Strategic Radiology Outreach Planning for Underserved Populations Using Geographic Information Systems

Daniels, Marcus J.; Game, Alina; Mollura, Daniel J.; England, Ryan W.

doi:10.1016/j.jacr.2020.08.022

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…In radiology, this analytic method has been used to reveal disparities in access to computed tomography (CT) facilities in less densely populated areas [ 25 , 26 ]. Additionally, a study of populations in Alaska, whose decreased access to health care when compared with the contiguous US mirrors that of northern to southern Canadians, demonstrated similar utility of GIS in global health outreach planning and resource allocation [ 7 ]. Our analysis builds on this body of work, and further demonstrates the wide geographical differences in access to care.…”

Section: Discussionmentioning

confidence: 99%

“…Researchers in Baltimore, Maryland, utilized GIS to identify regions with a high prevalence of gonorrhea in order to target future outreach efforts [ 6 ]. Daniels et al employed GIS in Alaska to identify regions with limited access to radiology services [ 7 ]. In turn, GIS has the potential to identify communities in northern Canada with limited access to health care and radiology.…”

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confidence: 99%

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Global health radiology planning using Geographic Information Systems to identify populations with decreased access to care

et al. 2021

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Background Communities throughout northern Canada face significant health care disparities including decreased access to radiology. A medical hybrid airship is under development which aims to serve remote populations, requiring strategic outreach planning. This study aims to use geographic information systems (GIS) to identify (1) high risk and medically underserved patient populations in northern Canada and (2) potential landing sites for a medical airship to allow for mobile delivery of radiology services. Methods The northern region of Canada extending from the Rocky Mountains to the Atlantic Ocean was analyzed using multi-variable, multi-weighted GIS modeling. Based on population distance from hospitals (50% weight), health centers (eg, clinic; 30% weight), remote communities (not connected to electric grid; 10% weight), and roads (10% weight), individuals were stratified into one of five health care accessibility index (HAI) categories (ranging from very low to very high severity). HAI (80% weight) was combined with population density (20%) to create a health care access severity index (HASI). Topographic and land cover data were used to identify suitable landing sites for the medical airship. A coordinate data set was made from georeferenced health care facilities, and infrastructure data was obtained from OpenStreetMap. Results GIS analyzed 815 772 Canadians. Of this population, 522 094 (64%) were found to live ≥60 km from a hospital, 326 309 (40%) were ≥45 km from the nearest health center, 65 262 (8%) were within 30 km of a remote community, and 57 104 (7%) lived ≥1 km from the nearest road. Combined, the HASI identified 44% of the population as having decreased access to care (high or very high severity). Lastly, 27.5% of land analyzed was found to be suitable for airship operations. Conclusions GIS identified medically underserved populations in northern Canada who may benefit from mobile radiology services. These techniques may help to guide future global health outreach efforts.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%