Using Tableau Technology to Share Risk and Protective Factor Data with Local Communities for Prevention Planning

Nicole Eisenberg, John S. Briney, Richard F. Catalano, Kevin P. Haggerty, Ilene Berman, Daniela Castillo, Cynthia Weaver

Introduction: Assessment of risk and protective factors is a key element of science-based approaches to community prevention such as the Communities that Care (CTC) and Evidence2Success (E2S) prevention systems. In these models, local communities form prevention coalitions that use local epidemiological youth survey data to prioritize needs and guide prevention planning efforts. Survey results are shared with coalitions and community members to help guide the selection of evidence-based preventive interventions. Reporting of risk and protective factor data has relied, to date, on traditional reporting formats (i.e., print reports, power-point slides), and it is very time-consuming when sites have large numbers of schools or neighborhoods, each requiring a local report. Additionally, prevention coalitions often request additional data displays or different data breakdowns that are relevant to their communities (e.g., levels of risk and protection by gender or ethnic group). This technology demonstration uses Tableau software to address some of these issues and offer a cost-effective solution.

Methods: We use youth risk and protective factor data collected using the CTC Youth Survey and the Evidence2Success Youth Experience Survey in different communities in the Unites States and internationally (e.g., in Chile, South America). Both instruments contain brief and reliable measures of risk and protection for youth in grades 6-12, and share many items and scales. Sample sizes vary by site (e.g., approx. 700 to 5,000 per site). Data were analyzed using SPSS statistical software and then exported into Tableau software for visualization.

Results: The results include graphs and maps that highlight how levels of risk and protection can be presented interactively and allow users to select, in real time, different views and breakdowns that are significant to their sites. For example, some communities may purposefully want to address racial disparities, and having data broken down by ethnic groups would be important for them. Geographical mapping of results can aid with visualization of cross-site comparisons. Reporting time is reduced, as is the time communities must wait for additional data requests.

Conclusions: The session demonstrates a promising technology for implementing and scaling prevention efforts. It provides information in real-time and allows those responsible for prevention planning to explore questions that relate directly to their needs without extensive technical assistance from external scientists. It facilitates cross site-comparisons and reduces costs associated with reporting, increasing the sustainability of prevention for local communities.

This abstract was submitted to the 2017 Society for Prevention Research Annual Meeting.