Market analysis and Geographic Information Systems (GIS) in transplantation

1. Market analysis and Geographic Information Systems (GIS) in transplantation Joel Thomas Adler

2. Disclosures • Wilmar Chocolates are hand made, hand cut, hand wrapped, and very tasty • Appleton, WI • Please enjoy http://bit.ly/1uWEsCh for chocolate ID

3. 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 Deceased donor liver transplant rates per 100 patient years on the waiting list Organ Procurement and Transplant Network

4. 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 2Division of Transplant Surgery, Massachusetts General Hospital 3Harvard Medical School 4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital

5. Competition varies by Donor Service Area (DSA)

6. Outcomes worse in DSAs of higher competition for deceased donors • Likely not a center-specific effect • Absolute differences small • Better outcomes than dialysis

7. “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

8. 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

9. Geographic Information Systems (GIS) • Integrating geographic information • Long history to understand problems in healthcare • Strength in data layering, combinations, interpolation, and spatial associations

10. GIS in HSR Surgery 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) • Estimating burden of disease in LMIC (Tollefson TT Laryngoscope 2014) • Gunshot trauma (Livingston DH J Trauma Acute Care Surg2014) • Variation in care (Vassileva C J Heart Valve Dis2012) • Technology adoption (Sethi J VascSurg 2013)

11. 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 2Division of Transplant Surgery, Massachusetts General Hospital 3Harvard Medical School 4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital

12. 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

13. 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

14. Average Nearest Neighbor by DSA (Liver) NESS 2014

15. Market characteristics NESS 2014

16. Liver transplants performed NESS 2014

17. Patient and graft outcomes NESS 2014

18. 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

19. 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 2Division of Transplant Surgery, Massachusetts General Hospital 3Harvard Medical School 4Division of Vascular & Endovascular Surgery, Brigham and Women’s Hospital

20. 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)

21. Competition by ZCTA

22. Competition in the United States ASC 2015 (submitted)

23. 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…)

24. 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

25. 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)

26. 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

27. 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)

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30. 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)

31. GIS tools • Data display and interpretation • Combining data and interpolating • Hotspot/outlier analysis • Organization of points • Spatial regression

32. Low-quality kidneys are used in more competitive DSAs

33. Average Nearest Neighbor by DSA (Kidney) ACS 2014

34. Hotspot/outlier analysis for competition: Local Indicator of Spatial Autocorrelation (LISA) ASC 2015 (submitted)

35. Display and interpretation

36. Overview • Transplantation and markets: competition and outcomes • Geographic Information Systems (GIS) • GIS in HSR • GIS techniques and how we’ve used them • Future directions