Pediatric Heart Transplants: rethinking the waitlist, by Power, Sweat...Almond et al.

 Here's a paper on the design of the waitlist for pediatric heart transplants.  It's accompanied by an editorial in the journal, and a discussion at Stanford Medical School.

Here's the article

Alyssa Power, MD,a,* Kurt R. Sweat, PHD,b,* Alvin Roth, PHD,b John C. Dykes, MD,a Beth Kaufman, MD,a Michael Ma, MD,c Sharon Chen, MD, MPH,a Seth A. Hollander, MD,a Elizabeth Profita, MD,a David N. Rosenthal, MD,aLynsey Barkoff, NP,a Chiu-Yu Chen, MD, PHD,a Ryan R. Davies, MD,d Christopher S. Almond, MD, MPH, Contemporary Pediatric Heart Transplant Waitlist Mortality  Journal of the American College of Cardiology, Volume 84, Issue 7, 13 August 2024, Pages 620-632

ABSTRACT

BACKGROUND In 2016, the United Network for Organ Sharing revised its pediatric heart transplant (HT) allocation policy.

OBJECTIVES This study sought to determine whether the 2016 revisions are associated with reduced waitlist mortality and capture patient-specific risks.

METHODS Children listed for HT from 1999 to 2023 were identified using Organ Procurement and Transplantation Network data and grouped into 3 eras (era 1: 1999-2006; era 2: 2006-2016; era 3: 2016-2023) based on when the United Network for Organ Sharing implemented allocation changes. Fine-Gray competing risks modeling was used to identify factors associated with death or delisting for deterioration. Fixed-effects analysis was used to determine whether allocation changes were associated with mortality.

RESULTS Waitlist mortality declined 8 percentage points (PP) across eras (21%, 17%, and 13%, respectively; P < 0.01). At listing, era 3 children were less sick than era 1 children, with 6 PP less ECMO use (P < 0.01), 11 PP less ventilator use (P < 0.01), and 1 PP less dialysis use (P < 0.01). Ventricular assist device (VAD) use was 13 PP higher, and VAD mortality decreased 9 PP (P < 0.01). Non-White mortality declined 10 PP (P < 0.01). ABO-incompatible listings increased 27 PP, and blood group O infant mortality decreased 13 PP (P < 0.01). In multivariable analyses, the 2016 revisions were not associated with lower waitlist mortality, whereas VAD use (in era 3), ABO-incompatible transplant, improved patient selection, and narrowing racial disparities were. Match-run analyses demonstrated poor correlation between individual waitlist mortality risk and the match-run order.

CONCLUSIONS The 2016 allocation revisions were not independently associated with the decline in pediatric HT waitlist mortality. The 3-tier classification system fails to adequately capture patient-specific risks. A more flexible allocation system that accurately reflects patient-specific risks and considers transplant benefit is urgently needed. 

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Here's the accompanying editorial in JACC

Getting to Transplant Should Not Be the Goal, by David L.S. Morales MD and Benjamin S. Mantell MD, PhD

And here's the Stanford article:

Heart transplant list doesn’t rank kids by medical need, Stanford Medicine-led study finds. More babies and children survive the wait for a heart transplant than in the past, but improvements are due to better medical care, not changes to wait-list rules, a new study finds. August 5, 2024 - By Erin Digitale

“The current system is not doing a good job of capturing medical urgency, which is one of its explicit goals,” said the study’s co-lead author, economist Kurt Sweat, PhD, who conducted the research as a graduate student in economics at Stanford University. "

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Kurt's job market paper was on heart transplants for adult patients.

Friday, April 26, 2024

Kurt Sweat defends his dissertation

Update: here's another commentary on the article:

Fewer Kids Now Die While Awaiting Heart Transplant, but There’s Room for Improvement Twenty years of data show mortality has dropped. Still, with one in eight children dying on the wait list, more needs to be done.  By Yael L. Maxwell

The Impact of prioritization on kidney and liver allocation in Israel

   Israel's  Organ Transplantation Law grants some priority on waiting lists for transplants to candidates who are first-degree relatives of deceased organ donors (i.e. whose family has given permission for someone's deceased organ donation) or who previously registered as organ donors themselves. (There's also a tiny priority for relatives of people who signed organ donor cards...)  Here are two papers that looks at the effect of those priorities on kidney and liver transplants, and how they interact with other priorities on waiting lists for Israeli organs.  

The first paper, on kidneys, concludes that the priorities are effective in reducing waiting time to transplant, and suggests that perhaps these priorities should not be so large compared to other existing priorities (e.g. for time on dialysis), or for priorities that could be established, e.g. for highly sensitized patients (who get high priority in the U.S., for example.)

Mor, Eytan, Meitar Bloom, Ronen Ghinea, Roi Anteby, Ronit Pasvolsky-Gutman, Ron Loewenthal, Ido Nachmani, and Tammy Hod. "The Impact of the Donor Card Holder Prioritization Program on Kidney Allocation in Israel." Transplantation (2024): 10-1097.

Abstract

Background: Since 2014, as part of a priority program within the Israeli Transplant Law, additional points were given to waitlisted candidates with donor cards. We assessed the impact on deceased donor kidney allocation.

Methods: This study enrolled all patients older than 18 y who underwent deceased donor kidney transplantation (January 2016–December 2019). Data were obtained from the National HLA Tissue Laboratory registry at the Sheba Medical Center. Patients were grouped by donor card status (ADI group) (not signed, 0 points; relative signed, 0.1 points; patient signed, 2 points; and relative donated, 9 points). The primary outcome was waiting time until kidney transplantation with and without the additional score.

Results: Four hundred forty-four patients underwent kidney transplantation during the study period: 281 (63%) were donor card holders (DCH) and 163 (37%) were not DCH. DCH with extra points waited 68.0 (±47.0) mo on average, compared with 94.6 (±47.3) mo for not DCH (P < 0.001). Donor card signers had a shorter time until transplant in a multivariable model. Without extra points, 145 recipients (32.6%) would have missed organs allocated to higher-scored candidates. Allocation changes occurred in 1 patient because of an additional 0.1 points, in 103 candidates because of an additional 2 points, and in 41 candidates because of an additional 9 points.

Conclusions: Additional DCH scores improved allocation and reduced waiting time for donor card signers and those with donating relatives. To enhance fairness, consideration should be given to reducing the score weight of this social criterion and raising scores for other factors, especially dialysis duration.

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There are many fewer liver transplants than kidney transplants, and the effect of priority is less clear:

Ashkenazi, Tamar, Avraham Stoler, and Eytan Mor. "The effect of priority given to donor card holders on the allocation of livers for transplant—evidence from 7 years of the Israeli priority program." Transplantation 106, no. 2 (2022): 299-307.

Abstract

Background. The Israeli Transplant Law grants priority in organ allocation to patients signing a donor card. Liver transplant candidates get additional 2 points on their Model for End Stage Liver Disease score for signing a donor card, 0.1 points for a relative holding a card, and 5 points if a relative donated an organ. We studied the effect of the priority program on waiting list mortality and allocation changes due to priority.

Methods. Using Israeli Transplant data of 531 adult liver transplant candidates with chronic liver disease listed between 2012 and 2018 we compared waitlist mortality and transplant rate of candidates with and without priority. Then we analyzed liver allocations resulting from additional priority points and followed outcome of patients who were skipped in line.

Results. Of the 519 candidates, 294 did not sign a donor card, 82 signed, 140 had a relative sign, and for 3, a relative donated an organ. The rates of waitlist mortality in these 4 groups were 22.4%, 0%, 21.4%, and 0%, respectively, and the transplant rates were 50%, 59.8%, 49.3%, and 100%, respectively. Of the 30 patients who were skipped because of priority, 24 subsequently underwent transplant, 2 are on the waiting list, and 4 died within 0.75, 1.75, 7, and 17 mo.

Conclusions. The 2 points added to the Model for End Stage Liver Disease score were associated with lower waitlist mortality and higher transplant rate for candidates signing a donor card without significantly affecting access to transplant during allocation. Further research and consideration of optimal policy when granting priority for candidates signing a donor card should continue.

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Earlier:

Stoler, Avraham,  Judd B. Kessler, Tamar Ashkenazi, Alvin E. Roth, Jacob Lavee, “Incentivizing Authorization for Deceased Organ Donation with Organ Allocation Priority: the First Five Years,” American Journal of Transplantation, Volume 16, Issue 9, September 2016,  2639–2645. 

 Stoler, Avraham, Judd B. Kessler, Tamar Ashkenazi, Alvin E. Roth, Jacob Lavee, “Incentivizing Organ Donor Registrations with Organ Allocation Priority,”, Health Economics, April 2016 Volume: 26   Issue: 4   Pages: 500-510   APR 2017

Increasing kidney transplants by reducing discards of risky kidneys

 Kidneys from deceased donors are too often discarded. Dr. Joshua Mezrich, a transplant surgeon at U. Wisconsin, writes in Stat about how to reduce the rate at which high risk kidneys are discarded (after being on ice for a long time while being rejected by many patients). He proposes that kidneys that can be identified as high risk even before being recovered from the deceased donor  be offered promptly to patients/transplant centers that have indicated a willingness to take them. It would require transplant centers to keep current blood tests available for patients who are candidates for high risk kidneys (who may be candidates in part because they are far from the front of the waiting list...)

Too many donor organs go to waste. Here’s how to get them into the patients who need them  By Joshua Mezrich, Stat, March 2, 2024 

"So here is the fix. High-risk kidneys should immediately be offered to transplant centers that opt into a high-risk program as an open offer to their wait list rather than to a specific patient, on a rotating schedule with weight put on proximity to the donor hospital. Ideally the offer should be made prior to procurement of the organ, with final acceptance once it is removed and anatomy and biopsy results can be reviewed by the accepting surgeon.

"If the biopsies show significant disease and the function of the kidney would be inadequate for a recipient, the receiving center can request both kidneys for a single patient, termed a dual transplant (which has been shown to have good outcomes). If a center accepts a kidney, it can then choose the patient who will benefit the most from the transplant and has a long predicted wait time for a low-risk transplant, with informed consent. That would entail a discussion with the patient about expectations regarding the quality of the kidney, how long and how well it might work, and how much longer they might need to wait for a lower-risk kidney. The ability to match the kidney to a recipient is important, as high-risk kidneys need to go into patients who can tolerate the slow initial function. Centers that opt into the high-risk program will need to maintain an updated list of informed patients who are predicted to benefit from these kidneys, who can be called in as soon an offer becomes available. For them, taking a chance beats remaining on dialysis.

Experiments for organ allocation (an idea whose time may be coming)

 Experiments to improve how deceased donor organs are allocated to waiting patients seem like a good idea...

OPTN Task Force sets goal of achieving 60K transplants by 2026, Jan 26, 2024 

“we need to move quicker, be more responsive, and deliver results for the patients we serve,” said Dianne LaPointe Rudow, DNP, president of the OPTN Board of Directors. “The reality is that while the number of transplants continues to grow, so does the non-use of available organs and allocations of organs out of the intended sequence of offers.

...

"The need is clear. In the case of kidneys, the most transplanted organ, the number of kidneys recovered from deceased donors increased by 56 percent between 2018 to 2023. Yet the number of kidney transplants only increased by 44 percent, meaning that approximately one quarter of kidneys recovered were not transplanted.

...

"Under a proposed variance for expedited placement, currently out for public comment, the task force intends to develop a series of rapid, small-scale tests of innovative organ placement approaches and assess their outcomes to evaluate whether they could be incorporated into future OPTN policies. The task force also has committed to prioritizing studies that evaluate potential frameworks for allocating hard-to-place organs to increase the number of transplants and lower non-use rates."

Pilot projects to speed deceased donor organ allocation

I won't use the word "experiments" for fear of the evil eye, but the OPTN is planning to authorize 'pilot programs' to try to speed the allocation of deceased donor organs. The idea is that after an organ has been rejected numerous times, and has started to be in danger of discard, it can be offered to a transplant center and patient that are likely to accept and transplant it, rather than proceeding to offer it to centers and patients in priority order. This is important, because too much waiting time on ice is one of the chief reasons that organs are rejected and eventually discarded.

Expedited Placement Variance his proposal recommends a new variance related to expedited organ placement and proposes modifying the OPTN’s variance process in order to allow for more rapid studies of potential improvement.

"Proposed changes

"Create a variance to govern pilot projects related to expedited organ placement.

Gives the OPTN Executive Committee authority to develop protocols for expedited organ placement.

This approach will allow the OPTN the ability to rapidly iterate on different protocols.

Update portions of the OPTN’s governance structure regarding variances.

This will allow for a more rapid and iterative approach when creating new variances."

Read the full proposal (PDF)

"The task force intends to conduct multiple iterative pilots or PDSAs with the community to identify effective practices to improve the efficiency of the organ allocation process. (Not all pilots or PDSAs will require a policy variance.) This proposal 1) creates a variance to govern the expedited placement pilots and 2) adjusts the OPTN’s governance of all variances. Additional variances or process improvement projects will focus on other topics to improve the efficiency of the organ allocation process.

"The Committee is issuing this proposal for a thirty-day public comment period, which is shorter than the usual public comment period. This is to allow the variance to take effect sooner than the end of regular public comment but allow the community time to comment on the proposed variance. It also is in line with the public comment periods for emergency and expedited policy changes."

Waitlist equity, when not everyone can wait a long time, by Afshin Nikzad and Philipp Strack

 Patients waiting for deceased donor kidneys are given priority in part by how long they have been on dialysis, while patients waiting for livers are prioritized according to how sick they are, sickest first.  When the wait is long, not everyone has an equal chance of surviving long enough to receive an organ. Here's a paper that suggests that service in random order (SIRO) has desirable equity properties. Efficiency depends on how patients' welfare and future prospects change while they wait.

Equity and Efficiency in Dynamic Matching: Extreme Waitlist Policies, by Afshin Nikzad and Philipp Strack, Management Science, forthcoming, Published Online:3 Oct 2023https://doi.org/10.1287/mnsc.2023.01212

Abstract: Waitlists are commonly used to allocate scarce resources, such as public housing or organs. Waitlist policies attempt to prioritize agents who wait longer by assigning them priority points (à la first come, first served). We show that such point systems can lead to severe inequality across the agents’ assignment probabilities unless they use randomization. In particular, deterministic point systems lead to a more unequal allocation than any other rule that prioritizes earlier arrivals, an axiom that ensures that agents who wait longer are treated (weakly) better. Among the policies abiding by this axiom, we show that service in random order (SIRO) leads to the most equal allocation. From a utilitarian perspective, we show that the planner faces no trade-off between equity and efficiency when the flow utility from waiting is nonnegative or negative and increasing over time. In these cases, SIRO is also the most efficient policy. However, when the flow cost of waiting increases over time, then the planner may face an efficiency–equity trade-off: SIRO remains the most equitable policy but may not be the most efficient one.

1. Introduction: Waitlists are a common way to allocate scarce resources, such as public housing,1 organs,2 or services such as call center support.3 There are many ways to decide who among the waiting agents receives an object once it becomes available. Some waitlists operate in a service-in-random-order (SIRO) manner and use lotteries to allocate objects to waiting agents, such as in the Diversity Immigrant Visa Program in the United States4 or Beijing’s license plate allocation.5 Many other waitlists follow designs akin to first come, first served (FCFS), in which whoever has waited for the longest time receives (priority points for) an object. For example, in the national kidney transplant waitlist in the United States, enrolled patients earn priority points for each day that they remain on the waitlist.6 Such rules ensure that an agent who waits longer is not treated worse than an agent with a shorter waiting time and otherwise identical characteristics.

"Prioritizing agents with longer waiting times, however, has a drawback: it implies that an agent with a longer lifetime, that is, an agent who can wait longer for an object, has a higher probability of receiving an object. This naturally leads to inequality in assignment probabilities across agents with varying lifetimes. For example, a first-come, first-served list would lead to many of the sickest patients never receiving an organ as they depart the system before having waited long enough to receive an organ. Such equity concerns, for example, play an important role in the context of organ allocations (Organ Procurement and Transplantation Network 2015). The high-level question we ask here is, what policy induces the least inequality among policies that give priority to agents who arrive earlier? Furthermore, is minimizing inequality aligned with the objective of a planner who maximizes the average of the agents’ utilities, or are there efficiency–equity trade-offs to be considered here?"

Tonya Ingram (1991-2022), health activist, died while waiting for a kidney

 Tonya Ingram, a poet and health activist who testified in Congress about the long waiting list for kidney transplants, died last month while still waiting.  Saturday's New York Times had a moving column about her activism, her struggle and her long wait.

Tonya Ingram Feared the Organ Donation System Would Kill Her. It Did. By Kendall Ciesemier (Ms. Ciesemier is a writer, a producer and an organ recipient.) Jan. 28, 2023

Here's her obit in the LA Times:

Tonya Ingram, an inspiring L.A. poet and ‘lupus warrior,’ died waiting for a kidney by Jireh Deng, JAN. 23, 2023

Market design isn't only about trying to allocate scarce resources effectively, it's also about working to make them less scarce.

Patient preferences for taking an offered kidney versus waiting for a better one

 Here's a paper whose title announces in its first two words that it's unusual for the transplant literature: "Patient Preferences."   It sensibly asks about preferences for a transplant now versus a long future wait.  That's relevant, because the waiting list for a kidney is often years long.

Patient Preferences for Waiting Time and Kidney Quality, by Sanjay Mehrotra, Juan Marcos Gonzalez, Karolina Schantz, Jui-Chen Yang, John J. Friedewald and Richard Knight, CJASN Aug 2022, CJN.01480222; DOI: 10.2215/CJN.01480222

Visual Abstract

Abstract

"Background and objectives Approximately 20% of deceased donor kidneys are discarded each year in the United States. Some of these kidneys could benefit patients who are waitlisted. Understanding patient preferences regarding accepting marginal-quality kidneys could help more of the currently discarded kidneys be transplanted.

Design, setting, participants, & measurements This study uses a discrete choice experiment that presents a deceased donor kidney to patients who are waiting for, or have received, a kidney transplant. The choices involve trade-offs between accepting a kidney today or a future kidney. The options were designed experimentally to quantify the relative importance of kidney quality (expected graft survival and level of kidney function) and waiting time. Choices were analyzed using a random-parameters logit model and latent-class analysis.

Results In total, 605 participants completed the discrete choice experiment. Respondents made trade-offs between kidney quality and waiting time. The average respondent would accept a kidney today, with 6.5 years of expected graft survival (95% confidence interval, 5.9 to 7.0), to avoid waiting 2 additional years for a kidney, with 11 years of expected graft survival. Three patient-preference classes were identified. Class 1 was averse to additional waiting time, but still responsive to improvements in kidney quality. Class 2 was less willing to accept increases in waiting time for improvements in kidney quality. Class 3 was willing to accept increases in waiting time even for small improvements in kidney quality. Relative to class 1, respondents in class 3 were likely to be age ≤61 years and to be waitlisted before starting dialysis, and respondents in class 2 were more likely to be older, Black, not have a college degree, and have lower Karnofsky performance status.

Conclusions Participants preferred accepting a lower-quality kidney in return for shorter waiting time, particularly those who were older and had lower functional status."

HT: Martha Gershun

Returning to your place in the queue following a failed kidney transplant

 Here's a forthcoming paper that proposes that rejections of marginal kidneys could be reduced if recipients were guaranteed a shorter waiting time for a subsequent transplant if a marginal kidney that they accepted failed.

Tunç, Sait, Burhaneddin Sandıkçı, and Bekir Tanrıöver. "A Simple Incentive Mechanism to Alleviate the Burden of Organ Wastage in Transplantation." Management Science (2022).

Abstract: Despite efforts to increase the supply of donated organs for transplantation, organ shortages persist. We study the problem of organ wastage in a queueing-theoretic framework. We establish that self-interested individuals set their utilization levels more conservatively in equilibrium than the socially efficient level. To reduce the resulting gap, we offer an incentive mechanism that recompenses candidates returning to the waitlist for retransplantation, who have accepted a predefined set of organs, for giving up their position in the waitlist and show that it increases the equilibrium utilization of organs whilealso improving social welfare. Furthermore, the degree of improvement increases monotonically with the level of this nonmonetary compensation provided by the mechanism. In practice, this mechanism can be implemented by preserving some fraction of the waiting time previously accumulated by returning candidates. A detailed numerical study for the U.S. renal transplant system suggests that such an incentive helps significantly reduce the kidney discard rate (baseline: 17.4%). Depending on the strength of the population’s response to the mechanism, the discard rate can be as low as 6.2% (strong response), 12.4%(moderate response), or 15.1% (weak response), which translates to 1,630, 724, or 338 more  transplants per year, respectively. Although the average quality of transplanted kidneys deteriorates slightly, the resulting graft survival one-year post transplant remains stable around 94.8% versus 95.0% for the baseline. We find that the optimal Kidney Donor Profile Index score cutoff, defining the set of incentivized kidneys, is around 85%, which coincides with the generally accepted definition of marginal kidneys in the medical community."

Queuing for ridesharing and organ allocation

 Queues for ridesharing drivers at airports (where some trips are much better than others) lead to lots of rejected trips by those at the head of the line, while they wait for a good one.  This is of course something that also occurs in deceased donor waiting lists.

Here's a paper that tackles the ridesharing problem:

Randomized FIFO Mechanisms by Francisco Castro, Hongyao Ma, Hamid Nazerzadeh, Chiwei Yan

Abstract: "We study the matching of jobs to workers in a queue, e.g. a ridesharing platform dispatching drivers to pick up riders at an airport. Under FIFO dispatching, the heterogeneity in trip earnings incentivizes drivers to cherry-pick, increasing riders' waiting time for a match and resulting in a loss of efficiency and reliability. We first present the direct FIFO mechanism, which offers lower-earning trips to drivers further down the queue. The option to skip the rest of the line incentivizes drivers to accept all dispatches, but the mechanism would be considered unfair since drivers closer to the head of the queue may have lower priority for trips to certain destinations. To avoid the use of unfair dispatch rules, we introduce a family of randomized FIFO mechanisms, which send declined trips gradually down the queue in a randomized manner. We prove that a randomized FIFO mechanism achieves the first best throughput and the second best revenue in equilibrium. Extensive counterfactual simulations using data from the City of Chicago demonstrate substantial improvements of revenue and throughput, highlighting the effectiveness of using waiting times to align incentives and reduce the variability in driver earnings."

"Many ridesharing platforms now maintain virtual queues at airports for drivers who are waiting in  designated  areas,  and  dispatch  drivers  from  the  queue  in  a  first-in-first-out  (FIFO)  manner.4 This resolves the congestion issues and is also considered more fair by many since drivers who havewaited the longest in the queue are now the first in line to receive trip offers.  At major U.S. airports,however, a driver at the head of the queue will receive the next trip offer in a few seconds under FIFO dispatching, if she declines an offer from the platform (see Figure 12).  As we shall see, thislowered cost of cherry-picking substantially exacerbates existing problems on incentive alignment.

...

"During busy hours, instead of accepting an average trip, drivers who are close to the head of the queue are better off declining most trip offers and waiting for only the highest earning trips.  Riders, however, have finite patience, despite being willing to wait for some time for a match.  When each driver decline takes an average of 10 seconds, 2 minutes had passed after a trip with low or moderate earnings (e.g.  trips to downtown Chicago) was offered to and declined by the top 12 drivers in the queue.5 At this point, it is very likely that the rider cancels her trip request, not knowing when a driver will be assigned, if at all.

...

"To  achieve  optimal  throughput  and  revenue  without  the  use  of  an  unfair  dispatch  rule,  weintroduce a family ofrandomized FIFO mechanisms.  A randomized FIFO mechanism is specifiedby a set of “bins” in the queue (e.g., the top 10 positions, the 10th to 20th positions, and so on).Each trip request is first offered to a driver in the first bin uniformly at random.  After each decline, the mechanism then offers the trip to a random driver in the next bin.  By sending trips gradually down the queue in this randomized manner, the randomized FIFO mechanisms appropriately align incentives using waiting times,  achieving the first best throughput and second best net revenue: the option to skip the rest of the line incentivizes drivers further down the queue to accept trips with  lower  earnings;  randomizing  each  dispatch  among  a  small  group  of  drivers  increases  each individual driver’s waiting time for the next dispatch, thereby allowing the mechanism to prioritize drivers closer to the head of the queue for trips to every destination without creating incentives for excessive cherry-picking."

Deceased organ allocation in the U.S., moving towards a more continuous system--Martha Pavlakis in Transplantation

 In the latest issue of Transplantation a clear description of how the transplant community is planning to move towards a more continuous way of allocating organs, in ways that have already begun (so that e.g. a lung transplant candidate in Manhattan won't be ineligible for a kidney from a deceased donor across the river in New Jersey).  One element of this that worries me is that a weighting system for priorities will be derived from focus groups of interested parties, using the Analytic Hierarchy Process, which is an orderly, matrix based process for aggregating opinions that doesn't have any ability to integrate different aspects being evaluated from the point of view of how they might effect relevant transplant outcomes, or consider how they might influence incentives for diagnosis and treatment. So I anticipate that organ allocation will continue to be in motion for the foreseeable future.

Continuous Distribution in Organ Allocation: Stepping Back From the Edge  by  Martha Pavlakis,  Transplantation: December 2021 - Volume 105 - Issue 12 - p 2517-2519, doi: 10.1097/TP.0000000000003886

"Organ allocation priorities are determined according to policies developed by the Organ Procurement and Transplantation Network (OPTN), which is operated by United Network for Organ Sharing (UNOS). In 2016, a significant shift began which will culminate in a transition of all organ allocation to be determined in the framework of an approach known as continuous distribution. The most reductive description of the change from current allocation to continuous distribution is that it will change from a classification-based (or bucket-based) system to a points-based system without hard borders. 

...

"The removal of hard boundaries in the continuous distribution system of allocation has been reviewed elsewhere6,7 and is best described by outlining the steps in its development. The steps include (1) identifying and categorizing candidate attributes; (2) building of a rating scale that assigns values for each attribute, such as candidate blood type, using UNOS and SRTR data; and (3) assigning weights to each attribute to determine how much that attribute will contribute to the candidate’s final score. This process has several parts: first, there needs to be a specific weight assigned to each attribute such that it can be prioritized against each of the other attributes. As a next step, the attributes need to be converted into points. (4) A framework will be built where a composite score is determined by combining weights and rating scales. To do this, a sensitivity tool called the analytic hierarchy process (AHP) will inform the development of the framework through a prioritization exercise.8 In these exercises, participants compare 2 attributes against each other and select their level of importance when considering a candidate for organ transplant. The information from multiple rounds of these exercises will be used to inform the weight of each attribute to the overall score. The AHP method was chosen because it has been used effectively by other healthcare groups to involve patients in making clinical decisions.9 The “participants” in this AHP method are the same participants that engage in public comment for policy change—member centers, individuals, OPOs, organizations with a vested interest in transplant such as the American Society of Transplantation, and the general public. Participants will weigh the trade-offs between effectiveness/benefit and medical urgency. Using focus groups, Oedingen et al convincingly highlighted the importance of preference studies to elucidate public preferences in organ allocation, which has multiple and sometimes competing goals.10

"Once the community has agreed on a proposed continuous distribution system, the SRTR will perform modeling to identify any potential unintended consequences of the proposal. The modeling will estimate the benefits of the new proposal and inform any needed improvements. (5) After considering community input through public comment, modeling and analysis, and committee project work, the kidney committee will then propose a composite score as a policy proposal. (6) Finally, a policy proposal will be presented to the OPTN Board of Directors for approval. Once approved, implementation of the policy is projected to take approximately 12 mo due to programming changes and education for the transplant community."

Counterbalancing Learning and Strategic Incentives in Allocation Markets by Kang, Monachou, Koren, and Ashlagi

 Here's a model with a new perspective on deceased organ waiting lists...

Counterbalancing Learning and Strategic Incentives in Allocation Markets by Jamie Kang, Faidra Monachou, Moran Koren, and Itai Ashlagi

Abstract: Motivated by the high discard rate of donated organs in the United States, we study an allocation problem in the presence of learning and strategic incentives.We consider a setting where a benevolent social planner decides whether and how to allocate a single indivisible object to a queue of strategic agents.  The object has a common true quality,  good or bad,  which is ex-ante unknown to everyone.  Each agent holds an informative, yet noisy, private signal about the quality.  To make a correct allocation decision the planner attempts to learn the object quality by truthfully eliciting agents’ signals. Under the commonly applied sequential offering mechanism, we show that learning is hampered by the presence of strategic incentives as herding may emerge. This can result in incorrect allocation and welfare loss. To overcome these issues, we propose a novel class of incentive-compatible mechanisms.  Our mechanism involves a batch-by-batch, dynamicvoting process using a majority rule. We prove that the proposed voting mechanisms improve the probability of correct allocation whenever agents are sufficiently well informed. Particularly, we show that such an improvement can be achieved via a simple greedy algorithm. We quantify the improvement using simulations.

Alternative kidney waitlist designs, by Agarwal, Ashlagi, Rees, Somaini, and Waldinger in Econometrica

 Here's a paper that seeks to take into account that patients waiting for a deceased organ transplant are forward looking, and make decisions based not just on their place in the current waitlist and the option being offered to them, but on what offers are likely coming, in equilibrium.

Equilibrium Allocations Under Alternative Waitlist Designs: Evidence From Deceased Donor Kidneys, by Nikhil Agarwal, Itai Ashlagi, Michael A. Rees, Paulo Somaini, Daniel Waldinger, Econometrica, Volume89, Issue1, January 2021, Pages 37-76

Abstract: Waitlists are often used to ration scarce resources, but the trade-offs in designing these mechanisms depend on agents' preferences. We study equilibrium allocations under alternative designs for the deceased donor kidney waitlist. We model the decision to accept an organ or wait for a preferable one as an optimal stopping problem and estimate preferences using administrative data from the New York City area. Our estimates show that while some kidney types are desirable for all patients, there is substantial match-specific heterogeneity in values. We then develop methods to evaluate alternative mechanisms, comparing their effects on patient welfare to an equivalent change in donor supply. Past reforms to the kidney waitlist primarily resulted in redistribution, with similar welfare and organ discard rates to the benchmark first-come, first-served mechanism. These mechanisms and other commonly studied theoretical benchmarks remain far from optimal. We design a mechanism that increases patient welfare by the equivalent of an 18.2% increase in donor supply.

"The estimated payoffs show that while some organs are systematically more desirable than others, there is substantial match-specific heterogeneity in values. For instance, organs from younger donors are preferred by all patients, but younger patients place a higher value on such organs. This and other sources of match-specific heterogeneity, such as immunological similarity, create scope for redesigning the allocation mechanism to improve match quality by incorporating detailed patient and donor characteristics into the priority system."

Vaccine delivery improving, with congestion

 A statewide vaccine appointment list is a good idea, but it can crash:

Massachusetts Vaccination Website Crash: What Went Wrong?  The state thinks the high volume of traffic may have been the cause, but they still aren't 100% certain

"Massachusetts’ COVID-19 vaccine appointment portal temporarily crashed Thursday morning as more than 1 million additional state residents became eligible to schedule a shot.

"Gov. Charlie Baker said the administration had run through different scenarios to try to avoid problems with the vaccine portal. He said people in the administration are in the process are trying to determine what happened.

"The state on Thursday for the first time began allowing those age 65 and older, people with two or more certain medical conditions, and residents and staff of low income and affordable senior housing so sign up for a vaccine shot. But it came with a warning that it could take up to a month to book an appointment.

...

"As of Friday morning, the issues appeared to have been resolved and the website seemed to be working properly. But vaccination appointments remained hard to find.

"People who went to vaxfinder.mass.gov on Friday to book an appointment were told none were available. A statement from state health officials said “a small number of appointments for other locations,” including pharmacies and regional collaboratives, would be posted over the next few days."

Multiple queues for Covid vaccines, as pharmacies join the supply chain

 It is good news that pharmacies are now being included among the places that can dispense Covid vaccinations, because not everyone is connected to another kind of health care provider.  But it will not end the congestion in getting appointments and delivering vaccines.

Having multiple waiting lists for appointments--i.e. for appointments at different pharmacy chains, health care providers, county vaccination centers--will add to congestion. People will have incentives to make appointments with more than one provider, because supplies at each provider are uncertain, so that some appointments may be cancelled due to shortages of vaccine on the appointed date.  After getting vaccinated, at least some people will neglect to cancel their other appointments, and so some doses of vaccine will not be delivered when scheduled. (Hopefully they won't be wasted).  So vaccinations will still be slower than we might hope.

Here's a CBS report:

Pharmacies now offering COVID-19 vaccines: Here's what you need to know BY KATE GIBSON

"The federal government this week started sending supplies of COVID-19 vaccines to 21 national drugstore chains and to independent pharmacies in a move to accelerate distribution. The program will be implemented in stages, based on available vaccine supplies, according to the U.S. Centers for Disease Control and Prevention.

...

"National drugstore chains CVS Health and Walgreens are among those getting supplies of COVID-19 vaccines from the federal government. But getting a shot isn't as easy as walking through the pharmacy door. Consumers are instead being discouraged from flocking to the stores, but rather get in line by making an appointment online or the phone. "

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Here's the CDC site:

Pharmacies Participating in the Federal Retail Pharmacy Program

Covid has slowed transplants in the UK

The Evening Standard has the story:

Organ transplant waiting list jumps to five-year high due to pandemic, new NHS figures show

by Naomi Ackerman 

"The number people waiting for an organ transplant has soared to five-year high as a result of the coronavirus pandemic, new NHS figures have shown.

"NHS Blood and Transplant (NHSBT) said this week that an estimated 6,700 people are currently in need of a transplant across the UK - up from 6,138 prior to the start of the pandemic.

"The health body has estimated that the increase in patients waiting - expected to be the highest since 2015-16 - comes after services were impacted by the effects of the pandemic.

...

"It is hoped that the waiting list can be shortened going forward following the introduction of a new law in May, making organ donation "opt-out" rather than an active choice.

...

"The law will see that families are still consulted before organ donation goes ahead - the reason is why health officials have implored people to make their wishes about donation known to their families.

"NHSBT has said that thousands of "transplant opportunities" have been missed in recent years. In 2018-19, it reported that 835 families declined to support organ donation - with many families saying they did not know what their relative would have wanted."

***********

HT: Alex Chan

Confusion in NYC high school wait lists

In August, the New York City Department of Education announced a change in the school choice assignment process--without announcing any details.  But the plan was that after the initial run of the deferred acceptance algorithm, they would institute some sort of wait lists. I blogged about it at the time, and was concerned by the lack of detail.

Here's a current story from Chalkbeat that suggests that the details are still opaque, but that families are learning that the waitlist position they were given isn't reliable:

‘How can you move back on a waiting list?’: NYC’s high school admissions tweaks spark confusion
By Alex Zimmerman  May 8, 2020

"students vying for the city’s most coveted schools are discovering that their position on high school waitlists can worsen over time, a situation that has come as a surprise to some families — adding anxiety to an admissions process that is already famous for its complexity.
...
"Every student who fills out an application and does not get into their top choice is automatically waitlisted. If you get your third choice school, for example, you’ll be on the waitlist for your No. 1 and 2 choices. Nearly 44,000 students did not get into their first choice high school this year, automatically placing them on at least one waitlist.

"The second way is that students can add themselves to any waitlist once the initial matching process is over, even for schools a student didn’t initially apply to.

"In general, students who initially applied to a school but didn’t get in and are automatically added to its waitlist should be ranked ahead of students who add themselves later on, officials said. But there are exceptions.

"The first major exception is if a student is in a higher priority group than someone who is already on the waitlist. Some schools, for instance, give preference to students who live in certain neighborhoods, which can override a student’s position on the waitlist even if they were added first. (Officials said this is the most common reason a student would see their position worsen.)

"Olga Ramos, the admissions director at Bard High School Early College Queens, pointed to a second reason families can move backward — something that surprised her at first.

"If a student got into their first choice school, and listed Bard as their second choice, they could still add themselves to Bard’s waitlist and be considered as if they had been automatically added — potentially bypassing students who were already on the list."

*********
Here's an earlier story in Chalkbeat by Mr. Zimmerman, indicating that the system was still pretty opaque as the school choice process got ready to announce admissions in March:

NYC high school offers are coming this week with a big change: waitlists. Here’s what you should know.  By Alex Zimmerman  Mar 18, 2020

Here's what was known then...

"What are these waitlists, anyway?

"New York City students must apply to high school, listing up to 12 schools they want to attend. A complicated algorithm, developed by a Nobel prize-winning economist, then matches a student to one of their choices.

"That fundamental algorithm is not changing. But for the first time this year, any student who does not get into their first choice school will automatically be added to the waitlist of every single higher-ranked school they didn’t get into.

"Every school that has more applicants than seats will have a waitlist. It’s a similar model that the education department uses for pre-K, kindergarten, and middle schools — something education department officials said is an advantage."

**********
Here's a story from the time of the initial announcement:

Goodbye round two applications, hello waitlists: NYC announces changes to high school admissions
By Christina Veiga and Alex Zimmerman   Aug 15, 2019

"Starting next year, the city will allow students to sit on waiting lists for schools they wanted to attend, but didn’t get into. The city is also eliminating the second round of admissions, which it now uses to for students who aren’t matched to a school they applied to during the typical process.
...
"“It’s like going to a store and getting the ticket, you know what number you are, and you know how many folks are ahead of you, and you’ll be able to watch the process go,” said Deputy Chancellor Josh Wallack. “You’ll also be able to talk with an administrator in a school who can give you a sense of how much waitlists move each year and that varies a bit by school.”
*****

I'm still confused about a different issue that I haven't yet seen addressed. In the original school choice system using the deferred acceptance algorithm, there was a second round in which students unmatched in the first round were asked for additional preferences over schools, so that they could be matched.  How were those unmatched students assigned to schools this year?

Here's my August post:

Friday, August 16, 2019 

Waitlists in NYC school choice--early reflections on yesterday's initial announcements

Transplantation rates for patients in non-profit versus for-profit dialysis centers

From JAMA,September 10, 2019  Volume 322, Number 10:
J::AMA
September 10, 2019 Volume 322, Number 10Association Between Dialysis Facility Ownership and Accessto Kidney Transplantation

Jennifer C. Gander, PhD; Xingyu Zhang, PhD; Katherine Ross, MPH; Adam S. Wilk, PhD; Laura McPherson, MPH; Teri Browne, PhD;Stephen O. Pastan, MD; Elizabeth Walker, MS; Zhensheng Wang, PhD; Rachel E. Patzer, PhD, MPH

"MAIN OUTCOMES AND MEASURES: Access to kidney transplantation was defined as time from initiation of dialysis to placement on the deceased donor kidney transplantation waiting list,receipt of a living donor kidney transplant, or receipt of a deceased donor kidney transplant.Cumulative incidence differences and multivariable Cox models assessed the associationbetween dialysis facility ownership and each outcome.
RESULTS: Among 1 478 564 patients, the median age was 66 years (interquartile range, 55-76years), with 55.3% male, and 28.1% non-Hispanic black patients. Eighty-seven percent ofpatients received care at a for-profit dialysis facility. A total of 109 030 patients (7.4%)received care at 435 nonprofit small chain facilities; 78 287 (5.3%) at 324 nonprofitindependent facilities; 483 988 (32.7%) at 2239 facilities of large for-profit chain 1; 482 689(32.6%) at 2082 facilities of large for-profit chain 2; 225 890 (15.3%) at 997 for-profit smallchain facilities; and 98 680 (6.7%) at 434 for-profit independent facilities. During the studyperiod, 121 680 patients (8.2%) were placed on the deceased donor waiting list, 23 762 (1.6%)received a living donor kidney transplant, and 49 290 (3.3%) received a deceased donorkidney transplant. For-profit facilities had lower 5-year cumulative incidence differences foreach outcome vs nonprofit facilities (deceased donor waiting list: −13.2% [95% CI, −13.4% to−13.0%]; receipt of a living donor kidney transplant: −2.3% [95% CI, −2.4% to −2.3%]; andreceipt of a deceased donor kidney transplant: −4.3% [95% CI, −4.4% to −4.2%]). AdjustedCox analyses showed lower relative rates for each outcome among patients treated at allfor-profit vs all nonprofit dialysis facilities: deceased donor waiting list (hazard ratio [HR], 0.36[95% CI, 0.35 to 0.36]); receipt of a living donor kidney transplant (HR, 0.52 [95% CI, 0.51 to0.54]); and receipt of a deceased donor kidney transplant (HR, 0.44 [95% CI, 0.44 to 0.45]).
CONCLUSIONS AND RELEVANCE: Among US patients with end-stage kidney disease, receiving dialysis at for-profit facilities compared with nonprofit facilities was associated with a lower likelihood of accessing kidney transplantation. Further research is needed to understand the mechanisms behind this association.

Here are the figures. "For-profit large chains" seem to give the slowest access to being put on the transplant waiting list, receiving a living donation, or receiving a deceased donation.

HT: Irene Wapnir

Waitlists in NYC school choice--early reflections on yesterday's initial announcements

Yesterday the New York City Department of Education announced a change in the school choice assignment process--I gather that after one round of deferred acceptance, they will do something else, involving interim assignments  and wait lists.  (The original design included a subsequent round of deferred acceptance, after disseminating to unmatched students a list of schools with vacancies, and eliciting new preference lists for this second round.)
The details of the new plan for the second round aren't yet clear (at least to me).

Here's the press release from the city:

Mayor de Blasio, Chancellor Carranza Announce Easier and More Transparent Middle and High School Admissions Process
August 15, 2019
Families will now have one form and one deadline for middle and high school admissions

"“We are changing the middle and high school application processes so families don’t have to go through the gauntlet just to get a placement. There will be one application round and one deadline to make everyone’s lives easier.”

“We’ve heard from families and educators that they want a simpler, more transparent, and more accessible system of school choice, and today we’re taking a step forward,” said Schools Chancellor Richard A. Carranza. “This common-sense change will make a real difference for families across the five boroughs, and improve our middle and high school choice process for years to come.”

The DOE is eliminating the second application rounds for middle and high school. The main round application process and timeline will remain the same, with middle and high school applications opening in October with a December deadline. Students will receive their offer in March. Families can still appeal for travel, safety, or medical hardships; if families have any hardship, they will be able to access in-person support at Family Welcome Centers, rather than wait to participate in a second process. The waitlists will open after offers are released and will be a simpler, clearer process for families, increasing:

• Transparency:  By knowing their waitlist position, families have a better understanding of their chances of getting into a preferred school option in the event that seats become available.
• Ease: This is a shorter process that requires less paperwork. Rather than having to complete a second application and wait weeks—often into May or June—for a second decision or offer, families will complete one process, receive one offer, and receive any additional offers based on waitlist position.
• Consistency:  Families will now have one admissions system at all grade levels, with the changes to the middle and high school process making it more similar to the elementary school admissions process. Currently the elementary school process has one round, and the middle and high school processes have two rounds with different names; now, families will not need to learn a different process each time a child applies to a new school—allowing them to focus on school options and not process."

************
From the WSJ:

New York City Introduces Wait Lists for Students Unhappy With School Placements
City’s complex school-choice system, in which students hope to be assigned to a top pick, has long been daunting for many families
By Leslie Brody

"In recent years, applicants who didn’t like their middle school assignments—given in spring for entry the next September—would have to go through an appeals process. High school applicants who didn’t like their offers would have to try a second round of applications and then appeal if need be.

"Under the new Department of Education system for fall 2020, students will be placed on wait lists for each school listed higher on their applications than the schools they were admitted to. They will be informed of their positions on wait lists and may be offered seats if they open up."

*******************
I got some emails about this. Here's my reply to a reporter...

"at this point I’m only an observer of NYC schools from a distance—I haven’t been involved in advising them for over a decade, and even then I worked only on the high school match, not anything involving middle schools.

So I don’t know anything about the current plans besides what I’ve read today ...

So I don’t have comments so much as questions.

1. How is the NYCDOE going to handle the timing of moving waitlists?  Many vacancies don’t become visible until just before (or just after) the official start of school, which means that there could be some complications right around that time, for families and schools.
2. How will students on multiple waitlists be dealt with?  Suppose a student waitlisted at multiple schools is admitted off one of them in the summer—if he or she accepts that new assignment, do his/her other waitlist positions remain?  
1. (If other waitlists have to be given up, this could be a complicated decision whether to accept a somewhat preferred school, or wait for an even more preferred one…  If other waitlist positions can be maintained, then the process may move slowly, as some students accept for one waitlisted position, and then a better one when it becomes available, and maybe another…)
3. How long will a student have to consider whether to accept a given waitlist position?

As with many questions of market design, the devil is in the details…"

and I added this in replies to followup emails asking for my thoughts on waitlists:

"I’ve always been cautious about waitlists, because some of the questions I asked you just don’t have good answers.  There’s a tension between wanting waitlists to move early and fast—to make planning easy for families and schools, and avoid disruption of the first week(s) of school, and wanting to give students the best chance at the schools they like best…"

"my colleagues and I never recommended waitlists to nyc, back at the turn of the century.:)
We thought it was important to reduce the number of “unmatched” students who had to be assigned to a school over which they  hadn’t had an opportunity to express preferences. This is why we had a second stage of the matching algorithm, in which lists of schools with still available places were disseminated to students unmatched in the first round, so that they could express preferences over these.

Another question about the new system is, how will such students now be assigned?  E.g. they might be assigned to the closest school to their home that has unfilled places.  In what order?  i.e. after some students are assigned this way, some schools will no longer have unfilled places, and students will have to be assigned to other schools.  The things I read today didn’t address that issue, but I gather that these interim assignments of unmatched students, which will turn out to be final assignments for students whose waitlists don’t move enough, will be made without having the students express preferences.

Another question about the waitlists: how will they be ordered?  According to the school priority/preferences that were used in the first round of matching?  Or perhaps unmatched students will be given preference? (that might sound attractive but I think it would be a bad idea, because it might make it seem desirable to be unmatched after the first round, which would interfere with eliciting student preferences altogether….)

My point is not to try to guess what design decisions have been made, but rather that there are lots of important decisions that have to be made to have a working system, and the initial announcements and news reports don’t reveal these. And they will have consequences.  So I hope that the system has been carefully designed."

Professional line sitters: Giving the gift of time

Have an urgent need to shop Black Friday bargains, but don't like waiting on lines in the cold and dark?  There's a business that will take care of that for you...

Professional Line Sitters Make Up to $35 an Hour — And This Is Their Busiest Time of the Year

Some links if you're too busy to read the story:
Skip the line (STL) inWashington DC
Same Ole Line Dudes in New York City
InLine4You an app for both sides of the market

"The Supreme Court website says security starts admitting people to oral argument sessions at 9:30 a.m., but “visitors may begin lining up on the Front Plaza as early as they feel comfortable” — which sometimes means four days in advance. The average SCOTUS wait time Goff gets tapped for runs about five hours (she charges $35 per hour), with occasional overnight requests for big cases like the travel ban and Masterpiece Cakeshop v. Colorado Civil Rights Commission."