Using Arcgis Storymaps to Guide State Policy Decisions and Targeted Treatment Services for Individuals at Risk for Gambling Problems

Background and Purpose: An increasing number of states – 38 plus the District of Columbia – have legalized online sports wagering and/or iGaming, generating an estimated $11 billion and $6 billion, respectively, in revenue. Evidence suggests that the expansion of online wagering may result in triple the rate of gambling problems, adding to growing public health concern. However, gambling treatment services are poorly attended, largely due to the glamorization of gambling as purely recreational and the stigma associated with problem gambling. Therefore, developing evidence-based methods to identify specific subgroups of residents disproportionately impacted by gambling is critical to guiding social work and public policy initiatives. The goal of the current study is to use GIS mapping to identify ZIP codes and counties with individuals most at risk for gambling problems, who may require targeted services and interventions.

Methods: We created a GIS StoryMap, comprised of a series of maps that provide additive information, culminating in a comparative risk level assessment in a Northeastern state. Demographic and socioeconomic data at the ZIP code level were sourced from the 2022 American Community Survey, and bet- and bettor-level gambling data were extracted from yearly reports to regulators in the state. Locations of counseling and support services were identified online through national and state-level provider databases and independently verified. ZIP code risk scores were calculated as weighted composite scores based on the prevalence and strength of indicators (i.e., point-biserial correlations) related to gambling problems.

Results: Five maps, accompanied by explanatory narratives, highlight geographic areas where residents are at increased likelihood for experiencing gambling-related harms and the availability of in-person treatment and support services, by ZIP code and county. Map 1 reflects demographic and socioeconomic indicators associated with problem gambling risk and allows users to interactively select variables relevant to their policies or services. The second set of maps illustrates the number of residents who participated in (Map 2) online sports wagering and (Map 3) online casino gambling (i.e., iGaming), in addition to concentrations of high-intensity betting. Map 4 depicts overall online gambling prevalence as well as the location of land-based gambling venues, including a 20-minute drive-time analysis. The final map, Map 5, showcases risk scores, based on population makeup and gambling participation, along with the availability of in-person supports and treatment services.

Conclusions and Implications: To date, no state with legalized online gambling or sports wagering has developed a data-based model to guide the deployment of prevention and treatment services to address the needs of specific populations at risk. This visual StoryMap, the first of its kind, provides geographically based depictions of those who are most likely to experience gambling-related harms. As rates of problem gambling continue to increase, it is critical for social workers across health, mental health, school, and child welfare settings to ensure that targeted, culturally appropriate services are available and accessible. Implications for using this technology to inform prevention, intervention and capacity-building initiatives are discussed.