Stakeholders cannot simply wait for the gambling evidence‐base to develop: We must all help to build it

For quality evidence to be built, close collaboration between diverse stakeholders is required. Only through the integration of a variety of tools and perspectives can we hope to make genuine reductions in gambling-related harm. Our opinion article, outlining how player-tracking technologies could enhance stakeholders' ability to understand and reduce gambling-related harm 1, led to four thought-provoking commentaries from leading academics in the field. Gambling policymakers often say that they will not hesitate to act on the latest evidence 2, 3. However, any harm-prevention approach as bold in scope as our proposed universal and independent player-tracking system would require a coalition of stakeholders to initiate, develop and assess its effectiveness for reducing gambling-related harm. We were therefore pleased that the Australian state of Queensland recently announced the introduction of a player-tracking system with many of our proposed features for land-based casino gambling 4, 5. In time, this development could help to provide evidence to test our opinions against. For now, we respond to the important points raised within the commentaries, which help to define the key overarching features of a well-developed player-tracking system. Muggleton 6 thoughtfully describes the benefits that can be yielded by linking self-report and naturalistic data together. One recent study has done exactly this, linking Problem Gambling Severity Index scores and gambling expenditure yielded from an open banking application to show that the average unharmed UK gambler loses only £16 per month compared to more than £200 per month for the average high-risk gambler 7. We hope that this initial study demonstrates the benefits of this approach to other stakeholders. Nikkinen 8 highlights how all gambling provision models raise their own issues. Although we, as outsiders, lauded the Finnish state monopoly's player-tracking system, an insider's view such as Nikkinen's provides alternative perspectives and corresponds with other critiques of states' dependencies on gambling revenue 9, 10. Nikkinen's suggestion of a personal gambling licence accords with many of our own suggestions regarding using player-tracking to enforce spending limits. The suggestion of a gambling literacy quiz (akin to a driver's licence theory test) is an interesting new suggestion, although we note the mixed evidence surrounding educational initiatives in gambling 11. Delfabbro 12 raises important points concerning user privacy and points to features of operator data (e.g. reversed withdrawals) that make for strong markers of harm. We acknowledge his expert view, based on working closely with operator data. One benefit of player-tracking could be a democratization of such data sets, enabling a larger pool of researchers to work with operator data—provided that the privacy issues raised can be solved. Allami 13 makes several valid points, including the administrative burden that existing self-exclusion systems place on gamblers—mirroring other aspects of the gambling environment where doing the right thing is made unreasonably hard to do 14-16. Both Allami and Delfabbro raise points about the challenges of administering a universal system in jurisdictions which regulate gambling on multiple levels (e.g. state/provincial, federal). We call upon legal and emerging technology experts to help find workable solutions to these challenges. For example, blockchain-based solutions have been suggested as one potential technology to support gambling harm-reduction initiatives 17. Although, for example, inter-state differences pose challenges for effective implementation, they can also provide opportunities for evaluation, as between-state differences can be assessed over time in a quasi-experimental fashion 18, 19. We must all come together to help build the gambling evidence base. Philip Newall: Conceptualization (equal); writing—original draft (lead); writing—review and editing (equal). Thomas B. Swanton: Conceptualization (equal); writing—review and editing (equal). There are no funders to report. None to declare. Data sharing is not applicable to this article as no new data were created or analyzed in this study.