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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
• Scarcity and allocation
• Liver transplant rates greatly vary across country
Organ Procurement and Transplant Network
Deceased donor liver transplant rates per 100 patient years on the waiting list
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 Hospital2Division of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
Competition varies by Donor Service Area (DSA)
Outcomes worse in DSAs of higher competition for deceased donors
Patient mortality Graft failure
HR (95% CI) P value HR (95% CI) P value
Competition
All patients0.99 (0.92, 1.07) 0.78 1.07 (0.99, 1.15) 0.08
Living donor0.94 (0.80, 1.11) 0.48 0.99 (0.85, 1.15) 0.89
Deceased donor1.11 (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 2014Halldorson 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 outcomeJoel 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 Hospital2Division of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
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 characteristicsAbsolute Nearest Neighbor (ANN)
Variable All DSAs(n = 446)
Single(n = 150)
Clustered(n = 164)
Random (n = 93)
Dispersed(n = 39)
P value
Population (millions)
5.28 (3.59 – 8.72)
3.33(2.46 – 4.71)
6.25(4.73 – 10.8)
6.34(4.49 – 9.35)
8.70(7.37 – 17.0)
<0.0001
Liver transplant centers
2 (1 – 3)
1(1 - 1)
2(2 – 3)
2(2 – 4)
3(2 – 4)
<0.0001
HHI 0.56 (0.50 – 1.00)
1.00(1.00 – 1.00)
0.52(0.39 – 0.61)
0.50(0.42 – 0.53)
0.51 (0.31 – 0.59)
<0.0001
New listings 166 (98 – 299)
72(40 – 117)
220 (161.5 – 425.5)
232(155 – 401)
285(168 – 602)
<0.0001
Deceased organ donors
139 (88 – 217)
87(52 – 131)
169.5(108 – 266)
151(114 – 257)
211(171 – 444)
<0.0001
Liver transplants 87.5 (55 – 162)
48(23 – 73)
109(74.5 – 205)
131(85 – 166)
179(88 – 436)
<0.0001
MELD score at transplant
25.1 ± 0.1 23.3 ± 0.2 26.6 ± 0.2 25.8 ± 0.3 23.9 ± 0.3 <0.0001
LDRI Unadjusted 1.51
(1.44 – 1.57)1.44
(1.36 – 1.53)1.52
(1.45 – 1.60)1.52
(1.49 – 1.59)1.54
(1.51 – 1.61)<0.0001
Adjusted 1.37 (1.31 – 1.43)
1.32(1.25 – 1.39)
1.39(1.32 – 1.44)
1.40(1.36 – 1.44)
1.39(1.35 – 1.43)
<0.0001
NESS 2014
Liver transplants performed
Variable IRR (95% CI) P valueAdult liver transplant centers 1.03 (1.01 – 1.06) 0.04Competition (inverse HHI) 1.33 (1.03 – 1.69) 0.03New listings (100s) 1.14 (1.10 – 1.17) <0.0001Donors (100s) 1.25 (1.17 – 1.32) <0.0001Population (millions) 1.04 (1.00 – 1.07) 0.02Geography (by ANN)
Single Ref -Clustered 1.25 (1.13 – 1.38) <0.0001Random 1.24 (1.09 – 1.41) 0.001
Dispersed 1.43 (1.10 – 1.85) 0.007MELD score (at transplant) 0.97 (0.96 – 0.98) <0.0001Adjusted LDRI 3.35 (2.54 – 4.43) <0.0001
NESS 2014
Patient and graft outcomes
MortalityVariable HR (95% CI) P value
Liver transplant centers 1.01 (0.98 - 1.04) 0.68Competition (inverse HHI) 0.99 (0.77 – 1.29) 0.96New listings (100s) 1.02 (0.99 – 1.04) 0.16Donors (100s) 1.05 (0.99 – 1.10) 0.04Population (millions) 0.99 (0.98 – 1.01) 0.08Geography (by ANN)
Single Ref -Clustered 1.02 (0.91 – 1.14)Random 1.03 (0.91 – 1.17) 0.65
Dispersed 1.03 (0.91 – 1.17) 0.62Adjusted LDRI 1.56 (1.47 – 1.66) <0.0001
Graft failureHR (95% CI) P value
1.05 (1.01 – 1.08) 0.012.17 (1.64 – 2.86) <0.00010.94 (0.91 – 0.97) <0.00011.13 (1.07 – 1.19) <0.00011.03 (1.01 – 1.05) 0.0002
Ref -1.51 (1.34 – 1.71) <0.00011.31 (1.14 – 1.51) 0.00021.01 (0.87 – 1.17) 0.901.68 (1.56 – 1.80) <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 Hospital2Division of Transplant Surgery, Massachusetts General Hospital
3Harvard Medical School4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital
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?
|  ̄ ̄ ̄ ̄ ̄ ̄ | | JANUARY 24 | | | | (SIGN BUNNY) | | _______| (\__/) || (• ㅅ• ) || / づ
Projects
• ZIP codes and SES of donors and recipients• Spatial organization of centers– Kidney transplants– Liver transplants
• 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) P value
Competition None 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