The relationship between gambling behaviour and gambling‐related harm: A data fusion approach using open banking data

Abstract

AbstractBackground and aimsUK‐based gambling policymakers have proposed affordability checks starting at monthly losses of £125. The present study combines open banking data with self‐reports of the Problem Gambling Severity Index (PGSI) and other relevant information to explore the harm profiles of people who gamble at different levels of electronic gambling behaviour.Design, setting and participantsThis was a data fusion study in which participants consented to share their bank data via an open banking application programming interface (API) and who also completed relevant self‐report items. Hierarchical hurdle models were used to predict being an at‐risk gambler (PGSI > 0) and being a ‘higher‐risk’ gambler (higher PGSI scores among those with non‐zero scores) using four specifications of electronic gambling behaviour (net‐spend, outgoing expenditure, incoming withdrawals, interaction model combining expenditure and withdrawals), and by adding self‐reported data across two additional steps. The study took place in the United Kingdom. Participants were past‐year people who gamble (n = 424), recruited via Prolific.MeasurementsSelf‐report measures were used of gambling‐related harm (PGSI), depression [Patient Health Questionnaire 9 (PHQ‐9)], age and gender; bank‐recorded measures of income and electronic gambling behaviour.FindingsUnharmed gamblers had an average monthly gambling net‐spend of £16.41, compared with £208.91 among highest‐risk gamblers (PGSI ≥ 5). Being an at‐risk gambler (PGSI > 0) was predicted significantly by all four types of gambling behaviour throughout all three steps [1.08 ≤ odds ratios (ORs) ≤ 2.92; Ps < 0.001), with only outgoing expenditure being significant in the interaction model (2.26 ≤ ORs ≤ 2.81; Ps < 0.001). Higher PHQ‐9 scores also predicted at‐risk gambling in steps 2–3 (1.09 ≤ ORs ≤ 1.10; Ps < 0.001), as did lower age (0.95 ≤ ORs ≤ 0.96; Ps < 0.001) and male gender identity in step 3 (2.51 ≤ ORs ≤ 2.95; Ps < 0.001). Being a higher‐risk gambler was predicted significantly by gambling behaviour only in the expenditure‐only (1.16 ≤ ORs ≤ 1.17; Ps ≤ 0.048) and withdrawal‐only (1.08 ≤ ORs ≤ 1.09; Ps ≤ 0.004) models, and was not predicted by income (0.98 ≤ ORs ≤ 1.14; Ps ≥ 0.601), age (0.98 ≤ ORs ≤ 0.99; Ps ≥ 0.143) or male gender identity (1.07 ≤ ORs ≤ 1.15; Ps ≥ 0.472).ConclusionThe UK government's proposed affordability checks for gamblers should rarely affect people who are not experiencing gambling‐related harm. At‐risk gambling is predicted well by different types of gambling behaviour. Novel insights about gambling can be generated by fusing self‐reported and objective data.