GIS Modeling for Primary Stroke Center Development

Anna Kate Sokol, M.U.P.Sr. GIS SpecialistCity of South Bend, IN

Essential GIS Question

What is the best place to put something?Definition of “best”Limiting factorsWhat tools to useHow 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 4Repeat 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 1 Step 2

Step 3 Step 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 SelectedHospitals

(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

69 47.3 % 3.2 3.2 3.3

10 mile service area for hospitals in urbanized areas

≥150 square miles, 20 mile for all other hospitals

54 40.0 % 8.1 5.2 13.3

15 mile service area for hospitals in urbanized areas

≥150 square miles, 15 mile for all other hospitals

74 50.7 % 7.4 6.1 9.8

20 mile service area for hospitals in urbanized areas

≥150 square miles, 20 mile for all other hospitals

48 32.9 % 9.6 7.2 13.7

25 mile service area for hospitals in urbanized areas

≥150 square miles, 25 mile for all other hospitals

30 20.6 % 12.8 9.0 19.7

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 FactorsAge, Race, and Gender

Kissela et al, 2004.

Results: Population Coverage by Race

ModelTotal 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

1401957 99.2% 7509980 94.3%

10 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals

1401355 99.2% 7522283 94.4%

15 mile service area for hospitals in urbanized areas ≥150 square miles, 15 mile for all other hospitals

1407427 99.6% 7507066 94.2%

20 mile service area for hospitals in urbanized areas ≥150 square miles, 20 mile for all other hospitals

1403162 99.3% 7516568 94.4%

25 mile service area for hospitals in urbanized areas ≥150 square miles, 25 mile for all other hospitals

1405463 99.5% 7501746 94.2%

Results: Population Coverage by Age

ModelUnder 35

35 to 44

45 to 54

55 to 64

65 to 74

75 to 84

85 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 BendAsokol@southbendin.gov