A meta‐analysis of problem gambling risk factors in the general adult population

Abstract

Background and Aims Few meta‐analyses have been conducted to pool the most constant risk factors for problem gambling. The present meta‐analysis summarizes effect sizes of the most frequently assessed problem gambling risk factors, ranks them according to effect size strength and identifies any differences in effects across genders. Method A random‐effects meta‐analysis was conducted on jurisdiction‐wide gambling prevalence surveys on the general adult population published until March 2019. One hundred and four studies were eligible for meta‐analysis. The number of participants varied depending on the risk factor analyzed, and ranged from 5327 to 273 946 (52% female). Weighted mean odds ratios were calculated for 57 risk factors (socio‐demographic, psychosocial, gambling activity and substance use correlates), allowing them to be ranked from largest to smallest with regard to their association with problem gambling. Results The highest odds ratio (OR) was for internet gambling [OR = 7.59, 95% confidence interval (CI) = 5.24, 10.99, P < 0.000] and the lowest was for employment status (OR = 1.03, 95% CI = 0.87, 1.22, P = 0.718). The largest effect sizes were generally in the gambling activity category and the smallest were in the socio‐demographic category. No differences were found across genders for age‐associated risk. Conclusions A meta‐analysis of 104 studies of gambling prevalence indicated that the most frequently assessed problem gambling risk factors with the highest effect sizes are associated with continuous‐play format gambling products.

Online Gambling's Associations With Gambling Disorder and Related Problems in a Representative Sample of Young Swiss Men

“…As regards our first aim, we found that both the amount of money spent on- and offline were associated with GD and gambling-related problems. These findings were consistent with a recent meta-analysis that found that both internet gambling and offline gambling activities were strong risk factors for problem gambling ( 39 ).…”

The impact of COVID-19 on gambling and gambling disorder: emerging data

“…GD is characterized by the continuation of gambling despite negative impacts, increased priority to gambling compared with other life interests, and impairment of control over gambling involvement [ 11 ]. Individuals with GD tend to gamble more frequently on all gambling formats, although faster-paced forms of gambling such as EGMs and certain casino table games are considered particularly problematic [ 12 ].…”

“…As well as affecting gambling availability, the social and financial effects of the pandemic might also influence people's desire to gamble. The stress and social isolation associated with the pandemic can lead to gambling as an emotional escape, a well-documented and frequent motivation for gambling that is linked to GD [ 12 ]. These impacts might also lead to increased relapse among people in recovery from GD [ 17 , 18 ].…”

Further Exploration of the Psychometric Properties of GamTest: A Rasch Analysis

“…This can be compared to the population in [ 7 ], where the level of gambling related problems (based on PGSI-scores) was higher than in [ 6 ] and can, thus, been seen as a medium-risk population. Additionally, the population in [ 7 ] was recruited at different gambling sites, which indicates a higher level of gambling seen as online gambling is associated with high risk [ 33 ]. Furthermore, when the GamTest is used in Playscan spending can also be used as a marker for low or high gambling.…”

“…In a sample where there are few risk factors, the revised scale-steps can be used, while if several risk factors are present, the non-revised version administered instead. A starting point for this procedure would be to use the ranking of risk factors presented in [ 33 ].…”