GIS Modeling for Primary Stroke Center Development Anna Kate Sokol, M.U.P. Sr. GIS Specialist City of South Bend, IN

Essential GIS Question  What is the best place to put something? Definition of “best” Limiting factors What tools to use How to measure

Presentation Outline  Project background  Research methodology  Research results  Measure model outcome  Other applications of model  Questions

Project Background: Research Intent  Using existing inputs (existing hospitals, census population, stroke data) create a model for determining the optimal placement and development of stroke centers within a given geography.

Project Background: Research Questions  What are the limiting factors upon which this model is based, and how are these prioritized in the model?  Does the selected model adequately provide access to the at-risk population? (Goal of model to cover at least 95% of the entire population.)  What is the impact on hospitals’ system capacity as a result of this model?

Project Background: Limiting Factors and Inputs  Tissue Plasminogen Activator (tPA) What is tPA?  According to the American Heart Association Website: In 1996 the U.S. Food and Drug Administration (FDA) approved the use of tPA to treat ischemic stroke in the first three hours after the start of symptoms. This makes it very important for people who think they're having a stroke to seek help immediately. If given promptly, tPA can significantly reduce the effects of stroke and reduce permanent disability. tPA can only be given to a person within the first three hours after the start of stroke symptoms.

Project Background: Limiting Factors and Inputs  Tissue Plasminogen Activator (tPA) As of the early 2000s only a 2% treatment rate with the drug nationwide for stroke patients.  Huge public health and economic benefit to broader usage of tPA. Socio-economic variances between those who receive tPA and those who do not  More commonly used to treat younger and\or white patients than older and\or black patients.  More often used in suburban or rural hospitals than in urban hospitals.

Methodology: Data  Hospital locations Michigan Hospital Association (MHA) 2000 Data  Strokes per hospital in a given year MHA 2003 Data  Census geographies and population data US Census Bureau 2000 Environmental Systems Research Institute (ESRI) Data

Methodology: GIS Datasets  Block group polygons converted to centroids representing population and accompanying demographic data.  Hospital locations geocoded.  Buffers around hospitals created representing different travel times to a hospital, or the hospital service area. For example a 20 mile buffer might be a half hour of one-way travel time.

Methodology: Identify Hospitals  Spatial join to find populations (centroids) within buffers. (Join points to polygon)  After joined, sort table to identify hospitals with the highest populations in these varying buffers.  Select the hospital with the highest population inside its buffer or service area.  Remove these centroids from population database, and repeat spatial join process to find the next most populated buffer.  Entire process termed the “Total Remaining Population” method, illustrated on next slide.

Methodology: Total Remaining Population Method for Hospital Selection Step 1 Identify all eligible hospitals, block group centroids, and designated hospital service areas in a given geography. Step 2 Identify hospital with largest population within its hospital service area. Record this as a selected hospital. Step 3 Remove the centroids that are within the selected hospital buffer from the eligible centroid dataset to establish the total remaining population. Step 4 Repeat steps 2 and 3 with the remaining centroids to find the hospital with the next highest population within its service area until ≥ 95% pop. coverage.

Methodology: Total Remaining Population Method for Hospital Selection Step 1Step 2 Step 3Step 4 Identify all eligible hospitals, block group centroids, and designated hospital service areas in a given geography Identify hospital with largest population within its hospital service area. Record this as a selected hospital. Remove the centroids that are within the selected hospital buffer from the eligible centroid dataset to establish the total remaining population. Repeat steps 2 and 3 with the remaining centroids to find the hospital with the next highest population within its service area until ≥ 95% pop. coverage.

Methodology: Sensitivity Analysis and Model Adjustment  Varying buffer sizes or hospital service areas across entire model.  Distinctions between urban, suburban, and rural areas when determining estimated travel times and hospital service areas.  Always covered greater than or equal to 95% of population.

Model Outcome: 20 Mile Hospital Service Area Option

Model Outcome: Varying Hospital Service Area Option

Model Outcome: Hospital Service Area Determination  After all hospitals are identified and created in their own layer, perform a spatial join to link population centroids to hospitals. (Join points to points)  This identifies the hospital closest to each population centroid. This may or may not be the same hospital as the buffer a centroid was initially identified as being located within. (See next slide)

Model Outcome: Hospital Service Area Determination

Model Outcome: Sensitivity Analysis Variation of Model Number of Selected Hospitals (Out of 148 total MI Hospitals) Percent of Total Number of Eligible Michigan Hospitals Average Distance from Centroid to Selected Hospital (miles) Average Distance from UA Centroid to Selected Hospital (miles) Average Distance from Non-UA Centroid to Selected Hospital (miles) 5 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % mile service area for hospitals in urbanized areas ≥150 square miles, 15 mile for all other hospitals % mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % mile service area for hospitals in urbanized areas ≥150 square miles, 25 mile for all other hospitals %

Model Assessment  No matter the model, one must have way to measure how good it is and if it accomplishes its goal.  In this case the question is as follows: does the model select hospitals that provide access to the population in the state most at risk for stroke?

Results: Does the model cover the at risk population?  Non-Modifiable Risk Factors Age, Race, and Gender Kissela et al, 2004.

Results: Population Coverage by Race Model Total Black Percent Black Total White Percent White 5 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % % 10 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % % 15 mile service area for hospitals in urbanized areas ≥150 square miles, 15 mile for all other hospitals % % 20 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals % % 25 mile service area for hospitals in urbanized areas ≥150 square miles, 25 mile for all other hospitals % %

Results: Population Coverage by Age Model Under to to to to to and Above 5 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals 95.8%95.3%94.7%93.5%93.0%93.6%93.3% 10 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals 96.0%95.6%95.1%93.5%92.8%93.3%93.1% 15 mile service area for hospitals in urbanized areas ≥150 square miles, 15 mile for all other hospitals 95.8%95.3%94.9%93.4%93.3%94.0%94.4% 20 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals 95.8%95.4%95.1%93.9%93.4%93.9%93.5% 25 mile service area for hospitals in urbanized areas ≥150 square miles, 25 mile for all other hospitals 95.8%95.3%94.9%93.4%92.8%93.0%92.7%

Other Applications for Model Methodology  Fire Stations Given existing locations and two or more proposed locations, which new location covers the most population x distance from station?  Schools How are populations of different races and ages distributed throughout school districts?  Voting Districts Are populations equally distributed across voting districts? What’s the best location for a new voting station?  Business What location reaches the most new customers?

Other Applications for Model Methodology  BMV location in South Bend Existing BMV locations in South Bend, Mishawaka, and Walkerton Proposed locations from the state and the city Analysis of population closest to each branch as they are now and under each proposal State analysis of population per branch was conducted using population per zip code versus South Bend analysis using population per block group.  Block group analysis much more detailed and specific When empirical evidence is presented, it is easier to explain logic to decision makers and help them make informed decisions.

Other Applications for Model Methodology  How to measure the model’s success.  What population breakdown is best for your analysis? Census Tracts, Block Groups, Blocks State-wide, County-wide, and City-wide analysis might have different needs Level of accuracy needed to measure success of model  Limiting factors in analysis Existing infrastructure Travel times Population distribution

Questions? Anna Sokol, M.U.P. Sr. GIS Specialist City of South Bend