Market analysis and Geographic
Information Systems (GIS)
in transplantation
Joel Thomas Adler
Disclosures
• Wilmar Chocolates are
hand made, hand cut,
hand wrapped, and very
tasty
• Appleton, WI
• Please enjoy
http://bit.ly/1uWEsCh for chocolate ID
Kidney and liver transplantation
• 620,000 people with
ESRD in the US
• 16,000 waiting for liver
transplantation
Deceased donor liver transplant rates per 100
patient years on the waiting list
• Scarcity and allocation
• Liver transplant rates
greatly vary across
country
Organ Procurement and Transplant Network
Market competition influences
renal transplantation and outcomes
Joel T. Adler, MD,1,2 Rosh K. V. Sethi, BS,3
Heidi Yeh, MD,2,3 James F. Markmann, MD, PhD,2,3
and Louis L. Nguyen, MD, MBA, MPH1,3,4
1Center
for Surgery and Public Health at Brigham and Women’s Hospital
of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School
4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
2Division
Competition varies by
Donor Service Area (DSA)
Outcomes worse in DSAs of higher
competition for deceased donors
Patient mortality
HR (95% CI)
P value
Graft failure
HR (95% CI)
P value
Competition
All patients
Living donor
Deceased donor
0.99 (0.92, 1.07)
0.78
1.07 (0.99, 1.15)
0.08
0.94 (0.80, 1.11)
0.48
0.99 (0.85, 1.15)
0.89
1.11 (1.02, 1.21)
0.01
1.18 (1.09, 1.28) <0.0001
• Likely not a center-specific effect
• Absolute differences small
• Better outcomes than dialysis
“Markets” and scarce resources
• Donor service areas
(DSAs) functioning as an
individual “market”
• Increasing market
competition associated
with riskier organs and
worse survival, but better
than alternative
• How can we use
geography to better
understand and
optimize?
Adler et al Ann Surg 2014
Halldorson et al Liver Trans 2013
Markets and GIS: why does this matter?
• Allocation linked to
geography
• Provide insight into
utilization patterns
• Justify our definition of
DSA markets
• Larger discussions of
allocation policy
Gentry AJT 2013
Geographic Information Systems (GIS)
• Integrating geographic
information
• Long history to
understand problems in
healthcare
• Strength in data
layering, combinations,
interpolation, and
spatial associations
GIS in HSR
Surgery
• Estimating burden of
disease in LMIC (Tollefson
TT Laryngoscope 2014)
• Gunshot trauma (Livingston
DH J Trauma Acute Care
Surg 2014)
• Variation in care (Vassileva C
J Heart Valve Dis 2012)
• Technology adoption (Sethi
J Vasc Surg 2013)
Everybody else
• Access to stroke care
(Adeoye O Stroke 2014)
• Need and access in CKD
(Rodriguez RA J Nephrol
2013)
• Environmental exposures in
children (Harrison F Int J
Health Geogr 2014)
• Health disparities and
mammography utilization
(Ayanian JZ JNCI 2013)
Market competition and density
in liver transplantation: relationship
to volume and outcome
Joel T. Adler, MD1,2, Heidi Yeh, MD2,3,
James F. Markmann, MD, PhD2,3, and Louis L. Nguyen, MD, MBA, MPH1,3,4
1Center
for Surgery and Public Health at Brigham and Women’s Hospital
of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School
4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
2Division
Market competition and density
in transplantation
• Transplant centers unevenly distributed in the
DSAs
• Competition and transplant center density are
likely important
• Incorporate the spatial arrangements into
models to better understand access and
outcomes
ACS 2014, NESS 2014
Density and Organization: Average
Nearest Neighbor (ANN)
• Geocoded transplant centers
• Categorized as clustered, random, or dispersed;
single as a special case
• Considers spatial arrangement more than distance
Average Nearest Neighbor by DSA (Liver)
NESS 2014
Market characteristics
Variable
Population
(millions)
Liver transplant
centers
HHI
New listings
Deceased organ
donors
Liver transplants
MELD score at
transplant
LDRI
Unadjusted
Adjusted
All DSAs
(n = 446)
Absolute Nearest Neighbor (ANN)
Single
Clustered
Random
Dispersed
(n = 150)
(n = 164)
(n = 93)
(n = 39)
5.28
(3.59 – 8.72)
2
(1 – 3)
0.56
(0.50 – 1.00)
166
(98 – 299)
139
(88 – 217)
87.5
(55 – 162)
25.1 ± 0.1
3.33
(2.46 – 4.71)
1
(1 - 1)
1.00
(1.00 – 1.00)
72
(40 – 117)
87
(52 – 131)
48
(23 – 73)
23.3 ± 0.2
6.25
(4.73 – 10.8)
2
(2 – 3)
0.52
(0.39 – 0.61)
220
(161.5 – 425.5)
169.5
(108 – 266)
109
(74.5 – 205)
26.6 ± 0.2
6.34
(4.49 – 9.35)
2
(2 – 4)
0.50
(0.42 – 0.53)
232
(155 – 401)
151
(114 – 257)
131
(85 – 166)
25.8 ± 0.3
8.70
(7.37 – 17.0)
3
(2 – 4)
0.51
(0.31 – 0.59)
285
(168 – 602)
211
(171 – 444)
179
(88 – 436)
23.9 ± 0.3
1.51
(1.44 – 1.57)
1.37
(1.31 – 1.43)
1.44
(1.36 – 1.53)
1.32
(1.25 – 1.39)
1.52
(1.45 – 1.60)
1.39
(1.32 – 1.44)
1.52
(1.49 – 1.59)
1.40
(1.36 – 1.44)
1.54
(1.51 – 1.61)
1.39
(1.35 – 1.43)
P value
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
<0.0001
NESS 2014
Liver transplants performed
Variable
IRR (95% CI)
Adult liver transplant centers
Competition (inverse HHI)
New listings (100s)
Donors (100s)
Population (millions)
Geography (by ANN)
Single
Clustered
Random
Dispersed
MELD score (at transplant)
Adjusted LDRI
P value
1.03 (1.01 – 1.06)
1.33 (1.03 – 1.69)
1.14 (1.10 – 1.17)
1.25 (1.17 – 1.32)
1.04 (1.00 – 1.07)
0.04
0.03
<0.0001
<0.0001
0.02
Ref
1.25 (1.13 – 1.38)
1.24 (1.09 – 1.41)
1.43 (1.10 – 1.85)
0.97 (0.96 – 0.98)
3.35 (2.54 – 4.43)
<0.0001
0.001
0.007
<0.0001
<0.0001
NESS 2014
Patient and graft outcomes
Mortality
HR (95% CI)
P value
Variable
Liver transplant centers
Competition (inverse HHI)
New listings (100s)
Donors (100s)
Population (millions)
Geography (by ANN)
Single
Clustered
Random
Dispersed
Adjusted LDRI
Graft failure
HR (95% CI)
P value
1.01 (0.98 - 1.04)
0.99 (0.77 – 1.29)
1.02 (0.99 – 1.04)
1.05 (0.99 – 1.10)
0.99 (0.98 – 1.01)
0.68
0.96
0.16
0.04
0.08
1.05 (1.01 – 1.08)
2.17 (1.64 – 2.86)
0.94 (0.91 – 0.97)
1.13 (1.07 – 1.19)
1.03 (1.01 – 1.05)
0.01
<0.0001
<0.0001
<0.0001
0.0002
Ref
1.02 (0.91 – 1.14)
1.03 (0.91 – 1.17)
1.03 (0.91 – 1.17)
1.56 (1.47 – 1.66)
-
Ref
1.51 (1.34 – 1.71)
1.31 (1.14 – 1.51)
1.01 (0.87 – 1.17)
1.68 (1.56 – 1.80)
<0.0001
0.0002
0.90
<0.0001
0.65
0.62
<0.0001
NESS 2014
Conclusions
• Market variables and ANN are most important
for graft survival
• Transplant center density has a measurable
impact on liver transplants and patient and
graft survival
• Increasing the number of liver transplant
centers within a DSA could provide better
access to liver transplantation
Market and socioeconomic factors in the
conduct of kidney transplantation
Joel T. Adler, MD1,2, Heidi Yeh, MD2,3,
James F. Markmann, MD, PhD2,3, and Louis L. Nguyen, MD, MBA, MPH1,3,4
1Center
for Surgery and Public Health at Brigham and Women’s Hospital
of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School
4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
2Division
Market and socioeconomic factors in the
conduct of kidney transplantation
• Kidney transplants dependent on market
factors (SRTR)
• Socioeconomic factors affect access to kidney
transplantation (US ACS)
• These factors may be spatially correlated to
better understand kidney transplantation
ASC 2015 (submitted)
Competition by ZCTA
Competition in the United States
ASC 2015 (submitted)
Spatial regression
• Classically linear (housing prices in Manhattan)
• Spatial error
– Omitted (spatially correlated) covariate
– Errors are not independent
• Spatial lag
– “Diffusion” process: events in one place predict and
increased likelihood of events in other areas
– Observations and errors are not independent
• Dependent on weights (queen, rook, K nearest
neighbor…)
Kidney transplants and SES
factors are spatially related
----------------------------------------------------------------------Variable
Coefficient
Std. Error
z-value
Probability
----------------------------------------------------------------------CONSTANT
40.56982
4.683726
8.661868
0.0000000
HHI_HSA_IN
27.54952
1.712949
16.0831
0.0000000
CROWDED
1.16356
0.2482719
4.686636
0.0000028
POVERTY
-0.1164395
0.1354502
-0.8596483
0.3899829
LOW_EDUCAT
-0.05833486
0.1134684
-0.5141065
0.6071775
HIGH_EDUCA
0.3939563
0.1205663
3.267548
0.0010850
UNEMPLOYME
0.8934559
0.2050763
4.356701
0.0000132
MPV -3.4443e-005
7.431e-006
-4.633904
0.0000036
MHI -9.5672e-005
7.181e-005
-1.332215
0.1827895
LAMBDA
0.459516
0.02209419
20.79805
0.0000000
-----------------------------------------------------------------------
ASC 2015
Conclusions
• Competition and SES effects diffuse among
neighboring HSAs
• Spatial autocorrelation plays a role in factors
influencing kidney transplantation
• Consider these issues in planning transplant
center location and organ sharing
ASC 2015 (submitted)
Conclusions: this does matter!
• Allocation and
utilization are linked to
geography
• Utilization patterns and
cost
• Justify our definition of
DSA markets
• Allocation policy
Gentry AJT 2013
Resources
• Center for Geographic Analysis
(http://www.gis.harvard.edu/)
• Open GeoDA
(https://geodacenter.asu.edu/ogeoda)
• ESRI ArcGIS
(http://www.esri.com/software/arcgis)
Three boys? Why not?
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Projects
• ZIP codes and SES of donors and recipients
• Spatial organization of centers
– Kidney transplants
– Liver transplants
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•
•
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Competition maps and access to transplantation
Market competition density index
Provider-induced demand
Disparities in donation rates (Bode)
GIS tools
•
•
•
•
•
Data display and interpretation
Combining data and interpolating
Hotspot/outlier analysis
Organization of points
Spatial regression
Low-quality kidneys are used in more
competitive DSAs
Variable
OR (95% CI)
Competition
None
P value
1.00
-
Low 1.20 (1.08, 1.32)
0.0005
Medium 1.05 (0.95, 1.16)
0.33
High 1.39 (1.26, 1.52) <0.0001
Average Nearest Neighbor by DSA (Kidney)
ACS 2014
Hotspot/outlier analysis for competition:
Local Indicator of Spatial Autocorrelation (LISA)
ASC 2015 (submitted)
Display and interpretation
Overview
• Transplantation and markets: competition and
outcomes
• Geographic Information Systems (GIS)
• GIS in HSR
• GIS techniques and how we’ve used them
• Future directions