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Lotteries are one of the most prevalent forms of gambling and generate substantial state revenues. They are also argued to be one of the least harmful forms of gambling. This paper is one of the first to examine exclusive lottery gamblers and compares their gambling patterns and problems as well other associated risky behaviours to those who are not exclusive lottery gamblers. Data were derived from two large surveys conducted with representative adult samples in France (n = 15,635) and Québec (n = 23,896). Participants were separated into two groups: exclusive lottery gamblers (ELGs) and non-exclusive lottery gamblers. Using multivariate analysis, study results reveal that ELGs, who represent two thirds of gamblers, generally exhibit less intensive gambling patterns and are less likely to report other risky behaviours. However, harms associated with moderate risk and problem gambling are found to be concentrated in specific subpopulations for both groups, primarily males, older individuals, and those who report lower income and education level. Given widespread participation in lotteries and concentration of harm within specific subgroups, these findings point to the need for prevention efforts despite the lower levels of harm associated with lottery gambling.

In 2010 France enacted a law to regulate supply and consumption of online gambling. Its primary aim was to protect citizens from gambling-related harm. This study aims to assess differences in gambling patterns and related harm between online gamblers who use licensed versus unlicensed sites. Participants (N = 3860) completed a self-administered online survey on gambling practices. Pairwise logistic regressions examined the association between the legal statuses of gambling sites people patronized and demographic variables and gambling types. Multivariate logistic regression models explored associations between gambling patterns and related problems according to the legal status of sites people have gambled on. Overall, 53.7 % of online gamblers report gambling exclusively on licensed sites. Those who bet on regulated activities on unlicensed sites, versus licensed sites, are more likely to be female, younger, less educated, inactive in the labor market and are more likely to perceive their financial situation to be difficult. Gambling on unlicensed sites is associated with more intense gambling patterns and more gambling-related problems compared to licensed sites. Findings demonstrate that gambling activities carried out on state licensed sites are associated with less overall harm to gamblers. Implications of these findings on future policy are discussed and prospective research directions are outlined.

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Background and Aims: Participating in online gambling is associated with an increased risk for experiencing gambling-related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at-risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at-risk online gamblers indicated by the Problem Gambling Severity Index (PGSI).Design: Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K-nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI.Setting: Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto-Québec (a provincial Crown Corporation) in Quebec, Canada.Participants: N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site.Measurements: Participants completed the PGSI, a self-report questionnaire with validated cut-offs denoting a moderate-to-high-risk (PGSI 5+) or high-risk (PGSI 8+) for experiencing past-year gambling-related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users' transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform.Findings: Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24-86.41) and 82.52% (95% CI = 79.96-85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants' betting behaviour and repeat engagement on the site.Conclusions: Machine learning algorithms appear to be able to classify at-risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade-offs between their sensitivity and precision.