Key findings

Both potential and severe adverse consequences from gambling were most prevalent amongst males, younger individuals, those identifying as Mixed, Asian, or Black (compared with White), and people with lower financial income.

The negative impacts of gambling were rarely confined to a single area of life. Instead, people tended to report negative impacts across multiple domains:

Among the 15 percent of participants who had gambled in the past 4 weeks and reported at least one potential adverse consequence, over a quarter (29.5 percent) experienced consequences across all 3 domains (resources, relationships, and health), while half (49.9 percent) reported consequences in at least 2 domains.

Of the 2.1 percent of participants who had gambled in the past 4 weeks and reported at least 1 severe consequence, nearly half experienced 2 or more types (44.5 percent), and 8.1 percent reported all 4 severe consequences (crime, significant financial loss, relationship breakdown, and violence).

An important caveat to these findings is that they do not account for differences in the type of gambling activities that people play. It is therefore possible that differences in activity type may partly account for the observed associations between demographics and adverse consequences. We plan to conduct further analysis to test this hypothesis.

Results from this secondary analysis of Year 2 (2024) GSGB data provide insight into who may be most at risk of experiencing negative consequences from gambling, and how different types of consequences co-occur. Our findings have important implications for harm reduction strategies, such as the need to ensure that interventions and safer-gambling messaging engage a diverse range of consumers. The new GSGB survey questions capture adverse consequences that other tools, such as the PGSI, often miss. The inclusion of these questions, alongside the PGSI, enables us to monitor both behavioural risk and the tangible impacts of gambling on people’s lives. This broader understanding of harm is essential for ensuring that regulatory decisions are guided by robust and comprehensive data.