National estimates of Australian gambling prevalence: f indings from a dual-frame omnibus survey - PubMed (original) (raw)
. 2016 Mar;111(3):420-35.
doi: 10.1111/add.13176. Epub 2015 Nov 6.
Affiliations
- PMID: 26381314
- PMCID: PMC5063184
- DOI: 10.1111/add.13176
National estimates of Australian gambling prevalence: f indings from a dual-frame omnibus survey
N A Dowling et al. Addiction. 2016 Mar.
Abstract
Background, aims and design: The increase in mobile telephone-only households may be a source of bias for traditional landline gambling prevalence surveys. Aims were to: (1) identify Australian gambling participation and problem gambling prevalence using a dual-frame (50% landline and 50% mobile telephone) computer-assisted telephone interviewing methodology; (2) explore the predictors of sample frame and telephone status; and (3) explore the degree to which sample frame and telephone status moderate the relationships between respondent characteristics and problem gambling.
Setting and participants: A total of 2000 adult respondents residing in Australia were interviewed from March to April 2013.
Measurements: Participation in multiple gambling activities and Problem Gambling Severity Index (PGSI).
Findings: Estimates were: gambling participation [63.9%, 95% confidence interval (CI) = 61.4-66.3], problem gambling (0.4%, 95% CI = 0.2-0.8), moderate-risk gambling (1.9%, 95% CI = 1.3-2.6) and low-risk gambling (3.0%, 95% CI = 2.2-4.0). Relative to the landline frame, the mobile frame was more likely to gamble on horse/greyhound races [odds ratio (OR) = 1.4], casino table games (OR = 5.0), sporting events (OR = 2.2), private games (OR = 1.9) and the internet (OR = 6.5); less likely to gamble on lotteries (OR = 0.6); and more likely to gamble on five or more activities (OR = 2.4), display problem gambling (OR = 6.4) and endorse PGSI items (OR = 2.4-6.1). Only casino table gambling (OR = 2.9) and internet gambling (OR = 3.5) independently predicted mobile frame membership. Telephone status (landline frame versus mobile dual users and mobile-only users) displayed similar findings. Finally, sample frame and/or telephone status moderated the relationship between gender, relationship status, health and problem gambling (OR = 2.9-7.6).
Conclusion: Given expected future increases in the mobile telephone-only population, best practice in population gambling research should use dual frame sampling methodologies (at least 50% landline and 50% mobile telephone) for telephone interviewing.
Keywords: Cellphones; dual-frame; gambling; mobile telephone; prevalence; problem gambling; sampling; surveys.
© 2015 The Authors. Addiction published by John Wiley & Sons Ltd on behalf of Society for the Study of Addiction.
Figures
Figure 1
Interaction effect of gender and sample frame (landline telephone frame versus mobile telephone frame) in predicting Problem Gambling Severity Index (PGSI) category. Data are presented only for the probability associated with non‐problem gambling status for males and females across sample frame. The simple slope for the mobile telephone frame was significant [b = 0.97, standard error (SE) = 0.30, P = 0.002, 95% confidence interval (CI) = 0.37–1.57], but the simple slope for the landline telephone frame was not significant (b = –0.08, SE = 0.36, P = 0.817, 95% CI = –0.80–0.63)
Figure 2
Interaction effect of gender and telephone status (landline telephone frame respondents versus mobile telephone‐only users) in predicting Problem Gambling Severity Index (PGSI) category. Data are presented only for the probability associated with non‐problem gambling status for males and females across telephone status. The simple slope for the mobile telephone‐only users was significant [b = 1.44, standard error (SE) = 0.56, P = 0.010, 95% confidence interval (CI) = 0.34–2.54], but the simple slope for the landline telephone frame respondents was not significant (b = –0.08, SE = 0.37, P = 0.816, 95% CI = –0.80–0.63)
Figure 3
Interaction effect of relationship status and telephone status (landline telephone frame respondents versus mobile telephone‐only users) in predicting Problem Gambling Severity Index (PGSI) category. Data are presented only for the probability associated with non‐problem gambling status for respondents in and not in a relationship across telephone status. The simple slope for the mobile telephone‐only users was significant [b = 1.04, standard error (SE) = 0.50, P = 0.039, 95% confidence interval (CI) = 0.06–2.02], but the simple slope for the landline telephone frame respondents was not significant (b = 0.99, SE = 0.80, P = 0.216, 95% CI = –2.56–0.58)
Figure 4
Interaction effect of health category and telephone status (landline telephone frame respondents versus mobile telephone‐only users) in predicting Problem Gambling Severity Index (PGSI) category. Data are presented only for the probability associated with non‐problem gambling status for respondents reporting low health (poor/fair/good) and high health (very good/excellent) across telephone status. The simple slope for the landline telephone frame respondents was significant [b = –1.19, standard error (SE) = 0.40, P = 0.003, 95% confidence interval (CI) = 1.98–0.40], but the simple slope for the mobile telephone‐only users was not significant (b = 0.12, SE = 0.46, P = 0.787, 95% CI = –0.78–1.03)
Comment in
- Commentary on Dowling et al. (2016): Is it time to stop conducting problem gambling prevalence studies?
Markham F, Young M. Markham F, et al. Addiction. 2016 Mar;111(3):436-7. doi: 10.1111/add.13216. Addiction. 2016. PMID: 26860244 No abstract available.
Similar articles
- Improving gambling survey research using dual-frame sampling of landline and mobile phone numbers.
Jackson AC, Pennay D, Dowling NA, Coles-Janess B, Christensen DR. Jackson AC, et al. J Gambl Stud. 2014 Jun;30(2):291-307. doi: 10.1007/s10899-012-9353-6. J Gambl Stud. 2014. PMID: 23288431 - Surveying alcohol and other drug use through telephone sampling: a comparison of landline and mobile phone samples.
Livingston M, Dietze P, Ferris J, Pennay D, Hayes L, Lenton S. Livingston M, et al. BMC Med Res Methodol. 2013 Mar 16;13:41. doi: 10.1186/1471-2288-13-41. BMC Med Res Methodol. 2013. PMID: 23497161 Free PMC article. - Inclusion of mobile phone numbers into an ongoing population health survey in New South Wales, Australia: design, methods, call outcomes, costs and sample representativeness.
Barr ML, van Ritten JJ, Steel DG, Thackway SV. Barr ML, et al. BMC Med Res Methodol. 2012 Nov 22;12:177. doi: 10.1186/1471-2288-12-177. BMC Med Res Methodol. 2012. PMID: 23173849 Free PMC article. - [Internet gambling: what are the risks?].
Bonnaire C. Bonnaire C. Encephale. 2012 Feb;38(1):42-9. doi: 10.1016/j.encep.2011.01.014. Epub 2011 Apr 8. Encephale. 2012. PMID: 22381723 Review. French. - A meta-analysis of problem gambling risk factors in the general adult population.
Allami Y, Hodgins DC, Young M, Brunelle N, Currie S, Dufour M, Flores-Pajot MC, Nadeau L. Allami Y, et al. Addiction. 2021 Nov;116(11):2968-2977. doi: 10.1111/add.15449. Epub 2021 Mar 11. Addiction. 2021. PMID: 33620735 Free PMC article. Review.
Cited by
- The Relationship Between Gambling Accessibility and Behavior Among Korean Adults.
Kim Y, Lee S, Park S, Lee J. Kim Y, et al. J Gambl Stud. 2023 Jul 14. doi: 10.1007/s10899-023-10236-9. Online ahead of print. J Gambl Stud. 2023. PMID: 37452234 - Gambling and Aging: An Overview of a Risky Behavior.
Fontaine M, Lemercier C, Bonnaire C, Giroux I, Py J, Varescon I, Le Floch V. Fontaine M, et al. Behav Sci (Basel). 2023 May 23;13(6):437. doi: 10.3390/bs13060437. Behav Sci (Basel). 2023. PMID: 37366689 Free PMC article. Review. - Prevalence of Problem Gambling: A Meta-analysis of Recent Empirical Research (2016-2022).
Gabellini E, Lucchini F, Gattoni ME. Gabellini E, et al. J Gambl Stud. 2023 Sep;39(3):1027-1057. doi: 10.1007/s10899-022-10180-0. Epub 2022 Dec 31. J Gambl Stud. 2023. PMID: 36586057 - A Gambling Just-In-Time Adaptive Intervention (GamblingLess: In-The-Moment): Protocol for a Microrandomized Trial.
Dowling NA, Merkouris SS, Youssef GJ, Lubman DI, Bagot KL, Hawker CO, Portogallo HJ, Thomas AC, Rodda SN. Dowling NA, et al. JMIR Res Protoc. 2022 Aug 23;11(8):e38958. doi: 10.2196/38958. JMIR Res Protoc. 2022. PMID: 35998018 Free PMC article. - Smartphone App Delivery of a Just-In-Time Adaptive Intervention for Adult Gamblers (Gambling Habit Hacker): Protocol for a Microrandomized Trial.
Rodda SN, Bagot KL, Merkouris SS, Youssef G, Lubman DI, Thomas AC, Dowling NA. Rodda SN, et al. JMIR Res Protoc. 2022 Jul 26;11(7):e38919. doi: 10.2196/38919. JMIR Res Protoc. 2022. PMID: 35881441 Free PMC article.
References
- Williams R., Volberg R., Stevens R. The Population Prevalence of Problem Gambling: Methodological Influences, Standardized Rates, Jurisdictional Differences, and Worldwide Trends In: Report prepared for the Ontario Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care. Ontario: Ontario Problem Gambling Research Centre and the Ontario Ministry of Health and Long Term Care; 2012.
- Neal P., Delfabbro P., O'Neil M. Problem Gambling and Harm: A National Definition. Adelaide, Australia: South Australia Centre for Economic Studies; 2005.
- ACIL Allen Consulting , Social Research Centre , Problem Gambling Research and Treatment Centre Third Social and Economic Impact Study of Gambling in Tasmania. vol. 2, 2013. Tasmania: Tasmanian Gambling Prevalence Survey; (prepared for the Tasmanian Department of Treasury and Finance); 2014.
- Davidson T., Rodgers B. 2009 Survey of the Nature and Extent of Gambling, and Problem Gambling, in the Australian Capital Territory. Canberra: Australian National University; 2010.
- Queensland Government. Queensland Household Gambling vSurvey 2011–12. State of Queensland: Department of Justice and Attorney‐General; 2012.
MeSH terms
LinkOut - more resources
Full Text Sources
Other Literature Sources
Medical
Miscellaneous