 2.219*** (0.086) Akaike Inf. Crit. 20,260.410 *p<0.05 **p<0.01 ***p<0.005 Discussion The current study aimed to estimate the typical number of people affected by a problem gambler, and to identify the most affected people and the types of gambling activities that have the most impact. Although figures on the typical number are often quoted in the literature, to our knowledge, this is the first study to directly investigate this point estimate. A key feature of the study is that we surveyed both gamblers and affected others. Best Estimates for the Number of Affected Others We found that a typical problem gambler reported affecting about four others, whereas those who were affected by a problem gambler on average estimated this figure to be six - including themselves. As mentioned above, this discrepancy is probably primarily due to (a) under-reporting by gamblers due to the self-presentation bias that is more common in self-report data compared to data reported by others (Nederhof, 1975), and (b) censoring of the sample of affected others. Again, censoring bias occurs because the survey response of the “affected other” necessarily includes the respondent themselves as “one” of those affected. With respect to censoring, this bias is likely to be significant (e.g. between 0.5 and 1.0) in the case of the low risk category, where it is plausible that a large proportion of associated gamblers truly do not affect even one person. However, for problem gamblers, it is likely that only a negligible proportion of gamblers truly do not affect anybody, and therefore the censorship bias is likely to be slight. With respect to under-reporting by gamblers, there are multiple psychological explanations for the minimisation of self-reported impact on others, such as common tendency to present oneself in a positive light (Greenburg, et al. 1982), attribute negative outcomes to external forces (Rotter, 1966), or positive memory biases (Walker, et al. 2003). Therefore, our interpretation is that the figure of six affected others per problem gambler is the most valid since it is least affected by underreporting. This is within, but at the lower end, of the range of figures commonly quoted (Productivity Commission 1999; Ferland et al. 2008). Taking into account censoring, and therefore rounding-down our point-estimates, we conclude that a typical moderate risk gambler affects about three people, and a low risk gambler affects one person. That is, both of these latter figures are on the low-side of our estimated ranges to account for the attenuating effects of censoring. Our separate estimates for number of affected others per gambler at each level of PGSI risk is useful as it can be weighted according to the specific population prevalence statistics for low, moderate and high risk (problem) gamblers to produce accurate estimates of total affected others in the population, and potentially be applied to international settings. Who is Most Likely to be Harmed by Another Person’s Gambling? In terms of demographic characteristics of affected others, current findings were similar to those from previous research. For example, Dowling et al., (2014) found that over 60% of affected others seeking counselling were the spouse or partner of a problem gambler, and almost 20% were the children of gamblers. This suggests people who live in the closest proximity and are dependent financially and emotionally on a problem gambler are most likely to be affected by their behaviour. What Gambling Games are Most Likely to Harm Affected Others? Dowling et al. (2014) also reported that over 40% of the concerned significant others in their sample were related to gamblers who were primarily EGM players. In the current study almost half of the affected others’ sample fell into this category. This likely reflects the high proportion of gamblers that play EGMs. EGMs feature a combination of risky structural characteristics such as rapid playing speeds and payout intervals, multiplier potential, reinforcing payout schedules, and attractive audio-visual effects. These features are more amenable to risky and problematic play than many other gambling products (Jackson et al. 2000; Blaszczynski, Walker, & Sharpe. 2001; Smith and Wynne 2004), therefore we might also expect that EGM players are more likely than other gamblers to export gambling associated harms to others (Doughney 2002; Breen & Zimmerman, 2002). Sports and race-betting were associated with greater (gambler reported) estimates for affected others harmed, whereas lotto and keno were conversely associated with lower estimates (per Table 3). Both sports and race-betting are dominated by male gamblers, whereas lotto and keno attract proportionately more female gamblers. Given the relative rarity of female sports and race bettors, it is difficult to determine whether gender or type of game dominates in our analysis. Future research may illuminate whether the type of preferred games or alternatively gambler-demographic factors are more influential in determining the dispersion and severity of harm that impacts affected others. Nevertheless, the finding that certain games, such as EGMs, sports and race-betting, are associated with a greater number of others being harmed is important in estimating population-level harm. These games not only cause harm to problem gamblers, but also export harm to a greater number of affected others; magnifying their effects on the whole community. Moreover, although we expect most western countries are likely to have broadly similar numbers of affected others to those estimated in this paper, jurisdictions outside of Australia may have fewer or more affected-others for every gambler based on different mixes of preferred gambling products. Limitations Any consideration of a numeric figure depends heavily on the threshold one uses to define being ‘affected’. Our operational definition of ‘affected’ is the occurrence of at least one of the items on the gambling harms checklist presented in Appendix C which represents a variety of financial, relationship, work or study related, emotional, and health related harms experienced by gamblers (Browne, et al., 2016). The downside of this approach is that we are unable to estimate the degree to which individuals, other than the participants themselves, are affected by the gambler. In addition to the potential bias caused by gamblers underreporting harms, it must also be acknowledged that affected others reports may also be subject to similar issues. It is difficult however, to predict whether affected others are susceptible to under-reporting harms due to lack of knowledge of the full extent of the gamble’s impact on others, or to overestimate the proportion of harm due to negative emotions regarding the person with the behaviour. Given that judging the quantity and extent of harm is intrinsically subjective, there is not an obvious solution to this issue. Nevertheless, the dual perspectives presented in the present study go some way to addressing uncertainty due to reporting bias. Finally, recruitment was done through an online panel, which is not representative of the general population of gamblers, and in which problem gamblers are over-represented. However, our results are provided with respect to, or control for, gambler risk category. Although an effort was made to allocate affected others and gamblers to their respective surveys in an unbiased manner (see Appendix A), the smaller amount of participants taking part in the second phase of recruitment meant that gamblers who also identified as affected others may have been slightly under-represented overall. Conclusion Accurate understanding of how many affected others are impacted by gambling problems, as well as a better understanding of who is affected, is helpful in efforts to reduce community harm. The costs of problem gambling are not limited to the immediate effects on the financial and emotional well-being of the problem gambler, but also extend to people intimately connected to the gambler through family and other social ties. These connections must be considered to understand the wider costs of problem gambling, and provide a foundational knowledge for the investment in appropriate interventions. 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