Disease maps as context for community mapping: a methodological approach for linking confidential health information with local geographical knowledge for community health research.
Health is increasingly understood as a product of multiple levels of influence, from individual biological and behavioral influences to community and societal level contextual influences. In understanding these contextual influences, community health researchers have increasingly employed both geographic methodologies, including Geographic Information Systems (GIS), and community participatory approaches. However, despite growing interest in the role for community participation and local knowledge in community health investigations, and the use of geographical methods and datasets in characterizing community environments, there exist few examples of research projects that incorporate both geographical and participatory approaches in addressing health questions. This is likely due in part to concerns and restrictions regarding community access to confidential health data. In order to overcome this barrier, we present a method for linking confidential, geocoded health information with community-generated experiential geographical information in a GIS environment. We use sophisticated disease mapping methodologies to create continuously defined maps of colorectal cancer in Iowa, then incorporate these layers in an open source GIS application as the context for a participatory community mapping exercise with participants from a rural Iowa town. Our method allows participants to interact directly with health information at a fine geographical scale, facilitating hypothesis generation regarding contextual influences on health, while simultaneously protecting data confidentiality. Participants are able to use their local, geographical knowledge to generate hypotheses about factors influencing colorectal cancer risk in the community and opportunities for risk reduction. This work opens the door for future efforts to integrate empirical epidemiological data with community generated experiential information to inform community health research and practice.
Beyer
Kirsten M M
KM
Department of Population Health, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA. kbeyer@mcw.edu
Comstock
Sara
S
Seagren
Renea
R
D016000:Cluster Analysis; D015179:Colorectal Neoplasms / Q000453:epidemiology*; D003256:Community Participation; D003219:Confidentiality; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D007484:Iowa / Q000453:epidemiology; D008297:Male; D008490:Medical Informatics / Q000379:methods*; D008875:Middle Aged; D012306:Risk; D012423:Rural Health; D019371:Topography, Medical