Towards Identifying and Reducing the Bias of Disease Information Extracted from Search Engine Data.

The estimation of disease prevalence in online search engine data (e.g., Google Flu Trends (GFT)) has received a considerable amount of scholarly and public attention in recent years. While the utility of search engine data for disease surveillance has been demonstrated, the scientific community still seeks ways to identify and reduce biases that are embedded in search engine data. The primary goal of this study is to explore new ways of improving the accuracy of disease prevalence estimations by combining traditional disease data with search engine data. A novel method, Biased Sentinel Hospital-based Area Disease Estimation (B-SHADE), is introduced to reduce search engine data bias from a geographical perspective. To monitor search trends on Hand, Foot and Mouth Disease (HFMD) in Guangdong Province, China, we tested our approach by selecting 11 keywords from the Baidu index platform, a Chinese big data analyst similar to GFT. The correlation between the number of real cases and the composite index was 0.8. After decomposing the composite index at the city level, we found that only 10 cities presented a correlation of close to 0.8 or higher. These cities were found to be more stable with respect to search volume, and they were selected as sample cities in order to estimate the search volume of the entire province. After the estimation, the correlation improved from 0.8 to 0.864. After fitting the revised search volume with historical cases, the mean absolute error was 11.19% lower than it was when the original search volume and historical cases were combined. To our knowledge, this is the first study to reduce search engine data bias levels through the use of rigorous spatial sampling strategies.

Neighborhood commuting environment and obesity in the United States: an urban-rural stratified multilevel analysis.

OBJECTIVE: Automobile dependency and longer commuting are associated with current obesity epidemic. We aimed to examine the urban-rural differential effects of neighborhood commuting environment on obesity in the US METHODS: The 1997-2005 National Health Interview Survey (NHIS) were linked to 2000 US Census data to assess the effects of neighborhood commuting environment: census tract-level automobile dependency and commuting time, on individual obesity status. RESULTS: Higher neighborhood automobile dependency was associated with increased obesity risk in urbanized areas (large central metro (OR 1.11[1.09, 1.12]), large fringe metro (OR 1.17[1.13, 1.22]), medium metro (OR 1.22[1.16, 1.29]), small metro (OR 1.11[1.04, 1.19]), and micropolitan (OR 1.09[1.00, 1.19])), but not in non-core rural areas (OR 1.00[0.92, 1.08]). Longer neighborhood commuting time was associated with increased obesity risk in large central metro (OR 1.09[1.04, 1.13]), and less urbanized areas (small metro (OR 1.08[1.01, 1.16]), micropolitan (OR 1.06[1.01, 1.12]), and non-core rural areas (OR 1.08[1.01, 1.17])), but not in (large fringe metro (OR 1.05[1.00, 1.11]), and medium metro (OR 1.04[0.98, 1.10])). CONCLUSION: The link between commuting environment and obesity differed across the regional urbanization levels. Urban and regional planning policies may improve current commuting environment and better support healthy behaviors and healthy community development.

Evaluation of Spatial Accessibility to Ohio Trauma Centers Using a GIS-Based Gravity Model.

Aims: Traumatic injury is one of the leading causes of death in all age groups. Ensuring adequate and effective access to trauma centers is key to improving the quality of care for injured patients. This study evaluates the spatial accessibility of Ohio trauma centers and identifies potentially underserved Ohio counties. Study Design: A gravity based accessibility model using a Geographic Information System (GIS) was implemented. Place and Duration of Study: The study was conducted in Nationwide Children’s Hospital in 2015. Methodology: A gravity based accessibility model using a Geographic Information System (GIS) was adapted to incorporate US census data, trauma center location data, and trauma center utilization data to quantify accessibility to trauma centers at both the zip code and county levels. An underserved index was developed to identify the location and clustering pattern of underserved regions within the state. Results: Most served counties were about 10 times more served than an average county while least served counties were about 4 times less served than an average county. Conclusion: Findings of this study are potentially useful for evaluating regionalized trauma care and provide evidence for trauma care system improvements.

Environmental impacts of the emerging digital economy: the e-for-environment e-commerce?

The Internet-led digital economy is changing both the production and consumption patterns at the global scale. Although great potential exists to harness information technology in general and the Internet in particular and improve the environment, possible negative impacts of e-commerce on the environment should also be considered and dealt with. In this forum, we discuss both the potential positive and negative impacts of e-commerce. Drawing from insights gained from the complexity theory, we also delineate some broad contours for environmental policies in the information age. Given the paradoxical nature of technological innovations, we want to caution the scientific community and policymakers not to treat the Internet as the Holy Grail for environmental salvation.