Integrating open-source technologies to build low-cost information systems for improved access to public health data

被引:32
作者
Yi, Qian [1 ]
Hoskins, Richard E. [1 ,2 ]
Hillringhouse, Elizabeth A. [1 ]
Sorensen, Svend S. [1 ]
Oberle, Mark W. [1 ]
Fuller, Sherrilynne S. [1 ]
Wallace, James C. [1 ]
机构
[1] Univ Washington, Ctr Publ Hlth Informat, Seattle, WA 98195 USA
[2] Washington State Dept Hlth, Washington, DC USA
关键词
Application Program Interface; Geospatial Data; Scalable Vector Graphic; Public Health Data; Java Server Pages;
D O I
10.1186/1476-072X-7-29
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Effective public health practice relies on the availability of public health data sources and assessment tools to convey information to investigators, practitioners, policy makers, and the general public. Emerging communication technologies on the Internet can deliver all components of the "who, what, when, and where" quartet more quickly than ever with a potentially higher level of quality and assurance, using new analysis and visualization tools. Open-source software provides the opportunity to build low-cost information systems allowing health departments with modest resources access to modern data analysis and visualization tools. In this paper, we integrate open-source technologies and public health data to create a web information system which is accessible to a wide audience through the Internet. Our web application, "EpiVue," was tested using two public health datasets from the Washington State Cancer Registry and Washington State Center for Health Statistics. A third dataset shows the extensibility and scalability of EpiVue in displaying gender-based longevity statistics over a twenty-year interval for 3,143 United States counties. In addition to providing an integrated visualization framework, EpiVue's highly interactive web environment empowers users by allowing them to upload their own geospatial public health data in either comma-separated text files or MS Excel T spreadsheet files and visualize the geospatial datasets with Google Maps (TM).
引用
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页数:13
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