Faculty Bibliography

OBJECTIVE:To describe how overly simple conceptualisations of how research is translated into public health policy impact impair effective translation. To suggest how alternative approaches to conceptualising impact, which incorporate recent developments in social and political sciences, can help stakeholders improve translation of high-quality public health research into policy impact. STUDY DESIGN/METHODS:Researchers often describe generating impact in terms of linear or cyclical models, in which the production of scientific findings alone compels action and leads to impact. However, such conceptualisations do not appear to have supported improved translation of research into policy and practice. Improving understanding of how research impact is achieved may identify areas stakeholders seeking to achieve impact could target. METHODS:Overview of theoretical and practical approaches to achieving public health policy impact from research. RESULTS:Despite much evidence that translating research into public health policy is more complex than linear and cyclical models suggest, stakeholders often revert to these heuristics, that is shorthand ways of thinking that allow simple but inaccurate answers to complex problems. This leads to potentially missing opportunities for impact, such as conducting research in collaboration with local policy makers and contributing ideas to the wider narrative through the media and public engagement. CONCLUSION/CONCLUSIONS:The process of translating research into impact appears more complex than that suggested by linear and cyclical models. Success involves a planned approach targeting multiple routes to impact, sustained over time.

Using nonideal kidneys for transplant quickly might reduce the discard rate of kidney transplants. We studied changing kidney allocation to eliminate sequential offers, instead making offers to multiple centers for all nonlocally allocated kidneys, so that multiple centers must accept or decline within the same 1 hour. If more than 1 center accepted an offer, the kidney would go to the highest-priority accepting candidate. Using 2010 Kidney-Pancreas Simulated Allocation Model-Scientific Registry for Transplant Recipients data, we simulated the allocation of 12 933 kidneys, excluding locally allocated and zero-mismatch kidneys. We assumed that each hour of delay decreased the probability of acceptance by 5% and that kidneys would be discarded after 20 hours of offers beyond the local level. We simulated offering kidneys simultaneously to small, medium-size, and large batches of centers. Increasing the batch size increased the percentage of kidneys accepted and shortened allocation times. Going from small to large batches increased the number of kidneys accepted from 10 085 (92%) to 10 802 (98%) for low-Kidney Donor Risk Index kidneys and from 1257 (65%) to 1737 (89%) for high-Kidney Donor Risk Index kidneys. The average number of offers that a center received each week was 10.1 for small batches and 16.8 for large batches. Simultaneously expiring offers might allow faster allocation and decrease the number of discards, while still maintaining an acceptable screening burden.

BACKGROUND:Despite providing survival benefit, increased risk for infectious disease (IRD) kidney offers are declined at 1.5 times the rate of non-IRD kidneys. Elucidating sources of variation in IRD kidney offer acceptance may highlight opportunities to expand use of these life-saving organs. METHODS:To explore center-level variation in offer acceptance, we studied 6765 transplanted IRD kidneys offered to 187 transplant centers between 2009 and 2017 using Scientific Registry of Transplant Recipients data. We used multilevel logistic regression to determine characteristics associated with offer acceptance and to calculate the median odds ratio (MOR) of acceptance (higher MOR indicates greater heterogeneity). RESULTS:Higher quality kidneys (per 10 units kidney donor profile index; adjusted odds ratio [aOR], 0.94; 95% confidence interval [CI], 0.92-0.95), higher yearly volume (per 10 deceased donor kidney transplants; aOR, 1.08, 95% CI, 1.06-1.10), smaller waitlist size (per 100 candidates; aOR, 0.97; 95% CI, 0.95-0.98), and fewer transplant centers in the donor service area (per center; aOR, 0.88; 95% CI, 0.85-0.91) were associated with greater odds of IRD acceptance. Adjusting for donor and center characteristics, we found wide heterogeneity in IRD offer acceptance (MOR, 1.96). In other words, if listed at a center with more aggressive acceptance practices, a candidate could be 2 times more likely to have an IRD kidney offer accepted. CONCLUSIONS:Wide national variation in IRD kidney offer acceptance limits access to life-saving kidneys for many transplant candidates.

BACKGROUND:The Organ Procurement and Transplantation Network implemented Share 35 on June 18, 2013, to broaden deceased donor liver sharing within regional boundaries. We investigated whether increased sharing under Share 35 impacted geographic disparity in deceased donor liver transplantation (DDLT) across donation service areas (DSAs). METHODS:Using Scientific Registry of Transplant Recipients June 2009 to June 2017, we identified 86 083 adult liver transplant candidates and retrospectively estimated Model for End-Stage Liver Disease (MELD)-adjusted DDLT rates using nested multilevel Poisson regression with random intercepts for DSA and transplant program. From the variance in DDLT rates across 49 DSAs and 102 programs, we derived the DSA-level median incidence rate ratio (MIRR) of DDLT rates. MIRR is a robust metric of heterogeneity across each hierarchical level; larger MIRR indicates greater disparity. RESULTS:MIRR was 2.18 pre-Share 35 and 2.16 post-Share 35. Thus, 2 candidates with the same MELD in 2 different DSAs were expected to have a 2.2-fold difference in DDLT rate driven by geography alone. After accounting for program-level heterogeneity, MIRR was attenuated to 2.10 pre-Share 35 and 1.96 post-Share 35. For candidates with MELD 15-34, MIRR decreased from 2.51 pre- to 2.27 post-Share 35, and for candidates with MELD 35-40, MIRR increased from 1.46 pre- to 1.51 post-Share 35, independent of program-level heterogeneity in DDLT. DSA-level heterogeneity in DDLT rates was greater than program-level heterogeneity pre- and post-Share 35. CONCLUSIONS:Geographic disparity substantially impacted DDLT rates before and after Share 35, independent of program-level heterogeneity and particularly for candidates with MELD 35-40. Despite broader sharing, geography remains a major determinant of access to DDLT.

Recent OPTN proposals to address geographic disparity in liver allocation have involved circular boundaries: the policy selected 12/17 allocated to 150-mile circles in addition to DSAs/regions, and the policy selected 12/18 allocated to 150-mile circles eliminating DSA/region boundaries. However, methods to reduce geographic disparity remain controversial, within the OPTN and the transplant community. To inform ongoing discussions, we studied center-level supply/demand ratios using SRTR data (07/2013-06/2017) for 27 334 transplanted deceased donor livers and 44 652 incident waitlist candidates. Supply was the number of donors from an allocation unit (DSA or circle), allocated proportionally (by waitlist size) to the centers drawing on these donors. We measured geographic disparity as variance in log-transformed supply/demand ratio, comparing allocation based on DSAs, fixed-distance circles (150- or 400-mile radius), and fixed-population (12- or 50-million) circles. The recently proposed 150-mile radius circles (variance = 0.11, P = .9) or 12-million-population circles (variance = 0.08, P = .1) did not reduce the geographic disparity compared to DSA-based allocation (variance = 0.11). However, geographic disparity decreased substantially to 0.02 in both larger fixed-distance (400-mile, P < .001) and larger fixed-population (50-million, P < .001) circles (P = .9 comparing fixed distance and fixed population). For allocation circles to reduce geographic disparities, they must be larger than a 150-mile radius; additionally, fixed-population circles are not superior to fixed-distance circles.

The Organ Procurement and Transplantation Network (OPTN) went up for competitive bid again this year, yet this contract has been held by only 1 entity since its inception. The OPTN's scope has grown steadily, and it now embraces several disparate missions: to operate the computing and coordination infrastructure that maintains waitlists and makes organ offers in priority order, to regulate transplant centers and organ procurement organizations, to follow and protect living donors, and to decide organ allocation policy in concert with the many voices of the transplant community. The contracting process and performance work statement continue to discourage both innovative approaches to the OPTN and competitive bids outside of United Network for Organ Sharing (UNOS), with evaluation criteria that either disqualify or strongly disadvantage new applicants. The performance work statement also emphasizes bureaucratic tasks while obligating the OPTN contractor to the specific committee structure that has impeded decision-making and tended to preserve the status quo in controversial matters. Finally, the UNOS computing infrastructure is antiquated and requires months to years to implement small changes. Restructuring the OPTN contract to separate the information technology requirements from the policy/regulatory responsibilities might allow more nimble and effective specialty contractors to offer their capabilities in service of the national transplant enterprise.