Lower-risk gambling limits: linked analyses across eight countries

Figures & data

Table 1. Datasets identified for risk curve analyses assessing the association between gambling involvement and development of harm.

Dataset	Region	Year	Survey Design & Sampling (Total N)†	ROC Sample N^††	% of ROC sample scoring 5–7 and 8+ on PGSI
The Leisure, Lifestyle, and Lifecycle Project (LLLP) and the Quinte Longitudinal Study (QLS) (datasets merged)	Alberta, Ontario, Canada	2009–2012	Longitudinal, random population sample + oversampling of at-risk gamblers (5929)	4930	3.0% 2.0%
Icelandic Gambling Project	Iceland	2005, 2007, 2011	Cross-sectional, random population sample (3358, 3004, 1531)	4817	0.6% 0.8%
Finnish Gambling Population Survey	Finland	2011, 2015	Cross-sectional, random population sample (4484, 4515)	6934	0.9% 0.6%
SWELOGS	Sweden	2008–2014	Longitudinal, random population sample + oversampling of at-risk gamblers (8827)	3132	1.3% 1.0%
Enjeu 2014- Enquête nationale sur les jeux d’argent et de hasard	France	2014	Cross-sectional, random population sample (15635)	8652	2.2%^††† 0.5%^†††
e-Enjeu - Enquête nationale sur les jeux d’argent et de hasard en ligne	France	2012	Cross-sectional, random population sample (4042)	3860	0.5% 0.5%
ENHJEU-Quebec	Quebec, Canada	2012	Cross-sectional, random population sample (12008)	7983	0.6% 0.5%
Victorian Gambling Study	Australia	2008–2012	Longitudinal, random population sample + oversampling of at-risk gamblers (15000)	3719	3.0% 1.0%
New Zealand 2012 National Gambling Study	New Zealand	2012	Cross-sectional, random population sample (6251)	4950	1.3% 0.9%
The Massachusetts Gambling Impact Cohort (MAGIC Study)	Massachusetts, United States	2013–2015	Longitudinal, random population sample + oversampling of at-risk gamblers (3139)	2617	1.8% 1.1%
Consolidated gambling prevalence surveys from Canadian provinces^1	Ontario, Manitoba, New Brunswick, Newfoundland and Labrador, Canada	2005–2016	Cross-sectional random population sample (19936)	15765	1.1% 1.3%

†Includes total sample size. If longitudinal design this represents sample size at time 1.

^††Survey respondents ≥ 18 years and who indicated they gambled in past year. Note. For each data set, ROC analyses were conducted for each gambling indicator (4), for each harm indicator (4) and by gender (2). Thus for each contributing dataset, 32 ROC analyses were conducted. Due to differing patterns of missing data across each of these analyses, the sample size presented should not necessarily be understood to be that associated with each ROC analysis conducted.

^†††Note. These prevalence numbers refer to the full sample.

Datasets from provincial gambling prevalence surveys conducted in Ontario (2005), Manitoba (2006, 2013, 2016), New Brunswick (2009, 2014), and Newfoundland and Labrador (2005, 2009) were merged to create a consolidated data source for the project. Datasets from other provinces could not be used because we could not obtain access from the data custodian or the survey data was considered too old (i.e. pre-2005). Based upon earlier comparative analyses, two Canadian longitudinal datasets were combined into one sample to maximize sample size. The datasets from Finland and Iceland also represented the merging of two or more cross-sectional surveys conducted at different time points.

Table 2. Spearman correlations between risk curve harm indicators and harm domains (N = 4,027).

PGSI Harm Indicators	Harm Domains
	Financial	Relationships	Emotional	Health	Work	SGHS
Financial harm- any	.44	.34	.40	.38	.25	.41
Bet more than afford	.42	.31	.38	.35	.20	.41
Borrowed money	.43	.44	.39	.42	.42	.31
Financial problems	.53	.46	.49	.49	.34	.47
Emotional harm- any	.36	.32	.41	.33	.23	.40
Felt like you had problem	.33	.33	.45	.34	.25	.38
Felt guilty	.43	.44	.39	.42	.42	.31
Health	.43	.46	.53	.51	.35	.46
Relationship	.28	.43	.32	.35	.38	.24
Two or more harms	.43	.37	.44	.40	.26	.45
Total harms (0–7)	.55	.58	.60	.57	.48	.50

SGHS = Short Gambling Harm Scale. PGSI = Problem Gambling Severity Index item. All correlations significant at p < .00001.

Table 3. Mean upper and lower limits by harm category.

	Expenditure		Percentage of Income		Frequency		Number of Types
Harms	Criterion 1	Criterion 2	Criterion 1	Criterion 2	Criterion 1	Criterion 2	Criterion 1	Criterion 2
Financial	$65.90	$140.90	1.8	5.0	6.0	8.3	3.2	3.7
Relationship	$62.50	$145.00	1.6	4.3	7.3	9.2	3.0	3.9
Emotional	$56.80	$117.80	1.5	3.3	5.5	7.8	2.9	3.4
Health	$72.80	$119.40	1.8	3.4	5.3	7.5	2.8	3.4
Two or more	$46.7	$114.2	1.6	4.4	5.8	7.3	3.2	3.9

Mean expenditure was calculated from the risk curves compiled for each of the 11 datasets. Criterion 1 provides the lower and Criterion 2 the upper boundary. CAD $.

Data availability statement

Requests for access to the data in which proposed use aligns with public good purposes will be considered individually by the author(s) from each jurisdiction (country) providing prevalence survey data. Access will be according to local regulation.