Buckeridge David

The success of public health campaigns in decreasing or eliminating the burden of vaccine-preventable diseases can be undermined by media content influencing vaccine hesitancy in the population. A tool for tracking and describing the ever-growing platforms for such media content can help decide how and where to invest in campaigns to increase public confidence in vaccines. The Vaccine Sentimeter, developed from the Healthmap project, aims to assist public health practitioners in maintaining or improving vaccine coverage through a real-time, online visualization tool of global media content on vaccines.

Objective The current analysis describes the scope and trends in United States content from the Vaccine Sentimeter’s results, while seeking to examine any possible links between media content, vaccine coverage, and reported vaccine adverse events in the country.

Electronic data that could be used for global health surveillance are fragmented across diseases, organizations, and countries. This fragmentation frustrates efforts to analyze data and limits the amount of information available to guide disease control actions. In fields such as biology, semantic or knowledge-based methods are used extensively to integrate a wide range of electronically available data sources, thereby rapidly accelerating the pace of data analysis. Recognizing the potential of these semantic methods for global health surveillance, we have developed the Scalable Data Integration for Disease Surveillance (SDIDS) software platform. SDIDS is a knowledge-based system designed to enable the integration and analysis of data across multiple scales to support global health decision-making. A ‘proof of concept’ version of SDIDS is currently focused on data sources related to malaria surveillance in Uganda.

Objective

To develop a scalable software platform for integrating existing global health surveillance data and to implement the platform for malaria surveillance in Uganda.

Unhealthy diet is becoming the most important preventable cause of chronic disease burden. Dietary patterns vary across neighborhoods as a function of policy, marketing, social support, economy, and the commercial food environment. Assessment of community-specific response to these socio-ecological factors is critical for the development and evaluation policy interventions and identification of nutrition inequality. Mass administration of dietary surveys is impractical and prohibitory expensive, and surveys typically fail to address variation of food selection at high geographic resolution. Marketing companies such as the Nielsen cooperation continuously collect and centralize scanned grocery transaction records from a geographically representative sample of retail food outlets to guide product promotions. These data can be harnessed to develop a model for the demand of specific foods using store and neighborhood attributes, providing a rich and detailed picture of the “foodscape” in an urban environment. In this study, we generated a spatial profile of food selection from estimated sales in food outlets in the Census Metropolitan Area (CMA) of Montreal, Canada, using regular carbonated soft drinks (i.e. non-diet soda) as an initial example.

Objective

To demonstrate a method for estimating neighborhood food selection with secondary use of digital marketing data; grocery transaction records and retail business registry.