Search and Selection Results
The database searches returned 384 papers for review and 162 of these were excluded as duplicates. Analysis of titles and abstracts led to a further 9 exclusions for not discussing gambling and 20 exclusions for not discussing harms. The remaining 193 studies were reviewed in full, resulting in a further 6 exclusions for conflating gambling severity scores (i.e. PGSI) with harms, 9 exclusions for only discussing harms to others, 46 exclusions for only mentioning harm as a concept in general terms, and 57 exclusions for only discussing harm minimisation. There were 8 studies not in English, 2 were short letters, and 3 were abstracts for conference presentations. There were 2 studies which could not be accessed in full, and full-text requests to the authors were unsuccessful. Finally, 1 systematic review into harms 36 was removed because it described the process by which a systematic review would be conducted but did not report any results.
This left 59 studies for review of which 22 were qualitative, 36 were quantitative, and 2 were mixed methods design. Of the mixed method studies 1 was predominantly qualitative and 1 was predominantly quantitative.
Figure 1. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA-P) Flowchart of Exclusions 37
MAIN RESULTS
Description of Included Studies
Of the 59 studies included in this review, 5 were cohort studies, 2 were case-control studies, 16 were cross-sectional, 21 were qualitative, and 2 were mixed methods. Of the qualitative studies, 2 used multiple methods, 11 were interviews, 2 were focus groups, 4 were narrative reviews and 2 were systematic reviews. Secondary data analysis was conducted in 13 of the studies.
The most common funding sources for this selection of studies were the Ministry of Social Affairs and Health Helsinki (5) and the Victorian Responsible Gambling Foundation (6). In total, the government funded 12 studies, responsible gambling foundations funded 9, general research funds were used for 5, gambling focused research funds were used for 5, Colleges and Universities funded 4 studies, and there were 2 funding contest awards (International Contest ONCE; Irish Research Council Innovation Award). In addition, 2 studies were funded by alcohol foundations, 1 by a business school, 1 by a casino, 1 by a non-gambling charitable foundation, 1 by an information company, 1 by a gambling authority, and 1 by a psychiatric association. Of the remaining 14 studies, 8 received no funding and 6 did not declare their funding status.
General Gambling Harms
Five studies include data on gambling harms generally, with some investigating specific harm locations, such as casinos or the workplace. Ricijas 38 reported that inappropriate social behaviour such as shouting at machines, aggression towards other patrons, appearing depressed, being withdrawn and excessive sweating were observed at all of the gambling venues included in their study. And Binde 39 found that participants identified gambling during work breaks and during work hours, poor work performance and lateness, depression and anxiety, tiredness and irritability, absences from work, tax authorities investigating staff wages, poor standards of self-care and belongings, and crimes such as embezzlement.
Jeffrey et al. 40 investigated how gamblers report and recognise harms in comparison to other individuals in their lives. They found that gamblers were more likely to report problems which impacted them individually such as lack of money, using work or study time to gamble, alcohol use, suicide attempts, hygiene issues, sleep problems, and feelings of shame or worthlessness. In comparison, spouses of gamblers reported shared harms such as missed bill payments and relationship tension or conflicts. The researchers suggested that this may mean gamblers are less aware of relationship dysfunctions. Another study 41 reported that harms in all domains accumulated more quickly in gamblers than in affected others.
Langham et al. 31 developed a taxonomy of gambling harms and found that many of the category domains interacted, or had individual specific outcomes. For example, cultural and relationship harms often appeared together due to the link between family and culture. They also reported that emotional harms were affected by all other domains, and criminality was often a second-order harm to address a primary harm such as financial issues. Financial harms reportedly led to a change in behaviour, however the crisis point was dependent on individual tolerance for deprivation. The level and type of relationship harm experienced appeared to be dependent on how the non-gambling person viewed gambling, and health harm was found to occur in recreational gamblers, but was not well documented. Finally, criminality was only found within those individuals who scored highly on risk severity measures.
Age
Twenty-two studies include data on age, and several of these found that being younger was associated with a higher risk of experiencing gambling harms 42-47. One study found that younger age groups (16-34) were at risk of dependence and social harms 42, and Ferrara et al. 45 found younger age groups showed higher rates of “problematic gambling” and a higher comorbidity with other addictions. In Breen 48 it was found that youths who were exposed to card gambling were more likely to gamble later in life to increase their income, and those who missed school had reduced lifelong aspirations and reduced opportunities. Salonen et al. 47 reported that financial harm, work and study harm, health harms, and emotional harm all tended to decline within the older age groups, and financial harm in particular was most common in the younger participants, and Splevins et al. 49 reported that students spent their pocket money or part-time job wages on gambling. Bergh and Kuhlhorn 50 reported that gamblers aged 20-34 spent more time gambling than those over 35, and Salonen et al. 51 also reported that females aged 18-24 increased their occasional gambling and consequently reported more harms.
In contrast, Raisamo et al. 52 reported that gambling involvement increased with age, and some studies found that younger gamblers were less at risk of financial harms 50, 52. Larsen et al. 53 found that alcohol use increased with age in lifetime problem gamblers, as defined by the DSM-IV criteria for ‘pathological gambling’, in opposition to the trend seen in a general population. Whereas Pitt et al. 54 found that children aged 8-16 showed little or no current harms as they were gambling at home with their families, spending small amounts of pocket money, or betting with activities such as push-ups against family members. Despite this, children developed false beliefs around gambling, such as that skill can be used to win, or that it is necessary for everyone to gamble at least once. Similarly, Melendez-Torres et al. 55 found that harms increased with age; however, they only researched participants attending school who would be categorised in the younger age groups of other studies.
Livazovic and Bojcic 56 found that older participants scored higher on risk severity measures, however they did not report a difference in harms. Browne et al. 57 found that age had no impact on harm profiles, and Lloyd et al. 58 found no association between age and gambling-induced thoughts of self-harm. Browne et al. 59 found that although younger age appeared to correlate with harm this was not statistically significant, and Raisamo et al. 46 reported that guilt was not associated with age.
The remaining studies researched the distribution of harms within a single age group. Anderson et al. 60 reported that seniors who gambled experienced arguments, broken relationships, anxiety, debt, exhausted pensions or savings, and shame. Heiskanen and Matilainen 61 found that gamblers from the generation categorised as ‘Baby Boomers’ had difficulty walking past a machine without gambling, and spent excessive time and money both online and offline, and some participants reported that they felt unable to ‘meddle’ in another person’s gambling problems, suggesting there may be less peer support within this age group.
Further research is needed to understand the distribution of harms across age groups as it was found by Estevez et al. 62 that sensation seeking and impulsivity were high in young gamblers. Anxiety, depression and psychoticism were partially mediated by impulsivity, and somatisation, obsessive-compulsive behaviour, interpersonal sensitivity, paranoid ideation and hostility were perfectly mediated.
Gender
Nineteen studies examined gender, and 5 of these found no difference between men and women 43, 50, 57, 59, 63. Despite this, several studies showed that men have a higher prevalence of harms than women 42, 44-47, 52, 55, 56, 58, however Canale et al. 42 and Raisamo et al. 46 found that men gamble more frequently and spend more money when gambling. Raisamo et al. 46 in particular found that when controlling for frequency and spends, gender was no longer significantly related to harm. And in complete contrast Salonen et al. 51 reported that while gambling was more common in young males, women displayed an increase in specific harms between 2011 and 2015 where men did not.
Breen et al. 64 found that women from small villages and men from towns were both more likely to be heavy commercial gamblers, however harms were the same and so this was likely due to usage level rather than gender. Livazovic and Bojcic 56 found that males in Croatia scored significantly higher on psychological, social, and financial consequences than females. However, they also scored significantly higher on risk behaviour and were more likely to score as a problem gambler on the Canadian Adolescent Gambling Inventory. Splevins et al. 49 found that men started gambling earlier than women did and found it more exciting. This led to increased spending and therefore an increased risk of harms such as substance use and interpersonal conflicts.
Despite this some studies suggested differences in how gambling harms present between genders. In Singapore, Goh et al. 63 reported that “tentative evidence… points to the risk of child neglect when the problem gambler is the mother.” They also found that verbal abuse was most commonly males towards their mother, but found no difference in cases of physical abuse between genders. McCarthy et al. 65 found that women were more likely to report mental health comorbidity than males, however causality was not discussed, and Raisamo et al. 66 found that while the most common harm was guilt for both genders, the second was disrupted schoolwork for females and conflict with friends for males.
Socioeconomic Status
There were ten studies examining socioeconomic factors, and more than half of these studies concluded that less affluent socioeconomic groups are more at risk of experiencing harms than more affluent groups 44, 58, 67-70. Angus et al. 67 found that clinical participants had significantly lower incomes than a community sample and a higher proportion of them reported harms. Currie et al. 44 concluded that participants who reported harms were more likely to be in a lower income bracket, and to have received no further education than high school. Similarly, Lloyd et al. 58 found that gambling related thoughts of self-harm, as well as acts of self-harm were more frequently found among the unemployed, although were not related to marriage status. Gambling related thoughts of self-harm were also found to be associated with parents gambling behaviour. And Skaal et al. 69 reported that urban residents were more likely to report psychological distress and those that scored as high risk of problem gambling on the PGSI were more likely to use alcohol.
Apinuntavech et al. 68 examined education level, and found that the average GPA of gambling participants was lower than non-gamblers. Gamblers subsequently had a higher risk of smoking, abusing alcohol and energy drinks, and reporting harms. The most common of which were psychological, in particular guilt, depression, anxiety, and considering suicide. These individuals also reported lying, perceived poor health, insomnia, debt, selling possessions, substance use, and school absence. Livazovic and Bojcic 56 found that lower achievers in school reported more psychological harms, however there was no difference between school types. However, Melendez-Torres et al. 55 reported that more harms and increased gambling behaviour were a result of feeling less school belonging.
Interestingly, Tu et al. 70 found that people in managerial or professional occupations were more likely to participate in gambling than people in routine (semi-skilled or unskilled) occupations. Melendez-Torres et al. 55 also found that participants from more affluent households were participating in more gambling than those from less affluent households. In light of this, they highlighted that more affluent individuals were reporting more harms, however Tu et al. 70 reported that although gambling rates in the most affluent groups dropped during times of recession, the rates within deprived communities did not. This suggests that less wealthy people may be more likely to gamble in times of economic stress. When controlling for confounding variables the most deprived groups were 4.5 times as likely to experience arguments or money issues.
The remaining studies found little to no effect from socioeconomic factors, with Browne et al. 57 reporting a difference of less than 5 points between individuals earning $15-30k AUD and those earning $101-150k AUD. Browne et al. 59 reported that part time work, unemployment, marriage status, lower education, and lower income all had large correlations, but these were statistically insignificant. And Livazovic and Bojcic 56 found that family life and parent’s education level had no significant effect on harms.
Culture
Twelve studies include data on culture and five of these discuss Australia and New Zealand 48, 71-74. The included studies largely focus on single groups or comparing indigenous people and migrants to a society, so there are significant gaps that future studies may address.
Hing et al. 73 interviewed Indigenous Australians and reported that female gamblers from small villages and male gamblers from towns both experienced similar harms. However, they were also heavy commercial gamblers, meaning they played at casinos and other commercial establishments. Hing et al. 72 interviewed counsellors who noted that cultural acceptance for gambling within Indigenous Australian communities was high, and so a strong support network was in place for individuals with a problem. Despite this, Indigenous participants’ highlighted isolation from the community as a key harm in a few studies 48, 71, 72, and missing key community events, neglecting children, lying, arguments, violence and breakups were found to lead to social isolation. Gamblers also admitted to hiding their losses due to shame, guilt and low self-esteem, which meant they were reluctant to seek help. In addition they reported financial problems, and outside criticism or lack of support 71, 72, as well as debt, lack of resources 48, 72, distress, cut off utilities, crime, loss of employment, and homelessness 72. Breen 48 also noted that many people would gamble within a group, increasing their behaviour, but also feelings of shame from losses and potential gossip. Similarly, Hing et al. 71 reported that participants were betting above their means, felt the need to spend more, borrowed or sold, and had health problems.
Goh et al. 63 found that families in Singapore were at risk of acute financial harms when the problem gambler was a parent, with households suffering double financial harms through loss of income and debt. When the gambler was a mother without income, they found that the father would leave employment to care for the children, resulting in an income reduction for the entire household. They also found that many people in Singapore viewed gamblers as self-centred, and siblings would often give up on them.
Kolandai-Matchett et al. 74 found that Pacific New Zealand people experienced similar gambling harms to other populations. However, the context of collectivist cultural values meant that additional harm dimensions were present, such as a loss of belonging or isolation, shame, loss of the community’s respect, disruption of trusting relationships, transference of communal responsibilities, and an overall loss of social cohesion. In a quotation from one of the interviewed participants, the researchers noted that the wider collective might exclude non-present or non-contributing members of the society. Similarly, Bramley et al. 75 found that migrants in the UK reported similar harms to the general population, including selling possessions, relationship breakdown, mental health problems, drug use and sale, homelessness, domestic violence, sex work and suicide. Despite this, participants felt that harms were exacerbated by a lack of ‘safety net’ and difficulty accessing informal support. Sub-Saharan African men in particular felt that when they lost money they lost community status.
McCarthy et al. 65 conducted a worldwide study which suggested that women from ethnic minorities, indigenous communities and specifically Maori and Pacific women in New Zealand were more vulnerable to gambling harms than European women were. Melendez-Torres et al. 55 also found that participants from white ethnicities were less likely to feel guilt from gambling, and a non-white British background was associated with more harms. Ferrara et al. 45 found that non-white males were most at risk of developing a gambling problem and addiction comorbidity, and Wardle et al. 76 found that although migrants were less likely to gamble they were more likely to experience harms than individuals born in the country. They found minimal evidence on specific harms experienced, but did report that Spanish migrants tended to spend over 300 euros daily and claim losses as wins, and Australian migrants experienced financial harm, shame, relationship issues, suicide, mental health issues, isolation and prostitution. Similarly, Currie et al. 44 found that in Canada, non-white men were more likely to have reported two or more harms in the last year.
Clinical
Five studies reported on a clinical sample and all of these found more harms within a clinical population compared to the general community. Angus et al. 67 reported that 100% of their clinical sample reported psychological harms, compared to only 14.85% of the non-clinical participants. And while they found a greater severity of harm in all domains for the clinical sample, they specifically found a 97.98% response on financial harms compared to 23.33% in the non-clinical sample. Similarly Bramley et al. 77 reported that a clinical sample with habitual gambling showed high levels of anxiety, financial difficulties and depression.
Salonen et al. 47 reported that while 11% of a general sample experienced at least one harm of any domain, they found that 88% of the clinical sample reported emotional harms, 87% financial or health, and 81% experienced relationship harms. The specific harms reported were similar for all domains apart from emotional harm, where the clinical sample reported more anger, as well as being more likely to promise to pay debts without intending to, more likely to steal, and more likely to feel like an outcast.
Shannon et al. 78 found that the highest rated harms within their clinical sample were reduced savings, going without, worry, frustration, and debt. The lower rated consequences included drug use, suicide, bankruptcy, self-injury, and educational problems. In contrast the general population rated debt, relationship issues, feeling constrained, going out less, poor self-control and lowered pride highest. Despite these different results the averaged distribution of harm was consistent across both samples, excluding reduced savings and decreased happiness.
Finally, Estevez et al. 62 reported that young adults within their clinical sample had more dysfunctional symptomology. Specifically anxiety, depression, hostility, out of character behaviour, and somatisation. They also found high comorbidity for alcohol, drug, gaming, shopping and sex ‘addiction’. Despite this they found no significant differences for eating behaviour or internet use, and when repeating the analysis discovered that impulsivity partially mediated anxiety, depression and psychoticism. While perfectly mediating somatisation, OCD symptoms, interpersonal sensitivity, paranoid ideation and hostility.
Military Personnel
Only one study reported on a military population 79, however this was a systematic review of existing literature. One examined study found that individuals would be quickly reprimanded for gambling, but meaningful assistance was slow to come, whereas another found that 21/25 active personnel who received treatment were retained in the military, compared to the 4 who lost their jobs. Several of the investigated studies highlighted comorbid mental health problems with gambling in the military, including suicide. It was also found that 9/35 gamblers receiving treatment had depressive disorder, 20% endorsed suicidal ideation and 3 participants had made actual attempts on their life.
Criminality
May-Chahal et al. 80 investigated harms within the British prison population and found that although the prevalence of gambling was higher in prisons, the prevalence of gambling behaviour prior to incarceration was significantly lower. They found that high rate offenders in their mid-20s were 5.3 times more likely to be frequent loss chasers than other categories, and occasional gamblers were less likely to use alcohol or drugs in prison, with nearly 2/3 of the problem-gambling group abstaining completely from substance use. The researchers suggest that this may be because the individuals’ ‘addiction needs’ are being met by their gambling behaviour.
Risk Severity
Nineteen studies include data on risk severity, which is the measure of behaviour that puts someone at risk of developing a problem with gambling or experiencing harms from gambling. Angus et al. 67 found that the number of harms experienced increased with PGSI classification, and significantly less low-moderate risk gamblers reported harms compared with problem gamblers. Problem gamblers were also more likely to come from the clinical sample, who had significantly greater severity of harms in all domains. Similarly, Delfabbro et al. 81 reported that ‘problem gamblers’ experienced more harm in general than lower risk groups. In fact, the number of gambling harms within the lower risk categories was close to zero in all but the financial and psychological domains. Ricijas et al. 38 also found that social gamblers had no consequences, moderate risk occasional gamblers experienced low-moderate harms, and high risk frequent gamblers suffered serious consequences. Specifically in terms of delinquency and cognitive distortions.
In contrast, Browne et al. 57 reported that the prevalence of harm within a non-problem gambling group was twice that of the problem category, and Raisamo et al. 46 found that most of the harms reported originated from low-moderate risk participants. However, when scaling for severity of harms, Delfabbro and King 32 reported that low and moderate risk participants experienced only a low-medium severity of harm. Interestingly, more severe financial harms, such as selling belongings, were found in the lowest risk group even when scaling. However, there was a significant number of participants from less affluent socioeconomic backgrounds, which the researchers suggest may impact these results.
In considering scaling, Browne et al. 82 reported that all individuals in the high risk category reported at least one harm, and while mild harms were broadly distributed across all risk groups, severe harms were repeatedly more prevalent in the highest risk group. Hing et al. 71 found that 93.8% of high risk gamblers spent more than they could afford to lose, and 92.9% felt the need to bet more each time for the same thrill. Family arguments were experienced by 18% of moderate risk gamblers, compared to only 0.9% of low risk participants, and 94.9% of high risk participants had a gambling related health issue. Browne et al. 57 found that only 10% of financial harms across the study population were in the problem gambling or pathological gambling groups and that more than 50% of cases where someone sold their belongings to fund their gambling were in recreational or low risk gamblers. In contrast to this, they found that more than 50% of social deviance harms are found within problem gamblers, and the remaining categories of harm were evenly distributed across the severity groups.
Langham et al. 31 reported that criminality was only found within high risk participants, and Skaal et al. 69 found psychological distress was only associated with problem gambling. Similarly, Splevins et al. 49 reported that high scoring participants were more likely to miss school, sell their personal property, commit illegal acts, and use cigarettes or drugs. Larsen et al. 53 found that harmful alcohol and marijuana use were common among high risk scorers, and Yani-de-Soriano et al. 83 reported the highest degree of harm across all domains was found in high risk participants. Specifically reporting that as risk scores increased, so did physical, mental health, social, and academic harms.
Browne et al. 84 conducted their study using disability weights, a health-related measure of quality of life which uses a ratio scale between 0 and 1, representing ideal health and death. They found that problem gamblers show similar disability weights to those of Bipolar Disorder or alcohol dependence, whereas the low risk group show disability weights equal to moderate anxiety. In addition, they reported that the less severe harms were experienced by a large proportion of the population, compared to the intense harms, such as suicide attempts, which were mostly confined to the highest risk participants.
Li et al. 41 found that selling personal items, absence from work or study, reduced performance, poor sleep and extreme distress had the highest correlation with PGSI categories. They also found that reduced spending on essentials, absence from work or study, feelings of worthlessness, relationship conflict, and feeling like an outcast were the most effective discriminators between the low and high-risk groups. Similarly, Ferrara et al. 45 found that participants rated as high risk were more likely to use alcohol or substances, have depression, dysthymia, anxiety, phobia, and anger, resentment, headaches, gastrointestinal problems, eating disorders, and criminality, as well as family conflict, less independence, less engagement in intellectual or cultural activities, and reduced expression of emotion.
In contrast, Livazovic and Bojcic 56 reported only a weak correlation between success in school and risk score, and May-Chahal et al. 80 found that nearly two thirds of high risk participants in the prison system were actually abstaining from drugs and alcohol.
Gambling Behaviour
Thirteen studies include data on gambling behaviours and many of these studies agreed that a higher frequency of play, and higher amount of spending per session, leads to more harms 42-44, 46, 50, 52, 85-87. In particular, Castren et al. 43 found that spending at least 1% of your monthly income increased harms, and daily gambling doubled them. Kildahl et al. 85 also reported that overconsumption of money and time, social consequences, and emotional consequences all increased linearly with gambling frequency.
Samuelsson et al. 87 found that low frequency stable gamblers only reported mild harms such as shame or guilt, whereas high frequency gamblers with decreasing use experienced substantial financial losses, frustration, alcohol use, and isolation. They also noted that periodic gamblers experienced financial, psychological, and relationship harms, including insomnia, isolation, and low self-esteem. The most severe harms, such as irrational thought and increasing spends, were found in the high frequency gamblers with increasing use. However they did find that financial harms and psychological distress could lead to a period of reduced play depending upon an individual’s support network.
Similarly to participation frequency Lloyd et al. 58 reported that number of years gambling was associated with thoughts of self-harm, and Rintoul et al. 86 found that gambling fast and intensely lead to more harm. Specifically highlighting multiple machine use, skipping meals, withdrawing money multiple times and betting over $3 per spin. Interestingly, Canale et al. 42 reported that most of the identified harms in their study were reported by non-high time and spend regular gamblers. Despite this, harm odds increased with greater frequency of play individually, suggesting a higher individual risk in high volume play, but a larger proportion of at least one harm among low volume players.
Five studies looked at motivations for gambling, and although Browne et al. 59 found no link between motivation of play and harms, Lee et al. 88 found that excitement, escape and challenge motives were linked with positive outcomes, but financial motivation led to harms. Lloyd et al. 58 also found that self-harm thoughts were associated with money as a motivator but was negatively associated with enjoyment motivations, and Kildahl et al. 85 reported individuals who were influenced by reward frequency were more likely to swap card decks rather than persevere with the same cards. This led to overconsumption of time and money, and negative social and emotional consequences.
Similarly, Mageau et al. 89 found that harmonious passion was related to positive emotions and thoughts, whereas obsessive passion lead to harms. Harmonious passion is when an individual chooses to gamble, whereas obsessive passion is when someone feels compelled to gamble. Mageau et al. 89 reported that in comparison to harmonious passion, obsession was strongly related to feelings of guilt, anxiety, and negative emotions, and negatively correlated with feeling in control and having fun.
Game Choice
Game choice also affected harms, and nine studies reported on this relationship. Breen et al. 64 found that card games led to financial losses and lost welfare benefits, whereas commercial gambling (i.e. Casinos, EGMs) led to financial hardship, family and relationship issues, mental health issues, crime, eviction, homelessness, domestic violence, neglect, relationship breakdown, depression, suicidality, theft, and sold belongings. Hing et al. 73 reported that heavy card players spent their pensions, borrowed money, and played all day and night. Similarly, heavy commercial players gambled alone, spending their whole pay and playing all day and night. They experienced debt, relationship issues, lost home, overcrowded housing, missed bills, lack of resources, abuse, neglect, self-esteem issues, depression, suicidality, theft, selling belongings and crimes against their workplace. Ferrara et al. 45 also found that sports betting was associated with high rates of addiction comorbidity, Mihaylova et al. 90 found that online poker players had higher annual debts, and Ricijas et al. 38 reported that sports bettors, VLT users, and virtual bettors showed severe psychosocial consequences.
When considering casino gambling Mageau et al. 89 also found more negative consequences than in lottery players. However, they also reported more positive outcomes overall in casino gamblers. Similarly McCarthy et al. 65 found that older women believed electronic gaming machines were less harmful than other games as they were able to socialise while gambling.
Castrén et al. 91 found that six out of twelve game type predictors were associated with more harmful consequences, including scratch games, betting, slot machines, non-poker online games, online poker, and non-monopoly games. They found that lottery play caused the lowest number of harms, and this finding is consistent with findings reported by Currie et al. 44 who found that frequency of play on lottery games did not increase the harms experienced, whereas electronic gambling machines, ticket gambling, bingo and casino games did.
Online vs. Offline Gambling
As well as specific game type six studies look at the broader categories of online or offline gambling. Castrén et al. 91 found only a weak link between online gambling and an increase in harms, however Mihaylova et al. 90 found that online poker players had a greater risk of alcohol dependency, illicit drug use, family issues, studying issues and financial issues in comparison to offline poker players.
Yani-de-Soriano et al. 83 found that online gambling was associated with binge drinking but not smoking, and around 60% of online gamblers scored as high risk for gambling problems. These increased risk severity scores in turn led to increased physical, mental health, social, and academic harms. Hubert and Griffiths 92 also found a link between online gambling and alcohol dependence, and they discovered that online gamblers were less likely to have jobs, children and a stable relationship, leading to unemployment and less money later in life. They further found that online gamblers were less able to control impulsivity and frustration, but despite this, they had fewer suicidal thoughts than offline gamblers, although actual suicide attempts were comparable in both groups.
Feelings of anxiety and guilt appeared to be higher in online gamblers relative to offline gamblers 88. However, Fulton 93 observed that secretive gambling increased financial harms due to the likelihood of concealed debt; and by living a double life secretive gamblers experienced increased stress, relationship conflicts, and emotional deterioration.
Sense of Coherence
Langham et al. 94 found that an individuals’ sense of coherence correlated strongly with gambling harms in all domains. Sense of coherence is the extent to which someone feels confident in the predictability of his or her environment, and that things will generally turn out as expected. They reported specifically that a stronger sense of coherence meant fewer harms, and that a weaker sense specifically led to reduced spending on essential items, increased negative health behaviour such as lost sleep, reduced physical activity, and poor nutrition, as well as stress related illness and depression. Weaker sense of coherence also resulted in feelings of failure, worthlessness, hopelessness, shame, anger and feeling the need to run away. Despite this, a weaker sense of coherence was not related to increased risk of suicide.
Quality Checks
In applying the Standard Quality Assessment Criteria 30 we found that several studies were not robust in their quality control. In particular, studies scoring below 0.5 on the assessment may not be an accurate representation of gambling harms, whereas studies that scored above 0.9 may present the most reliable data on harm distribution (Table 3).
Table 3. Highest and Lowest Quality Assessment Scores 30
Study
|
Highest Scores
|
Study
|
Lowest Scores
|
Angus et al. (2019)
|
1.00
|
Browne and Rockloff (2018)
|
0.86
|
Browne et al. (2019)
|
1.00
|
Browne et al. (2017)
|
0.86
|
Browne, Goodwin, and Rockloff (2018)
|
1.00
|
Hing et al. (2014)
|
0.86
|
Delfabbro, Georgiou, and King (2020)
|
1.00
|
Li et al. (2017)
|
0.86
|
Langham et al. (2017)
|
1.00
|
Tu, Gray, and Walton (2014)
|
0.86
|
Larsen, Curtis, and Bjerregaard (2013)
|
1.00
|
Splevins et al. (2010)
|
0.82
|
Mihaylova, Kairouz, and Nadeau (2013)
|
1.00
|
Salonen et al. (2018)
|
0.77
|
Raisamo et al. (2015)
|
1.00
|
Yani-de-Soriano, Javed, and Yousafzai (2012)
|
0.77
|
Salonen, Alho, and Castren (2017)
|
1.00
|
Goh, Ng, and Yeoh (2016)
|
0.71
|
Canale, Vieno, and Griffiths (2016)
|
0.95
|
Hing, Breen, and Gordon (2012)
|
0.71
|
Estevez et al. (2015)
|
0.95
|
Anderson, Rempusheski, and Leedy (2019)
|
0.68
|
Hubert and Griffiths (2018)
|
0.95
|
Apinuntavech et al. (2012)
|
0.68
|
Jeffrey et al. (2019)
|
0.95
|
Langham et al. (2016)
|
0.68
|
Kildahl et al. (2020)
|
0.95
|
Pitt et al. (2017)
|
0.68
|
Lee, Chung, and Bernhard (2014)
|
0.95
|
Samuelsson, Sundqvist, and Binde (2018)
|
0.68
|
Lloyd et al. (2016)
|
0.95
|
Wardle et al. (2019)
|
0.68
|
Mageau et al. (2005)
|
0.95
|
Breen (2012)
|
0.64
|
Raisamo et al. (2013)
|
0.95
|
Breen, Hing, and Gordon (2012)
|
0.64
|
Ricijas, Hundric, and Huic (2016)
|
0.95
|
Fulton (2019)
|
0.64
|
Shannon, Anjoul, and Blaszczynski (2017)
|
0.95
|
Heiskanen and Matilainen (2020)
|
0.64
|
Skaal et al. (2016)
|
0.95
|
Rintoul, Deblaquiere, and Thomas (2017)
|
0.64
|
Browne et al. (2020)
|
0.91
|
Hing and Breen (2015)
|
0.61
|
Castren et al. (2018)
|
0.91
|
Kolandai-Matchett et al. (2017)
|
0.61
|
Currie et al. (2006)
|
0.91
|
Bramley, Norrie, and Manthorpe (2020)
|
0.57
|
Livazovic and Bojcic (2019)
|
0.91
|
Paterson, Whitty, and Leslie (2020)
|
0.57
|
May-Chahal et al. (2017)
|
0.91
|
Bramley, Norrie, and Manthorpe (2019)
|
0.54
|
Melendez-Torres et al. (2019)
|
0.91
|
McCarthy et al. (2019)
|
0.46
|
Raisamo et al. (2019)
|
0.91
|
Binde (2016)
|
0.43
|
|
|
Bergh and Kuhlhorn (1994)
|
0.39
|
|
|
Delfabbro and King (2019)
|
0.36
|
|
|
Ferrara, Franceschini and Corsello (2018)
|
0.29
|