Modeling travel distance to health care using geographic information systems Anupam Goel, md


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Modeling travel distance to health care using geographic information systems

  • Anupam Goel, MD

  • Wayne State University

  • Detroit, MI (USA)


Author’s background

  • Initially, a research project

  • Determine the distance to the closest mammogram center for Vermont women ages 40 and older

  • Applications to other public health settings



Learning objectives

  • Define GIS

  • Recognize potential data sources for GIS projects

  • Recognize some strengths and limitations of using GIS technology



Performance objectives

  • Recognize variations in measuring geographical access

  • Critically review an article using GIS



Geographic Information Systems (GIS) overview

  • A method to visualize, manipulate, analyze, and display spatial data, information linked to a specific place

  • Additional description of GIS



Geographic Information Systems (GIS) overview (cont.)

  • Can include spatial data from many sources

  • Applications include: environmental modeling, government or military uses, and business forecasting



Software choices

  • Available GIS programs

  • This presentation uses ESRI software, namely ArcView 3.2a and Network Analyst 1.1b



Methods to measure access

  • Distance to closest facility

  • Straight-line

  • Driving distance

  • Number of facilities within a given distance

  • Travel across political boundaries



Using this methodology for mammography utilization

  • All relevant mammography facilities

  • Assign women to representative points throughout the state

  • Road atlas

  • The shortest road distance from each group of women to a facility



Mammography facilities

  • Mammography Facility Registry

  • Subset of mammography facilities within Vermont and the surrounding counties

  • Mobile mammography centers and Canadian centers not included



Estimating a woman’s location

  • Eligible women within each Vermont ZIP code (Claritas, Inc.)

  • Assigned these women to the ZIP code population centroid (Geographic Data Technologies, Inc.)



Estimating a woman’s location (cont.)



Road network

  • Census 2000 county road networks

  • All counties within Vermont (n=14)

  • The US counties surrounding Vermont (n=10)

  • Canadian roads were not included



Distance to closest facility

  • Applied mathematics

  • Graph theory

  • Network optimization

  • Dijkstra’s algorithm – a method to find the shortest path from a node to all other nodes connected by a network



What we found

  • Median distance to travel for a mammogram in Vermont was 11.2 km (range, 0.5-49.1 km)

  • Women in the most populated ZIP codes traveled less for a mammogram than women in the least populated ZIP codes



Limitations

  • Mammography from work instead of from home

  • Mobile facilities not included

  • No women surveyed for their actual driving distance

  • Driving distance, not driving time



Implications of this project

  • Two ways to place new facilities:

  • 1) Reduce the longest distances traveled (place new facilities in rural areas)

  • 2) Reduce the average distance Vermont women travel (place new facilities in urban areas with less access to mammography)



Next directions

  • New data sources

  • Census 2000, Utilization files

  • New questions

  • Utilization in other areas, targeting interventions

  • New analytic approaches

  • Adjusting for covariates, spatial statistics



Acknowledgements

  • University of Vermont

  • Benjamin Littenberg, Richard G. Pinckney, Division of GIM

  • Austin Troy, SNR

  • Berta Geller and VBCSS

  • National Cancer Institute funding

  • 3 P30 CA22435-17S3 and 1 R03 CA101493-01



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