Background: The Appalachian region of the United States (U.S.) is a National Cancer Institute-designated priority region, which experiences elevated cancer risk and a disproportionate cancer burden. Tennessee is one of the largest and most diverse states in Appalachia in terms of race/ethnicity, income, and location compared to similar largely rural states in the region like Kentucky and West Virginia. For instance, half of the state is classified Appalachia rural and poor (eastern) while the other half non-Appalachia (western) tend to be urban, rural, poor and yet understudied. Few studies have comprehensively assessed cancer incidence trend and treatment pattern by rural and urban type, and Appalachian and non-Appalachian counties in the state. Objective: To examine the spatial distribution of cancer incidence and treatment patterns based on place of residence, sociodemographic and tumor characteristics. Methods: The main data source was cancer incidence data from the Tennessee Department of Health (TDH) all adult residents in Tennessee who were diagnosed and treated for any of the ten top cancers in the state between 2005 and 2014. The data was extrapolated to generate rural Appalachia, urban Appalachia, rural non-Appalachia and urban non-Appalachian counties dataset in ArcGIS. Next, analysis was performed to assess cancer incidence by type, treatment, and distributions across the 95 counties in Tennessee Results: Overall our preliminary findings revealed that even though the incidence rates for rural Appalachian residents were lower they turned to be diagnosed at more advanced stage than those dwelling in non-Appalachian urban counties most especially breast cancer cases. In addition, residents in rural area were more likely to drop out of treatment compare to their urban counterparts. Discussion/Conclusions: The determinants of cancer disparities occur at multiple intersecting levels. Understanding these issues is essential to enhance the design and implementation of programs and policies to meet the needs of each specific environment and patient.

Open data solutions for population health are continuing to evolve, moving from static data catalogs and specialist visualizations targeting analysts and scientists, towards multiple-dimension web platforms that engage a greater number and diversity of users. These newer platforms are built with the recognition that simply providing access to data is no longer sufficient: creating value from open health data requires answering users’ questions in the context of each user’s unique situation. To accomplish this lofty goal – which some have termed Open Data 2.0 --, these newer applications provide graphs, charts, data-driven narratives, Application Programming Interfaces (APIs), search engine optimization, and journalistic stories that allow for even greater information sharing and insight. These help patients, providers, network administrators, researchers, and policy makers understand population health, health industry trends, healthcare quality and costs, and many other aspects that drive decisions on providing and accessing healthcare. The Centers for Disease Control and Prevention (CDC), Division of Healthcare Quality Promotion (DHQP) are currently building such a solution that embraces Open Data 2.0 concepts, the Antibiotic Resistance Patient Safety Portal. This portal will provide enhanced methods to study and share how Hospital-Associated Infections and related issues vary across the nation, providing data throughout spatially driven, interactive mediums to drive intuitive decision-making. Through implementing the portal, DHQP will broaden the types of data provided, organize content in ways that are relevant to both general users and advanced analysts, and attract larger audiences through modern sharing and social engagement techniques. The concepts of the Patient Safety Portal ultimately align with the Division’s greater mission to streamline their technological tools internally, and to unify messaging to the public about its programs and achievements.

Nonspecific symptoms, which manifest in later stages of Lyme disease (LD) may also be caused by a variety of autoimmune, viral, or complex diseases. Patients lacking erythema migrans or who test negative under CDC guidelines, but suspect LD may search online symptoms in vein. As a result, patients with lingering and undiagnosed symptoms turn to alternative lab tests, often from specialty, commercial labs. This study was developed to identify the efficacy of Google Trends searches for alternative Lyme-related lab tests. Extending the literature beyond basic searches for symptoms or disease, this study examines dynamics among symptom, disease, and lab test searches on Google. It further compares them with actual cases of LD and tick prevalence as reported by the CDC. The search terms used for the Google Trends analysis over the period from 2011 through 2015 include: (1) "lyme" and "lyme disease" for disease, (2) "tick bite", "joint pain", "stiff neck", "circular rash", and "brain fog" for symptoms, and (3) "Igenex" for the alternative lab test. A series of spatial, temporal, and spatiotemporal analysis illustrates noticeable similar patterns between the search frequency and the actual LD and tick prevalence. The public health and medical communities benefit from this research through improved knowledge of undiagnosed patients who are searching for alternative labs to explain their lingering symptoms.