Abstract
AbstractInjury from causes such as falls, traffic accidents, or violence is a major public health issue globally. Injury prevention research aims to identify vulnerable populations and places by analyzing the spatial patterns of demographic and socio‐economic risk factors associated with elevated injury rates. The stakeholders in injury prevention and control are often distributed across government and public health institutions, non‐profits, and even the private sector (e.g. insurance firms). While this situation calls for distributed, online research tools, their implementation may conflict with health data confidentiality and license limitations for socio‐economic data. In this article, we present the Online Injury Atlas for Ontario, which was designed with the explicit goal of making use of, and contributing to, the Canadian Geospatial Data Infrastructure. We propose a service‐based architecture that integrates publicly accessible map services with protected data layers. Thereby, we demonstrate the benefits of using spatial data infrastructures alongside private data at different levels of protection. In addition, we discuss the extensive data processing needs and specific cartographic design requirements of a Web atlas in the health and social sciences domain.
Highlights
Injuries are the greatest single contributor to potential years of life lost for Canadians before 65 years of age
We extend prior work by providing map layers related to the social determinants of injury, which are important to support end-users in public health in understanding injury risk and developing strategies for injury control
Extensive consultation of end-users from organizations with a mandate of injury prevention and injury research ensured that the atlas would be useful for its intended audience
Summary
Injuries are the greatest single contributor to potential years of life lost for Canadians before 65 years of age. The rates of injuries caused by different mechanisms (e.g. violence, self-inflicted, traffic-related, or falls) vary greatly. In a special issue of the Annual Review of Public Health, Cromley (2003) reviews GIS applications to disease surveillance, while McLafferty (2003) assesses GIS use for health services planning. Starting with the seminal paper by Armstrong et al (1999), researchers have devised a number of algorithms for geographically masking (i.e. modifying) patient locations from health records. Acknowledging this possibility, Wartenberg and Thompson (2010) are concerned that the additional processing and the lack of an agreed-upon masking approach may inhibit public health research. In the work presented here, we use the traditional approach of aggregating individual records to geographic units of analysis, and not reporting outcomes below a threshold number, e.g. five cases
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