Abstract

Traditionally, gambling and problem gambling research relies on cross-sectional and retrospective designs. This has compromised identification of temporal relationships and causal inference. To overcome these problems a new questionnaire, the Jonsson-Abbott Scale (JAS), was developed and used in a large, prospective, general population study, The Swedish Longitudinal Gambling Study (Swelogs). The JAS has 11 items and seeks to identify early indicators, examine relationships between indicators and assess their capacity to predict future problem progression. The aims of the study were to examine psychometric properties of the JAS (internal consistency and dimensionality) and predictive validity with respect to increased gambling risk and problem gambling onset. The results are based on repeated interviews with 3818 participants. The response rate from the initial baseline wave was 74%. The original sample consisted of a random, stratified selection from the Swedish population register aged between 16 and 84. The results indicate an acceptable fit of a three-factor solution in a confirmatory factor analysis with ‘Over consumption,’ ‘Gambling fallacies,’ and ‘Reinforcers’ as factors. Reinforcers, Over consumption and Gambling fallacies were significant predictors of gambling risk potential and Gambling fallacies and Over consumption were significant predictors of problem gambling onset (incident cases) at 12 month follow up. When controlled for risk potential measured at baseline, the predictor Over consumption was not significant for gambling risk potential at follow up. For incident cases, Gambling fallacies and Over consumption remained significant when controlled for risk potential. Implications of the results for the development of problem gambling, early detection, prevention, and future research are discussed.

Highlights

  • Gambling availability has increased markedly in recent decades (Arvidsson et al, 2016)

  • The results of the confirmatory factor analysis (CFA) analysis indicate a mediocre fit between the threefactor model and the data (χ2[41] = 1077.742; p < 0.001; Root-Mean-Square Error of Approximation (RMSEA) = 0.071; p < 0.05; 90% CI [0.067, 0.075]; Comparative Fit Index (CFI) = 0.939; Tucker-Lewis Index (TLI) = 0.918; Normed Fit Index (NFI) = 0.94)

  • When that path was freed, the modified model indicated a significantly better fit (χ2[40] = 665.356; p < 0.001; χ2 diff[1] = 412.390; p < 0.001; RMSEA = 0.056; p < 0.05; 90% CI [0.052, 0.060]; CFI = 0.963; TLI = 0.949; NFI = 0.936)

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Summary

Introduction

Gambling availability has increased markedly in recent decades (Arvidsson et al, 2016). This increase has been associated with growth in gambling participation and expenditure. In Sweden, based on the Problem Gambling Severity Index, 0.4% (95% CI 0.28– 0.53%) of adults are estimated to be current problem gamblers, 1.3% moderate-risk gamblers and 4.2% low-risk gamblers (Public Health Agency of Sweden, 2016b). This means that approximately one in 10 gambling participants experience at least some form of reduced control over gambling and/or adverse consequences

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