Towards Next Generation Health Data Exploration: A Data Cube-Based Investigation into Population Statistics for Tobacco

Increasingly, experts and interested laypeople are turning to the explosion of online data to form and explore hypotheses about relationships between public health intervention strategies and their possible impacts. We have engaged in a multi-year collaboration to use and design semantic techniques and tools to support the current and next generation of these explorations. We introduce a tool, qb.js, to enable access to multidimensional statistical data in ways that allow non-specialists to explore and create specific visualizations of that data. We focus on explorations of health data - in particular aimed at helping to support the formation and analysis of hypotheses about public health intervention strategies and their correlation with health-related behavior changes. We used qb.js to formulate and explore the hypothesis that youth tobacco access laws have consistent, measurable impacts on the rate of change in cigarette smoking among high school students over time. While focused in this instance on one particular intervention strategy (i.e., limiting youth access to tobacco), this analytics platform may be used for a wide range of correlational analyses. To address this hypothesis, we converted population science data on tobacco-related policy and behavior from Impacteen to a Resource Description framework (RDF) representation that was annotated with the RDF Data Cube vocabulary. A Semantic Data Dictionary enabled mapping between the original datasets and the RDF representation. This allowed for the creation and publication of data visualizations using qb.js. The RDF Data Cube representation made it possible to discover a significant downward effect from the introduction of nine youth tobacco access laws on the rate of change in smoking prevalence among high school-aged youth.