Using Spatial Analysis Tools 

k

Implementation Science

Mar S. Bauer, MD

Center for Organization, Leadership, & Management R

VA Boston Healthcare System

Rob Penfold, PhD

Assistant Investigator

Group Health Research Institute, Seattle, WA

Diane C. Cowper Ri l p ey, PhD

Rehabilitation Outcomes Research Center North Flori

Georgia Veterans Healthcare System
Cyberseminar Outline

• Introduction: Bauer• Overview of spatial analysis with emph

on relevance relevance to implementation implementation metho

metho

Penfold

• E l xamp es of appl i y ng spati l a tools to

implementation issues: Cowper Ripley

• Q&A: Presenters & Audience
Question:

“Who s’ out there anyway? anyway?”
Why Pay Attention to Geograp

Factors in Implementation Eff 

o

Pre‐Implementation (assessment):• Is VHA an hermetically sealed, top‐down,

hierarchically driven healthcare system?

• Or do local factors play a role in provider (and

system) bh i e av or at the VISN and VAMC l 

eve

• Are these factors explainable solely in terms 

ad i i t ti dministrative sttl ruc ural ft ? ea ures?

• Or do local social/spatial context factors play 

provider/patient/system behavior?
Why Pay Attention to Geograp

Factors in Implementation Eff 

o

I l t ti Implementation (t t s ra egy di ) es gn :• Social context factors• Resource availability & distribution

• Travel & communication issues

• Maintenance & sustainability of implementat

strategies

• Information & influence flow—intended and

unintended
Spread of Newer Antipsychotics fo

Bipolar Disorder & PTSD

HSR&D IIR 10‐314 (2011‐2014)

We h h ypot esize that b h ot geographic factors, consisten

classic diffusion theory, and organizational factors, as a

in more recent applications of diffusion theory to disse

within healthcare organizations, will shape SGA spread therefore, identify opportunities for intervention.
Regional Variability in Use of Ne

Antipsychotic Prescribing for P

(n=705,085)

Fig 1. Estimated Probability of Being Prescribed 

0.340.36

with Any of Five SGAs for PTSD Patients

(FY2003 to 2010)

0.32

0.29

0 280.300.32

bility

0.27

0.24

0.240.26

0.28

Probab

0.200.22

South Northeast Midwest West

Region

(Analyses courtesy of Austin Lee, PhD)
Spatial Analysis Tools in Implement

• Site-specific customization• Social context often defined geographically Social context often defined geographically• Geographic units as policy boundaries• Specific tools

• Location-allocation

• Minimum set coverage Minimum set coverage• Spatio-temporal cluster analysis (hotspots)• Geographically weighted regression
Site Customization

• Implementation of interventions and/or policies g

requires customization to local resources or requires customization to local resources or

circumstances

• Capacity constraints

• Differences in workplace culture/procedures• Aggregate differences in patient needs

• Location-allocation (p-median) implementation

• Optimal location of new facility or site of serv

Optimal location of new facility or site of serv

• Optimal re-allocation of resources given a kn

of service points
Intervention: improve “acces

• Increase the number of sites• Increase capacity at existing sites Increase capacity at existing sites• Subject to budget constraints• Location-allocation problem

• How many sites should be added and where 

achieve the accessibility goal(s)?

• “P-median problem”

• Wh t h ld th it ( # li i i ) b 

What should the capacity (e.g., # clinicians) b

each site given the distribution of demand?
Minimize distance to a central 

Source: Schietz
Location‐Allocation with 4 sit
Minimum Set Coverage Proble

• What is the minimum number of service points r

to cover a geographic area with to cover a geographic area with “service service”

• E.g., how many CBOCs would be required su

no veteran must drive more than 30 minutes one.

• How many broadband internet access points 

needed to implement remote telehealth acceneeded to implement remote telehealth acce

population of veterans “fairly”?
xDSL coverage in Franklin Count

Grube
Various Tradeoff Solutions
Social Context & Networks

• Social context is often geographically defined 

( p y) homophily)

• VAMC vs. VISN, rural vs. urban, county vs. s

state vs. region

• Various levels of spatial aggregation associat

Various levels of spatial aggregation associat

different cultures/norms

• Social context often implicated in the success/fa

i t ti n erventions

• Social networks often defined/constrained geog

because p p eo le interact with their local 

colleagues/patients/neighbors more often

• Spatio-temporal cluster analysis is one appro

defining the spatial extent of social interaction defining the spatial extent of social interaction
Space Time Scan Statistic

• Uses

• Geographical and temporal surveillance of a 

Geographical and temporal surveillance of a 

to detect clusters of events (e.g., Rx of a new 

• Test whether a p y rocess is randomly distribute

space, time or space-time

• Evaluate the statistical significance of cluster

• Multiple types of scan statistic for different distrib

• Poisson-based Bernoulli space based, Bernoulli, space-time permut

time permut

multinomial, ordinal, exponential, normal.
Space‐time Cluster of “adoptio

Source
Interpretation/Significance

• Who are the clinicians or patients within the spa

cluster?

• How do the resources, people, etc within clus

differ from those outside the cluster?

• Do clusters occur in the same places across 

interventions/policies?

• Target dissemination/implementation resources 

Target dissemination/implementation resources 

clusters with LOW uptake of a desired change
Policy Boundaries

• Most policy interventions are implemented for a 

jurisdiction and these are often geographically djurisdiction and these are often geographically d

• VAMC, VISN, state

• Geog p ra hic entities become convenient units fo

evaluating the impact of policies 

• intervention versus control units• E i t f tt E.g., impact of state-l l M di id li i level Medicaid policies o

veterans’ use of VHA services

• Approaches

• Geographically Weighted Regression• Conditional Autoregressive Models
Geographically Weighted Regre

• Version of “multi-level” or “mixed” modeling whe

regression coefficients vary across sub regression coefficients vary across sub-groups i

groups i

population

• Coefficients vary with geographic units in GW

• GWR allows the investigator to model “tailoring” 

that may optimize the implementation of interventhat may optimize the implementation of interven

• E.g., neighborhood variation in racial/ethnic 

comp , osition, socio-economic status

• GWR also facilitates evaluation of spatial variati

impact of an intervention

• Spatial analysis of regression model residual
Effect of Race on CHD Mortal

Source: Gebrea
GWR hotspots for TB in Housto

Sourc
Summary

• Geography plays a prominent role in implementa

• Often receives little consideration however Often receives little consideration however

• There are a host of “off-the-shelf” tools 

• SatScan: www.satscan.org (freeware)• P-median java applet: http://www.hyuan.com• GWR SAS macro: 

• Chen VY and Yang TC. SAS macro programs to geographically weighte

linear modeling with spatial point data: applications to health research, C

Methods and Programs in Biomedicine 2012;107:262-273

• ArcGIS
Using Spatial Analysis Tools in 

Implementation Science

Diane C. Cowper 
Overview: Two Examples

 Access to Acute Stroke Care in V

◦ RRP (PIs: Glenn Graham, Huanguang 

 Expansion of Telehealth 

◦ Office of Rural Health 
Example 1: Acute Stroke C

 The National Institute of Neurologic Disord

S k d Ad d C d L f S Stro

ke and Advanced Car

diac Life Support g

recommend “door to treatment” of one houp y 

ersons who are candidates for thrombol

yti

 The most commonly used drug for thrombo

therapy is tissue plasminogen activator (tPA)

 Timely and effective tPA administration after 

onset can improve patient survival, limit funcdeficits, p qy and im

prove 

qualit

y of care

 Time lost is brain lost
Objectives

 We wanted to get a look at the big picture of acute s

within the VHA

◦ Look at both ischemic and hemorrhagic stroke◦ Travel time analysis to acute stroke care (shorter t

time=b ) etter patient outcomes)

 60 Minutes to stroke care was the bench m

study

◦ Partnership with Non-VHA entities to reduce ove

to obtain acute stroke care
Methods

 Identify VHA patients with acute stroke by ZIP c

FY06-FY10

◦ Used ICD-9 Codes 431-437◦ Aggregated patient locations up to the ZIP code level

 Identify VHA facilities that have specialized strok Identify Non-VA stroke center locations Draw travel times around all VHA and Non-VA s

locations
Methods (cont ) Methods (cont

.

)

 From the VACO (VA Central Office) Neurology Offic

facilities could deliver 3 different levels of stroke care

◦ Primary Stroke Care (PRC) *30 VHA locations

 24/7/365 stroke care

◦ Limited Hours Stroke Care (LHSC) *28 VHA locat

 9-5 M-F stroke care

◦ Supporting Stroke Facility (SSF) Supporting Stroke Facility (SSF) 55 VHA locations *55 VHA locations

 Limited resources for stroke care

 From VACO identified 1074 Non-VA stroke care cen

the nation
PLACEHOLDER

 MAPS AND SCENARIOS HERE 

– 

OF SLIDES
Scenarios

 Stroke coverage – current

◦ Scenario 1 (upgrading 1 Limited Hou

facility)

◦ S i 2 (M i VA i ) Scenario 2 (Maximum VA capacity)◦ Scenario 3 (VA Stroke Centers + to

VA Stroke Centers)

◦ Scenario 4 (Maximum VA + Non-VA ◦ Example is for VISN 7, but can be do

VISNs

 742 patients with stroke FY06-FY10



3
6
Coverage Under Different Scen

◦ Baseline: 136 (18.3%)◦ Scenario 1 (upgrading 1 VAMC): 273 (36.8%)◦ Scenario 2 (Maximum VA capacity): 339 (45.7◦ Scenario 3 (VA Stroke Centers + top 3 non-

Centers): 303 (40.8%)

◦ Scenario 4 (Maximum VA + Non-VA Capacit

(82.9%)
Implications

 Facilities and patients are not dist

the same in all VISNs

 G S ca e p e t y access gaps 

IS can help identify access gaps 

specialty care 

 Assist in Assist in build or buy decisions “build or buy” decisions Show current maximum capacity  A i i h l Assist in where new tele-specil a ty

CBOCs would be most beneficia
Example 2: Broadband Coverag

 Ex

pansion of Telehealth in VHA

◦ VAMC to CBOC◦ VAMC to Patients’ Home◦ CBOC to Patients’ Home

 Move care closer to where rural V

reside

 Office of Rural Health 
Broadband Coverage

 Assemble database The National Broadband Map 

to sea c , a a y e a ap b oa 

r

c

h, a

n

aly

ze and

map b

roa

d

availability across the United Stat

◦ Created and maintained by the NTI

collaboration with the FCC, and in 

partnership with 50 states, five territ

the District of Columbia.
Broadband Coverage

 Downloaded data from 

http://www.broadbandmap.gov/dadownload

 National shape file not available, s

downloaded and merged each sta

 Mapped national coverage Mapped VISN coverage Identified enrollees outside of cov
BROADBAND COVERAGE BROADBAND COVERAGE - NATIONAL NATIONAL
Broadband Broadband Coverage Coverage –VISN 1
Broadband Covera

ge 

–

VISN 3
Broadband Coverage Broadband Coverage –VISN 8
Broadband Coverage Broadband Coverage –VISN 23 VISN 23
Broadband Coverage Broadband Coverage –VISN 21 VISN 21
Implications

◦ May have a tele-medicine program t

promise in pilot phase

◦ To roll out, need to see if roll out is 

feasible

◦ GIS can help with understanding spa

capaci f h l ity for new technology

◦ In addition to broadband, have also 

downloaded and can map wireless c
Contact Information

GeoS

p (

atial Outcomes Division (

VRHRC-ER, Gainesville, FLRORC-REAPGainesville FL Gainesville, FLDiane C. Cowper Ripley, PhD (Diane.Cowper2@va.gov)

Eric R. Litt, BA

(Eric Litt@va gov Eric.Litt@va.gov)Lauren Wilson, BS(Lauren.Wilson1@va.gov) 
VA GIS Users Group

 VA GIS Users Grou

p 

s includes em

from across the VA, not only in VH



S a e o t: h

a

r

e

P

oint: 

http://vaww.dwh.cdw.portal.va.gos/

gg p is/SitePa

ges/Home.as

p

x

 GIS Day is November 14 – This y

participating join us! participating

... join us!
Q ti ? uestions

?

Rehabilitation Outcomes Research Center