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

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