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. 2020 Dec 11;17(24):9266.
doi: 10.3390/ijerph17249266.

A Longitudinal Analysis of Gambling Predictors among Adolescents

Álvaro Botella-Guijarro  1   2 , Daniel Lloret-Irles  2 , José Vicente Segura-Heras  3 , Víctor Cabrera-Perona  2 , Juan Antonio Moriano  1
Affiliations

A Longitudinal Analysis of Gambling Predictors among Adolescents

Álvaro Botella-Guijarro et al. Int J Environ Res Public Health. .

Abstract

Although gambling is forbidden for minors, the prevalence of gambling among adolescents is increasing. In order to improve preventive interventions, more evidence on predictors of gambling onset is needed. A longitudinal study was proposed to (1) establish the prevalence of gambling; (2) identify factors associated with gambling behavior the following year; and (3) adjust a model to predict gambling behavior. A cohort of 1074 students (13-18 years old) was followed for 12 months. The prevalence of gambling reached 42.0% in the second measure. Boys gambled 2.7 times more than girls, and the highest percentages of gambling onset showed up between 13 and 14 years old. Gambling onset and maintenance was associated with gender, age, sensation-seeking, risk perception, self-efficacy for not gambling, parents' attitude towards gambling, group pressure (friends), subjective norm, exposure to advertising, accessibility, normative perception, gambling in T1 and parents gambling behavior. Gender, gambling in T1 and risk perception were significant in all three logistic adjusted regression models, with the fourth variable being sensation seeking, peer pressure (friends) and accessibility, respectively. It is suggested that universal prevention should be aimed preferably at children under 15 years old and to alert regulators and public administrations to the directly proportional relationship between accessibility and gambling onset.

Keywords: adolescence; gambling; generalized linear model (GLM); risk factor.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Gambling frequencies by age (%) for T1 (n = 774 individuals who gambled within the past 12 months).
Figure 2
Figure 2
Gambling frequencies by age (%) for T2 (n = 1023 individuals who gambled within the past 12 months).
Figure 3
Figure 3
At-risk and problem gamblers by age and gender (%) for T2. The percentage refers to both the percentage of total males or females who are classified as a risk (n =116) or problem gambler (n = 70) and the percentage of total youths of each age who are classified as a risk or problem gambler.
Figure 4
Figure 4
ROC curves for derivative and validation samples, model A, AUC = Area Under the Curve.
Figure 5
Figure 5
ROC curves for derivative and validation samples, model B, AUC = Area Under the Curve.
Figure 6
Figure 6
ROC curves for derivative and validation samples, model C, AUC = Area Under the Curve.
Figure 7
Figure 7
Possible scenarios for model A. Note. The X-axis represents the values of the variable risk perception (0, 4). The dots represent the score on the sensation-seeking variable. The horizontal line represents the threshold for a person to be classified as a gambler or not at T2.
Figure 8
Figure 8
Possible scenarios for model B. Note The X-axis represents the values of the variable risk perception (0, 4). The dots represent the score on the Peer pressure (friends) variable. The horizontal line represents the threshold for a person to be classified as a gambler or not at T2.
Figure 9
Figure 9
Possible scenarios for model C. Note The X-axis represents the values of the variable risk perception (0, 4). The dots represent the score on the accessibility variable. The horizontal line represents the threshold for a person to be classified as a gambler or not at T2.
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